From ddea6113f45675b07adc150f64e9af87e9241a9a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tr=E1=BB=8Bnh=20V=C5=A9=20=C4=90=E1=BB=A9c=20H=E1=BA=A3i?= Date: Wed, 24 Sep 2025 22:34:34 +0700 Subject: [PATCH 01/88] Adding AR1 and VAR1 Data --- environment.yml | 2 +- src/conf/wandb.yaml | 4 +- src/eval.py | 1 + src/models.py | 5 ++ src/plot_utils.py | 2 +- src/samplers.py | 114 ++++++++++++++++++++++++++++++++++++++++++++ src/schema.py | 3 +- src/tasks.py | 12 +++-- 8 files changed, 134 insertions(+), 9 deletions(-) diff --git a/environment.yml b/environment.yml index b88835ac..cdd5704c 100644 --- a/environment.yml +++ b/environment.yml @@ -4,7 +4,7 @@ channels: - defaults dependencies: - pip=21.2.4 - - python=3.8.12 + - python=3.9 - pytorch=1.11.0 - pip: - jupyter==1.0.0 diff --git a/src/conf/wandb.yaml b/src/conf/wandb.yaml index 4cc61db6..2f371b92 100644 --- a/src/conf/wandb.yaml +++ b/src/conf/wandb.yaml @@ -1,5 +1,5 @@ wandb: project: in-context-training - entity: your-entity + entity: hai-trinh220970-ho-chi-minh-city-university-of-technology notes: - log_every_steps: 100 + log_every_steps: 100 \ No newline at end of file diff --git a/src/eval.py b/src/eval.py index fb5a0360..8ba52579 100644 --- a/src/eval.py +++ b/src/eval.py @@ -326,6 +326,7 @@ def conf_to_model_name(conf): (3, 2): "Transformer-xs", (6, 4): "Transformer-small", (12, 8): "Transformer", + (4, 8): "Transformer", }[(conf.model.n_layer, conf.model.n_head)] else: return conf.wandb.name diff --git a/src/models.py b/src/models.py index e65b240a..f07fd789 100644 --- a/src/models.py +++ b/src/models.py @@ -71,6 +71,11 @@ def get_relevant_baselines(task_name): (XGBoostModel, {}), (AveragingModel, {}), ], + "noisy_linear_regression": [ + (LeastSquaresModel, {}), + (NNModel, {"n_neighbors": 3}), + (AveragingModel, {}), + ], } models = [model_cls(**kwargs) for model_cls, kwargs in task_to_baselines[task_name]] diff --git a/src/plot_utils.py b/src/plot_utils.py index 32579d1e..2bd41bad 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -102,7 +102,7 @@ def collect_results(run_dir, df, valid_row=None, rename_eval=None, rename_model= normalization = 1 for k, v in m.items(): - v = v[:xlim] + # v = v[:xlim] v = [vv / normalization for vv in v] m_processed[k] = v processed_results[model_name] = m_processed diff --git a/src/samplers.py b/src/samplers.py index 84779fd8..3f6eda2c 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -14,6 +14,8 @@ def sample_xs(self): def get_data_sampler(data_name, n_dims, **kwargs): names_to_classes = { "gaussian": GaussianSampler, + "ar1":AR1Sampler, + "var1":VAR1Sampler, } if data_name in names_to_classes: sampler_cls = names_to_classes[data_name] @@ -56,3 +58,115 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): if n_dims_truncated is not None: xs_b[:, :, n_dims_truncated:] = 0 return xs_b + + +class AR1Sampler(DataSampler): + def __init__(self, n_dims, bias=None, scale=0.9, sigma=0.0, init_state=None): + super().__init__(n_dims) + # phi là số thực, có thể lấy trace nếu scale là ma trận + if torch.is_tensor(scale) and scale.ndim == 2: + self.phi = torch.trace(scale).item() + elif isinstance(scale, (int, float)): + self.phi = float(scale) + else: + raise ValueError("scale phải là số hoặc ma trận 2D torch.Tensor") + + self.sigma = sigma + self.bias = bias + self.init_state = init_state if init_state is not None else torch.zeros(n_dims) + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + xs_b = torch.zeros(b_size, n_points, self.n_dims) + + for b in range(b_size): + if seeds is not None: + torch.manual_seed(seeds[b]) + + state = self.init_state.clone() + for t in range(n_points): + # noise = torch.randn(self.n_dims) * self.sigma + state = self.phi * state + if self.bias is not None: + state += self.bias + xs_b[b, t] = state + + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b + +class VAR1Sampler(DataSampler): + def __init__(self, n_dims, bias=None, scale=None, sigma=0.0, init_state=None): + super().__init__(n_dims) + if scale is None: + self.phi = 0.9 * torch.eye(n_dims) + else: + assert scale.shape == (n_dims, n_dims), "scale phải có shape (n_dims, n_dims)" + self.phi = scale + + self.bias = bias + self.sigma = sigma + self.init_state = init_state if init_state is not None else torch.zeros(n_dims) + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + xs_b = torch.zeros(b_size, n_points, self.n_dims) + + for b in range(b_size): + if seeds is not None: + torch.manual_seed(seeds[b]) + + state = self.init_state.clone() + for t in range(n_points): + noise = torch.randn(self.n_dims) * self.sigma + state = self.phi @ state + noise + if self.bias is not None: + state += self.bias + xs_b[b, t] = state + + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b +def test_ar1_sampler(): + n_dims = 3 + n_points = 5 + b_size = 2 + phi = 0.5 + sigma = 0.0 + init_state = torch.tensor([1.0, 2.0, 3.0]) + + seeds = [42, 123] + + sampler = AR1Sampler(n_dims=n_dims, scale=phi, sigma=sigma, init_state=init_state) + xs = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) + + print("Output shape:", xs.shape) # should be (b_size, n_points, n_dims) + print("First batch:\n", xs[0]) + print("Second batch:\n", xs[1]) + + # Test reproducibility + xs2 = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) + assert torch.allclose(xs, xs2), "Output not reproducible with same seeds!" + print("Reproducibility test passed.") + + # Test AR1 dynamics roughly + print("\nCheck AR1 dynamics:") + for t in range(1, n_points): + expected = phi * xs[0, t-1] + print(f"t={t}, previous*phi: {expected}, current: {xs[0, t]}") + + +def test_var1_sampler(): + n_dims = 2 + n_points = 4 + b_size = 2 + phi = torch.tensor([[0.5, 0.1], [0.0, 0.7]]) + sigma = 0.1 + init_state = torch.tensor([1.0, 2.0]) + seeds = [42, 123] + + sampler = VAR1Sampler(n_dims=n_dims, scale=phi, sigma=sigma, init_state=init_state) + xs = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) + print(xs) + + +if __name__ == "__main__": + test_var1_sampler() diff --git a/src/schema.py b/src/schema.py index 98d00914..40f72489 100644 --- a/src/schema.py +++ b/src/schema.py @@ -40,6 +40,7 @@ "linear_classification", "relu_2nn_regression", "decision_tree", + "noisy_linear_regression", ] training_schema = { @@ -47,7 +48,7 @@ "task_kwargs": merge(tdict, required), "num_tasks": merge(tinteger, nullable, default(None)), "num_training_examples": merge(tinteger, nullable, default(None)), - "data": merge(tstring, allowed(["gaussian"])), + "data": merge(tstring, allowed(["gaussian","ar1","var1"])), "batch_size": merge(tinteger, default(64)), "learning_rate": merge(tfloat, default(3e-4)), "train_steps": merge(tinteger, default(1000)), diff --git a/src/tasks.py b/src/tasks.py index 2dc0a1ea..1ea43e5c 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -74,13 +74,16 @@ def get_task_sampler( class LinearRegression(Task): - def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1): + def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1,uniform=False): """scale: a constant by which to scale the randomly sampled weights.""" super(LinearRegression, self).__init__(n_dims, batch_size, pool_dict, seeds) self.scale = scale if pool_dict is None and seeds is None: - self.w_b = torch.randn(self.b_size, self.n_dims, 1) + if uniform: + self.w_b = torch.rand(self.b_size, self.n_dims, 1)*2 -1 + else: + self.w_b = torch.randn(self.b_size, self.n_dims, 1) elif seeds is not None: self.w_b = torch.zeros(self.b_size, self.n_dims, 1) generator = torch.Generator() @@ -178,12 +181,13 @@ def __init__( pool_dict=None, seeds=None, scale=1, - noise_std=0, + noise_std=0.01, renormalize_ys=False, + uniform=False, ): """noise_std: standard deviation of noise added to the prediction.""" super(NoisyLinearRegression, self).__init__( - n_dims, batch_size, pool_dict, seeds, scale + n_dims, batch_size, pool_dict, seeds, scale, uniform ) self.noise_std = noise_std self.renormalize_ys = renormalize_ys From 39b86fe6ac45ec0934f6985d667d37d113e3e6e8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tr=E1=BB=8Bnh=20V=C5=A9=20=C4=90=E1=BB=A9c=20H=E1=BA=A3i?= Date: Wed, 1 Oct 2025 11:32:02 +0700 Subject: [PATCH 02/88] change init_state --- src/samplers.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/samplers.py b/src/samplers.py index 3f6eda2c..45a3cc06 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -61,7 +61,7 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): class AR1Sampler(DataSampler): - def __init__(self, n_dims, bias=None, scale=0.9, sigma=0.0, init_state=None): + def __init__(self, n_dims, bias=None, scale=0.9, sigma=0.5, init_state=None): super().__init__(n_dims) # phi là số thực, có thể lấy trace nếu scale là ma trận if torch.is_tensor(scale) and scale.ndim == 2: @@ -73,7 +73,7 @@ def __init__(self, n_dims, bias=None, scale=0.9, sigma=0.0, init_state=None): self.sigma = sigma self.bias = bias - self.init_state = init_state if init_state is not None else torch.zeros(n_dims) + self.init_state = init_state if init_state is not None else torch.ones(n_dims) def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): xs_b = torch.zeros(b_size, n_points, self.n_dims) @@ -95,7 +95,7 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): return xs_b class VAR1Sampler(DataSampler): - def __init__(self, n_dims, bias=None, scale=None, sigma=0.0, init_state=None): + def __init__(self, n_dims, bias=None, scale=None, sigma=0.5, init_state=None): super().__init__(n_dims) if scale is None: self.phi = 0.9 * torch.eye(n_dims) @@ -105,7 +105,7 @@ def __init__(self, n_dims, bias=None, scale=None, sigma=0.0, init_state=None): self.bias = bias self.sigma = sigma - self.init_state = init_state if init_state is not None else torch.zeros(n_dims) + self.init_state = init_state if init_state is not None else torch.ones(n_dims) def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): xs_b = torch.zeros(b_size, n_points, self.n_dims) From c58161054bb5d747c7dc077fc555080e4a7a346a Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Sat, 4 Oct 2025 18:40:01 +0700 Subject: [PATCH 03/88] Add Ridge and other relevant functions --- src/models.py | 268 ++++++++++++++++++++++++++++++++++++++++++++++++ src/samplers.py | 243 +++++++++++++++++++++++++------------------ src/tasks.py | 68 ++++++++++++ 3 files changed, 482 insertions(+), 97 deletions(-) diff --git a/src/models.py b/src/models.py index f07fd789..0fabece0 100644 --- a/src/models.py +++ b/src/models.py @@ -76,6 +76,16 @@ def get_relevant_baselines(task_name): (NNModel, {"n_neighbors": 3}), (AveragingModel, {}), ], + "ar1_linear_regression": [ + (LeastSquaresModel, {}), + (RidgeModel, {"alpha": 0.1}), + (RidgeModel, {"alpha": 1.0}), + (RidgeModelWithVarianceAdjustment, {"alpha": 1.0, "ar_coef": 0.5}), + (FeasibleGLSModel, {"ar_coef": None}), + (GLSModel, {"ar_coef": 0.5}), + (NNModel, {"n_neighbors": 3}), + (AveragingModel, {}), + ], } models = [model_cls(**kwargs) for model_cls, kwargs in task_to_baselines[task_name]] @@ -480,3 +490,261 @@ def __call__(self, xs, ys, inds=None): preds.append(pred) return torch.stack(preds, dim=1) +class RidgeModel: + def __init__(self, alpha=1.0): + """ + Ridge regression model with L2 regularization. + alpha: regularization strength (larger values = more regularization) + """ + self.alpha = alpha + self.name = f"ridge_alpha={alpha}" + + def __call__(self, xs, ys, inds=None): + xs, ys = xs.cpu(), ys.cpu() + if inds is None: + inds = range(ys.shape[1]) + else: + if max(inds) >= ys.shape[1] or min(inds) < 0: + raise ValueError("inds contain indices where xs and ys are not defined") + + preds = [] + + for i in inds: + if i == 0: + preds.append(torch.zeros_like(ys[:, 0])) # predict zero for first point + continue + train_xs, train_ys = xs[:, :i], ys[:, :i] + test_x = xs[:, i : i + 1] + + # Ridge regression: (X'X + alpha*I)^(-1) X'y + # Add regularization term to diagonal + XtX = train_xs.transpose(-2, -1) @ train_xs + Xty = train_xs.transpose(-2, -1) @ train_ys.unsqueeze(-1) + + # Add alpha * I to diagonal + reg_matrix = XtX + self.alpha * torch.eye(XtX.shape[-1], device=XtX.device) + + try: + ws = torch.linalg.solve(reg_matrix, Xty) + pred = test_x @ ws + preds.append(pred[:, 0, 0]) + except torch.linalg.LinAlgError: + # Fallback to least squares if singular + ws, _, _, _ = torch.linalg.lstsq(train_xs, train_ys.unsqueeze(2)) + pred = test_x @ ws + preds.append(pred[:, 0, 0]) + + return torch.stack(preds, dim=1) + + +class RidgeModelWithVarianceAdjustment: + def __init__(self, alpha=1.0, ar_coef=0.5): + """ + Ridge regression with variance adjustment for AR(1) data. + alpha: regularization strength + ar_coef: AR(1) coefficient for variance adjustment + """ + self.alpha = alpha + self.ar_coef = ar_coef + self.name = f"ridge_var_adj_alpha={alpha}_ar={ar_coef}" + + def __call__(self, xs, ys, inds=None): + xs, ys = xs.cpu(), ys.cpu() + if inds is None: + inds = range(ys.shape[1]) + else: + if max(inds) >= ys.shape[1] or min(inds) < 0: + raise ValueError("inds contain indices where xs and ys are not defined") + + preds = [] + + for i in inds: + if i == 0: + preds.append(torch.zeros_like(ys[:, 0])) + continue + train_xs, train_ys = xs[:, :i], ys[:, :i] + test_x = xs[:, i : i + 1] + + # Create AR(1) covariance matrix for variance adjustment + n = train_xs.shape[1] + ar_cov = self._create_ar1_covariance(n, self.ar_coef) + + # Weighted Ridge regression: (X'V^(-1)X + alpha*I)^(-1) X'V^(-1)y + try: + ar_cov_inv = torch.linalg.inv(ar_cov) + XtV_inv = train_xs.transpose(-2, -1) @ ar_cov_inv + XtV_invX = XtV_inv @ train_xs + XtV_invy = XtV_inv @ train_ys.unsqueeze(-1) + + # Add regularization + reg_matrix = XtV_invX + self.alpha * torch.eye(XtV_invX.shape[-1], device=XtV_invX.device) + ws = torch.linalg.solve(reg_matrix, XtV_invy) + pred = test_x @ ws + preds.append(pred[:, 0, 0]) + except torch.linalg.LinAlgError: + # Fallback to regular ridge + XtX = train_xs.transpose(-2, -1) @ train_xs + Xty = train_xs.transpose(-2, -1) @ train_ys.unsqueeze(-1) + reg_matrix = XtX + self.alpha * torch.eye(XtX.shape[-1], device=XtX.device) + ws = torch.linalg.solve(reg_matrix, Xty) + pred = test_x @ ws + preds.append(pred[:, 0, 0]) + + return torch.stack(preds, dim=1) + + def _create_ar1_covariance(self, n, ar_coef): + """Create AR(1) covariance matrix: V[i,j] = ar_coef^|i-j|""" + indices = torch.arange(n, dtype=torch.float32) + diff = torch.abs(indices.unsqueeze(0) - indices.unsqueeze(1)) + return torch.pow(ar_coef, diff) + + +class FeasibleGLSModel: + def __init__(self, ar_coef=None): + """ + Feasible GLS for AR(1) data with unknown AR coefficient. + ar_coef: if None, estimate from residuals; otherwise use fixed value + """ + self.ar_coef = ar_coef + self.name = f"feasible_gls_ar={'est' if ar_coef is None else ar_coef}" + + def __call__(self, xs, ys, inds=None): + xs, ys = xs.cpu(), ys.cpu() + if inds is None: + inds = range(ys.shape[1]) + else: + if max(inds) >= ys.shape[1] or min(inds) < 0: + raise ValueError("inds contain indices where xs and ys are not defined") + + preds = [] + + for i in inds: + if i == 0: + preds.append(torch.zeros_like(ys[:, 0])) + continue + train_xs, train_ys = xs[:, :i], ys[:, :i] + test_x = xs[:, i : i + 1] + + pred = torch.zeros_like(ys[:, 0]) + for j in range(ys.shape[0]): + x_j, y_j = train_xs[j], train_ys[j] + + # Step 1: OLS to get initial residuals + try: + w_ols, _, _, _ = torch.linalg.lstsq(x_j, y_j.unsqueeze(-1)) + residuals = y_j - (x_j @ w_ols).squeeze() + except torch.linalg.LinAlgError: + pred[j] = 0.0 + continue + + # Step 2: Estimate AR coefficient from residuals + if self.ar_coef is None and len(residuals) > 1: + # Estimate AR(1) coefficient using Yule-Walker equations + ar_coef_est = self._estimate_ar_coef(residuals) + else: + ar_coef_est = self.ar_coef if self.ar_coef is not None else 0.0 + + # Step 3: Create covariance matrix and perform GLS + if len(residuals) > 1: + n = len(residuals) + ar_cov = self._create_ar1_covariance(n, ar_coef_est) + + try: + ar_cov_inv = torch.linalg.inv(ar_cov) + XtV_inv = x_j.transpose(-1, -2) @ ar_cov_inv + XtV_invX = XtV_inv @ x_j + XtV_invy = XtV_inv @ y_j.unsqueeze(-1) + + w_gls = torch.linalg.solve(XtV_invX, XtV_invy) + y_pred = (test_x[j] @ w_gls).squeeze() + pred[j] = y_pred + except torch.linalg.LinAlgError: + # Fallback to OLS + y_pred = (test_x[j] @ w_ols).squeeze() + pred[j] = y_pred + else: + # Not enough data for GLS, use OLS + y_pred = (test_x[j] @ w_ols).squeeze() + pred[j] = y_pred + + preds.append(pred) + + return torch.stack(preds, dim=1) + + def _estimate_ar_coef(self, residuals): + """Estimate AR(1) coefficient using Yule-Walker equations""" + if len(residuals) <= 1: + return 0.0 + + # Compute autocovariances + n = len(residuals) + gamma_0 = torch.var(residuals) + if n > 1: + gamma_1 = torch.mean(residuals[:-1] * residuals[1:]) + ar_coef = gamma_1 / gamma_0 if gamma_0 > 1e-10 else 0.0 + # Ensure stability + ar_coef = torch.clamp(ar_coef, -0.99, 0.99) + else: + ar_coef = 0.0 + + return ar_coef + + def _create_ar1_covariance(self, n, ar_coef): + """Create AR(1) covariance matrix""" + indices = torch.arange(n, dtype=torch.float32) + diff = torch.abs(indices.unsqueeze(0) - indices.unsqueeze(1)) + return torch.pow(ar_coef, diff) + + +class GLSModel: + def __init__(self, ar_coef=0.5): + """ + GLS with known AR(1) covariance structure. + ar_coef: known AR(1) coefficient + """ + self.ar_coef = ar_coef + self.name = f"gls_ar={ar_coef}" + + def __call__(self, xs, ys, inds=None): + xs, ys = xs.cpu(), ys.cpu() + if inds is None: + inds = range(ys.shape[1]) + else: + if max(inds) >= ys.shape[1] or min(inds) < 0: + raise ValueError("inds contain indices where xs and ys are not defined") + + preds = [] + + for i in inds: + if i == 0: + preds.append(torch.zeros_like(ys[:, 0])) + continue + train_xs, train_ys = xs[:, :i], ys[:, :i] + test_x = xs[:, i : i + 1] + + # Create AR(1) covariance matrix + n = train_xs.shape[1] + ar_cov = self._create_ar1_covariance(n, self.ar_coef) + + try: + ar_cov_inv = torch.linalg.inv(ar_cov) + XtV_inv = train_xs.transpose(-2, -1) @ ar_cov_inv + XtV_invX = XtV_inv @ train_xs + XtV_invy = XtV_inv @ train_ys.unsqueeze(-1) + + w_gls = torch.linalg.solve(XtV_invX, XtV_invy) + pred = test_x @ w_gls + preds.append(pred[:, 0, 0]) + except torch.linalg.LinAlgError: + # Fallback to OLS + ws, _, _, _ = torch.linalg.lstsq(train_xs, train_ys.unsqueeze(2)) + pred = test_x @ ws + preds.append(pred[:, 0, 0]) + + return torch.stack(preds, dim=1) + + def _create_ar1_covariance(self, n, ar_coef): + """Create AR(1) covariance matrix""" + indices = torch.arange(n, dtype=torch.float32) + diff = torch.abs(indices.unsqueeze(0) - indices.unsqueeze(1)) + return torch.pow(ar_coef, diff) \ No newline at end of file diff --git a/src/samplers.py b/src/samplers.py index 45a3cc06..e8370e34 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -60,113 +60,162 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): return xs_b +# class AR1Sampler(DataSampler): +# def __init__(self, n_dims, bias=None, scale=0.9, sigma=0.5, init_state=None): +# super().__init__(n_dims) +# # phi là số thực, có thể lấy trace nếu scale là ma trận +# if torch.is_tensor(scale) and scale.ndim == 2: +# self.phi = torch.trace(scale).item() +# elif isinstance(scale, (int, float)): +# self.phi = float(scale) +# else: +# raise ValueError("scale phải là số hoặc ma trận 2D torch.Tensor") + +# self.sigma = sigma +# self.bias = bias +# self.init_state = init_state if init_state is not None else torch.ones(n_dims) + +# def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): +# xs_b = torch.zeros(b_size, n_points, self.n_dims) + +# for b in range(b_size): +# if seeds is not None: +# torch.manual_seed(seeds[b]) + +# state = self.init_state.clone() +# for t in range(n_points): +# # noise = torch.randn(self.n_dims) * self.sigma +# state = self.phi * state +# if self.bias is not None: +# state += self.bias +# xs_b[b, t] = state + +# if n_dims_truncated is not None: +# xs_b[:, :, n_dims_truncated:] = 0 +# return xs_b + +# class VAR1Sampler(DataSampler): +# def __init__(self, n_dims, bias=None, scale=None, sigma=0.5, init_state=None): +# super().__init__(n_dims) +# if scale is None: +# self.phi = 0.9 * torch.eye(n_dims) +# else: +# assert scale.shape == (n_dims, n_dims), "scale phải có shape (n_dims, n_dims)" +# self.phi = scale + +# self.bias = bias +# self.sigma = sigma +# self.init_state = init_state if init_state is not None else torch.ones(n_dims) + +# def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): +# xs_b = torch.zeros(b_size, n_points, self.n_dims) + +# for b in range(b_size): +# if seeds is not None: +# torch.manual_seed(seeds[b]) + +# state = self.init_state.clone() +# for t in range(n_points): +# noise = torch.randn(self.n_dims) * self.sigma +# state = self.phi @ state + noise +# if self.bias is not None: +# state += self.bias +# xs_b[b, t] = state + +# if n_dims_truncated is not None: +# xs_b[:, :, n_dims_truncated:] = 0 +# return xs_b +# def test_ar1_sampler(): +# n_dims = 3 +# n_points = 5 +# b_size = 2 +# phi = 0.5 +# sigma = 0.0 +# init_state = torch.tensor([1.0, 2.0, 3.0]) + +# seeds = [42, 123] + +# sampler = AR1Sampler(n_dims=n_dims, scale=phi, sigma=sigma, init_state=init_state) +# xs = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) + +# print("Output shape:", xs.shape) # should be (b_size, n_points, n_dims) +# print("First batch:\n", xs[0]) +# print("Second batch:\n", xs[1]) + +# # Test reproducibility +# xs2 = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) +# assert torch.allclose(xs, xs2), "Output not reproducible with same seeds!" +# print("Reproducibility test passed.") + +# # Test AR1 dynamics roughly +# print("\nCheck AR1 dynamics:") +# for t in range(1, n_points): +# expected = phi * xs[0, t-1] +# print(f"t={t}, previous*phi: {expected}, current: {xs[0, t]}") + + +# def test_var1_sampler(): +# n_dims = 2 +# n_points = 4 +# b_size = 2 +# phi = torch.tensor([[0.5, 0.1], [0.0, 0.7]]) +# sigma = 0.1 +# init_state = torch.tensor([1.0, 2.0]) +# seeds = [42, 123] + +# sampler = VAR1Sampler(n_dims=n_dims, scale=phi, sigma=sigma, init_state=init_state) +# xs = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) +# print(xs) + +# code này là thêm: class AR1Sampler(DataSampler): - def __init__(self, n_dims, bias=None, scale=0.9, sigma=0.5, init_state=None): + def __init__(self, n_dims, rho=0.9, noise_std=1.0, bias=None, scale=None): super().__init__(n_dims) - # phi là số thực, có thể lấy trace nếu scale là ma trận - if torch.is_tensor(scale) and scale.ndim == 2: - self.phi = torch.trace(scale).item() - elif isinstance(scale, (int, float)): - self.phi = float(scale) - else: - raise ValueError("scale phải là số hoặc ma trận 2D torch.Tensor") - - self.sigma = sigma + assert 0 <= abs(rho) < 1, "|rho| must be < 1 for a stable AR(1)" + self.rho = float(rho) + self.noise_std = float(noise_std) self.bias = bias - self.init_state = init_state if init_state is not None else torch.ones(n_dims) + self.scale = scale def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + # Shape: (batch, time, dims) xs_b = torch.zeros(b_size, n_points, self.n_dims) - for b in range(b_size): - if seeds is not None: - torch.manual_seed(seeds[b]) - - state = self.init_state.clone() - for t in range(n_points): - # noise = torch.randn(self.n_dims) * self.sigma - state = self.phi * state - if self.bias is not None: - state += self.bias - xs_b[b, t] = state - - if n_dims_truncated is not None: - xs_b[:, :, n_dims_truncated:] = 0 - return xs_b - -class VAR1Sampler(DataSampler): - def __init__(self, n_dims, bias=None, scale=None, sigma=0.5, init_state=None): - super().__init__(n_dims) - if scale is None: - self.phi = 0.9 * torch.eye(n_dims) + generators = None + if seeds is not None: + assert len(seeds) == b_size + generators = [] + for seed in seeds: + g = torch.Generator() + g.manual_seed(int(seed)) + generators.append(g) + + # Initialize x_0 ~ N(0, I) + if generators is None: + xs_b[:, 0, :] = torch.randn(b_size, self.n_dims) else: - assert scale.shape == (n_dims, n_dims), "scale phải có shape (n_dims, n_dims)" - self.phi = scale + for i in range(b_size): + xs_b[i, 0, :] = torch.randn(self.n_dims, generator=generators[i]) + + # AR(1): x_t = rho * x_{t-1} + eps_t, eps_t ~ N(0, noise_std^2 I) + for t in range(1, n_points): + if generators is None: + eps_t = self.noise_std * torch.randn(b_size, self.n_dims) + else: + eps_t = torch.zeros(b_size, self.n_dims) + for i in range(b_size): + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i]) + xs_b[:, t, :] = self.rho * xs_b[:, t - 1, :] + eps_t - self.bias = bias - self.sigma = sigma - self.init_state = init_state if init_state is not None else torch.ones(n_dims) - - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): - xs_b = torch.zeros(b_size, n_points, self.n_dims) - - for b in range(b_size): - if seeds is not None: - torch.manual_seed(seeds[b]) - - state = self.init_state.clone() - for t in range(n_points): - noise = torch.randn(self.n_dims) * self.sigma - state = self.phi @ state + noise - if self.bias is not None: - state += self.bias - xs_b[b, t] = state + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias if n_dims_truncated is not None: xs_b[:, :, n_dims_truncated:] = 0 + return xs_b -def test_ar1_sampler(): - n_dims = 3 - n_points = 5 - b_size = 2 - phi = 0.5 - sigma = 0.0 - init_state = torch.tensor([1.0, 2.0, 3.0]) - - seeds = [42, 123] - - sampler = AR1Sampler(n_dims=n_dims, scale=phi, sigma=sigma, init_state=init_state) - xs = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) - - print("Output shape:", xs.shape) # should be (b_size, n_points, n_dims) - print("First batch:\n", xs[0]) - print("Second batch:\n", xs[1]) - - # Test reproducibility - xs2 = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) - assert torch.allclose(xs, xs2), "Output not reproducible with same seeds!" - print("Reproducibility test passed.") - - # Test AR1 dynamics roughly - print("\nCheck AR1 dynamics:") - for t in range(1, n_points): - expected = phi * xs[0, t-1] - print(f"t={t}, previous*phi: {expected}, current: {xs[0, t]}") - - -def test_var1_sampler(): - n_dims = 2 - n_points = 4 - b_size = 2 - phi = torch.tensor([[0.5, 0.1], [0.0, 0.7]]) - sigma = 0.1 - init_state = torch.tensor([1.0, 2.0]) - seeds = [42, 123] - - sampler = VAR1Sampler(n_dims=n_dims, scale=phi, sigma=sigma, init_state=init_state) - xs = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) - print(xs) - - -if __name__ == "__main__": - test_var1_sampler() + +# if __name__ == "__main__": +# test_var1_sampler() diff --git a/src/tasks.py b/src/tasks.py index 1ea43e5c..19e64180 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -346,3 +346,71 @@ def get_metric(): @staticmethod def get_training_metric(): return mean_squared_error +class AR1LinearRegression(Task): + def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, ar_coef=0.5, noise_std=1.0): + """ + AR(1) Linear Regression: y_t = x_t^T w + epsilon_t + where epsilon_t = ar_coef * epsilon_{t-1} + u_t, u_t ~ N(0, noise_std^2) + + scale: a constant by which to scale the randomly sampled weights + ar_coef: AR(1) coefficient for error terms + noise_std: standard deviation of innovation noise + """ + super(AR1LinearRegression, self).__init__(n_dims, batch_size, pool_dict, seeds) + self.scale = scale + self.ar_coef = ar_coef + self.noise_std = noise_std + + if pool_dict is None and seeds is None: + self.w_b = torch.randn(self.b_size, self.n_dims, 1) + elif seeds is not None: + self.w_b = torch.zeros(self.b_size, self.n_dims, 1) + generator = torch.Generator() + assert len(seeds) == self.b_size + for i, seed in enumerate(seeds): + generator.manual_seed(seed) + self.w_b[i] = torch.randn(self.n_dims, 1, generator=generator) + else: + assert "w" in pool_dict + indices = torch.randperm(len(pool_dict["w"]))[:batch_size] + self.w_b = pool_dict["w"][indices] + + def evaluate(self, xs_b): + """ + Generate AR(1) linear regression data with correlated errors + """ + w_b = self.w_b.to(xs_b.device) + batch_size, n_points, n_dims = xs_b.shape + + # Generate linear predictions + ys_linear = self.scale * (xs_b @ w_b)[:, :, 0] + + # Generate AR(1) error terms + ys_ar1 = torch.zeros_like(ys_linear) + for b in range(batch_size): + # Generate AR(1) process for errors + errors = torch.zeros(n_points, device=xs_b.device) + for t in range(n_points): + if t == 0: + # Initial error + errors[t] = torch.randn(1, device=xs_b.device) * self.noise_std + else: + # AR(1) error: epsilon_t = ar_coef * epsilon_{t-1} + u_t + errors[t] = self.ar_coef * errors[t-1] + torch.randn(1, device=xs_b.device) * self.noise_std + + # Add AR(1) errors to linear predictions + ys_ar1[b] = ys_linear[b] + errors + + return ys_ar1 + + @staticmethod + def generate_pool_dict(n_dims, num_tasks, **kwargs): + return {"w": torch.randn(num_tasks, n_dims, 1)} + + @staticmethod + def get_metric(): + return squared_error + + @staticmethod + def get_training_metric(): + return mean_squared_error From 3c427cfd640ff248a4c36f87f1689c22a3e23d6f Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Sat, 4 Oct 2025 18:46:30 +0700 Subject: [PATCH 04/88] Add ridge and other relevant functions --- src/models.py | 268 ++++++++++++++++++++++++++++++++++++++++++++++++ src/samplers.py | 243 +++++++++++++++++++++++++------------------ src/tasks.py | 68 ++++++++++++ 3 files changed, 482 insertions(+), 97 deletions(-) diff --git a/src/models.py b/src/models.py index f07fd789..0fabece0 100644 --- a/src/models.py +++ b/src/models.py @@ -76,6 +76,16 @@ def get_relevant_baselines(task_name): (NNModel, {"n_neighbors": 3}), (AveragingModel, {}), ], + "ar1_linear_regression": [ + (LeastSquaresModel, {}), + (RidgeModel, {"alpha": 0.1}), + (RidgeModel, {"alpha": 1.0}), + (RidgeModelWithVarianceAdjustment, {"alpha": 1.0, "ar_coef": 0.5}), + (FeasibleGLSModel, {"ar_coef": None}), + (GLSModel, {"ar_coef": 0.5}), + (NNModel, {"n_neighbors": 3}), + (AveragingModel, {}), + ], } models = [model_cls(**kwargs) for model_cls, kwargs in task_to_baselines[task_name]] @@ -480,3 +490,261 @@ def __call__(self, xs, ys, inds=None): preds.append(pred) return torch.stack(preds, dim=1) +class RidgeModel: + def __init__(self, alpha=1.0): + """ + Ridge regression model with L2 regularization. + alpha: regularization strength (larger values = more regularization) + """ + self.alpha = alpha + self.name = f"ridge_alpha={alpha}" + + def __call__(self, xs, ys, inds=None): + xs, ys = xs.cpu(), ys.cpu() + if inds is None: + inds = range(ys.shape[1]) + else: + if max(inds) >= ys.shape[1] or min(inds) < 0: + raise ValueError("inds contain indices where xs and ys are not defined") + + preds = [] + + for i in inds: + if i == 0: + preds.append(torch.zeros_like(ys[:, 0])) # predict zero for first point + continue + train_xs, train_ys = xs[:, :i], ys[:, :i] + test_x = xs[:, i : i + 1] + + # Ridge regression: (X'X + alpha*I)^(-1) X'y + # Add regularization term to diagonal + XtX = train_xs.transpose(-2, -1) @ train_xs + Xty = train_xs.transpose(-2, -1) @ train_ys.unsqueeze(-1) + + # Add alpha * I to diagonal + reg_matrix = XtX + self.alpha * torch.eye(XtX.shape[-1], device=XtX.device) + + try: + ws = torch.linalg.solve(reg_matrix, Xty) + pred = test_x @ ws + preds.append(pred[:, 0, 0]) + except torch.linalg.LinAlgError: + # Fallback to least squares if singular + ws, _, _, _ = torch.linalg.lstsq(train_xs, train_ys.unsqueeze(2)) + pred = test_x @ ws + preds.append(pred[:, 0, 0]) + + return torch.stack(preds, dim=1) + + +class RidgeModelWithVarianceAdjustment: + def __init__(self, alpha=1.0, ar_coef=0.5): + """ + Ridge regression with variance adjustment for AR(1) data. + alpha: regularization strength + ar_coef: AR(1) coefficient for variance adjustment + """ + self.alpha = alpha + self.ar_coef = ar_coef + self.name = f"ridge_var_adj_alpha={alpha}_ar={ar_coef}" + + def __call__(self, xs, ys, inds=None): + xs, ys = xs.cpu(), ys.cpu() + if inds is None: + inds = range(ys.shape[1]) + else: + if max(inds) >= ys.shape[1] or min(inds) < 0: + raise ValueError("inds contain indices where xs and ys are not defined") + + preds = [] + + for i in inds: + if i == 0: + preds.append(torch.zeros_like(ys[:, 0])) + continue + train_xs, train_ys = xs[:, :i], ys[:, :i] + test_x = xs[:, i : i + 1] + + # Create AR(1) covariance matrix for variance adjustment + n = train_xs.shape[1] + ar_cov = self._create_ar1_covariance(n, self.ar_coef) + + # Weighted Ridge regression: (X'V^(-1)X + alpha*I)^(-1) X'V^(-1)y + try: + ar_cov_inv = torch.linalg.inv(ar_cov) + XtV_inv = train_xs.transpose(-2, -1) @ ar_cov_inv + XtV_invX = XtV_inv @ train_xs + XtV_invy = XtV_inv @ train_ys.unsqueeze(-1) + + # Add regularization + reg_matrix = XtV_invX + self.alpha * torch.eye(XtV_invX.shape[-1], device=XtV_invX.device) + ws = torch.linalg.solve(reg_matrix, XtV_invy) + pred = test_x @ ws + preds.append(pred[:, 0, 0]) + except torch.linalg.LinAlgError: + # Fallback to regular ridge + XtX = train_xs.transpose(-2, -1) @ train_xs + Xty = train_xs.transpose(-2, -1) @ train_ys.unsqueeze(-1) + reg_matrix = XtX + self.alpha * torch.eye(XtX.shape[-1], device=XtX.device) + ws = torch.linalg.solve(reg_matrix, Xty) + pred = test_x @ ws + preds.append(pred[:, 0, 0]) + + return torch.stack(preds, dim=1) + + def _create_ar1_covariance(self, n, ar_coef): + """Create AR(1) covariance matrix: V[i,j] = ar_coef^|i-j|""" + indices = torch.arange(n, dtype=torch.float32) + diff = torch.abs(indices.unsqueeze(0) - indices.unsqueeze(1)) + return torch.pow(ar_coef, diff) + + +class FeasibleGLSModel: + def __init__(self, ar_coef=None): + """ + Feasible GLS for AR(1) data with unknown AR coefficient. + ar_coef: if None, estimate from residuals; otherwise use fixed value + """ + self.ar_coef = ar_coef + self.name = f"feasible_gls_ar={'est' if ar_coef is None else ar_coef}" + + def __call__(self, xs, ys, inds=None): + xs, ys = xs.cpu(), ys.cpu() + if inds is None: + inds = range(ys.shape[1]) + else: + if max(inds) >= ys.shape[1] or min(inds) < 0: + raise ValueError("inds contain indices where xs and ys are not defined") + + preds = [] + + for i in inds: + if i == 0: + preds.append(torch.zeros_like(ys[:, 0])) + continue + train_xs, train_ys = xs[:, :i], ys[:, :i] + test_x = xs[:, i : i + 1] + + pred = torch.zeros_like(ys[:, 0]) + for j in range(ys.shape[0]): + x_j, y_j = train_xs[j], train_ys[j] + + # Step 1: OLS to get initial residuals + try: + w_ols, _, _, _ = torch.linalg.lstsq(x_j, y_j.unsqueeze(-1)) + residuals = y_j - (x_j @ w_ols).squeeze() + except torch.linalg.LinAlgError: + pred[j] = 0.0 + continue + + # Step 2: Estimate AR coefficient from residuals + if self.ar_coef is None and len(residuals) > 1: + # Estimate AR(1) coefficient using Yule-Walker equations + ar_coef_est = self._estimate_ar_coef(residuals) + else: + ar_coef_est = self.ar_coef if self.ar_coef is not None else 0.0 + + # Step 3: Create covariance matrix and perform GLS + if len(residuals) > 1: + n = len(residuals) + ar_cov = self._create_ar1_covariance(n, ar_coef_est) + + try: + ar_cov_inv = torch.linalg.inv(ar_cov) + XtV_inv = x_j.transpose(-1, -2) @ ar_cov_inv + XtV_invX = XtV_inv @ x_j + XtV_invy = XtV_inv @ y_j.unsqueeze(-1) + + w_gls = torch.linalg.solve(XtV_invX, XtV_invy) + y_pred = (test_x[j] @ w_gls).squeeze() + pred[j] = y_pred + except torch.linalg.LinAlgError: + # Fallback to OLS + y_pred = (test_x[j] @ w_ols).squeeze() + pred[j] = y_pred + else: + # Not enough data for GLS, use OLS + y_pred = (test_x[j] @ w_ols).squeeze() + pred[j] = y_pred + + preds.append(pred) + + return torch.stack(preds, dim=1) + + def _estimate_ar_coef(self, residuals): + """Estimate AR(1) coefficient using Yule-Walker equations""" + if len(residuals) <= 1: + return 0.0 + + # Compute autocovariances + n = len(residuals) + gamma_0 = torch.var(residuals) + if n > 1: + gamma_1 = torch.mean(residuals[:-1] * residuals[1:]) + ar_coef = gamma_1 / gamma_0 if gamma_0 > 1e-10 else 0.0 + # Ensure stability + ar_coef = torch.clamp(ar_coef, -0.99, 0.99) + else: + ar_coef = 0.0 + + return ar_coef + + def _create_ar1_covariance(self, n, ar_coef): + """Create AR(1) covariance matrix""" + indices = torch.arange(n, dtype=torch.float32) + diff = torch.abs(indices.unsqueeze(0) - indices.unsqueeze(1)) + return torch.pow(ar_coef, diff) + + +class GLSModel: + def __init__(self, ar_coef=0.5): + """ + GLS with known AR(1) covariance structure. + ar_coef: known AR(1) coefficient + """ + self.ar_coef = ar_coef + self.name = f"gls_ar={ar_coef}" + + def __call__(self, xs, ys, inds=None): + xs, ys = xs.cpu(), ys.cpu() + if inds is None: + inds = range(ys.shape[1]) + else: + if max(inds) >= ys.shape[1] or min(inds) < 0: + raise ValueError("inds contain indices where xs and ys are not defined") + + preds = [] + + for i in inds: + if i == 0: + preds.append(torch.zeros_like(ys[:, 0])) + continue + train_xs, train_ys = xs[:, :i], ys[:, :i] + test_x = xs[:, i : i + 1] + + # Create AR(1) covariance matrix + n = train_xs.shape[1] + ar_cov = self._create_ar1_covariance(n, self.ar_coef) + + try: + ar_cov_inv = torch.linalg.inv(ar_cov) + XtV_inv = train_xs.transpose(-2, -1) @ ar_cov_inv + XtV_invX = XtV_inv @ train_xs + XtV_invy = XtV_inv @ train_ys.unsqueeze(-1) + + w_gls = torch.linalg.solve(XtV_invX, XtV_invy) + pred = test_x @ w_gls + preds.append(pred[:, 0, 0]) + except torch.linalg.LinAlgError: + # Fallback to OLS + ws, _, _, _ = torch.linalg.lstsq(train_xs, train_ys.unsqueeze(2)) + pred = test_x @ ws + preds.append(pred[:, 0, 0]) + + return torch.stack(preds, dim=1) + + def _create_ar1_covariance(self, n, ar_coef): + """Create AR(1) covariance matrix""" + indices = torch.arange(n, dtype=torch.float32) + diff = torch.abs(indices.unsqueeze(0) - indices.unsqueeze(1)) + return torch.pow(ar_coef, diff) \ No newline at end of file diff --git a/src/samplers.py b/src/samplers.py index 45a3cc06..e8370e34 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -60,113 +60,162 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): return xs_b +# class AR1Sampler(DataSampler): +# def __init__(self, n_dims, bias=None, scale=0.9, sigma=0.5, init_state=None): +# super().__init__(n_dims) +# # phi là số thực, có thể lấy trace nếu scale là ma trận +# if torch.is_tensor(scale) and scale.ndim == 2: +# self.phi = torch.trace(scale).item() +# elif isinstance(scale, (int, float)): +# self.phi = float(scale) +# else: +# raise ValueError("scale phải là số hoặc ma trận 2D torch.Tensor") + +# self.sigma = sigma +# self.bias = bias +# self.init_state = init_state if init_state is not None else torch.ones(n_dims) + +# def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): +# xs_b = torch.zeros(b_size, n_points, self.n_dims) + +# for b in range(b_size): +# if seeds is not None: +# torch.manual_seed(seeds[b]) + +# state = self.init_state.clone() +# for t in range(n_points): +# # noise = torch.randn(self.n_dims) * self.sigma +# state = self.phi * state +# if self.bias is not None: +# state += self.bias +# xs_b[b, t] = state + +# if n_dims_truncated is not None: +# xs_b[:, :, n_dims_truncated:] = 0 +# return xs_b + +# class VAR1Sampler(DataSampler): +# def __init__(self, n_dims, bias=None, scale=None, sigma=0.5, init_state=None): +# super().__init__(n_dims) +# if scale is None: +# self.phi = 0.9 * torch.eye(n_dims) +# else: +# assert scale.shape == (n_dims, n_dims), "scale phải có shape (n_dims, n_dims)" +# self.phi = scale + +# self.bias = bias +# self.sigma = sigma +# self.init_state = init_state if init_state is not None else torch.ones(n_dims) + +# def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): +# xs_b = torch.zeros(b_size, n_points, self.n_dims) + +# for b in range(b_size): +# if seeds is not None: +# torch.manual_seed(seeds[b]) + +# state = self.init_state.clone() +# for t in range(n_points): +# noise = torch.randn(self.n_dims) * self.sigma +# state = self.phi @ state + noise +# if self.bias is not None: +# state += self.bias +# xs_b[b, t] = state + +# if n_dims_truncated is not None: +# xs_b[:, :, n_dims_truncated:] = 0 +# return xs_b +# def test_ar1_sampler(): +# n_dims = 3 +# n_points = 5 +# b_size = 2 +# phi = 0.5 +# sigma = 0.0 +# init_state = torch.tensor([1.0, 2.0, 3.0]) + +# seeds = [42, 123] + +# sampler = AR1Sampler(n_dims=n_dims, scale=phi, sigma=sigma, init_state=init_state) +# xs = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) + +# print("Output shape:", xs.shape) # should be (b_size, n_points, n_dims) +# print("First batch:\n", xs[0]) +# print("Second batch:\n", xs[1]) + +# # Test reproducibility +# xs2 = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) +# assert torch.allclose(xs, xs2), "Output not reproducible with same seeds!" +# print("Reproducibility test passed.") + +# # Test AR1 dynamics roughly +# print("\nCheck AR1 dynamics:") +# for t in range(1, n_points): +# expected = phi * xs[0, t-1] +# print(f"t={t}, previous*phi: {expected}, current: {xs[0, t]}") + + +# def test_var1_sampler(): +# n_dims = 2 +# n_points = 4 +# b_size = 2 +# phi = torch.tensor([[0.5, 0.1], [0.0, 0.7]]) +# sigma = 0.1 +# init_state = torch.tensor([1.0, 2.0]) +# seeds = [42, 123] + +# sampler = VAR1Sampler(n_dims=n_dims, scale=phi, sigma=sigma, init_state=init_state) +# xs = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) +# print(xs) + +# code này là thêm: class AR1Sampler(DataSampler): - def __init__(self, n_dims, bias=None, scale=0.9, sigma=0.5, init_state=None): + def __init__(self, n_dims, rho=0.9, noise_std=1.0, bias=None, scale=None): super().__init__(n_dims) - # phi là số thực, có thể lấy trace nếu scale là ma trận - if torch.is_tensor(scale) and scale.ndim == 2: - self.phi = torch.trace(scale).item() - elif isinstance(scale, (int, float)): - self.phi = float(scale) - else: - raise ValueError("scale phải là số hoặc ma trận 2D torch.Tensor") - - self.sigma = sigma + assert 0 <= abs(rho) < 1, "|rho| must be < 1 for a stable AR(1)" + self.rho = float(rho) + self.noise_std = float(noise_std) self.bias = bias - self.init_state = init_state if init_state is not None else torch.ones(n_dims) + self.scale = scale def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + # Shape: (batch, time, dims) xs_b = torch.zeros(b_size, n_points, self.n_dims) - for b in range(b_size): - if seeds is not None: - torch.manual_seed(seeds[b]) - - state = self.init_state.clone() - for t in range(n_points): - # noise = torch.randn(self.n_dims) * self.sigma - state = self.phi * state - if self.bias is not None: - state += self.bias - xs_b[b, t] = state - - if n_dims_truncated is not None: - xs_b[:, :, n_dims_truncated:] = 0 - return xs_b - -class VAR1Sampler(DataSampler): - def __init__(self, n_dims, bias=None, scale=None, sigma=0.5, init_state=None): - super().__init__(n_dims) - if scale is None: - self.phi = 0.9 * torch.eye(n_dims) + generators = None + if seeds is not None: + assert len(seeds) == b_size + generators = [] + for seed in seeds: + g = torch.Generator() + g.manual_seed(int(seed)) + generators.append(g) + + # Initialize x_0 ~ N(0, I) + if generators is None: + xs_b[:, 0, :] = torch.randn(b_size, self.n_dims) else: - assert scale.shape == (n_dims, n_dims), "scale phải có shape (n_dims, n_dims)" - self.phi = scale + for i in range(b_size): + xs_b[i, 0, :] = torch.randn(self.n_dims, generator=generators[i]) + + # AR(1): x_t = rho * x_{t-1} + eps_t, eps_t ~ N(0, noise_std^2 I) + for t in range(1, n_points): + if generators is None: + eps_t = self.noise_std * torch.randn(b_size, self.n_dims) + else: + eps_t = torch.zeros(b_size, self.n_dims) + for i in range(b_size): + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i]) + xs_b[:, t, :] = self.rho * xs_b[:, t - 1, :] + eps_t - self.bias = bias - self.sigma = sigma - self.init_state = init_state if init_state is not None else torch.ones(n_dims) - - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): - xs_b = torch.zeros(b_size, n_points, self.n_dims) - - for b in range(b_size): - if seeds is not None: - torch.manual_seed(seeds[b]) - - state = self.init_state.clone() - for t in range(n_points): - noise = torch.randn(self.n_dims) * self.sigma - state = self.phi @ state + noise - if self.bias is not None: - state += self.bias - xs_b[b, t] = state + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias if n_dims_truncated is not None: xs_b[:, :, n_dims_truncated:] = 0 + return xs_b -def test_ar1_sampler(): - n_dims = 3 - n_points = 5 - b_size = 2 - phi = 0.5 - sigma = 0.0 - init_state = torch.tensor([1.0, 2.0, 3.0]) - - seeds = [42, 123] - - sampler = AR1Sampler(n_dims=n_dims, scale=phi, sigma=sigma, init_state=init_state) - xs = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) - - print("Output shape:", xs.shape) # should be (b_size, n_points, n_dims) - print("First batch:\n", xs[0]) - print("Second batch:\n", xs[1]) - - # Test reproducibility - xs2 = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) - assert torch.allclose(xs, xs2), "Output not reproducible with same seeds!" - print("Reproducibility test passed.") - - # Test AR1 dynamics roughly - print("\nCheck AR1 dynamics:") - for t in range(1, n_points): - expected = phi * xs[0, t-1] - print(f"t={t}, previous*phi: {expected}, current: {xs[0, t]}") - - -def test_var1_sampler(): - n_dims = 2 - n_points = 4 - b_size = 2 - phi = torch.tensor([[0.5, 0.1], [0.0, 0.7]]) - sigma = 0.1 - init_state = torch.tensor([1.0, 2.0]) - seeds = [42, 123] - - sampler = VAR1Sampler(n_dims=n_dims, scale=phi, sigma=sigma, init_state=init_state) - xs = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) - print(xs) - - -if __name__ == "__main__": - test_var1_sampler() + +# if __name__ == "__main__": +# test_var1_sampler() diff --git a/src/tasks.py b/src/tasks.py index 1ea43e5c..19e64180 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -346,3 +346,71 @@ def get_metric(): @staticmethod def get_training_metric(): return mean_squared_error +class AR1LinearRegression(Task): + def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, ar_coef=0.5, noise_std=1.0): + """ + AR(1) Linear Regression: y_t = x_t^T w + epsilon_t + where epsilon_t = ar_coef * epsilon_{t-1} + u_t, u_t ~ N(0, noise_std^2) + + scale: a constant by which to scale the randomly sampled weights + ar_coef: AR(1) coefficient for error terms + noise_std: standard deviation of innovation noise + """ + super(AR1LinearRegression, self).__init__(n_dims, batch_size, pool_dict, seeds) + self.scale = scale + self.ar_coef = ar_coef + self.noise_std = noise_std + + if pool_dict is None and seeds is None: + self.w_b = torch.randn(self.b_size, self.n_dims, 1) + elif seeds is not None: + self.w_b = torch.zeros(self.b_size, self.n_dims, 1) + generator = torch.Generator() + assert len(seeds) == self.b_size + for i, seed in enumerate(seeds): + generator.manual_seed(seed) + self.w_b[i] = torch.randn(self.n_dims, 1, generator=generator) + else: + assert "w" in pool_dict + indices = torch.randperm(len(pool_dict["w"]))[:batch_size] + self.w_b = pool_dict["w"][indices] + + def evaluate(self, xs_b): + """ + Generate AR(1) linear regression data with correlated errors + """ + w_b = self.w_b.to(xs_b.device) + batch_size, n_points, n_dims = xs_b.shape + + # Generate linear predictions + ys_linear = self.scale * (xs_b @ w_b)[:, :, 0] + + # Generate AR(1) error terms + ys_ar1 = torch.zeros_like(ys_linear) + for b in range(batch_size): + # Generate AR(1) process for errors + errors = torch.zeros(n_points, device=xs_b.device) + for t in range(n_points): + if t == 0: + # Initial error + errors[t] = torch.randn(1, device=xs_b.device) * self.noise_std + else: + # AR(1) error: epsilon_t = ar_coef * epsilon_{t-1} + u_t + errors[t] = self.ar_coef * errors[t-1] + torch.randn(1, device=xs_b.device) * self.noise_std + + # Add AR(1) errors to linear predictions + ys_ar1[b] = ys_linear[b] + errors + + return ys_ar1 + + @staticmethod + def generate_pool_dict(n_dims, num_tasks, **kwargs): + return {"w": torch.randn(num_tasks, n_dims, 1)} + + @staticmethod + def get_metric(): + return squared_error + + @staticmethod + def get_training_metric(): + return mean_squared_error From 767436f1ab3564153d1a017b3bd441c1936d235a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tr=E1=BB=8Bnh=20V=C5=A9=20=C4=90=E1=BB=A9c=20H=E1=BA=A3i?= Date: Mon, 6 Oct 2025 13:25:37 +0700 Subject: [PATCH 05/88] Update fetures --- src/conf/toy.yaml | 51 ++++++++++++++++++++---------------- src/eval.py | 2 +- src/models.py | 67 +++++++++++++++++++++++++++++++++-------------- src/plot_utils.py | 6 +++++ src/samplers.py | 1 - src/schema.py | 1 + src/tasks.py | 1 + 7 files changed, 86 insertions(+), 43 deletions(-) diff --git a/src/conf/toy.yaml b/src/conf/toy.yaml index c3566bab..906fd460 100644 --- a/src/conf/toy.yaml +++ b/src/conf/toy.yaml @@ -3,31 +3,38 @@ inherit: - wandb.yaml model: - n_dims: 5 - n_positions: 11 + family: gpt2 + n_dims: 5 + n_embd: 128 + n_head: 8 + n_layer: 4 + n_positions: 100 training: - task: linear_regression - data: gaussian - task_kwargs: {} - batch_size: 64 - learning_rate: 0.0001 - save_every_steps: 1000 - keep_every_steps: 100000 - train_steps: 5001 - curriculum: - dims: - start: 5 - end: 5 - inc: 1 - interval: 2000 - points: - start: 11 - end: 11 - inc: 2 - interval: 2000 + batch_size: 32 + curriculum: + dims: + start: 5 + end: 5 + inc: 0 + interval: 1 + points: + start: 21 + end: 21 + inc: 0 + interval: 1 + data: ar1 + keep_every_steps: 100000 + learning_rate: 0.0003 + num_tasks: null + num_training_examples: null + resume_id: null + save_every_steps: 1000 + task: ar1_linear_regression + task_kwargs: {} + train_steps: 50001 out_dir: ../models/linear_regression wandb: - name: "linear_regression_toy" + name: "linear_regression_toy with 5 points" diff --git a/src/eval.py b/src/eval.py index 8ba52579..b92b679d 100644 --- a/src/eval.py +++ b/src/eval.py @@ -209,7 +209,7 @@ def build_evals(conf): evaluation_kwargs = {} evaluation_kwargs["standard"] = {"prompting_strategy": "standard"} - if task_name != "linear_regression": + if task_name not in ["linear_regression", "ar1_linear_regression"]: if task_name in ["relu_2nn_regression"]: evaluation_kwargs["linear_regression"] = {"task_name": "linear_regression"} for name, kwargs in evaluation_kwargs.items(): diff --git a/src/models.py b/src/models.py index 0fabece0..3ca664c1 100644 --- a/src/models.py +++ b/src/models.py @@ -672,28 +672,57 @@ def __call__(self, xs, ys, inds=None): return torch.stack(preds, dim=1) def _estimate_ar_coef(self, residuals): - """Estimate AR(1) coefficient using Yule-Walker equations""" - if len(residuals) <= 1: - return 0.0 - - # Compute autocovariances - n = len(residuals) - gamma_0 = torch.var(residuals) - if n > 1: - gamma_1 = torch.mean(residuals[:-1] * residuals[1:]) - ar_coef = gamma_1 / gamma_0 if gamma_0 > 1e-10 else 0.0 - # Ensure stability + """Estimate AR(1) coefficient using Yule-Walker equations (returns a torch.Tensor scalar).""" + # Ensure residuals is a torch tensor + if not isinstance(residuals, torch.Tensor): + residuals = torch.tensor(residuals, dtype=torch.float32) + + if residuals.numel() <= 1: + # return tensor scalar on same device + return torch.tensor(0.0, dtype=torch.float32, device=residuals.device) + + # Use unbiased-ish estimators: + n = residuals.shape[0] + # gamma_0: variance (use unbiased? here regular torch.var with unbiased=False to match mean-of-squares) + gamma_0 = torch.var(residuals, unbiased=False) + gamma_1 = torch.mean(residuals[:-1] * residuals[1:]) + + # avoid division by (near) zero + if gamma_0.item() <= 1e-10: + ar_coef = torch.tensor(0.0, dtype=torch.float32, device=residuals.device) + else: + ar_coef = gamma_1 / gamma_0 + # ensure tensor type & correct device + if not isinstance(ar_coef, torch.Tensor): + ar_coef = torch.tensor(ar_coef, dtype=torch.float32, device=residuals.device) + else: + ar_coef = ar_coef.to(dtype=torch.float32, device=residuals.device) + + # clamp safely as tensor ar_coef = torch.clamp(ar_coef, -0.99, 0.99) + + return ar_coef # tensor scalar + + def _create_ar1_covariance(self, n, ar_coef, device=None, dtype=torch.float32): + """Create AR(1) covariance matrix V[i,j] = ar_coef**|i-j|. + ar_coef may be float or torch scalar; this returns a torch.Tensor (n x n). + """ + if device is None: + # default CPU + device = torch.device("cpu") + + # make ar_coef a tensor scalar on correct device + if not isinstance(ar_coef, torch.Tensor): + ar_coef_t = torch.tensor(ar_coef, dtype=dtype, device=device) else: - ar_coef = 0.0 - - return ar_coef + ar_coef_t = ar_coef.to(device=device, dtype=dtype) - def _create_ar1_covariance(self, n, ar_coef): - """Create AR(1) covariance matrix""" - indices = torch.arange(n, dtype=torch.float32) - diff = torch.abs(indices.unsqueeze(0) - indices.unsqueeze(1)) - return torch.pow(ar_coef, diff) + indices = torch.arange(n, dtype=dtype, device=device) + diff = torch.abs(indices.unsqueeze(0) - indices.unsqueeze(1)).to(dtype=dtype) + + # use torch.pow with tensor base and tensor exponent + # (ensure ar_coef_t is broadcastable) + return torch.pow(ar_coef_t, diff) class GLSModel: diff --git a/src/plot_utils.py b/src/plot_utils.py index 2bd41bad..006ed39b 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -37,6 +37,12 @@ "3-Nearest Neighbors", "2-layer NN, GD", ], + "ar1_linear_regression": [ + "Transformer", + "Least Squares", + "3-Nearest Neighbors", + "2-layer NN, GD", + ], } diff --git a/src/samplers.py b/src/samplers.py index e8370e34..04d0cfbf 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -15,7 +15,6 @@ def get_data_sampler(data_name, n_dims, **kwargs): names_to_classes = { "gaussian": GaussianSampler, "ar1":AR1Sampler, - "var1":VAR1Sampler, } if data_name in names_to_classes: sampler_cls = names_to_classes[data_name] diff --git a/src/schema.py b/src/schema.py index 40f72489..88f8ae92 100644 --- a/src/schema.py +++ b/src/schema.py @@ -41,6 +41,7 @@ "relu_2nn_regression", "decision_tree", "noisy_linear_regression", + "ar1_linear_regression" ] training_schema = { diff --git a/src/tasks.py b/src/tasks.py index 19e64180..2da4cb57 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -60,6 +60,7 @@ def get_task_sampler( "quadratic_regression": QuadraticRegression, "relu_2nn_regression": Relu2nnRegression, "decision_tree": DecisionTree, + "ar1_linear_regression": AR1LinearRegression, } if task_name in task_names_to_classes: task_cls = task_names_to_classes[task_name] From 0b54ebe66f7bf11c69c423c23581fc3a2ab64b0f Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Thu, 9 Oct 2025 18:57:47 +0700 Subject: [PATCH 06/88] Add ar2 and vr2, draw figure 3 --- .vscode/settings.json | 4 ++ src/conf/ar2.yaml | 15 ++++++ src/eval.ipynb | 68 ++++++++++++++++++--------- src/eval.py | 31 +++++++++++-- src/models.py | 10 +++- src/samplers.py | 105 +++++++++++++++++++++++++++++++++++++++++- src/tasks.py | 27 +++++++++++ 7 files changed, 232 insertions(+), 28 deletions(-) create mode 100644 .vscode/settings.json create mode 100644 src/conf/ar2.yaml diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000..ba2a6c01 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,4 @@ +{ + "python-envs.defaultEnvManager": "ms-python.python:system", + "python-envs.pythonProjects": [] +} \ No newline at end of file diff --git a/src/conf/ar2.yaml b/src/conf/ar2.yaml new file mode 100644 index 00000000..14b4197e --- /dev/null +++ b/src/conf/ar2.yaml @@ -0,0 +1,15 @@ +defaults: + - base + +training: + data: ar2 + data_kwargs: + rho1: 0.5 + rho2: 0.3 + noise_std: 0.1 + + curriculum: + points: + start: 5 + end: 40 + step: 5 \ No newline at end of file diff --git a/src/eval.ipynb b/src/eval.ipynb index 10c5a98a..bdbff506 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -2,10 +2,21 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "ed6cfeb1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mRunning cells with 'Python 3.13.0' requires the ipykernel package.\n", + "\u001b[1;31mCreate a Python Environment with the required packages.\n", + "\u001b[1;31mOr install 'ipykernel' using the command: 'c:/Users/CaoHuuThienHoang/AppData/Local/Programs/Python/Python313/python.exe -m pip install ipykernel -U --user --force-reinstall'" + ] + } + ], "source": [ "from collections import OrderedDict\n", "import re\n", @@ -205,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -227,7 +238,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -246,7 +257,18 @@ "\n", "models = relevant_model_names[task]\n", "basic_plot(metrics[\"standard\"], models=models)\n", - "plt.show()" + "plt.show()\n", + "\n", + "# Figure 3 and 4\n", + "for model_name in models: \n", + " if \"gradient_alignment\" in metrics[\"standard\"][model_name]: \n", + " alignments = metrics[\"standard\"][model_name][\"gradient_alignment\"]\n", + " plt.figure(figsize=(6,4))\n", + " plt.plot(range(len(alignments)), alignments, label=f\"{model_name} gradient-w alignment\")\n", + " plt.xlabel(\"# in-context examples\")\n", + " plt.ylabel(\"normalized inner product\") \n", + " plt.legend()\n", + " plt.show()" ] }, { @@ -259,7 +281,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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xIlQGajAts8O2xlATHt2JV/ftsAFQURRQBAh22DJuSFEU4iKGQ7EjhOyn2tYe+l85sz8uI5wwN73zZvLYNa47cACX7NN3s943ZcokjjjiKL755isGDRrMhRdewk03XU84HCKRSLDffgdw6aVXAPD4449SXr6GUChEdXUVvXr15k9/mo3T6WL+/A/5298eRlVVTNPk97+/joaGev7v/55DCIvvv/+OP//5LoQQzJ59Oy0tLWiazkUXXcK+++4PwD77jOPcc8/ns88+YZ999qOurg673UZFRTlVVZUccshEDjjgIB5//FHq6uo47bTTmTp1GgBr1qzmr3+9m7a2VpLJJKeeOo3Jk0/o8rgXXHDxtvvgd3Ay+GWgsn11l77eLZ/jtyGX5sKu2zg4rzf/rFqcavrsP462eIh8Zz4bW/IlLuJUtFWTNI1O20xhURWsY2C2Ex3btinsVlob7OJWnKSVJJSIEE6E8Tv95DtyYUcPJoqgOdlCXbABt81FsbcQh+JkF1ofosc99EV5jwQ+gHDC5KEvyjc7+AGEw2GefPJZAOLxOHfffS9utxvDSHL55Zfw+eefpgPUkiWLeeqpf+L1ern88kv497/fZsqUE3nssUeYMeMGRo/eA9M0icWi7LnneCoqyolGo1x22ZVAqul1ypQTOf74KZSVreLCC8/j//7v1fTC/g6Hk6ee+icAt946k1WrVvHgg49iWSa/+tVkQqEQjzzydxobG5k69Vccd9wU7HY7N998PbNm3U7//gMIh8OcffYZjBq1O/37D+h03F2JDH6bEEzGCRhxHKpGsdO3zW54GjpZTh/9jQR9XT7Ko0EWBurZR+9FQsSx03WWdENJUtFaRdzoflpE3EhQHa6lr683WNs/sKwNdgkrTqI92IUSYRJmEmO9gB1JxEh6EhR7ilCsHbOf0lQM6iL1NEfaEELQZgaJJKMUevPJsWdv12wcOwpFUQhbQeyqAxu2bfJAeMnefXu05nfJ3psf+ACOPXZS+mfLsnjggXtZtOg7hBA0NzexfPmy9Wpn++Lz+QAYOXIUVVWp5QvHj5/Avff+hUMPPYx9992fQYMGdzpPOBxm+fKfmDz5eAAGDBjIkCFD+eGH7znwwIMBmDRpcof3HHzwIdjtdgD69u3HfvsdgKqqFBYW4vP5qa+vQwjB6tWruemmdU2riUSC1avL0sFvw+PuKmTw24S1TZ6FdjeObbgCixCCLIefxnALB+f24dmqxcxvqmBCdjERI4Zd7xz8LMWgMlhDJBnrcJz/NlVQ4vAwwpeXfj0QDdJoa6bQmb9dJs6rqkJSJIiZcYKJMOFEpFOw25BA0BhuIWEm6eUrQRc7Rk0VUjf4qBWhqq3j5w2QNA2q22oJOIIUewtxa+5danGCiBWmvLUaRVEo8uaTZcva6oeAS/bpu0U1s57mcq3rf3/hhX8SDAZ44olncDgc/PnPfyQeXzfC2m5fd39Y28QJcMUVV7NixXK++eYrrr/+Wk477QymTDlxq8qSOp99vfNpG/yeOr+iKGRnZ3fqw9zYcXcVu95j62aqirUCqTl+W5LNYWNcqhOnzcFBeb0B+LS5iqRl0RYL0Gk1HsWiOlRHMLYuj5sQgr+t+Y4/r/iCqxd/yKpw67ptQH2okZDRw3nfFEFMRKgKV7OiZTWrmstpCDURSUQ3GvjWF4iFWN1aQXwHmauoqNCSbGZ1a0WnwLeWAILxMGUt5dTFGrCUnmmy2xqKCmIblytJgsr2JveEkaSytYbVgXIiVhjlF343CQaD5Ofn43A4qK+vZ/78jzJ635o1qxk8eAhTp07j6KOPZcmSHzvt4/F4GDJkGG+9NQeAsrJVrFixjFGjdt+qMvft2w+n08nbb89Nv7Z6dRnh8M6RD7InyZrfJqyb5rBlqYw2TiHHlUX/RJT+Lj+rowEWttWxv81Owkqu67NTBLWRelraywKpwPdExSJerV0OQEJY3Lr8cx4adTgePfU+0zKpDtYyILsv2jasVa2t5YWSYZqjrUQSsfSI0y0VTcYoa62gl78E/884WMdSDOrC9TS1N3NuimGZ1AbqCdiDlHgL8Wien30Qj6JC1IxQH2zCEoI+vlI0sfX/1IVqUdVWQ2y9JncBhOIRIokKctx+Clz52BXHL7I/9Ne/PpUbbriOadNOobCwkAkT9srofQ8//AAVFeVomobX6+OGG27ucr9Zs25j9uzbeeGF59A0nZkz/7jVibx1Xeeuu+7l3nvv5p//fAbLssjNzeX222dv1XF/CWRWhy6sv2r+7Uvf54EVn3Ju3925bY/jUcS2DYAJ4qxsXs1T5Yt4pvJHjizozzWDJjAgtw9e1YeiCBrijdQGGjuMMn264geeq1qCpihcN2gv/q96KasibRyS14frB+/dYeWMHHcWfTyl6VGKW5oVIHVTjdIaD9AWC5Awklv/AWxAU1WKfYXk2nO2eCDMllyfoijErAiVwVoiiegWnVdTVfLcOeS78rZJsOlOd9enqBAxozRGmgjGQpjtDyQ+h4c+/l5bVSZFEVRFamhar3WhKzZNb+8PzUIR2kazOvz442JKSzuvuShJ21J19RpGjtyt0+u/8IaKrbc2iW2py7fVq7t0xak6cNtd6abPz9qbPgOxILqu0pxopS7YMfD9s3Ixz1UtQUXh+sH7cGh+X24asi8uVefDpgrm1q3qcI7WSICmRMsWLSWlqgqWYhAw21jVtoZVzWtoCDX1SOADMC2L6rY6aiN1CGXrapOZSjVztlDWWrHFgQ9SZa8PNVHWWk7IDCJUsV2W71IUiIkoFcEqylrW0BoNpAMfpJpnywNVmMqWfWeKAg3xJprDbZvcN2kaVLXVsqp1DSEr+ItvCpV2XvKv5iasXdqslyurR45vtS931s/lZ4A7i5CZZEFbHaFEhMZYEzXBeqz1Kuf/V7WUf1T+iApcN3ivdNDs7fJx5cA9AXhkzbcsD7ek3yMQ1AUbiFiRjMqkqgoGScJWiOpILStaVrOmpSq1As1WNm9GzCRv15dxx4ovWBxs6nIfgaAh3ExFcMtv2JmyFJPqcA1VbTVdTh0BiFsm/6j4kT8t/1+HftXuRJMx1rRWsrJlFQ2x+va+TLHN+zMVBeIiSmW4mlUt5bRE2zCtrr+f0BYGQEWBNiPQ6QFsUyLJGGtaKqkJ127W+SRpe5F9fpuwdo5fP29Oj/XluPX2OX+5vSmLtPFRUwV755RQ1VbbIfC9WrOMJyoWoQBXD5rAxPyOo+MOze/L94EG5tav4o/LPueR0Uek+/8My6Q6UEP/7M4j6lJTEyxiVpyoESUQCxFNxkiaya1dyQ1I9U/+GGzi7YYy5jdVEGufmP9RUwUX9xvL5KKBXdaQ2mJBkmaSPlml7fPqtkFh2qmqQtgMUR2o32ht74dAI39Z9RWV7QONPmyq4PD8fvy2zygKHd2PkrOEIJqME002UK804bA5yHH68dg9OFXnVk3wF0IQJ0ZTpJm2aBDDymxQSygeoTxQRR9/r4xH1kasKNWB2i3q07WEoC22cyTdlXY92y34zZ49m3feeYeqqirmzJnD0KFDO+3z0EMP8dZbb6GqKjabjSuvvJIDDzwQgBkzZvDZZ5+lO4CPPvpoLrrooh4tc8IyqY+HUFHo7c7psU58m2LH5/ByUF4fnq78kc9aqklYJnZ1Xf/i67UreHTNdwBcOXA8RxT07/JYF/Ufw9JQMysirfxl1VfcNGTfdGCJJGPUReoptLLaB60kiZsxgokIwXiQhGl0WjFmazQlorzXsIZ3GsrSwQNgtC+fYoeH9xrXcP/qBSwJNXH5wD1xqJ37UyPJGGUtFRT7CvDbs1C2wbxFoVjUxZppCDV1e70RM8mT5T/wZt0KBNDX5WN3XwH/bijjvcY1fNhUwZTiwZzWawQ+3d7lMdYyhUUkESWSiKKpGi6bg2yXH4/uwaE6WD+uKMq6TAcCgSnM9B8LE8MyCba0UNlcn3HQW18oHqEiwwBoKEmqullMQZJ2dtst+B122GGcddZZnH766d3us/vuu3POOefgcrlYunQpZ5xxBp988glOZ2rO2/nnn88ZZ5yxvYpMTTSAAPLtTty2nsuyblmCbIefvm4/A91ZrIq0saCtjn1ySgGYW7eSh1YvBOCyAeM4pnBAt8eyqxo3Dt2Xixe9x8fNVbxRt4IpxUPS25sjbaxpq6I5GEzV7gxjq5drW59hWXzRWsO/68v4srUWq/3YuTYnRxb056iC/vR2pSYCj88u5p5VX/Ne4xrKIm3MHLofxU5Pp2MmzCQVrTV4HK0Uegrw6h62pPV17aCW6mA9oXjXOb4Avm6t5a+rvqE+EUFTFE4tHc60XiOwqxq/Lh3GkxU/8GFTBS/XLOPt+jJO6zWCKcWDOzysdMe0TELxCKF4BF3VcNld5Dj96KqOYRkkTIOkmUjNkbQMTGFhCYFlWenal9/v2qLAt1YmAVCoFlWBGqLJjplCTCF4vPx7VkfaODy/Hwfm9e7yoUWSdnTbLfiNHz9+k/usreUBDBs2DCEEra2tFBcX92TRurV2sEuRw4NGz/4Dd2kuHLqdg/L6sCrSxkdNleyTU8q/68u4r2wBAJf0H8NxRYM2eaxeTi+/HziBPy7/nL+t+Y4R3jyGeXOBVJNZU6SFQHTLB3ZsKGQkWBZu4avWWt5vXENr+w1TUxQOyOnF0QUDGJ9dhLbB6IeJ+X3p7/Iza9lnrIi0cvEP73P94L0Zn935+xaI9JD6LJePQnc+TtWZ+eRyVdAUb6Yu1Njt/MOgkeDRNd/xbsNqAIZ4svn9wAkM8mSn9ylxerlhyD6cXDKUv5cv4ttAPY+Xf8/rtcs5u88oJub3Q8uwc8+wTIKxUIe5m9tKYyLK2/VljPTlMS6rqNP2UDzCmrZK+mX17hwAFUFtuI7ABuUyheAvK7/ivcY1AHzTVsfDqxdyeEF/ji0cQH93z/SLS1JP2GH7/F5//XX69u3bIfA99dRTvPjii/Tp04ff//73DBq06UCwvu6GXHeloMBHW2vqJt7b6ycv24fb7tqs822uuF7IJGswT1f8wOet1fw3UMk9q74G4PKh45nWv/Nw3bV0VetQG5jsH8LSWDMvV/zE7Su+4Jl9J+Ffr/bq92/ZtUSNJEuDzSwNNLG4rZHFgSYqIx37dQZ4sji+1xCOLhlArmPj5xnjd/GP/MnMXPQJnzVWcf3Sj7lw8Fh+M2BUtyMlTRI0GHXkurIp9Ofj7KJWXlDgS/8cioeoCtYTFGHcHht0sebph3Xl3LnkC5oSUeyqyvRBY5jWbzf0TqsNpEzw92J8SSn/a6rmwWULWBFq4c6VX/Fa3QouGTKOffNLe3SkZ3ffX000xLNlP/Bm1QqSwkJF4arhEzil7/Au9282G+mf0wdP+99tIQTVgTqSWrzDOUxhcesPn/Fe4xpcms5ZA0Yxv76CJYEm/lW7nH/VLmf37AJ+1XsoE4v64dRStxa7tuOs2iNJ69vu8/wmTpzIo48+2mWf31pffvkl1157LU8++SQDBw4EoK6ujoKCAlRV5fXXX+e+++7j/fffR9Myr5Ft7jy/e5Z/xJ0/fcgZvUdy19gTe3wdxzgxVjavYfq3/2Zl+yhTgHP7jObUXl3fvBQU8j055LiyWN1a2WEKQsIyufLH/7Is3MJ+OaXcMnQ/FEXB73cRCGy65mdYFisirSwLNfNTuIVloWbKowE2bHG0KSqDPNkM9+YyMa8vw725m33jt4TguarFPFO5GID9c0q5ZtBe6QE73XHodgq9+WTZ/OnvJz0PThG0JtuoDdZ322/Vkojx4OqFzG9OrcM40pfH7wdOoI/L1+X+XTGF4IPGNTxV8QMN7YNnxvgLOLFkKHtll2RcE8xUV99fdSzEC1VLea9xNWb7P+mRvjx+bB9RO7V0GOf0GY3aRVncNid9s3rjUB20JQNUtFV1GDVqCsFdK7/kP43lOFWNPw0/kNH+AgCWh1t4q24VHzSVE2n/jL2ajcML+nFs4UBGZBWw3+CxXV7HjjbPb8qUSfzlL/d1ufbmtjJ37pvsvvse9O3b9XV/881XPPTQAySTCRKJBPn5+TzwwKOo3TyESZvW3Ty/Ha7mt3DhQq655hoefvjhdOADKCpa13QzZcoU/vznP1NbW0uvXr16rCxrV3cpdfrQVQ3T7NnnBKfqxGVzcHBe73TwO6v3yG4Dn6ooFPkKyHfkoaDQy1dM+Xo3LruqceOQfblo0Xt81lLNa7XLOamk+4cOSDX9fdVay+ct1XzVWkvY7Dg0XkVhkDuLod4chnlyGerNob8rC9tW/uNUFYUze49kiCeHO1Z8yact1ZT/8B9mDt2Pfm5/t++LGwkqW6tpcbRS5MnHo6dq94aSpDZUT2s00GWfphCCD5rKeWj1twSNBE5V49y+u3N80aAuA8TGaIrCEQX9OTivD2/UruD5qiV8G2jg20ADBXYXxxQO5JjCAeT3QMtBRTTI81VL+KCxHAuBCkzM68tpvYbT353FO/Wr+WvZ17xY/RMN8Si/HzS+U99kJBmjvK2SQm8BVYGaDQKfxZ0rvuKDpnJcqs6fhh/IKH9+evsQTw6XD9yT8/vtwYdNFbxVt4ql4WZer13B67UrOLxwIO90E/x2RfPmzSE7O7vL4GcYBn/4wzU89NBjDBmS+nf6009Lt8tcUQDTNDerMrGz26GC3/fff8+VV17J/fffz8iRIztsq6urSwfAjz/+GFVVOwTEnrB2jl9vT9Z2WbJKWJDjyuaoggF81lLN/jm9ug18mqJS7C8kz5GLsFLLTPl0H4XefGoDDekbfonTw9WDJjBr2Wc8Xv49I7x57OPv3eFYNbEQn7dU83lLNd8HGtODVCDVf5jqM8xhqCeXQZ7sHh3gsE9OKQ+NPpxbfvqU1dEAl/7wH64ZNIED83p3+551S2xVkuX0YtrzWdVS2W3mi0Ayzn1lC9K1vXFZRVw5YM8uB9tsDruqcUrpMI4uHMDb9WXMq19FdSzEM5U/8s/KxeybU8KkokHsmVW02QF2Q2WRNp6vWsJHTRUIUg8lRxb057TS4ekBRQBHFfYnz+7k1mWf80FTOU3JKLcM3Q/vBiNUI8kY5a2VHabWmMLijhVf8mFTRSrwjTiQUb58uuLSdI4pHMAxhQNYEW7lrfpV/KdxDcsjLV3uv6HYjw8S+3Y29MRatLoX55jrcI783Ra9/bPPPuGpp54gkYhjs9m44orfM2rU7jQ1NXab36+rHH41NVUsXbqYe+65i7/97WEuvfRK9tpr7/R5IpEIkUiE3Nx1C9QPG7bu3/+33y7grrvuAGDs2HF88sl8/vKX+xk0aDD77DOODz74BLc7Nf1m/d9vvvkGystXk0wm6d27DzfcMBO/388333zNPffcyfDhI1i27CcuuOBi+vTp22Xuv1gs2p5GaSW6rtOvX/+dfom07Rb8brvtNt59910aGxs5++yzyc7OZt68eUyfPp3LLruM0aNHM2vWLGKxGDffvG7tuzvvvJNhw4Zx3XXX0dTUhKIoeL1eHnnkkR7POrw2+PV15263tQo9upsil5cHRh3W7T66qtErq5gsPavDqEchIN+ZS8yI0xJZtxrHAbm9OKl4CK/WLue25Z/zbMFx/BRs4vOWav7XUs3qaCC9r4rCGH8B++aUsk9OKaXOzPtJt5VeTi/3jzqMe1Z9zYdNFdy6/HOOaRvA2b1HkWPvOtUTgCUsWqIBzECy28D3dWstd638iuZkDJeqc1H/MRxd0H+bPl37dDu/Lh3GySVD+TZQz9y6VXzWUsWnLdV82lJNscPNsYUDObpgwEavZ31CCGKWyepIG6+tWsGH9eUA6IrCUQUDmFo6nJJugvf47GLuGXkoNyz9mO8CDVz543+5ffiBneYpdhf43Fqqxjeym8C3ocGebC4bMI7pfXfn8cofMnpP/MeHeibwARgh4j8+tEXBr7KygieffJz77nsIj8fLqlUrufLKS3njjbfwen3d5vfrLoffvHlzOf30MznggIM6ncvv9zNlyomccsoUxo4dxx57jOGoo46hqKiYRCLBTTf9gVtuuZ099xzP+++/yyuvvJTRNVx11dVkZ6emiD366EM8++zTXHLJZUBqAe215TQMg3PPPavL3H9rF8P+v/97FYBAINDt+XYW2y343Xjjjdx4442dXn/88cfTP7/66qvdvv/pp5/uiWJ1yxKCmljqC+6/DZPYbopDdeCxu2mNdv2Xy6bp9Mkqxat1k1vQUinxFBFPxjtkJDi37+78GGpiaaiZyR+9grFe1HRrOntll7BvTikTsos3OW9ta9l1G7muLJKWSUukrcsJ1C5N5/rBezPCm8tj5d/zdn0ZHzZWMLV0GCeVDE0PqMhU3DL5e/n3vF67Akj1h103aC9KMgzumqrhc3iIGTFiye5zKa5PVRTGZRUxLquI5kSMfzeU8Xb9KmrjEZ6s+IF/VP7I/jm9OKKgHyoKbUac1mT7HyNOazKW/r3NiBNfb0CTTVE5pjAV9DY22X6twZ5s7h81keuXfszqaIDLfvgPfxp+IAPXG8m6lmFZ/HnFF8xvrsSt6fx5+EHstl66rEy5NJ2+ru6brNfnGHlJj9b8HCMv2aK3/u9/n1NVVcmFF56Xfs00DZqamnC73d3m98skh19Xrr56BqeddgZff/0Vn3/+Kf/4x1M89dQ/icdjOBxO9twzNWr+8MOP5I47bsvomG+9NY933nkLwzCIRqP07btuoYs+ffoyevQeAFRUlHeb+2/IkKGsXr2au+76M+PGjWf//Q/I6Nw7sh2q2XNH0hgPE7dM/Lodv61nR3muz7IEOa4s2trnGK7Podvpk9ULt+raaE1UEzq9/CWsbq1ID/SwqSo3DtmHixe9T8BIUOxws09OKfvmlDLaV7DVfXaboqDgtNnJ8+Tit3nRsYMi8Nu9VAfruqypKYrCiSVDGZ9dzN/LF/F5SzVPV/7I3PpVnN1nFIfn98uo+XB5uIU7VnxBeTSIpiic1XskU0uHZzQQRVEUfA4PhZ4CPJoLQ5gEk0Eaw81Ek/GM50jm2p1M6zWCqaXDWdBWx9y6lfyvpZr5zZXp5tdNsSsquXYnhxb344S8QeRtZh9ikcPDvSMnMvOnT1kUbOTKxf9l5tD9OkyFMCyLP634go/bA98dww/qkCdyc+VmWLN1jvzdFjdL9izBPvvsx8yZf+y05cknH+82v9/W5PDr1as3vXr15oQTfsUVV/yOTz6Z32UGifVbKzRNQ7Q/RK6fY/Dbbxfw2msv8/jjT5OTk8M777zN66+/lt7ucq37OySE2Gjuv+eff5mvv/6Szz//lEceeZDnnnsJh6Pn5j/3NBn8ulG5/hy/7TyJ1625sev2DgHBZXPSN6sXdhwZ1UJdqpsSXyGVbTXp5qwih4fHdj8SnCq5pn27dKSriorb7iLfk4tP86CgIkRq3h4i1U85KMdJbbiB1mhbh6a3tfq6/Nw6bH++bavnsfLvWB5u5a6VX/FazXIu6LcHY7MKuzy3KQQvVafWQjWFoK/Lx4zBezPEk1maGLfNSaG3AJ/uBaFgWaCikaVn48/JImyEaAg3E0lEuix3VzRFYUJ2MROyi2mIR/h3Qxlftdbi0mzk2Bxk6Q6ybQ6ybc7U/9f73alqmzVatys+3c4dIw7izhVf8lFzJdcv/ZjfD5zAEQX92gPf//i4uQqPZuOOEQcxvH1+6JbKtmUW/HZUe+21L0888RirVq1k4MDU1KrFi39kt91Gdpnf78QTTwbW5fAbPHgIkUiEJUt+ZMqUE/F4PIRCXdduI5EIixZ9x1577YOiKASDQWpqqiktLaVfv/7E43G+/XYBY8aM44MP3icYXDfFqHfvPixe/CMTJuzNu+++nX49GAzi9XrJysoikUgwZ84b3V7r+rn/jjkmld199eoyCgoKCIfD+P1+Dj74UPbeex8mTz6KQCBAQUHBVn/GPxcZ/LpRGUv1mW2PCe4b0tDxO300hFLD1L0OD338pRtdjkpVlQ7TOIQQZNuzibrjNITXLSCdZ3fh9275zTNTuqrhc3jJc2fjUt2p9ESpeNeBEKBho7e3FL8jVQvsLmPEmKxCHhx1OP9pLOepikWsjLRy7ZKP2Ce7hPP67U6/9ZrYamIhZq/8Mj3Uf0rxYM7ru3tGg3Xsuo0CTx7Z9ixUoXUuNKBYCl7Vh9fvTaURijYRikc2a3m4AoebM3uP5MzeIze98zZkVzWuH7IPBeXf80rNMu5c+SX18TArIq180h74Zo84KL0wQlc8dhemZZIwjY2u+5m3HVtNtoVLL72ow4jH5557iVtuuY3bb59FPB4nmUyy++5j2G23kRvN79ddDr8pU07k/vv/ynPPPdNpwIsQgldeeYm//OVO7HY7pmly1FHHcMghEwH44x//1GHAy/pzoC+//Cpmz74dj8fLYYcdmX59333349//fotf/3oKWVnZjBkzjsWLOyfThY3n/luxYgUPP3w/AJZlcdZZ5+zUgQ9kPr8uFRT4uPWLd5i1+D1OKR3OA+NPAWv7zrOJEWFlUzk+h4devpJuc7GpqoIIBki2NGMvLsHaoL9OKCZrglUdVhHZmprDpth1G9lOPznObBxKZrXUtRQFkiJJbbj7KQprxUyDV2uX82LVUqKWgYrCpKKBnNl7N76PNfGXJV8StQxybU6uHjSBCV2sGrMhXdXIcWWR787FxuYlZFVUiJlRmmKttMWCGWex3xKZfH82TSfXlY3T5qAmUE/C7PqB4l81y3lkzbfpT9rbXuPrLvApikKBJ5cid2Fqge21i6HHQ8SScZJmx+XyIkIwebd9ujzWjjbPb2ezPeYl/hLsNPP8dhRr5/iVuLyoaB2G/29L6YWMhYViJVHMJMJM4raSFDocFPpLEFY3gQ8Lo76RaG0twjAxolHcffogXOuyiStCo5e3mDKjvNsRkBmVEwVVVVAVtf2PgqZq2DUbNs2OTdWwaTZcmhObYseyxGYPEhIC9PZaoNfhoS7Y0O1N26npnN5rBMcWDOAflT/ydv0q5tSt5O36VRjtJz4otzeXDxjXYWWbrqiKgs/ppcidj1N1rWuW3ZyyW+BQXPT2uClwxWiJtdESa+uxvIfdWRvA81y5qUWzhcCZ46CirbrDAKi1flUyhHyHizuWf4Fd1Zg94mCGertuFt5weo2CglNx4bK7yHPkkrCSJKw4oWSEYDxMwkiQJdf9lHZQMvh1o3LtNAdX9jY7ZirQgWImUaxUkBOJGFYiikhEEaaJZZqpQKiq5NkdEK9Ezy3FVO0dgolqJIhXVxNvaU3nxjHCUUIry3D36Y2SlZ3e367Y6eUvZk1rVcbNcpqq4tBTaXgcuh1V0VAVFY32/ysaCu3ZB8R6gU6Qcf9Xt4RCji0bT66LmmA9gVio22CUY3dyxcA9+VXxEB4r/44vW2vx6DYu6TeWw/P7brJf0213UeTNx6ul+vW2tuiWJbDhoMhVRJ4rh2AyTFOkhVgytvWfy0Zoqka2y0eeKxeX6sKyRLqVw46T/tl9qQ7V0BrtnGLowNze7DY2D5uidvugoGs6vfzFZOn+TouKC7HuwUVXbXicPgqdFgmRwERmhOgpr78+7+cuwk5NBr9urF3Uuo9n61MZKYqCKhIQbsUINCMsA2EaiG4SjwIIy0LEolhEMRNRbHklWI5sFEVBhAJEKisxIp2f5K1kkvCaNbhK4uj5BVjtA0x8uo8iXz41bfXdnnP9gOexe3AoDhS6CAjtN7ttmQ1iQ0KADQd9/b1ocbRSF2rcaGqdfm4/tw8/kOXhFvrn5mBbLxnBuhRBpIOCoqgUeHPJdeTi1uzbfB6nEAINGzm2bHKyswgbUZqjzQQTkW3aJKopKn6nlwJ3XrrW2lXTviZ0+nh7YdcaaAw3dwrEGxs5mhplXIJb9Wb0OaX2UbDjQFV37gEv0i+XDH7dqGqfZ9fPs+Vz/FRVQTXjiFAzyUATVobzwzZkJWIk6tZgz4ljxQXhmnoso/sanDAtIlU1OONxHKW9MBUNyxLk23OJumOYrCuHpqg4bA6ynT68tvZEq2sDXuoe1iNUYQECS9lEs5ilkmvPw5PjoT7SSFs0uNEBFkM8OWiWRiiRwDQFhmlhWhZJw8I0BZYQ+J1uitx5BBttNCUD5PodFOW6sevqNp/PmfocFdyqG4/XTVzECSSCtERaiRuJLX58UBUFn8NLgScPt+ZOrfKzwcE0xUJJhBE2N5aiIYRKsbsIu26nNpBZPkC33UUff2l7/+0uMzxA2gXI4NeFQCJGWzKGQ9UodGa+wPFaqqqgGFGsQBOJYAvW1vb7qCqq7iC6cjnx1gC6LxdFc278ZiQEsYYmrHgcZ58+WDYnQiiUeIpothoxbIIspx+fw4NLc6EqCknDImGkcs6ZliCRtDBMi2yvfZsGBh2TYHkFRiKBf0A/FPvGUxMJIbDjoI+3F7nOELWhBsLrZV9XFAXDtIgmTAKhOLpNIxTu/KBh0zSK/fn4tGyiIYElUjf/xtYYwUiS4nwPOV57l6M7twUhwI6DAoeTXEcOYSNMY6QFYZkIIUhYRjogKSikKqwKipJqYF5bg/Xa3RTkFOJpz2vY1bOAhoHVXEUy2IJmd6Jn5YMzC0vYybXlYs+2URmo2WifZJbTRy9fMZqwbbdFHiRpe5HBrwsr2xqB1DQHXc38I1IU0IwoZmsDyVAbYhs0b6m6DcVSiK4uJxlK9dckW2rRvTng9CGEgtWyEsXhR3F3HnqcCIQwV5bh7tsbPH40oVPqKcVnJUgmoa0tSX0iRCxhYpoC07Qw1+svAmhs0VKBwbf1gUExDRqWr6J6TR2WZeFtDJI7eACO7GwcttRAmu6CurDArXoZmOWmJdlGfbiJUDRGKJokHEmSNFNRwKN3rk36XW5KvIWIuJ1wrHO0iCdMymsCBHwOSvI8OGxaj9V0hBCoaPj1LLJddkI1FcSjYRw5OeheH8LpRCip2ndqMFQqECrt/xXmZdHcFOk2oa8uEhiN5RjtqabMeBSzvgLVVo/Nn4fiycFv8zEg20ZVsIZQPNLh/QoKuZ4sSjzFKNt5lLMkbS/yb3YXVrY1A1DocKNvqlkOUFXQjRA0rSFes5xkW9MWBz4hBFasFrP1G1QlhogkCK1clQ58AMIyU82oTatJfn4H8XnnEHvjDIxlr3d5wzZjMcKrViNaGkgaJuU1EZaVtbKyopXq+jBNrTHCkSSxuEHSsDrVwuJJk/LaAGU1AeKGtUWT4xUFkrEYFYuWUFlWg9Xe3xlqDVHzw080ralmdU2AupYIsaSZHhzU1XGEULFbPryiAMVwEY4Y6cC3IV1V6ZVdQG9PbxIRG/HkRvpZBbQE4iyvbKUxEOvBHk1QhYnVWEfwpxWYTSH0iMCsaiaxogJrRTm2+lbccQuXsONUndhxYMOOjq3bRRcUBXQzSrJuVTrwrc9Kxok3VZOoXobVXInbsuiX1Zscd1a6dTuVKSSfXp6SXS7wXXvtVZxxxlTOOus0LrjgHJYt+6nbfadMmcS0aaek/x6vfW3lyhXbo6jdCgaDPPvs091ur66uZp99xjF79p86vHbUURM3eeyGhgYuvvj8jMqxzz7jiEQim71te9pktSY10fIo3nrrLez2nl3zcUdREUoFv2LVjRmKodvWTXQW7XdkVVFQhAFmAjMeJB5s2egAFoBo3EQgcDtSH7uwEojIGqzwSkRoZer/4VVgpubkJTUvluckhGO/TpHAql9AYvEzEG9NTTIzYyS//CtmxSc49rkG1ZPP2o4gBYGSiBFc/hPCn4O/IA/dbWKhYAmwhNL+s4pQwBIqJiqive9PkArK0ZjJmpoA+Tkucn0OFDr3M21IUSBpCULNQZqXrSLc0tZpn2QsQVvZany9SmhL5lLXFMHl1MnxO/G5bDjsGgoQS5oEwgma2mLE4gZCgMuey6A8Hw3hRloiQYQQuOx2LAO8TifF3gKUpINE0sJpB6V9pn3qigEUEkIhkTDTwS6ZtKisDdIWilOa78Ht0DPPGL8JiiIgFCJaU0MyFO60XZgWRjiKEY6i1NajOR3Ys/zoPj+4XIhuWiIURUFLtJFoqNhk37JlJLFa6zHDrdi82Qzw5dKs2IhEQnizcvE7crpN36Wk2l9JJC10TUFTlY6jfXdiN988C6831c0xf/6H3HbbLJ555vlu949EIrz99jwmTTquR8pjGMZmL94fDAb55z+f4cwzf9vtPm63m/nzP+T008+kd+8+GR+7oKCAhx9+bLPK01O2RfqlTX6ymqahaRrxeHzXCX7tq4LkBOKUf/UDNrtGjs+BrilgWShmDBGPYCViCGHhKikFtx2sLm46ikIsYdAaiGC2LcIWX4VhrMGeXA3RchBdDDqwZaOofkS8HDXwD4Ttf1i+M0AvQiQCmEuex6r9InX4rEHoo8+BaB3J75/EqvmK6JzfYht5FkrpvijtN6VYwiQQjmOJMvyF+ViaHc3lQLc7sFQFoShYipoaEWma6EKkgoOmoygaqAooKoqiEo2rNLbYyfY7cdi0VPBVVBRVRdjdWEp7H5ECbeEE4bYgoVWriQW6X7TYNEzayqvwFiXQ8goJRpOEIkl0TcXj0tE1lUA4QdLo+IART5goSZVidyl9shNY4UasQBMO1YkrahFvqydpWNiSqWkkhmURTAoCSYu2pEWJU6UkLwtfVhZx3UHcUkkkU4EwEEoQiRkU5brJz3am1wJN3ejXX01n03+nFAWUZIJEXS3xpk0/KEFqxK8RiWJEoih19Wh2B7ZsPzHyUaMGQtNQdB1F11FjLcSba9ZrcVA6PC8JFFRdR1FU2p94sCIJIo1lmNElaJoNl82Gva4Nw9eAnpWF6rCj2GxYpgVYJJOCmGESjRsYpgWKhq5rOBw2bHYHuk1Ht+mp1XzaPx8lg+sEeGj5p9z504eEt2Iuanc8up1rhx3CJUP23+h+awMfQCgUQlU33sJx3nkX8MQTj3HkkUdjs3VcfamxsYG//OVO6upqicfjHHHEUfz2t+cCcP/9f2Xhwm9IJpNkZ2dzww0zKSkppbq6mrPPPoNJk47j66+/YsqUEznooIO7PI5lWdx992y++eYrbDYbLpebxx9/irvvvoNQKMiZZ56K0+nk8cef7lRum83OtGln8re/Pcwf//jnTtt/+GERDz/8AOFw6t/r+edfxP77H5gu3zvvfADABx/8h7/97SEcDgcTJx7Oo48+1CGt0ksvvcBHH/2XtrY2fve7K5g4cV2mmuee+wfz539EPB7nwgt/l962dt1Q0zTJycnhuutuoE+fvl2mX2psbOCFF57DbrdjWRa33z6b/v0HbPQ7W19GjxVnnXUWV1xxBRdccAHFxcUdmr369Mn8yWFnUdE+wb0YO6ZpICJJAokQHjWJZsawDIP1b37hinI8ffqiuO2I9Z6644ZFoKUOGt7G1/YOmtnU4TwCBcXVB9UzCNUzEMUzGD13N4w2lVhdLUrsfyjBl1CSP6E0zcKM7oGx6gdIhkCzow0+CbXf4akbmqcU2/5/xPjxaUTDdyS/exSl9mv03c4ijptAOJFuEo23thKPrxvooNp0NJst9cfhRHe50JwO0HRMyyBpJYnHYh1u2GGgVVXI8tjxex1o7X8lNLsTW04xcbufmuYoIhwmWl5OPLRBM4dlwAa1GCEEwdp63Ik42SW9aEuqGKZFW6j7G6LDpuFUTDzxIOHyav69uoXKOLTEggQMhaCl0SY0AqZKwISg2fGGpiswJS/GmcX15Ge5ycrJwpaVhWmzYQmBYiQwAhGaggZ+u0DXNHSbiqJoCEVJPRgoSqrtW1XX/Q4IRQM1NR/SbA0Qqa3DjCc6/N3JlLAERiyGURvDHg0SCsVQNB1VVyAexkhGUWx2FJsdoemYApKWhSnAqalolgHxIGYkipJMYCWTiC5Ge679lhRFBbsd1e1FeHzEUIgmDRKxeGouKqnapqKqaLqO3WHH7nBg11QcuoZqmohYDFXVyMnfeNABeHjl5z0S+ADCRoKHV36+yeAHcPvtt/Lll/9DCMG99z640X1HjNiN4cNH8NprLzN16rQO22bNuplzzjmPsWP3JJlM8rvfXcCIESPZe+99OOus33LZZVcC8MYb/+Khh+7ntttSy5a1tbUyYsRu6e2XXnpRl8fJzs7mm2++4oUXXkFV1XSKoauvnsHZZ5/R7eLUa5188q+ZOvVXLFv2U4egHwwGufPOP3HPPfeTn19AY2MDZ599Js8//3KH9zc1NXHHHbfx97//g759+/LCC//sdA6Px8NTT/2T7777lhtvvK5D8FNVjWef/T/WrFnN9OlnM2bM2PbP7SYeeeTvDBgwkDfffJ2ZM2/kySefATqmXwI47LCDePHFV8nPLyCRSGBtxtKCkGHw++MfUyuaf/rppx1eVxSFJUuWbNYJdwYV7bnw+ttcuIwAyWiIcCJJWAjcTh2Py8b6D4XCMNIBUPU4iEWihOoXotTPwRv6HIXUl2LYSoi7didp70/SPgDD3hebw022z4nXqaPZnSSbW4nV16SO69wHYR8JTc9hrPwSK/A/AJSc/uijLkJxd1zQWXFko4+9HKvqY8ylLyDqvibRvIxI/2mI7NFdXqsQEIqbBKOCoGUQMOMErSABSyUoNLLsKsf3spGVn4uhacSTSZLtq8ZblqAlGCcSM8jNcuJy6BiJGC1rVpBQXPg8OTRXVHcIfHHToGrRM0xomsMn/iOoH3Que2fbca73gUaa23DEk+T06U1AdXSq7amqgtOm4rLiEGhiaW0Tr64O8u9WG1Gx8XllCgKfKvDr4NYUlkcVXmmE91otzi9qYUprDQYmutOBze8Ht5uEZRFNJGgxDDRVQddUHHYNu03FrquomopNTY3KTPe5KgqqzYlqQby+nkQgiKJqKLoNRdVRNA1F1RCoqaZ0VU/VoLtpQlTa125NmhYtwTiBYByFKEq4mVigNVWZs9bOTwFNjeET7yEUD41iFElR2P7ZqagKaJqCTdfQNRVNVVAVBVUF0xIkDYt4MkEiGcGyUl0AutOJ3evDl50FLidJkRowoCmgGCZGcyvRcJhgPIaZSCCsVN+wK8tP6f6bDjoXD9q3R2t+Fw/aN6N9167B+fbbc3nggXv5618f2Oj+F1xwMZdccj7HHTcl/Vo0GmXBgm9obV2XyDcSibB6dRl7770Pn3/+Ka+88hLRaBTT7HjDdjgcHH74kZs8zqRJkzFNg9tvn8X48RPYf//O+QE3xuFwcPbZ03nkkQe55pp1KYwWLfqO6uoqrrzy0vRriqJQWVlBVlZ2+rUff/yBYcOGp9MjHXfcCdx33z0dznHEEUcBMGrUaBoaGojH4+ksEGs/r379+jNs2HB++GERigKDBw9lwICBAEyefDx33fVnwuFU98D66ZcAxo+fwK23zuSAAw5i//0PoFev7hNedyWj4Ld06dLNOujOrrI9+BUnDKKBjrW1SCxJImni99ix6esGBAjDJLjqJzT7EqzGubjjq1OvoxJ1703YfzQJ1+6d+u4SSYuGlgjJbD/eYCuJ2pp0O5oQAqtqAeay78EQoCrYeqmoeVVg/hdhnQAbTCJWFAWt90GouSOIf/931LZleJc/Sjx/H4J9TuapYBEL4y7aTIWgqRKwVIxNTOZ7tjHJZXnlHJKv4szNwePzExeCRDyOaRjEkya1TRG8LhtJ08KwFLx2hZZlX6CqOj6nn6ihsbCpjewf7ma/+AIADg28zeJFP3Fl1rUMyCvlsGwY401lPoiHI5hlZfh79ybs8BJLWNhtKk7FwpaI0FrRyNzKEHPqkyyOqEDqH9UejhgTvAYekcSnWvhVC7/W/n/Vwqta6VoqKCxPOrivyc93MQd3Vdl5qV7hkpxW9nGFUJqaUHUdu8eLOycb1eNJTQ5XUl9RIgGJJKhqqolY10HXVGyaiqqqJJtaSTQ0IIxklwsCiFgr5vKXsWq/RvH1Qisag14yHq1oNIrNDZqNpAWJpEk4aqSbG91uOxgJ1GgLiVDnvI82sZqcxD/QSd00PbxJkkJiyh7ExO4k6Y1hKsQTa2+8qb47JdWq3+U8SiMWw4jFoLEBzW7H5nJiGCZGPN7eEtKZEKnRw5m4ZMj+GdXMtpdjjpnMHXfcTltbKx9//BEvvvgCAKeffhZHH31ser9+/fqz774HdKj5WJaFosBTTz2LrndsDq2pqebee+/hqaeepbS0F99//x0333x9ervT6Uq3rG3sOADPP/8KCxZ8zVdffcFDD93PP/7Rff9kVyZPPp7nn3+W775bkH5NCMHgwUN49NEnOu1fXV29Wce321P/Jtf2zW0Y6DfX+umXAO64424WL/6Rb775iksuOZ9rr72B/fbL/O/QZvWmVldXU1dXR3FxMSUlJRm/b/bs2bzzzjtUVVUxZ84chg4d2mkf0zS57bbb+Pjjj1EUhfPPP59TTjllk9u2tds+Wk5NLIQC+EwdulieyTAtWgJx3E4Vt9MGZi0i9AF68gsU4miApeUQ9h1G2H8klr7xDNhOlxuzNUhVZSVOm4rHaUOJ1aeaMJtTNWulYA/0Eaeimp+gRN5Hif4HEV+I5TsdHJ1rdSGyiQy5FEfdh7gq38DR+D/CLctI5Pwa7EMImQU0W1mAgkNJBQZf+5+1wcKnWnwRdVGWtHFdXT77BaJc3lpDb1ctDp8Pb14uwuslbpokYjGCkQQ2hx2vTSNUVpa6WQLhUISXGuL8qvwOBphVtKl+1vQ9k37Vr7JbYhXPNF/FzclLua5lf/J0mJgtODwHBooEgTXl+EqK8fh8iEAL31c08kZNgvebBRELQMWnmhztiXCcL8QAu4HDYevQrNs9wRBbjAeKYnwccfFwSxZrkjaurS9ggjPK73LbGEiSWFsrsbbW9j4zZb13rx0F1fnIigCEhdpeU9Rtqdqhqiqppe3WvINZNhfMVE1HtJVhtJVhLPsXqHZEznDMnJGYeXtg+Qeg6A7sampZOZdmEm+tJxHdYHFrIfCID/Fbc1CwSNAXQynGKX7ARj028R4+8z0McokpuxNTdydBf1BUzM0Y0GMmEpiJnmmi/LlEIhGCwQBFRakF0D/++CP8fj9+fxaTJ5/A5MkndPve6dMv4De/OT19c/d4PIwZM5Znnnmac86ZDkBdXS26rhMOh7HZdHJz87Asi3/965Vuj7ux46hqaizGPvvsx4QJe/PJJx9TVVVFv379icViGQ2W0TSNCy64mAceuDf92ujRe1BRUcE333zFnntOAFIpnEaM6Lgw9MiRo7jttqVUVlbQu3cf5s2bu9FzbWju3Dc555zzKC8vZ9mynxg1ajSKonD77bNYvbqM/v0H8NZbcxg6dBgej6fT+w3DoLa2hpEjRzFy5CgqKytZtmzptg9+9fX1XHXVVXz77bdkZ2fT2trKHnvswT333ENRUdEm33/YYYdx1llncfrpp3e7z5w5cygvL+fdd9+ltbWVKVOmsO+++9K7d++NbtvWSuv/w9+WvYLTMGmLOXGocfxKAsUyUqM71/5fGCjCxNBUbL0VbLmpWmCcQURtB2EbcBwJ3YkVi7E6InipEr5ugT1z4Jx+UOBI3USdLhe2WJRgRQXCsoiYJkb997iXP4ZiJcDmQxtxOmrxXqlmNU5GOPZCDT6DYpSjtT2A5ZiAcO6NYgXBCpCIt+AwW3GJIGp+EDwOzHKT3EgzNzQ+ipajoHoUcNkwnTkIWz4mOZhKLia5mEpO6v/4uSBH49WAlydbs/gs6uLrKifTsgKckWjF0dKC7nTiyM7GlZ2DoSnoySTBVWWppi8BH0Zc/K96FTe13YNPRGh09keZcBOF9kJi/Q7Esfg+fA2f89fAbF73TGKW6xxebrTxciP0d8Lh2QYHhKv4Iaowp8Hip/Xu96MdcY73hTjUHcWhdnHzFiZYCRQrgWIm23+Oo1jJ9OtC92F4+3OQB/ZxR3kt4OMfrX6+irk4u9rJcb4w52a3kaNZ3dZwNsa0IGmYEAeEwN76La7y11ATqeZEUTAOZdCvINqM0fgDSstitEgVStP36E3fo694AcuWTTJrBFr2SJS8kZjx7E6BTxFhcqzncYpUupqQcjAB9ThQdBAmdrESl/gOp1iETjNe8SEe47+YSS/xeD+SiVySeh+MrNEIvfvM8JaAlQkbbZZKiW5QpJvoG2k4UMzOI1p3RNFolOuvv5ZYLIaqqvj9fu66696MpvUUFhZxzDGTeP75Z9OvzZp1O/fe+xdOP/3XQGqE5Q03zGTw4CFMnHgEp512MtnZ2ey33/4sXLigu0N3e5xYLMaf//xHTNPENE323Xd/Ro0ajaqqHHXUMZx++q/x+/1dDnhZ38SJh/Pss0+npx6krvuv7U2+d5NMJunVqzd3331vh/fl5eVx3XXXc9VVl+F0Otl//wPRdR2nM7Pl7EzT4KyzTiMWi3HddTeQm5vKIjJz5h+5+eYbME2DnJwcbrml62z1lmXxxz/OJBQKoSgKRUVFXHLJpV3u252MUhpdfPHFlJaWctVVV+F2u4lEItxzzz1UVlby6KOPZnyyiRMn8uijj3ZZ8zv//PM58cQTOfroowG49dZbKS0t5bzzztvots3xwgsvdkgkOXjwUEaNGkMymWTevH8BUPTTIwxu+GazjgvQVjqQJb7JBMXahwGFeP4g5rR4+LSxY9OPDYuDPK1cMNzLcL+bqpUrWNqemLIg+CO717yIikk4ewxiyOkEFRdLg4H0P8TUhGeT/f3f44nNRWHTtZyEpRKp1bHXhTs1cio2UFwKqktJ/d+poDgBRcPCR0I4CFl2qk0/NYaPkHBjCAejnCp9bBrNhkaLoRJXsmg0+iKEQqul8594X4Y0/5srw8+gIljjGc3Kkl8hdBeazc7YXsOxhCC45J/0rngZVZg0OPry19zL+Y85kLDoPJTZpZiMt7dwiLOVI3NSzSo/hUMkok0MDv2H4sD3KEkjNZ0hw/H3pmKjxdWPZvdgmt0DqbH15r+JUj6K5eBSIgzU6zjGtYIx9iZMHCSFC7fmo7crDwsXC4NBNpwZUGC308+VCiJft7XijVUzvGEeOdHVAESdJRj9fk3cN4SFgY7TPxxGgIGJcgoiK9ADS9GMjnP2EpoHy5aN6swlqftpJUGpazEOewzD5mCRdRyqc2/cugshEpQF6vEkGnEnm3AnGshJVuBP1qPFQ12PvfHokOVBycoj4ciiNumgxsyiwshhlZFDvZVFm+UhaHmICBda+wzELE1QoMQYmVzG/tZPFIcX43bB0L+Udfm5y5RGO7dwOJyulc2d+wZvvvkGjz325M9cqs62KqXRN998w3333Zcezut2u7n22ms58MADt1kBa2pqKC0tTf9eUlJCbW3tJrdtDptNw2Zbd0P1ep0UFPhIJpPp198afgrflg5kUNLBmEg/3g5n41eqOcS9kEPdC/CpkVRPvwIRZQA0eVErFpJVvYrcwpW05ZSwMJHFf6N5VLW4AAu7ChOcbYxxhfg0nMW3MS//Cefy5beCc/JbOdKloaoKRW3fslvNK6hYVOTsh23YGfh1HTORIDWWoX36gQIKGhHnkeT0P5Zw7XME22qICS8x003MchO3vPwkBvBMay/qrRwG6klOy64h211HbngF3ngtRUY9WrQakgYiKbAC690JFVCcBqo7gT1LJd+nUGBX2GPD2S4CsjQY0P6xhvRc3okeyUNtR3F14FEmxz8CYGXeYazOPzQ1qAMBRgJXvAEUqMzfhzotj1GVz1MQL2dW3Q1M63Madb2PZ16TysfNgmI9zn6OJsbY27ArAqem4bA50Jq/Y2Tl+3hDq7ruuVTX/lGwVB1TsWMoDizNjdOWjRJrQovUkB9ZQX4kNUHZ0jSmeO1ofoHNZ6DY6boG0N6FUeiykxQukjjT/9dUL14lB8vQmdDwA1ktqfIlNRer8idilhxKf68fh2V1GlKftGcRzt6bXPehRI0kP9UuITe8gpzwcnIiq7GbYTDDEKvCDqxtFEo1REYYyYuElTeoV/PIsoIcKjr3C65l6W6Sdhs2RwItGUOEBIQNCLchqtuwO2BAlsqgLAXVq3T5OYikwAwIkgEQQQtlvScBxSWD2y/VSy+9wAcfvI9pmvj9fv7whxt/7iJtloyCX1ZWFitXrmT48OHp11atWoXf79/Iu3Y8hx56dKfJyg0NqafqSZNOAmDJsg/5alkDI1QnxwQXMiXnK3Ra0/svTfTju9i+DHKNpNTmh2JwKB/iLn+ZofXzmB/J4p+uUwHIUU1Oyo5wxsgc3O5i4tEYZ9hsLIvbeXBxhK+aLe6r8/J/Njd32H5kZM3LKAjiA0/BP+QsVA2UZBif2cY4X1aHcjv8flylJdQJSOZfgVIAmoACt5tkMMTN30b4dzDVQXxedhtnZYVRFD/gx9FrN+LxJEFI9UvFG9AiVWjR6vb/V6HFmxBRMKMCs8lEaA7M7AGYOf0wvXn8mFT5ISawKzH8SoQRjiCD9RV41SZOtL3AkaEX0OMGlmonMvA35OaMYcP0qOH2Sd69NRvk70Y8+3q0smext37PqDVPMTi2hnG9TwDf+n9N/WjhCuwNn2Nf+hWqGUkNdVFAzdJI5O+FlT0eYdajU49NqUMXtWisrT0l2v8EgToARFLHDAqskIUVFKgJE9qiWG3trZU2jVZPFnWOPDSbiVM3cOkxPLY4flsYXUugKwlcrKvBCUtgVFoYtRbZ7RV/rUDFWZxkjP4ugv9ihj1YeDnc7SdJLxJKP5JKPywlNfQ8FkuyOqmz0D6BL80D+VZxYLgFeVYrg6ngatc/GaWsQCRgeaQXjXE/BVYLRWYjHhFjgFkFQAQnq/VS1mi9WK31YrWW+rnNVkSuw05fm0Ffm0GTqVDmjzAw/A0HJb5hXGwxnngUo96CejA1G5Y/B1uWB0U3IdCGCIQhtq45WAEUB6g+Fc2voGi71ioxu5Kzzz6Ps8/evNa3HUlGwe+8887jt7/9LSeffDKlpanJmK+99hqXX375NitISUkJ1dXV7L777kDH2t7Gtm1rFU2pJY2GhubhMVL9JwY5BNmTl8IHcX/rCGJCRW0RTPKGmeQL854+Bd1XyA3Bhzkv9Dw+YiR6HccR3ggOFbS6CL7Bg/H0H0JCsTM+EuKv8So+zYHHqiz2bnmLCaHUygmLe51BTv9foxiAAbrmw5XjRom2kYiEUG02PMXFWB4PgWg0PedqrTXNQW5covBD0IVTFdyY38TB7o1k/VZULGcRlrOIJOPWvW5G0SLV2AI/YW9ZiBatRm9ait60FEtzsXv27vTO3pP7jKOYG84GQMPkQvVlzm96Ed0wUexgH+jA9NRhiAhC2aAvSVE6zBAXupvw4PMx6j7AVfk6zroP0EOrCA86B6E5sDd9hb3hc/Ro1bpDuEDPVUnkjaXFfiKWkt3lgBdFhLFTi04tNlGHTg26VQtYmPYcjLwczLwcTHKwEjoEW1AD1eiBlajJCDmtTeTQceQvQBII4CCkugmrLqKak4Rqo2+ylhwjFQwjvhys0kI87iQqQTTCKCTbH6paQYCTxekmyJDI46fkYD6JDeOT2DB+SOSRIFXlHuJIcIirigs8D5GjNhMTPuYnfkubsjt+1aJNsxCKiV+E0ZOtxFQP5Uoeawwb5Ukb5UmdNUkbFUmdmFCpiMN38fWvKIuFjmNZ7j+c751hDjF/YED4Wxxti9Bi9Wgt9dDSsbVUqHaSvqEYWcMw/f3B6UEhihAxlMhXDOr+b58k/Wwy6vMD+Pzzz5k7dy719fUUFhYyefJk9t03s7kza22sz++1115j3rx5PP744+lBLc899xx9+vTZ6LbN0dQU2uQyVb/+9x+Ybzj5e+TfTDALiCrjSDCgvbkOmgyVp9qymBv0YG7Q0PY7833Ob34QFYtY0aHE+0/F5vagOH0kFAfO0lKEqhJcU4HZnpLIXfYy/lWpSZx3eM7ln+4TGOWG6SUw0rPu+E6bRnauC93voi0UJBHtnMtvWUjwhx+gPgGFDrhvgouRdoNwdXV65CWwydGQCgqKujaTABimQI3WYG9ZiK15YYfgIzQn9b4xPKEeTDwR5qbQo+gYmL5SbP0dOPRKACwcRJT9CakHozmLyB/cD01TaV61hlgk1mGNRAAtVIZ35ROoiRaE6gSRRFm7Go6moeUK9FwV092bNu0kEkpqbpCqqrhdNkzDRNNUVHXtMH4ltUgN7Vk31vvuTEsQS5hEYknMDVckERZapCrV9xatQTEjqEYYxYigGGEUM7KuXBtYpfVmtvdcPrXvmX7NpVj0tiUZagsx1N7CQFsLhWoTcbMSn7KGQfoqPGrH79YUGi2iN4raF4diwyM+bB/N2Y8W7TeYyoZ16o7fZlcde5aABlOjPKmng6JbFYx3xhjliOPoosKmxuqwtS7C1voDihnD8A8lmbUbhncQqJ2H4gNEou9xxDX/6nKb7POTtofu+vw2Gfy2xdqet912G++++y6NjY3k5OSQnZ3NvHnzmD59OpdddhmjR4/GNE1uvfXW9ET66dOnM3Xq1HQZutu2OTIJfi+v/ow5ld9wU7IPor7z4sBrlSd1HmvJ4tuYg31dMaZmBRlsT2Jr+RbPyidRhEmyz5EERvyOZPuqIoqqpuZSmak1N72rnsO7+kUECs3DLuL/HEfzXD0E1vYl2aDEDr3cGn3zfRRl+yn12RjsTuKJNJBcL6D9t0Fw+08Qt2CUH27fDXLtCja7HbfdjtHQSKSpEWFZuF0OjPYJ26qqpCc+q2rH9DmpQJGaixiMJFJLWpG6CdqbF2JrXtAhEK4VKzyYaJ+TQFGxixV4xfs4RapGLdDRiifhGDANxVGMIlTCVdWEGptT89iSVnoKgWKEcK96FnvbDwgU8Odiz21DywKhegiok4go+6JpOnabhtOuoWkqPq+daKTruXXdS00ijyVNotEkRobLcqWyx8ZRjPagaIYRyQgtlp0f3WNYY7hYk9SpaK9xtVobX4/Qrhgc4ynjSOcSdrcvp0AtQ6c2tSbpekLKoQTUydDNwuuqqmL3+lEcHkQyhhmNYCbjnR4yelrAWMAxV/y9y20y+EnbwxYHP4CjjjqKV155BZ9v83Pb7UgyCX4Amhmk4pNPCNU2kfFSVIqK3e5A8/gxm37AveBPKFaCaPEhtI24AtZfiV8IfCuexFP+OgKVtt2uIFZyKAAhU/ByA7zaCF1k3klzagq9PBrFdhOHIvigIfX6sUXw+yFgX38QhaLg9nhwmCY0N+B3KiRiqZqfsKz2VUk2cZ0CInGTcCzZ4QaqxupTgbBlIVqsjkjfk0kUdJ5rY1eqyLXPRw1/se5FzQ26F0X3AU4sQ8MSTkzhIGk5MYUTSzhQg5W47d+h26MIFCLKAUTsx+KwZ2G3qehaeyqk9mtwu+1EIls+D80SayeWJ9MBf1sJmGqqtmXaqdZ9lFtO6qImw72C/Qpt7FVgJ9umQiJBsq0NIxLGiLSim2uwswZNNGDa9yRkdm49WcvucqF6cohYdpKGhaaq6BrYVQMtGceMhzASSaxtmFG+O61qBZMu7rx+JMjgJ20fWxX8nnvuOT744IOdfm3PTIOfgxANX36BEQgSMwSGpYCqpNZStNlQbDZUm46i29AdDsxQmHgoTkzYSRipbOH2lkVkf3crqhkjVrAvwd2vw+F0IISF7bsHcVe9jVB0WkddTbywc7BQXE4CuUWsjCiUt8WpiSSoDaf+1ISThJIdm9pU4HdDNKb1t6HpOopmQ7HZUXU7im5Hs9vIyfLg1hTUQCuBhhasRAJhJtunBFiptTstK7Xmo2WkkqyaJojUNmGlsqKHo0micZNOATOVgK7Ttbi8LgoH9cee5SXZuhSj8v+wGv4LYvNuvkl1MAn3VHRH39Qi49006XUb/NrXolTU1ELdiqqhKApW+1qXGxKkFs4Ox5IYxrYLgnavF3dpCXFNJxqJdLkytqpp2Bx2bIqKroAVjpBsC5CMRdCE1WWztarrOLzZxHUvsYTosuarqgo2TcGuGGhWEpGMgSpQNB3N4cAIh0lGo5hdfB5bIuyKc+S5V3a5TQY/aXvYquC3/ijPDm/eydb2zDT4OW0xolWVqfWrlNTKYpF4knDMIJEw0Gw6mt2F6vKRVB2omg2jtpZgbUOHfHr2wDJyFt6c6hsq2RvnIbeQ+OI+zFX/Rqg2WkZfTyJ/fKfzu7J82Hr1ps3Qui1vKGFS2x4QWxIG4wsd7JaX6oc00TBFqhnPsixcThu9C704bals7AUFPprqWiCZQCSSmNEIRjiMGYsjzPaAt96cQkwDhIUiTIRpIIwEyXicYChCIpHEMq0usxSoqkpuQQ45A/ogbGqHVDtCmGBGEEYYjPah+1YExYqTbKnFCDaAiIAVBZHAso9Gce7FRldiU1VsHi/egnwSSTO1fqbWvuC01j4/ZW2aC6W9AxAFLIFIGhihEEYohBmPb5AWSCFpCiJxE8Nsz3coUsFFtE9ByaSFQNV1PMVFKFnZhKNRzM2YNK/bbNjsdmwouOwasXAUKx7HjMewEgaabsOyewjH2ifVd0Frz7qgORzobg82rxvNYU+NKsbEjARQEeiWCbEY8ba2dDAUlkj1mSqg6yo2TUmNaDUtLNNclwBZUdEcDjSnE93vBY+D0Ycc3mV5dsTgFwgEOO64ozjhhBO56qprfu7iMH/+R3z33QIuvbTrBwhp07Y4+AkhqKiooLS0dLNzS+1oMg1+LpdBZM0qzGj76hSKgmZzgNNDQvPQEldoi6nEE2b6eF6Hij3QRKimHrsOXpc91QfVtoLY+7+HeBs4slL/15zYDvkTsZw9aA3GO9ysPPk5qMW9aItvfY40RYH8bBcleW7U9WpkBQW+9BSPtfspigJGEpFIQCKBEY1ghsOY8QRW0ugQ3NbmdENYxGJxotH2oGkksOJhjHgch10jr08p7uICTDORUQqftTS7EzMUIVpdjbWJZbQUXUd3urDn5mIvKEb155JTUkRzcxjFMhDCANNEsczUz0YSy0giknGEkUgtZqmAottQbU4UzZZq90yaGNEoRiiMlTQwkwaoSjrbfSJpkEha6WbjdJ+cEKmashCpmrNlgmni8LlxFBURFYJ4JIJlWanm4834khVFwaar+H1OYvEkqqphc7uxZxUSFw6MhIFqmQjDSAXF9gwMmsuF4nRiajYSaBhoGKaFYVrp06tq6thr+3p1m4rbruNUDFwkUWIRRCiYeiA0jVTGi/Y1QDWbhma3o3ncKE47FmBZBqaRQAiF3nt0fsCDHTP4vfLKi3zwwX8oK1vJm2/+u1Oqoi2xJXn5pG1niye5K4rC8ccfz4IF3S/B80tjUzVUVQWnC82dher0IWxOhGrDKQTFgC9q0NgWJRhONQ8puk7OoP6UFPpINtRjrW2WzBmC44j7ib9/JcSawebBfuhstMLR+BRwO3WCkSTBcAJnfh5WfhGtG+vsy/QadJXSAi+5PkeX2d3XJ0TqIQdVB6cOTjd6Vg42RUDSgGQcKxbDCIUxwqlgkFrqS8HpdKLbnbSF4oQtO06/n0Kfii/Pj+KyYcQ2f3krMxFDdTvwDhxIrLqGxAYroKh2O7rLjS07C93nQ/fngCcbS3VgtD+MpBZUVgE7aKT+kPp17cjP1MWb7SN52xcTFkB7jU5VFBwIiMcRyThmOIwRSdW4HJaFZRokEiZJwyIWN4glDAxLYJmpxa8VRcHu1vGWFCGyc2lNChAmms/CrlioikjVpg0jFYyTcSwjjmmYWIaRStipprIvuF06LoeOXVfJ8rsIhZPY/LmoWYVYqn1d2cW6NUdTM0kUTMtqr7GChcA0BYZhYVgWhiFIGqkarWmBx6njbU8gbNc1dG1tpop8FCyIx7AiUYxQCM3pQHW7UewOaB8MJ4SCpqRSRQnLRMuwzzz03/sJ/ftPiHj3OR+3lOLw4j36eryHXrbJfefMeYPf/e5y/vGPp5g//0P+97/PGTx4cDpl0cqVK7jmmit59dU3iUTC3HvvPaxcuZx4PM6ee07g8suvQtM0LrpoOkOHDuWHHxbh92dx111/5fe/v4y2tjbi8Ti77TaSGTNuxGazkUwmufvuO1iw4BtycnIZOnQoTU1N/PnPdzF37pt8+unH/PnPd/HNN19z7713M3LkKBYt+h5FUfjjH/+czoLwyCMP8p//vIvfn824cXvy9ddf8vTTz23zz/OXIqPHkREjRlBWVsagQbvGjB3d5kDL7YWiORBoGGuDx3q1Ro9Tx+v2EUukMoI77FoqiriLsdntRCqr0mtBqtn9cRz1IMZPr6EPPBo1dwiQ2l0FcnwOCgf0IujJpbppI3PyMuR26fQp9OF26JsMfN1JZ+fW9NQfpwd7bj52y4REApGIY4TDGKEQajxBnqbgddtwuF34+vYGnx/FiKKGWjFCzZvMLr4hy0iCouDq2xut2UOipSXVVJeVheq0o9jtaG4/ijsbU20P8Bkuztwx87ja3mLZRdYF0R5G7A6wO9C8fvT2Gi/JJMIwUrWgeBwrFseMRUnEEiTiBtFYHMXpRi0oIqjYiYaM9Vod1i47Awo2FFVB0/1oDhVNETg1C7cdvA4FGyaaSEIiimUkEKaJ6nBh9/bCtPtSy6p1k3V9bdJdTVHQ9M6jQpX2Zt8Nu2nXbx1Z97NIjbi1u8Duwp6bl/4cBUD6eW2Dz3YTCWHXCv/3/h4JfAAiHiL83/s3GfyWL19GW1sb48fvRVNTE3PmvMFvf3su99xzVzr4zZ37JpMmHYeiKNx77z2MGzeOG264GcuymDnzBubMeYMpU04EoKqqir/97Ul0PfXv8NZb/0RWVnb7zzczZ84bnHjiyfzrX69SV1fLCy+8gmmaXHzxdAoLu14zedWqVdx44y3MmHEjTz31d5566gluvfV2Pv74Iz799GOeffZFHA4Hf/jDz99ku6PLKPjttddeTJ8+nV/96ledBrycfPLJPVa4n4tmc2Corm5vimsJCxztaY3EejcMJScPj64TLi/HSqRqhqqvF/bxnRdeVTQNV68StLwCHIqCx+2gviVCayC+WSvtQ+pmlpvlpDTPg6YqWxz4upO6Eapgd4Ldie7LxqYAiTgkErjjMXSvD8vuQFiA6kLJcmPz5UOsDSPQhBmPZt7UJwRGIoqem4UtJxuhpAbU6N4cFHcOpuZMXeM2vs6NFKf9M1VAt6f+AIoHdEXBpoDTNMBob2p1uTCEQp4pMC0L0xLEEybxpEkskWo2NU3RnrpGweXQyPY6cDl1HDZtXW4/JRWgNGGiWAaOXD+hltiW5MTtdD0dg1XmMuk+2ByeQy/r0ZqfJ8Na37HHTkZRFA45ZCL33HMnpaW9iETCrFixnP79B/Dee/9OLxb9yScfsXjxDzz/fCqdUSwWo7BwXY7No446Jt3caVkWzz33LJ9//imWZREIBNKLQH/zzVccffQkdF1H13WOPPJovv12YZdl7NevH8OGpcZgjBo1mk8+md9+jK857LAj0ml/Jk06jieffHwLPq1dR0bBb8GCBfTq1Ysvv/yyw+uKovwig9/WEkKg+Px4BwwgvKYcM9Z5QjqkMqi7+/RBzc5JD6Jw6Cp9i7zkZbloDsTam6RSgwlS3Ump0aSpio5IP33rmkpJgYc8v2Orb4qbc51CkA4EitubWu5SdNzHVGwo7nx0dy62eAgj2IgZDaUG1nRnvQcsK5lE1XVsvnwUT866mt52CnqZSH8WigY2DWykFtcGbJqCrT2nmceRSou0dnEbw0w1P2pqKjHu2qCy/sPUupqqCoodVbcBXf+d2ll5D70so2bJnpJMJnn33bex2ey89VYqPY9hGMyb9ybHHnsc8+bNYdy4PenffwAlJanVpYQQ3HnnPd0mUV0//9y7777Nd98t5NFHn8Dj8fD0009QXl6+2eVcf661qmpbnSNvV5ZR8Hv22Wc3vZPUgRAgXB48AwcQWbMGIxzpsF1zOvD07YvweDs9RQsL3HYNb6EXoEOS0dT/1wU+q702oqoKHoe+zZ/ItxUhwEQFux+1wI+ejKaG2SvtiyUrkGoKFOnVdFJSQVBoNqwdMOhtifWbXTVVQWufA7qjfne7gvnzP6Rv3/4dshIsWvQds2bdzIMPPsp55/2GysoKJk06Pr39wAMP5plnnuLaa69H0zRaW1uIRCKUlvbqdPxgMER2dg4ej4dQKMi77/6b4cNTgzDGjRvPO++8zeGHH4lpmrz//rvk5xdsVvnHjRvP3//+KKeddjp2u4O33563hZ/EriPjIUgtLS189NFHNDY2ct5551FXV4cQguLi4p4s307PsjtxDxhArKKCRFtqdX3d48bdry/C4drofXzDm6GCgqauHbvRuS9lZ7l5WhZYmgs016Z3Xt9OHvSkHdecOW9w1FHHdHht9Og9EEJQXV1N//4DWbDgG/74xz+lt19xxdU8+OB9nHnmqamRuDYbV1xxdZfB79hjJzF//odMnXoiOTk57LHHWOLx1KKqJ554MitWLEvn+Ovff8Bml/+ggw5m0aLvOOOMqfj9WYwcOZpgsPtsHlKG8/y+/PJLLr30UkaNGsWCBQtYuHAhX375JU8++eRm5fP7uWU61WHDqQDbgiZM4lWVWEkDZ58+WPqWLRW3LfTE9e1I5PXtOFRVIS/P2+W2HXGqw89lbW68RCLBNddcwcSJR3DCCb/aomNYlsWf/nQr+fkFXHjhJT1U4p3HVuXz+9Of/sS9997Lvvvuy4QJqdT2e+yxB99///22LeUvmKlo2Hv1ASGw1I2v7yhJ0q7l0ksvIplMkEgkmDBhLyZNOm6zj3HrrTdTU1NNPB5n2LARnHnmb3qgpL8cGQW/qqqqdAaHtSM9bTab7GzdTJaibnyFEkmSdklPPvnMVh9j9uy/bIOS7DoyyjQ5aNAgPv744w6vffbZZ12mJpIkScrUtp6OI0nrE8LqarlhIMOa34wZM7jgggs45JBDiMVi3HzzzXzwwQc8/PDD27KckiTtQlwuJ8FgGz5fVoe5w5K0tYQQmKZBW1sLHo+ny30yTmZbV1fHm2++SXV1NSUlJRx//PE73UjPn3PAy45EXt/ObWe6vo0NeEkmk1RUVBDtIjGzJG0tXdfIyckhPz8/tVzlhtszPVBRURHTp0/fpoWTJGnXZbPZGDhw4M9dDGkXlVGfnyRJkiT9kmy3PBtlZWXMmDGD1tZWsrOzmT17Nv379++wz7XXXstPP/2U/v2nn37ioYce4rDDDuOBBx7g+eefT6+dN27cOGbOnLm9ii9JkiT9gmy34Ddz5kymTZvGCSecwBtvvMHNN9/MM890HN575513pn9eunQpv/nNbzjwwAPTr02ZMoXrrrtuexVZkiRJ+oXaLs2eTU1NLF68mMmTJwMwefJkFi9eTHNzc7fveeWVVzjuuOM6LOQqSZIkSdtCtzW/adOmZTT8+LnnNp0ssaamhqKiIrT2le01TaOwsJCamhpyc3M77Z9IJJgzZw5PP/10h9fnzZvHJ598QkFBAZdeeiljx47d5LnX192os64UFPg269g7G3l9O7df+vVJUk/rNvidcsop6Z/Ly8t59dVX+dWvfkVpaSnV1dW8/vrrnHTSST1SqPfff5/S0lJGjBiRfu3UU0/lwgsvxGaz8emnn3LxxRfz1ltvkZOTk/Fx5VSHFHl9O7ed6fo2NtVBkn5O3Qa/X/1q3aKqv/71r3niiScYMmRI+rXjjjuO66+/nssu23QOrpKSEurq6jBNE01L5aCqr6+npKSky/1fffXVToG1oGBdio/999+fkpISli9fzl577bXJ80uSJEnS+jLq81u5ciV9+/bt8Frv3r1ZtWpVRifJy8tjxIgRzJ2bShI5d+5cRowY0WWTZ21tLd988w3HHddxYde6urr0z0uWLKGqqooBAzY/9YckSZIkZTTac8KECcyYMYPLL7+c4uJiampqePDBBxk/fnzGJ7rllluYMWMGDz/8MH6/n9mzZwMwffp0LrvsMkaPHg3Av/71Lw499FCysrI6vP+ee+7hxx9/RFVVbDYbd955Z4faoCRJkiRlKqPlzVpbW5k1axbvvfcehmGg6zpHHnkkN954Y5e1tx2V7PNLkde3c9uZrk/2+Uk7qoxqftnZ2fz1r3/Fsiyam5vJzc3tcq00SZIkSdoZZBzBVq5cySOPPMLDDz+MqqqsWrWKpUuX9mTZJEmSJKlHZBT83n77bU4//XTq6up4/fXXAQiHw9xxxx09WTZJkiRJ6hEZNXvef//9PP300wwfPpy3334bgOHDh8uanyRJkrRTyqjm19zczLBhwwDSq74oiiITUEqSJEk7pYyC38iRI3njjTc6vDZv3jx23333HimUJEmSJPWkjJo9b7jhBs4991xeeeUVIpEI5557LmVlZTz55JM9XT5JkiRJ2uY2GfyEENjtdubOncv8+fM55JBDKCkp4ZBDDsHj8WyPMkqSJEnSNrXJ4KcoCscddxwLFizg2GOP3R5lkiRJkqQelVGf34gRIygrK+vpskiSJEnSdpFRn99ee+3F9OnT+dWvfkVxcXGHUZ4nn3xyjxVOkiRJknpCRsFvwYIF9OrViy+//LLD64qiyOAnSZIk7XQyCn7PPvtsT5dDkiRJkrabjILf+oQQrJ8IQi5wLUmSJO1sMgp+dXV13HrrrXz99dcEAoEO25YsWdIjBZMkSZKknpJRtW3mzJnYbDaefvpp3G43//rXv5g4cSKzZs3q6fJJkiRJ0jaXUc1v4cKF/Pe//8XtdqMoCsOHD+f222/n1FNP5de//nVGJyorK2PGjBm0traSnZ3N7Nmz6d+/f4d9HnjgAZ5//nkKCwsBGDduHDNnzgQgGo3yhz/8gR9//BFN07juuus49NBDN+NSJUmSJCklo+Cnqiq6ntrV7/fT3NyM1+ulrq4u4xPNnDmTadOmccIJJ/DGG29w880388wzz3Tab8qUKVx33XWdXn/iiSfwer289957rF69mtNPP513331XrjIjSZIkbbaMmj332GMPPvroIwAOOOAArrjiCn73u98xatSojE7S1NTE4sWLmTx5MgCTJ09m8eLFNDc3Z1zQt99+m6lTpwLQv39/Ro0axfz58zN+vyRJkiStlVHN784778SyLACuv/56nnjiCSKRCL/5zW8yOklNTQ1FRUVomgaApmkUFhZSU1NDbm5uh33nzZvHJ598QkFBAZdeeiljx44FoLq6ml69eqX3Kykpoba2NqPzr5WX581434IC32Yde2cjr2/n9ku/PknqaRkFP7/fn/7Z6XRyySWX9EhhTj31VC688EJsNhuffvopF198MW+99RY5OTnb5PhNTSEsS2xyv4ICHw0NwW1yzh2RvL6d2850faqqbNZDpyRtLxkFv/vuu6/bbZdffvkm319SUkJdXR2maaJpGqZpUl9fT0lJSYf9CgoK0j/vv//+lJSUsHz5cvbaay9KS0upqqpK1xRramrYe++9Mym+JEmSJHWQUZ9fbW1thz+LFi3iySefpLy8PKOT5OXlMWLECObOnQvA3LlzGTFiRKcmz/UH0CxZsoSqqioGDBgAwNFHH82LL74IwOrVq1m0aBEHHnhgRueXJEmSpPVlVPP785//3Om1+fPnM2/evIxPdMsttzBjxgwefvhh/H4/s2fPBmD69OlcdtlljB49mnvuuYcff/wRVVWx2Wzceeed6drgueeey4wZMzjiiCNQVZVbb70Vr1c2p0iSJEmbTxHrr1W2GSzLYsKECXzzzTfbukw9Rvb5pcjr27ntTNcn+/ykHVVGNb+KiooOv0ejUebOndupz06SJEmSdgYZBb8jjjgCRVHSC1q7XC5GjBjBHXfc0aOFkyRJkqSekFHwW7p0aU+XQ5IkSZK2G5mPSJIkSdrlZFTzO/jgg1EUZZP7ffjhh1tbHkmSJEnqcRkFv7POOovXX3+dM888k9LSUqqrq/nnP//JlClTMl7fU5IkSZJ2FBkFv3/961888cQTFBUVpV876KCDOO+88zjnnHN6rHCSJEmS1BMy6vOrr6/H7XZ3eM3tdm9WSiNJkiRJ2lFkVPObOHEiF110ERdddBHFxcXU1NTwt7/9jYkTJ/Z0+SRJkiRpm8so+M2aNYsHHniAmTNnUl9fT0FBAccccwy/+93verp8kiRJkrTNbfHyZjsjubxZiry+ndvOdH1yeTNpR5VRn9///ve/9BJnDQ0NXHfddfzhD3+goaGhRwsnSZIkST0ho+A3a9asdBb2O+64A8MwUBSFm266qUcLJ0mSJEk9IaM+v7q6OkpLSzEMg08++YQPPvgAm80m8+lJkiRJO6WMgp/X66WxsZHly5czaNAgPB4PiUQCwzB6unySJEmStM1lFPzOOOMMTj75ZJLJJNdffz0ACxYsYODAgT1aOEmSJEnqCRmP9iwrK0PTNPr27Zv+PZFIMGzYsB4t4LYkR3umyOvbue1M1ydHe0o7qoxqfgADBgzY6O+bUlZWxowZM2htbSU7O5vZs2fTv3//Dvs89NBDvPXWW6iqis1m48orr0z3K86YMYPPPvuMnJwcAI4++mguuuiizSqDJEmSJMFmBL+tNXPmTKZNm8YJJ5zAG2+8wc0338wzzzzTYZ/dd9+dc845B5fLxdKlSznjjDP45JNPcDqdAJx//vmcccYZ26vIkiRJ0i/Udsnn19TUxOLFi5k8eTIAkydPZvHixTQ3N3fY78ADD8TlcgEwbNgwhBC0trZujyJKkiRJu5DtUvOrqamhqKgoPVdQ0zQKCwupqakhNze3y/e8/vrr9O3bl+Li4vRrTz31FC+++CJ9+vTh97//PYMGDdqscmxO30NBgW+zjr2zkde3c/ulX58k9bTt1uy5Ob788kvuu+8+nnzyyfRrV155JQUFBaiqyuuvv855553H+++/nw6omZADXlLk9e3cdqbrkwNepB3Vdmn2LCkpoa6uDtM0ATBNk/r6ekpKSjrtu3DhQq655hoeeuihDlMpioqKUNVUcadMmUIkEqG2tnZ7FF+SJEn6hdkuwS8vL48RI0Ywd+5cAObOncuIESM6NXl+//33XHnlldx///2MHDmyw7b1cwd+/PHHqKraIbmuJEmSJGVqu2V1WLlyJTNmzCAQCOD3+5k9ezYDBw5k+vTpXHbZZYwePZqTTjqJqqqqDkHtzjvvZNiwYfz2t7+lqakJRVHwer1ce+21jBkzZrPKIJs9U+T17dx2puuTzZ7SjkqmNOrCznRz2RLy+nZuO9P1yeAn7ai2S7OnJEmSJO1IZPCTJEmSdjky+EmSJEm7HBn8JEmSpF2ODH6SJEnSLkcGP0mSJGmXI4OfJEmStMuRwU+SJEna5cjgJ0mSJO1yZPCTJEmSdjky+EmSJEm7HBn8JEmSpF2ODH6SJEnSLkcGP0mSJGmXI4OfJEmStMuRwU+SJEna5Wy34FdWVsbUqVM56qijmDp1KqtXr+60j2mazJo1i8MPP5wjjjiCl19+OaNtkiRJkrQ5tlvwmzlzJtOmTeOdd95h2rRp3HzzzZ32mTNnDuXl5bz77ru8+OKLPPDAA1RWVm5ymyRJkiRtju0S/Jqamli8eDGTJ08GYPLkySxevJjm5uYO+7311luccsopqKpKbm4uhx9+OP/+9783uU2SJEmSNoe+PU5SU1NDUVERmqYBoGkahYWF1NTUkJub22G/0tLS9O8lJSXU1tZuclum8vK8Ge9bUODbrGPvbOT17dx+6dcnST1tuwS/HUVTUwjLEpvcr6DAR0NDcDuU6Ochr2/ntjNdn6oqm/XQKUnby3Zp9iwpKaGurg7TNIHU4JX6+npKSko67VddXZ3+vaamhuLi4k1ukyRJkqTNsV2CX15eHiNGjGDu3LkAzJ07lxEjRnRo8gQ4+uijefnll7Esi+bmZt5//32OOuqoTW6TJEmSpM2x3Zo9b7nlFmbMmMHDDz+M3+9n9uzZAEyfPp3LLruM0aNHc8IJJ/Ddd99x5JFHAnDJJZfQp08fgI1ukyRJkqTNoQghNt0J9gsh+/xS5PXt3Ham65N9ftKOSq7wIkmSJO1yZPCTJEmSdjky+EmSJEm7nF1qnp+qKj2y785IXt/ObWe5vp2lnNKuZ5ca8CJJkiRJIJs9JUmSpF2QDH6SJEnSLkcGP0mSJGmXI4OfJEmStMuRwU+SJEna5cjgJ0mSJO1yZPCTJEmSdjky+EmSJEm7HBn8JEmSpF2ODH6SJEnSLmeXWttzU8rKypgxYwatra1kZ2cze/Zs+vfv/3MXa5uZOHEidrsdh8MBwNVXX82BBx74M5dqy82ePZt33nmHqqoq5syZw9ChQ4FfzvfY3fX90r5HSfpZCCntzDPPFK+//roQQojXX39dnHnmmT9zibatQw89VPz0008/dzG2ma+++kpUV1d3uq5fyvfY3fX90r5HSfo5yGbPdk1NTSxevJjJkycDMHnyZBYvXkxzc/PPXDKpO+PHj6ekpKTDa7+k77Gr65MkaduQzZ7tampqKCoqQtM0ADRNo7CwkJqaGnJzc3/m0m07V199NUII9txzT6666ir8fv/PXaRtSn6PkiRlQtb8diHPPfccb775Jq+++ipCCG699dafu0jSFpDfoyRtPRn82pWUlFBXV4dpmgCYpkl9ff0vqtlp7bXY7XamTZvGggULfuYSbXvye5QkKRMy+LXLy8tjxIgRzJ07F4C5c+cyYsSIX0xTWSQSIRgMAiCE4K233mLEiBE/c6m2Pfk9SpKUCZnJfT0rV65kxowZBAIB/H4/s2fPZuDAgT93sbaJiooKLr30UkzTxLIsBg0axI033khhYeHPXbQtdtttt/Huu+/S2NhITk4O2dnZzJs37xfzPXZ1fY8++ugv7nuUpJ+DDH6SJEnSLkc2e0qSJEm7HBn8JEmSpF2ODH6SJEnSLkcGP0mSJGmXI4OfJEmStMuRwW8nNGnSJL744oufuxjSRrz22mucdtppP3cxJEnqhgx+O6F58+ax9957/9zFSKusrGTYsGEYhrFDHUuSJKk7MvhJkiRJuxwZ/HZCEydO5LPPPgPggQce4PLLL+faa69l7NixTJo0iUWLFnX7XtM0efTRRzn88MMZO3YsJ554IjU1NQAsWLCAk046iT333JOTTjqpw5qRZ555Jvfeey+nnnoqY8eO5ZxzzkmnCTrjjDMAmDBhAmPHjmXhwoUAvPLKKxxzzDFMmDCBc889l6qqKgAee+wxTjnllHTt7vnnn2fSpEnE4/Fuj7U+y7J47LHHOPzww9l77725/PLL/7+9ewuJqmvjAP53mtIhO0ykjYe0jFQsCsfx0MFDap4QE83UGhRFc6SkTIzAzCtNHSOTQqkxwxDEkg6C2YEaC6+8KCQbQU3LdFRoRjOVcY8+38XLu788vZ2+jxea9bvas/baz15rzcxeszbDfjA2NgYAKCgoQFZWFl9XqVQiOTkZRITx8XFkZGTAx8cHnp6eyMjIwPDw8Lw+Xr58me+jQqGAXq9HTk4OpFIpYmNj8enTJ76+i4sLamtrERQUBG9vb5SUlGBubm7Jce/t7UVKSgq8vLwQGhqK5uZmfl9raysiIiLg7u4OX19fVFdXL/v+MQzzP/Iv5RFkfsOBAweora2NiIgqKipo586dpFaryWg0UllZGcXFxS177I0bNygyMpJ6e3tpbm6ONBoN6XQ60uv1JJPJ6N69e8RxHDU1NZFMJiOdTkdERHK5nIKCguj9+/c0PT1NcrmclEolERENDAyQs7MzcRzHn+fp06cUHBxMPT09xHEcXbt2jeLj44mIaHZ2lo4ePUoVFRXU19dHMpmMOjs7l4210K1btyguLo60Wi0ZDAbKz8+n7OxsIiKampqikJAQamxspPb2dvLy8iKtVktERDqdjlpaWmhqaoomJiYoKyuLMjMz+bhyuZyCg4Ppw4cP9OXLFwoPD6eQkBBqa2sjjuMoNzeXzp07x9d3dnYmuVxOer2eBgcHKSQkhBoaGoiIqLGxkRISEoiIaHJykvz8/Oju3bvEcRx1dnaSl5cXdXd3ExHRvn37qL29nYiIxsbG6O3bt//8AWAY5rexld8fwMPDA/7+/lixYgUOHTqErq6uZeveuXMHp06dgpOTE8zMzODq6gqxWAy1Wg1HR0dER0dDKBQiMjISTk5OePHiBX9sTEwMtm7dCgsLC4SFhUGj0Sx7nvr6ehw/fhzbtm2DUCiEQqGARqPB4OAgBAIBSkpKcPv2bWRmZiItLQ1ubm4/3N/6+npkZ2dDIpFg1apVOHnyJB4/fgyj0QiRSITS0lIUFxcjNzcX+fn5kEgkAACxWIzQ0FCIRCJYWloiMzMT7e3t82LHxMTAwcEBa9asgZ+fHzZv3oy9e/dCKBQiLCwM7969m1c/PT0d69evh62tLZKSkvgHan9LrVbDzs4OsbGxEAqFcHNzQ2hoKFpaWgAAQqEQPT09+Pr1K9atW4cdO3b88FgwDPNrWDLbP8DGjRv5bQsLCxgMBhiNRjQ3N6OgoADAXxOkSqXC8PAwHBwcFsUYHR2Fra3tvDJbW1uMjIzwr62srPhtkUiEqampZds0NDSEoqIilJSU8GVEhJGREdjZ2cHe3h7e3t5obW3FsWPHfqq/Q0NDOHHiBASC//52EwgE+Pz5MzZt2oTdu3fD3t4eOp0O4eHhfJ3p6WlcvHgRr169wvj4OABgcnISs7OzfPLbb8fS3Nx80dgu7PO3qZLs7OwwOjq6qL2Dg4Po6OiATCbjy2ZnZxEVFQUAqKioQGVlJS5dugQXFxfk5OTA3d39p8aEYZifwya/P1hUVBR/gf2bRCLBx48f4ezsPK/c2toaQ0ND88q0Wi18fX2/ex4zM7NFZTY2NlAoFIvO/ze1Wo3Xr19jz549KC0t5ROyLhVrIYlEgqKiInh4eCy5v66uDhzHwdraGiqVChkZGQCAmzdvoq+vDw0NDbCysoJGo0F0dDToN57trtVqsX37dgB/TcpLZVewsbGBp6cnampqloyxa9cuVFZWguM41NXV4fTp02htbf3lNjEM833stqeJiYuLw5UrV9Df3w8iQldXF/R6Pfz9/dHf34+mpiZ+1djT04OAgIDvxtywYQMEAgEGBgb4soSEBFy/fh3d3d0AgImJCTx69AgAoNPpcP78eRQWFqK4uBjPnz/nL/ZLxVooMTER5eXl/B9odDodnj17BgDo6+tDeXk5lEolSktLoVKp+Nuzk5OTMDc3x9q1azE2NoarV6/+/AAuUF1djfHxcWi1WtTW1iIiImJRnYCAAPT39+P+/fvgOA4cx6GjowO9vb2YmZnBw4cPMTExgZUrV2L16tXzVrQMw/x/sG+ZiUlJSUF4eDhSU1MhlUqRl5cHg8EAsViMqqoq1NTUwNvbGyqVClVVVT+UBFYkEkGhUCAxMREymQxv3rzBwYMHkZaWhjNnzkAqlSIyMhIvX74EAFy4cAGBgYHw9/eHWCxGYWEh8vLyoNfrl4y1UFJSEgIDA5Gamgp3d3ccOXIEHR0dMBqNyM3NRXp6OlxdXbFlyxZkZ2fj7NmzmJmZQXJyMgwGA3x8fBAfH/9Dq9rvCQoKQkxMDKKjoxEQEIDDhw8vqmNpaYnq6mo0NzfD19cX+/fvR1lZGWZmZgAADx48QGBgIKRSKerr66FUKn+7XQzD/DOWz49hfpGLiwuePHkCR0fHf7spDMP8JLbyYxiGYUwOm/wYhmEYk8NuezIMwzAmh638GIZhGJPDJj+GYRjG5LDJj2EYhjE5bPJjGIZhTA6b/BiGYRiT8x/ShwV9QGvUeAAAAABJRU5ErkJggg==\n", 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", 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\n", 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", 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\n", + "image/png": 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YCwC4Mc4/XGNF7kGK//Fhf7oUFMoc5aQVHSHbnotP8px6pzMgSYDko9xXxr6Cg5R7yzijz0dJIc9aWOlLgNvrwelzVbubIvkospfgU5RjnWScFNlLyCrNwe4TNZYzYbc7CAoKEYFPqHOSJKFWawgNjcBqtVa5TdMNfsdqEU6v/0M8WH325lVTFAhRm4kNjkaWTv8cw6NaICPxS+FRCl3+D97PMvcG1pnV/mmFWpgs9LLE4vR5+W921fP4Fbkd/FWaxx9FWawpSOeH3EMsyT7AqvwjeBVflfsAeHxecsryOFh8BKuvvM4/uCRJwiu5KXQVcqA4jUOFRyl32UgvycTus5/2cW1eOzaXv6NPkcvB3NSN7C0vxKf4cHirD2J2rwOHu/J6n6JQ7ChFlsUH95kQgU84myRJrvbxRBNu9vRzHOvqHqzWntVnOIoCoVoL3mAf2WW5VTbhqWQVkSb/OMY8ayHefwxFiNQZ6R0ax/qiDL7LPcigiCTWFqSjkiSujq2YK/XG+BR+L87im+wDXB/bBpNaA/g/tJdkH2BR+nac1Qx1WFOQzmOteqGVqx/nZ3c7OFycQaw5ijBtKCeJlzWmSF7ynEUU2ooqNRG7vR6OlGTQPDQRjVK7Z7OSDAXWAnzH7vmi9B2szDtEocvO8yn9KXNaCdOGVWrKlWWJYmtpYL9/KnOW4zG6kcWfkSA0OE3+r9bh84IKzJpz0J1fkYjUh+NTfOSW5VdogjNo9MQGRxOkMiFJEkFaI5lludhcFWs7V8a0ZH1RBstzD5LnsuMDLg1PqjBGEODC4HA6myP5s9TfSeaG+LYctZfx0sFN7CjLB6C1KZQQtQ69SoVeVqOTVawuSOfXokwe2/MLT7bpg1GlqfZyvD4vmSXZuIM8RBkiTrszjCxLWL1WskpzsLqqr905PS6OlGSSHJKASqm+XP9k81opc/qbPnKdNn7MPwTAX6V5OI6N9/MoHuR/PPdzK27KnNVnYXF6XFi9doLl4BqXpTGQJH9zML7G03A0btytuN2uY51wjtCiRUsAWrduwxNPPHlWznnkyBEef9yfxP+mm25h2LArTrGHUJeabPBTTmz21IBZrTsnvfcUH0TrI/D5fORZC5AliVBjCDHGKGRFjaL4y2aQTLSwNCPXlk+BrRCvz1+16myOIkEfxFFHOctzDwIwJu7vlGwnhp8b41P4szSPr7L2IUnwfvpOXIqPMI2eqc27cnFYPP90ZcwFzNi9lj9L83h41xqea9vvpF8MfIpCblkeLq/rtLLCKJKXHEcR+eUF1Q66P5HNZedoWRaJwfE1OpcieckszQ3cv88z9+I99ka7FR9/lebROywel8+F/h/j/WweG05P9c8DAYpsxYSYg/HVQc23IZBkKHOXUWAvItGcgNxIAuCiRe8D/hzCt98+lg8++LTCeo/Hg1pdtx+Xq1evokOHjjz00CO12u9slOVsHvd81XSutBL/B+DxDi9mjZ467+pZ3ZkViWhTFAo+TFojIZqQKpsNJZ9MjCGKYK2JzLJs7G4nsiQxMrolbx7+C4BeobE0M/6dnzLMGIrPKVPmtNLFHEUbUyh7rUW8c2Q7AJdGNGNicufA88F/am4M4ZV2lzB991r2Wou4f9dqXkjpT4S2+oHgCv6sMG6vh4TgWNSSGlD8zYUSFZp4j9d2FRS8iofssrwaDbo/UamjnEw5mwRT7El7W8qyRI6jKFB7LnQ5WHHsC0Of0HjWF2WwqSSbnqGx2L129GrDCftCUXnJKctiddtx+JyNKhFAdbyShzxrPgW2YnyKjyJdERHaiDPuhQuw4PcjzP4lDaur7jPumLQqpvdrzuReSbXab/To4Vx66VA2b95Iy5YXMHHiZJ544lGs1nJcLhcXX9yXe++9D4C3317IkSOHKS8vJzMzg/j4BJ57bjZ6vYG1a1fz73+/gSzLeL1eHnhgOnl5uXz66Ucoio9t2/7i+efnoigKs2c/S1FRESqVmrvvnkzv3n0A6NWrK3fccSe//rqOXr0uJicnB61WQ3r6ETIyjjJw4CD69u3P228vJCcnhxtvvJkxY24C4PDhQ7zyyouUlBTjdru54YabGDFiVJXHveuuSXV3489zTTj4+TmO1TZCNPpzOm5L8knEmWJB4aTPyxQFjLKJeHMMaYXpeBUfl0Ymsyh9B06ft0KtT6NSE2UKR3apsbps+IBbEtrx+N51hGv0TGvRjZ6hp55WKk4fxCvtLmHG7rUctpcybecqZrW+mAtMJ08wXu60kuo5hEqSUTgh0P3jxirHLsynKPhO82Fhka0ElSQRa4qttrnV5rWRX14QeP1l1l7cio8+ofFcF9ea9UUZbCjOZjJQ5rQSrg0PPPdz+JxYXTbeS9/B//KP0Cc0jssik2lhslQ4h8frodxtJVx7bloO6oMk+YeJZJXlYDuh809eeQHm0GA0VP1FqjYW/HHkrAQ+AKvLy4I/jtQ6+AFYrVYWLfoAAKfTyYsvzsNoNOLxuJk6dTK//bY+EKB2797F4sUfEhQUxNSpk/n+++8YPfpq3nrrTWbMeIwOHTrh9XpxOOxcdFE30tOPYLfbmTJlGuBveh09+mquvHI0aWkHmThxPJ9++lUgsb9Op2fx4g8BeOqpWRw8eJDXX1+Iz+flqqtGUF5ezptvvkN+fj5jxlzFyJGj0Wq1zJz5KE8++SzJyc2xWq3cfvtY2rfvSHJy80rHbUqabvBTjn/I+TtWhJykZnPWilCLz32jykSIwUyhrZhgtZZZrS+myO2gfXBEYJswgwWT1ohd7SVYF0SJo4yeobG822kokVojBlXN3+4IrYGXLxzIY3vWscdayN3bf6JfWAK3JlxIsjGk2v3cXg91mRFUURRK3M4qB6HkW4uRkIk1RaP8MwBKPrLKcgNNqaVuZ2CIyE3xKbQ0hRCk0pDpKCfDUY5OrQ0895MkKHeVU+Sy83mmP2B+lb2fr7L308pk4bLI5gyKSArUnovsJYRrQ+EsDZWpypmOe5QkahSsJUmiwFVAdllepQ5Ybq+HPHs+8cZYFOXMrn1yz6SzWvOb3LP2gQ/giiuGB372+XzMnz+P7dv/QlEUCgsL2L9/3wm1s94EB/uf/7Zr156MDH/i+m7dujNv3ktccslgevfuQ8uWF1Q6j9VqZf/+vYwYcSUAzZu3oFWr1uzYsY1+/QYAMHz4iAr7DBgwEK3W/zuYlNSMiy/uiyzLREVFERxsJjc3B0VROHToEE888XfTqsvl4tChtEDw++dxm4omH/yO93i0aM7v/I6KDyKMYZQ6yvD4vHS3xFRYr1VpCDNYAttGmsIpc1rxKT6SDKc3bY9Zo2POhQN4L30HS3NS+aXwKOsKjzIwPJFbEtqRaDg7HT18isKu8gJ+KTjKL4VHyXfZmZTcmdExrSptm28tRJIkYgxRgQ9gSZLIdxVR7vx7fM/X2ftxHLtvrYP836QvColmTeFRNhZnk2gIDjz3UySFIlsJq/PTcSs+2prCaBUUys/5R9hvLWa/dStvHf6LJ9v0obslBofbid1nRy/VXW7Yk5FliRJ3CcGa4NPqZCRJElZfOSaV6ZRfwFyKk9zy/EqB77hiWymh+hAM0plNWzS5V9Jp1czONoPh7/f0k08+pKyslHfffR+dTsfzzz+N0/n3OFqt9u+m7+NNnAD33fcgBw7sZ/PmjTz66MPceONYRo+++ozK4j/f3zVuWVb947X//JIkYbFYKj3DPNlxm4rG8bT6DDiOjfML0dTNjA5nk0E2EGa0VLkuzBSK5oTnTkaVEbP+zHMqGlRq7k7uzPtdruDK6JaoJImfC9IZ/9f3vJ62FU8d9vQ4aC3m9bSt3LhlGdN2/szX2fvJc9lRgAWH/uR/+Ycr7aMAeeWF5DjyQDqetcdO3gnNnVaPmyXZBwB/re+4bse+QGwszsKnKNg9diQJ7F47Do+T7/PSALgqthVTmnfls4tG8ugFPelkjsSt+Fh0ZDvKsabbYmdZrcb8+RTfaQ3alySJYncJGaXZWD1VD949+f5g9ZZztCSLcvfJU+75p8sqxO2tPqGBV/GRXZ4PciNt8z1BWVkZERER6HQ6cnNzWbt2TY32O3z4EBdc0IoxY25i2LAr2L17Z6VtTCYTrVq1YcWKpQCkpR3kwIF9tG/f8YzKnJTUDL1ez3ffLQssO3QoDau1cc0leTqabs3vHx1eGkLw8/kUwg1hFDtKK4yD06o1hOlCKnYs8UGk0V/7q+5be21EaA3c27wrY+La8nHGbr7PS+ObnANkOst5olXvWjWp/pNPUfg8cy/vHd0R6IkZrTXSLzyBfmEJ7HcV8/r+LcxN3UiwSkuPfzy3VFDILStAkiQiDRFkleRW+MD+JucAVq+bTubICs3Ex2vPf5Xm4fJ5KXNZiTREUmwrIdVazD5rESaVhj7HesVqZRWXRCTRJyyem7Ys44CtmJ1lBbQ3R1DqKCPKEF6zMX+SQkZpNoW2ciIMYehlfY3SxUkSlHtKySjJwuPzUmArIsgcVKvmc4fPTnppJi6Pm2xrPi1CTEjVdBqy++wU2WvQ6cdpo8RVQiT1MzHwuXL99Tfw2GPTuemm64iKiqJ79x412u+NN+aTnn4ElUpFUFAwjz02s8rtnnzyGWbPfpZPPvkIlUrNrFlPn/FE3mq1mrlz5zFv3ot8+OH7+Hw+wsLCePbZ2Wd03Magyc7qUL70cexrXuP9tsOYG9OOH/vfQQdzQj2X8NT8z2DyySj5e+7DWHMUkbpIFEWpkFlfkuCoNTMwhU9d2l1WwBN711HicdHKFMqzbfoSehrzIea77Mw+sIE/S3MBf6aay6Oa09oUGsj+YTYbeGn7H3yetRedrGJOygAuDA6vdCxJkgjWGSlz2AKdbexeD2O3LqfU42J2Sn+6hkRX2Gfith9JtRXzfNt+9IlIonloIgeLjvBa6ia+ytrHiKgWTG1xUaVzLU7fwccZu+kflsATrXsjAclhiQSdasyf5CPblotLZaek1I5apSZUbybcEIZO1lUbBCUJbD4rh4szAoFdJatoGZpU4ymZ3Lg4VHIEh/vv4RvxITGEa8MrPT+UJIWj1qwqf3cO20op87oqfJHQqbVclHwhZcUVh4acbFaHnTt3ERdXOeeiINSlzMzDtGt3YaXlotkzUPM7v5/5HacoChatBf2xsXc6tZZQnaXKzg+KApHGMNRVZGqRJQmDRodZH0S40UJ0cCQJITE0C43HUINacEpwOPPaDSJWZ2K/tYgpO1dxtJYzV/xamMld21byZ2kuFo2OZ9v05b4WF9GmijkOxyd1YGhkMk6fl8f3/MIhW+UaiaIolDqsFZIHfJtzgFKPi5SgMLqYo064fhm1rArU/jYVZ+P2usm3FWJzOflfnr+JdeixfKr/NDK6JTIS6wozyHPaUIBCe/FJmzJ9kpej1qxjOUb9PF4PedZCUosOkW3LwYkDRfIiy1LgHkiSvxZ2pDizQo3W6/NS6CiuUXOrV/JwpDSjQuADyLMW4KZy/le7z06Jo7TCMqfPy9uHt3Hnth+YtvNntpzwBczpcZFrzRfpyoQGQzR7Hgt+ofXQ2/N0qVARHRTBkaJMIkyhqNFUO2uDXjZgMZjJtxb595VlgrQmIoyhmNQmpGPff45P0SNJoA/Rc6g4/ZQDvBMMwbzabhCP713HPmsRU3eu4oEW3XErXo7YSzlsK+WwvZQcpw2DSo1ZrSVEo8Os1uJVFH4t8s+r1y0kmodb9jhpzVGSJKa1uIhSj4vfijJ5ZM8vzGt3CdG66jtabC/N471js9qPjb+wwgdzqNGMVtbQ3RLDp5l72FCczURFId9axB/FWRR7nCQbzLSpZnhHhNZAv7B41hQeZWlOKuOSOmB12rAaytGp9GgkTSBhAYBXcpNelkWZo+pnLW6vh5zyfPJtRahlFVqVBqPWgEGtR5ZkMkqzq5yLscRRRoTBedLhBors42hpZqVsQQAuj5t8WwGxxthA709JhtyygkBiAIAdpfm8eHAjGSeU/7W0LbzV8bJAGjyr24ZJDmm0Qz6ExqUJBz8/l8/nn8tP1XAGKSsKBGvMWIzlhGhDTtrl/fhzwnKXDaPGQLghFIPKgOLzPxf8Z9BUFNCio1lIPIdKjp5yGqZQrZ4XLxzIM/t/Y0NxNrP2ra9yO7vPQ6HbASd8/qoliTuSOnJ1TCvkGtQYVJLMY6168cjutWwvy+eBnf4B+AlV9DrNcpTzr32/4lEUro5pVeE5oVpWEW4IQ1EU2gdHYlSpSXeUke2wEqM38UPeIQAui0w+aU1mdGwr1hQeZUXuQcYm+JtV0oqOolap0MhqDBo9Bo0Brawmqyy3whi56nh9Xrw+L06PK5CSTUKq9suN2+uhxFUaaPauRPKRUZ5FaTVBF6DQXopFHxLorWr1lAeCtN3r4d0j2/k25wAK0MxgZmrzrryatoXD9lI+zdjDrYntTnldgnC+OWfBb/bs2fzwww9kZGSwdOlSWrduXWmbBQsWsGLFCmRZRqPRMG3aNPr16wfAjBkz+PXXXwMPgIcNG8bdd999+gU64YMiWK1DJavqJDnzuSL5JOKColEp6lPmpdHLepJDEtFK/uTdNblOvWwgKSSew8VHT9rbD/w9Qp9q04d3jmxnY3E2sXoTzQxmmhnMJBnMxOmDcPq8lLidlHiclHlclHlcdDBH0qyWwzB0soqn2vThkT2/sKe8kGm7fub5tv254ITB51aPmyf2rqfU46K7JYY7m1XsMWcxmDHIBrz4MGp1dA2JZl1hBhtLsukjx/NHURYyEkMiTv48ql1QOK1MFvZbi/k5P52hUcn4FB8ujw8X7mN5SotqdX1V+WfgK3Y72VGWT5/QOCRJotBWTJgutFJuUiSFbFsuRVU0EZ/I6/OSbc2nuTkRgJwyfxLwUreTe3f8j0ynFZUkcUNcW26KT0Erq5jSvCsP7FrNp5l7GBSRVOUXEEE4n52z4Dd48GBuvfVWbr755mq36dixI+PGjcNgMLBnzx7Gjh3LunXr0Ov9zWF33nknY8eOraMSHcs+IkkEq7XIkoT3HKU3qyuy79SBD/y1Pw21m7XCn1nGSGJIHEeKM06Zd1MlydzVrBN3NetU5fpgOGmKtNoIUmuZkzKAf+1bz5aSXB7ctZpn2vSlvTkCr+Lj2f2/c9heSjODmccu6IXqhGmk1Co14Qb/DA6yJGPWBdPdEuMPfsXZOLwefChcHBp3yg48kiQxOqYVc1M3siR7P5dFNjvrz7zKPS7u3/kz6Y4yHmjRjWFRzf21RHcZIWrL32WTIdeRH2juPpHV4ybXZaP5CckKyh1WSgylSMhYj6Wbeyd9O5lOK8kGM9Mv6FnhC0ZHcyRDI5P5Ie8Qr6ZtYU5K/7N2zYJwNpyzDi/dunUjNvbkqbX69euHweD/gGzTpg2KolBcXHzWy2bWNN7UVGdCUSBIFUxCSGyVnWbqk0Gl5uk2fekXFo/V62bGnrX8UZTFW4e3sbEkG7Nay9Nt+gSmcjouVG9GL/uDmqIomHVB9LD4fy+3luTwXa5/bN/QyOTAPhGmMCKDwtFrdJWaZweGJxKi1gaGPdTEXyW5vLDrd77O2kearaTGmVqOB/Z0h79j0eeZewPTLeXbigJj7SQJCp2FlWYOAchxWpm4fSV3blvJqvwjgeUKCllluf7ptlDYVVbAd7lpqCWJx1v3rhD4jpuQ1BGzWsufpbmsKjhSab0gnM/O22d+S5YsISkpiZiYvzOZLF68mM8++4zExEQeeOABWrZsWatjntjl2qvXBB4/WXSGartjN0SRkXXbBBVBEBGWEIrsJRQ7SnH76jKB2clJSKhkFSFmY5XPvWZfdAkv7PqdbzMOMHPvenwoqCWZOZ0voU1YZIVt1bKK5uFxBGn/7iTj8uhp64umZZCF1PJi0h1lhGr1DElqgVr29whtcWwft9dNuctKqcNKibMUt9eNAlyd2IbFadtZXnCQixMSq70Wj8/Huwe3sfjgtgpXEqbV0z0slu7hsQyISqx2Fo2X92xkU0kOFo0OjSyT7ihjh6uQvpEJSEjIBi/hxlByy/Mpd5URFFyx5prjsDL9r7VkH6vZvZq2ma7RsSSZKjY9yz4dC3ZuBeDm5HZ0iK44POQ4MwamtOnGMzt/5d9HtjGyxYWkxDeevyOhcTsvg9+GDRt49dVXWbRoUWDZtGnTiIyMRJZllixZwvjx4/npp59QqWpeIzlxnJ/d7u/erSBhkjXk55fXSXb6+nbiOL+6ZpJC0GmMWD1WCmxF2FyO005MXR1ZktGo1OjVOoK0RgwaAxGhZtLzcskqza3yfPckdEbvU/F5ln9W+6nNu9JSbaa0tGLvxqigcJxlCnbf3/dHliVwqegaHEVqeTEAg8OTsJX7fz/CTZZ/7KPCJIVgVAeR4cym0FbMZaHN+E/aDlblHCE1r4BIXeV0UXlOG88f+IPtZflIwHWJbSiyO9hSkkOhy8EP2Wn8kJ3G/L1a7mzWiUsjKjahrsg5yGdHdqOWJGa26s1eaxH/PvwX7x3YRkedf8zjIVcWZUZHlc3U+S47D+xaTaajnNamUKJ0RtYVZjB968/Mbz+4wsTFS7L3s6+siCitkWsiWlW6jyfqHxRHh+AItpflM2/PHyzQJ1cYq3iycX7nm9Gjh/PSS69WmXuzrixb9i0dO3YiKanq58mbN29kwYL5uN0uXC4XERERzJ+/EFlu8qPS6tx5F/y2bt3KQw89xBtvvEGLFi0Cy6NP+PY5evRonn/+ebKzs4mPrzwnXW0o+OfyO1fTGTVkiqKgQo1ZFYLZbMbqtZJRmn3KIRE1ZdIaiA2OQi/rUUnqwPALo8ZAuDYMOUQiszSnQhd88D97m9CsI62DQlEUGBhRufalUakJ04dWGkTu8ymYdcH0sMTyRdY+4O8mT1mSCdNbKu3j/5IkE24IpcRRWmHYw+uHtjIwPJFEg5kEfRB6lZrfizKZm7qRUo+LMI2eRy7oSf/EZpSW2lEUhSP2MraW5vBzfjq7yguYm7qR73PTmNK8K8nGEP4qzeO1Q1sAmNL8IjqYI2lhtPDB0Z1sL8tnT3khbYPCKHWWY3XZKgW+QpeDh3etIdNRzgVGCy+k9EdG4qCthIO2Et489GdgIH+By87iY8NDJid3PmXmHkmSmNr8IiZuX8kXR3YzsXkW7YJjTrpPU7Z8+VIsFkuVwc/j8fDIIw+xYMFbtGrl7xC4d++eczZ20uv11qoy0dCdV8Fv27ZtTJs2jddee4127Sp2n87JyQkEwF9++QVZlisExFo7oZZn1p7b6YwaBUUiSBVEsiWR9GrGkNWUSpaJMIUTYQj3T46qEHiWFTidAmHaMOQQmaMl2VWmbBsQXn2TY5jBUm0GFYNKT5fQGDqbI4nUGgOzVpi0BgwqY7W9Yw0qA8E6E8X2Mq46Nuzh16LMwPhFgEitgbxj96Z7SAwPXdCd0BOSCEiSRDOjmWZGM6OiL+Cn/MO8dXgb28vymbj9R66MvoD/5R/GqyhcE9OKy48NujepNYyIasnnWXv5MnMvj7fujaIoeJSK96XI7eDh3WtId5TRwhjC7JT+BB+bjeKJVr2YsmMVy3IP0skcxcCIRN46vA2b10MvSyy9Q+OqvZ8alTrQC7iZ0czYhHa8l76DAqfN37uplhw7X8fx52zwnIWck+og9J2no293z2nt/uuv61i8+F1cLicajYb77nuA9u07UlCQX+38flXN4ZeVlcGePbt4+eW5/Pvfb3DvvdPo0aNn4Dw2mw2bzUZY2N/Zi9q0+XvKsj//3MLcuS8A0KVLV9atW8tLL71Gy5YX0KtXV1atWofR6G91OPH1zJmPceTIIdxuNwkJiTz22CzMZjObN2/i5Zfn0LZtCvv27eWuuyaRmJhU5dx/Dof92DRKqajVapo1S27wKdLOWfB75plnWLlyJfn5+dx+++1YLBaWL1/OhAkTmDJlCh06dODJJ5/E4XAwc+bfue/mzJlDmzZtmD59OgUF/vyNQUFBvPnmm2c46/DfvT3P9xkdzlfHxwQ2D0kkozyL4lpmeAEwaPTEBkcTVIMZBnw+hRC1BSlE4uix/JY14a/1Va7BHaeWNITqgpl74cDAMgmJCFPYyeda9EGEMZxSh5V2wRE83aYPO8sKSLeXke4oJdNRTp7LjkqSGJfYgWtjW590PKMkSVwamUzP0DgWHdnOityD/Dd7P+DPQzrhHz1pr4ptxVfZ+/il8ChZjnJi/5HIvMjtYPqxORmTDWZmpwyo8DzxAlMoE5t1Yv6hrbyStgmb182qgiNoJZlJyV2qrHFISFiMZqJNERwtzQpMRHxzfAp3XdiNlrrkGuUp/SfnzgVnJ/ABeMpx7lxwWsHv6NF0Fi16m1dfXYDJFMTBg6lMm3Yv33yzgqCg4Grn96tuDr/ly5dx88230Ldv5d6xZrOZ0aOv5rrrRtOlS1c6derM0KGXEx0dg8vl4oknHuFf/3qWiy7qxk8/reTLLz+v0TXcf/+DWCz+IWILFy7ggw/eY/LkKYA/gfbxcno8Hu6449Yq5/47ngz700+/AqC0tLTa8zUU5yz4Pf744zz++OOVlr/99tuBn7/66qtq93/vvffORrFQaBhJrc9nsqImISgejZxLvrUo0DFFLavQqNQYNXrUKg0+nw+v4sWnKHh9XvRqPZHGcP9YxRp+XiqKglkdQlKIRHpp1inHIEqSRFRQBBpJV+0zXZ9P8c+VeEISZ71Gi0llOmVruFFlJFhnosRRRq/QOHqdUFvyKj6yHFZMKk2t8p6a1Vrua3ERQyOTWXj4L1SSdGzIRsVgFKE1MCg8iR/zD/N19n4mJ3cJrCtw2Xl49xqO2MtI1AczJ2UAlio60oyMbsmfpXn8UniUV9I2A/6ZL2L1lTPnqGUV0cGRhGlDQZGICYoizZ0eqIUHV9NRpyZ07Saf1Zqfrt3k09r1999/IyPjKBMnjg8s83o9FBQUYDQaq53fryZz+FXlwQdncOONY9m0aSO//bae//xnMYsXf4jT6UCn03PRRd0AGDLkMl544ZkaHXPFiuX88MMKPB4PdrudpKS/p45KTEyiQwf/l6r09CPVzv3XqlVrDh06xNy5z9O1azf69Olbo3Ofz86rZs9z6cQPQhH8zpykyMQYo9GqtXh9XowaA1pZg1bSIknysfstVcp9eTq1BEVRCFIHkxgik16ScdIAGGkKI1wXhnKK8+hlPVqVJpBCLNwYiox8yifB/tpfKGXO8kpNtSpJPqPB3ynB4bzaftBJt7kurg0/5h/m+9w0bkloh1mtJc9p46Hda8hwlJNsMDMnZUC1wVeSJB5o0Y0D1iKynFbi9UFcF9em0nZGjZ44cwxG2RT42zGpTESawsguyzvtazxO3+6e026WPLsUevW6mFmznq60ZtGit6ud3+9M5vCLj08gPj6BUaOu4r777mHdurVVziBxYs1cpVKhHGumOHGOwT//3MLXX3/B22+/R2hoKD/88B1LlnwdWH98aBkcyxt8krn/Pv74CzZt2sBvv63nzTdf56OPPkenaziZsf6p6XYhEsGv7ikS4dpwovSRmOQgNOhQFAmfTwnkufT5Kv477VMpEKQykRgSj6aaThmhxhCijFE1ymijlbUYjgUIrVqDWRtc49qoSWXCqD23E4IG6Yz+oRvGELqFROPweVmak0qO08oDu1aT4SinpdHCixcOPGWt06TW8GSbPvSyxPLIBT0r9PyUgDCjhWRLEgbJWOFLoz91XjjGBpQXt7Z69OjN77//ysGDqYFlu3b55+M72fx+1c3hZzKZKC+vunZrs9n444/fAve4rKyMrKxM4uLiaNYsGafTyZ9/+js+rVr1E2Vlfz9mSEhIDJRr5crvAsvLysoICgoiJCQEl8vF0qXfVHutJ5v7Lzc3B5VKZsCAS7jvvgcoLi5q8E2fTbbmd5yChFndeP94z7VzOVzkeABMConnyD9qgMH6IOJMMUg1nOnc51Ow6M2U2Muw6M1oJG2lmlz15ZCINIVhddnOyfUbNXqahSSSa8snr7yA6+LasKkkhyXZ+1mRc5Bcl43WplCeT+mPWV19wusTNTeG8HTbyk1ZIYZg4oNiq50xXvbJxAVHkVZ09Iyu6Xxx7713V+jx+NFHn/Ovfz3Ds88+idPpxO1207FjZy68sN1J5/erbg6/0aOv5rXXXuGjj96v1OFFURS+/PJzXnppDlqtFq/Xy9ChlzNwoL/2//TTz1Xo8HLiGOipU+9n9uxnMZmCGDz4ssDy3r0v5vvvV3D99aMJCbHQuXPXQJD8p5PN/XfgwAHeeOM1AHw+H7feOo7IyMgqj9NQNNn5/Mq+vh/Hr+/w3AWDmHDzG1wYXH3PtobkbI7zqy+nuqZ/znVn0OhJtiSiVjTV7lMVNy4Ol6STGBKPjlq2BsgKB4sPHcvneYpNJZnmMXF4HWB127G5bLi9Hjw+zykDrkalprklEZ1kwIWT1MJDuL0e7t7+E6nH5t5LCQrj+bb9K2W3OVGwPgiH23HSJmO9Wkvz0GanvI+SBDmOPGSdlyg5tsbj/MR8fmfmXIxLbAzEfH7/IAV6e0JII262aQr8eUhNNLPEY9IaSAyJq3XgA9DJ2mNJr0+jGdwnEWkKR+LkNU2tSkOSJY6kkDiCVWZiDTG0tDTngrBkWoYlExMciaqaVHIqSSbeHINe9v++6iQdZn0wkiRxc3wKAB2CI3gh5eSBL9xoIdmcQGJIXLVNxipZRZw5tkb3UVEg0hCOrgHNjCIIotkTCYv63D6vEere8QCYbElCpahOa9ymzwcRhghqOIKikiB1EAatvtoxj0E6I3HBMeglfaCzgqIooIAKDSpJg9FgJEhrJLM0p8IUSBISUcERmNXmwLUpCoGB9v3CE3jHOJQEfVCFRN7/FGGyEGvyN2MGqYJIDIkjvaTiJLkSEtHBEQSrg2r8XFbyyYQHh+Kx1mhzoQ4sWbK8vovQoDXZmp/nWJYQWZIw1fC5iHB+UxSQfacX+AK8p59NQ1JkkkLiSLLEEW4MQa/RoZZVyJJEuMlCM3MiOk6eUMHnUzBIJppbmhEVFI58LJCFGkOI1IVX2tegMhB0bELfZgZztYFPAiJMocSY/n5+539mGlSpBmgxmonQhdW6Q1KIPrhRpAgUmoYmW/NzH5vBXSerUckyXq/4oxXOnEbREaLWEaoNxYcPl8+FR/FglI3+NvYakhUVMYZogjRGChwlxJqiUarYX/FBhCGcMkfloRbH+QNfGDGm6EodV04MgOklmahltf9cNewoVKk84s9IaCCabPBz+TxoAZ1KI/5ghTrnrzVJaNGhlXSnlTpWUcCkCiYoOOikwcioMmDUGil3Vm5z9GeqCa0y8J14nuMBUJZkVEqT/VgQmpAm+1vu8vqD36kS9wpCfTtlLez4UAunrcK0T2pZRUxwJKHa0GoDX+AQxwLg8Z8FobFrss/8XMd6NehUNU+tJQjnK9OxzjbHGTR6kkMTCNOG17i51Z+I4GyV8Pz38MP3M3bsGG699Ubuumsc+/btrXbb0aOHc9NN1+E7YYaR0aOHk5p64FwUtVplZWV88MF71a7PzMykV6+uzJ79XIVlQ4eePJMQQF5eHpMm3VmjcvTq1RWbzVbrdefSKYOf1+tlyJAhuFx1M23N+cJ17JmfQaVBTGckNHSSz1/7kyUJi8FMc0siBskkOqDUwsyZT/Lhh5/x/vufcPPNt/LMM0+edHubzcZ33529Hpcez8nz1lalrKyMDz98/6TbGI1G1q5dzdGj6bU6dmRkJG+88Vaty3Q2eL2n2SX7BKds81OpVKhUKpxOJ1pt4+kVebxrt1488xMaiWB1ELHmaMJ0p27mPN8s2L+eOXtXY62juSFPZFJrebjNQCa36nPS7YKC/s7DWl5e7p/o+CTGj7+Ld999i8suG4ZGU3E8ZH5+Hi+9NIecnGycTieXXjqU//u/OwB47bVX2Lp1M263G4vFwmOPzSI2No7MzExuv30sw4ePZNOmjYwefTX9+w+o8jg+n48XX5zN5s0b0Wg0GAxG3n57MS+++ALl5WXccssN6PV63n77vUrl1mi03HTTLfz732/w9NPPV1q/Y8d23nhjPlarPw3bnXfeTZ8+/QLl++GHVQCsWvU//v3vBeh0OgYNGsLChQsqTKv0+eefsGbNz5SUlHDPPfcxaNDgwDk++ug/rF27BqfTycSJ9wTWHc8b6vV6CQ0NZfr0x0hMTKpy+qX8/Dw++eQjtFotPp+PZ5+dTXJy85O+Zyeq0QOvW2+9lfvuu4+77rqLmJiYCglVExOrn0PtfOY6Nu+ZeOYnNBaSoiJCF35GOVPryxupv52VwAdg9bh4I/W3UwY/gGeffYoNG35HURTmzXv9pNumpFxI27YpfP31F4wZc1OFdU8+OZNx48bTpctFuN1u7rnnLlJS2tGzZy9uvfX/mDJlGgDffPNfFix4jWee8actKykpJiXlwsD6e++9u8rjWCwWNm/eyCeffIksy4E8mw8+OIPbbx9bbXLq46699nrGjLmKffv2Vgj6ZWVlzJnzHC+//BoREZHk5+dx++238PHHX1TYv6CggBdeeIZ33vkPSUlJfPLJh5XOYTKZWLz4Q/76608ef3x6heAnyyo++OBTDh8+xIQJt9O5c5dj9+0J3nzzHZo3b8G33y5h1qzHWbTIX5M9cfolgMGD+/PZZ18RERGJy+XCV8sBujX65H/6aX9G8/Xr11dYLkkSu3fvrtUJzxfHa34GWUaSmvazDqHxaIiBD2BSy95nteY3qWXvGm17PAfnd98tY/78ebzyyvyTbn/XXZOYPPlORo4cHVhmt9vZsmUzxcVFgWU2m41Dh9Lo2bMXv/22ni+//By73V6p+U6n0zFkyGWnPM7w4SPwej08++yTdOvWnT59Ks8PeDI6nY7bb5/Am2++zkMP/T2F0fbtf5GZmcG0afcGlkmSxNGj6YSEWALLdu7cQZs2bQPTI40cOYpXX325wjkuvXQoAO3bdyAvLw+n0xmYBeL4/WrWLJk2bdqyY8d2JAkuuKA1zZu3AGDEiCuZO/d5rFZ/L+YTp18C6NatO089NYu+ffvTp09f4uMTanUPahT89uzZU6uDNgTuY98S9JIKCQXlFGmpBEE4eya36lOjmtm5cvnlI3jhhWcpKSnml1/W8NlnnwBw8823MmzYFYHtmjVLpnfvvhVqPj6fD0mCxYs/QP2PNHNZWZnMm/cyixd/QFxcPNu2/cXMmY8G1uv1hkDL2smOA/Dxx1+yZcsmNm78gwULXuM///m4Vtc4YsSVfPzxB/z115bAMkVRuOCCVixc+G6l7TMzM2t1fK3WH+iOJwo/0+d0J06/BPDCCy+ya9dONm/eyOTJd/Lww49x8cU1/x2qVW/PzMxMtm7dSlZWVm12Y/bs2QwaNIg2bdqwb9++Krfxer08+eSTDBkyhEsvvZQvvviiRutOV+ixfJ6J2iBkd/33PBIEof7YbDZycrIDr3/5ZQ1msxmzOYQRI0bxwQef8sEHn1YIfMdNmHAXX375eaAHo8lkonPnLrz//nuBbXJysikoyMdqtaLRqAkLC8fn8/Hf/35ZbZlOdpyioiIcDge9el3MpElTMJmCyMjIwGQy4XA4atRZRqVScdddk3jrrYWBZR06dCI9PZ3NmzcGlu3atbNSx6l27dqzd++eQKeZ5cuXURvLln0LwJEjR9i3by/t23egffuOHDiwj0OH0gBYsWIprVu3wWSqPLGyx+MhI+Mo7dq159Zbb6dHj97s21e7SlqNan65ubncf//9/Pnnn1gsFoqLi+nUqRMvv/wy0dHRp9x/8ODB3Hrrrdx8883VbrN06VKOHDnCypUrKS4uZvTo0fTu3ZuEhISTrjtdF5jCcQBRagNeWwlSSJDoGScITZTdbufRRx/G4XAgyzJms5m5c+dV6N9QnaioaC6/fDgff/xBYNmTTz7LvHkvcfPN1wP+HpaPPTaLCy5oxaBBl3LjjddisVi4+OI+bN26pbpDV3sch8PB888/jdfrxev10rt3H9q374Asywwdejk333w9ZrO5yg4vJxo0aAgffPBeIHD7r/uVY02+L+J2u4mPT+DFF+dV2C88PJzp0x/l/vunoNfr6dOnH2q1Gr2+ZknhvV4Pt956Iw6Hg+nTHyMsLAyAWbOeZubMx/B6PYSGhvKvf1U9W73P5+Ppp2dRXl6OJElER0czefK9VW5bnRpNaTRp0iTi4uK4//77MRqN2Gw2Xn75ZY4ePcrChQtPtXvAoEGDWLhwIa1bt6607s477+Tqq69m2LBhADz11FPExcUxfvz4k66rjU8++SwwkWSnPc8SlJ+G1OdWpPZ3sG7nvkpNn23btqNt23bY7XZ++GFppeO1a9eJVq3aUFZWxv/+912l9Z07X0RyckuKigpZs+anSusvuqgniYnNyM/PZd261ZXW9+zZl9jYOLKyMvnjj3WV1vftO5CIiCjS0w+zefMfAGg0Ktxuf/PCgAFDCA0N49ChVP78c3Ol/QcPvpzg4GD279/Lzp1/VVo/dOhIDAYDe/bsZM+eynOADR9+FRqNhh07/uTAgco1+tGj/X+wW7du4vDhgxXWqdVqRozwz2y9adPvHD16pMJ6vV7PsGFXAvDXX3+Qlna4wnqTKYhLL/V/C1+37mfy8yvOJm6xhDJw4KUArF79Y4XnJgAREZH07XsJAD/+uCLQs+246OhYevfuB8D333+Lw+GosD4hIYlu3XoBsGzZ15W+aTdr1oIuXboBsGTJ5/xT584dSU5ui9vtZvny/1Za31h+9zIz0+nUqfJ0MiCmNGrorFZroFa2bNk3fPvtN7z11qJ6LlVl1U1pVKOa3+bNm3n11VcD3XmNRiMPP/ww/fr1q7MCZmVlERf395x6sbGxZGdnn3JdbWg0KjQaf/vz8W906pJNlNlvBhQ0moq3IzhYT2RkMDbb3/udyGz2r9dqfdWsNxAZGYwkOatcb7EYiYwMxuu1Vrk+NNS/3uEwVrPeRGRkMGVlFdcf/zkszERERDCFhYYq9w8PNxESEkxOjr7K9RERQRiNRjIyql4fGRmMRqMhKKj69QBBQbpK6zUadWC90aittF6n0wTWn3hNx+n1f683GCrvf+J6vV5Tab3BoK2w3uWquN5o/Hu9TqfB63VXu16rVSNJFb9DBgXpAuurujfgvz9ut7vK9Y3ld6+0VExz1Fh9/vknrFr1E16vF7PZzCOPPF7fRaqVGtX8LrvsMl577TXatm0bWLZnzx7uvfdefvzxxxqf7GQ1v5EjR/Lss8/SsWNHAN5++21ycnJ4/PHHT7quNk6czLb0o9twbv0v6mZqCrq8R0hCChHNWyE18JbPpjiZbUPUVK5JTGYr1LczqvmNHz+e//u//+Paa68lLs4/GPPrr79m6tSpdVbA2NhYMjMzAwHuxNreydadrkNlErGApCgYyn/BWhyNprgciyVIJHwRBEFo5GrU2/P666/nlVdeoaioiJ9//pmioiJeeuklxowZU2cFGTZsGF988QU+n4/CwkJ++uknhg4desp1p+tQsxHs0TdHFSyhLf0Zt8OJo7yMMru70raSJKGm8nJBEAShYTplzc/r9TJ06FBWrFhB7941Gyj6T8888wwrV64kPz+f22+/HYvFwvLly5kwYQJTpkyhQ4cOjBo1ir/++ovLLvMP8Jw8eXIge8zJ1p2uEcOu4ebSZsx1XYvFnUapPRVPeQR5qiAM0WrUx54JShLI9gI85YWoIprjpernN4IgCELDUaNnfkOHDuXLL78kODj4VJue10585idJUKb4+OztWxhj/J6N0giap0xCiWyJWqcnMSoICQXZVoArPxPF50UXEY8vKPq8HhLRVJ4lNXRN5ZrEMz+hvlX3zK9GzZ7Hc3tu2LCBI0eOkJ6eHvjXkMWb9SQk+ptPW3rWsjmnDL3ipKjUQZnNhVyehzM/A+VYNhh3cS6y116fRRYEQRDqQJPN7QkgqdR0ankROQWJRKvS+d/BLaTERxMSkYQ9+wiyUopO9ffYP5/Hja8kBzm8GSdM4yUIQiNRWlrKyJFDGTXqau6//6H6Lg5r167hr7+2cO+90+q7KI3OKYOfoiisXLmSuLg41OrGNwOCKigcXdglUPg+Q7Q/M3NDL+b1kSgpLsShVREdpkfJXo6kMqCKGoK7vBi9KRSf1lzfRRcEoY6tXPkd7dp14Mcfv+fee++rNFXR6fB4PKf92dm//wD69x9wxmUQKjvlOyJJEldeeSVbtlSfgqchs0t6iByKUvghl+g381hWEf/Z4eO6eAmH3YZ9x1zUJb/4N9ZYUIV2w12UiTrGhFcRnV8EoS6U//wa5d8/h+IsP/XGtSTpggga9ihBl0w55bZLl37DPfdM5T//Wczatav5/fffuOCCCwJTFqWmHuChh6bx1VffYrNZmTfvZVJT9+N0Ornoou5MnXo/KpWKu++eQOvWrdmxYztmcwhz577CAw9MoaSkBKfTyYUXtmPGjMfRaDS43W5efPEFtmzZTGhoGK1bt6agoIDnn5/LsmXfsn79Lzz//Fw2b97EvHkv0q5de7Zv34YkSTz99POBWRDefPN1/ve/lZjNFrp2vYhNmzbw3nsf1fn9bCxq9MwvJSWFtLS0s12Wc0pRQCXL6HU61GGtcBo6o5G8jDSu442DkFpSSnj2k6hLfkE5dpvcB15C8ZTjddihvKBGef8EQTg168+vnZXAB6A4y7H+/Nopt9u/fx8lJSV069aDESOuZOnSbxg+fGSFpM3Lln3L8OEjkSSJefNepmvXrixa9AEffPApRUWFLF36TWDbjIwM/v3vRbzyynxUKhVPPfUc7733ER9/7B+2dXzb//73K3Jysvnkky+ZP/9Ndu/eVW0ZDx48yFVXXctHH33O4MGXsnixf/aFX35Zw/r1v/DBB5/xzjvvkZ5+pNpjCH41qov36NGDCRMmcNVVV1WazPbaa689a4U722RZIjLUQKHXgt0yBL19C+NCfmaV/SKScp5Fp87EqwqjJO5xwgr/Dda9uA4uRG75AN6CHPTaICStUcwFKAhnyHTJlLNa8zPVsNZ3xRUjkCSJgQMH8fLLc4iLi8dms3LgwH6Sk5vz44/fB5JFr1u3hl27dvDxx/7pjBwOB1FRUYHjDR16eaC50+fz8dFHH/Dbb+vx+XyUlpYGkkBv3ryRYcOGo1arUavVXHbZMP78c2uVZWzWrBlt2vgzbbVv34F169YeO8YmBg++NDDtz/DhI1m06O3TuFtNR42C35YtW4iPj2fDhg0VlkuS1KCDH4AEWCJCccUOxpfzBgm+NJZGTydYLifd1wx14mMomgjywiYTYb0fcr8nX9UDl6kLQc40Ylu0BLVeBEBBOANBl0ypUbPk2eJ2u1m58js0Gi0rVvhreh6Ph+XLv+WKK0ayfPlSuna9iOTk5sTG+rNLKYrCnDkvVzuJ6onzz61c+R1//bWVhQvfxWQy8d5773LkSO1rZ1qtNvCzLKvOeI68pqxGwe+DDz449UYNmFqSiUxsTkFqX0wl3xMsl/OroxN3FzzIJJORkbHg1iRQFnYD5sIPMee+Tm7Cq5TkK+hwExGfgGIKw6vUanpEQRDOE2vXriYpKbnCrATbt//Fk0/O5PXXFzJ+/G0cPZrO8OFXBtb36zeA999fzMMPP4pKpaK4uAibzUZcXHyl45eVlWOxhGIymSgvL2Plyu9p29Y/9qxr12788MN3DBlyGV6vl59+WklERGStyt+1azfeeWchN954M1qtju++W36ad6LpqPGndVFREUuWLOGdd94BICcn57RmVjgfKQoYgkMwJI/BK5uxBl/GvrBHKVeMvJoKaVZ/ta48ZBQuXStU3kJCChYDUFhUhjUnHW9uKmpPObIsngMKQkOzdOk3DB16eYVlHTp0QlEUMjMzSU5uwZYtm7nkkkGB9ffd9yCyrOKWW27g5puv57777iE3N7fK419xxXCsVitjxlzNgw/eR6dOXQLrrr76WiIiIrjxxmu55567SE5uTlBQ1YkBqtO//wB69uzN2LFjGD/+NiIiImt9jKamRhleNmzYwL333kv79u3ZsmULW7duZcOGDSxatKhW8/nVtxMzvEDFjBSyLKEUHyUr7TClNn8ez+f2KnyXA8lGeLsL6FUSatdRIjMeQFLcFMQ8itPYjTCzntBgHcgyanMEsjm63tKgNZXMIQ1dU7kmkeGlZo7PjedyuXjoofsYNOhSRo266rSO4fP5eO65p4iIiGTixMlnqcQNxxnN6vDcc88xb948evfuTffu3QHo1KkT27Ztq9tS1iOfT0FtshAWmo/LW4bD6WXaBbCrFA7Z4J6dGox4sHriGam5kUmm9/FlLqQg+knUqkRMRg0aRcFdlINOlpHNsRVSqSlIiAeDgiBU5d5778btduFyuejevQfDh4+s9TGeemomWVmZOJ1O2rRJ4ZZbbjsLJW08ahT8MjIyAkmtj/f01Gg0je5hq09tRKMzEB7iIafAhkFSmN3TzG2/lLC3+O9ZHV5hBD3Vf3CRbi+W3Puxe8dQqr+eCIsJRVFwFeei05nwaYKRJCi3e7A6PESHGs7rvKCCINSPRYveP+NjzJ79Uh2UpOmoUfBr2bIlv/zyS4WZ23/99dcqJ6VtyBQk1OYwdA4b4RYTNo2ZRE0Yr19i42i5izCjmjBvIZK1GKfnUb4+8h+uNq5CU/ghbuuvONs8hNbcCsXrxV2YgSa6JcV2icNZpf7mH7MOlXgmKAgBiqKI8bLCWaMoPqr79apR8JsxYwZ33XUXAwcOxOFwMHPmTFatWsUbb7xRl+Wsd4oCki4Ytd5AeGw0DquW8nwbSWY9SWb/mByL0YRc4MFhlfhf0GS+yenDnPCFRDsP4t02GXfCGNRJt+B1SLjzM8lxhuD2+BOBltrchAVrReunIAAGg56yshKCg0NEABTqlKIoeL0eSkqKMJlMVW5Tow4v4O/d+e2335KZmUlsbCxXXnklMTExdVrgs+1kHV6Ok2UJ2evAK+vw+BQOZpZgtXn+Xi9JhBvcOLMPUmp3ceNGcLjtfJb8MSnu7/zTIEUMxNt8BgWlLrTRSRS4jXi9CkaDmlYJFs7mn3lT6UjR0DWVazpZhxe32016ejp2u+NcFE9oYtRqFaGhoURERCDLlQc21Dj4NQY1CX7/5HB7ST1aEqi9AWjUMmFyGeXZR1iW6eWFfRCtgy/b7yE27xkkn53SqEmUBw3hsFvH03ugmVnPg90SaBEfQrDhzJPlVqepfKg2dE3lmk4W/AShPolR2adg0KqJiwyq0G7s9vgok8wEhUcxLBpamiDHCR8WtKU44k4AgvPeYUfeYSZscLC3yMHKw8X8mVtOXrFdNPEIgiDUs3M2R1FaWhozZsyguLgYi8XC7NmzSU5OrrDNww8/zN69ewOv9+7dy4IFCxg8eDDz58/n448/DuTO69q1K7NmzTrr5VYUhbBgHQ6XEY/Xh06jQqtRoVHL6NUmNBoV95Zmct9fPj5Ih+Ex/dEFbcdYvorkwpdRfLOJ1evIcsCne3Pok2jG7vKg14gZIQRBEOrLOQt+s2bN4qabbmLUqFF88803zJw5k/ffr9i9d86cOYGf9+zZw2233Vahh+no0aOZPn36uSpygKIoxEeYAj+f2FBsim3GJSoDfTJTWZ/n4+1DYJLvYJx3D6016bwfv5jgpIlcvwG25NrZlnqAKH0CwZGReGVthWZYQRAE4dw4J82eBQUF7Nq1ixEjRgAwYsQIdu3aRWFhYbX7fPnll4wcObJCItf65PMp+HxKpZ6aRr0Wr8HC1F4tUEmwNBs+zdRzf+EDeNBwET8S4fiVG47lvn1zt428gwewHt0LJZmoaFxjJQVBEBqCamt+N910U42eTX300aknS8zKyiI6OhqVyt/Up1KpiIqKIisri7CwsErbu1wuli5dynvvvVdh+fLly1m3bh2RkZHce++9dOnSpdK+J1PVg/fIyOBaHaMqeoMWHzLXpJTy+a5cLBqJaV1b4/aNR531JqH5b3J7cgpfZISyrRT+KlcR43Jj8ZSgcoE2shmyVnfG5TiuLq7pfCOuqWFojNckNE7VBr/rrrsu8PORI0f46quvuOqqq4iLiyMzM5MlS5ZwzTXXnJVC/fTTT8TFxZGSkhJYdsMNNzBx4kQ0Gg3r169n0qRJrFixgtDQ0Bof93R6e9aEJEkYtTJjW0UQp9dwaYKK0LJMijxDwLgVg+13Ig8/wFsxfXghpw9v7GtN52AvahlUpXbUJeWoI5rhkc68lttUehE2dE3lmkRvT+F8VW3wu+qqv5OqXn/99bz77ru0atUqsGzkyJE8+uijTJly6jm4YmNjycnJwev1olL556DKzc0lNja2yu2/+uqrSoE1MvLvKT769OlDbGws+/fvp0ePHqc8/9l2vFNMeLCOy5ND0WlkjDqF8twMSqImo8vJR7Yf4CJW8EXUCjI8ERQf6UOoZijBYa3x2MpRcg6iiWyGV20Qg+AFQRDOsho980tNTSUpKanCsoSEBA4ePFijk4SHh5OSksKyZf5JIpctW0ZKSkqVTZ7Z2dls3ryZkSMrJnbNyckJ/Lx7924yMjJo3rx5jc5/LkhAbIQJWZZwun2Ua8IIiYwiOjIKQ9c30XZagCviKkoJJ16dT2vnN6h33UNZ+goAvE47rpyDqFxl1abjEQRBEOpGjXp7du/enRkzZjB16lRiYmLIysri9ddfp1u3bjU+0b/+9S9mzJjBG2+8gdlsZvbs2QBMmDCBKVOm0KFDBwD++9//cskllxASElJh/5dffpmdO3ciyzIajYY5c+ZUqA2eD4IMasJC9OQX2dFo1UTGt0QqOITHVo4U3AZD69ZkBY9l2ubdDNOt4jrTKtSHX6HY4yI4eTS4XbhyD6OJvQCvrK/vyxEEQWi0apThpbi4mCeffJIff/wRj8eDWq3msssu4/HHH6+y9na+OlvP/E7k9vooLncREaJHAlQ+B+7sg/jcTiRJoqjcyYIddt5Ig8fDl3C74QMArNF3YW5xLRqVhMYchhTeDJ/v5OeqSlN5ltTQNZVrEs/8hPNVjWp+FouFV155BZ/PR2FhIWFhYVXmShNAq5aJDjUEgqxPpUcbmYgzJw3F6yXYoOX6JCefHPXxTMFoTDFqrlcvxpTzb4q9LoJa3IBUXowuKBSfxlzPVyMIgtA41TiCpaam8uabb/LGG28gyzIHDx5kz549Z7NsDZKiUKF2qSjg1ZrRRTVDYw5Hq9cTHx7EzLZgkOGR7BG87TiWEi1/MeUH3iev2I49Pwu1fBpVP0EQBOGUahT8vvvuO26++WZycnJYsmQJAFarlRdeeOFslq3RUBQFjzYEKbwZqri2hLZIYdhFrXm7fyihGnghfygvWyehIBFc+BGezKWkp+dSnJONxyfmOxMEQahrNQp+r732Gu+99x5PPfVUYKB627ZtRc2vlnw+BZ8i4VUZ0IREkhzXjPeHxJJggAVFg3mhbCIA5oL38TnzyE8/Ql5uEbnFdkQdUBAEoe7UKPgVFhbSpk0bgEAtRJIkUSM5A8EGDWqNTKglkv8MiiIlGN4pGcxqR3dkxUZI/ju4nE58pbkUFNspKXeJIRCCIAh1pEbBr127dnzzzTcVli1fvpyOHTuelUI1CQrEhJlwecEYEsM7/SPoFSbxeNF4yn16DLY/SM36HXtRHmaVg/wSGyIHtiAIQt2oUfB77LHHmDdvHmPHjsVms3HHHXfw6quv8sgjj5zt8jVqRp2K5PgQHF4JtSWWV3tbuLVFBP+23gxAq/J3uGdTKb/sO4qs+Ch3uOu5xIIgCI3DKYc6KIqCVqtl2bJlrF27loEDBxIbG8vAgQMxmUznooyNlqJAkE5F8zgzh7JLMYUmcG0rL6VRQ8k+spYY1X5GqT9mysYJXJ+/n1mD22I2WlBEFVAQBOGMnDL4SZLEyJEj2bJlC1dcccW5KFOToij+GmCLuBAOZZVgCkskyOPBFz8J5eiDjA36geX2fnyR1pYb9u8lzNgGtcki5gEUBEE4AzVq9kxJSSEtLe1sl6XJUhTQqWWax4bgRIc2Ih6foQXlltFIKLwSsRA1bhbttVGQtg+pNFvMAygIgnAGapThpUePHkyYMIGrrrqKmJiYCr08r7322rNWuKZGq5ZJig0mr0hNULiNUve1GMp/JdaTzuOW95idcxupBTLBhgz09nLU4Ql45LqbB1AQBKGpqFHw27JlC/Hx8WzYsKHCckmSRPCrYxpZJj4yCLc5GZ3kosw9CUvGTMYGfc8A/RZ+OXIrrcMHoZZLUXxpaKIvwFOzt1EQBEE4pkafmh988MHZLodwAsWnoNZoiUpugVHjw6p/HtfhhSRyiJt4Efvu7/C0uQdohVyUgRyWhE8RgwAFQRBqqtbZqRVFwefzBf4JZ49HZcIYEYc5rgemLm/wjvMuCrxmDI6duP+ahDv1NVyl+UjWfJFwQBAEoRZqVPPLycnhqaeeYtOmTZSWllZYt3v37rNSMMH/RUMxhqEJsoJSSPNmlzN4cx8esHzBWNMKvFnfAipcqqnoNAY8GjF1jCAIQk3UqOY3a9YsNBoN7733Hkajkf/+978MGjSIJ5988myXr8nzKTKq0FhUWgODmhlICjLxr6L/40vNv1AkDd6s/+JO/wJv0VE0kn8QvKgECoIgnFyNgt/WrVt57rnnSElJQZIk2rZty7PPPsuiRYtqfKK0tDTGjBnD0KFDGTNmDIcOHaq0zfz58+nduzejRo1i1KhRFYKr3W7nvvvu49JLL2XYsGH8/PPPNT53Q+eRtKijkwmJiefOtgYAXs64kKLIyQC4D75Jxq7vyUpNJS29gLwSBw63FyREc6ggCEIVatTsKcsyarV/U7PZTGFhIUFBQeTk5NT4RLNmzeKmm25i1KhRfPPNN8ycOZP333+/0najR49m+vTplZa/++67BAUF8eOPP3Lo0CFuvvlmVq5c2WSyzHhlPVJwHNf0CeXVvZs5WOrm6/J+XB+ahbnoM4Iy51IgWVAZTeQ5TGR5FbQ6FRaTjmCTFoNWhQiDgiAIfjWq+XXq1Ik1a9YA0LdvX+677z7uuece2rdvX6OTFBQUsGvXLkaMGAHAiBEj2LVrF4WFhTUu6HfffceYMWMASE5Opn379qxdu7bG+zcGiqKg1esZ1zURgLePqHgi/3q20x9ZcRCU8SypB7YRTBkqlYTd7iEr38qB9GKRF1QQBOEENar5zZkzJ9Cz89FHH+Xdd9/FZrNx22231egkWVlZREdHB+YCVKlUREVFkZWVRVhYWIVtly9fzrp164iMjOTee++lS5cuAGRmZhIfHx/YLjY2luzs7Bqd/7jw8ModQiIjg2t1jPPBXf1b8u8tGaSXOlmWBSuZxHuR+fTU7cK7fxZD/3iaFhHhtIk00zbcxIWRJhwehWbhQajkhln/a4jv06mIaxKE+lOj4Gc2mwM/6/V6Jk+efFYKc8MNNzBx4kQ0Gg3r169n0qRJrFixgtDQ0Do5fkFBeYWcmJGRweTlldXJsc8lWZb4YNSF/JJaSJHTQ6m9jK8KppPom0E7bRqvWGZzR/ajbMi2Bfa59cIonhrWBoNGVY8lPz0N9X06maZyTbIsVfmlUxDqW42C36uvvlrtuqlTp55y/9jYWHJycvB6vahUKrxeL7m5ucTGxlbYLjIyMvBznz59iI2NZf/+/fTo0YO4uDgyMjICNcWsrCx69uxZk+I3Oj6fQsvoYBSXF5fbh0EbTrDXjCPjCXyZj3Gxfjs/Jb/MB9J0djqD+C3Tyid78hjTIYYeLcJFUmxBEJq8Gj3zy87OrvBv+/btLFq0iCNHjtToJOHh4aSkpLBs2TIAli1bRkpKSqUmzxM70OzevZuMjAyaN28OwLBhw/jss88AOHToENu3b6dfv341On9jpFOriAg1olbJ2F0+ytVh6GO7YG3+DD45iHjPRqbo5vNKOy9DmgXj9iks/jMLt8+LSnGh9pSj9tpOfSJBEIRGSFIU5bSqAWvXrmX58uXMnj27RtunpqYyY8YMSktLMZvNzJ49mxYtWjBhwgSmTJlChw4dmD59Ojt37kSWZTQaDVOmTGHAgAEA2Gw2ZsyYwe7du5FlmYceeoghQ4bUqsyNpdnzRE6PD4/Xh93pQfF50dlyKD7wM5ajM5EVB1bzMHZY7uWmXx3oZFg1PJQWIWp8XjcqnRF1zAV4lVon+jmnGsP79E9N5ZpEs6dwvjrt4Ofz+ejevTubN2+u6zKdNY0x+B0nHRvTFxKkpiBtH/bDP6PseRQUN66o65mSPoqCkkPcFn2IkZHpYE1FMjUjqO8b+Azh9V38k2pM79NxTeWaRPATzlc1euaXnp5e4bXdbmfZsmWVntkJ9UdR/EMh1Do9mvAkZAbi5Ancu/+FNvdz3tR9gRSlgAJK7rF9rPtx7vsEfae78dLwOsIIgiCcrhoFv0svvRRJkjheSTQYDKSkpPDCCy+c1cIJp8eLClV4IjplGHjtuPfNRpIkDnsT2WBvQXBwc4bHO/Cmf4TrwL/RtboWjDH1XWxBEIRzpkbBb8+ePWe7HEId86JBHZEEvlHIod1AZaAwT2LGzzaMpdCrvZ7Q/LUo9nQcu97F0G2GqP0JgtBknN89HYQz4kGDOrIZ6uBYJFlL7xgtPcIlbF54N1VB22oSAK6Di1BKM0RCbEEQmowa1fwGDBhQowTJq1evPtPyCHXMI2lRRzZDLs5G0hqYMSCCq78+wAdpHu4ddBP6wx/hK9mBfcd8jH1eELU/QRCahBoFv1tvvZUlS5Zwyy23EBcXR2ZmJh9++CGjR4+ucX5Pof54ZT1yRHMURaFvMHSJzmZrTjkvrTvKgy0fRLPl/3Af/hjlwgnIYa0RcxQLgtDY1Sj4/fe//+Xdd98lOjo6sKx///6MHz+ecePGnbXCCXXnxCEeD/RtztivtrNoezZ/5cUy19CTRPcf5P/xHLp+b2Iw6JFFG6ggCI1YjZ755ebmYjQaKywzGo21mtJIOH9c1iqcGy6MQpYkNmaXMyH9BryKjJy3gv9tXMvBrFLKHG4k8URYEIRGqkYfb4MGDeLuu+9m/fr1pKamsm7dOiZPnsygQYPOdvmEs0BRYO4VbflyVApTu8RhCkvha9tA1JIX+77Xufu73azanUt6rhWPTxEdYQRBaHRqlOHF6XQyf/58vv/+e3Jzc4mMjOTyyy/nnnvuQa/Xn4ty1onGnOHluJpekyRJ5BTZyS204fH6UJxHaP7XcNS4mFzwID/YezOiZRjjO8TSJj6E0GAt1FM+7Kb8PjUkIsOL0JCcdnqzhkgEv4okWcLp8lJmd1NmdRF8YC66wwvwITO9cDJf2wYSrFVx24VR3NmzGTGheurjt6Wpv08NhQh+QkNSo2bP33//PZDiLC8vj+nTp/PII4+Ql5d3VgsnnF2KT0Grlokw60iODSak52MoCbch42Nu2HweiVxJmcvL639mcd+K3ZTYXKIJVBCERqFGwe/JJ58MzML+wgsv4PF4kCSJJ5544qwWTjg3FAVQQK3REdp7JnKzOwEYr/s3X16wFKNaYlV6CU//LxWHW4yDEASh4avRUIecnBzi4uLweDysW7eOVatWodFomvR8eo2VT2chuMt92DQG3AdepYvjPT5v7uDK/dfy/s4cmoXouLtPc1SyqAIKgtBw1ajmFxQURH5+Phs3bqRly5aYTCYAPB7PWS2ccO75FPAFRWPsOAlN6+mATIr9U5Ykv4UWNy/8ns7yndkgYp8gCA1YjWp+Y8eO5dprr8XtdvPoo48CsGXLFlq0aHFWCyfUD0UBxRSJseOd2NUGXLufpZ1nJSsTDnJj1oM8+JNMgsVA1yRLvfUAFQRBOBM17u2ZlpaGSqUiKSkp8NrlctGmTZuzWsC6JHp71o4kSahcpdgP/IBz50xw5lCmBHNv/lSOaHvw1Y2dSAoPQjnLjwHF+9QwiN6eQkNSo5ofQPPmzU/6+lTS0tKYMWMGxcXFWCwWZs+eTXJycoVtFixYwIoVK5BlGY1Gw7Rp0wLPFWfMmMGvv/5KaGgoAMOGDePuu++uVRmE2lEUBa82GEOry1GZEnHs+BfBRRtYFPEsr5Zez4SvYeGoziRHmkQNUBCEBqXGwe9MzZo1i5tuuolRo0bxzTffMHPmTN5///0K23Ts2JFx48ZhMBjYs2cPY8eOZd26dYGB9HfeeSdjx449V0UW8DeBetQmtIldkIyv49z9Bu4j/2FayGe0s6fxf189wqtXdKBjQqgYBiEIQoNxTrI3FhQUsGvXLkaMGAHAiBEj2LVrF4WFhRW269evHwaDAYA2bdqgKArFxcXnoojCKXgkLarIFpi6Poyu/Wy8chCXGTYwTnqVcd/uYP2BPDxNJ1+CIAgN3Dmp+WVlZREdHR0YK6hSqYiKiiIrK4uwsLAq91myZAlJSUnExMQEli1evJjPPvuMxMREHnjgAVq2bFmrclT17CEyMrhWx2gIzuo1RZjxhN9CmSWKol8ncrVpNQVlZu787g4WDJcZ1iEOs0lb56cV71PD0BivSWiczlmzZ21s2LCBV199lUWLFgWWTZs2jcjISGRZZsmSJYwfP56ffvopEFBrQnR4qSta1ElDMTjmYt90HxOCvyW/2MId31zF80U2ru2aUKfjAMX71DCIDi9CQ3JOmj1jY2PJycnB6/UC4PV6yc3NJTY2ttK2W7du5aGHHmLBggUVhlJER0cjy/7ijh49GpvNRnZ29rkovlAFj6RF13YMxk5PAfCI5X0u06ziwZ8PsmJnNpJ4ACgIwnnsnAS/8PBwUlJSWLZsGQDLli0jJSWlUpPntm3bmDZtGq+99hrt2rWrsO7EuQN/+eUXZFmuMLmucO55FRXaDhPQXfggALNDF3CxdhP3/3SALenFogOMIAjnrXM2q0NqaiozZsygtLQUs9nM7NmzadGiBRMmTGDKlCl06NCBa665hoyMjApBbc6cObRp04b/+7//o6CgAEmSCAoK4uGHH6Zz5861KoNo9jw7VHhx/D4dZ+pinIqWm/KeJF/XnuW3XERMsO6Mjy/ep4ZBNHsKDYmY0qgJfACdC7Liwb72TtxHv6HEZ+bq3GexhLdmydiuGNRn1sAg3qeGQQQ/oSE5J82eQuPnk9QY+r+JKrIPIXIp70U+S3peBvd8uwsxAl4QhPONCH5CnfFJOkyDPkQ2p5CoyubdiOf434GjPL8mTTz/EwThvCKCn1CnfGozpiGfIxni6KA9wGthLzP/9zRm/nQAu0fMBSgIwvlBBD+hzimGOExDvgRNCIMMm3nS8jYLN6XTa+HvLNmdK2qBgiDUOxH8hLPD3AbTJR+CrOXGoB95P+ZlVI6j3PXtLkZ/uJX9Bdb6LqEgCE2YCH7CWSNFXoyx71ug0tNH/SurYqfyUOjnbMnIZeCiTTy3+iA+0RlGEIR6IIKfcFbJiSMJvvI31LHDUONioukz1sVPZYjuV1794zDD399CRpmjvospCEITI4KfcNYpxiSMQz7CNPBT5KALCCWPBeEv8u/IV/grq5iB72xkxf78+i6mIAhNiAh+wjnh84EUfylBI9ei7/gEqEwM0a3n3YR3KHV5uP3rHTz8wz4cHm99F1UQhCZABD/hnPLJOrSdphF0yQcg6+jHSr5t91/UssR//sxkyOLN/HAgnyaUeEgQhHoggp9wzvl8CkQPwNh/EUgq2pV8xG/9fqNZiJ79hTZu/WoHl/1nM6vSCkUQFAThrBDBT6g3cvwwDBcvACTCUufyy+A9zLqkJeEGNdtyyrnx820M/3ArP+7Lw+MTA+QFQag7IrF1E0gufL7z7H0Lx6ZHABlD/0W4Yq/g7Y1HWbAhnWKHBwCjRqZrrJluCSFcFBtMt/gQwgya+i34GWiI79OpiMTWQkMigl8T+ABqCNzbX8C5bS7IGgwDP0QVOwSb28ubG9L5bEc2h4srD4foHBvMVSlRXNkmkjizvh5Kffoa6vt0MiL4CQ2JCH5N4AOoIVAUBffmR3HtfQtk7bEAOBjwX9POtAL+OFrCxqPFbMosZUeuFaf376bQngkhjGobyZCW4SSF6M/7meQb6vt0MiL4CQ2JCH5N4AOooVAUBfemh3HtWwSyDv3Aj1DHXlLpmiQJHB4fP+zP5+tdOaw6WIjT+/f7atKqaBNupHWEibYRJlqGGYg0aQk3agg3aDBqVPUeHBvy+1QdEfyEhkR9rk6UlpbGjBkzKC4uxmKxMHv2bJKTkyts4/V6eeaZZ/jll1+QJIk777yT66677pTrhMZBkiQ03eagoODetxjHmrHoB3wIkVdW2E5RQKeSubJtFFe2jcLq8vL9gXy+2Z3L5sxS8m1utmSVsSWr6uCiV8uEGTTEBGmJN+uJDdYRH6wjzqzDotegV8toVRI6tXzsZ///erUKg0ZGPs9rlYIgnNo5C36zZs3ipptuYtSoUXzzzTfMnDmT999/v8I2S5cu5ciRI6xcuZLi4mJGjx5N7969SUhIOOk6ofGQJAlttzkAgQBotywBQ/dq9zFpVVxzYTTXXBgNQKHdzd4CK3tyrezOs3KwyEaBzU2R3U2h3YPD4yOzzElmmbPaAHkyWpWEQa0KBEeDWkav8QdHtSwhS6CSJGRJQpZBLUvoVH8HUp1axhKsx2F3+beRCPwv8XdgPTHG+hQFn+L//3gdVyv7j3U8UGtV8rHz+veVJAn52D0NHPOEY0vH1kn8vb2iKChKxfPJkoRKPvZP4tg1Sv6yKP6pin2KQrNyF61MGvHlQGgQzknwKygoYNeuXSxevBiAESNG8PTTT1NYWEhYWFhguxUrVnDdddchyzJhYWEMGTKE77//nvHjx590ndC4SJLsD4A+L+4D75O9ZCSSLqLG++uAjsf+AaA99s8CSAQ+3L0+8CoKXp8S+F+BwAc6/P3h/vf/J3lK4Kn1pTYqZYqJNZ3f4JLOF9d3UQThlM5J8MvKyiI6OhqVSgWASqUiKiqKrKysCsEvKyuLuLi4wOvY2Fiys7NPua6mqnr2EBkZXKtjNASN5ZqU4W9R8LOBsm3/RrFn1emx5WP/qhwsIVXzs3BSUZSgT1Aaze+f0Lids2bP84Ho8NIAdXiOpF4zKcgrqO+S1KmwsCAKC8vruxh1Kjw6msIydYXfP9HhRThfnZPgFxsbS05ODl6vF5VKhdfrJTc3l9jY2ErbZWZm0rGjv8HqxNreydYJjZvKGIlkbFjj+E5FHRyM5GhEX1IAlT4YyhrXNQmN1zlJbxYeHk5KSgrLli0DYNmyZaSkpFRo8gQYNmwYX3zxBT6fj8LCQn766SeGDh16ynWCIAiCUBvnrNnzX//6FzNmzOCNN97AbDYze/ZsACZMmMCUKVPo0KEDo0aN4q+//uKyyy4DYPLkySQmJgKcdJ0gCIIg1IYY5N6Yno8hrqmhaCrXJJ75CecrMauDIAiC0OSI4CcIgiA0OSL4CYIgCE1OkxrnJ8uVRyxXtayhE9fUMDSFa2qM1yg0Dk2qw4sgCIIggGj2FARBEJogEfwEQRCEJkcEP0EQBKHJEcFPEARBaHJE8BMEQRCaHBH8BEEQhCZHBD9BEAShyRHBTxAEQWhyRPATBEEQmhwR/ARBEIQmp8kGv7S0NMaMGcPQoUMZM2YMhw4dqu8i1drs2bMZNGgQbdq0Yd++fYHlDfXaioqKmDBhAkOHDmXkyJHcc889FBYWAvDnn39y5ZVXMnToUMaNG0dBQUE9l7bmJk2axJVXXsno0aO56aab2L17N9Bw36cTvf766xV+/xry+yQ0MUoTdcsttyhLlixRFEVRlixZotxyyy31XKLa27hxo5KZmalccsklyt69ewPLG+q1FRUVKb///nvg9QsvvKA88sgjitfrVYYMGaJs3LhRURRFWbBggTJjxoz6KmatlZaWBn7+8ccfldGjRyuK0nDfp+N27Nih3HHHHYHfv4b+PglNS5Os+RUUFLBr1y5GjBgBwIgRI9i1a1egltFQdOvWjdjY2ArLGvK1WSwWevbsGXjduXNnMjMz2bFjBzqdjm7dugFwww038P3339dXMWstODg48HN5eTmSJDXo9wnA5XLx1FNP8a9//SuwrKG/T0LT0qSmNDouKyuL6OhoVCoVACqViqioKLKysggLC6vn0p2ZxnJtPp+PTz75hEGDBpGVlUVcXFxgXVhYGD6fj+LiYiwWS/0VshYee+wx1q9fj6IovPPOOw3+fXr11Ve58sorSUhICCxrDO+T0HQ0yZqfcP57+umnMRqNjB07tr6LUieeffZZVq9ezbRp05gzZ059F+eMbN26lR07dnDTTTfVd1EE4bQ1yeAXGxtLTk4OXq8XAK/XS25ubqUmxIaoMVzb7NmzOXz4MPPmzUOWZWJjY8nMzAysLywsRJblBlmbGD16NH/88QcxMTEN9n3auHEjqampDB48mEGDBpGdnc0dd9zB4cOHG837JDR+TTL4hYeHk5KSwrJlywBYtmwZKSkpDaK56VQa+rW9/PLL7NixgwULFqDVagFo3749DoeDTZs2AfDpp58ybNiw+ixmjVmtVrKysgKvV61aRUhISIN+n+68807WrVvHqlWrWLVqFTExMbz77ruMHz++wb5PQtPTZGdyT01NZcaMGZSWlmI2m5k9ezYtWrSo72LVyjPPPMPKlSvJz88nNDQUi8XC8uXLG+y17d+/nxEjRpCcnIxerwcgISGBBQsWsGXLFmbNmoXT6SQ+Pp65c+cSERFRzyU+tfz8fCZNmoTdbkeWZUJCQpg+fTrt2rVrsO/TPw0aNIiFCxfSunXrBvs+CU1Pkw1+giAIQtPVJJs9BUEQhKZNBD9BEAShyRHBTxAEQWhyRPATBEEQmhwR/ARBEIQmRwS/Bmj48OH88ccf9V0M4SS+/vprbrzxxvouhiAI1RDBrwFavnx5hQTQ9e3o0aO0adMGj8dzXh1LEAShOiL4CYIgCE2OCH4N0KBBg/j1118BmD9/PlOnTuXhhx+mS5cuDB8+nO3bt1e7r9frZeHChQwZMoQuXbpw9dVXB9JvbdmyhWuuuYaLLrqIa665hi1btgT2u+WWW5g3bx433HADXbp0Ydy4cYHpd44nn+7evTtdunRh69atAHz55ZdcfvnldO/enTvuuIOMjAwA3nrrLa677rpA7e7jjz9m+PDhOJ3Oao91Ip/Px1tvvcWQIUPo2bMnU6dOpbi4GIBZs2Zx7733BradO3cut912G4qiUFJSwl133UWvXr3o3r07d911F9nZ2RWu8ZVXXglc48SJEykqKuKBBx6ga9euXHPNNRw9ejSwfZs2bXj//fcZPHgwPXv2ZPbs2fh8virve2pqKrfffjs9evRg6NChrFixIrBuzZo1XHHFFXTp0oV+/frx7rvvVvv+CYJQR+pzMkHh9FxyySXK+vXrFUVRlNdee01p3769snr1asXj8Sgvvviict1111W779tvv62MGDFCSU1NVXw+n7J7926lsLBQKSoqUrp166b897//Vdxut7J06VKlW7duSmFhoaIoijJ27Fhl8ODBysGDBxW73a6MHTtWmTt3rqIoipKenq60bt1acbvdgfP8+OOPypAhQ5QDBw4obrdbWbBggTJmzBhFURTF6/UqN910k/Laa68paWlpSrdu3ZSdO3dWe6x/eu+995TrrrtOycrKUpxOp/LEE08o06ZNUxRFUWw2m3LZZZcpX331lbJx40alR48eSlZWlqIoilJYWKh8//33is1mU8rKypR7771XufvuuwPHHTt2rDJkyBDl8OHDSmlpqXL55Zcrl112mbJ+/XrF7XYrDz30UIXJWVu3bq2MHTtWKSoqUjIyMpTLLrtM+fzzzxVFUZSvvvpKueGGGxRFURSr1ar0799f+fLLLxW3263s3LlT6dGjh7J//35FURSlT58+gQlgi4uLlR07dpz8F0AQhDMman6NwEUXXcSAAQNQqVSMGjWKPXv2VLvtF198wdSpU2nRogWSJNG2bVtCQ0NZvXo1zZo1Y/To0ajVakaMGEGLFi34+eefA/teffXVNG/eHL1ez7Bhw9i9e3e15/n000+58847admyJWq1mokTJ7J7924yMjKQZZnZs2fzwQcfcPfddzN+/HguvPDCGl/vp59+yrRp04iJiUGr1XLPPffwww8/4PF4MBgMzJkzhxdeeIGHHnqIJ554gpiYGABCQ0MZOnQoBoOBoKAg7r77bjZu3Fjh2FdffTVJSUkEBwfTv39/EhMTufjii1Gr1QwbNoxdu3ZV2H7ChAlYLBbi4uK49dZbA4mqT7R69Wri4+O55pprUKvVXHjhhQwdOjQw0atarebAgQOUl5cTEhJCu3btanwvBEE4PU1yMtvG5sTEwXq9HqfTicfjYcWKFcyaNQvwB8h33nmH7OxskpKSKh0jNze3wkSkAHFxceTk5AReR0ZGBn42GAzYbLZqy5SZmclzzz3H7NmzA8sURSEnJ4f4+HgSEhLo2bMna9as4eabb67V9WZmZjJ58mRk+e/vbrIsU1BQQHR0NJ06dSIhIYHCwkIuv/zywDZ2u53nn3+eX375hZKSEsA/64LX6w1MKnvivdTpdJXu7T+v+cQpiOLj48nNza1U3oyMDLZt2xaY4Rz8zc9XXnklAK+99hpvvvkmL730Em3atOGBBx6gS5cutbongiDUjgh+jdiVV14Z+IA9LiYmhiNHjtC6desKy6OioirMxQb+mbn79et3yvNIklRpWWxsLBMnTqx0/uNWr17N1q1b6d27N3PmzOGpp56q9lj/FBMTw3PPPcdFF11U5fqPPvoIt9tNVFQU77zzDnfddRcAixYtIi0tjc8//5zIyEh2797N6NGjUc4gt3tWVhatWrUC/EE5Kiqq0jaxsbF0796dxYsXV3mMjh078uabb+J2u/noo4+47777WLNmzWmXSRCEUxPNnk3Mddddx6uvvsqhQ4dQFIU9e/ZQVFTEgAEDOHToEEuXLg3UGg8cOMDAgQNPecywsDBkWSY9PT2w7IYbbuCtt95i//79AJSVlfHdd98B/klOH3/8cZ599lleeOEFVq1aFfiwr+pY/3TjjTcyb968QAeawsJCfvrpJwDS0tKYN28ec+fOZc6cObzzzjuB5lmr1YpOp8NsNlNcXMzrr79e+xv4D++++y4lJSVkZWXx/vvvc8UVV1TaZuDAgRw6dIglS5bgdrtxu91s27aN1NRUXC4X3377LWVlZWg0GkwmU4UarSAIZ4f4K2tibr/9di6//HLGjRtH165deeyxx3A6nYSGhrJw4UIWL15Mz549eeedd1i4cGGNJlc1GAxMnDiRG2+8kW7duvHnn39y6aWXMn78eO6//366du3KiBEjWLt2LQAzZ85k0KBBDBgwgNDQUJ599lkee+wxioqKqjzWP916660MGjSIcePG0aVLF66//nq2bduGx+PhoYceYsKECbRt25bk5GSmTZvGww8/jMvl4rbbbsPpdNKrVy/GjBlTo1rtqQwePJirr76a0aNHM3DgQK699tpK2wQFBfHuu++yYsUK+vXrR9++fXnxxRdxuVwAfPPNNwwaNIiuXbvy6aefMnfu3DMulyAIJyfm8xOE09SmTRtWrlxJs2bN6rsogiDUkqj5CYIgCE2OCH6CIAhCkyOaPQVBEIQmR9T8BEEQhCZHBD9BEAShyRHBTxAEQWhyRPATBEEQmhwR/ARBEIQm5/8BDuW4KRimQfQAAAAASUVORK5CYII=", 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\n", 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", 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\n", 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", 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\n", 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", 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\n", + "image/png": 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+ezWZmfXXXBSE5lRYuIXevQ+pt13M0qbxRLY7M8mRRvKJyR0B+KZsC9q2B2m6RkFtET7dvy+LKQiCIDQTEfxoPJffzmyyFYvJTO+YJDKtTirCAZbVlET3hzWVrTUFhBHZ3gVBEA50IvgBDsXM7jI1mGULsdYYJEnihJSOACwsy61zTFANke8uxJDE6BdBEIQDmQh+bA9+u6brBgm2OBRJ5oSUyH2KnysLoqmOtvOG/Pg10f0pCIJwIBPBD3CYLE0aqGJX7NgtdtKsTgbGpRIydGZs/B1tp3kOhmFQHaxFlsXcP0EQhAOVCH6AswktPwBDh2RHZOjx1R0HEmuy8Ft1Ec/lrmDnQbPuoIewEd4nZRUEQRD2ngh+gL2RRLYNcZocWE0Wsu0xTO9+JGZJZk7JJj4u3hA9JqSG8am+fVFUQRAEoRmI4EfjWdwbomAmwR4LQJ/YZG7uMhiAl7f8yY+VBdHjqgI1NLJGtyAIB6AJE8awadPGfXqNuXO/IC9vS6P7//hjCRdffD7nnXc2EyeextSpl6HrYgDdviBWeKHxLO4NMQyDWGssZd4qNF3juOT2FAW9vLF1FQ9v/I3HDjmWnq5EfCE/QT2EmaafWxCEg9u8eXOIj4+nffv6k/tVVeW//72Z5557mW7dugOwbt1aJGn/jB/QNG2PUsS1diL4sWctPwCbbMNlcVATiKxmcU5mT4oCXhaU5XDXuh95pvfxpNuceFUvCeamDaYRhLYs8PezBFbMANXT/Cc3ubAdeiu23v/5Vw//+ecfeeON1wiFgpjNZq677kb69OlHRUV5o/n9GsrhV1RUwNq1q3niiUd56aXnufrq6xkyZGj0Oj6fD5/PR2JiUnRbjx49oz+vWLGMRx99GIABAwby44/f8/jjT9OlS1eGDRvIokU/4nBEFqne+fe7776DvLxcwuEw2dntuOOOacTGxvLHH0t54olH6NmzF+vXr+Pyy6+iXbv2Deb+CwT829IobcJkMtGhQ8dWv0SaCH40fcDLdoYOiY54agMeDAwkSeLaTgMpDXlZVlPKa1tXcke3YVT6qkmIT9jdFEJBaPOCfz+3bwIfgOoh+Pdz/yr45edv5fXXX+Gpp57D6XSxefMmrr/+aj7/fD4uV0yj+f0ay+E3b95czj33PI466uh614qNjWXChNM488wJDBgwkP79D2XUqNGkpaUTCoW4667/cs89D3DYYYP45puFfPTRB02qww033ER8fGSg3osvPsesWW8ydeo1AOTkbI6WU1VVLrnk/AZz/+Xm5uD1enjvvY8BqK2t3ePn8kAjgh/g2EUi28Y4FQdWs5lAODLPzyTL3Nh5MOctn8ePlflUh4MoskxA82OV7M1dZEE4qFh7T92nLT9r76n/6qG//voLBQX5XHHFpdFtmqZSUVGBw+FoNL9fU3L4NeSmm27jnHMms3TpEn755Sf+7//e4I033iYYDGC12jjssEEAjBx5Ig8/fH+Tzjl//jy++mo+qqri9/tp335H6qh27drTt29/ALZuzWs091+3bt3Jzc3l0UcfYuDAQRx55FFNuvaBTAQ/9rzlByBjIsEeT1G4NLot1epgcHwGv1UX8XVZLmdm9sAd8mCzOWhD64cLwh6z9f7Pv+6W3LcMhg07gmnT7qu35/XXX2k0v9/e5PDLysomKyub8eNP5brr/sOPP37fYAaJne8FKoqCsW2+8c45BlesWMYnn3zIK6+8SUJCAl999SWfffZJdL/dvuOLuWEYu8z99847H7J06e/88stPvPDCs8ye/QFW6543HA4UYjwi4PwXLT/DMIizxGL5x7qgJ6d2BmB+6WYMw6AqUItO82enFgRh3xsy5HB+/fVnNm/eFN22evXfQMP5/bbbnsNv4sRJnHTSyaxZE3mM0+nE42m4devz+fjtt1+iX5TdbjdFRYVkZmbSoUNHgsEgK1YsA2DRom9wu3dk0MjObhct18KFX0a3u91uXC4XcXFxhEIh5sz5nMbsnPtvu+3dnaWlJSiKzDHHHMd1191IdXVVq+/6FC0/9my0584skoWOcdlsqSkguG2Zs6EJ6SSZbeQHPPzlLmdAXCp+LYBDbpvZkgWhNbn66ivrjHicPfsD7rnnfh54YDrBYJBwOEy/fodyyCG9d5nfr7EcfhMmnMbTTz/J7Nlv1RvwYhgGH330AY8//ggWiwVN0xg1ajTHHjsCgPvue7DOgJf09PToY6+99gZmzHgAp9PF8cefGN1++OFHsGDBfM46awJxcfEceujAaJD8p13l/tu4cSPPP/80ALquc/75F5OSkrK3T3eLEvn8eoziyokzOTQ261+fN0SQvJoC/OEAAG9uXcXsgjWMSGrPf7sNJdmZQJYzc78ku20reeJau7ZSJ5HPb9+ZMGEMjz/+VJPvJ7ZVIp/fLriUveu3tmClY1w7XNZI6250aick4IfKfGrDQWrFcmeCIAgHlP0S/GbMmMGIESPo0aMH69evj27Pyclh4sSJjBo1iokTJ5Kbm9vg4zVNY/r06YwcOZITTjiBDz/8sFnLF2Pe+4noJsy0j80m1uYizepkUHw6YUPn6/IthNQwRZ4SAoYfSXzdEAShGXz22TzR6tsL++Wj+Pjjj2f27NlkZdXtWpw2bRqTJk3iq6++YtKkSdx9990NPn7OnDnk5eWxcOFC3n//fZ555hny8/ObrXx2uXlWYVEME+1isnBZnYzZNvBlXsm2gS/+GjZXbSHPnY/f8IkgKAiC0IL2y0fwoEGDyMjIqLOtoqKC1atXM3bsWADGjh3L6tWrqaysrPf4+fPnc+aZZyLLMomJiYwcOZIFCxY0W/maksm9qWRDIdmRwND4DBLNNrYG3KxylwOg6TrV/lo2V+axpXZrJAjup6WLBEEQhB1abLRnUVERaWlp0ZFViqKQmppKUVERiYmJ9Y7NzMyM/p6RkUFxcfEeX3PnG+8Bq0IAUCSZjJT4f1WHxsSqFjzUMj67G2/krGRhVR5HZrevc4yBSqVWTkyMlQR7814/JSWmWc93IBB1ah0OxjoJB6c2NdVh59GeHn9kaoJFVigv9zTrJHRJkjCpFo6La8ebrOR/xblMyepLbANriHo9OXSMb4eF5pks2lZGEbZ2baVOuxrtKQgtqcXuPGVkZFBSUoKmRSaAa5pGaWlpve7R7ccWFhZGfy8qKqozx+Xf0IzIdS2yQnMvvrk980O2I5bD4tIIGzrflDWcxiSohthaW4gmqc1aBkEQ9swtt9zA5MkTOf/8c7j88otZv35do8dOmDCGSZPOrJNuaH+kRNodt9vNrFlvNrq/sLCQYcMGMmPGg3W2jRo1YrfnLisr46qrLmtSOYYNG4jP13BO013t2592G/w0TWPkyJGEQqFmvXBSUhK9evVi7tzIagJz586lV69e9bo8AU466SQ+/PBDdF2nsrKSb775hlGjRu3V9dVtb1qzXD+FR3PchrPJNpwWOyenRQa+vJr3F/et/4WfKwsJ/yM/ly/kp8BdhCE3LW+XJAGSIQKmIDSju++ezttvv89bb73Lueeez/33T9/l8T6fjy+/nLfPyqOqe/737Xa7efvtt3Z5jMPh4PvvF5Ofv3WPzp2SksLzz7+8x2XaF7Y3mvbGbrs9FUVBURSCwSAWy78bFXn//fezcOFCysvLueiii4iPj2fevHncc8893HbbbTz//PPExsYyY8aOFBlTpkzhmmuuoW/fvowfP54///yTE0+MrFwwdepU2rVr96/Ksp1q6FgAq6zUSTmkG+DxhYl1mveqQRjJ/JDAEfGZHJvUju8qtvJ9ZT7fV+YTY7JwTGI7Ria2p3d8MgA1ATdmpZhMZwaG3nD0lWUI6EG8YS9VvhokSaJjXAekRo4XhNbiuQ0/8ci6xXjV5v2SDZGUZbf0OJap3Y7c5XEu1477lR6PB1ne9d/VpZdezmuvvcyJJ56E2Vx30Fx5eRmPP/4IJSXFBINBTjhhFBdeeAkATz/9JMuX/0E4HCY+Pp477phGRkYmhYWFXHTRZMaMOYWlS5cwYcJpHH30MQ2eR9d1HntsBn/8sQSz2Yzd7uCVV97gsccexuNxc955Z2Oz2XjllTfrldtstjBp0nm89NLz3HffQ/X2r1q1kueffwavN7IM22WXXcmRRw6Plu+rrxYBsGjR/3jppeewWq2MGDGSF198rk5apQ8+eJfvvvuWmpoa/vOf6xgx4vjoNWbP/j++//47gsEgV1zxn+i+7euGappGQkICt956B+3atW8w/VJ5eRnvvjsbi8WCrus88MAMOnbstMvXbGdNuud3/vnnc91113H55ZeTnp5eZ4RiU4LQnXfeyZ133llve5cuXRqds/fKK69Ef1YUhenTd/0tbE+p2xaBtezU8pNkKCnzUVblJyHOSkaSE7Mi/et8fE7FgdNi445uw5jSvh/fVuSxqDyPzb4a5pZuYl7pJu7peiRHJEcG81R4azApZuItcejoYBjoGBgYqLpKlacGX8iPqke+9UhIeMJuYpTYvXsyBKGFPb/pl30S+AC8aojnN/2y2+AH8MAD9/L7779iGAYzZz67y2N79TqEnj178cknHzJx4qQ6+6ZPv5uLL76UAQMOIxwO85//XE6vXr0ZOnQY559/Iddccz0An3/+Kc899zT33x9ZtqympppevQ6J7r/66isbPE98fDx//LGEd9/9CFmWo+ts3nTTbVx00eRGF6fe7owzzmLixFNZv35dnaDvdrt55JEHeeKJp0lOTqG8vIyLLjqPd96p+zldUVHBww/fz6uv/h/t27fn3XffrncNp9PJG2+8zZ9/ruDOO2+tE/xkWWHWrPfYsiWXKVMu4tBDB2x73u7ihRdepVOnznzxxWdMm3Ynr78eacnunH4J4Pjjj+b99z8mOTmFUCiEru9Za7BJwe+++yIrmv/00091tkuSxJo1a/boggeK7cHPqkSeAkmCak+IKncQJKioDuD1h8lKdRFrN/+rAKhgIt4eR7G7jFSrg4mZPZmY2ZO1NVW8t3UNP3kKeDxnCd3jRpFstmFgUFJbTrlcGQl5hoFhgNFIE9TAoNRTTky8CwwxcVBova7qcvg+bfld1eXwJh27fQ3OL7+cyzPPzOTJJ5/Z5fGXX34VU6deximnTIhu8/v9LFv2B9XVVdFtPp+P3Nwchg4dxi+//MRHH32A3++v131ntVoZOfLE3Z5nzJixaJrKAw9MZ9CgwRx5ZP38gLtitVq56KIpvPDCs9x8844URitX/klhYQHXX391dJskSeTnbyUuLj667e+/V9GjR89oeqRTThnPU089UecaJ5wQuTXVp09fysrKCAaD0SwQ25+vDh060qNHT1atWokkQdeu3enUKXKraOzYcTz66EN4vV6gbvolgEGDBnPvvdM46qijOfLIo8jKyt6j56BJwW/t2rV7dNLWYPs9P4usoOhBVDVMqLqWeN2PbnNR4VcIBDVyCmpJibeTmmhH2cObgYZhEGeNpdxbGW2thTUdZ8jM5ITeVKoB1gQqeHDdLzza+1gUSdrWymv6Nxh/OECt6ibOFCcyxgut1tRuRzapZba/jB49locffoCammp++OE73n//XQDOPfd8Tjrp5OhxHTp05PDDj6rT8tF1HUmCN96Yhekfc4iLigqZOfMJ3nhjFpmZWfz115/cffft0f02mz3as7ar8wC8885HLFu2lCVLfuO5557m//7vnT2q49ix43jnnVn8+eey6DbDMOjatRsvvvhaveN3HnTYFBZLJNBtn862t/fpdk6/BPDww4+xevXf/PHHEqZOvYxbbrmDI45o+ntoj5oLhYWFLF++nKKioj152AEp2vIzdIJFGyhcs5KqLRvwlORjVBURY408NbpuUFLpY1N+DTW+EOzh7TWrbMVpifSBa7pBaaWfcFhHliQuTupLrGxhpaec2fmr/1U9DKDUUyHSJgnCXvD5fJSU7Jg7/MMP3xEbG0tsbBxjx45n1qz3mDXrvTqBb7spUy7no48+iI5gdDqdHHroAN56683oMSUlxVRUlOP1ejGbTSQmJqHrOp9++lGjZdrVeaqqqggEAgwbdgRXXXUNTqeLgoICnE4ngUCgSYNlFEXh8suv4uWXX4xu69u3P1u3buWPP5ZEt61e/Xe9qWC9e/dh3bq10UEz8+bNZU/MnfsFAHl5eaxfv44+ffrSp08/Nm5cT25uDgDz58+he/ceOJ3Oeo9XVZWCgnx69+7D+edfxJAhh7N+/Z410prU8istLeWGG25gxYoVxMfHU11dTf/+/XniiSdIS0vbowseKNRtUx3MkkJVlQevL4RmGCwug17+ano4YgiYEwmFI8f5Aiq5hbXEOC2kJTlx2RSMXQzOjHx5k5CAZGcC1X43ZdV+guEdQSpWsXJxcj+eKl3K2wWr6RubwoC41F2WO6Rr/FVbxtKaElyKmXOzeuEPB6gJu0kwx+/dkyIIbZTf7+f2228hEAggyzKxsbE8+ujMJq3AlJqaxujRY3jnnVnRbdOnP8DMmY9z7rlnAZERlnfcMY2uXbsxYsQJnHPOGcTHx3PEEUeyfPmyxk7d6HkCgQAPPXQfmqahaRqHH34kffr0RZZlRo0azbnnnkVsbGyDA152NmLESGbNejMauCP1fnJbl+9jhMNhsrKyeeyxmXUel5SUxK233s4NN1yDzWbjyCOHYzKZsNlsu32+ADRN5fzzzyEQCHDrrXdER/lPm3Yfd999B5qmkpCQwD33NJytXtd17rtvGh6PB0mSSEtLY+rUqxs8tjFNSml01VVXkZmZyQ033IDD4cDn8/HEE0+Qn5/Piy++uLuHHzDefff9aCJJ+/rnGVi6jG+GXcSAzAks27iBL91JLPQkYZF0To0tY2LfbFxpPQgFA6z7a0eiSgkwmWT69TuUbt16UFPr5rtFCzBgxz06A7r06Edqege8vip+/eVrwuqOwBfUJaT0bLLS4/i0dDULAluJMWRuDGQQQ6SboGPf7riS4llbVMC3G9eyVvGzSQ4Slna8ZNO6H0HvsJWCNTnYTDYsZhPhbQH2mGNGkpCQyObcDfy5Ytm2ULzD8cePJiYmhg0b1vH333/We75GjToFu93O2rV/s3Zt/RxgY8acitlsZtWqFWzcuL7e/gkTIn+wy5cvZcuWzXX2mUwmxo6NZLZeuvRX8vPz6uy32WycdNI4AP788zdycurOk3Q6XZxwQuRb+I8/fkt5eVmd/fHxCRx77AkALF78dZ37JgDJySkcddRxAHz99fzoyLbt0tIyOPzw4QAsWPAFgUCgzv7s7PYMGjQMgLlzP6n3TbtDh84MGDAIgM8++4B/OvTQfnTs2JNwOMy8eZ/W29+zZ2969uyN3+/nq6/m1Nvfu3d/unXrgdvt5n//+7Le/kMPPYyOHbtQVVXJd999U2//YYcNpV27DpSXl/Ljj4vr7R869CgyMjIpKirkt99+rLf/qKOOJTk5la1bt/DHH78BYDYr9d57hYVb6d+/fjoZECmNWjuv1xttlc2d+zlffPE5L7/8eguXqr7GUho1qeX3xx9/8NRTT0WH8zocDm655RaGDx/evKXcj7bPDlAMGcOAvJCVbzyRbx8hQ+b9mjRyVwW4J7V+884AwqpOUYUP3VqN3+vBF6zfzVDtDiLb/IQDYUySCUOJfGMpCpl5szwTb7HCTUN1Rtvbs9ZTQq4pxGxLBYdpDgrkEOX5S8nd7MGvq7DTLJMs3Uy8YeJvxc8bW1cyI20guqFvm7i/4yWVJPDqXkq8pYT1MJZmWsBbEAThgw/eZdGib9A0jdjYWP773/oj+g9kTWr5nXjiiTz99NP07Nkzum3t2rVcffXVfP311/u0gM1p5+XNvnvxFA7Z+B0/DbuMrhljuWQZ5PrgzCzo6YInNoJXgxSbzC1DO9Ivae8ysZtNMiaTxMoKN7d9m4s7FAmq2TESdxxhoVYP8kDxL7j1+qPdki12+sQkMzg+nUFx6SRabIR1nUv+XEBR0MuNnQdxUmonbCYLAzsdgrsqhC6plPkrqPBVoek6Jlmhc2IHrDStW+JA0laWAmvtRDJb4UC0Vy2/Sy+9lAsvvJAzzjiDzMzIZMxPPvmEa6+9ttkLur+o4cjwWXvlT1iDi3nKJWGKlehoM+FzjafvYUO5fy38Vatz83ebOaVzIuf0SCHJ/u8yQIRVnR+3unng9zyCmsERmbFsrvGT7w7zXZ7GiI42piT35wv3BjLsLro54+nqTKCrM54Ec/2AZZZlLmzXh4c2/sZb+X8zIrk9qCEqvFWoukGhuzSaWR5A1TXKvBW0i8na5b1KQRCEtqBJwe+ss86iXbt2zJ07l3Xr1pGamsrjjz/O4Yc3be7MgShDirSwugVzSXWGyNjeIxgEa8kT6JnTebp/T2Zvhde3wJzNlSzIrWJUhwTO6pFMmmPPuhC/3lLFE8sK0A0Y1SGeawdk8Vuxm+m/5vH5BpXDMhR62BL5r+tw0hIcWM27X3P02KR2vF+4ls2+Gr4o2cQZGd0p8ZZTW+tHbyDC1QTcJNq9OOT6o6cEQRDakt0GP03TGDVqFPPnz2/Vwe6fspP7EMz9izmBU/m8ZBCj0wzOyAKH+384a78ioeQRtKxHOL99CkckGrxdYuN/BQHm5lTyZW4lx7eP59SuyaTYzdhNMqadlkIyDIPakEaZP0yZL8zflT4+XB/J6TexezIX9U5DkiQOz4hhaHoMvxW7+XSdxoX9TKiqTnGll6RYOy67mV0FQFmSuLhdX+5c9yPvFqxhdEonYg29TuCrCgX4vGQjRyVm0dWZQKm3nE5xjkaXUBMEQWgL9svangciS6dh1K74go9DgwnGduKkjhCWJWosnTCFi7D6/yKh+GEqMh+kq8vKPa4gl3eL4/VciYW51SzcEvm3nVmWcJhkLIpMTVAlpNcPWlf0S+fUrsnR3yVJ4sr+GSwv8/BzgcqR2TLdEmU0zaCsyk8wrJIQa9vlZMwh8en0jUlmpbucD4vWcU3i4Oi+ZTUlPLzxN6rCQf5XvoXX+p+EJ+ijdh8siSZJ7DQ5V8y2FwThwNakSe7b1/b8/fffycvLY+vWrdF/rdWfWRMYnPA2G82duKMHWLe13EwmM8EOt6Ga0rGEcogve5btS6dk6LXc10vlk/FdGN0pgRS7GadZRgbCukHNttZeSDdwmWU6xloZnOZidMcE7j+iQ53At12G08LE7ikAfLBWR90WOAwMajwhSit90dVoINLas5ktpLiSaBefgVkxcXH7vgB8XLSeiqAfzdB5Y+sqblvzPVXhIDISxUEf80s3R5ZQ85SjS3s3KV6SIWD48epuqtUqSgNlFHgKKPIVIYmV1gRBOMC12bU9v82pREfmqt4x9IyJzPGSJEiMs+Gyu/Ao09HXXIfd+xPh6g54Es4AIOD1kq7m8+Dh2dRo7QiGdQzDIKgZ+FWdoKYTZ1Wwm+qnSmrMWd2T+Savmi01IX4ptDI8e0dgCoV0qmrDZCbFkuSIxWVxYpNtKJKCYRgE7EH66BrDEjL4taqIp9YtpcDrZpW7HBk4L/sQOjriuHf9L8zOX8OJKR0BqA7VkGRJrLckmiRJu0zsK8sSfs1Hqbuc2oC33r1FRZKJs8Zhk+yNnEEQDly1tbWccsooxo8/jRtuuLmli8P333/Hn38u4+qrr2/pohx0djvVwTAMtm7dSmZmJiZT6078vvNUh9pgmDXVfroFiykviCzXFuO0kBK3Y2RlqPxntLXTAKhMu5Wgc0h0n6IoOFKyqJLio6vA7AlFkUiMs1FVG0RVdZYUu7nz5y3YTTIvjkknK8aOWTEjGQroEi67jezkGIx/dCmqUohNVVtYV1vO5X8tjN4hTDLb+G/XofSPS8UwDK79exFrPJVckN2bydmHYDGZ6ZLQEQkZzVBRdZWQHiaohrCazNgVGxbZiiIp6LqBJEGYMBX+Sip91btcfzTBHku7mOxmG1XaVqYFtHYHw1SHjz56n0WL/kdOzia++GJBvVRF/4aqqq3+s7M1+9dTHSRJYty4cSxb1vgSPK1RnM3MqO4uclYWY7JYMCsSSfEO2JZKCMCSfARqh4tQt7xOYulj1CRdgi/mRJAkNE0jUFWCMzWGUHjPrm21KrRLjSHWYcZiVigo8TA4PYYjM2P5qbCWO/5XzkPDO5Fq3/HyBPwBXFYL8S5LndaaGSspziRCaphRKZ1YUJbD4Ph0bukyhHhzZGFZSZK4pH0/blq9mA+L1nFKWhfigM1VWzAw0HQd7R/BTJZkzIoJh9lOjNWJZuiUeSsIqbuvbG3Qg9/hF60/ock83z6NZ8GDGEHP7g/eQ5LVheuk23Edd81uj50z53P+859r+b//e4Pvv1/Mr7/+QteuXaMpizZt2sjNN1/Pxx9/gc/nZebMJ9i0aQPBYJDDDhvMtdfegKIoXHnlFLp3786qVSuJjY3j0Uef5MYbr6GmpoZgMMghh/TmttvuxGw2Ew6Heeyxh1m27A8SEhLp3r07FRUVPPTQo8yd+wU//fQDDz30KH/8sZSZMx+jd+8+rFz5F5Ikcd99D0WzILzwwrP8738LiY2NZ+DAw1i69HfefHN2sz+fB4sm3Z3p1asXOTk5+7os+50hyXgtyZjSu5Hesw/2rK7YMrthTUyPpnNXss9ByTgVyVCJL3+JxPKnkfTI/LlwMIgl7MakNP0mV3ysla5ZccTYTei6QUqsjVhXZCDRNQMy6RJnI98T4rpvN5Fbu2OenmFAUYUPVavb8jMMg3hLHA6zjes6D2TW4WO5v8dR0cC3Xf/YFAbHpePTVN4tjCwAG1RDhNRwvcAHoBs6QTVElb+GvOpCCmqKdxv4QrpGUNfQdJ1yX8Uu7/1JEuLeoBDl/fbpfRL4AIygB++3T+/2uA0b1lNTU8OgQUMYO3Ycc+Z8zpgxp9RZtHnu3C8YM+YUJEli5swnGDhwIK+/PotZs96jqqqSOXM+jx5bUFDASy+9zpNPPoOiKNx774O8+eZs3nnnQ3Rdjx776acfU1JSzLvvfsQzz7zAmjWNL3K/efNmTj31DGbP/oDjjz+BN96IZF/44Yfv+OmnH5g1631effVNtm7Na/QcQkST2uJDhgxhypQpnHrqqfWS2Z5xxhn7rHD7miRJeIIS8TEOLHYH2+OKHOvArOuEq0uRJAlzl6nIMT0Jb3wSm/s70sK51KTfgl/OIFRbjiMpllr/rq+lyBJpSU5S4yPdqju33jKTnfgCKvGYePToTkz7ZQsry33c+F0O9x3RgUO2rS4TCKqUVfvJSHLUebxsKKS6UvBX59M9JpHafxTGophRZIWL2/dhycpivijeyGnp3Ui1/rtVazTD4PfqIjZ4qigOeikKeikOeikP+XEoJp7vewKKrOyy9Rc0gpT7KslwpCE1Zy5CKbL2qkjv1Lo4j7tmn7b8nE1s9Z188lgkSeLYY0fwxBOPkJmZhc/nZePGDXTs2Imvv14QXSz6xx+/Y/XqVbzzTiSdUSAQIDV1x8L0o0aNjnZ36rrO7Nmz+OWXn9B1ndra2ugi0H/8sYSTThqDyWTCZDJx4oknsWLF8gbL2KFDB3r0iKy01adPX3788ftt51jK8cefEE37M2bMKbz++isNnkOIaFLwW7ZsGVlZWfz+++91tkuS1GqD3/YFqJ0OM2kJdYOJbkgo8ZmYDY1wTQUASurxSK6uhNdMR/ZvISHvRuI6XodPPhqLKUjY5iAc1tF0HQxQFBmTImGxKNitJmIdFlx2U4MfynaLibQkBwUlHpxmhQeO7MiDv2/l1yI3t/2Yw93D2jMoLZJtubzaT5zLit1Sd0BNrMlFrM1JIBwirOmYt7VGXVYnWTHpaIZKUA1xbFI7FldsZVb+39zYZXC9suyKqut8W5HHuwVr2Rpo+H6VT1N5c+sq7ug2jHJfJe1cWfXqrEphtlYX4AsH0A2dbGdGsyTj1QyDvGIPSXE24hz/LgGx0DJcx13TpG7JfSUcDrNw4ZeYzRbmz4+09FRVZd68Lzj55FOYN28OAwceRseOncjIyAQivS6PPPJEo0lUd84/t3Dhl/z553JefPE1nE4nb775Gnl5e94623m6mSwre50jry1rUvCbNWvW7g9qhQwDkuPtSA3M99YMCSUhC5OmoXqqAZAdHbAc+hzhDU+gl3+LvPlhEvsmY1ESScpIJxgGVTcwMDDLMmZFRlGkSKDdlpV9Z9tHVhpGpPvT7Q1R6wlhVWTuHtqeJ5cV8HVeNdN+zuO2IdkMz4pD1QyKKrx0zoytO/9dkogxJ5BXnEuN24fTbqFTchpZMalYZDMGBjE2Jxe268MPlfksLMvljMweJJhtrKot489t/0pDPjo74ujhTKRnTCI9nYnEma0sLMvl/cJ1FAcjy8KlWRwcl9yeTJuLdKuTDKsTA7jkzwUsrtjKxMwe9JAVAg4/1p1af7qkke8uwrdt6bUqXw0SElnO9L0LgBIUlnmpcQdxe0OkJthJTXTsWcJKoc36/vvFtG/fsU5WgpUr/2T69Lt59tkXufTSC8jP38qYMeOi+4cPP4a33nqDW265HUVRqK6uwufzkZmZVe/8breH+PgEnE4nHo+bhQsX0LNnZBDGwIGD+OqrLxk58kQ0TeObbxaSnJyyR+UfOHAQr776Iueccy4Wi5Uvv5z3L5+JtqPJQ5Cqqqr47rvvKC8v59JLL6WkpATDMEhPT9+rAuTn5zN16tTo7263G4/HU6+V+cwzz/DOO+9EuxUGDhzItGnT9uraZkXBYTE1OrRfMxSUpGzQNVRfpKUjKXbMPW5HtaagFXyAmjcbOX4AtpAXizmGfy4D0NiEb4UwUsCDbk+MHrO9+1NVdRRZ4obDsoixKHyysYJHluST6bTQJd6O2xuiyhMicdvgF003KC734fFrxNli8fnCpNhS8VfbWV/pJsZpJinWRqozGU/Ay+iUTswt3cz1qxbh0cL11pDZHgjZlrPYLMmEtw3dzLbFcE5WT45P7oAiyfxzBZpx6V35uGg9r+et4sFewyn3V5HttEcCv6RT6C3GHajbtVXpq0YCMl0ZO9Jt7AkJSqsCVFYHos95cYUPb0AlO9WFzSyLVqCwS3PmfM6oUaPrbOvbtz+GYVBYWEjHjp1ZtuwP7rvvwej+6667iWeffYrzzjs7cnvEbOa6625qMPidfPIYvv9+MRMnnkZCQgL9+w8gGAwCcNppZ7Bx4/pojr+OHTvtcfmPPvoYVq78k8mTJxIbG0fv3n1xu2v3+DxtSZOyOvz+++9cffXV9OnTh2XLlrF8+XJ+//13Xn/99WbP5/fAAw+gaRp33313ne3PPPMMPp+PW2+99V+fe+epDtD04eYmI0S4NAct4ItuM1QPwSWTQPNh6f8sloyhyCmd0XczvF+WQApUo1YWAwamjO5o2/L3SZJEWY2f/JIdwcEwDGYuK2TBlirSHWaeHdGVGIuC1aLQrV08/pBKQZmXQCCSUikl1YrPHyDoU1C1nSbHyxJJcVYCpkryfGVctOJLArqGWZLp6Uqkf2wq/WNTSDU72OitZr23ivW+StZ7q/BqYTrYYjk9tTtDYjLQNIOwqmMxySTG2lB2WtqtJhzk/BXz8Wkqjx1yLAPj0+mS0B6bbKPIV0KZt7LR5ybZGU+GKwPZkNHRUXUV1VBxuMzUuv1ISNH7zRISFtmCCTOVniBbi90NftEwm2UyU1zEOy382wXdJKn57yGKqQ4H5lSHlrI9N14oFOLmm69jxIgTGD/+1H91Dl3XefDBe0lOTuGKK6bu/oEHub3K6vDggw8yc+ZMDj/8cAYPjtwn6t+/P3/99VezFjIUCjFnzhxee+21Zj3v3lIlC+bUTkjleTtagCYXSvopaAXvo+a/jxLXG1PIh25qeBCJJEnImh+9uoSQpyryaSrLmLUAKJGFpg3DICnWhscfpro2GH3c1EMz2FjjZ2N1gEeW5jP98PYEQxqbC2sIBLXoh35Q01mW7yPLLCNLdaOwrhuUVQVwxNrBL/NEzxH4CNPFHo+sS/iDGv6QStiv006PpZ0lluMtHdDjDNx6CJdsQUaixr0j5VIwpBHWDFITbJjkSAdjnNnKGRk9eCv/b17PW0m/mGTK/VVYTRbKvZGEspu91Ty86Xe6OuK5utNA7ErkbVjurabGF0BRJJAi0y80XccVttYbxCMhYbfYSHWkU1IeQNcNqgIqumHUybwRDuvkFdVSbjORkuAg1mFBkfcsmPl1H1bFhqSLTlRh37j66isJh0OEQiEGDx7CmDGn7PE57r33boqKCgkGg/To0YvzzrtgH5T04NGk4FdQUBBd1Hr7N2+z2dzsN1sXLVpEWloavXv3bnD/vHnz+PHHH0lJSeHqq69mwIABe3T+hr6BpqTENPnxeoKLcHk+qrsKMNB6TKKi6BP0ih+xU4SNDKwpafUfp4ZQa8pRPSUYkoo9ZsdEerNJw5JctwyxsXZyCmtw+3ZMLXhkZHcu/OJvfi9282luNRcfGrnpbrZEPuiXF7t54Mcc8t1BUhxmJvRIYXz3FJL/kX3CYpJJcsWjeQwSFQd+vx4NnmaTgrmBlWlcWOtt21mNTyUlwY7LHrnWhd37MadkE6s9FfwZrOCYOAc+QsTE2lheVcJNaxbjUcPk+GrIC7p5dMBxpNkcVNQGKa2uBknCYTURH2PF5bREn5N/CoRUVpfmkBGfQV6xwRVfbyCk6VzQL4Pz+2Vg+ccUlCpvGG9YJyXOTmKcDbs18vY3DANNNzB0A0mW6kxdCahBSisKsRgW2idkYTU13/q2e/Leay0OxjrtD6+//tZen2PGjMeboSRtR5OCX5cuXfjhhx/qZG7/+eef6d69e7MW5uOPP+b0009vcN/ZZ5/NFVdcgdls5qeffuKqq65i/vz5JCQkNPn8/7bbc2eyORnJYhCqLMbQnSipJ6IVz6Nm3SwCchZmJRZNigQLWdaRfLWoNcVogYbnQpjCxchSbL3u0gSHhZraAG5vpKXlBG4ZlM1dP2/hleUFdHSYOCwtBr+q8dqqEuZsjnQn2kwyZb4wrywv5PUVhRyRGcvYzon0T3ZGv7jYXS7CwQq8av3EuXtKliSsZjM1VRpxJjvxdicBbzFnZ/bkhS0reG7dH/S1JqFIEr9UFXL/+l8IGTrDEjLI89Wyzl3JRb/M49ZOw0gI2tC3Nck8niAVVT5cDjNpyS6CgRCaDrphoKoGYU3HHwgTCGn8ne/hoZ8DeMORJ/HVFYUs3FTBtQMy6ZPsRJYlDN2I3p0sK/Ng3dZ1vD3oRUb/gs2i0C7VhSJLSBIU+0so3Tbgye3xkx2T1SwtQNHtGWEYRp2pU4LQnAxDb3BAIzTxnt+KFSu4/PLLOfbYY/nyyy+ZMGECixYt4vnnn6dfv37NUsiSkhJGjRrFt99+26SAdtppp3HbbbcxZMiQ3R67XXMEP4jcA5L91YQr8lHduYT+uAgkGeugt7Fl9oO4NKSAG7W6BNXvqdfHZugqevl3GJoPU9YErFnd0eT6CWtV3SCnqAavT41um7W6hLfXlhFrUbiiXwb/t7qEEl8YRYJzeqRw2ZD2/Ly5grmbK/m5qJbt1e0SZ+PcnqkcnhmD3WrCTTlF26Zx7Hn9JVw2G0n2eGwmO5KuYKgyBhKZKU68VJNbWchlfy+kLOTjxk6DQJJ4cvNSdAzGpHbm6k4D8aohpq//hb/cZZglmQsT+zLIuWMAlVcPUxz24lZClAd81KhBatUQtXoQv65yuDOLQ5QsHvk1RE0QhmTZOadHJk8syWerO9JtPL57PBcPSCDOZkE2lEhZNRlVN1BVvcHuT5fDTIf0GEKSny3VWyPTV7aJt8fsMgA29f6gCH6RCduKYiUmJk4EQKFZGYaBpqnU1FRhNst06FD/3nKTgh9EgtMXX3xBYWEhGRkZjBs3bq9Heu7sxRdfZM2aNTz11FONXj8tLdKluGbNGi688ELmzp1LSkrThwQ3V/CDyIecEvYQKsklsOpu9PLvUDLPwNrjGmSrA81Xi/GP5pyh+dGKv0Qt/BCCZQCY+8zA0eM0NFtig9cJ6zo5BbX4tg1o0QyDu37awh+lOwbFdI23ccPALLrE24mLs1NTE2lllvvDfJlTybycKqqCkcd3irNxbs8URvaIYUtVHkFVxRs22OqW2VIDZT6JkKYT0jTCWiTLhGZEBuqYZQmLScFhtmBC4ZBEJ2M6JtQZ8ALgdMoU+PJZULqRNytXYpdM+I3I9c/O6MnFHfpGB5/UBEI8tfEPfvDkA9DNlArKtqCn775laqvqgL+wA90TZa4fYiM9Ng5fOMxHqz18sT6IZkCsBY7rYOLYDmZSXBasJnNk2TZzDGrARFitP0opId6M31ROga+KHF8NQ+J3LO6wPQDKhowkSWjohPUQPjWAZMjEW2J2GwBF8IvMrdu6dSt+f6DB/YKwN0wmhYSEBJKTk5Hl+l9Wmxz89rVRo0Zxxx13cPTRR0e3TZkyhWuuuYa+ffty66238vfffyPLMmazmWuuuYZjjjlmj67RnMEPIn/Y1BQSyPuB0IorQbZhHfwOkrlurjwjXINa+Bla0WegbruebAM9gJI+FvuA6cjJnRqdFhFSdTYX1hLYFsBqgyrXLN5MuT/Meb1SOaNbcjQA7Rz8tgtqOgtyq3h/XRkVgR1BsGuSwtryIFtrVf6trnE2rhuYRbeEHffkFFlCcQTZUJbHPUU/UhSOzA2cmNCTkXEdsVkUYpwWJAnKq/yEVZ23i3L4MbyBnYdkWiSZNJOTLFsMTszEyhZiFCuxioX8gI/PateCBDHubO7r3gu7ue4bvNCtM2tVmE3VkedVkWBgusyx7U10TZCwWSxkx6Vh1pz4AzvuX1stCn6lmnWV+dxX9DOV4QBHJWZxc5fBOJTIPdY4ewyxVheeoA+/GqDWH6CkykuM3cbgjt2wset1TWMTLNTWBP/d1I4D1L/p9hSElnLABL/9obmDH0SmQYSKNhBYfj169R+Y2l+Iqf3kyOR1z1rUojno5YthWytGiumFKfscJGtKJGCa47Ef+SmWzF5oUuMryAdVnZydAmBA1QnrBjH/WOmloeC3XUjT+Sq3ivfWl1Hu3xHwzLJEt3g7vZLsdImzY1UkFElCkSVMsoTVLKPrEAhraIaBqhu4QxrvriujxBdGluC0rsmc1ysVmykSgOw2hbJwEb9VbOGjqnUcH9OBwc6MOuWxmEyEVJXv81TeWa2i22oxOdyoATuEHMRIVkZ0MHNidxs1nhDlfoNyn0GF32BZiU6RVIrUbg1IBkc6s5ic2Bv5H91numGwtkJncZ7GnyV69L5fhlMiziYRVA1UQyGsSfjDOv1SnPz36HSKagt4vPg31gR2dA13sMdyT/cjyLbvGNRhANWeEDXuILphIMsSXTIS6JbUEXO9WZ8RqhSmxqgk5NfJcKa3SADcX9M3RPATDlQi+O1l8JMkCdlTgm/jPMKrbgJTLKYOF6EVz8XwbooeJycMwZR9NlJs3+jKLqE/LsAIFGLp+wSOXqehmnc9Ui6o6uQW1eIPNN5Sy0yPobY2gC+gNt6S1HS+y6/BHdboleigS5yt3sjIpvCrGm+tLuWzjRXoQIbTzCV90sl0WnBaFJLjJYrdde+ZQWSQTEZcEjHmGGb+voVP1kbmT47ponByFxNLi3W+zlHJd+/6rZnqkBg3oJa3alYQNnQOc6RzRfqh1AQDFIQ9FIQ8FIbduPUwWWYXScRSUOrk960K7lDjAad9rEKPHlv4xrcJl2zmsuT+vFe1lsKwB4ds4ubOQzgyOQtV06moCUS7pLeLc1nokJpEh5gsZKPumLKwFCSvugCTXcJd6yfJmUC6Mx1pFwFQkmm29FCSDAE9gDvkwWl24JAdzRYERfATWhMR/JrhvotCmHDhBvxLpmB41u7YYYpFSTsJJX0Msr3+qg/h3FfQ8t9HyRiPY9D9EJe1y0SyEOkCzS2qrfeBazLJpCc56No+keoaH76gRkWNn1pPqMF7Ws1pXaWPJ5cXklPT8L0bhwnax8l0TZDokWhieMd0YqRYHvwlj//l1SBLMLmPhaOydwRgwzBYV6nzdY7G6nKdWCsk2SWS7VLkf4fEoWkKTrPEhkAVz5b9QcDQsMkKgV3kGgSIlS0kS7H0NWXT05KCxQQ2BXQUXlgWokAvR+rwF0hwbcphHGJPJqCrvFmxkuX+UgDGJXSlly0Rv6oRMiL/VEOnhy2JdKuDjGQnyc442sVkIRkykgR+3U9eTQFBNURsrD06dzG5kQAoSQa1qodKfyXJ9kRc5ph/FQQlSUJHxav5qPRV4wn50HQNRVZIsMeSbE/CIll3+97bHRH8hNZEBL9mCH6SBLKvHN/6zwn/fQdSTA9M6acgJw9HkhufF6a71xH6cypYknAc9RmmrJ7oTRhGH9J0cgt3BMBYl4XMZCd2i4nkZFe0TrIsEQhr1HpDVNQECATVfbbMl6obfLqxnN+K3XjCOr6whies4Qvr9ZZPkyWIt5qoDKjYFJnpR3VgcHsTBbUl+EP1B7k47GYCARVZkpAkGVmWkCUJVdMJa5HnoEz28mj+79SoIRyKiQ72OLItLlJw4JDM5IfdbAnVsiVYg8/Y8cVhgD2VsxJ6kmiK3KMr9Ae4v/hnNCWMUt6Bazp0o2dSpGvZMAwW1Obwec2GenXazmSYuCn9MAalphPntJLsjCPTmYlH9ZBfU0RIC7OoPI+vKnKZnNGLvrGRAVs7B0BJBq/qpdRbjifow8BAlmTi7bGkOVIwS7tetFuSJJAMwnqYkB7CF/ZRFaghGA5jNFByi2Im2ZVIgiUehcaX+9sdEfyE1qTR4Ddp0qQmDT+ePbv1JEvcV8EPQJE01KINaMHd5DbaiWEYBJdOhmAJlv5P4+h1KqrStDRDYU1nS7GbuBgrKbE7pkk0VCdJAt0ATyBMZU0Aty+MugetQYl/ruDZdIpJokyt5rf8Cgo9Vv4qDrChyo9mQKLVxL1HdKBbgh2TImN1aJR4SwnrKhaTBasS+RfncuD3a5FCGFLkHxKGopJfU4hs0kmOsxPWNarDQVIs9m3vXYmwFgn+Hl84MqfPMChTfSz3lzKvZhNBQ8MqKYyJ68JxrvY8U/YH64NVuMLxuNf3Q5Ekzu1tYmimglmJ/D2s8pfxVW0OOgZhVaHUA76gAuYAkrMWq2TilozBHNUuC0WSiXPE4An6CKth3i9cx2tbVwJglRXu7XEkA+Mio5iTnfEk2hMo81ZSE3CjN9DMs5jMpLqSibfEoyBjYKAbGqqhoW375wn58Ia8hLQwqqY1GPAaYjfbiLW5sCoWLIoFRVIwSSZMkrLbZftABD+hdWk0+H366afRn/Py8vj444859dRTyczMpLCwkM8++4zTTz+da65puTQke2pfBr/I3L9KgqV5ux5JIElIsowkySDLBNc+hVb4EUrmabiGPojuTGly68wgMrJyT+okSRAM61R7glTVBjGIBDdJAkWJtKhMJhmLWcGsyJhMkdRMum7g9oVx+0KEQnqddUN3x+GQkRSNgE9G0wwCqk5ubYB2MVac5h0DdiQJYmPM6JpBWAVN09F0g5gYW4ODeBRZIiXFhMeowB/e9XD5sKZT4w3h3RYEAarUAB9UrWWZvwQAp2zGq4eJlS3ckX44CzcofJMb6UI1ydAhTqJrvEyXBJkYi8S8TSqryiLPQ6wFUp0GG52rkeLKsUsm7ul6JAOTIguxa4bBC7kr+LxkIxLQy5XEak8FZknm7u6HMywhsmKPLMl1gp5ugKpF1lGNPk+A0+pAkRVCWhhd19GNSDqthgLmdm41xE+VBXxfkU+tGmRsWhdOSNm+QHldEhKKLKPICjazlRRHEg7FsctuVxH8hNakSd2eZ511Fg888ADdunWLbtu4cSO33347H3zwwT4tYHPal8EPQJF01OKNaAFvdJskKyhWO4orHslkAVnBkBSQFSTAv/YzgsuuBGsKzqPnoKR3a9K37MY0tU6SJEVbBBIS8j/Wu2w0BRMGobCOP6hGg0korO2ybbG9A2FX7zRFlkhJsJMSbyesGfgCYarcQfxBFafTWif4SVLkQzUhxkp2qguVMPnuonrZIhoS1nRqvSG8/jDqtuzFq/xlvFe1hjLVjwTckDqY7tvmXS4rNfPVpjA51Q0HV6sCozqbGNlRwa/C7d/50TLXIMWV45BNzDjkGDo74nh442/8UFmAWZKZmjWAgc4MXi/4k8WerShI3N5tGEcn7cgLVxEO8HHBeuaXbgZgYmZPTs/shkWuv/zcrnjUEL9UFbK4YivLakpQ//EitLPFcH52b45Oyq43UnZnsiQTY3OS4kjEoTgbDIIi+AmtSZOC32GHHcbPP/+M1bpjjcdAIMBRRx3F0qVL92kBm9O+Dn4ASqiaUEkessmM4opHdsShmxyRHruGgkl1Hp6FIyFUjnXgi9h7jEOVd72W5q7sz8nTsixFlhkLqlS5Q3h8oUgg3MM+UofNRFaKq06yX2lbczQQ0jBZTFRV+1CUbTkSZQlZlrCY5Oi0QF3SKPQWU+Wr2e31JEkipGoEw/q2+4eRxQQWVuSQYnbQRUogGNJIcLjIcGbi8+h4wjobvV6WFFXyZ4mXUq/OwPTI6NRY646g8f7qMP/bEia+61pqrGU4ZBPtHbGs9VTiVMzc3G4wmXoMDocFjzfIx9Xr+dqdiwzc3GUIHR1xfFS0jsUVW9H+8USmWOxc2K7PtnRSDQcqjxpipbucP2vL+Ku2jE3eKrbHKRnoH5vKMUntMMsyb+evpmhbfsbOjjjOz+5NT1ciMSZLo0F2V0FQBD+hNWlS8Lviiiuw2+1ce+21pKenU1RUxLPPPovX6232lEb70v4IfrKkI4c8GGYnurT7wQMm1Yv7h+vQCj9ByToL1+EPoVnj//X1W2rlEFmWCKs6gbBGKBxZHSYcjnSPhlQdTTPQdSPSNbdtLU1ZlkiOt5GW6Gj0wxwidSovd+8+qEo6ZYEKVF3FJJswyQom2YSy7YNc0zU0Y1u2CEPDG/LjC/mi64lup+kG4ZBEgpKG11u3BWyzKmgmH/k1xQTCYf6pJmhw++IgYV2nV/91rFUjo0OTLXZuaz+M2JAFv2qQ55Xp5NIxyTCnZhPzajfVOY8EDLCnMTK2I349zKfV68kPR1q2nRxxnJXRA80wKA/7KQ/6KA/7KQn6yPXV1GmFmySJPjHJHJ3UjqMSs0gw77g/rOo6C8tyebtgNWWhut3KVlnBpZiJMVk4NC6VyVmHEGfe8aVMlmScVgfJjgRcJhfokgh+QqvSpOBXXV3N9OnT+frrr1FVFZPJxIknnsidd95JYmLDy3IdiPZH8NtTsqQTWvMhgT+uQrKm4xwxHyWlI5rWfCPuWkIklkUWh5akSEBRtwVAVTdQNR1ZkohxmHY7fH9P6rRjkFb9btt/lg10PJqXUm8F3pAv+kXFZrbQMb4d1dUGReXeevMlTSYZi13Dr/qQJRlFUpBlGQmJsB7m8Z+3MH9TmN4pkNp5M9VSkMsyDsUUkKgKGMxcEqLQY5Ado3BeH4VO8TILajbzac0GrJLCka4sjo/pgMOwszhPQ5FgZEeZJf5i5tRupDzc+KAqkyTR05VE/9gU+sWm0MuVhF0xs6shSyFdY17JZr4sy6EqHMCjhup1jzoVM+dm9WJ8etc6rcLtqaVSnIlkJ6dSWxWs8zgR/IQD1R5NddB1ncrKShITExtcK+1AdyAGP0kCyVNM7bzhEK7EOvAlLBlDUZxxGGYnhmxqdLJ6Qw6EOjW3fV0nSTJwax7KvBWomkqH+Gws2JAkqPaGyC/1EA7r/3jMtsFGxo77o7IsYTHLVGteJn68kYAGtw6z0DPFhKrqFHt1Zi4JU+E3kLeNwJUlOLGTwildTVToXuIUK4phYvEWjS83q3i3NS5P6KhwRk8TGjpLtGL+9pUTa7KQbLGTbLGTZLGTbLbTyRGHTdkxsT4Y1qn1BUmJ2/VyazszDIOAruFWQ5SGfLydv5o/aiKDgjKsTqa078dRiVn1RoNnpSSTLKXVeb+K4CccqJoc/DZt2sSCBQuoqKjg7rvvZvPmzYRCIXr27Lmvy9hsDsTgB6DoATzfTUUr/Awl+xzMHS8BSUI2W1DsMSjOOHSzC53df+E4UOrUnPZbnWQD1QhjxlLn3mMgrOP2hfAFwvgDGqoW6c6VJAmzScZhMxHjsOCwRRbJ3lri4fXVBcxaWU7PJJkbhljIrdF5emkITwgOSXLw8PHdeGt5AZ9sLEc3IMMlcX4fM4UenTkbVKq3NaA6xUnk1UYWFx/fzcSYriZkWSI1wY7TZt5lt3owrFNS6UPTdJIS7MTaLSiyjEUx4TDb8Yb9BMLBRh+/syXVxby05U+2+GsBOMSVxKnp3TgyMQvzti/CGcmJpMoZIvgJrUKTgt+XX37J9OnTOfHEE5k7dy7Lli1j5cqVPP7447z55pv7oZjN40ANfpIE4XUfEFhyJZItC8thb9b9Vi1JWFPbN5r5YWcHSp2a04FQp0j3rYRuGIS35ROUJLCalOhUkO3H5Zd5yS3zcv6Xa/GEdcZ1M7Fws0pAgyHpMdwxpB1pSU5qavysrvDx5PIC8mrrBqF2MRITe9sYmuVkeYnEQz+WYQBnH2JiRAcTiiwR57IS4zDXy6oBkYBdWunFMCRsZjNxdied01JwWe1YZSsyMqqhUh2qocxbQUitf//ynzRDZ35JDv+Xv4qabbkgE8xWTkrpxJi0zhya1U4EP6HVaFLwGz16NE8++SQ9e/Zk8ODBLFmyhHA4zPDhw/n111/3RzmbxYEa/ADkQDm1XwyFcDWWfk8jxx5SZ7/J7kRO64pu7Lr1dyDVqbm0tjqphsGGvGpe/6uIt1aXRrePbB/P9QOzMMlSnQXIg5rOO+vK+GBdGSkOmcl9Yzm5WwoWrBiqgqoZLNhSwSO/FQBwSX8zQzMj990sZpl4lw2n3RQd+arpEh6vhgkH7/3tIclm5cxuqVitJjqmx+ycOANJAhWVykAVFb6q6Io5EhKSJEXXod15/qBPC/NN2RbmlGwid1tLUAKOTevA84eeRYJ5x0INIvgJB6omZXKvrKykR48ewI4BBdv/MITmIdkSMKUcjVr4BaG/rkGK7YOSfBxK8tFIlgTUgB9byIu+m8WvhZZnliUykp2c2jWJzzZWUBvSOL1bEpf2SW9wLp1VkbnokDTO7plKnENC1hVCPh2fYQCRSfbHZyVQ3Vfj5ZXFvPFXmCSHjf5pkUnoWkhBV6wkxTixma3U1urUBlVu/C6HjdvmJ8bICse2i6fMbiY9wR79EmgYoGAi1ZZKvDWOkB5EkiKDdyITSWR0dMp85bgDHnTDwKGYGZfelVPSuvC3u4I5JZv4oTKfb0u28GN5Dqdk9N5fT7Ug/GtNCn69e/fm888/Z8KECdFt8+bNa7Ys7gLokglb35vwhWrRK3/GqF2FWrsKdfNzyPGHYupwMZorHjk5do8GwAj7n2FAvMtCZqKDx4/uRJk/zMBUV50viw67CS1sxusPR+8v2mWJUAC2B7x/Or1bMrUhjffWlTHzNw9n9kzm7J5pxJlN6GEDdyXU6gbLiz3c+1seNUGNWItCbUjjqeWF9Eh0YKrw4rSbcf4jFZZhGJixYG5kLdr2rmw8dg8l7jJ821bTkSSJPrHJ9IlN5opwf6pNOqekHvLv18IThP2oSd2emzZt4pJLLiE7O5sVK1YwdOhQcnJyeP311+nYseN+KGbzOJC7PSEy5y9YtBE95EGv/AWtfDF61RIwVDAnYhv2LtbsPmiyrdFzHGh1ag6ttU5BVWfj1uo6WTVkWSI10UGPzknUVPtw+8KUVPrx+kP1pmZYzQoxLgsuu5miCi/BoIZhGLz4VzGfbYrkGbQpMmM7J3JGt2QSbCYW5FbyzPIiVMNgQKqT24e046llhfxYWEuPBDuPH9OJOKeFrllxu1zRpTGGpFHVyH3CtIQEMiyZdabpiG5P4UC12+BnGAb5+fkkJCTw/fffU1hYSEZGBsceeyxOp3N/lbNZHOjBT5Z0tH8sj2aobkIrb8LwbsLU9TqcvS9Dj0lvdJTfgVan5tBa6yRJEqXVfgpKI5PTrRaF7FQXsQ4zyck71UkiGgSDIRWH3URSrB2HzYTFJEcWQA/rbCmpxeuL3JNbW+lj9toyfi+OnMOqSPROcrCsNPLemdAlicv6pqPIEu6QxlX/20ipP8yZ3ZK5tG86yQl2EmOsmE0KFrMM26ZsNLVeYSNIbdhNla8GvxrEGwjjsrvon9q1zqLpIvgJB6omtfwOPfRQli1b1irn9u3sQA9+kgSyt4xgWX6d7VrZt4TXPYBky8R++DuYMw9Bo+Hlpw60OjWH1lwnHdhcUIPJJJOZ7MRqkjGMRuokRfI12sxyg+u7qoZBfomHaveOkaEbqvzMXlvKL0WRc5kkiasHZHBSx7ojg/+u8HHT95vRDXjgyA4MSotBkrZN2Dcpka5QuxmbRcFmlgFpl8FQkiJ18wRClHtqKPdW43SZ6ZfaWQQ/oVVo0j2/Xr16kZOTQ5cuXfZ1edo0wwBsMchmC3p4R147OflopC1vYAQKCRd+gzmxHdjiW6ycQtPJQPv0GMxKZPjILr9qGmBRGg58EAls7dNjMCkSFdUBDKBbgp17Du/Apmo/3+RVc3R2HL0Sd4y2tFoUYp0W+khwXq9U/m91KY8uzeeF47uSaDMTDkeWofP6I12YJkXCYlGIc1pxOSLB0GKSUbXI9A5t2//BkEZlbQB/QCUy1jOBnIBK72Rxw09oHZoU/IYMGcKUKVM49dRTSU9Pr3Pj/owzztjrQowYMQKLxRJdOPumm25i+PDhdY7x+/3897//5e+//0ZRFG699VaOO+64vb72gUaTbVjSOmEEfWjeGvSQH10FJess1E0zUfPfRW0/BsUejxj30jpYlObrMZGB7FQXZrNCWaUvmpmiS7ydLvE7VnGRJEiItZGR5MBqVpAkmNgjhRVlXv4s8/LQ71s5/5A0usXbse2ULknVDFS/is+vIklgMSkoJhl9W3opTTPqrIWq6QbzcyuZtbqUmpDGy+NlxvdMbbb6CsK+0qTgt2zZMrKysvj999/rbJckqVmCH8DTTz9N9+7dG93/2muv4XK5+Prrr8nNzeXcc89l4cKFre6+Y1OoigMcDmRXMiY1iBEOoNhOR817C8O7iXDR95gTs9BNB1/dhSYwIC3BTrzLSrU7SFVtgEBoxwhRi1khI9lJQowFDNB1g/QkJ4GQxq2Dsrnyfxv5q9zHTd/nIEvQMdZGzwQ7PRMddIm30T7GikWJdM8GwxqEGx59urTEzcsri9mybYL+wIwYjunYetb6Fdq2JgW/WbNm7ety7NaXX37Jww8/DEDHjh3p06cP33//PaNHj27hku07ug66bAWrFXOyGVP2mag5L6FufQetw4nIiS4x7aGtMsBqkklPdJASb8PjVymv8aPIEpnJzm0DZXYcLgPt0lwEwxqPDO/E55srWFfpJ6c2wOaayL/5uVWRYyVoF2Olc5yNznE24iymyAo3284lSbB4aw1LSiIDedIdZi7tm86ZA7JIdJjFe1JoFZoU/HYWWcR356HMzdOlc9NNN2EYBocddhg33HADsbGxdfYXFhaSlZUV/T0jI4Pi4uI9ukZDN95TUlrLpPEYbAOnULJ1NnrNn5hq/iCmXScUm6Peka2nTk0n6rRraUAnPdIladpFN6vVZsHpsNK/fQIA/rDGugoff5d5WF3uZWOVn7yaAFtqg2ypDfLt1sbzIzrNChf1z+CsQ9KwKDKSJAa3CK1Hk4JfSUkJ9957L0uXLqW2trbOvjVr1ux1IWbPnk1GRgahUIgHHniAe++9l8cee2yvz/tPB/poz91RLBkomeNR82ZTs/p11LhBKK5EdLMDA6nxUYStnKhT85EkiRibibzi2mjLsJPdRKf28YxtHw9AQNXJ3dYizK0N4AvrkWTMAIaBAaTYzZzWNZl4mwm/J4gfcNhi6v2NidGewoGqScFv2rRp2Gw23nzzTSZPnszs2bN55plnOOaYY5qlEBkZGQBYLBYmTZrElVdeWe+YzMxMCgoKovkDi4qKGDp0aLNcv7UwTHZsPafgyf8QvfJngkV/oLg6IVvsmGISwBqDYYj7gELjDMMgMcaKP2inrMrf4OhTm0mmZ6KDnon1exUa47CbSEtyYKi7Sc4oCAeIJvVZLl++nAcffJBevXohSRI9e/bkgQce4PXXX9/rAvh8PtzuyDdgwzCYP38+vXr1qnfcSSedxPvvvw9Abm4uK1eurDci9GCn6wam5B6YMk4GQN06G11T0QJegmX5hIo2EMhfhylcSwML/QsCEPk7y0hy0q1dPBnJThx2Eybl371hLGaZ7DQXXbPiSYl3NHmivCC0tCa1/GRZxmSKHBobG0tlZSUul4uSkpK9LkBFRQVXX301mqah6zpdunRh2rRpAIwfP56XX36ZtLQ0LrnkEm677TZOOOEEZFnm3nvvxeVqe90pmmzF3usq3AVfoJctIli9HCVxKHLi4cjxA9H9ZgLuCkzOGExxqegWV6PzxoS2SwIcVhNOm5n0RDuBsI4vqOIPqNHkvIZhoG27j6iqOvq2kaPbuzXjYyykJTqwmBQR9IRWp0krvFxxxRWcfvrpnHDCCdx9993k5uZis9nw+/0HxEjQpmrt9/y2UwjjX/oEodw3IbgjZQ6SGUvKEMiYhBzTA0lWMDnjUOJS0EzOVvsB1Vpfp105UOu0I2tL3e0GoOs6mr4tABpgt8rslOmowTqJe37CgapJwa+2thZd14mPjycQCPDaa6/h8/m44IILSE1tPRNaD5bgt30ZtEDpVgxfTmQR7MpfMNzriHxMSSgZp2DqcDGSyYWkmLCmdURtpemQWuvrtCttpU4i+AkHqiYFv4PFwRL8ABQ01KL1aJEcOAAYoUqUsk/x5bwP6GCOx9zpCuSU4zE7Y5FTO+82Ge6BqDW/To1pK3USwU84UDXpnt9TTz3V6L5rr7222QojNJ0uKZji0zCqitDVMBgGkiURV6//oMYfR3jT0xi1qwivfxipeD5G95uxxyWDJb6liy4IgtDimhT8/jmZvKysjCVLljBy5Mh9Uihh9wwDDGcSZnsskhpA89Wi+2pBkpGdnbH0fRKtdCFqzssYtX8RXn0XpthZmDNiGs0IIQiC0FY0Kfg99NBD9bZ9//33zJs3r9kLJDRdpAvXBCYXUlwMSlw6NruEnwJUTyVS2iiUxMMJ/fkfDF8uoc2zMMffjORI3HV2AUEQhIPcv74BdNRRR/HNN980Z1mEvWAYBrohozhjISEba0YXTK44JEscpi7/AUDNe4tQ8Upko+GFigVBENqKJrX8tm7dWud3v9/P3LlzoyuzCAcWwwBVcSInd8IaU4NqtaMVH41e8T3BdU9gSTsEyZkiWn+CILRZTQp+J5xwApK0I7Oz3W6nV69e0SwLwoFJNySwxGNKi8F+6J14F49Hr/iRwMbPsfc9H1WytHQRBUEQWkSTgt/atWv3dTmEfUhDwZTeH0vnSwlteIbQ+iextDseKbFz9AuNLEtIWghJC6GZnaJVKAjCQa31TfoS/hUNE/b+1yA5O2MEi/GvegpF9WJSvSi+MvSyTYSL1hMo2oQSOrjmnwmCIPxTk1p+xxxzTHTZo11ZvHjx3pZH2Id0WyL2ftPw/XIB6tZ3CWwaiaGF0Gr+Qq9dhV67ClQvDHwaa/dT0PY83aMgCEKr0KRPt/PPP5/PPvuM8847j8zMTAoLC3n77beZMGECffr02ddlFJqJYYCl8yhCuWNRi+YSWHIh27K01RFcdS+mlP7bukX3ezEFQRD2uSYFv08//ZTXXnuNtLS06Lajjz6aSy+9lIsvvnifFU5ofipm7IPuwf3VLxCqAGsacmyfyL+YnoTXPYThz8P/5wxijnys1a4HKgiCsCtNCn6lpaU4HHUTWzocjmZJaSTsf1J8J1zHfEy4uhjJmlxnn7n7rYT+vBqt4GMCm47D2vNMsSKMIAgHnSYNeBkxYgRXXnklP/30E5s2beLHH39k6tSpjBgxYl+XT9gHdB1MKT1QXJkgScgmM4rVgTk2GXvn4zF1vAAwCK66D70qp156G0EQhNauSVkdgsEgzzzzDAsWLKC0tJSUlBRGjx7Nf/7zH2w22/4oZ7M4mLI6NKapdZIkUMJewMBQLBiKBZAwDJD8pbi/OgXDswElYwwxw59r0e7Ptvw6tSYiq4PQmoiURm3gA2hPSRLo+T/g/e5MMMJYD30cW+/zWqz7U7xOrYMIfkJr0qRuz19//TW6xFlZWRm33nor//3vfykrK9unhRNahmGAKesILN2uACC4+kG00jXIovtTEISDRJOC3/Tp01GUyLf+hx9+GFVVkSSJu+66a58WTmg5Ggq2ATcjx/WFUAW+pbeAp7hJ8z0FQRAOdE0a7VlSUkJmZiaqqvLjjz+yaNEizGYzw4cP39flE1qQYXbiHPY07v+NR6/6De+Su4g56klUk+jGEgShdWtS8HO5XJSXl7Nhwwa6dOmC0+kkFAqhqupeF6CqqopbbrmFvLw8LBYLHTp04N577yUxMbHOcbfddhs///wzCQkJAJx00klceeWVe319oXGGAVJKX+yDn8L/62VoBZ/gW94ex6BbxaLYgiC0ak0KfpMnT+aMM84gHA5z++23A7Bs2TI6d+681wWQJIlLL72UoUOHAjBjxgwee+wxHnzwwXrHXnbZZUyePHmvryk0nWFIWLqMQffcRXDl3YTWP4Xiao/lkPPRDNEFKghC69Sk4HfZZZdxwgknoCgK7du3ByAtLY37779/rwsQHx8fDXwAhx56KO++++5en1doPpqhYO87Bd27lfDmV/CvuB0lJhup3fFi+TNBEFqlA2qqg67rXHzxxYwYMYLzzz+/zr7bbruNJUuW4HA4aNeuHTfeeCNdunRpoZK2TarPTdmX5xPYOg/ZmkzquPnYsw5t6WIJgiDssQMq+E2fPp2SkhKeffZZZLnuQNSSkhJSUlKQZZnPPvuMp556im+++SY6CrUpxDy/vaeEa3AvPB29ejmSsxMxJ36O7sjaZ9cD8Tq1FmKen9CaHDD5/GbMmMGWLVuYOXNmvcAHkW7W7dsnTJiAz+ejuLh4fxezzdPMcbhG/B+SowOGNwfP/yYiB8tbuliCIAh75IAIfk888QSrVq3iueeew2JpeBThzoto//DDD8iyXCfLhLD/GI4sYka8j2TLQK9dg/d/56CoB1crRhCEg1uLZyvdsGEDL730Eh07duTss88GIDs7m+eee47x48fz8ssvk5aWxq233kpFRQWSJOFyuXjhhRcwmVq8+G2SYQDx3XAd9y7u/52OVrUMz6LJOEe+jy63nrVeBUFouw6oe377mrjn17wkSYLS33EvOgvUWkwZo7Af9xaG1LxfSsTr1DqIe35Ca3JAdHsKrZNhGJA6BNexs0BxoBZ9ReCnq5CMcEsXTRAEYZdE8BP2imEYSOlH4Rr+BshWwls+xrdwAnKwtKWLJgiC0CgR/IS9ZhggZY/EOfxNMMehlf+Ke+5xUPqzSIQrCMIBSQQ/oVkYBsjtTiR21HzkmF4YgWI835yG+vcLNDBzRRAEoUWJjyWh2RgGGPE9iRk9D1P2aWCE8S+/E//iS5F1f0sXTxAEIUoEP6FZGUZkIrzz2BexHXofyBbCWz/F9+35YOx9FhBBEITmIIKfsE9ohoK5z5W4jv0QTC7U4kWEfrtFJMMVBOGAIIKfsM8YhoSUeRTOo18DyURo0/+hrnqqpYslCIIggp+wbxkGSBkjsQ97EgD/invRt3zawqUSBKGtE8FP2C+UzpOw9b0ZMPD9PBWj/PeWLpIgCG2YCH7CfqP0vRVL57NBD+L7dhJ4Nrd0kQRBaKNE8BP2G0mSsAybiSn9aIxQFd6vJ4Ant6WLJQhCGySCn7B/SWZsR/8fSkJ/DF8B3q9GY9Sua+lSCYLQxojgJ+x/5ljsJ36BkjwEI1CK76sxGFV/tXSpBEFoQ0TwE1qGyYV95CeY0o+JdIEuHIdevqSlSyUIQhshgp/QchQ7tuPexdzuZFDd+L45Fb34u5YulSAIbYAIfkLLkq1YjnoDc6czQfPj+/ZstKJvW7pUgiAc5ETwE1qcJJuwHP48lm4XgB7Cv/hctOLFLV0sQRAOYiL4CQcESZIxD34Mc7cLQQ/iXzwJtWhxSxdLEISD1AER/HJycpg4cSKjRo1i4sSJ5Obm1jtG0zSmT5/OyJEjOeGEE/jwww/3f0GFfUqSZCyDH40EQC1I4LtzUcU9QEEQ9oEDIvhNmzaNSZMm8dVXXzFp0iTuvvvuesfMmTOHvLw8Fi5cyPvvv88zzzxDfn5+C5RW2JeiAbDr+aAFCCw+F/9WcQ9QEITmZWrpAlRUVLB69WreeOMNAMaOHct9991HZWUliYmJ0ePmz5/PmWeeiSzLJCYmMnLkSBYsWMCll17aUkUX9hFJkrEMeRwwCG+cRfGnY5GsyS1drGblV2R0TW/pYjSroCMB07BXUOJ7tnRRBGG3Wjz4FRUVkZaWhqIoACiKQmpqKkVFRXWCX1FREZmZmdHfMzIyKC4u3qNrJSW56m1LSYn5lyU/cB0sdTLGvELFtw7cf72E4S9q6eI0K62lC7APhAOlJFnd2A+S959wcGvx4Lc/VVR40HUj+ntKSgxlZe4WLFHzO+jq1PdB2g+7m4qyipYuSbNKTHRRWelp6WI0q6S0NCrdJjw7vf9kWWrwS6cgtLQWD34ZGRmUlJSgaRqKoqBpGqWlpWRkZNQ7rrCwkH79+gH1W4LCwUtxpCA5bC1djGZliolBChxEX1IAxRYD7oOrTsLBq8UHvCQlJdGrVy/mzp0LwNy5c+nVq1edLk+Ak046iQ8//BBd16msrOSbb75h1KhRLVFkQRAEoZVr8eAHcM899/D2228zatQo3n77baZPnw7AlClTWLlyJQDjx48nOzubE088kbPOOoupU6fSrl27liy2IAiC0EpJhmEYuz/s4CDu+bVOok6tQ0N1Evf8hAPVAdHyEwRBEIT9SQQ/QRAEoc0RwU8QBEFoc1p8qsP+JMtSk7a1dqJOrUNbqNPBWEfh4NCmBrwIgiAIAohuT0EQBKENEsFPEARBaHNE8BMEQRDaHBH8BEEQhDZHBD9BEAShzRHBTxAEQWhzRPATBEEQ2hwR/ARBEIQ2RwQ/QRAEoc0RwU8QBEFoc9ps8MvJyWHixImMGjWKiRMnkpub29JF2mMzZsxgxIgR9OjRg/Xr10e3t9a6VVVVMWXKFEaNGsUpp5zCf/7zHyorKwFYsWIF48aNY9SoUVx88cVUVFS0cGmb7qqrrmLcuHFMmDCBSZMmsWbNGqD1vk47e/bZZ+u8/1rz6yS0MUYbdd555xmfffaZYRiG8dlnnxnnnXdeC5dozy1ZssQoLCw0jjvuOGPdunXR7a21blVVVcavv/4a/f3hhx82/vvf/xqaphkjR440lixZYhiGYTz33HPGbbfd1lLF3GO1tbXRn7/++mtjwoQJhmG03tdpu1WrVhmXXHJJ9P3X2l8noW1pky2/iooKVq9ezdixYwEYO3Ysq1evjrYyWotBgwaRkZFRZ1trrlt8fDxDhw6N/n7ooYdSWFjIqlWrsFqtDBo0CICzzz6bBQsWtFQx91hMTEz0Z4/HgyRJrfp1AgiFQtx7773cc8890W2t/XUS2pY2ldJou6KiItLS0lAUBQBFUUhNTaWoqIjExMQWLt3eOVjqpus67777LiNGjKCoqIjMzMzovsTERHRdp7q6mvj4+JYr5B644447+OmnnzAMg1dffbXVv05PPfUU48aNIzs7O7rtYHidhLajTbb8hAPffffdh8PhYPLkyS1dlGbxwAMPsHjxYq6//noeeeSRli7OXlm+fDmrVq1i0qRJLV0UQfjX2mTwy8jIoKSkBE3TANA0jdLS0npdiK3RwVC3GTNmsGXLFmbOnIksy2RkZFBYWBjdX1lZiSzLrbI1MWHCBH777TfS09Nb7eu0ZMkSNm3axPHHH8+IESMoLi7mkksuYcuWLQfN6yQc/Npk8EtKSqJXr17MnTsXgLlz59KrV69W0d20O629bk888QSrVq3iueeew2KxANCnTx8CgQBLly4F4L333uOkk05qyWI2mdfrpaioKPr7okWLiIuLa9Wv02WXXcaPP/7IokWLWLRoEenp6bz22mtceumlrfZ1EtqeNpvJfdOmTdx2223U1tYSGxvLjBkz6Ny5c0sXa4/cf//9LFy4kPLychISEoiPj2fevHmttm4bNmxg7NixdOzYEZvNBkB2djbPPfccy5YtY9q0aQSDQbKysnj00UdJTk5u4RLvXnl5OVdddRV+vx9ZlomLi+PWW2+ld+/erfZ1+qcRI0bw4osv0r1791b7OgltT5sNfoIgCELb1Sa7PQVBEIS2TQQ/QRAEoc0RwU8QBEFoc0TwEwRBENocEfwEQRCENkcEv1ZozJgx/Pbbby1dDGEXPvnkE84555yWLoYgCI0Qwa8VmjdvXp0FoFtafn4+PXr0QFXVA+pcgiAIjRHBTxAEQWhzRPBrhUaMGMHPP/8MwDPPPMO1117LLbfcwoABAxgzZgwrV65s9LGapvHiiy8ycuRIBgwYwGmnnRZdfmvZsmWcfvrpHHbYYZx++uksW7Ys+rjzzjuPmTNncvbZZzNgwAAuvvjiaPqd7YtPDx48mAEDBrB8+XIAPvroI0aPHs3gwYO55JJLKCgoAODll1/mzDPPjLbu3nnnHcaMGUMwGGz0XDvTdZ2XX36ZkSNHMnToUK699lqqq6sBmDZtGldffXX02EcffZQLLrgAwzCoqanh8ssvZ9iwYQwePJjLL7+c4uLiOnV88skno3W84oorqKqq4sYbb2TgwIGcfvrp5OfnR4/v0aMHb731FscffzxDhw5lxowZ6Lre4PO+adMmLrroIoYMGcKoUaOYP39+dN93333HySefzIABAxg+fDivvfZao6+fIAjNpCWTCQr/znHHHWf89NNPhmEYxtNPP2306dPHWLx4saGqqvHYY48ZZ555ZqOPfeWVV4yxY8camzZtMnRdN9asWWNUVlYaVVVVxqBBg4xPP/3UCIfDxpw5c4xBgwYZlZWVhmEYxuTJk43jjz/e2Lx5s+H3+43Jkycbjz76qGEYhrF161aje/fuRjgcjl7n66+/NkaOHGls3LjRCIfDxnPPPWdMnDjRMAzD0DTNmDRpkvH0008bOTk5xqBBg4y///670XP905tvvmmceeaZRlFRkREMBo277rrLuP766w3DMAyfz2eceOKJxscff2wsWbLEGDJkiFFUVGQYhmFUVlYaCxYsMHw+n+F2u42rr77auPLKK6PnnTx5sjFy5Ehjy5YtRm1trTF69GjjxBNPNH766ScjHA4bN998c53krN27dzcmT55sVFVVGQUFBcaJJ55ofPDBB4ZhGMbHH39snH322YZhGIbX6zWOPvpo46OPPjLC4bDx999/G0OGDDE2bNhgGIZhHHnkkdEEsNXV1caqVat2/QYQBGGviZbfQeCwww7jmGOOQVEUxo8fz9q1axs99sMPP+Taa6+lc+fOSJJEz549SUhIYPHixXTo0IEJEyZgMpkYO3YsnTt35ttvv40+9rTTTqNTp07YbDZOOukk1qxZ0+h13nvvPS677DK6dOmCyWTiiiuuYM2aNRQUFCDLMjNmzGDWrFlceeWVXHrppRxyyCFNru97773H9ddfT3p6OhaLhf/85z989dVXqKqK3W7nkUce4eGHH+bmm2/mrrvuIj09HYCEhARGjRqF3W7H5XJx5ZVXsmTJkjrnPu2002jfvj0xMTEcffTRtGvXjiOOOAKTycRJJ53E6tWr6xw/ZcoU4uPjyczM5Pzzz48uVL2zxYsXk5WVxemnn47JZOKQQw5h1KhR0USvJpOJjRs34vF4iIuLo3fv3k1+LgRB+HfaZDLbg83OCwfbbDaCwSCqqjJ//nymTZsGRALkq6++SnFxMe3bt693jtLS0jqJSAEyMzMpKSmJ/p6SkhL92W634/P5Gi1TYWEhDz74IDNmzIhuMwyDkpISsrKyyM7OZujQoXz33Xece+65e1TfwsJCpk6diizv+O4myzIVFRWkpaXRv39/srOzqaysZPTo0dFj/H4/Dz30ED/88AM1NTVAJOuCpmnRpLI7P5dWq7Xec/vPOu+cgigrK4vS0tJ65S0oKOCvv/6KZjiHSPfzuHHjAHj66ad54YUXePzxx+nRowc33ngjAwYM2KPnRBCEPSOC30Fs3Lhx0Q/Y7dLT08nLy6N79+51tqemptbJxQaRzNzDhw/f7XUkSaq3LSMjgyuuuKLe9bdbvHgxy5cv5/DDD+eRRx7h3nvvbfRc/5Sens6DDz7IYYcd1uD+2bNnEw6HSU1N5dVXX+Xyyy8H4PXXXycnJ4cPPviAlJQU1qxZw4QJEzD2Ym33oqIiunXrBkSCcmpqar1jMjIyGDx4MG+88UaD5+jXrx8vvPAC4XCY2bNnc9111/Hdd9/96zIJgrB7otuzjTnzzDN56qmnyM3NxTAM1q5dS1VVFccccwy5ubnMmTMn2mrcuHEjxx577G7PmZiYiCzLbN26Nbrt7LPP5uWXX2bDhg0AuN1uvvzySyCS5PTOO+/kgQce4OGHH2bRokXRD/uGzvVP55xzDjNnzowOoKmsrOSbb74BICcnh5kzZ/Loo4/yyCOP8Oqrr0a7Z71eL1arldjYWKqrq3n22Wf3/An8h9dee42amhqKiop46623OPnkk+sdc+yxx5Kbm8tnn31GOBwmHA7z119/sWnTJkKhEF988QVutxuz2YzT6azTohUEYd8Qf2VtzEUXXcTo0aO5+OKLGThwIHfccQfBYJCEhARefPFF3njjDYYOHcqrr77Kiy++2KTkqna7nSuuuIJzzjmHQYMGsWLFCk444QQuvfRSbrjhBgYOHMjYsWP5/vvvAbj77rsZMWIExxxzDAkJCTzwwAPccccdVFVVNXiufzr//PMZMWIEF198MQMGDOCss87ir7/+QlVVbr75ZqZMmULPnj3p2LEj119/PbfccguhUIgLLriAYDDIsGHDmDhxYpNatbtz/PHHc9pppzFhwgSOPfZYzjjjjHrHuFwuXnvtNebPn8/w4cM56qijeOyxxwiFQgB8/vnnjBgxgoEDB/Lee+/x6KOP7nW5BEHYNZHPTxD+pR49erBw4UI6dOjQ0kURBGEPiZafIAiC0OaI4CcIgiC0OaLbUxAEQWhzRMtPEARBaHNE8BMEQRDaHBH8BEEQhDZHBD9BEAShzRHBTxAEQWhz/h+aj9br82eZfQAAAABJRU5ErkJggg==", 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\n", 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", 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Jvo0kCfSaqnqdVSLBEEbAD3bXAboC4WBx9dHZu1XD2tdsNnvs9w8/fI/a2hqmTHkHi8XCo48+SDAYjK03m7d/hmxrjgS44YabWbduLQsWzOOOO27lvPMuZPz40/eoLNHzbR/aJMvK3x5Hzy9JEh6Pp8E9xZ0d91DRxocJ1L8H19wanGGA2+xElRWGJkTnppxVHv2mVhtom5MvN8liRTGb6jdTbhWu8xIqKkDemq1BCofwFxRh7NiGaxhEamrEWCihVaitrSUpKQmLxUJJSQmzZv3arP02bdpI585dOOec8xkz5iRWrvyrwTYOh4MuXbrx9ddfAbBhw3rWrVtDr16H71GZs7PbY7Va+eab6bFlGzduwOttW7kId8chUYMLGzqKJOFoQdobi2zFbrYxLDGLKfnL+L2qkJCuURfyEzEiyOy8F2GbIcuoDscOAa7+H26gvBLZasWUmkawoBgt0HBAfLiqBlNqBEM6RJ4zodU6++xzufPO2zj//LNISUlhwICjmrXfyy+/QH5+Hoqi4HS6uPPOexrd7v77H2Ly5If58MP3URSVe+99MHb/bXepqsoTTzzLs88+yXvvvYOu6yQkJPDww5P36LhtgWTsqxTNB0h5eV2sQ4P3u4fxfT+Zl9sP4r0ux5F70h0t6uxQq9WwsXIzVy37nrXeKu7vegyDE7LolNAem3zgqvzJyS5KS2v33wmrK6hds5LAx9F2fOs5M5DU7ffeJEXBmpRAoLS8fu1t23pZwtWlM7rN2eQp9vs17QeHyjXJskRiYuOv7V9/rSAjo+EcgYKwNxUWbqJnz8MaLG/jTZRRhrRtouWWsat2zKqJoQnRqbtmVWzGwKA2XHdINbnJViuy1YUU3wkMDb1sVb31hqbhLy6tF9wMXSOy8hO0kmUYukGktvaQes4EQTjw2naA26Fy6lBa3jHEJJlwW1wM3Zojbm5ltJmyJlCLIbXBsQJNMVtRTKbt03aVLt/lLpG/3ie84EXCcx7CMAxCVdVIxiH0nAmCcMC17QAXS40j4dyNGty2MXFZNjed7R58WoT5VcUEIyF8kcZzqLVJioLqdCAn9wZ2HeD0spVElr4NgOHdglGVixYIgv8Qes4EQTjg2naA21qDM9h5qpydsSk2rKo5VoubVZGPbhhU+CsPmUmHdd1Ade0w4LvsL4wmamNG2EdozoNgaGByAKDlz8bQdSJ1tYfMcyYIwoHXtgPcVkasBrcbEc6QiLO6GZoYvQ+3rZmyNuglaAR3sXPbIVutyK50sCdDqJbIsncaDXLhBS9h1BYgeTpiHhSdZFbb/D8A0UwpCMJ+1aYD3LYOoobEbjVRRo8BLrODbLubTjs0U0Z0jZrgITS+y2xFNZsx9ZoASESWvkXolzsxQtt71Gn5v6Gtmw6yGfPgu5AzjgbFilGxGsNbghYIYjQyjEAQBGFfaNMBbpttNbjdHRBhka1YVAvDdmimBKj0V6MR2VvFPLhtvQ+ndj0V8/GPgdmFXvA/gl9fjl6Zi+ErJ/T7EwCY+v4TOb4TkmpBzugPgFbwPwxNQ6+rE82UgiDsF208wG2PaM1JldMUCYl4W8NmymAkhDfSeKr0tkbXDVRndKyTknk0lhNfQ4rvjFFXQPDbKwn+fCsEq5HT+6N0PyO2n5I1BABt8xwAQlVVbTGjntBKjB9/Mrm56/bpOaZP/5K8vE1Nrl+wYB6XXDKBiy46l3POOZ2rr/4nelucwf0gsN9mMtmwYQOTJk2iqqoKj8fD5MmTycnJqbfNZ599xttvv40sy+i6zllnncWECRN2/6Q7dDJx7SKb964O4zA5yLbH0cnuIddXxYLqYgbFZ1Duq8Qd5+ZQuLUkWa1IioKhaciuDCyjXyb859No67/FqFwHljjMg25HkrZ/b1IyBxGWZPQtizDCPiIBBQJ+sNh2ciZBaL1mzPgKj8dDdnbDAe6RSITbb7+Fl156jS5dugKwevWq/XarQ9M0FOXQmVFovwW4e++9l/PPP59x48Yxbdo07rnnHt55551624wePZrTTz8dSZKoq6vjlFNO4aijjqJ79+67d9Id2iR39x7cNlbZis1kYWhiFrm+Kr4v3cSg+Ax8IT9+zY9Vavsf2JLFgmJSiWydYFZSLZgGTUJO6klk7TRMfS9HsifV38fqiU7UXLoMvfBPpPbHoXnrkK223W4yFlqnwF8vElg8GSL7YI5E1Yn1iNuw9rxmt3b/3/9m89ZbUwiFgphMJm644V/06nU45eVlTeaHaywHXFFRAatWreDpp5/g3/9+mWuvvZGjjhoYO4/P58Pn85GQkBhb1q3b9s+3xYsX8sQTjwHQt28/Zs+exVNPPU+nTp05+uh+/PTTbOz26CxKOz6+5547ycvbSDgcJiurHXfeeS9ut5sFC+bz9NOP0717D9asWc3ll19Fu3bZjeaOCwT8W1P05KKqKu3b57T66b72S4ArLy9nxYoVvPXWWwCMHTuWBx98kIqKChISEmLbOZ3bp/sJBAKEw+E9/GazrZOJ1KxccLs6lMfmZlRyDu9s/os5FQWUBn0kW+xUBqrIdNjbfs4zRUV1OIgEtvcelSQJteupqF1Pbbi9JIFhIGcNRi9dhrZ5Nkr74whXVWFNTN6dPq1CKxb866V9E9wAInUE/3pptwLc5s35vPnm6zz33Es4HE7Wr8/lxhuvZdq0r3E6XU3mh2sqB9yMGdO54IKLGDJkaINzud1uxo8/nbPOGk/fvv3o0+cIRo8+kdTUNEKhEHfffTv33fcwRx7Znx9+mMmnn37crGu46aab8Xiic1q++upLvPvu21x99XVAdFLnbeWMRCJceumERnPHbZug+aOPPgOgpqamxc/lwWa/BLiioiJSU1NjVWNFUUhJSaGoqKhegAP48ccfefrpp8nLy+Nf//oX3bp1a9G5dpwTT7eb8RMNc6lxLpKT9yxlizUk4zXqOD4lmx+KN/F9VR5XdOmLLkewuRTs5oa1OF3XkeW9f6tzT69ldwXCSfgi/l1vKElY09IIlZcT6jGCskWvohf+gdtlQlbBaZUwOevPX3igrmlfEte0naXn1fu0BmfpefVu7fr773MpKNjMFVdcFlumaRHKy8ux2+1N5odrTg64xtx88yTOO+9C5s+fx9y5c/jPf97irbfeIxgMYLFYOfLIaMeskSNH8dhjDzXrmF9/PYPvvvuaSCSC3+8nO3t7WqJ27bLp3bsPAPn5eU3mjuvSpSsbN27kiScepV+//gwePKRZ5z6YHXTZBEaMGMGIESMoLCzk6quvZujQoXTs2LHZ++842bLPu72moYSkPZ74VpIhEjA4ObEjPxRv4ov8NZyZ3AWzrLCZEhJMCRhGtFYTIUxtuBZ/OECGPX2vNscdyEl8pbBEXV0IQ9t50kpbSjKSw0OgvIaAnILkzsaoyaNy3TyU1CMIF5YixW9/Ug6ViYlbu5ZOtrwja89rdrsJcd8yOProY7j33gcbrHnzzdebzA+3JzngMjOzyMzMYty407jhhmuYPXtWo5kLdmzBUhQlNvZ0xxx1ixcv5PPPP+H1198mPj6e7777hqlTP4+tt9m2f/E2DGOnueM++OAT5s//k7lz5/DKKy/y/vsfY7HsYevXAbRfelGmp6dTXFwcSw6oaRolJSWkp6c3uU9GRga9e/fml19+2YMzb+tkIuFqZi64nR5Nh3hbHD1diXSye6iKBPm1PDpkoMJbBZJBmBClwVJyKzeSX1VEdaCWEKE9PvfBYtt9uJ0xu52Y0tOJaAamuDiQJJSsY4DorCYA4aqqQyuvnnDQOuqoQfz++/9Yvz43tmzFimhaqJ3lh2sqB5zD4aCurvFaqs/n448/5sbG6NbW1lJUVEhGRgbt2+cQDAZZvHghAD/99AO1tdu/TGRltYuVa+bMb2LLa2trcTqdxMXFEQqF+OqraU1e685yx5WUFKMoMsOGHc8NN/yLqqrKVt9MuV9qcImJifTo0YPp06czbtw4pk+fTo8ePRo0T+bm5tKpUycAKioq+OOPPxg1atTun3jHXpR7IcDB9gwD49M689T6+Uzdso6RSe3xh4NsqtmML+wnrG0fGxfWIgQiAUwtyEV3UFNUTJ44tJJSjEbuOSoWM9asduhbc79JdhuK2YSWNRhWfIS++X8YR15NxOeHoB/2oHerIOyOa6+9sl5Pwvff/5j77nuIhx++n2AwSDgc5vDDj+Cww3ruND9cUzngxo8/neeff4b333+nQScTwzD49NOPeeqpxzGbzWiaxujRJ3LcccMBePDBR+p1MklLS4vte/31NzF58sM4HE5GjNj+uTho0DF8++3XnH32eOLiPBxxRL9YIPy7neWOW7duHS+//DwQvbUyYcIlJCcn7+nTfUDtt3xwubm5TJo0iZqaGtxuN5MnT6Zjx45MnDiR6667jt69e/PII48wZ84cVFXFMAzOOussLrroohadZ8cmyrovb8c/6yWe6DSMWy79gGTznt8PkWXYWJNPia+K8xdOpyYS4rmewznMldjkPnE2F+2d7fZaM+WBbvqSJdBrqggUFhHxb5+ZRFIUnB3agysudq2yLBHK24S/pITAZ6dBsBrL2LeRPR1w5rQHT/RLzoG+pn3hULkmkQ9u3xk//mSeeuq5Zt/fO1Q1lQ9uv92D69SpE5988kmD5a+//nrs9zvuuGOfnd9t2jvd+PWtzZTVgVpOTOnAfwtXM23Lup0GOF/IT4gQJtpGLU43AJcHe2cHkdJS/GVlGJqOLT0Vye2p15tU16PNlIGycpTMQWjrv0XbPAfZ04FQVRWW+AQxXEAQhH2iTc9kounRe34yEjZl78Vym2rDpKicktoJmejUXRWhpudY3NZM2dboigklPQNXp47Y01JQk5IbHSoh2WwoZjNK1mBg+6wmEa8PKXToTFgtCC01deoMUXvbA206wIW2BjiTrO7VmQLMkhmXxUmqxcGg+EwihsGMktwG21WGAxQFojebKwPVbXIORsMA3eZETc9Eb+rtZLagOu3IGQNANmOUrcDwlqCHw+giR5wgCPtImw5w2zp7mGSFvRlddN0gweZBlmTGp0W/XU0vXk9Y1zEMg79qy3h47e+ct3A6ly75jjx/TayZsq3a2SB3XTcweeKRVFusN2Vk/bcAhCsrRW9KQRD2iYNuHNzeFNYjmIjW4IJhDXUvBjm7YsdhttHHnUyOzc1Gfw0vbVzE6roK1vmqYttpGHxfuolsm7tt9aZsIdlmQzaZUDqPRcv7BW3dDNReFxLx+rCEgsCux1EJgiC0RJuuwW1rolRlBa8/slebCA0dkhwJyJLMuK21uBkl61nnqyJONXNeRndu7xztHvxj2SZ0w2izzZTNYZgtqA47cvqRSI40DO8W9C0L0EJhDH8zZkbZSg76kYPN314QhENXmw5wG6u3prIxZKpqA3u1mRLAoTiwmSyMSGpPL1cSPZwJ3NppAB/0G8sl2b05LrEdaRY7pSE/y2pK23wz5c4YBpg9HiRJRul0EgDauhlAdNB3c8hBP76NG/Ft3IgcEkFOaLlbb72JCy88hwkTzuPyyy9hzZrVTW47fvzJnH/+WfVS2eyPdDu7Ultby7vvvt3k+sLCQo4+uh+TJz9Sb9no0cN3eezS0lKuuuqfzSrH0Uf3w+dr/B76ztbtT7sMcJqmMXLkSEKh1vfB7A+HAagLgDcQIRDa+fRSLSUjk+hIwKaoPNPzeJ7vNYITknMwy9FBpLIkMTwpOgboh7K8XfambOvZwWWHHdmkonQ6ESQZLf83jEAV4bo6IrvI9C2Hg/g2bSTiDxDxB/Bt2oQSFj0whZa55577ee+9//LOOx9ywQUTeOih+3e6vc/n45tvZuyz8kQiLU+YXFtby3vvvbPTbex2O7Nm/cLmzfktOnZycjIvv/xai8u0L2i7mA6wOXZ5D05RFBRFIRgMYja3rvtH8bZo/K4LgTcQxheIYHHuvWswDHCZnFhUM8FI418Ahidl80HBSmZV5HNth75UBqpxO931xn5JkkTQCFATrMVtdmGRLG1zbJjZimq3oYdTkNOPQi/8HW3DTKTDziZQWIhscYK1YVYGJRzEu3EDEd/2IBjx+vHn5WFt3x59D1MhCfvHS2vn8PjqX/A28beyJxyqmVu7HcfVXQbvdDunc/tkD3V1dbvs4HTZZZczZcprjBo1BpPJVG9dWVkpTz31OMXFWwgGg5xwwmj+7/8uBeD5559h0aIFhMNhPB4Pd955L+npGRQWFnLxxRdy8smnMH/+PMaPP52hQ4c1ehxd13nyycksWDAPk8mEzWbn9dff4sknH6OurpaLLjoXq9XK66+/3aDcJpOZ88+/iH//+2UefPDRBuuXL1/Gyy+/gNcb7eX9z39eyeDBx8bK9913PwHw008/8u9/v4TFYmH48JG8+upL9VL2fPzxh/z6689UV1dzzTU3MHz4iNg53n//P8ya9SvBYJArrrgmtm7bPJeaphEfH89tt91Ju3bZjab2KSsr5cMP38dsNqPrOg8/PJmcnA47fc121KxOJhMmTOCGG27g8ssvJy0trV5No127ds0+2f5m2jobT0STmb+ljkS3lQS3pdEppnaXiokEWxxFtaWNrm9vc9PFEc9abyVzKwsZabLUG/QtyQYVoUqKa0sJaxFK5XJcFieJ9nhssg2MtlOr0w0weTyEqmtRO59MqPB3Iuumo3Q/i1BZOXW+LZg9cZgTEsHuQDdAjoTwbdpExNuwSTJUWwf5eVizc9D34jhHYd94OXfuPgluAN5IiJdz5+4ywAE8/PAD/Pnn7xiGwbPPvrjTbXv0OIzu3Xvw+eefcM4559dbd//993DJJZfRt++RhMNhrrnmcnr06MnAgUczYcL/cd11NwIwbdoXvPTS8zz0UHQKrurqKnr0OCy2/tprr2z0OB6PhwUL5vHhh58iy3JsXsibb57ExRdf2OSEyduceebZnHPOaaxZs7peYK+treXxxx/h6aefJykpmbKyUi6++CI++KD+RBzl5eU89thDvPHGf8jOzubDD99rcA6Hw8Fbb73HkiWLueuu2+oFOFlWePfdj9i0aSMTJ17MEUf03fq83c0rr7xBhw4d+fLLqdx77128+Wa0Rrpjah+AESOG8t//fkZSUjKhUAhdb1mtrlmfCg8+GJ1le86cOfWWS5LEypUrW3TC/SlsRJ8Mw1D4ZXM1Q7M9BEMaZrXxlllJpsWZuQ3DIM4SR5mvst4clDsamZTNWm8lP5blMSyxHYFIAIvJQlAPUFRbQo2/DmPrxNARXaPSX011oBaH2UayIwGn4mozNTrF4UBWFYysY8CagFG9Cb3sL/AMQA9HCJSWE6yowux2YklMJFBcQrjO2+TxQtW1SJvzsGa3R5MOnUzFrdFVnQbt0xrcVZ0GNWvbbXNGfvPNdF544VmeeeaFnW5/+eVXcfXV/+SUU8bHlvn9fhYuXEBVVWVsmc/nY+PGDQwceDRz587h008/xu/3N2hqs1gsjBw5apfHOfnksWhahIcfvp/+/QcweHDD/HI7Y7FYuPjiibzyyovccsv29DjLli2hsLCAG2+8NrZMkiQ2b84nLs4TW/bXX8vp1q17LPXOKaeM47nnnq53jhNOGA1Ar169KS0tJRgMxrIPbHu+2rfPoVu37ixfvgxJgs6du9KhQzRDzNixp/LEE4/i9Ub/xndM7QPQv/8AHnjgXoYMGcrgwUPIzMxq0XPQrAC3atWqFh30YBELOIbM3KIaagMRvMEIFlO09rRj0AhpOpVVQdITWp641CJbcFtdlHsrG11/XGI2/960hD+riqgOB6kMVKMZOsW1pYS0cKP76IZObdCLN+Qn2ZlIsi0JSW8DtTmLDdVmQ49oqJ3GEPnrA7R106HzgNgmhqYRrKwmWFndrEMGK6tByseS1Q5dFkHuYHV1l8HNqmHtLyeeOJbHHnuY6uoqfvvtV/773w8BuOCCCYwZc1Jsu/btcxg0aEi9Goyu60gSvPXWu6hq/abLoqJCnn32ad56610yMjJZunQJ99yzfRpCq9UWawXb2XEAPvjgUxYunM+8eX/w0kvP85//fNCiaxw79lQ++OBdlixZGFtmGAadO3fh1VenNNi+sLCwRcc3m6PBbNvk1Xt632zH1D4Ajz32JCtW/MWCBfO4+up/cuutd3LMMc1/D7WoF2VhYSGLFi2iqKioJbsdMNrWGpzHYiaoGSwsq0MP1KH4ypCDVbFOlSFNZ2NhDeVVAUKRFlbhiA5kTrR6ULYmNpWQsKhmkhzxtPdkkmJzcmRcGpphMKt8MzX+WjZXFTUZ3Oode2sg3Fy7mYi06+0PdroB5uQkAJROJwOgbfwZPdS8JJhawe9om//XYHmwopJgQT6ysXc7Eglth8/no7h4S+zxb7/9itvtxu2OY+zYcbz77ke8++5H9YLbNhMnXs6nn34c6xnocDg44oi+vPPO27Ftiou3UF5ehtfrxWRSSUhIRNd1vvji0ybLtLPjVFZWEggEOProY7jqqutwOJwUFBTgcDgIBALN6qCiKAqXX34Vr732amxZ7959yM/PZ8GCebFlK1b8xd/n3e/ZsxerV6+KdVSZMWM6LTF9+pcA5OXlsWbNanr16k2vXoezbt0aNm7cAMDXX39F167dcDgcDfaPRCIUFGymZ89eTJhwMUcdNYg1a1pW2WpWDa6kpISbbrqJxYsX4/F4qKqqok+fPjz99NOkpqa26IT7U449msL9cE8K+OHX9UWMdZZQ6zFjNpuwpCgEVSd5W2rxBaJvllpfmASXucVNglbFhtviQpZl4iwubIoNVVIBgxpzLSOSs5lXvYUfyjZxSlontuWqg+g3qvnVxaSY7bS3uxs9fpW/lmAkRKY7HWjdWaJlpxvVZiVCFnLKEegliwms/R6yxux0v8jqLwjPexYA06DbUDvV/yAKllciSRLmrCx0RE1OqM/v93PHHbcSCASQZRm3280TTzzbrN7LKSmpnHjiyXzwwbuxZfff/zDPPvsUF1xwNhDtuXjnnffSuXMXhg8/gfPOOxOPx8Mxxwxm0aKFTR26yeMEAgEeffRBNE1D0zQGDRpMr169kWWZ0aNP5IILzsbtdjfayWRHw4eP5N13344F5+h1P7O1efZJwuEwmZlZPPnks/X2S0xM5Lbb7uCmm67DarUyePCxqKqK1dq8FFeaFmHChPMIBALcdtudsfRo9977IPfccyeaFiE+Pp777ms8a7mu6zz44L3U1dUhSRKpqalcffW1jW7blGaly7nqqqvIyMjgpptuwm634/P5ePrpp9m8eTOvvvrqrnbfrz788L+xZIOd1r5DWvEsvMNu5+gVRyNj8GDqeuLMEoosgSTRrmMPzAldCAb8rF76K7IsYbdsj/s9e/ahS5du1NbW8uOP3zQ43xFHHElOTicqKyv49dcfGqw/8siBJGYm8ue6JUxY/SMhyeD2QDpJRrQ5IuGwHF6rWsfcymjTwGGajeERNx30aNW/4xE9cMa7qSwuI39FLhISNosVSY/WFocNG0l8fAIbN+ayePGCBucfMeJEXC4Xa9eu5q+/ljRYP3r0KdhsNlat+otVqxrmkDr55NMwmUwsX76YdevWNFg/fnz0j3LRovls2rS+3jpVVRk7NprheP7839m8OW/7ynAYVdMZYikiPOchAu4O/Nnx+nr7280Wju0aTYGxbu4UMnO3d402kFnf/Sp69T8LgLm5q6jZOmBcNqlIFgtJSSkMGXI8AN9//3Wsx9g2qanpDBp0LADffvslgb8NVcjKyqZ//6MBmD798wbfmNu370jfvv0BmDr14wbPzRFHHE5OTnfC4TAzZnzRYH337j3p3r0nfr+f7777qsH6vfHea9euPWVlJcye/UuD9QMHDiE9PYOiokL++GN2g/VDhhxHUlIK+fmbWLDgDwBMJoVwOFpL3vbeKyzMp0+fhqlKQKTLae28Xm+sdjV9+jS+/HIar7325gEuVUN7lC5nwYIFPPfcc7Fusna7nVtvvZVjjz1275Zyr4vG7jiLwhFxsKhaZlnAySC1FllWCIcjBCrLSMvoRHFwW5u4gaYb0QC4l9gUO26Tjd6ajQWqj4WKj1GRONbKAT7O+4MKLYhdVghpOisUPysUPx01SzTQ/e37h4FBSAuhoKBKDdvsWw1VQTIMlHZDCZudWGs2kFy5gFJPvwYD8iPrvyUjN/rNOTdjPCbNS3bx9+SseQ09pxdyUo962+vhCDIgYSBLQCiIpEUgHMLQdSSzOdqjSBCEnfr44w/56acf0DQNt9vN7bffdaCL1CLNqsGNGjWK559/nu7du8eWrVq1imuvvZbvv/9+nxawpXZMeFr7ybUE/vgPtpPu5dWqI3liLQyMh6f7yJhNCoFg9Bu51elkjj+BiCbTO8lBfJyVnFTnXuu5KElQHipn6qYl3LHqNzKsToYmZPHfwlUYQC9XErd3HohJlpm6ZR1fbllH3db7c10c8dzSaQAd7HGx47ndNry1QTLj0og3x7e4U8zBQJIktKICfFuKCS98hciKaJdnKaErpsP/DznzGCRJIrLxJ8JzHgRDRz3in5h6XYBhGITnPoa2/luwxGEZ/TKyu2HvKnOcC0PT0IJh9Egk1qvIHOfCmtMhlnV8XxEJT0UNTtg/9qgGd9lll/F///d/nHnmmWRkRAcrfv7551x//fW73vlA2vqBpsoyI9MUnl6nMb/SoMpfi1tzgCRFx8Isq+X9/FoUCb4Y1w1FAz0oY5INkEBTnQ1uwLa0GC6Ti6MSMog3WSgM1PFR4Spk4KKswzg/swfK1hrFxe16cXZ6N74uWc+nRWtY663k6mU/8H/tenFGeleUrbUbzdApqClGipOJU+NaVD5J4oAPOzAMAzUhHrmsHPWIidgSMqid/zZGxRpCv9yBlNANJWswkWX/iQa3wy/G1OuCreWXMB19C0agAr3wT0I/3Yxl9MtItoR65whVNx5cQtW1KMVbMKVn0gq/GwiC0EzNqsEBzJ07l+nTp1NSUkJKSgpjx45l0KDmjTvZn+rV4D6+isCf72EfeDJhl8q6kg2ksJkEpRa/4xjKkq/nyXUq07d3rOKC9grXdFHwOM247WYUVUVN64K2h82BkgT5dZt5fNVsPi5aTYrZzu2dB9LLndTkPn4twmubljC9JHpvq7criVs6HUW3lCRqaqL3m1RZITsuA6fq+tvsKNGfsBEmouvo6BjoRPQImq7hMDmwytYDWvuTJAjnbSJQXkFcnI2q8iq0tV8S/usDCFTEtlN7XoB6xMQGnQGMsI/g9zdgVKxGSuiKZcRTSJbGO+k0OLcs42jfDsmz7zKKixqcqMEJ+0dTNbhdBjhN0xg9ejRff/11q5iqa8cAV/XqUMLrFmPKVlATG95zmacN4sKiG5BllX9kw+sbwaHAZwMh3qaSkeRAlsCS2gHN6tnjsvn0OlaVb+T3ikL6xqXgbOYUU39WFvHU+vlUhAPYZJWbug9gmCsz9oFvUlTaezJxKE50dIJaAJ/mpyZQRyASRNN1DENH3+GlNikqCTYPCbZ4TJgOWI1ODnipXbsOt9NCdXU0aBuRANraL4ms/Qol+zjUPpc02dPNCFQS/PZqjLoCJGcG5mEPIcd3at65TSacnTuhW2y73ng3iAAnApywfzQV4HZ5p33HuShbG8maDICS0A+1w5WUZNzPsKLXOL34MbyGnQHKXJ5PfI5nemlMyJboEwdeDaZvgXBYi03OrNWWIe+FPgk2xY7bbOPYxKxmBzeAo+LTef3wUQxNyMKvR3h4xVzeyFsWa5YMaxHyqgop8BaytnI9uRWb2Fy1hZpAHaFIGE3X6gW3bfsU15WRW7mR8lAFutzy8X97hc2ByVn/w1FSrag9zsZ66ruYjrh0p924JWs85hOeQUroilFXSPDbq4hs/KlZp9bDYfz5+Sh66x9fKAhCQ8362N42F+Wff/5JXl4e+fn5sZ+DmWxLAUBJHY2aeQae9KPoFJ/IknAXLiq9mzrdzmjb/xgWfA4MjfO29lP4pADChkGNNwhIaAEfcmjPUz/IyCTYPbu1r9tk4a4uR3NrpwEoksTHRat5r2BFbH1IC1PmrSQQDqK1YL6xUCRMQfUW1lduwG/49nu+Ot0AS3JS81IZSRK21BRsyUmww+ayIxXLqBdROo4GLUB49v2EF7yCoe96IGy4zkuwaAsiqbggtD1tei7KWLvb1v9sFpUxaTK/lumUyF3JS76b7hUPYPPOwSiRGJR8Hdk2hfKAn0XFZRwbV0HAcGJN7Ifmq0KKc+yVziYmpbzevJWSJOG2OHFbXVT6q/GH/Wh6wyAlSRInJOfgcdi4a+ks3tm8ArOscE5G9wbbtpQ/HCSvqoBsTwZ22bFfmywlpxu5rgpo+kuEpMjY0tNRk5Jjsc1fWrZ9vWrBNOh25MTuhOe/SGTlR+iVa1GyBmNEfBD2Y4R9EPEjZwxEzdmeGytYXo7J4wFH6x5ALzRPTU0Np5wymnHjTuemm2450MVh1qxfWbJkIddee+OBLkqbs8sAZxgGM2fOJCMjA1VtXTO2S45EAOSttSaTIjMqXcYq6XRzgsvUjXLLPSQW3Y/dOxtLcBXfJvqw4It+1vqAItB7PoxmGorJlbzHnU3Mkhmn2U6lPzozuNVkIc2ZjFt1ARLxZg8BzU9VsIbqQG2jaXhGpOVQ5Q3wRO6fvJG3DIusMD6tS71tNvlq+Lk8jzx/LT4tjFcLR/+PhLEoKhOyDmN4Yna95r+QFmZTVQHZcZk4lP0X5HQkXJkZeL0hwl5vg2wPsknF3i4LKS4+1uvRlJGJAQR2DHKShNrtdKT4ToRm3Yu+ZQH6loaD37X134IWQu0UnTnF0A1CZWVYXC4a+V4htDEzZ35Dz569+f77b7n22hsapMHZHZFIZLc/H4cOHcbQocP2uAxCQ83qRXnEEUewcOFC5L1xI2of27GTiRGswVq2FJ8Rh7G1xuQNahSX15+d3hRYRdKWB5H0aCeHgGGmKJJIklXFpeVDXD+svR/HkpqDZo3f4zL69Dryq4tIsHtIsMajojYIJpIkoRGmLuKluK6MwA7JPd1uGzU1fmYUr+fZDdEP8Bs7HMlR8en8Up7Pj6WbWOer2mU5BsSlcX3HfqRa6s8DZ1JU2sVl4FT23ljAXUlOdlFeWo3hrSNYWka4phZD11GsFuzZ7cDhblB7lg2dUGFBvSC3jeEtIbLqUwwthGSyg8mGpNowvKVEVn4EkoJ5xJMoaf0AkFQFZ5fOGBb7Xr0m0clkeyeTup+fp+7bRzCCzZt3tCUkixPnmDtwHn/dLrf9xz/O55prruc//3mL0047g99/n0vnzp1j6XByc9dxyy038tlnX+LzeXn22afJzV1LMBjkyCMHcP31N6EoCldeOZGuXbuyfPky3O44nnjiGf71r+uorq4mGAxy2GE9mTTpLkwmE+FwmCeffIyFCxcQH59A165dKS8v59FHn2D69C+ZM+c3Hn30CRYsmM+zzz5Jz569WLZsKZIk8eCDj8Zm33/llRf58ceZuN0e+vU7kvnz/+Ttt9/f689na7NH4+B69OjBhg0b6NSpeb3TDhay1Y2j50j8KxZGA5wk43BYcEk2IuEI/uoqAEyeXpiyPkQOF1Pud/H8GjNvFkiMTPDyimMicvVCdN8mtFoXsj1+j7/l21QHHeLbYZVt6LpBYzHEMAxkVOLUOBweB2X+csp9VWg75EM6ObUjQT3CK5uW8OyGBUgbYFvRHIqJoYlZ9HVHe2s6FBN2RcWhmFhQXcy/Ny1hXvUWLlvyHRe36824tM6xMXZhLUJ+deHWIOfao2bZltCRweHG6nRj9dURrqzClJSIYbE1WgZdkjFnZAIGwbJyYHurtORIwXTkVU2eKbLyY0K/3o1l9EvInhyMiEakohJTpqNVDpxvDbw/P79PghuAEazD+/Pzuwxwa9euobq6mv79j6K8vJyvvprG//3fpTz99BOxADd9+pecfPIpSJLEs88+Tb9+/bjzznvQdZ17772Tr76axvjx0SnoCgoK+Pe/30RVVQzD4IEHHiEuzrP193v46qtpnH76mXzxxWcUF2/hww8/RdM0rrpqIikpjc/ju379eu666z4mTbqLt956g7femsIDDzzMb7/9ypw5v/Huu//FYrFw++0Hvnn1YNesAHfUUUcxceJETjvttAYJT88888x9Vri9RbHaMcUlI1nsYLIiVQTRvH7sSEihWpI8dhQJMDtxW3ROy6zjvc0GP1Q4KEk8jjT/d4Q2f4Hi7Iga8qGre/YtX9IlzDRvDJphgIJKuj0Nt8VNcV0JEtsj7OnpXQnqGm/mL0eVZAZ50hmRnM1ATzrmJlLHjEnpwFGedF7euIhfKzbzyqbF/FyWx82dB9DeFh1Hti3IpbmSsak2zJIZRVL2y4e/bgA2JyaHK3q+nZxSl2TMmVkEjQCVRQW4zM5Ga8Q7UvtdiV5XhJ7/G6Gfb8My5mUkWyKhyipMSUlgsuz1axLAcfx1+7QG52hG7e2rr6Zx0kljkSSJ444bztNPP05GRiY+n5d169aSk9OB77//NjaB8ezZv7JixXI++CCaKicQCJCSkhI73ujRJ8aaJnVd5/3332Xu3Dnouk5NTU1sYuIFC+YxZszJqKqKqqqMGjWGxYsXNVrG9u3b061b9L56r169mT171tZjzGfEiBNiKWVOPvkU3nzz9d14tg4dzQpwCxcuJDMzkz///LPeckmSDvoAJ6sqUlIHdMOIfugZ4LSbKSn3kZmeQ3x4C7p/e5OlRVXIcKmcmBpmWhG8VXsit6vfYZR+j97xsr3S2WR36LqBTbLRPi4bzRzA582PdVQ5L7MHR8dnkGS24Wrm8IMEs5W7ug5ieEUhL2xcyCpvBdcs+4HrOvTjhOQcYGuQqypCkWVMiopVteKyOLAqViRJQgKMrRnHJQnMshn2Ys665gZTTYKqeDuSnkTZli04FCsO1YHURDZ0SZIxD74rOki8fCXBX+7AcsJzaIBWU4OclHzAZ3ppi5zHX9esJsR9JRwOM3PmN5hMZr7+Opr6JRKJMGPGl5x00inMmPEV/fodSU5OB9LTM4BoS8rjjz/dZKLNHfOXzZz5DUuWLOLVV6fgcDh4++0p5OXlNbrfzuw43liWlT3OsXYoa1aAe/fdd3e90UFKkqQGH5R2i0qix068x4YZK+GS9WgB/9a1BnFOC+dkhfmyCN4pbsc1HQ/HFVxKoOBrFNuFe6Wzye6SdIl0VwpBb4S8miIiW4PcjnNVtsQxCRn0cSfzwsaF/FiWx+O581hSU8o1OX2xKtG3h6braHqIQDhElb8GWZJ2qMVLsX8tqpkUZxIu1Ymxn5KzShLURGqp9Neguiy4rO3xFhRQ5yvBY3VjlW2N1gAl1YrluEcIfncVRvkqQnMexDz0QYLl5dgTEjBEdvA2Z9asX8jOzqk3G/6yZUu4//57ePHFV7nssn+weXM+J598amz9sccO45133uLWW+9AURSqqirx+XxkZGQ2OH5tbR0eTzwOh4O6ulpmzvyW7t2j94X69evPd999w8iRo9A0jR9+mElSUnKLyt+vX3/eeONVzjvvAsxmC998M2M3n4lDR7N7jVRWVjJ16lTeeOMNAIqLi9myZcsu9jo4KRJkJtuRJYmIZMaU3B7ZvD3Hkc2s0MVjYkgihAz40BfNO6ZvmYYW8sM+uo/QEk7VTTt3OupeyGDtUE3c1uko/tWxP2ZJ5rvSjVy7/Ec2+Woa3V43jK1BT0fTNTRdI6JreEN+NlZsZn31Jrx6LZK0H5oz0SipK8cgWuOsUjRM7bMxeTyUeisIaP4m95VsCZiPfwzMTvT82WgbfyTiD6DXta2OIULUV19NY/ToE+st6927D4ZhUFhYSE5ORxYuXMDxx28fQnLDDTcjywoXXXQuF1xwNjfccA0lJSWNHv+kk07G6/Vyzjmnc/PNN9CnT9/YutNPP5OkpCTOO+9MrrnmcnJyOuB0Nt4xpylDhw5j4MBBXHjhOVx22T9ISkpu8TEONc3qRfnnn39y7bXX0qtXLxYuXMiiRYv4888/efPNNw+6fHA79qKE5vdkUyNeQqWbwDCQVTNBxcai4jBnTN+EYWgsyb4Gm16C1uk+PJ1ORErpfMAm6t12TZIkUROpJr+6qF7nkx3JkoQsyVt/ojUvRVYIRkL1xuJts8FXzYNr5pIfqMUqK1yQeRhDEjLJtDqblRhyG0mScJjtJDsScCgOZOSdNvvtTo9DSYLKcBU/bv6LX8vzOTGlA1m26Fg2h2rGUhtAikRIsSUiocQGk4cqK9EC23ulRtbNIPz740jOTCynvoPF48HasTM6zbteGQ3CEQyztV7TtehFKabq2tG23GqhUIhbbrmB4cNPYNy403brGLqu88gjD5CUlMwVV1y9j0rceuxRL8pHHnmEZ599lkGDBjFgwAAA+vTpw9KlS/duKQ8gzeTAlNYJkDEUMxbDIMFXxeldvHy8powPfCdyqfU/aEVfEEgfiiNUg25q3sS++4phGLjVOLLiDDZXb4kFOQkJi8mMx+rGZXYiSzKKJCOjxAKdXwtQ4iuj2l+LvsPMJx3scbzUeyTPbVjAj2V5TMlfxpT8ZaRbHBzlSWeAJ43D3cnYlJ2/dQzDoC7oxRv0YTGZibfF4Ta7sMqWvTbWTENjc00x96/5HyUhH58WreHk1I5clBl9owccJmTJjMluIdWWsjXASpg8HrwbNsSCnNJxNJG/PsSozUfL/YZwt3FYfV6w7/zbsSSB5K0jUFSEFgxhSUnGlBCPrhy4uT2Fg9e1115JOBwiFAoxYMBRnHzyKS0+xgMP3ENRUSHBYJBu3Xpw0UX/2AclbTuaFeAKCgpimQNiE/yaTG3q5qdhgCZt7T2nG0gSJMfbObdbMj/kVfFC+QgmZH6E2bcEb/kaLHY7SooTzTiwYwMNw8Bj8qC7DUrqynCY7cRb3dgUOzLK9hrFtv8M0DAwY6GdM5MEax3F3jK8QV/sVpVNUbmt01EMTcji1/LNzKveQlHQy7TidUwrXgeAUzHhMVmIN1nxmKwkmW0cl9iOw1yJ9cuHQSAcpChcQqlcjsNiJ9HmwabYUCXTbvfKlCSoDlXzQf5ySkI+4lQztZEQXxXn8kPpJs7O6MYZ6V2xKSqldRXYTXYcsjP6RFhtONq3jwa5UBhJVlH7XEx49gNElv0HpeMoQuXlmBxNjwOUtTCR0lL8pWUYW/8OfJsLUMvLsaamoHg8VAdqCBPGxME/Sbmw77355ju73mgXJk9+ai+U5NDRrADXqVMnfvvtt3oZvP/3v//RtWvXZp9ow4YNTJo0iaqqKjweD5MnTyYnJ6feNi+99BJff/01sixjMpm48cYbD1jWcMMAt8NEosPMxMPTmfxnhC99wzjDPpNI4VQq7TnEOypRXElo2oH9uq7rBvEmD654BybJHOta3/gIu+0MHeyykxy3nepwNcV1ZYQi0YmHJUnimIRMjknIRDMMVtdV8GdVEX9WbSHXW0WdFqZOC7M5sP1+5Bdb1nKYM5EzM7pyTHxmbFzdNhFdo9pfS42/DrNqwmm2E2d1Y1OssXPuuIuOBoYU/fmbCBHWVBbyUeEqAO7ocjSJJhtv5C/l98oi/rP5L74qzmVS54H0jUthS20JHTw2ZEOJBi27A3tOe7wbNqGHwyjtjyey/H2Mqly0tV8Rsp6LOehHstiIdb8FMAyMulp8hUVE/IGG5fIH8OblYZRtQcuIoywcIjsu65AOcoZhtKh5WxBawjD0JqeybdY9uMWLF3P55Zdz3HHH8c033zB+/Hh++uknXn75ZQ4//PBmFWLChAmcccYZjBs3jmnTpvHZZ5/xzjv1v9H89ttv9O/fH5vNxqpVq7jwwguZPXt2bCxJc+zuPbjGSBKUVgfI21LLzbNyCVav5bu0G9AlC8XZb2DzpGBv1x2rzYbdomztNr9bp2qRfXFvZ1vuuC3eEqr8NTsNjrphUBsJURkOUBkOUhkOkOut4uuS9bFM5BkWB6eld2VEUvZOhy5IgEk1kZaQQNCvEdbCRHSNiB5BN3QUSSHRHo/TtD14S5JEeaiMWxd9zfSS9Qz0pPNQ9yGxYy6pKeX1TUtY7a3EpZr5d+8TSLbYSXUm7dBUufWWXF0N3o2b0MMRtPw5hH69A6zxWMd/iOr01P9gNqL/aKEwRhPtrJIEQSNEqbcMT0YqNfEeJEmmvScTk9H4+DrZ0DB8PnC6dvn+kSQJA73RoL8/tPQe3Pr161EUCy5XnAhywl5lGAaaFqG6uhKTSaZ9+4b3epud8LS4uJgvv/ySwsJC0tPTOfXUU0lLS2tWQcrLyxk9ejR//PEHihId1zFw4EBmzpxJQkJCo/sYhkH//v2ZMWNGs88TPdfeC3AAmm6wJr+KVeVervh+He8m3ccx1mVUJ0zA6xmPMzWLGjUBRZbJSHbgtO58kPHesE87L0gG1eFqttSVxmpzzeXXInxXuoHPitayJbh9bGGm1Ul3ZwLdHAl0cybQ2eFpMAh92/RjTTGrJuKsbuItblRZ5fvNi7lk0deAxGt9RsUGqG+jGwZ3rZrNvOotHO5K5vHDhmFWVDrEt8MmbR+oL0kSRk0lvk35aOEwwW+vxChfiXrEP2MZxJtLkiBkhCjxlhHRNJxuB0pmNpVSGJvJSnZcJmYaCXLVFfjyC7AmJ6KmpKI3MUQh2qmoBgN9axb3FhVvr2hpgAuHw+Tn5+NvpLYrCHtKVRXi4+NJSkpqdCrJZs8OmpqaysSJE3erEEVFRaSmpqIo0T9cRVFISUmhqKioyQA3depUsrOzWxTcgEb/0JKT93CWeFXBbjdzWhcPb+SfyjHWZbiqP0dKORElWE1yfBI1YZVKb5ikRAdO275vjtrja9rZsXGTFkqgqKaEmmDtLho6t3MDE+IP54Iuvfi1JJ9P81azvLqUgkAdBYE6fiyLDnq1yioDk9IZlpzNkORM4rYO0XC7d554NIiPkkgAVVb5d/4SdOCMrC70Tm18yqP7jziWC+d+xdLaUj4rW8tlnfpQSzWp8fHIkkR4a3ZzzRGHXQrizd+MecDFBL+9lcjKD7H2ORWLLR6T0vSYRz3sxzt/ClrtFqTDz6HOEo/JolAaCTOtOo+zPW4Ss9II6xEq9TJyPFk4Lc4d9g9Ts6UWl8MEvhrk4jCurEzM8Q3nPC2pK6O6pgJZkkl1x+M0Oxpssyu6ER3asbNr2pWWvPdMJhMdO3bc7XMJwp44KNMD/Pnnnzz33HO8+eabu974b/Z2DQ6i4+Z0TePi3umct74vswOHM8S6lFD+u4RSL8OhFBK0pFEd0oiEI7RPdTWzg/nu2V/dz+OlRBTFvDWFT4CQ1vwaXX9bCv27pRDRdTb4q1lVV8HqugpW1VWwyV/DryX5/FqSjwz0dCVxXHp72itOcuxxxO1iqqwFVcXMLSvArqick9qNmho/TosD3dDxh/yxgGwCbu14FJNWzWJK7hIOsyTQy51ETY0v2ryxNcu5rus4FDOEvfhM6XgSu2MuX0Xx3CmEep6DzWzDYbJhls1IhhSrOelb5hGa+ySGd+t40LUzIXMQSzufyC1yHbWGxlcrS3nROgazqlKDn9raANlxWdtrcrVV1BVXbm/brvZTVVaNLTUFNSkZXVKQJKgKV1FQUxzrKRsJbqK9K6vFA+q9updyXznpzjTMkrnFtcCW1uAE4UBqdhPlnmhJE+WiRYu44YYbePnll+nZs+dunGvvBziAQFhjQ2EN01fn8+WyRUxPvRkDmOR/hj5p7Tnh8E54Q2YMICPFSarHus+akPb3+CpZlgjpIQJ6kNpgLXUhLxFNQ98aIFqqNOhjbmUhcyoLWVJTgva3YySYrOTY3eTY4ujhTKSPO5n4rbU8zTC4ctn3bPBVc2m73pyb2R2TotIhPhuzZKY2UktJXTn+8PYmsTfylvLfwtUkm228evgo3I3cE5QkifiITG1uLnLxCuLnPICuWKk89l40VyaSrGBWVBwWB+ZIAG3+q0gbfgAgEteeUHwXbHm/IOkRwpLM5+m9eTvnGDab7WSa7Tzeazgp5mgN1W6y0t7TDrOhEMjNJVTb+MQBzpz2SPEJVIYrKawpRtN1KkMBPCYLsiSTEZdKojmx+dPGyQYbqvOoC3oxqyYyXWm41F3f99uRCHBCa7JfAhzARRddxJlnnhnrZPLpp582mAJs6dKlXHfddTz33HP06dNnt86zrwKcLEtsqfTjrall9oq1eLa8wPHqTH729+Oy8jtxqHDhYWmc3jkJRZbokBmHy7Zv7scdyAHEsiyhGRphI7y1BhQhpIUJamG8IR+BcKBFQc8bCfNnVRHL/eWsqa5go6+aQCOD1rNtLvq4U1AlmS+2rCXVbOfNI8ZglhXS3akkW5K29taLjo+rCddQWldOIBIiouvcuOJnVtVVMDg+k3u7Dmq0w4NVNWMtraSmqIi435/AUrIEAEOxEHFlEXG3Q7MlYt/wPXKoBkM24e12Gr5OJ6FLCp9WrSBz7ZecumUFCga6YuaePucxzZ1EksnGoz2OJWfrlGoui4Mc2U3duk0YkQAY0aStO7LEOQlkp5BXVURE15iSt4yPi1YzIimb2zodhVk10cHTDou086Zd2DqsIlJNXmVhrAORIskku5JIsiYiNbMmKAKc0JrstwCXm5vLpEmTqKmpwe12M3nyZDp27MjEiRO57rrr6N27N2eccQYFBQWk7nBP5fHHH6dbt27NPs++CnAQbUXaWFyDpW4Lvi0rSc67BsXwcY/3Lt6vjE7L88iQLI7JSkSSoHOWB1Xe+42VB+MMGZIkYUgavoifCn8ltSFfbJ7M5tjWyUQ3DEqCPjb6q8n1VrG0towVtWUNgt7tnQcyPCkbu9lGx7hsJKN+x4xooItQ7CulzFtJUcDLFctm4tMijE7OIU61EDI0QrpGUNfQDQNVknEqJpSaOjzeCk5c9RXZNQVYA5UNyhtK6EbtEZeiOTMIGDqT/ZuYFalCBu6OmDhlzTdYSpYQjOvApUdexBLdi0s182j3Y+nmTMCuWnAUlWP3RwhN/yeGtxil42jU7megeHKIoFGn+ZDapVNkBHgidx6/lOfHzn9dh36cktoJp8VBjrsd0i7GY+pShPklq/m0YAVDE9vV65QTZ3WR6khCkVQUSUGVo0MpjG0TlO9ABDihNWkywJ1//vnN6tb7/vsHV7K9fRngAGoDYWoqqggUrsVa+glxFe8QNrXj39JTPLNewa7AB8MTyE5NRrXHkZpg32m6l91xMAa4HUW7ygepCdVS5a+Ozllp6Oi63uTwg531ogzrOqu9FSypLmFpbSnpFifXd+iHIivkxGdtHcDdOEPW2VSzmdpAHb+U5fPwut9bfD2dIhHOCPgZ7KsmzVtG2NOR9VlHsUjzsihSy2KtjiojggOZe+wd6K+6IRIk6ad/IQeqKOt/HXcnpTE7UI5NVnmw2xCOd6ZRs3Yd7rXTMP/1Uf0ypx9JXYcT8Cf2xMhI5aaihSytKcWuqJyU0pFPi9ZgkmSe7zWczo540twppFqTmxw0L0kSpcFSLpv3Ob9XFmGVFW7uNIBhie1i28iSjCJHZ7xRZAWzYsZutpFoSWCHiW5EgBNalSYD3BdffBH7PS8vj88++4zTTjuNjIwMCgsLmTp1KmeccQbXXXfg0l80Zl8HOEmG4nIf4dKNeMsKScm/DjVSTFXiRP5VMIZfyqCLE/7dTyHO7SIxKwtHfNMfPrvjYA9w20S/IOlEiBDRdXQ0NEMjFAlRE/ISCAdi82HuaphAYxIdHjIdGfU+gBsTJsSGqjyCkRA/lm1is78OsyxjkRXMW39kJCKGTtjQkZCJVFaxxVfNb+EqSoztnWuyZAshQ6+3DCBbtnKvLYccZXtzobvgJ6wL3iTiyqL0+Ed5Rq3km4pNmCSZe5N6Mri6lsQfb0bSgpiPuY1w6V/o62ciaSEA/O52TDz8DJaoCklmGw93G0JHh4dn1y9gRsl6MiwOXu59Am6zlXZxGbibuJ8WJsS7uXO4Y+UsZLYnxT07vRuXZPdCkRqv/UmSRIY7lURzQuy4IsAJrUmzmijPPvtsHn74Ybp06RJbtm7dOu644w4+/vjjfVrAltrXAQ4grOlUlFfh27wGpeJXEoofR5NdbMh4iYsXOygMwGnpcFMXCZPFQlrXw7C73HvtflxrCXBNkaStY8b0cKzjCmaNyuo6tGZOVGlWTXSKb49qNG9Ihk/3sqlqM5EmJqWuVz4k4g2FunW5hEIh/tK8/BSuZFakiipja0CWFI5QXPRVnRztTiXHHk+wpgZt69hBWZJIiXOjfXoJkq+U6r5XIPcZxzPBfD4pWo0EfLxmFt0L5xFI609o8G2EtDBGoAbbpp9Q13+HNVjNG9lHMaPnWB7sfixJlmjwDOoa1y3/kfW+aoYmZHFXl6NRleiA+GRbEvIOzbWSBBtq8zlt7n8pCnq5on0fJCT+vWkJOgZHuFO4s8vReJrouapIMlmejK3j7gwR4IRWpVnDBHJzc8nOzq63LCsri/Xr1++TQh3szKpMUnICmimHEkkjVD0Dc+AvMite5PlOJ/GPFd34osjMER6D4clBKvI3YOR0x2G3iEl4Yev9HVAx4ZRNuB0u3B4LxUoVET2MLxzAF/YTioS2zmpSPyhJSKQ6kzBhbnbrr0NxkuZOobB6yy47wRgYeE0yjtRU9KIiektOeqtOrjGy+EvzYpdkOso2TKoJR2oKemI8YcnAGU5Br6omUl2FW7aT7Eyg8ohLCP1vMs7Vn1PVfgiTuh9JSlDnp02z6Vo4D01SqD3sXIxwdOLnv2SJtzN6oFll3lv0EWduWcEpx92BxRFHIBKt2VkVlbu7DOKqZT8wq2Iz00vWMz69CyV15dQFvaS5UnEqDgwDfLqfV3P/pCjoJcfmZlxqZ1RZppPDw0Nr57K4poSrl/3AuZndcSgmrLKCVVGxyApZVhdxJgsFNVsweRTscsvH3QnCgdSsADdgwAAmTZrE9ddfT1paGkVFRbz44ov0799/X5fvoGQY0bFx5vhksrRafOo1aEuvxub7k778yYJMM3MDPZhXcDhl1qNAklCKC4mkZuFxmPdqc2VboOsGJsWEBSsW2YrT6kKyRXtrhvQwESOMf2vQC0SCWFQzHrNnl02TOzIMgwRzPEF7iFJv+S63D0ZCKAlu3C4nWkUVgapKCAY5XI3WVGwuF5b0DHxWhUAkGpz8EsRlpJLToTtSlRc5UIecMwpp+YcoNXmYN/xMyBXHWXocp62fiwy8nXkES5QA4yNe3g1t4Y9INAefw92OckcKid4S6tbOQc1oR0iSSHYmocoyGHBTx/48vO53Xtm4mKNUD12T0ggYOpuq8km0J5BsT2Bx8Xo+2LwCgGs79IvuC/RxJ/Nyr5E8sHYuq+oqeH7DwgbPgUVWeKjbEI6ISyGvupAOnmxg300wIAh7W7OaKKuqqrj//vv5/vvviUQiqKrKqFGjuOuuu5qcieRA2R9NlDtSw3UEt6xHq15BuOQnwuULUEMbY+vDhsqW1HuwxPfFltkJuzsej9PS/LFLjWjtTZSN2dU1RZs1o0HPQEc2dm+OAkPS2VxXiD8cQDd0DMNAN4zYEANJkpCI5s2TJRnd0JElGZtmINX5olkG4uLQEzzU6eFYqiEJ8NjjSHekohgqkgRus0HRwuWE1s0k/Nv9GLZEykc+haloEXHznyNodjJmwMWUmbY3s9qQOd2czFmWFFLWz8T11wcEU49APvcVHOkZeMzxGIZBgbeICl8Vr25YwmfFa/BIKjc6OzAmrRNqfDwBczT/3c3Lf2RuZSEjkrKZ1HkgNpMVs2qi2h99rkO6xhdb1rLZX0tA1wjoEYKaRmU4wEZ/DTZZ5YnDhkV7fpqsHN6+K96q+r1jRROlcLBq0TABXdepqKggISGh0Xm/Dgb7O8BJElBVQLgqmuXXF9QoLSlEr1vC+qLfGGxeQKmeQEHG07RPbYeWkENCvIM4u3m3g9yhGOD2JkkG3dCiPTuJ9uzUDR0JCVmSAAlFkpGQ0QyNgBagKlBDSAuhaAYhdIJaGEVWMCkKFtWC2+LEY/bADuPJkpNdFK9Yi3dzAcGvJ2JUroMjLkFbOx3FW0Jt73+wJHswd/o34DUinOVpz/nWdFwRg0g4jOGvJGnmtWDoWM7/lMQjRxLZOsmyJkUorttC7YaN3Jj/Bwu16GDxo1U3NzpyyHYn8oc5zA25s7ErKm/2GUOyxRHtdao6KPGXUVpXXi8X4I50w+CxdX/wc3k+caqZp3seT7bNTbvkFBKk+p2mRIATDlbN/hqcm5vLt99+S3l5Offccw/r168nFArRvXv3fVm+g55hgOpORvPVoIcC2C0Kbk8KlcpQrOZB/JV3Dz1Nq1mx8SXmh29joN1FcbmESZGxmRufVFfYtwwdJBRUdnj+/z4iZuvnt4yCSTET54ojbITwa0EC4QA2kxVVMmGWTShSNO9eY7FCTUzGVFWF3udSQr/cDovfQsFAc2Xhbz+cXnYHUw/rQxVh4sw2VElG1Q3sER29uoZwxgDMBb9jrPgGvccgsEUDiYpCYq1GTXUNk+2dmREu5/VAAb9Havi/6uVcHEhnargMgMvbHU6K1Um8zY1TdWLokGJNwqqaKawpbjSzuyxJ3NrpKOq0MPOqtjBp5Sye7TmcdCPS8LkShINUs6ph33zzDRdccAHFxcVMnToViKZOf+yxx/Zl2VoNTTZjik9lW1Iij9OC1aKSbjNhbX8DXsPOMOs8fs/9hukrC7FLPvJLaomIe3Gthq4bKIYJp+wk2ZqEQ3ZikSxIhoyuNxwQHdtPVrBlZKC0G4yUdBjbIqd1wDW4HHE4srLQTDI2RSWkhfFFgtToIaoUDVtqKo6eZwEQWTudYEUFshxtPjUqK9BKq0iwunFumMnZ+fN425LDsWocfnReDhZQqAfpIFsZ57eQGII0R/L2IGxIxKlxtPe0w2ZqPB2VKsvc02UQPV2JlIb8TFo5i8pQy4ZyCMKB1KwA9/zzz/P222/zwAMPxDICdO/enVWrVu3TwrUWhgGGzYPJGYekqEiyRLLHhqrI2Kyp+FKuAuDWuP/wwep1vPHnBhyGj/Jqb5OJ+oSDV0tbliWnG2tSAqa+V4CkIGcNQc0YRGZWFyxJKfWaCSVJis5OEt+O1Ph2OLqPRHKkYXi3EFj5I0TCSN5afAWF6JqOeeUXOJe/i+uv9+n64y08sWkej8lJJEomVCSus7ZD8weRt5SjbSpADvmR0ZHlaFOsTbKR42lHosOD02LHbrZiM1mwqGbMqgmrEh2Y3sEeR36gliv++Ia6rZ1qBOFg16wmyoqKith0WdtmN4lmXxafztvohoyc2B6zJ4ykhbFqIXDVUV5agV8ajDewFEftTJ5LeJpxa5/AH1zLVYc5qIukE5eciCbtu8mZhQNLN8Ccmka4ZgDy+I/A6sHscmJKzSBJVZBlheLaUiyqmRRHIk7FiWFIaBqYk5JQu5xMePEUwiunoh1xEsHyimiC1rxZhBe/Dkho8Z1QKtfhWPsVJ+V+y9DsYRR1HIVHdWIzW7DLNgLlFYSqa5BVFdliRrHZUGxWzBYL7ZyZGEa0EULXo3cmdTQK64rBX8Oj3Y/lxr9+5q/qUn4oXsup6S2fCF0Q9rdmBbiePXsybdo0xo8fH1s2Y8aMZmfzPlTohgyyJfpjArs9EZ85CUndSLV2MebASjqRz32eKdy26Wq6OOoY6stFqyklLj4OxZEAJiuGaqGZ452FVsIwmbGmpeINhZFVFWtWFrqsgA4JpgQc8XYskhljh3Q8AJLdgaXX6YSXvIWW/xt1uSuQLB70ijWE5jwMgNr3n1h7nUtd0Xz05R9h3rIA18YfcG76mUDHUTj7TYw1TeqRCHokAoEAVEeHJEiqgjMnB1zure+7aE9SBZksVwbS1ptuT/c8nsWBckakdN5/T5wg7IFm9aLMzc3l0ksvJSsri8WLFzNw4EA2bNjAm2++SU5Ozn4oZvPt716Uu6IZBrU1ddTkrSFSsYLkgluRjBDf+QfypX84F/XoS0e3mdREOxZVRlZNyBYbiiMOLA4MxYpu1K8pH+hr2hcOhWtSMAhsXI/J40GKT2xWjV2SQK8oo+LtC9AL5qL2uwo1ZyTBby/H8JWidByDadCkWItKxAhTU7oc/voYy+b/IWGA2Y3p8P9D6ToOSW78O61iteDo1BG9kftxhqxTUFtEpb+a9KQEUuR00YtSaBV2GeAMw2Dz5s3Ex8cza9YsCgsLSU9P57jjjsPhOPhmNjjYAhyAJEuEaispWrsSqeQ7PKUvIW2dEbBc9yDHDUNKHElSRvd62QckRUF1xkN8u3ofhgfDNe1th8o1yeEgmC20pH+RrIWonvkGgR8nIbmzweTAKF+JnNwb88inkZT605Vtm+y6rngZjmXvoW9ZFF3uzsbU70rktCMbpOYBMMe5sXbIQadh795tQc7qUESAE1qNZtXgjjjiCBYuXHjQjn3b0cEY4CD6IWDUbKFwwwZ81YUoVb/gr/iZdkphbJuwtQumtNFYUo9HMkXzhkmKgiWtExF1+5eJg+Wa9iZxTU2TZYlQ3noqXz0eAhUASI40LCe+imSNx5qciKyoBCoq0EPbJ4GWZAld09E3zyG88GWM2oLtBzW7kGxJSLYEJHsSSscxKGn9sKenoaSlN1q7NGSdgFKHQ4sTAU5oFZp1D65Hjx5s2LCBTp067evytFm6biC7ksnI9FNpVqgwn0Wh7TTuWL6Gk60/cZpzDpbAWti4lsCmV1ESjkZJGYUcfxSR6lLkZIe4L3eI0nUDc1IKaueTiCx/D1Qb5uMeQbLGY4n3YM7IBEXFmZhApLyCQHkFejiMoRtIkoTSbghyxkAiqz9HW/MFhrcEQrUYoVqM6g0AaBt/wjLqefyyjMNmQ4rzNAhyki6T7kmhpkr0ohRah2bV4J555hm++uorTjvtNNLS0ur1njzzzDP3aQFb6mCtwW2jGiHCxbnU1XopqfAzq1Rj0l9gk4JM6fInfYxfsPiXxpowJVs2lr4vYs3qQ8QU/ZZ8sF3T3iCuaedkCXx/zcP382SUTiehpPXFHOfC1r49mmzavp0sQdBPpLycQEVlvRrdNoahQ7AGw1+G4S9H2/AD2oaZYE/GeuK/UePScXTuiG5umClcZBMQWpNmBbiLLrqo8Z0liXfeeWevF2pPHOwBDkAN1xIqyaOmzkdZpZ8pG3Xe3ASqBDd2hvHJldjrfsVZ+x1yuAQl4wxsh09CTuqIbhyc17SnxDU1Q20Vdes3gAGqw469Qwd0tfF0QbIsQSiIVl1NsKKciC/Q5AA+Q48Q+uFG9JKlW+/rPYMlPh5rZhaYTKCaYtm9RYATWpMWzUXZGrSGACdJErIWQAr7qS4vp6S4jKdXBPh4c7Tc49Lh+k5gC28gueAWkGQsfd/A3vk4Iib3QXlNe0pc067JuoZvzWoMJBwdO6CbG5+BZEeSBLJhoHvrCFdUEKqpxdA0jL+1dxv+cgJf/xP8ZSjdTsM84AYkRUFWVRSrGdXhQLHZ8WQkU1kbql8uEeCEg1SLp2Q3ts68vk1r6HhysDEMA022gMWCu108Wlw6d6RU03NVMQ8vrGJaEWzwwoOHdcDhGoWj9jt8q59F8nTHlinSlRyqDEXFkpyEYnc0K7hBtNKmIYHDhdnlxhwMYGgR0PRooAuH0cNhguUqlqEPEPz+erTVXxBJ6I7aaQyapqEFg4Sqa0GScHvsgGmX5xWEg0GzAlxxcTEPPPAA8+fPp6ampt66lStX7pOCHSo0zcDttOMNwNBOVrISE5g0axNLazQmLoLJ3c9jiDwH1buEkpXTiLf+H1an/UAXWzgADMNATUxGlySanel1B7pugMkS/dlKAkyyhGK34dV1TANuIPzHE4T/eArZ0wE5sdveuwBB2M+aVf269957MZlMvP3229jtdr744guGDx/O/fffv6/Ld2gwIC3Rjttppp3TzgvDu9AryU5JEC5b6uLj8HkAOEqnUJ63lvyiCiJtq2VZaCYNaa9P6abrBlJcPLb0dNSup6B0PgX0EMEfbiT4w02E/nyayMpP0Ap+J1xZsOsDCsJBoln34AYOHMjPP/+M3W6nf//+zJ8/n6qqKs4991y+/fbb/VHOZmsN9+CaJEFpdYAtpV78YY3Xl21hxoYKDENjWsqtHGbeyCbH2WQOvhef7CIzyblHiVMPJq3qdWqm1nZNMgbhwgJ8RQWEfp6EvmVBw40kmbiJX2Duevz2/cQ9OOEg1awmSlmWUdXopm63m4qKCpxOJ8XFxfu0cIccA1LirFhNCvnFdVx9RAZndEliWm4JD+VfygfJd5NaN5W7vxnJFYOPwmU347abxCTNwl6hI2FKT8caDsGIpzDqCjFq8jFq8tFr8jBqN2MyG8ju9ANdVEFolmYFuD59+vDrr79ywgknMGTIEG644QasViu9evXa1+U75BgGuO0mOmXFkVdcQxpwTb9sQj2cLFr4HX2l2QwLTuH2n5J47USwWzNRJNHRR9g7dEnBkpmFFgoRkTLBlQmZR0dXShKZ/XpSrYlOJkLr0KwmypqaGnRdx+PxEAgEmDJlCj6fj3/84x+kpKTsj3I2W6tuovwb3TDYXOalqiaI3SJhLV2Aa+X/oRgBbq+4kqS0E7hhcHvis9qj6607dVFrfp2a0pqvSQ768W3ciB4Oo2tatFNLEwFONFEKBysxDu5gJ4EvqFFeHcAU8WIsfxpXyesAvFRzJl07nseYw7NwpecQafmoj4NGq3+dGtGar0mSQNIiEA5jRCLooSB6MEhidjpVofotBiLACQerZn0iPvfcc02uu/766/daYYRGGGA3KzhTnYQ0OyH1GvwrzJjyX+Fq96fM3FzMGse1HIaGLTkLzeQQ9+SEPWYYYMgqWFSwgORwoUoSpjgntNKgLRx6mhXgtmzZUu9xaWkp8+bNY+TIkfukUEJDum6gShLWlAwSrJewXkpCzZvMKOtvrN5QRrH5DtK0ICanB8WdjKHa69VkBWFPtbHGHuEQ0KwA9+ijjzZYNmvWLGbMmLHXCyTsXMSQcaVkkZRzPEtDccQXPkQ3ZSVV626lNHILnvTemL3VqK5EVFcSmmwRH0yCIBySdrv73ZAhQ/jhhx/2ZlmEZlKd8djTszmsc19muyfzV6gDHqMI14abCCy8Gv+m6YTL8wkVrUX2ldfL/iAIgnCoaFYNLj8/v95jv9/P9OnTSU8X42EOFM0chzu7C+N1mTt+e5ButR9xpuMn3IE1sOFJ/HmvoqaOQm93FtYOxxJRGqY+EQRBaMuaFeBOOOEEJEmKNXXZbDZ69OjBY4891uwTbdiwgUmTJlFVVYXH42Hy5Mnk5OTU22b27Nk8/fTTrFmzhosuuojbbrut+VdyiDEMiMg2Ejt05Y4I3Dz7Yp4qOp+TbXO40jOTDqxFK/wcrfg7ZNO7mNoPQUM50MUWBEHYb/bbMIEJEyZwxhlnMG7cOKZNm8Znn33WIJfcpk2b8Pl8fPvtt4RCod0KcG1umEAj/n5Nuh6mdEMuHy4r5tm1Bn4dhjnW80TyOyRGliG5euI67gPwtDtoO54cCq9TWyDywQmtyX6ZAqO8vJwVK1YwduxYAMaOHcuKFSuoqKiot1379u3p0aNHbFowoXkUxYQ7qyPn9u/EB8fH08Mt8au3Iyfm/YtaEjBq/8K35BnkYM2uDyYIgtBGNCuSDBs2rFkdFX755ZdGlxcVFZGamoqiRJvIFEUhJSWFoqIiEhISml/aZmjsm2RyctvLofb3a9J1A8lkpqMriQ9ygrw8L583lsJVpdfybvL9hPPewWh3LIl9zkJuZi6x/e1QeJ3agrZ4TULb1KwAN2HCBKZOncpFF11ERkYGhYWFvPfee4wfP/6gm4/yUGyi3MamSEgOM4VlIc7qkkaWy8ED/4PXa09loutLKv+8A83ZDTmlB39L6HzAHUqvU2smmiiF1qRZAe6LL75gypQppKamxpYNHTqUyy67jEsuuWSX+6enp1NcXIymaSiKgqZplJSUiF6Ye5lhGFhUmQ7pbiprQ5hVhTsHGjz+x/kMsiyjFxuo/PNeEoe/imRLEDOeCILQpjXrHlxJSQl2e/0s0na7vdnpchITE+nRowfTp08HYPr06fTo0WOvN08KWxmQ4DLTuV0cpx6Wwu3HdOLmyhvw62ZMlT9SMn8KRtkG1HANiqQjhskJgtAWNSvADR8+nCuvvJI5c+aQm5vL7Nmzufrqqxk+fHizT3Tffffx3nvvMXr0aN57771YNvCJEyeybNkyAObPn8/QoUN56623+Oijjxg6dCi//fbbblyWYBhgUmTapTg5rVc6Fx89mEeqL46u3PQ8xUvfo2bzGiJb1iB7S1H1oBgQLghCm9KsYQLBYJAXXniBb7/9lpKSEpKTkznxxBO55pprsFoPrg4Lh/I9uKZIskRZdYCvlm7CufJGxth+ByCkpmFqdz7W9FEoFjvmlA5EVPsujrZviNepdRD34ITWRKTLaYV255okCap9YaYvWMmfSz7lMsdn5Jiik2gb5lRM2edh7XQ+ckqnA9IBRbxOrYMIcEJr0qwmyt9//z02XVdpaSm33XYbt99+O6Wlpfu0cMLeYxgQZzdz+lE9uHDI2dzkf54by69nXTgTKVRMZN2z+Jc+iBysO9BFFQRB2CuaFeDuv//+2Bi2xx57jEgkgiRJ3H333fu0cMLeZRgGNouJvod14T+jMklLP56xJc9wQ/kNhA0Vregrgpt+QBa34gRBaAOaNUyguLiYjIwMIpEIs2fP5qeffsJkMnHsscfu6/IJe5lhgGKykNapG7cmpTJ0dT53/HEsr9fmcZX7cwLLH8XcbihYRA9XQRBat2bV4JxOJ2VlZcybN49OnTrhcDgAiEQi+7Rwwr4jIeH2xDOkb08+GN+D9wNnUhBJwvCuw7/8FWTpIBsJLgiC0ELNCnAXXnghZ555JjfffDMXXHABAAsXLqRjx477tHDCvmUYYDMpdMxMYUL/rjxYFR20H1r7KkZV7gEunSAIwp5pdi/KDRs2oCgK2dnZscehUIhu3brt0wK2lOhFuXtCmsHAV//HA5b7ON62ECVtDM4R7+y3FDvidWodRC9KoTVpdjaBDh06xILbtscHW3ATdp9Zkbj12I7cX3UpQcOMtuVbInkzxSwngiC0WvslXY7QOpzTOw2bpyMv15wGgG/hXUha4ACXShAEYfeIACfEyJLEXcd15LXa8eRH0jC8GwksegJZalNzAQiCcIgQAU6oZ1SnRHqnJ3FP5WUAhFY9T2jpiygiyAmC0MqIACfUI0kS943sxKxgX16uOxvQCSy9D//8B5HRDnTxBEEQmk0EOKGBozLiGNkxgaeqzuEd7XIMJEKrnqP6t5sw9LDIOiAIQqsgApzQqHuGd0KVJO4vGsUN5TcQMlSkvPf47O3zuOaLRdSFRG1OEISDmwhwQqO6JTr4ZkI/Luudhj95LPf678arWxlj+ZkTym7llumLAHFfThCEg1ez5qIUDk190l10HtmFWl+IULgzRmkXQouvYLhtAZGKu3lj3stcNqDDgS6mIAhCo0QNTmiSYYDdrJAWb6N9qou0w0YTd8KnhGQXo2x/Yl18MwsKqw50MQVBEBolApywS4ZBbPozJflIEk74iBBWxtt/Yek311PhDx7gEgqCIDQkApzQcklH4z7ubcKGylnW6Xz9+a3o4nacIAgHGRHghN2iZp6A1v9FNENmPO8xc8aDB7pIgiAI9YgAJ+y2pO5nUdDlIQCGVD/Lj99NJiKqcoIgHCREgBP2yGEDL+ePxJsAGFj2OFP+cw2/rC8/wKUSBEEQAU7YC0aMuZPcnPvRDJkLzB+x7vur+Mcni9hU7T/QRRME4RAmApywV/QZfA3WIVOIYOZc5w+Mr76dEa//xrNzN6E3L6euIAjCXiUCnLDXWHJOxTPqMwzVzQm2ebyRcD8v/7aUiz9fjjcUOdDFEwThECMCnLB3JR+D88RvkGxp9Les4vPUO1izcQmj/rOA9ZWiyVIQhP1HBDhhr5Pc3bGPmYkc150ctZCpqZNo75vFqLfn80Ou6IAiCML+IQKcsE9I9kxsY2Ziyj4Fh+TntaTH+D/Lh1z06RKe/t9GNDGcQBCEfUwEOGGfkVQH5iFvYe17NyBxQ9x/eTnxcV6avYIzP1pCQU3gQBdREIQ2TAQ4YZ+SJAn1sBuwDf8vkina+WRq2h2sL8zl+Dfn89XqkgNdREEQ2igR4IT9Qkkfgf2kH5HdXemg5DMt/R7iIpu5bOoKrpuxktqA6GUpCMLeJQKcsN9Izo7YRn2NkngkSWzh63b3cZilgP8uL6bP07/w3NxNrCytwxDj5gRB2Av2W4DbsGED55xzDqNHj+acc85h48aNDbbRNI3777+fkSNHcsIJJ/DJJ5/sr+IJ+4lkicc64jOU1MHYIqVMy7iPU1KL2Fjh55FZGzjuzfkM+PcfTJq5hh9zyynzhUTAEwRht0jGfvr0mDBhAmeccQbjxo1j2rRpfPbZZ7zzzjv1tpk6dSpfffUVr7/+OlVVVYwfP54PPviArKysZp+nvLwulrsMIDnZRWlp7V67joNBW7gmI+In8NvFaIXfg8nF6l5v8Z8N6fyQW065P1xvW6dZob3HSnuPjfZxVlKdFhxmJfpjiv5vU2VUWUKVJWRZQpGivyuShCyDIm37XUKWQCJ6fzD6f/3HwPb/JQlJYus+0X1j19DEtW3bJDnZRVlZbWw/SZKa2KP1aOy9J8sSiYnOA1QiQWjafglw5eXljB49mj/++ANFUdA0jYEDBzJz5kwSEhJi2/3zn//k9NNPZ8yYMQA88MADZGRkcNlll7XgXCLAtRaGFiL4vyuI5E0D2YxkSQQgrBsEIjrBiE5EN9rcVF+NhbmWXGGjYXI/xU4vTtRBr9OzS//YMhHghIOVuj9OUlRURGpqKoqiAKAoCikpKRQVFdULcEVFRWRkZMQep6ens2XLlhadq7E/tORk126W/ODVVq7JGP8R5T9dQ+3yKRj+IiD6pnQCTglQDmTphL+LGFVUm2razPtPaNv2S4Dbn0QNrhXq8zjZxzxAeWnbmuUkIcFJRUVd7LHBzmtqf28e3XG/xn6PLdtl9c+IbWM0cvyWyEpNpcprqvf+EzU44WC1XwJceno6xcXFaJoWa6IsKSkhPT29wXaFhYUcfvjhQMMandB2KfZkJLv1QBdjr1JdLqTA9kCwu4HlYLpzZ7K7wNuGvlwJbdp+6UWZmJhIjx49mD59OgDTp0+nR48e9ZonAcaMGcMnn3yCrutUVFTwww8/MHr06P1RREEQBKGN2W/DBO677z7ee+89Ro8ezXvvvcf9998PwMSJE1m2bBkA48aNIysri1GjRnH22Wdz9dVX065du/1VREEQBKEN2W/DBPYXcQ+udRLX1DqIYQJCayJmMhEEQRDaJBHgBEEQhDZJBDhBEAShTWpz4+BkuWGn6saWtXbimlqHQ+Ga2uI1Cm1Dm+tkIgiCIAggmigFQRCENkoEOEEQBKFNEgFOEARBaJNEgBMEQRDaJBHgBEEQhDZJBDhBEAShTRIBThAEQWiTRIATBEEQ2iQR4ARBEIQ2SQQ4QRAEoU1q0wFuw4YNnHPOOYwePZpzzjmHjRs3HugitdjkyZMZPnw43bp1Y82aNbHlrfXaKisrmThxIqNHj+aUU07hmmuuoaKiAoDFixdz6qmnMnr0aC655BLKy8sPcGmb76qrruLUU09l/PjxnH/++axcuRJova/Tjl588cV677/W/DoJhxijDbvooouMqVOnGoZhGFOnTjUuuuiiA1yilps3b55RWFhoHH/88cbq1atjy1vrtVVWVhq///577PFjjz1m3H777YamacbIkSONefPmGYZhGC+99JIxadKkA1XMFqupqYn9/v333xvjx483DKP1vk7bLF++3Lj00ktj77/W/joJh5Y2W4MrLy9nxYoVjB07FoCxY8eyYsWKWG2htejfvz/p6en1lrXma/N4PAwcODD2+IgjjqCwsJDly5djsVjo378/AOeeey7ffvvtgSpmi7lcrtjvdXV1SJLUql8ngFAoxAMPPMB9990XW9baXyfh0NLm0uVsU1RURGpqKoqiAKAoCikpKRQVFZGQkHCAS7dn2sq16brOhx9+yPDhwykqKiIjIyO2LiEhAV3XqaqqwuPxHLhCtsCdd97JnDlzMAyDN954o9W/Ts899xynnnoqWVlZsWVt4XUSDh1ttgYnHPwefPBB7HY7F1544YEuyl7x8MMP88svv3DjjTfy+OOPH+ji7JFFixaxfPlyzj///ANdFEHYbW02wKWnp1NcXIymaQBomkZJSUmD5r7WqC1c2+TJk9m0aRPPPvsssiyTnp5OYWFhbH1FRQWyLLfKWsH48eP5448/SEtLa7Wv07x588jNzWXEiBEMHz6cLVu2cOmll7Jp06Y28zoJbV+bDXCJiYn06NGD6dOnAzB9+nR69OjRKpqGdqW1X9vTTz/N8uXLeemllzCbzQD06tWLQCDA/PnzAfjoo48YM2bMgSxms3m9XoqKimKPf/rpJ+Li4lr16/TPf/6T2bNn89NPP/HTTz+RlpbGlClTuOyyy1rt6yQcetp0Ru/c3FwmTZpETU0NbrebyZMn07FjxwNdrBZ56KGHmDlzJmVlZcTHx+PxeJgxY0arvba1a9cyduxYcnJysFqtAGRlZfHSSy+xcOFC7r33XoLBIJmZmTzxxBMkJSUd4BLvWllZGVdddRV+vx9ZlomLi+O2226jZ8+erfZ1+rvhw4fz6quv0rVr11b7OgmHnjYd4ARBEIRDV5ttohQEQRAObSLACYIgCG2SCHCCIAhCmyQCnCAIgtAmiQAnCIIgtEkiwB2kTj75ZP74448DXQxhJz7//HPOO++8A10MQRCaIALcQWrGjBn1JiU+0DZv3ky3bt2IRCIH1bEEQRCaIgKcIAiC0CaJAHeQGj58OP/73/8AeOGFF7j++uu59dZb6du3LyeffDLLli1rcl9N03j11VcZOXIkffv25fTTT49NJbVw4ULOOOMMjjzySM444wwWLlwY2++iiy7i2Wef5dxzz6Vv375ccsklsdQu2yZEHjBgAH379mXRokUAfPrpp5x44okMGDCASy+9lIKCAgBee+01zjrrrFgt7YMPPuDkk08mGAw2eawd6brOa6+9xsiRIxk4cCDXX389VVVVANx7771ce+21sW2feOIJ/vGPf2AYBtXV1Vx++eUcffTRDBgwgMsvv5wtW7bUu8Znnnkmdo1XXHEFlZWV/Otf/6Jfv36cccYZbN68ObZ9t27deOeddxgxYgQDBw5k8uTJ6Lre6POem5vLxRdfzFFHHcXo0aP5+uuvY+t+/fVXTjrpJPr27cuxxx7LlClTmnz9BEHYSw5kMjqhaccff7wxZ84cwzAM4/nnnzd69epl/PLLL0YkEjGefPJJ46yzzmpy39dff90YO3askZuba+i6bqxcudKoqKgwKisrjf79+xtffPGFEQ6Hja+++sro37+/UVFRYRiGYVx44YXGiBEjjPXr1xt+v9+48MILjSeeeMIwDMPIz883unbtaoTD4dh5vv/+e2PkyJHGunXrjHA4bLz00kvGOeecYxiGYWiaZpx//vnG888/b2zYsMHo37+/8ddffzV5rL97++23jbPOOssoKioygsGgcffddxs33nijYRiG4fP5jFGjRhmfffaZMW/ePOOoo44yioqKDMMwjIqKCuPbb781fD6fUVtba1x77bXGlVdeGTvuhRdeaIwcOdLYtGmTUVNTY5x44onGqFGjjDlz5hjhcNi45ZZb6iXw7Nq1q3HhhRcalZWVRkFBgTFq1Cjj448/NgzDMD777DPj3HPPNQzDMLxerzF06FDj008/NcLhsPHXX38ZRx11lLF27VrDMAxj8ODBsSShVVVVxvLly3f+BhAEYY+JGlwrceSRRzJs2DAURWHcuHGsWrWqyW0/+eQTrr/+ejp27IgkSXTv3p34+Hh++eUX2rdvz/jx41FVlbFjx9KxY0d+/vnn2L6nn346HTp0wGq1MmbMGFauXNnkeT766CP++c9/0qlTJ1RV5YorrmDlypUUFBQgyzKTJ0/m3Xff5corr+Syyy7jsMMOa/b1fvTRR9x4442kpaVhNpu55ppr+O6774hEIthsNh5//HEee+wxbrnlFu6++27S0tIAiI+PZ/To0dhsNpxOJ1deeSXz5s2rd+zTTz+d7OxsXC4XQ4cOpV27dhxzzDGoqsqYMWNYsWJFve0nTpyIx+MhIyODCRMmxCZP3tEvv/xCZmYmZ5xxBqqqcthhhzF69OhYMlBVVVm3bh11dXXExcXRs2fPZj8XgiDsnjab8LSt2XEyW6vVSjAYJBKJ8PXXX3PvvfcC0SD4xhtvsGXLFrKzsxsco6SkpF6ySoCMjAyKi4tjj5OTk2O/22w2fD5fk2UqLCzkkUceYfLkybFlhmFQXFxMZmYmWVlZDBw4kF9//ZULLrigRddbWFjI1VdfjSxv/w4myzLl5eWkpqbSp08fsrKyqKio4MQTT4xt4/f7efTRR/ntt9+orq4GorP9a5oWSzy643NpsVgaPLd/v+Yd09tkZmZSUlLSoLwFBQUsXbo0lukaok3Fp556KgDPP/88r7zyCk899RTdunXjX//6F3379m3RcyIIQsuIANfKnXrqqbEP0W3S0tLIy8uja9eu9ZanpKTUy+UF0QzNxx577C7PI0lSg2Xp6elcccUVDc6/zS+//MKiRYsYNGgQjz/+OA888ECTx/q7tLQ0HnnkEY488shG17///vuEw2FSUlJ44403uPzyywF488032bBhAx9//DHJycmsXLmS8ePHY+zBnOJFRUV06dIFiAbelJSUBtukp6czYMAA3nrrrUaPcfjhh/PKK68QDod5//33ueGGG/j11193u0yCIOyaaKJsg8466yyee+45Nm7ciGEYrFq1isrKSoYNG8bGjRv56quvYrW/devWcdxxx+3ymAkJCciyTH5+fmzZueeey2uvvcbatWsBqK2t5ZtvvgGiiTDvuusuHn74YR577DF++umn2Ad6Y8f6u/POO49nn3021mmloqKCH374AYANGzbw7LPP8sQTT/D444/zxhtvxJpSvV4vFosFt9tNVVUVL774YsufwL+ZMmUK1dXVFBUV8c4773DSSSc12Oa4445j48aNTJ06lXA4TDgcZunSpeTm5hIKhfjyyy+pra3FZDLhcDjq1UwFQdg3xF9ZG3TxxRdz4okncskll9CvXz/uvPNOgsEg8fHxvPrqq7z11lsMHDiQN954g1dffbVZCThtNhtXXHEF5513Hv3792fx4sWccMIJXHbZZdx0003069ePsWPHMmvWLADuuecehg8fzrBhw4iPj+fhhx/mzjvvpLKystFj/d2ECRMYPnw4l1xyCX379uXss89m6dKlRCIRbrnlFiZOnEj37t3Jycnhxhtv5NZbbyUUCvGPf/yDYDDI0UcfzTnnnNOs2umujBgxgtNPP53x48fz/+3doQ2EUBBF0VcAPSDxaEgINdAVtIDHYhCUhtoCNtlks2KTyTl6xLib+eZP05RlWd5mmqbJvu+5rivjOGYYhmzblud5kiTneWae5/R9n+M4sq7rz3sBn/kPDj7oui73fadt23+vAnzJBQdASQIHQEmeKAEoyQUHQEkCB0BJAgdASQIHQEkCB0BJL1HrNYo8Jm55AAAAAElFTkSuQmCC\n", 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", 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\n", 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", 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CUbkeI1iF5vejuOL2+BAhEDTElMM775Nn1tLY7Y7av9977228Xg+vvvomVquVhx++n3B4Z5SzxbJz9GjHECfADTfczIYN61myZBF33HELZ599HpMmnbZftsSut/NBU5aVv7yOXV+SJBISEurNYe7pvB2FAyZ+d911F3fddVe97WvXrt3tMZ999lnt32+88UZLmIUBOBpRkscwDAYlZmOVFQrDfipCAbrskulFkgDJIKiFsCo2JP3gWb+kSwq4E7DFJUAoiFpTQ6SyEjUEctpA9OIlaMVLiKZkY0rP7AD+sKAj4vV6SUlJwWq1UlpayoIF33Paaafv9bgtW/Lo0aMnPXr0JBAI8Oefq+qJn9PppGfP3nzxxSwmTJjI5s2b2LBhHQMGDNovmzt37oLNZmPu3NmcdFLMw8vL20xqaipO597nYg9mWn3Ys9XYxT1xmBojfuC2OOnpTGSlt5w1vgr6qSEcVgdBLYgv7Kcm5CGqq3RN6IRVsrek9a2CbgBWO0q6A2dqKv7164lmDkcvXhJb79fzxNi8n3nP86cCQXvkzDPP4s47b+Wcc84gLS2NQw89rFHHPffcTLZuzUdRFFwuN3feeXeD7aZOfYBp0x7kvffeQVFM3HPP/bXzffuKyWTiscdmMGPGdN5++010XScpKYkHH5y2X+c9GJCMPY31HWTsGu3p//pRAvMe4KXOI/jfIafy3bFT9hq8ohLlpqX/5cPCtVyQ04/Luw5FQiKkRtC3L5kA6JSQRYIpoSW7slsOVBShJEmoRQX4Vy8k/MWlSI5UrKd+hLtrF0hI3vsJmkBHiYxs7zQ12nPVqtVkZdXPuSgQNCeFhVvo379fve1tL1b/QPEXz68xjwBm2czA7Zle1vgqCUbDBKKhOsIH4A37Dvr0ZzsWwEuJ3cGagBEow/BsIVJVRWO6LrJaCQSC1qTDi9+OgBcaM1NlSAxL6gTExG93TnMwGkI11Ab3HUxIVhuKxVon1ZnqD0K4/rKIeseGg8hGy4S2CwQCwd7ouOK3I9qTPdfyq3OEYdArLo0EsxWPGqE43HC28KgWJaJ3gMoPVhsmuxV5u/hpRYvRVRVtLwVuZVkiWl6BVl0lPECBQNAqdGDxi7FD/Bo782lRrPR1xea01vgarkChGwYBNXDQ39gNw8AcF7fT8ytZjqFFiVRVIf1lKLgO4RDh6mpCxSVI0Q7wkCAQCNocHVf8dlG7PdXy+ysW2UK/uBSgrvhFdZ3/K1rP9Su/YXlNKd6w/6Av12IYoDicyK40pPhcUIPo5atQAyEINjz0KUmg1dSgR6Jo4QjRsrKD/iFBIBC0PTqu+NUOe0p7rOX3V2RkhiZkATHx0w2Db8vzuXjFPJ7fspzVvgo+LFpLSA0TNTqAV2O1IZvNsWwvxOb9DF3fbY0/SdMIV1TUvg5VVCAFGh4+FggEgpai44pfbcALOPdSy29XdN3g0JRYePZ6fxXXrPwfD234leKwn062WGqjPzxlBCJhIlq0+e1ua5jNmJyO2qFPrWgRAJGqaiS9ftCP7vOihkK1rw1VI1RcjMwehkkFAoGgmem44lcb3Sk1OuBlB+m2eHJsbqKGznp/FclmGzd2G8bLh5xAZ7ubkK6xxl+JP3rwz/vpemzeT04/BGQTRsVajHANaiiMEQzUaStjECkvqxdYG/F40T01B/17JWjbTJo0no0bN7ToNWbP/pz8/C273b9kySIuvvgCzj//LCZPPo0pUy5H18WDYUvQ4TO8NCXacwdm2cLEzJ58XLCGv6V347SMntiU2Fs5JC6d/KCX5TWljEjuRKo9pVGrKNozks2ObHUhpx0SS3W25XtMvU4hWl2NxRVXmzzACPiI+gL1T2AYhIqKcbpcaPLBnytV0HGZM2cWCQkJdO5cf3G/qqrcfvu/ePbZl+jZsxcAa9euOWCxA5qmNalEXHun44rfdgwkHE0IeIFYkusLuhzCpPT6xXQHx6fxWckGlntKiWhRosbBn/xastlRLGaUbuPQi5egbpobE78aD5aMCMhmJAmiFZUYuo6ha6h/foCc1Kt2uFQNhohWVKCkZe4xwbjg4CS06hlCy6eB6mv+k5tc2Abfiq3/Nft0+E8/LeT1118lEgljNpu54YZ/MmDAICoqyndb36+hGn5FRQWsWbOaJ554jBdffI5rr72Rww4bUXudQCBAIBAgKWlnhqTevfvU/r18+VIee+wRAIYMGcrChQt4/PGn6d69B4cfPpRvvlmIwxFLUr3r67vvvpP8/Dyi0Sg5OZ248857iIuLY8mSxTzxxKP06dOXdevWcsUVV9OpU+cGa/+FQsHtZZQ2YjKZ6NIlt92nSOvw4gc0KeAFYiH+bosTT6j+D3VQXCoSsNpbgS8aJqyFMR3s3owcq/mndB5NdNEMjPLV6DV5kJCL4feDOwEpHCTi8QCgbfkWddmLYLJjO/lNJGcaAKHSclwJCRhmWyt2RtAahFc92zLCB6D6CK96dp/Eb9u2rbz22ss89dSzOJ0uNm3ayI03Xstnn32By+XebX2/3dXwmzNnNueeez5HHnl0vWvFxcUxadJpnHHGJIYMGcohhwxm3LiTSE/PIBKJ8O9/38699z7IsGHD+frr+Xz88YeN6sNNN91MQkIsR+gLLzzLW2+9wZQp1wGwefOmWjtVVeWSSy5osPZfXt5m/H4f77//XwA823/L7ZkOK37GLgEv7iYEvMSOBbvJjoRUr6ZfnMlCD2cC6/3VrPKW0ykuHZfdfVB7M7F5PzehMhtKl2PRNsxB2zgXeehVRKqqsCcmESmtidX8MwzUP7f/aNUgkcUzsY6+P3aeaBS1pgYl1SbKInUwrP2ntKjnZ+0/ZZ8O/eWXnyko2MaVV15au03TVCoqKnA4HLut79eYGn4NcfPNt3H22eexePEifv75R/7zn9d5/fW3CYdDWK02hg2LjZQcf/wJPPLIA4065xdfzOHLL79AVVWCwSCdO+8sHdWpU2cGDjwEgK1b83db+69nz17k5eXx2GMPM3TocI444shGXbst02HFb9elDk2J9tyBWTZjUhSiWv2IxsFxaaz3V7OsppQjU3PBYRz0837YbMhmE0r3v6FtmIO6aT6mwZcR9fmx+ryEK2JrIvXSFRiVa8EaD1oEfesCtG0/oeSMAiBSXoEjKQVD7jhzDwKw9b9mn4clWxaDww8fxT333F9vz2uvvbzb+n77U8MvOzuH7OwcJk48lRtuuIaFCxc0WEFi17lARVEwtieW2LXG4PLlS/m///uIl19+g8TERL78ci6ffvp/tfvt9p3VZwzD2GPtv3ff/YjFi3/j559/5Pnnn+Gddz7Eam2/FVw6brTnLgEvTfX8ICZ+lr/UAZQkiQS7m8OTY/k/l3tKiaiRDpHqTLLYUKwW5JT+SHGdIVSJXvgbeiRKsKAAbfsPUl39AQCmXqdiPuRiAKKLnsJQY4vi1XAY3df+h1QEBweHHTaSX375iU2bNtZuW716FdBwfb8d7KjhN3nyOZx44t/488/YMU6nE5+vYe82EAjw668/144Seb1eiooKycrKokuXXMLhMMuXLwXgm2++xuvdWUEjJ6dTrV3z58+t3e71enG5XMTHxxOJRJg16zN2x661/3awY7iztLQERZEZPfpYbrjhn1RXV7X7oc+O6/ntMq7m2gfxw5BwWpz4I7Gbts1kIcOdRpw5jqMlE4r0Det8ldREQoT1MGa5afOK7Q0DCXNcHFFfAKXbiajLX0LdOBclZxRRb2wRu16zBb3gJ1AsmHpPAosbddOXGFUbUP94E/OQK8CASFk5trgEdMTaB8GB5dprr6oT8fjOOx9y770P8OCDUwmHw0SjUQYNGky/fv33WN9vdzX8Jk06jaeffpJ33nmzXsCLYRh8/PGHPP74o1gsFjRNY9y4kzjmmDEA3H//Q3UCXjIyMmqPvf76m5g27UGcThfHHXdC7faRI0cxb94XnHnmJOLjExg8eGitSP6VPdX+27BhA8899zQAuq5zwQUXk5qaur9vd6vSYev5+WbfTfC7GTzZ7Sj+feWn2PchKMWne8mvLiTZkUCKPRkTJgwDgkaACQteZ5W3nPt7H8GEnP5kO7P2Wi+wOWjNOnGS34t3w0YMfxmhT84AScZ22n+RbAkARH6ZjrZhFkqPk7EcfjMAevmfhOddBZKMdfwryAndkGQZV49uGA53q/eppegofRL1/FqOSZPG8/jjTzV6PrGjIur5/QVjl8TLDmXfojGtspWuiZ3IsKejGKZaZ9IiWxiWEHsqW15Tii8SQO8AGUwkqxXFbEZypMTSnekqWt7XABiharTNXwJg6ntG7TFySl+UXhPB0Ij++gSGoWPoOtGKSrHoXSAQtBgHRPymTZvGmDFj6N27N+vWravdvnnzZiZPnsy4ceOYPHkyeXl5DR6vaRpTp07l+OOPZ+zYsXz00Uf7bVNUj9WSM8vKPi8itUgW7JKjXmSiWTIzIikHgGWeUqKaSlgLNXCGgwyLFcUeW6Zg6n4SAOrG2PyDuu5T0CLI2SOR4+s+7ZsHXwq2JPSyP9A2fAFAxONBCneA90wg2Ec+/XSO8Pr2gwMifscddxzvvPMO2dnZdbbfc889nHPOOXz55Zecc8453H333Q0eP2vWLPLz85k/fz4ffPABM2fOZNu2bftlU3R73knzfkQV7m7A2DAMRqXkYpFkNgVqqAgH2OopImQE9+jNSBJEiYDcPkeidd3AkpwEgJxzBFjiMKo2oJWtRF0bizAz9Tur3nGSxY15eCzSL7rsBYyIFz2qolZXH/SVMQQCQetwQMRv+PDhZGZm1tlWUVHB6tWrmTBhAgATJkxg9erVVFbWr5H3xRdfcMYZZyDLMklJSRx//PHMmzdvv2zaIX4mWWn2NWWGAQk2N/23lz5a4SklGA2RV72VGrXhHJaSDDVqDZur8/G11GLfA4DsisNksyIpFpSuxwEQXXgfhGuQknojp8XWFJkcNiTTzgcPpcsY5PTBEPGirv0UgHBFJZJ28EfKCgSCA0+rRXsWFRWRnp5eG1mlKAppaWkUFRWRlJRUr21WVlbt68zMTIqLi5t8zV0n3ovMMQWyKGZSUhqekN8fQqqZI9I6saymlNXBSk6Oi+Xqq9GrsJhlMl1pmLbnAw1EghR5S6jWvFgdCiHZT6fkdEz76JWmprqbrR/7gj2cRai4mOghp1Kx9hMMfwkAccPOw57gAFnG2a0rWiBIqKio1oUOH34ZVZ9NQVv7MUkjzkc2yzi2l4Vq7T61BKJPAkHr0aGWOuwa7ekNhkkETJJCebm32b0/WZYY4o55u7+VF+Lx7CzuWlMToNzhIcuVTiAapNBbQljd6eH4pBAOvRSH3HRRbgtRhLJiwxeIopk7IyV0x6jeiORII5w6ikhNEEtCPIZuBquJsFRBuLoaAMPVHymlH0b5aqqWfoypz+kENheQlZxMRfXBNf/XFj6n5qap0Z4CQWvSatGemZmZlJSUoGmxwBNN0ygtLa03PLqjbWFhYe3roqKiOmtc9oUdw55WWW6RVFqGYXBoUmccioltIR9l4Z3VDAygKlDDxso8tlQX1BE+AN0wKAtUIrXXWFybHUtcHJIkYep7JgCmgf9Akk1Isow1LRXdAB0JW1YWijW2BlKSJMz9zwMguvp9DC2KGggQqa4WkZ+CFueWW27ivPMmc8EFZ3PFFRezbt3a3badNGk855xzRp1yQweiJNLe8Hq9vPXWG7vdX1hYyOGHD2XatIfqbBs3bsxez11WVsbVV1/eKDsOP3wogUADFVz2su9Astfbq6ZpHH/88UQizTv3kpycTN++fZk9O5ZNYPbs2fTt27fekCfAiSeeyEcffYSu61RWVvL1118zbty4/bq+uiPaU5KQW0BkDAPcVieD4mILQZd7Suu1CakRdKPhJRC+cICA1vpfkH0hFviSjCTLmLqfiO2M2Zh6xuZ2zXFuJOfOoTHdYsWek420/UOQc0YiJXSFQBna5vkYukGosAg5Kub+BC3L3XdP5e23P+DNN9/j3HMv4IEHpu6xfSAQYO7cOS1mj6rWT524N7xeL2+//eYe2zgcDhYs+I5t27Y26dypqak899xLTbapJdjhNO0Pex32VBQFRVEIh8NYLPuWpeSBBx5g/vz5lJeXc9FFF5GQkMCcOXO49957ue2223juueeIi4tj2rSdJTIuu+wyrrvuOgYOHMjEiRNZsWIFJ5wQy1wwZcoUOnXqtE+27GCn+CnIYS+6ufnnKmLr/TL5paqIZTWljE3N3WP7gpCPX6uKGJeai9NkpixQQRe3g93oY9vG6cTsdBDx+pCssfd2V69vB4YBUlw8ttQUgiWlSJKMqf+5RH98AHXVOyjdTkQPhwkVFGDt0gW94y5NPah5dv2PPLr2O/xq8z/kOE0Wbul9DFN6HrHHdi7XznuAz+dDlvc83HDppVfw6qsvccIJJ2I2110rXF5exuOPP0pJSTHhcJixY8dx4YWXAPD000+ybNkSotEoCQkJ3HnnPWRmZlFYWMhFF53H+PEns3jxIiZNOo2jjx7d4Hl0XWf69GksWbIIs9mM3e7g5ZdfZ/r0R/D5vJx//lnYbDZefvmNenabzRbOOed8XnzxOe6//+F6+1eu/IPnnpuJ3x8LvLv88qs44oijau378stvAPjmm//x4ovPYrVaGTPmeF544dk6ZZU+/PA9vv/+W2pqarjmmhsYM+a42mu8885/WLDge8LhMFdeeU3tvh15QzVNIzExkVtvvZNOnTo3WH6pvLyM9957B4vFgq7rPPjgNHJzu+7xM9uVRs35XXDBBdxwww1cccUVZGRk1Ak/b4wI3XXXXdx11131tnfv3n23a/Zefvnl2r8VRWHq1D0/hTWVHcOeFllB9ZQhp7hp7gQsZsnMyOROPLt5KV+Xb0GRJP7RaQApFnuddkFN5b2CP/m4aB1RQ2eDv4pbehyGL+Qn6AhgkxzNa9gBQDckLCnJRLw7I1d3eH1/HWY2DAlzRgaq30/U50fpcizqitcwvAVo+d9D4njC1TWYXOXIKekHdYWMjspzG39uEeED8KsRntv4817FD+DBB+/jt99+wTAMZsx4Zo9t+/btR58+ffm///uIyZPPqbNv6tS7ufjiSxkyZBjRaJRrrrmCvn37M2LE4VxwwYVcd92NAHz22Sc8++zTPPBALG1ZTU01ffv2q91/7bVXNXiehIQElixZxHvvfYwsy7V5Nm+++TYuuui83San3sHpp5/J5Mmnsm7d2jqi7/V6efTRh3jiiadJSUmlvLyMiy46n3ffrXufrqio4JFHHuCVV/5D586dee+9t+tdw+l08vrrb7NixXLuuuvWOuInywpvvfU+W7bkcdllFzF48JDt79u/ef75V+jatRuff/4p99xzF6+9FvNkdy2/BHDccUfzwQf/JSUllUgkgq43zRtslPjdf38so/mPP/5YZ7skSfz5559NumBbYYfnZ5EV1IAXW6T5vT/DMBia1InzsvvxXuGfzCvL47uKrZyZ1YfTM3thkxW+r9jGS/krKNueI1QG/le+hXOy+5Jjd1MeqKSz24HeDr0/2R2HYrOihcINen27oksK9uxstE2b0KNg6n820V8fR131Dsagv4FhECwqwWV3YDhEAMXBxtXdR7ao53d195GNarsjB+fcubOZOXMGTz45c4/tr7jiaqZMuZyTT55Uuy0YDLJ06RKqq6tqtwUCAfLyNjNixOH8/POPfPzxhwSDwXrDd1arleOPP2Gv5xk/fgKapvLgg1MZPvxQjjiifn3APWG1Wrnoost4/vln+Ne/dpYw+uOPFRQWFnDjjdfWbpMkiW3bthIfn1C7bdWqlfTu3ae2PNLJJ0/kqaeeqHONsWNjU1MDBgykrKyMcDhcWwVix/vVpUsuvXv3YeXKP5Ak6NGjF127dgNgwoRTeOyxh/H7Y7mBdy2/BDB8+KHcd989HHnk0RxxxJFkZ+c06T1olPitWbOmSSdtD0T1KAAmScbQdTRPGXKKC91ovsgKwwC72c6FnQdwXEpnXsn/nR+rCnlz2yq+KN1EhtXJSm85AD2diVyTO4QvyzbzRelm3i5YzW09RuAJ+wk6glgl+16u1vYwFDPW5CQCBUVY4hv2+urgdGFLTSVQWITS7USiv7+BUbWByJafIHEouqoSKNiGo1sPdKXuV1eWgFAArDYxNNoOmdLziEZ5ZgeKk06awCOPPEhNTTU//PA9H3zwHgDnnnsBJ574t9p2XbrkMnLkkXU8H13XkSR4/fW3MJnqDocWFRUyY8YTvP76W2RlZfP77yu4++47avfbbPbakbU9nQfg3Xc/ZunSxSxa9CvPPvs0//nPu03q44QJp/Duu2+xYsXS2m2GYdCjR09eeOHVeu13DTpsDBZLTOh2LGfb33m6XcsvATzyyHRWr17FkiWLmDLlcm655U5GjWr8d6hJd4nCwkKWLVtGUVFRUw5rk0QCsQAUk7+AgjI/JcXlRPxVRDWjWSMLrbIFs2Iix+7m3t5HML3fMfR0JlAeCbLSW06cycINXYcxc8Bx9HMnc052X0ySxDfl+WwJetB0jYpQVbuMdjQMA1N8AorNiiV1917fDnTdwJSUhGIxIykWTH0nA+Bb8nrtUKfqDxIpLkSWYhlxZEND8lYT2rwJ/6bNEAru6RICQYMEAgFKSnauHf7hh++Ji4sjLi6eCRMm8tZb7/PWW+/XEb4dXHbZFXz88Ye1EYxOp5PBg4fw5ptv1LYpKSmmoqIcv9+P2WwiKSkZXdf55JOPd2vTns5TVVVFKBTi8MNHcfXV1+F0uigoKMDpdBIKhRoVLKMoCldccTUvvfRC7baBAw9h69atLFmyqHbb6tWr6k019O8/gLVr19QGzcyZM5umMHv25wDk5+ezbt1aBgwYyIABg9iwYR15eZsB+OKLWfTq1Run01nveFVVKSjYRv/+A7jggos47LCRrFvXNCetUZ5faWkpN910E8uXLychIYHq6moOOeQQnnjiCdLT05t0wbaCbIo9RbgD61FDVVRH4whvykdLljGbzaQm2rGalP2eXzJJZmwmKxE15mkeEpfKMwOO59uKfApDPiZm9CTOtDOQKN3q5KS0bswq2chbW1dxV6+RVAe92E1VWBQzsiQjI8UCQyQF2WjbSzUNqw1bSvLevb4d7c1WrMnJBIqKMfU8GXXl20SLf0cqWoSSFSsZE66oRLHZwIBwZSVqMFS7UF71eFDsLjEvKGgSwWCQO+64hVAohCzLxMXF8dhjMxqVXi8tLZ2TThrPu+++Vbtt6tQHmTHjcc49N7bUx+FwcOed99CjR0/GjBnL2WefTkJCAqNGHcGyZUt3d+rdnicUCvHww/ejaRqapjFy5BEMGDAQWZYZN+4kzj33TOLi4hoMeNmVMWOO56233qgV7li/n9w+5DudaDRKdnYO06fPqHNccnIyt956BzfddB02m40jjjgKk8mEzWbb6/sFoGkqF1xwNqFQiFtvvbM2yv+ee+7n7rvvRNNUEhMTuffehqvV67rO/fffg8/nQ5Ik0tPTmTLl2gbb7o5GlTS6+uqrycrK4qabbsLhcBAIBHjiiSfYtm0bL7zwwt4ObzO8994HtYUkszf8h9ziH5ByTES7ncS31aORkFCsNiK6jCRJ9O7Tj6GHDCYcCvLll7Pqna9//0Po2bM3Xq+X//1vbr39gwcPo2vX7mwu2cSP339fb3+nft1JTE/BV+Vh0/Kdc6fVqDxsK0SV4KVBJ5Ac0Mn7Y0dCcKn2v32HD6J/1/6UbC1myZJfATCbFaLR2PDC6NHHk5iYRF7eRpYvX1Lv+scddxJut5v169eyatWKevvHjTsZu93OmjWrWLOmfg2w8eNPxWw2s3LlcjZsWFdv/6RJsR/siuWL2Lz9aW4HJpOJCRNila0XL/6Fbdvyd+40dMxRldG9+hNd+Q7q8peImNws7X0zEXM8AA6rlaN6xsqULNq8nsrtkWmSLCM77CQkJHHMMWMB+O67r+rMmwCkpKRy5JHHAvDVV1/URrbtID09k5EjjwJg3rzPCYXqLrLPyenM8OGHAzB79v/Ve9Lu0qUbQ4YMB+DTTz+s994MHjyI3Nw+RKNR5sz5pN7+Pn3606dPf4LBff/u5eZ2p6qqku+//7re/mHDRtCpUxfKy0tZuPC7evtHjDiSzMwsiooK+fXXhfX2H3nkMaSkpLF165Y9fvcKC7dyyCH1y8mAKGnU3vH7/bVe2ezZn/H555/x0kuvtbJV9dmvkkZLlizh1ltvrQ1hdTgc3HLLLSxbtqx5rTyAWLd3XQFc3vm4KMHAwFCjKLKEYRhU1oTYUFCNLxTd5+sYBthMNmhCYdYETIzUYkEdb277q+gYELOUkBohv6YATWr6eqADSZPnUSUZefuyGlO/yQQT+2JRvfTJexOM7fMGu0sqruugtcPoIIGgnfHhh+9x/vlncc45ZzB79ufcfnv9iP62TKM8vxNOOIGnn36aPn361G5bs2YN1157LV999VWLGtic1Elv9t8bCP38GsqAYVjMvxOyD6Uy8y6QJJwJSWjuTKpDsXkoWZKId1vJSnFgMTU9I4wuqayv2lw79NkoWyNBLlj2BRFD5/mBx9PDmbjbtnazjS7xOZix1EsxZcgG1eEqTLKJOFNci2SzaQnkSAjf+g3o0Sguc4Cy98+DYAWmfmdjHnrlHo+1padiye50QIoH7ysivZnw/AQHhv3y/C699FIuvPBCpk+fzrvvvsv06dO5+OKLufTSS5vd0ANFkTc2jPWlfxgRHNiCS7H4l4Fh4K+qgPLNJNsimE0yumFQ5QmxfmsNld5IU5w4YOe8396QkHCYbZgVE8kWOydnxGp1/aee91eXYDREfs02VHaKqyTFKsrn1eRTUFNMsa8MlbbtIdbBZseaFBN8xZGM5ah7QFJQV7+HtrX+MNyuRGs8sJcJf6UdBhAJBILmo1Hid+aZZ/Lkk09SVVXFt99+S1VVFY8//jiTJ09uaftajMpATCh+KlN4svrvAFQXvMGdq1QWlBsE/T7CRZtJkr04rLFQ3UhUI7/Yw5ZiLxFNb3QEpmEYJNkTsJutyA0k7JSQcFrsdEnMpntCLhnuVCRgclZvbLLCL1VF/FZVRH7QwyZ/NWt9laz0llMZ2TkPFYiG2OLZRiASRJNUioMlbK7aii/sxwBC0TAVwcq9Zq1oK+i6gTk5CdkcC+hR0g7BNOQKACI/PYzuLajT3tA19KqNGP5StHAEI+Df7bllCaIlRcjG/qdIEggE7ZO9hgpqmsa4ceP44osvGDmycQtF2wP9Ux2ENsEJnRy8axzPNvVLepi2ku39mjtXj+OQOJjSPUI/LR9XUipmRyo1AQPDgCpPGH9QJSvVSaLLutfIQsOAOFM87kQ3YS2EL+qnJuQlrEWxmaykOpNwmVygS2BAvDmeKmsNBjAxowcfFK7lzrX1vR2zJPPvXiMZmRgr9xSIBNlctZUar59QA7kwKwJVxFnd2PawZjBCBItsjtnS2tgcWBITIBwTMlPfM9HL/kDf+gORBXdjHjYFvWwleukf6OUrIRoAazy2Se8TrarCEhff4NCn4fMSLCnDYTIhJ6eJyFCBoAOyV89v19yeByNHdnIxtb8Fe9YFANyR+D7ZZj8rPHD5Mrh3lcb6bcWYPQUkOKTaEc9IVKO4IoDWyHklwzBAl7BKdlKsKXSLz6VHYi7d4jrjkt11xEYyZNKcKciSzJmZvennSibD6qSTzU1XRzw9nYl0dcQTNXSmrvuJ7yt2JqgNqqEGhQ9iWW1K/GUg1w8IkSQIG0G2VOdT7C9FklpfEGIJslOQti+SlSQJy8jbkNzZscXvX9+IuuJV9KLfYsInmyFcg5b3P6JeH0Tqf2dlQyNUVIShaYSKS5EjB1epJIFA0DgOSG7PNsn2p33D4sBkMRN2jiRs64cjtJovcp/h6eA1vFXg5Osy+L4cTsuq5OJ+OgnpnWIeIBCOqPjDUdy2+tkX9n5pCTOW3QaguEwuEuxx6IbOUwPqlxsxDINX8v/gw6K1PLT+F0Kaxri03DptAlqU/xatY3bJJk5O7855Of3whvzU2DwkmBNqry1JENSDbKnZRkSNElYrkWWZdHtqsyTV3vF12ScHy+7AHB8PlduXMlhcWI6+n8h3d4DFhZw2EDl1EEraQLTiZUR/ehB1/SxMPU9GDwQgbucaSkmS0CoqifpinqQWiRAuLcGc07ndBAIJBILmocPm9twRK+8NSdg6p+Mr2UZN8sWkFN6JK/gbtyo3csaga3i8aBBflcIHBfBJYTWTugQ5d0gudsmMYUClJ0y8w9LskYWGDqmOZLxhH1GtfvCGJElc2nkgNsXEm9tWMX3TIsK6ynlxA4noGnNKNvFuwZ9UqzHv581tqxgan04/dzIlvnKciU5MmJEkCOhB8rcLX+ydMSj1lSNLEqnWZIz9TPkW1INEdZU4U+MWuu+KrhvYsjIxlVai+rfnP03sju3UD+q1VTqPJrr4aYzKtegV64gmxmOJ30XkI0GCJSV1jglXVmFOTASHqEAuAI/Hw8knj2PixNO46aZ/tbY5LFjwPStWLOXaa29sbVMOOvY67GkYBvPnz2flypWsWbOmzr/2K3w70XSDsCUBm8OJau1GWfZ0ItaeKFoFPSumMj3lNV4bHOboZIgY8GFemNM+XcvMFQUU+sL4/BHC0ZYJnLBKNlKc9esb7kCSJM7P6cflnQcBMDNvGQ+v/plLVszjuS3LqVbD9HMlc1xKZwzg8U2LiOgaYTVCeaAcWZYI6IE6wrcDwzAo8ZZREdm/1GoqUbZ5CtlaU0B5pAL2YTjV5HDg6NQJ2bxnD1syWVG6xZLpqhtmEfUFkLYPfcoSREpK0CN/6aemEy4uRm6XdaMEzc38+XPp338gX301j2h039f37sq+1OXbwdFHjxbC10Ls1fOTJIlTTjmFpUt3n4KnXbKLC+INQ0pSBlJoM5olm/Ksh3BV/xd31Ye4PHMYZV5Bvx7Xsza3G2/nw//KYNbGKubnVfP0Md3JSnWR5N79EOa+m2iQZE2gylxDKLr7OdczsnpjU0w8vXkpn25bD0CuPY6LOg1kZGImUUNnra+S/KCXdwv+5MJOA6gM1mAxWSjzVRLRYj/yFZ4yltaUcEZmL1wmC7phUOwpRY6XSbYmoutGk/qoSxrbPIUEt9teVFNKxBkhw5mOpDct+bRhd+LolI0/Lz+2kH03mHpMQFvzMVre12jDrkbz+ZCTbBjeGsKV1TG7/CVElzyLqfdpKOmDiXh9mKsrkZNSxPBnK+H79ml88x7CCPv23riJSFYXrhPvwHXsdXttO2vWZ1xzzfX85z+vs2DBd/zyy8/06NGjtmTRxo0b+Ne/buS///2cQMDPjBlPsHHjesLhMMOGHcr119+EoihcddVl9OrVi5Ur/yAuLp7HHnuSf/7zOmpqagiHw/Tr15/bbrsLs9lMNBpl+vRHWLp0CYmJSfTq1YuKigoefvgxZs/+nB9//IGHH36MJUsWM2PGdPr3H8Aff/yOJEncf//DtVUQnn/+Gf73v/nExSUwdOgwFi/+jTfeeKfZ38+DhUbdgfr27cvmzZv33rBdEbvLJcbZyUh2INnjiUtKQpFlkBR8iWdSnv0IUXM25ug2kgvvoreylrv7Srx9KByaAGHNYH5+FRU1wd0lHNlvFExkuFKRGlhcuOuWk9O7c0ePEQxJTOdf3Q/lhUEnMCopKxYkIiv8s9uhSMD7hWvY6K9G03UKakpqhe+rsjxu/fN73i34k3+t/p6a7YKlGTqFnmI21WyhNFSGT/cSJoSGuudlE7JBob8Yb3jnkgMDg3J/FfmebahS056qDQOk+ETsmel7XGcpJ3RFTh0A0QBa3jdEqypRDI1gURGGrmMYOtEfH0DP/57ID1Mxwl4wIFRciiSqxbca/m+fbhHhAzDCPvzfPr3XduvXr6Ompobhww9jwoRTmDXrM8aPP7lO0ubZsz9n/PiTkSSJGTOeYOjQobz22lu89db7VFVVMmvWZ7VtCwoKePHF13jyyZkoisJ99z3EG2+8w7vvfoSu67VtP/nkv5SUFPPeex8zc+bz/Pnn6t3auGnTJk499XTeeedDjjtuLK+/Hqu+8MMP3/Pjjz/w1lsf8Morb7B1a/5uzyGI0ag5v8MOO4zLLruMU089tV7Ay+mnn95ixrUo2x/x7RYTSYmxMiJSQlcCVpVgIEKVN0SUHpRlTyeh/HkcvgUkFz9Aeeb9dLJ35cpuBouWwjf51Vw+KJNgWMNuUVrEzDiTmyRnPIZhYFYsWBQzZtkEEhR5SglGYxGLx6Z0ZmK33ng89SsbDIhL4ZT0HnxWsoHHNy1i5oDjUCQZwzD4sGgtr+T/AYDbZGFDoJqbV3/HI32PJtliR9N1PCEfnlDs5qTIMibZhM1kJcmRgFNxIrMz840kQ0mwnOpATYN98oR8RLV8Mtzp2BUbZsncqDlTwwBTahq2cJhQeeVu2yk9TkYvW4m6YTbR3qcQ2rYV1R9L3Kut+Ri99PdYw1Al0aXPYRl5K1o4TLSsDHNWdrMXNRbsHeex17Wo5+dspNf3t79NQJIkjjlmDE888ShZWdkEAn42bFhPbm5XvvpqXm2y6IULv2f16pW8+26snFEoFCItLa32fOPGnYTJFLvF6rrOO++8xc8//4iu63g8ntok0EuWLOLEE8djMpkwmUyccMKJLF/ecOrILl260Lt3LNPWgAEDWbhwwfZzLOa448bWlv0ZP/5kXnvt5QbPIYjRKPFbunQp2dnZ/Pbbb3W2S5LUfsVP3i5UioJhxIYYZYsTe2I6ilGE3eqi2hfGG5CoTr0WSY9gD/xCcvF9lGc+SE9nJt0csCmgsaLMS2ayE4fV0SJrxgxDIseZvf3vukOPufFWtnoL8IUDez3PJZ0H8ktVIev91XxUuI4zs3rzwpYVfFIcGyq9qstgRifncOufC8gLerhp9Xc81nc0ada6leQ1XUfTI4TVCJ6QD5vZQpIjEbfFhU22UR6uoMxXjgHohsHmQA25jjiUXRb4B6Nh8irzMZvMOM124m1x2E12zJj3OPSoI2PJzEILhYn6A0iyHEtmrchIioIWDqN0OYbo4pkY5avRytcR0rrHjq3ZQnRZ7IZgGnoV6vJX0TZ+gZZ7HErmcIKlZRi6hiUjq169QEHL4jr2ukYNS7YU0WiU+fPnYjZb+OKLmKenqipz5nzO3/52MnPmzGLo0GHk5nYlMzO2rtYwDB599IndFlHdtf7c/PlzWbFiGS+88CpOp5M33niV/Pyme2cWy87oZVlW9rtGXkemUb/wt956a++N2hn2kRdhNUuYeo+t3abrBoo7GcVfDeEgyXFWXA4zVZ4Q1Rk3IhU9hC24guSie6nIfpBx6Sk8vxnmby5nVKckUhPtDY4jS9L2Qdb90MXdeUYmzHSJy2Gbt4iaUMO5Ii2KuXao8IZuw7h9zQ+8uW0Vq7zl/FJdhEmSuKX7YRybEqvK/Hi/Y7jtzwVsCFRz46pvebTfaLJtDednNDAIRsMU1BRjVkw4LHZ84QC6YaDqOo9s+JXvK7fRxR7HFV0O4dCEjF2OhYgaJaJGqQp6MCsmEuxxZNrT9xhhqitm7F06Y1NVJFkBZcc/E2pxUawYbtexaOs+Qd0wC8uhN2DoKpGfHgY9gtLtRMz9zgI9irr8FaK/Tkee8DqSyU6orAI1EMCRnQMud5vODypoPhYs+I7OnXPrVCX4448VTJ16N8888wKXXvoPtm3byvjxp9TuP+qo0bz55uvccssdKIpCdXUVgUCArKzseuf3en0kJCTidDrx+bzMnz+PPn1i+SaHDh3Ol1/O5fjjT0DTNL7+ej4pKalNsn/o0OG88soLnH32uVgsVubOnbOP70THodFRB1VVVXz66ae88sorAJSUlFBcXLyXo/bOtm3bmDhxYu2/MWPGcNhhh9VrN3PmTEaOHFnbburUqft1XXPn4WRd9gayK6XOdg0z5vRumONTkGQFq0kmI8lBWnI8oS53ErH1waSVk1w0lb8lVyMD32/zE4iECDRQ/UGSwBdSKawItFhBWtkw0Skum0RH/M7rImEzW8mOz6B7Ui6d43MwyQrDEzIYl5pL1ND5pboIh2LioT5H1wofQLzZymP9RtPPlUxpJMBNq75lXulmCkK+PXq2UU2lJuhF0zVUXeeh7cIHsCXo4Y41P3Dnmh/ID3p2e3yFvxqftvvUZDvQzTYMuwvdakc3WdAlJVYMNyEB2WzC1HMCANqmrzDUEOrq9zAq/kRypGIeHqv7Zep3NlJiDwxfEeqKnTc91R/Et2kzWlkJcovN5graErNmfca4cSfV2TZw4CEYhkFhYSG5ud1YunQJxx67c83tDTfcjCwrnH/+WZx77pnccMM1lJaWNnj+v/1tPH6/n8mTT+Pmm2/gkEOG1O477bTTSUlJ4eyzT+eaa64gN7crLlfDD5u74+ijRzNixEjOO28yl176D1JSUpt8jo5Go6o6/Pbbb1x77bUMGDCApUuXsmzZMn777Tdee+21Zq/n9+CDD6JpGnfffXed7TNnziQQCHDrrbfu87l3reogSZCc7KK8vOE5BkkCJeJFrS5GDfhqN0bCHrQ//okU3ETUkstlVQ/wQ5WdO4YmcsbQnuSkOne5hoQnGCG/yIuuG3TLicdpbbnhNEM28MvVlFd7SXEk1pmLkySJikgFhTXFeNQI16z8HxFd4/7eR+y2YkRQU7l77UKWe8pqtyWarQxwpzDAncJhCZnk2Ouvj4sJ3y/8UFmAUzHzQO8j+dNXwdsFqwloKjISp2R057zsfsSb6yf8dljsdE3ogrw9IrQpFRBkWSKSv4VQeQWheVdhlK/G1Hcy6tr/gq5iGTMdJevQ2vZ6xVrC82JVIqwnPoee3APz9jWcSBLWhHisWZkYFnuzDmmLqg6iqsOu7KiNF4lE+Ne/bmDMmLFMnHjqPp1D13Ueeug+UlJSufLKKS1kcfthd1UdGnUnfuihh5gxYwYjR47k0ENjN45DDjmE33//vVmNjEQizJo1i1dffbVZz7s79lSl2TBANbtR0pwogUqiVSXo0QgWixtj4DQif9yIOZjHHQn/4aSqK5m1sZozBgYIR22YFRlZlqjyhdla7EPdXl+uqDxA9+y4phaFaHx/dInOidm49UAsM4uxc6Q1tmwiEZ/djxH08vKgE1Akqc48HIDFZEbVNHRDx66YeLDPUXxRupkVnlJWesqpiob5obKAHyoLeH7LCg5LyOC0jF4MjU9DkiRUXefBDb+wcLvwTet7NL1dSQyIS+H41C78Z+sq5pZu4tPiDcwp2cSoxCxOTOvK0Ph05O2fRyASpDJUSZottcnDjrGUaMmEq6ow9ZhAtHw16p+xBfFKr4m1wiebTehRFTm5N6a+Z6Kufp/wz49QOfp+XLZ44ixxSAaEq6qJ+vzY0lMxJyWjKyaxHELQ7Fx77VVEoxEikQiHHnoY48ef3ORz3Hff3RQVFRIOh+nduy/nn/+PFrD04KFRnt+hhx7KokWLgFjk52+//Yau64wcOZJff/212YyZN28ezz//PJ999lm9fTNnzuSjjz4iPj6e1NRUrr32WoYMGdLAWVoGLeglXLwZY/sSANW7icofLwY9yuUV/+Z/wcHMPTmTQwb0JT3JQXFFgIKymKcZjGqYZAmzIpOd5iI7tfWGI/yRABsq8ojq9RfeuiwOcuIz8Yb9FHtL0f6y8NswDLYGvCyvLmFJZQnflmwhrMcm3Ls5E5jcpQ8/lxfyXWk+LpOZmcPG0i8+pd511nureGH9Mn4qL0DfLs8ZNicTsrpzSk5P0m1OFEmhe1Jn4mxNz7xiaBredeuIVJRS9sZ4jKgfJS6b5MnvIFscSCYTji6dCRYWoQeDGNEQZe+fje4pINx3EpFBZ2M320h2JmHfpRSVbLNiS8/AkpyEbBIBMfuL8PwEB4LdeX6NEr+zzjqLKVOmcNRRR9WK38KFC3nxxRebNRjmsssu46ijjuKCCy6ot6+srIyEhATMZjM//vgjN998M1988QWJibsv8vpXdh32hKYPPZlUH5GSPPTt2VDUre+hbnmVKiOZYwqf5Iwubm4+rh+S1U1BaexaK8v9/PunLeS4rDxxTFfcNjO9uiRiknQkQ0OjaXlB98be+iRJ4FE95FcXom8XNwmJZGc8Gc4MJD3mtdZEPRR4ihpMrbaDmmiYOaWb+Kx4A5XRnQmiXYqZR7Z7fHuiNBzgq7I85pXlUbx9PaBDMfHsgOPJsbuJs7nIjetESnJck4cIJW81vs15hJe/grr6I5QxD2FLH4YBODvnoCSnYng9+DZtxtBVKrd8h33BvQBEUvrhHXQhUnwnkuyJOBT7Tm9PkrDEubCmpSPZbGA2b48WjhUt9kZ9xJvjYS8p4fZ32HO/8qW2EPsy7JmZ2XmPIzACwf5gGDrFxVvp128fxW/58uVcccUVHHPMMcydO5dJkybxzTff8NxzzzFo0KBmMbKkpIRx48bx7bffNkrQTjvtNG677bYGg2N2x/6KnySBHKwmUpaPoWsYhkZkxfUYvjV86B/D04EpfD4uGSM+C00zWFUe4Lpv8gioMZG5+pBULu/nxiGFibNoSMgo6d3RaL71gY3pkyRDkb+YMn8liiST5k4lxZpU54Ydy/npJ7+mcK8V6KO6zoLKrXxStJ7KaIh7ex1BL1fjH0p0w2CFp4x3ClazwlNGf3cyj/c7FpMk0ykhkx6ZnRq8qUoS6DoNzsVJkkbVmt8pLS8iqkaQTWaS7PEkpWRh7ZyLjowkgVZcQNHWDVQHaqjM/5buqz7AHPVhyCYCPU4m0OsU4pzJxFvikHZ9f2QJ2WxGsVowuVxIThsVup/yaIB4u5sMRxqSsfvPdX/ET5KgKlqNBMSZ45CMpmXLaSmaKn6bNm1CUay43fFCAAXNimEYaJpKTU0VZrNMly71RxgaJX4QE6fPP/+cwsJCMjMzOeWUU8jIyNj7gY3khRde4M8//+Spp57a7fXT09MB+PPPP7nwwguZPXs2qamNDwneX/GD7QLoKyVcUQiGgR7YQnjZlUhGlEvK7+D03sMZnmZlvVfn6sURvCockiCzolrHIsN/hkFnh0xGqhObScaamoPuTG22J/jG9kmXVPK9BSTbk3abcDpW5ihEfk1BbYqyfcG+vTq9PxJE03e/LsmrRrh0xZdURkNc0eUQTs/shcVkZmiXPvhrNCQJVCNKUA/jCXsJRcM4LQ6cFjtm2YxFsiBLMj7VT3mgAnO1h6pNm2rPb7XbSes3EJc7DVmPiV8w6qHoj6V8W7mVO4ObSI4EeWHLYnoXxNa0qs4MvIMuxJQ1nCR7ws5gmF0wJI2yUBW6LGHr2oVqVOJsLrLdGShGw579voqfJEFltIqimpLYvKzZRporBbfZ3eo1GJsqftFolK1btxIMirJSgubHZFJITEwkJSUFWW6giHhjxa+lGTduHHfeeSdHH3107bbLLruM6667joEDB3LrrbeyatUqZFnGbDZz3XXXMXr06CZdoznED2JJko3qAqLVsbDmyNYP0be8RLGWxJPGk5yS7eLa36EmCkcnw9S+MG0dzCuF4QnwxEBw2M1kJDlQzBYsmT1RJcueL9pImtInQ9aRDXmvwhslQrE/lkkmqkXRm/CVcVud5MRlYZbMhLQg1WEP1SHPbr3JX6uKuGvtQiySzPODxtLZHkeXtHT0iIIn5CUQDTY4FKvICqbt/4LRMLqhEydbiG7MIxyMLYaPz+2Kx2HCZraR7c4ADPKqt1JZU8N5K+fh26Wy+1W+Gi5b8wVmXyEAkdQBBPudSVz2oThNjtpST7qkUR6orM2y44hPQO6cjUcL4zDb6BSfhVWybY+43Rlk5Y634qkONemhR5KgIlJJkae0dsgaYsPWTqudNGcyTsW11yHXlqKp4icQtCZtRvwOBM0lfgCKpKNX5BP1VgE6FYuuwxlew2eBY3jAdy2VETg8ER7sDxZZoipicN5i8Kjw794wLk0nNdGOy2HDkpSJEZfZLKH0LRVCL8sSUUMlqocJRIN4In5C0dAe5wQT7fFkudKRDdNfzhPBF/VTEaiqFapdmb5xEV+W5dHHlcSM/mNIjHc0mLJtr0gSP2xZx+9l27g4ewDJ2dkEtFj+TovJjIxMVdjP9au+YXOghiNM8RxlSuDJUD5hDPpjZUbxWtI2fIGsxq4fTh8MA88jPms4uqFTHqiol3TcnZFJJC2RoBrBYjKT5U5HlmTCapjQ9n92pxmLbifFmtyomomSZFARqaLIU4qqa1RGQ6RY7HXayJJEgj2eTGdanff8QCHET9CeEOK3H0KhEEUry0MN+AjWbCHy+1VYpQjzAiPwmTpxdGYWkjUb1ZyFrHtYVbKezRUbGGrdyCHWTSCZsQ19DpOrC+bMHmiybb/7eCDWj8U8GIgYUbwRL+WBKsLRCMb2yM1YAE0CmY502MN8lCQZBPUQ1WEPnpCXsBoTJp8a4fLf51MWCXJp54Fc1mdIHfEzDIPicACXyYzbVN9jDmoqc0s38XHROsoisePiTVZu7j6cwxOz6pzn/vW/8EPlNnLtcbycMBDZH2CDFuDewGaKjAjxkon7lFRGbFmAfdOXyFpM6KKZw4lmDCXkzEBz51CqmHg3XEIXxcYkaxoJXbvidVqIaioSdZP7WExmkq1W/MEIbmcCKY5kDF0GGq6aIclQHq6g2FOKX41y77qfWFpTwuC4NCZn9WZYfHqdOTO72UZ2XDpOxXVAM9QI8RO0J4T47adQKHoItWQzaiTEN799wBGRpiWTNRKPwt7/HszxqUhJnfb7ZnWgF09LEujo+DQ/Ff7Y8F+yM4l0W0qji+DGvMEoATVARaAKb9jPoupi7ljzA2ZJ5s2RE0jRrZSGA3xTns//yreQtz1LTKbVSQ9nIj2dCfRwJrLGV8EnxRvwbhfSro54kk02FntiRWz/ntGTSzoPwizLvF+whle3/oFDMfHcwOMZaErAt3kzajSCx1B5MJDHYs2LBIwxJXKJ5KLbpq9w5H2FpNWtAFFojWO9M4X3swfjTxvIHXE96darD9WyVjtMrMgKLsmEVFkD/hpCoSiyYsLljCMxLgWz3YWSlFKbWFuSIaQFqQrVUBGowqdGuGvNQv7wlte5dndHAmdm9WZ0ck7tuk2TrJDqSiHFlnTA5gKF+AnaE0L8mkEoTJqfSHEe5dVeFm9aQ19LHja1CFO0cPu/IjA5iZi7UyJ35768HhRqicxKvx2FCJaBT6IkDcGa2QNVse/9gnugNTOH7LhZ2xR7o4byGkTWyavZijfs58lNi/midDPdnAk4ZVOdm75TMRPVNSK7uVBfVxJnZfXl8MRMFFnhg4I/eW3rH2iGQS9nIuPTujFj8xIMYGqvIxiVlIVFNuHSIFpaTrCqiqim8ma4mA8iJUQxkIGTzMlcZNjJKlxEoGYT/prNZPvLsW6fL4xKMucPOZstcdncnNiLv/UbhleP4FDMmL0hFm1dx5feIjYR4kJzBoeYYusYbWYrqc4U4nO7oSfE44v6qAhWE9geJORXo9yx5gdW+ypINtu4t/cRLK8p5ZPi9bXLTDKsDq7qMphRSbHckhIQb3eT7kzDKltjSQ9a8NcuxE/Qntit+J1zzjmNCj9+5532UyyxpcRPkkAOVeMv3kJBsQdtl2KrcU4LyfE2ZEmi2hehoibIq3kGb+TD3cnv8w/7R+DshXXwM1jiUpBSurCHWq175WBImxUhxKaqfKrDQS7/fT6lkVjFCoskMyopmzEpnRken4EsQX7QywZ/Fev91WzwV+E2WTgtoyeD4lLrfX9Xeyt4aP0vlER2VsC4IKcf5+f0B0CWZOJsLlQ1gjUYIVxSStBTQ7EW5s1wEfOjleiAGYmhJje/qR4MIMNQuE23Mirve+xbF1BuS2DisHPxmG2c4Mrk7E79+V/BOr7yFlNi7PQYzUjcbu/CaHNsWYhZMeF2JSB3zqJMC9YOI3vVCLf/uYC1/irSLA4e6zearO2JxiO6xtdlW/iwaC0F20tOnZ3Vh390GoCyvf8mWcFqspBgi8NhdmCVrcgozV6BRIifoD2xW/H75JNPav/Oz8/nv//9L6eeeipZWVkUFhby6aef8ve//53rrmu9MiRNpaXED2I/ctlXSv76DfgCESQJ4pxWkuKstenMDKCoIkBNMMqFS6AyFOSXnGtxGlWYe9+OKX0s1oxuqOamZzVpiT61FpIkURmtpKC6mLW+SuZUbKK/I5kjErNxmvYvKYBXjfDEpsUsrCxgVGIW9/QaVZtWLd2dSoYjjepINUXeUizIWHwhQiUlhAN+8tUgb4SL+E6tBmJZ4U+3pHGBNQO7pIAWJXHhfZhrNpOfNoC/9z2RkFT355UimTnWnEhYMfg8WIYEXGPLYZJl55IdR0IiUucsvGqYmmi4tsJGhtXJY31Hk2Fz1uuXZhj8X9E6Xsn/HR0YHp/O7T0PJ+4vc6KyJGMxmYmzukixJ6PsJTAm9tbEQlVlSarnOapGlIgeJapHsTkUrNG684xC/ARtlUYNe5555pk8+OCD9OzZs3bbhg0buOOOO/jwww9b1MDmpCXFD2JLIEJl+WzZkEdinJUEZ/1gjLCqU1weYG6Rxv1r4dL4b7jd/SxYUrAOewNzXBpKale0fVy4fDCIH8SCYfJ926gOeomLs+9btOd2zIoJVddqPR3DMNga8pJtc9d6Rwl2NznuHCRdQpIkAnqAAk8hEU3FISlYQlHCFRWEPR7WhD18H63mWHMi3f8yTC37S0n6/i5kNUBB3zO5Pasv2/Qwh5viGWNOZIDixFK9EWfFH/zPZOdRl4sKi5OzLelcYs2s9VYrUuKZFSljbukmyiNBsm0uHm2gtuJfWVZTyoPrf6ZGjZBhdXJvr1F0dyY02NZmtpDlSse1m3WehmzgidRQFqgEw0CWZBRZif1fkgmpYaK6WpsLNjMliTQ5U4ifoF3QKPEbNmwYP/30E1brzjyHoVCII488ksWLF7eogc1JS4tfDI1QRQk23Y8eCdemQtuBJMl4IwZVQTjjey/b/FF+7XwLyfpmTF0uwtTpXEyuBEzJnVAbl3e8DgeL+AGoRNlYnYfNYWqy+MVKOllIdiTiNrvxRr0Ue8tQG1hkbzdbyU3ojOkvC9I1Kco2bxGekA9ZkrAqFmyqjl5VQ6S6Ck3VMAw9VmBY12vF1VK0hIRFT2JIMtWj7iKa3AuICaPrzw+xFf5S5zobHcn8ltCJcEpfommD+VL3sVbfOTTbzRHPg32Oql3aICGR5EzAMAw8YR/qX5ablIYD3LvuJ9b7q7DKCudl92NIfBrdHQmY/rLYV5ZkUpyJpNpTkLdnpJFk8Kl+Sv1l+MM7h1/3hhA/QXuiUeJ35ZVXYrfbuf7668nIyKCoqIhnnnkGv9/f7CWNWpIDIX47lgEAyGoYIxpCD3owIgEkmxvF7sIw2ckrj/DhymIe/W0rE+NW8kTcPSDbsA7/D5IlGZPdhZLSqcnLHw4m8duRh7RKq6TGs1MMFFnBbrbiMDuIaBEiWgRN19G3C5HT4iDZkYDjLyWdfJqXbZ6iOgvsTbJCbmIn7FLDHpUh6ZQESqkK1tQKp8VkxqpLyLqBZBhIgGSAjEGooIiA14Nz1bs4N36BZkuketSd2LZ8g2PzfCRdxZDNRHOPBm8J5sr1SNrOdYIr3JlcOvgMFMXKsbYUJnQbRFd3Qu3QrCxJpLlTSLOmIEkyIT1ETdhDVbCmdqkIQFjXeHrzUuaX5dVus0gyPV2J9HMlMzgujUMTMmo9TYfFTpY7HUVSKAtUUB301Ft/2RBhXWONr5KVnjL8ss69fceTbN45LCvET9BWaZT4VVdXM3XqVL766itUVcVkMnHCCSdw1113kZS05+TFbYkD4/nVR5ZjN5hYAuTY9YMRjTX5VVwwdw2lAZXvc6eRo/6Gkn4S5p7/BECx2jCndEEzOxodpXcwiR/EvBCPVEV+aQk2k4VEezxOixObbANDqn3Q0AwNzdAwMLDIlgajTSUJIoTZ5inCFw4gSxLZ8RkkmpP2GPwhSQZhI4Iv6qMq6CG0y8J8CQmryYzb5ibe4sYajlC+/k+qPeU4v78XS9X6OucK5hxJuP9k3Gm5eHx+otEQ5qqN1JStwLnlW1LDXgrSBqIf9k+ssglHUjKmjDTCJpmorpHmTq63MF6SJDSieKM+yvw7s80YhsHCygJ+ripkja+SraG634sjErO5qdsw4rbXVFRkBQnqeMfrfFWURgKouo5q7PxXHPbzh6ecdf5K1F3eu1eGn8GEjJ1JhIX4CdoqTVrqoOs6lZWVJCUlNZgrra3TWuLXELIsUVIV5MVf85m5rJBj4gp5Je4GJHQsA59Ajh8Ya2e2YEnthGaJb1R03sEmfgDuRAtllTXYlZjg7W+Qoi5pFPlLUCSFTGd6o5dlSNtdvKAWpCbsRTcM4q1u7IoNGROGYSBJEkZlGd6tWwl589HnTEGOeIim9EMdfBGO9IGYJQtJ8S4qa7yUBytrM8QovkISf7gPOeojkHscvoEXgiRhMluwueNIy+qCKy4Fw2JrcD2oJIGKSpGvhOpgTb3BSk80zBpfJat8FXxavJ6AppJstnFLj8MYGp9ep+1qbwWvb13Jck/Dlclrr0lsLeVAdyrHderO5MzD63w+QvwEbZVGi9/GjRuZN28eFRUV3H333WzatIlIJEKfPn1a2sZmoy2JH4BuwKq8Ss78fDWVIZV53d+gZ3gWSGZM3aagZIxHkiQkxYQ1oyuqae83kdbuU0vQMsPTBjrGPldEkKQdVSXq/3xkCSLbthIqK8cIFBP1F2NKHYAimzG2t4+Pt1NTE0SXNCpD1fjDsWFdc8U6En5+GEmP4us7mUDPk5EVhRR7Ig6TA0kxYXY7sSQlIbnc6DRgv2xQHqqg3FeJRVaIGFq9NHTFIT+PbPyVVd4KJOCMzN5c2GkAecEa3ti6kt+qi4HYespD4lIxSTJmORboYpZk4kwW+rtT6O9OxrU9olTM+QnaE40Sv7lz5zJ16lROOOEEZs+ezdKlS/njjz94/PHHeeONNw6Amc1DWxM/SYJqX4THFmzi5d+LGRIX5c1Ob+ComQeAnHo85h7XIyl2TK545NRue10D2Np9agnaY58UXSWwaRNRn7/OdkmWscTHkdQli6qtxUQ8HnQ1SlW4Gk/IDxhYC38jbvFMJAwiI27E3vNvWCVrXc9fkrC4nVjT0uqJoCRJyFqEQHUppYVbkNNTqJK0eoErmqHzbsEa3t62Gh2DFIud8u3p4GyywmmZvTg9s1eDKeQaQoifoD3RqHDCp59+mjfeeIM+ffowd+5cAPr06cOaNWta1LiDHcOILYI/o28a768pY5kHvlUuY1RaXxLKnkMv+5qIfwPmPnejybmYwj50s7iRtAc02YS9Uye0jRvRI1GQwOx0YstIR3LHY0txY5asWMIh1OpqlEobilyGN+xD6ToayQjDkhexLJqJ4srByBxe9wKGQcTjI+rzY3Y5saalI7vcGJEwanU1gYpK9EgYt2EiWFFNQlY6HjVYJwGDIsmcn9OPYfHpPLzhV4rDfiySzCkZPZic1YcEsxWB4GClUeJXWVlJ7969gZ0lWWLDPqIA5f4iAd0y3JzWI5n/rC7lza0SQwYeRdSSS1LJY5gCeUSWT8Hc+w5MzjiU1NgCeG3H03WHSU7X/jBsdhzZWYSKS7CmpaIkJqKj1M6JGQYYFhtKeibOlGTs3nR8hduQIjpG33OI+CvR1nxE5H//RIrPRck+HDnrcOS0gUhy7Kdr6DtFULHZ0KNR9OjOIU4TZtwRCYdmIy05A1WPEtFUQmqY6mANUU2lnzuZFwaO5aeqAobEp9erFrEnTIoJi2LGZXGS5kyCfV+OKRAcUBolfv379+ezzz5j0qRJtdvmzJnTbFXcOzpuu5lzB2bw0bpyFlfpfFsGx6Z2oix7GvFlz+PwLySy7lGqlb5YdBflIQu6bmCzKXRJdwsBbKMYBkjxiThcbgyTmd3lLDcMA0M2IyUkE2ex4s/bghaOYB52NQDaxjkYNXmoNXmw+n0wO1EyD0XpMR45cziSJGPoBmqgvvIYahAkE+HiclyuBDSzC4cJZItEki2eAm8xvnAAp8nM2NTc2uN2RLFadQnJbCJiaMiSjEk2YZJkTIoZh9mORbZgkS1gSCQ7XJT529fwtKDj0qg5v40bN3LJJZeQk5PD8uXLGTFiBJs3b+a1114jNzf3AJjZPLS1Ob9d8Uc0/j1vLe/8GYuuOyIJrukOOTZILvo31tBqPImT0bteScDViUBIQ5YkuubE4bbtXJzdlvrUXHSkPkkSSKEggbw81O0Vzg0til62Er3wF7SCXzFqNu9s78pG6XUKpu4nIVnjAdD9JejbfkTbuhC9ZDlSfGesJ76APSMLS6cudUTYkDTKghWU+yvRDWP7GkawhFVkfwBrBOw2N7b0VBR3PDo7ih/XL78kcnsK2hN7FT/DMNi2bRuJiYksWLCAwsJCMjMzOeaYY3A66+cYbMu0ZfGTZIkNhR5eX1bAO6tLCag6ZgnOzIHLU1eRU3o3umSnrOtL2DsNoShgQgLcDjM9chJq84e2pT41Fx2xT3IkSGDLFlR/fW9O9xWh5f0Pbf3nGP6S7QdYUHJGoXsLMP6ythBA6XM61sOux9UtF8MVX2efJEFI81FTUUy4shI5GMaOGats3bkMRJKwuJxY01KR3PHo1J/yEOInaE80yvMbPHgwS5cubZdr+3alLYsfQCiqsWFrDaW+MG+uKWXu5ioAki3wavJ9DFRW8E5wEo95LsAbNUh3mHnm2O4MzE0iyW3BMNpen5qDjtonRY0Q3LKFiNfX4H5D19ALf0Fd9yl64W87d5jsyFmHoeQcgWRPIfLNv8DQsIx9Cmu3kTi790DbPmcoS2D4vIRLSwn5alC1KNbdJAkAakXQkpKM7HKB2Vr7mxLiJ2hPNGrOr2/fvmzevJnu3bu3tD0dGrtFITHOiqrp3DAkmwndk3lu2TZWVYS4u/xsPklfwWnWL3ham4CXREoCUV5fXcItbituhxmTLAKQDiY0kwVbbheUsjKiHi9aOIKh7cy+IskKSs4RKDlHoHu3oW/7CcndCTlzKJKyM1LTNOA81D/+Q/TnachJrxItj8eckYnh9xMuLSVS48HQdWQkLMSEz/CXoub9D33bj0hxOZgPuRTJkRKLMvX6iHh9KBYL5jg35sREJMeeE24LBG2NRnl+Tz75JLNmzeLUU08lIyOjTpTn6aefvt9GjBkzBovFUps4++abb+aoo46q0yYYDHL77bezatUqFEXh1ltv5dhjj23Sddq65wcQ0XQ2bK0hEo3d5AwMVlcFUANVHFF6GxnRRVS4/sbm9Bs5Z4EH3YCnjunG6F4pZCU7SU52tbk+7S9t8XPaX5rSJ0mSkAwNwiH0YJBoTQ1qIIgWjuz9YGJzhuF5V2JUbUDpdSq2Uf/E7HYT8XjriKkR8aNtXYC2+Sv04qXUiaQy2TENughTn7/XRpruYiAmu430Ab2pidR9ABOen6Ct0ijPb+nSpWRnZ/Pbb7/V2S5JUrOIH8TWEvbq1Wu3+1999VVcLhdfffUVeXl5nHvuucyfP7/dzTvuDatJpntOPMWVAWq8YXQd+ic6Scp2IlkvhE2LSPLNR0s5lfP6dOc/f1bxzPIi+qQ4SXQ3LQm2oH1gGEasjrzVAVYH1qQUrJEIut9LpKKCqD+AodUfp5RNCrLZjBZRsIy6nfAXl6Ot+4Ro56PRM4buPH+wkuiqd9HWfw47kmzLZuScUSidR6Nt+QZ960LUpc+hbfwC86HXo+xyPIYRC87RVGD/6i0KBAeKRonfW2+91dJ27JW5c+fyyCOPAJCbm8uAAQNYsGABJ510Uitb1rwYRkwAc9PdeOKsFJf78QdVPBGZ5MwRBEuOwO7/EWfFh5yffi1zN8usqw7yVX4VSXFWstLjWrsLghZG1w0wmSE+CVtCEraAn2h1FZFqD5IiY3K5MLtdSBYrktWGWlmOX9cxDfwH6u+vxYY/J7wOehR19fuoa/4PtFhkqZx2CErXE1C6jEayxNaUmnKPQyv4heiipzFq8oh8fSNyp6MwdRuHnHkokkk8dAnaH01KbA3bn0KNXdMX7X8QzJgxY3C5XBiGwbBhw7jpppuIi6t7Ex8yZAj/+9//aqtI3HvvvXTp0oWLLrpov6/flglHVUorg1TUhLArOuR/i3nFhQD4e7/I3PJ0blsRJd4i8eUZPenZNYvEhJ2V4A3DQNUMFFmqrS4hODjRQiFksxlJUeps11UV/6bNRCrKqfjvxahlazCnD0Ct3IwRjaVfs+YejeuwyzGn7n70xVDD+Je/jW/JG6DGPETJZMPSZRS27mNIPvZcbFm5LdU9gaBZaZT4lZSUcN9997F48WI8Hk+dfX/++ed+G1FUVERmZiaRSIQHH3wQv9/P9OnT67RpDvFrD3N+DSFJErpuYEgGpmAV3oXXYJR+iZQ8mtKka7hmmcziajg5A6aPTkVK7ERNWELVDFRVxzAgK81ZZz1ge6K9fE5N4UD3SVYjBDZtJFK4mvAXl4Meq2koZx2GedDFyCl96x4gEQtocbtRg4E6Sy50fyla3tdo+d9jVOxMcShZHMRfOQdz52E7ryvm/ARtlEYNe95zzz3YbDbeeOMNzjvvPN555x1mzpzJ6NGjm8WIzMxMACwWC+eccw5XXXVVvTZZWVkUFBTUil9RUREjRoxoluu3dWKlcmJZN3AkYu91BYGy/2FUfE9KzXKeShvFtYGjmVXck79vqWFgUEN15eAJ7ZwHKqkI4s4xi2wwHRTdZMGenYMWiWIedTv6th9Rep2KkjawTjvFasHscmFOTIhFcJosWNUo0fIyQqXl6KqK7ExD7n8O5v7nxBbUb/0BLX8B+LcivmCC9kKjxG/ZsmV8++23OBwOJEmiT58+PPjgg5x11lmceeaZ+2VAIBBA0zTcbjeGYfDFF1/Qt2/feu1OPPFEPvjgAwYOHEheXl5tVYmOhm5IWLKGEO1zB2reGxDMJ0Ody0dpc8mLZrBgw7HkVZ2Cz6ZRHLVSHojij2qM75pEWpKdOHv79P4E+4/kjsOenoahHQe5x+2yQ8LsdGBNS0N2OMESS59nQKzulmzClJGFKy6OUFExEY+XHeldZGc6cp/TMfU9g+yh/anRxPdL0D5olPjJsozJFGsaFxdHZWUlLpeLkpKS/TagoqKCa6+9Fk3T0HWd7t27c8899wAwceJEXnrpJdLT07nkkku47bbbGDt2LLIsc9999+FydczhFN0Sh7XLKchJR2H4N6CWfE209BtyKSbX/B6flW3j3srrYZcsHKvKAwzPiWNAl6QGcnMIOgK6bmBKScMaCBCuqtkpeulpyK44dElGBxpKQqrrBtic2Lp2w1xVQbikDC0aaTDKVCBoDzRqzu/KK6/k73//O2PHjuXuu+8mLy8Pm81GMBhsE5GgjaW9zvk1hEn1Ey7eWLtOKxAKs3TtjwzwPIFNCvGZfhF/2iaSkpLKh2ur2VgT4h/90rjzuB7EOxpXn62t0J4/p93Rmn1SomFCBQWYk5OQd5OqbE9IEki6hhEJYwSDqF4vaiBIer8e9Tw/MecnaKs0Svw8Hg+6rpOQkEAoFOLVV18lEAjwj3/8g7S0tANhZ7NwMImfJAFV24jWlO3YQnlNEKl6Ic6tj2CgUJF5L0rq4fwaSWbK15uxm2Q+mNiXw3qktivvrz1/TrujNfsUmz9u0MHbJ2RZAk0lIcFBZU243j4hfoK2SKOGPXdddmCz2ZgyZUqLGSRoHIYBSkIGVpsDzVeFFgqQ6LZRrRyNz7MGV82nJJY+Tpn5MUam2xiZ5ebnQi+vLiuiV2Y8SS5Lvaz8go6BYTRvWIquGyApKBYLEN5re4GgLdAo8Xvqqad2u+/6669vNmMETUPDBLYkZEcSSjSENezF7K9ha/Af2KKbMAV+J73icaTM6dw1JJ4JhV5mb65k8pYqju2XTvtOUy4QCAT7TqPEr7i4uM7rsrIyFi1axPHHH98iRgmahq4Dig3JaSMluzOK1U404R5Cy69C8q9B2fIiA7tfzymdTHy2VeWl5YUM7ZxYWwlCIBAIOhqNEr+HH3643rYFCxYwZ86cZjdIsO8YBigmE3JcBqZoBEufe4j8fgNa8Swkawo39T2NuQXw3dYafssvZ2zfTGSpPc3+CQQCQfOwzyNfRx55JF9//XVz2iJoJjQUTEnZmFMHY+p+HQDqltfplHcdt3SKZeSY8eNGfJ6a1jRTIBAIWo1GeX5bt26t8zoYDDJ79uzazCyCtocqWTCndMFQT0EyJ6BuehYjsJmLuJPU5KN5oOoCZv32JycO70d8fJyoBSgQCDoUjRK/sWPHIklSbUJru91O3759a6ssCNommsmOOTUHQz8SOWEo6rb30ba9zwT7AkZbF/HipvPplmCjc5duJCa4cFpNNDHPuUAgELRLGiV+a9as2XsjQZvDMMCwJWJJihCuKMTc5R8o6WMJbXgOd/Uv3Ox6iQcX6fTxnc2RvbuSFGcnKc6K0VwLwAQCgaCNIqLdD3J03cBwpWJNykQ2W5FtWTgHPognM7ZW8/a4V/n2jy946ucNbC31UVQRQMTACASCg51GeX6jR49GasQd8bvvvttfewQtgG5ISO4MzM4kCNWgespJ7nYaVZEaXBVv80TSU1y81c11FRHuHJVLvNOC3aLs/cQCgUDQTmmU+F1wwQV8+umnnH/++WRlZVFYWMjbb7/NpEmTGDBgQEvbKGgGDMNAk8xIjhRM9kTMES9R+Qr8v1fhrJnDi8nTOKvsPq6cH+Vlk8yYvumiOo1AIDhoaZT4ffLJJ7z66qukp6fXbjv66KO59NJLufjii1vMOEHzYxixpRBYEkjo4sZsvgvfohqc3oW8lfYgpxY/yGM/2xjWObHdJcAWCASCxtKoOb/S0lIcDkedbQ6Ho1lKGglaD9VQcGZ2JeGwB9Fcg0mQqnkz9X62lBfy0YptiGI1AoHgYKVR4jdmzBiuuuoqfvzxRzZu3MjChQuZMmUKY8aMaWn7BC2MihlLWg/cwx/FcPSkk6mYN1Lu58Xf1lBS5RPBLwKB4KCkUSWNwuEwM2fOZN68eZSWlpKamspJJ53ENddcg81mOxB2NgsHU0mj3bGvfTJpQcJblxJccjWEtrEk3JvZ7keYdvqRyFLrBgWLz6l90FCfREkjQVulUeJ3sCDEb/dIEigRL6H83/AunoJZLWNBaDBJhz7FkUMGomqt9zURn1P7QIifoD3RqEf6X375pTbFWVlZGbfeeiu33347ZWVlezlS0F4wDNAsbqw5w3ANfRw/8RxtW0710tsJVuQjSR3mGUkgEHQAGiV+U6dORVFi674eeeQRVFVFkiT+/e9/t6hxggOLYYBuS8TW+Qgsgx7Dpzs40vwTv391K3K4Wsz/CQSCg4ZGLXUoKSkhKysLVVVZuHAh33zzDWazmaOOOqql7RMcYAzDQLclktpnLF8UTWVo6Z30jnxJ/nf3knr0I5htdmQJUQdQIBC0axolfi6Xi/LyctavX0/37t1xOp1EIhFUVd1vA6qqqrjlllvIz8/HYrHQpUsX7rvvPpKSkuq0u+222/jpp59ITEwE4MQTT+Sqq67a7+sL6mMYBpoljr8dewZ3vlPDP00PEl/2Ni9/1pM+g05ncE4icU4LFkUWibAFAkG7pFHid95553H66acTjUa54447AFi6dCndunXbbwMkSeLSSy9lxIgRAEybNo3p06fz0EMP1Wt7+eWXc9555+33NQV7Z8cc4KXjz+fdWZs4z/oup4Qf4+Q56cTHd2Z8t2QmH5JJl1SnyAQjEAjaHY2a87v88st5/fXXee+99xg/fjwA6enpPPDAA/ttQEJCQq3wAQwePJjCwsL9Pq9g/zEM6JKewoWT76fKNpxExcfzyY+zsdLD44u38bd3lzFvVbEYAhUIBO2ONrXUQdd1Lr74YsaMGcMFF1xQZ99tt93GokWLcDgcdOrUiX/+85907969lSztWGiaTvmWNfjnnYAUKWW9cyI3VFzJmvIAZlnitqO6cvNxPXA7rK1tqkAgEDSKNiV+U6dOpaSkhGeeeQZZruuUlpSUkJqaiizLfPrppzz11FN8/fXXtVGojUGs89t3ZBm0ggX4vz0DDJVA74e4e8soPtlQAcCJXZOYMaEPSU5rs88Dis+pfSDW+QnaE22mnt+0adPYsmULM2bMqCd8EBtm3bF90qRJBAIBiouLD7SZHRZdBznraGwDbwfAsf4+njoizN1HdMGqSMzbXMmEt5byR2ENsizWRAgEgrZNmxC/J554gpUrV/Lss89isTRcSWDXJNo//PADsizXqTIhaHkMA8wDr8eU9TfQQ6gLz+PyzMW8M6kPmU4zG6pDTHp/BT9sqmhU/UeBQCBoLVp92HP9+vVMmDCB3Nzc2jyhOTk5PPvss0ycOJGXXnqJ9PR0LrzwQioqYjdVl8vFLbfcwuDBg5t0LTHs2TzI0Rp88yehV/8OgClrPJW97+WKr6v4rciL26zw9ukDGNklsVmCYcTn1D4Qw56C9kSri9+BRIhf86HoIYKLHyKy4SUwomBJgv73cNHy/izYWkOcReG9MwZyaKeE/RZA8Tm1D4T4CdoTbWLYU9D+0GQbjsP+jXP0x8hxAyBSCcuu5420xzg2x44nonH2R3+wtMAj0qIJBII2hxA/wT6jYkbOORL3uM+x9LoJZBtGyZe82uk1jsiJwxPROOvD31lR6GltUwUCgaAOQvwE+0UsE0w89sNuw3X0WyBbMLZ+zDtDfmNkdhzVYZUzP/id34u9wgMUCARtBiF+gmZBMxSknONwHPpo7PXye3h/TIARWXFUhVVOfXc5326sFAIoEAjaBEL8BM2GYRjIPc7H0vMiMFS0ny7hw0lpHNslAV9U47z//sGHfxQLARQIBK2OED9Bs2M59GGUlMMwwuUYCy/i7dP7MblfGlHd4No5a5j5c75YBygQCFoVIX6C5kcyYz/mTSRbOlrlUvQlt/L0yf248fDOGMD932/ijvnrQOifQCBoJYT4CVoGayqOY98C2Uxkw5tEl9/HHcfk8tjYnigSvLK0gFPfXsbSwhoxDCoQCA44QvwELYaUNAz7iCcBifCqp/DPG88F/SReP3UATrPMT9tqOOmtZZz5/gqWF4nlEAKB4MAhxE/QoijdzsZ53MdI1mS08t/wzzqKsY7F/HTZYZw/IB2bIvNdXhXj3lzK2R+uYHmxEEGBQNDyCPETtDhSxjE4T/4BU9qRGJFqAt+dS9K6+5j+t+78cMmh20VQ4pvNVYz7z1JOf385P2+tbm2zBQLBQYwQP8GBwZqObeyn2AbdDpJCZM2LBL8cTyeHn8dP7suPlx3GPwZmYDPJ/LClmknvLmf8W0v5ZlNls9cHFAgEApHYugMkF25rGOW/Evj+YoxQMZKzE44xHyDF9UaWJYo9IZ77JZ+3/yjGG9EAGJjh5vrDO/O3nikoB0mtwPbwOTUVkdha0J4Q4tcBbkBtESNUQuibs9CqfgezG/vRb6BkHAOAJEnUBCO8tGgbry0toCKkAtA1wc51Iztzev90LEr7HrRoL59TUxDiJ2hPCPHrADegNosWILzwCqLbvgBJwXbYdEw9LqjTJKRpfLCqjCd/2ESRLwJAhsvClBGdOWtgOnFWc2tYvt+0q8+pkQjxE7QnhPh1gBtQW8YwdNTl9xFePRMAS79rMA++t04GmNRUN8VlHj5cUczMX/LZWB0EQJbgkAw3x3ZN4piuSQzNdGNuJx5he/ucGoMQP0F7QohfB7gBtQfUjW8R+vVmMNR6AlinTxLMWlPKS79tY0mRB22Xb6/TrDAg3UWq00ya00qa00KK00yW28qQzDiS7M3nJYZVHatp34W2vX5Oe0KIn6A9YWptAwQCAFP387Hb0gl+fz6R1c8gWRIx97+hfkMDTu6dxil90vGEony/uZJvN1WyML+avJoQv26r2e01eiU7GNk5gcNzEhie7cZuUohoOhFNJ6TqRDQDffuzoETsxi0h4Y2orCn3s6bMz7pyP+sqAlQEonSOt3FodjwjO8dzaHYcvZKdyHtIVxPVdIp8YYq8ESJFXsyaTprTQqrDjMuiiHynAsEBRHh+HeDpuz2hbvk/QgsvBwyshz2GuefFe+2TJMX+bakKsq7cT7E3QpkvTKk/Qrk/ypaaEKsr/ES05vuqS8BfzxZnNZHltmKSJczK9n+yTEjVKfCEKPFF6h2zA7tJJs1pITfRTu8UJz2THfROcdAr2YluGKws9bGqxMeqMj9/lHgp9IRJsptJc1nIcFlJd1lIc1qwmxVsJhmLImFVZKwmGVU38EU0/BEt9v+ohi+sUhNWqQmpeMIq1SEVX0TFYVaIs5qIs5pwW03EWRV0I+bpBlWdsKoTUjVU3UCWJGSJ2v+num3cfXQumW5bbb+E5ydoq7QJz2/z5s3cdtttVFdXk5CQwLRp08jNza3TRtM0HnjgAX744QckSeLyyy/njDPOaB2DBS2GqctpWKNewr/eRPi3W8AcD6kX7vEYw4j96xRvp1O8HWB7vlCpNm+oP6yxuKCGn/Or+XVbNStL/QDbBSomFBZFRpFBN0A3DHY8FloViS5xNronxYSpT6qL7Hgb6yoC/LK1mkUFNawo8VEaiOIJq7u1UwKS7SbSHRaSnRaqAhEqgioVoShBVWdLTYgtNSG+z6tq1HvljWhsqQk1qu2B4uReKXXETyBoq7QJz++CCy7g73//OxMnTuSzzz7jv//9L2+++WadNp9++imzZs3i5Zdfprq6mkmTJvHuu++Sk5PT6OsIz6/9EFn1FJHl94FkIv2Uj/G7jmq2c0uSBBLomoEkx17LEuz6S5CkmAgahoGhbx8ClXYIrVGnnSRJGEB+VZCKQISobhBVDVTdIKJrWGSZrDgbGS4LVpOCJEFSooPq6iCGYaAbOp6wRrE3wqaqIOvKfayvCLC+MsDGylhwT+8UB31TnPRLdTEgw0Vugp2qYJRSf4QSX4RSf4RSX4RgVCOi6YQ1o3ZIV5ElnGYFp1nBZTVt/79CvM1Mgk0hzmom3mbCbVUIRQ08kSiekIonrOEJq0iAzRTzIm0mGZtJwSRL6LqBZsSGilUdUuNtDM9w1XkfhecnaKu0uvhVVFQwbtw4fv31VxRFQdM0RowYwfz580lKSqptd/nll3Paaadx4oknAnDfffeRlZXFpZde2oRrCfFrT4SX3Ud09VMgm5Csqa1tTrMiKzK6pu+50Y6x1V2nApvya93X4/YRkyMR0+EvoyT0qd0mxE/QVmn1Yc+ioiLS09NRFAUARVFIS0ujqKiojvgVFRWRlZVV+zozM5Pi4uImXauhH2FqqnsfLW+7HCx9MsY+SqVFw7P8GYxgUWub06xorW1ACxANlZJs9WI/SL5/goObVhe/A4nw/Noh/afS+bDbqSiraG1LmpWkJBeVlb7WNqNZSU5Pp9JrwrfL9094foK2SquLX2ZmJiUlJWiaVjvsWVpaSmZmZr12hYWFDBo0CKjvCQoOXhRHKpLj4AqiMLndSKGD6CEFUGxu8B5cfRIcvLR6Oozk5GT69u3L7NmzAZg9ezZ9+/atM+QJcOKJJ/LRRx+h6zqVlZV8/fXXjBs3rjVMFggEAkE7p9XFD+Dee+/l7bffZty4cbz99ttMnToVgMsuu4w//vgDgIkTJ5KTk8MJJ5zAmWeeyZQpU+jUqVNrmi0QCASCdkqrR3seSMScX/tE9Kl9INKbCdoTbcLzEwgEAoHgQCLETyAQCAQdDiF+AoFAIOhwtPpShwOJLNfPmt/QtvaO6FP7oCP06WDso+DgoEMFvAgEAoFAAGLYUyAQCAQdECF+AoFAIOhwCPETCAQCQYdDiJ9AIBAIOhxC/AQCgUDQ4RDiJxAIBIIOhxA/gUAgEHQ4hPgJBAKBoMMhxE8gEAgEHQ4hfgKBQCDocHRY8du8eTOTJ09m3LhxTJ48mby8vNY2qclMmzaNMWPG0Lt3b9atW1e7vb32raqqissuu4xx48Zx8sknc80111BZWQnA8uXLOeWUUxg3bhwXX3wxFRUVrWxt47n66qs55ZRTmDRpEueccw5//vkn0H4/p1155pln6nz/2vPnJOhgGB2U888/3/j0008NwzCMTz/91Dj//PNb2aKms2jRIqOwsNA49thjjbVr19Zub699q6qqMn755Zfa14888ohx++23G5qmGccff7yxaNEiwzAM49lnnzVuu+221jKzyXg8ntq/v/rqK2PSpEmGYbTfz2kHK1euNC655JLa7197/5wEHYsO6flVVFSwevVqJkyYAMCECRNYvXp1rZfRXhg+fDiZmZl1trXnviUkJDBixIja14MHD6awsJCVK1ditVoZPnw4AGeddRbz5s1rLTObjNvtrv3b5/MhSVK7/pwAIpEI9913H/fee2/ttvb+OQk6Fh2qpNEOioqKSE9PR1EUABRFIS0tjaKiIpKSklrZuv3jYOmbruu89957jBkzhqKiIrKysmr3JSUloes61dXVJCQktJ6RTeDOO+/kxx9/xDAMXnnllXb/OT311FOccsop5OTk1G47GD4nQcehQ3p+grbP/fffj8Ph4LzzzmttU5qFBx98kO+++44bb7yRRx99tLXN2S+WLVvGypUrOeecc1rbFIFgn+mQ4peZmUlJSQmapgGgaRqlpaX1hhDbIwdD36ZNm8aWLVuYMWMGsiyTmZlJYWFh7f7KykpkWW6X3sSkSZP49ddfycjIaLef06JFi9i4cSPHHXccY8aMobi4mEsuuYQtW7YcNJ+T4OCnQ4pfcnIyffv2Zfbs2QDMnj2bvn37tovhpr3R3vv2xBNPsHLlSp599lksFgsAAwYMIBQKsXjxYgDef/99TjzxxNY0s9H4/X6KiopqX3/zzTfEx8e368/p8ssvZ+HChXzzzTd88803ZGRk8Oqrr3LppZe2289J0PHosJXcN27cyG233YbH4yEuLo5p06bRrVu31jarSTzwwAPMnz+f8vJyEhMTSUhIYM6cOe22b+vXr2fChAnk5uZis9kAyMnJ4dlnn2Xp0qXcc889hMNhsrOzeeyxx0hJSWlli/dOeXk5V199NcFgEFmWiY+P59Zbb6V///7t9nP6K2PGjOGFF16gV69e7fZzEnQ8Oqz4CQQCgaDj0iGHPQUCgUDQsRHiJxAIBIIOhxA/gUAgEHQ4hPgJBAKBoMMhxE8gEAgEHQ4hfu2Q8ePH8+uvv7a2GYI98H//93+cffbZrW2GQCDYDUL82iFz5sypkwC6tdm2bRu9e/dGVdU2dS6BQCDYHUL8BAKBQNDhEOLXDhkzZgw//fQTADNnzuT666/nlltuYciQIYwfP54//r+9ew2JamvjAP7XplSyThNo3tIyUrEoZhwvXbykpo6JiWZqTYqmqZSYiX0xEwLNW2RSKDZmGIaY0kWwKzUWfhI0PJmCmnYZR4VmNFMZ9+jzfpD2m5qn2/tyINfv08yatZ+91pqZvWZthvX8/feix05PT6OsrAy+vr4QiUQIDQ3lt99qbW1FWFgYnJycEBYWhtbWVv64I0eOoLi4GJGRkRCJRIiLi+PT73zZfNrZ2RkikQhtbW0AgLq6OkilUjg7O+Po0aNQKpUAgPLycoSHh/Oru5s3b2Lfvn3QarWLxvrazMwMysvL4evrC1dXV6SmpmJkZAQAkJ2djZSUFL5uYWEhYmJiQEQYHR1FYmIi3Nzc4OzsjMTERAwODs7p48WLF/k+JiUlQaPRID09HWKxGGFhYfjw4QNf397eHlVVVfDx8YGrqyvy8/MxMzPzzXHv7e1FbGwsXFxc4O/vj8bGRv61pqYmBAYGQiQSwd3dHRUVFYu+fwzD/I/8m8kEmV+zZ88eam5uJiKikpIS2rp1KykUCtLpdFRUVETh4eGLHnv16lUKCgqi3t5empmZoc7OTlKr1aTRaEgikdDt27eJ4zhqaGggiURCarWaiIhkMhn5+PjQmzdvaHJykmQyGRUWFhIR0fv378nOzo44juPP8/jxY/L19aWenh7iOI6uXLlCERERREQ0PT1Nhw4dopKSEurr6yOJREIdHR2Lxprv+vXrFB4eTiqVirRaLWVlZVFaWhoREU1MTJCfnx/V19dTS0sLubi4kEqlIiIitVpNDx48oImJCRobG6OUlBRKTk7m48pkMvL19aW3b9/Sp0+fSCqVkp+fHzU3NxPHcZSRkTEnOaudnR3JZDLSaDSkVCrJz8+PamtriYiovr6eIiMjiYhofHycPDw8qK6ujjiOo46ODnJxcaHu7m4iItq1axefAHZkZIRevXr1zx8AhmF+G1v5/QGcnJzg6emJZcuWYf/+/ejq6lq07q1bt5CamgpbW1vo6enBwcEBQqEQCoUCNjY2CAkJgUAgQFBQEGxtbfHs2TP+2NDQUGzcuBGGhoYICAhAZ2fnouepqanBsWPHsGnTJggEAiQlJaGzsxNKpRL6+vrIz8/HjRs3kJycjPj4eDg6Ov5wf2tqapCWlgYzMzOsWLECJ06cwMOHD6HT6WBkZISCggLk5eUhIyMDWVlZMDMzAwAIhUL4+/vDyMgIxsbGSE5ORktLy5zYoaGhsLa2xqpVq+Dh4YH169dj586dEAgECAgIwOvXr+fUT0hIwJo1a2BhYYHo6Gh+o+qvKRQKWFpaIiwsDAKBAI6OjvD39+cTvQoEAvT09ODz58/466+/sGXLlh8eC4Zhfs2STGb7p/l642BDQ0NotVrodDo0NjYiOzsbwOwEKZfLMTg4CGtr6wUxhoeH5yQiBQALCwsMDQ3xz01MTPjHRkZGmJiYWLRNAwMDyM3NRX5+Pl9GRBgaGoKlpSWsrKzg6uqKpqYmHD58+Kf6OzAwgOPHj0Nf/7+/3fT19fHx40esW7cO27dvh5WVFdRqNaRSKV9ncnIS58+fx4sXLzA6OgpgNuvC9PQ0n1T267E0MDBYMLbz+/x1CiJLS0sMDw8vaK9SqUR7ezuf4RyYvf0cHBwMACgpKUFpaSkuXLgAe3t7pKenQyQS/dSYMAzzc9jk9wcLDg7mL7BfmJmZ4d27d7Czs5tTbmpqOicXGzCbmdvd3f2759HT01tQZm5ujqSkpAXn/0KhUKCtrQ07duxAQUEBzp07t2is+czMzJCbmwsnJ6dvvl5dXQ2O42Bqagq5XI7ExEQAwLVr19DX14fa2lqYmJigs7MTISEhoN/Y212lUmHz5s0AZidlU1PTBXXMzc3h7OyMysrKb8bYtm0bSktLwXEcqqurcfLkSTQ1Nf1ymxiG+T5223OJCQ8Px6VLl9Df3w8iQldXFzQaDTw9PdHf34+GhgZ+1djT0wMvL6/vxly7di309fXx/v17viwyMhLl5eXo7u4GAIyNjeH+/fsAZpOcnjlzBjk5OcjLy8PTp0/5i/23Ys0XFRWF4uJi/g80arUaT548AQD09fWhuLgYhYWFKCgogFwu52/Pjo+Pw8DAAKtXr8bIyAguX7788wM4T0VFBUZHR6FSqVBVVYXAwMAFdby8vNDf3487d+6A4zhwHIf29nb09vZiamoK9+7dw9jYGJYvX46VK1fOWdEyDPP/wb5lS0xsbCykUini4uIgFouRmZkJrVYLoVCIsrIyVFZWwtXVFXK5HGVlZT+UXNXIyAhJSUmIioqCRCLBy5cvsXfvXsTHx+PUqVMQi8UICgrC8+fPAQBnz56Ft7c3PD09IRQKkZOTg8zMTGg0mm/Gmi86Ohre3t6Ii4uDSCTCwYMH0d7eDp1Oh4yMDCQkJMDBwQEbNmxAWloaTp8+jampKcTExECr1cLNzQ0RERE/tKr9Hh8fH4SGhiIkJAReXl44cODAgjrGxsaoqKhAY2Mj3N3dsXv3bhQVFWFqagoAcPfuXXh7e0MsFqOmpgaFhYW/3S6GYf4Zy+fHML/I3t4ejx49go2Nzb/dFIZhfhJb+TEMwzBLDpv8GIZhmCWH3fZkGIZhlhy28mMYhmGWHDb5MQzDMEsOm/wYhmGYJYdNfgzDMMySwyY/hmEYZsn5D+qvAqq1CD2xAAAAAElFTkSuQmCC\n", + "image/png": 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", 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\n", 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", 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\n", 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", 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" ] @@ -550,7 +572,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -590,7 +612,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -604,7 +626,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.13.0" } }, "nbformat": 4, diff --git a/src/eval.py b/src/eval.py index 8ba52579..da90003b 100644 --- a/src/eval.py +++ b/src/eval.py @@ -185,9 +185,13 @@ def eval_model( all_metrics.append(metrics) metrics = torch.cat(all_metrics, dim=0) - - return aggregate_metrics(metrics) - + results = aggregate_metrics(metrics) + + if prompting_strategy == "standard": + grad_alignments = compute_gradient_alignment(model, task_sampler(), xs[0]) + results["gradient_alignment"] = grad_alignments + + return results def build_evals(conf): n_dims = conf.model.n_dims @@ -209,6 +213,10 @@ def build_evals(conf): evaluation_kwargs = {} evaluation_kwargs["standard"] = {"prompting_strategy": "standard"} + evaluation_kwargs["gradient"] = { + "prompting_strategy": "standard", + "task_sampler_kwargs": {"compute_gradient": True} + } if task_name != "linear_regression": if task_name in ["relu_2nn_regression"]: evaluation_kwargs["linear_regression"] = {"task_name": "linear_regression"} @@ -390,6 +398,23 @@ def read_run_dir(run_dir): assert len(df) == len(df.run_name.unique()) return df +# Figure 3 and 4: +def compute_gradient_alignment(model, task, xs, n_points=40): + w = task.get_ground_truth() + + alignments = [] + for k in range(n_points): + direction = torch.rand_like(w) + direction = direction / direction.norm() + + x = direction.requires_grad_(True) + with torch.enable_grad(): + pred = model(xs[:k], task.evaluate(xs[:k]), x.unsqueeze(0)) + grad = torch.autograd.grad(pred.sum(), x)[0] + alignments = (grad @ w) / (grad.norm() * w.norm()) + alignments.append(alignments.item()) + return alignments + if __name__ == "__main__": run_dir = sys.argv[1] for task in os.listdir(run_dir): diff --git a/src/models.py b/src/models.py index 0fabece0..2ae4d25a 100644 --- a/src/models.py +++ b/src/models.py @@ -747,4 +747,12 @@ def _create_ar1_covariance(self, n, ar_coef): """Create AR(1) covariance matrix""" indices = torch.arange(n, dtype=torch.float32) diff = torch.abs(indices.unsqueeze(0) - indices.unsqueeze(1)) - return torch.pow(ar_coef, diff) \ No newline at end of file + return torch.pow(ar_coef, diff) + +# class AR2Model: +# def __init__(self): +# self.ar1_coef = None +# self.ar2_coef = None +# self.name = "AR(2)" + +# def fit(self, x_train, y_train): diff --git a/src/samplers.py b/src/samplers.py index e8370e34..3538b0ad 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -15,7 +15,9 @@ def get_data_sampler(data_name, n_dims, **kwargs): names_to_classes = { "gaussian": GaussianSampler, "ar1":AR1Sampler, - "var1":VAR1Sampler, + # "var1":VAR1Sampler, + "ar2":AR2Sampler, + "vr2":VR2Sampler, } if data_name in names_to_classes: sampler_cls = names_to_classes[data_name] @@ -219,3 +221,104 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): # if __name__ == "__main__": # test_var1_sampler() +class AR2Sampler(DataSampler): + def __init__(self, n_dims, ar1_coef=0.5, ar2_coef=0.3, noise_std=1.0, bias=None, scale=None): + super().__init__(n_dims) + assert abs(ar2_coef) < 1, "|ar2_coef| must be < 1 for a stable AR(2)" + + self.ar1_coef = float(ar1_coef) + self.ar2_coef = float(ar2_coef) + self.noise_std = float(noise_std) + self.bias = bias + self.scale = scale + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + xs_b = torch.zeros(b_size, n_points, self.n_dims) + + generators =None + if seeds is not None: + generators = [torch.Generator().manual_seed(int(seed)) for seed in seeds] + + for t in range(2): + if generators is None: + xs_b[:, t, :] = torch.randn(b_size, self.n_dims) + else: + for i in range(b_size): + xs_b[i, t, :] = torch.randn(self.n_dims, generator=generators[i]) + + # AR(2): x_t = ar1_coef * x_{t-1} + ar2_coef * x_{t-2} + eps_t + for t in range(2, n_points): + if generators is None: + eps_t = self.noise_std * torch.randn(b_size, self.n_dims) + else: + eps_t = torch.zeros(b_size, self.n_dims) + for i in range(b_size): + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i]) + xs_b[:, t, :] = (self.ar1_coef * xs_b[:, t - 1, :] + + self.ar2_coef * xs_b[:, t - 2, :] + eps_t) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b +class VR2Sampler(DataSampler): + def __init__(self, n_dims, ar1_mat=None, ar2_mat=None, noise_std=1.0, bias=None, scale=None): + super().__init__(n_dims) + + if ar1_mat is None: + ar1_mat = 0.5 * torch.eye(n_dims) + if ar2_mat is None: + ar2_mat = 0.3 * torch.eye(n_dims) + + # Check + assert ar1_mat.shape == (n_dims, n_dims), "ar1_mat must be n_dims x n_dims" + assert ar2_mat.shape == (n_dims, n_dims), "ar2_mat must be n_dims x n_dims" + + self.ar1_mat = torch.tensor(ar1_mat, dtype=torch.float32) + self.ar2_mat = torch.tensor(ar2_mat, dtype=torch.float32) + self.noise_std = float(noise_std) + self.bias = bias + self.scale = scale + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + xs_b = torch.zeros(b_size, n_points, self.n_dims) + + generators = None + if seeds is not None: + generators = [torch.Generator().manual_seed(int(seed)) for seed in seeds] + + # Initialize first two time points + for t in range(2): + if generators is None: + xs_b[:, t, :] = torch.randn(b_size, self.n_dims) + else: + for i in range(b_size): + xs_b[i, t, :] = torch.randn(self.n_dims, generator=generators[i]) + + # VR(2): x_t = A1 * x_{t-1} + A2 * x_{t-2} + eps_t + for t in range(2, n_points): + if generators is None: + eps_t = self.noise_std * torch.randn(b_size, self.n_dims) + else: + eps_t = torch.zeros(b_size, self.n_dims) + for i in range(b_size): + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i]) + + # Matrix multiplication for each sample in batch + xs_b[:, t, :] = (torch.matmul(xs_b[:, t-1, :], self.ar1_mat.T) + + torch.matmul(xs_b[:, t-2, :], self.ar2_mat.T) + + eps_t) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + + return xs_b \ No newline at end of file diff --git a/src/tasks.py b/src/tasks.py index 19e64180..69fa233c 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -414,3 +414,30 @@ def get_metric(): @staticmethod def get_training_metric(): return mean_squared_error + +# class AR2RegressionTask: + # def __init__(self, ar1_coef=0.5, ar2_coef=0.3, noise_std=1.0): + # """ + # AR(2) Regression Task: y_t = ar1_coef * y_{t-1} + ar2_coef * y_{t-2} + epsilon_t + # where epsilon_t ~ N(0, noise_std^2) + + # ar1_coef: AR(1) coefficient + # ar2_coef: AR(2) coefficient + # noise_std: standard deviation of innovation noise + # """ + # self.ar1_coef = ar1_coef + # self.ar2_coef = ar2_coef + # self.noise_std = noise_std + # def evaluate(self, xs): + # batch_size, seq_len, dim = xs.shape + # ys = torch.zeros(xs) + + # ys[:, 0:2, :] = xs[:, 0:2, :] # Initialize first two values + + # for t in range(2, seq_len): + # ys[:, t, :] = (self.ar1_coef * ys[:, t-1, :] + + # self.ar2_coef * ys[:, t-2, :] + + # self.noise_std * torch.randn_like(xs[:, t, :])) + # return ys + # def get_metric(self): + # return lambda pred, target: ((pred - target) ** 2).mean(dim=-1) \ No newline at end of file From c2478578847b57718d91f92ac6220bc3924b6f08 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tr=E1=BB=8Bnh=20V=C5=A9=20=C4=90=E1=BB=A9c=20H=E1=BA=A3i?= Date: Mon, 13 Oct 2025 23:21:18 +0700 Subject: [PATCH 07/88] adding littles --- src/conf/toy.yaml | 51 +++++++++++++++++++++++++++-------------------- src/eval.py | 2 ++ src/schema.py | 3 ++- src/tasks.py | 1 + 4 files changed, 34 insertions(+), 23 deletions(-) diff --git a/src/conf/toy.yaml b/src/conf/toy.yaml index c3566bab..b7e340e1 100644 --- a/src/conf/toy.yaml +++ b/src/conf/toy.yaml @@ -3,31 +3,38 @@ inherit: - wandb.yaml model: - n_dims: 5 - n_positions: 11 + family: gpt2 + n_dims: 5 + n_embd: 128 + n_head: 8 + n_layer: 4 + n_positions: 100 training: - task: linear_regression - data: gaussian - task_kwargs: {} - batch_size: 64 - learning_rate: 0.0001 - save_every_steps: 1000 - keep_every_steps: 100000 - train_steps: 5001 - curriculum: - dims: - start: 5 - end: 5 - inc: 1 - interval: 2000 - points: - start: 11 - end: 11 - inc: 2 - interval: 2000 + batch_size: 32 + curriculum: + dims: + start: 5 + end: 5 + inc: 0 + interval: 1 + points: + start: 11 + end: 11 + inc: 0 + interval: 1 + data: ar2 + keep_every_steps: 100000 + learning_rate: 0.0003 + num_tasks: null + num_training_examples: null + resume_id: null + save_every_steps: 1000 + task: ar1_linear_regression + task_kwargs: {} + train_steps: 50001 out_dir: ../models/linear_regression wandb: - name: "linear_regression_toy" + name: "ar2_10_points" diff --git a/src/eval.py b/src/eval.py index da90003b..3c074b28 100644 --- a/src/eval.py +++ b/src/eval.py @@ -217,6 +217,8 @@ def build_evals(conf): "prompting_strategy": "standard", "task_sampler_kwargs": {"compute_gradient": True} } + + task_name =["linear_regression" if task_name == "ar1_linear_regression" else task_name][0] if task_name != "linear_regression": if task_name in ["relu_2nn_regression"]: evaluation_kwargs["linear_regression"] = {"task_name": "linear_regression"} diff --git a/src/schema.py b/src/schema.py index 40f72489..e58a423a 100644 --- a/src/schema.py +++ b/src/schema.py @@ -41,6 +41,7 @@ "relu_2nn_regression", "decision_tree", "noisy_linear_regression", + "ar1_linear_regression", ] training_schema = { @@ -48,7 +49,7 @@ "task_kwargs": merge(tdict, required), "num_tasks": merge(tinteger, nullable, default(None)), "num_training_examples": merge(tinteger, nullable, default(None)), - "data": merge(tstring, allowed(["gaussian","ar1","var1"])), + "data": merge(tstring, allowed(["gaussian","ar1","var1","ar2"])), "batch_size": merge(tinteger, default(64)), "learning_rate": merge(tfloat, default(3e-4)), "train_steps": merge(tinteger, default(1000)), diff --git a/src/tasks.py b/src/tasks.py index 69fa233c..c4b455a4 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -60,6 +60,7 @@ def get_task_sampler( "quadratic_regression": QuadraticRegression, "relu_2nn_regression": Relu2nnRegression, "decision_tree": DecisionTree, + "ar1_linear_regression": AR1LinearRegression, } if task_name in task_names_to_classes: task_cls = task_names_to_classes[task_name] From 4216fb8629e3b7496b1be248b5c9e4c29a9fcfbc Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Sat, 4 Oct 2025 18:46:30 +0700 Subject: [PATCH 08/88] Add ridge and other relevant functions --- src/models.py | 8 -------- src/samplers.py | 28 ++++++++++++++++++---------- src/tasks.py | 4 ++-- 3 files changed, 20 insertions(+), 20 deletions(-) diff --git a/src/models.py b/src/models.py index 2ae4d25a..5fd6286c 100644 --- a/src/models.py +++ b/src/models.py @@ -748,11 +748,3 @@ def _create_ar1_covariance(self, n, ar_coef): indices = torch.arange(n, dtype=torch.float32) diff = torch.abs(indices.unsqueeze(0) - indices.unsqueeze(1)) return torch.pow(ar_coef, diff) - -# class AR2Model: -# def __init__(self): -# self.ar1_coef = None -# self.ar2_coef = None -# self.name = "AR(2)" - -# def fit(self, x_train, y_train): diff --git a/src/samplers.py b/src/samplers.py index 3538b0ad..3ed7ef2b 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -218,9 +218,6 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): xs_b[:, :, n_dims_truncated:] = 0 return xs_b - -# if __name__ == "__main__": -# test_var1_sampler() class AR2Sampler(DataSampler): def __init__(self, n_dims, ar1_coef=0.5, ar2_coef=0.3, noise_std=1.0, bias=None, scale=None): super().__init__(n_dims) @@ -233,19 +230,26 @@ def __init__(self, n_dims, ar1_coef=0.5, ar2_coef=0.3, noise_std=1.0, bias=None, self.scale = scale def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + # Shape: (batch, time, dims) xs_b = torch.zeros(b_size, n_points, self.n_dims) - generators =None + generators = None if seeds is not None: - generators = [torch.Generator().manual_seed(int(seed)) for seed in seeds] + assert len(seeds) == b_size + generators = [] + for seed in seeds: + g = torch.Generator() + g.manual_seed(int(seed)) + generators.append(g) + # Initialize first two time steps for t in range(2): if generators is None: xs_b[:, t, :] = torch.randn(b_size, self.n_dims) else: for i in range(b_size): xs_b[i, t, :] = torch.randn(self.n_dims, generator=generators[i]) - + # AR(2): x_t = ar1_coef * x_{t-1} + ar2_coef * x_{t-2} + eps_t for t in range(2, n_points): if generators is None: @@ -254,9 +258,11 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): eps_t = torch.zeros(b_size, self.n_dims) for i in range(b_size): eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i]) - xs_b[:, t, :] = (self.ar1_coef * xs_b[:, t - 1, :] + - self.ar2_coef * xs_b[:, t - 2, :] + eps_t) - + xs_b[:, t, :] = ( + self.ar1_coef * xs_b[:, t - 1, :] + + self.ar2_coef * xs_b[:, t - 2, :] + + eps_t + ) if self.scale is not None: xs_b = xs_b @ self.scale if self.bias is not None: @@ -264,6 +270,7 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): if n_dims_truncated is not None: xs_b[:, :, n_dims_truncated:] = 0 + return xs_b class VR2Sampler(DataSampler): def __init__(self, n_dims, ar1_mat=None, ar2_mat=None, noise_std=1.0, bias=None, scale=None): @@ -321,4 +328,5 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): if n_dims_truncated is not None: xs_b[:, :, n_dims_truncated:] = 0 - return xs_b \ No newline at end of file + return xs_b + diff --git a/src/tasks.py b/src/tasks.py index c4b455a4..d324413a 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -295,7 +295,7 @@ def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, depth=4): self.target_tensor = torch.randn(self.dt_tensor.shape) elif seeds is not None: self.dt_tensor = torch.zeros(batch_size, 2 ** (depth + 1) - 1) - self.target_tensor = torch.zeros_like(dt_tensor) + self.target_tensor = torch.zeros_like(self.dt_tensor) generator = torch.Generator() assert len(seeds) == self.b_size for i, seed in enumerate(seeds): @@ -441,4 +441,4 @@ def get_training_metric(): # self.noise_std * torch.randn_like(xs[:, t, :])) # return ys # def get_metric(self): - # return lambda pred, target: ((pred - target) ** 2).mean(dim=-1) \ No newline at end of file + # return lambda pred, target: ((pred - target) ** 2).mean(dim=-1) From ab49e97924812b76487a670c7781c82991b9d12d Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 13 Oct 2025 23:57:03 +0700 Subject: [PATCH 09/88] NST --- src/eval.py | 4 +++ src/models.py | 67 +++++++++++++++++++++++++++++++++-------------- src/plot_utils.py | 6 +++++ src/samplers.py | 3 +++ src/schema.py | 2 ++ 5 files changed, 63 insertions(+), 19 deletions(-) diff --git a/src/eval.py b/src/eval.py index 3c074b28..1b35c5f2 100644 --- a/src/eval.py +++ b/src/eval.py @@ -213,6 +213,7 @@ def build_evals(conf): evaluation_kwargs = {} evaluation_kwargs["standard"] = {"prompting_strategy": "standard"} +<<<<<<< HEAD evaluation_kwargs["gradient"] = { "prompting_strategy": "standard", "task_sampler_kwargs": {"compute_gradient": True} @@ -220,6 +221,9 @@ def build_evals(conf): task_name =["linear_regression" if task_name == "ar1_linear_regression" else task_name][0] if task_name != "linear_regression": +======= + if task_name not in ["linear_regression", "ar1_linear_regression"]: +>>>>>>> 767436f (Update fetures) if task_name in ["relu_2nn_regression"]: evaluation_kwargs["linear_regression"] = {"task_name": "linear_regression"} for name, kwargs in evaluation_kwargs.items(): diff --git a/src/models.py b/src/models.py index 5fd6286c..b190d424 100644 --- a/src/models.py +++ b/src/models.py @@ -672,28 +672,57 @@ def __call__(self, xs, ys, inds=None): return torch.stack(preds, dim=1) def _estimate_ar_coef(self, residuals): - """Estimate AR(1) coefficient using Yule-Walker equations""" - if len(residuals) <= 1: - return 0.0 - - # Compute autocovariances - n = len(residuals) - gamma_0 = torch.var(residuals) - if n > 1: - gamma_1 = torch.mean(residuals[:-1] * residuals[1:]) - ar_coef = gamma_1 / gamma_0 if gamma_0 > 1e-10 else 0.0 - # Ensure stability + """Estimate AR(1) coefficient using Yule-Walker equations (returns a torch.Tensor scalar).""" + # Ensure residuals is a torch tensor + if not isinstance(residuals, torch.Tensor): + residuals = torch.tensor(residuals, dtype=torch.float32) + + if residuals.numel() <= 1: + # return tensor scalar on same device + return torch.tensor(0.0, dtype=torch.float32, device=residuals.device) + + # Use unbiased-ish estimators: + n = residuals.shape[0] + # gamma_0: variance (use unbiased? here regular torch.var with unbiased=False to match mean-of-squares) + gamma_0 = torch.var(residuals, unbiased=False) + gamma_1 = torch.mean(residuals[:-1] * residuals[1:]) + + # avoid division by (near) zero + if gamma_0.item() <= 1e-10: + ar_coef = torch.tensor(0.0, dtype=torch.float32, device=residuals.device) + else: + ar_coef = gamma_1 / gamma_0 + # ensure tensor type & correct device + if not isinstance(ar_coef, torch.Tensor): + ar_coef = torch.tensor(ar_coef, dtype=torch.float32, device=residuals.device) + else: + ar_coef = ar_coef.to(dtype=torch.float32, device=residuals.device) + + # clamp safely as tensor ar_coef = torch.clamp(ar_coef, -0.99, 0.99) + + return ar_coef # tensor scalar + + def _create_ar1_covariance(self, n, ar_coef, device=None, dtype=torch.float32): + """Create AR(1) covariance matrix V[i,j] = ar_coef**|i-j|. + ar_coef may be float or torch scalar; this returns a torch.Tensor (n x n). + """ + if device is None: + # default CPU + device = torch.device("cpu") + + # make ar_coef a tensor scalar on correct device + if not isinstance(ar_coef, torch.Tensor): + ar_coef_t = torch.tensor(ar_coef, dtype=dtype, device=device) else: - ar_coef = 0.0 - - return ar_coef + ar_coef_t = ar_coef.to(device=device, dtype=dtype) - def _create_ar1_covariance(self, n, ar_coef): - """Create AR(1) covariance matrix""" - indices = torch.arange(n, dtype=torch.float32) - diff = torch.abs(indices.unsqueeze(0) - indices.unsqueeze(1)) - return torch.pow(ar_coef, diff) + indices = torch.arange(n, dtype=dtype, device=device) + diff = torch.abs(indices.unsqueeze(0) - indices.unsqueeze(1)).to(dtype=dtype) + + # use torch.pow with tensor base and tensor exponent + # (ensure ar_coef_t is broadcastable) + return torch.pow(ar_coef_t, diff) class GLSModel: diff --git a/src/plot_utils.py b/src/plot_utils.py index 2bd41bad..006ed39b 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -37,6 +37,12 @@ "3-Nearest Neighbors", "2-layer NN, GD", ], + "ar1_linear_regression": [ + "Transformer", + "Least Squares", + "3-Nearest Neighbors", + "2-layer NN, GD", + ], } diff --git a/src/samplers.py b/src/samplers.py index 3ed7ef2b..193d7b4a 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -15,9 +15,12 @@ def get_data_sampler(data_name, n_dims, **kwargs): names_to_classes = { "gaussian": GaussianSampler, "ar1":AR1Sampler, +<<<<<<< HEAD # "var1":VAR1Sampler, "ar2":AR2Sampler, "vr2":VR2Sampler, +======= +>>>>>>> 767436f (Update fetures) } if data_name in names_to_classes: sampler_cls = names_to_classes[data_name] diff --git a/src/schema.py b/src/schema.py index e58a423a..11919181 100644 --- a/src/schema.py +++ b/src/schema.py @@ -42,6 +42,8 @@ "decision_tree", "noisy_linear_regression", "ar1_linear_regression", + "ar2_linear_regression", + "non_stationary_linear_regression", ] training_schema = { From 4288f99f77f09baeb857a31fa25600677ea3a9e3 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Tue, 14 Oct 2025 00:02:45 +0700 Subject: [PATCH 10/88] NST1 --- src/samplers.py | 45 ++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 42 insertions(+), 3 deletions(-) diff --git a/src/samplers.py b/src/samplers.py index 193d7b4a..4ed2955c 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -15,12 +15,9 @@ def get_data_sampler(data_name, n_dims, **kwargs): names_to_classes = { "gaussian": GaussianSampler, "ar1":AR1Sampler, -<<<<<<< HEAD # "var1":VAR1Sampler, "ar2":AR2Sampler, "vr2":VR2Sampler, -======= ->>>>>>> 767436f (Update fetures) } if data_name in names_to_classes: sampler_cls = names_to_classes[data_name] @@ -333,3 +330,45 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): return xs_b +class NonStationarySampler(DataSampler): + def __init__(self, n_dims, coef_base=0.5, coef_amplitude=0.4, noise_std = 0.1, bias=None, scale=None): + super().__init__(n_dims) + self.coef_base = float(coef_base) + self.coef_amplitude = float(coef_amplitude) + self.coef_noise_std = float(noise_std) + self.scale = scale + self.bias = bias + def get_transition_matrix(self, t, n_points): + t_norm = t / (n_points - 1) if n_points > 1 else 0.0 + time_varying_factor = self.coef_base + self.coef_amplitude * torch.sin(2 * math.pi * t_norm) + A_t = time_varying_factor * torch.eye(self.n_dims) + return A_t + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + xs_b = torch.zeros(b_size, n_points, self.n_dims) + generators = None + if seeds is None: + assert len(seeds) == b_size + generators = [torch.Generator().manual_seed(int(seed)) for seed in seeds] + if generators is None: + xs_b[:,0,:] = torch.randn(b_size, self.n_dims) * self.noise_std + else: + for i in range(b_size): + xs_b[i, 0, :] = torch.randn(self.n_dims, generator=generators[i]) * self.noise_std + for t in range(1, n_points): + A_t = self.get_transition_matrix(t, n_points) + + if generators is None: + eps_t = self.noise_std * torch.randn(b_size, self.n_dims) + else: + eps_t = torch.zeros(b_size, self.n_dims) + for i in range(b_size): + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i]) + xs_b[:, t, :] = (torch.matmul(xs_b[:, t-1, :], A_t) + eps_t) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + + return xs_b From ded21a243a622791eea8ad32714693e3be4cc831 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 13 Oct 2025 23:26:30 +0700 Subject: [PATCH 11/88] figure_3_and_4 --- src/eval.ipynb | 142 ++++++++++++++++++++++++++++++++++++++++++++++++- src/eval.py | 50 +++++++++++------ 2 files changed, 173 insertions(+), 19 deletions(-) diff --git a/src/eval.ipynb b/src/eval.ipynb index bdbff506..2c6d1d41 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -608,11 +608,149 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "395fe757", + "metadata": {}, + "outputs": [], + "source": [ + "# Figure 3(a): Function visualizations – sweep query along random directions\n", + "import math\n", + "\n", + "# Use existing model/task/data objects if defined earlier, else load them\n", + "try:\n", + " _ = model\n", + "except NameError:\n", + " model, conf = get_model_from_run(run_path)\n", + "\n", + "try:\n", + " _ = task_sampler\n", + "except NameError:\n", + " from samplers import get_data_sampler\n", + " from tasks import get_task_sampler\n", + " n_dims = conf.model.n_dims\n", + " batch_size = conf.training.batch_size\n", + " data_sampler = get_data_sampler(conf.training.data, n_dims)\n", + " task_sampler = get_task_sampler(\n", + " conf.training.task,\n", + " n_dims,\n", + " batch_size,\n", + " **conf.training.task_kwargs\n", + " )\n", + "\n", + "model = model.eval()\n", + "\n", + "# Helper: get true weight vector w if available\n", + "def _get_true_w(task):\n", + " return task.w_b[0, :, 0].detach().cpu() if hasattr(task, \"w_b\") else None\n", + "\n", + "# Helper: project a vector onto the row-space of X (k x d)\n", + "def _project_to_row_space(vec, X):\n", + " # X: (k, d); vec: (d,)\n", + " if X.numel() == 0:\n", + " return torch.zeros_like(vec)\n", + " # SVD gives X = U S Vt; row-space projector on R^d is P = V V^T with same rank as X\n", + " _, _, Vt = torch.linalg.svd(X, full_matrices=False)\n", + " P = Vt.t() @ Vt # (d x d)\n", + " return (P @ vec)\n", + "\n", + "@torch.no_grad()\n", + "def _estimate_range_quantiles(num_samples=4000):\n", + " # empirical 0.5%–99.5% quantiles of ||x|| for training distribution\n", + " xs_samp = data_sampler.sample_xs(n_points=num_samples, b_size=1)[0] # (num_samples, d)\n", + " norms = xs_samp.norm(dim=-1).cpu()\n", + " low = torch.quantile(norms, 0.005).item()\n", + " high = torch.quantile(norms, 0.995).item()\n", + " return low, high\n", + "\n", + "\n", + "def plot_function_visualizations(num_dirs=3, ks=None, T=15.0, num_steps=200, seed=None):\n", + " torch.manual_seed(seed if seed is not None else torch.seed())\n", + "\n", + " if ks is None:\n", + " # Use ~d/2, d, and min(2d, max_points in training curriculum)\n", + " d = conf.model.n_dims\n", + " max_pts = conf.training.curriculum.points.end\n", + " ks = [max(1, d // 2), d, min(2 * d, max_pts)]\n", + "\n", + " # Prepare tasks/contexts for each k\n", + " task = task_sampler() # single-task batch\n", + " w = _get_true_w(task)\n", + "\n", + " # Precompute norm band\n", + " band_low, band_high = _estimate_range_quantiles()\n", + "\n", + " fig, axes = plt.subplots(1, num_dirs, figsize=(14, 3.8), sharey=True)\n", + " axes = axes if isinstance(axes, (list, np.ndarray)) else [axes]\n", + "\n", + " for p in range(num_dirs):\n", + " ax = axes[p]\n", + "\n", + " # Random direction u (unit vector)\n", + " u = torch.randn(n_dims)\n", + " u = u / (u.norm() + 1e-8)\n", + "\n", + " ts = torch.linspace(-T, T, steps=num_steps)\n", + "\n", + " # For each k, build a fresh context and sweep the query\n", + " for ki, k in enumerate(ks):\n", + " xs_ctx = data_sampler.sample_xs(n_points=k, b_size=1) # (1, k, d)\n", + " ys_ctx = task.evaluate(xs_ctx)\n", + "\n", + " preds = []\n", + " for t in ts:\n", + " xq = (t * u).view(1, 1, -1)\n", + " xs_in = torch.cat([xs_ctx, xq], dim=1)\n", + " ys_in = torch.cat([ys_ctx, torch.zeros_like(ys_ctx[:, :1])], dim=1)\n", + " out = model(xs_in, ys_in, inds=[k]) # predict at query position\n", + " preds.append(out[0, 0].item())\n", + " preds = np.array(preds)\n", + "\n", + " label = {\n", + " ks[0]: f\"#dims/2 in-context examples\",\n", + " ks[1]: f\"#dims in-context examples\",\n", + " ks[-1]: f\"#dims * 2 in-context examples\",\n", + " }.get(k, f\"k={k}\")\n", + " ax.plot(ts.numpy(), preds, label=label, lw=2)\n", + "\n", + " # Ground truth line (if available)\n", + " if w is not None:\n", + " gt = ts.numpy() * float(torch.dot(u, w).item())\n", + " ax.plot(ts.numpy(), gt, color=\"C0\", lw=2, label=\"ground truth\")\n", + "\n", + " # Projected ground truth when k < d: show once as reference\n", + " # Use the middle k (d) context for projection, for a stable view\n", + " k_proj = ks[0]\n", + " xs_ctx_proj = data_sampler.sample_xs(n_points=k_proj, b_size=1)[0]\n", + " w_proj = _project_to_row_space(w, xs_ctx_proj)\n", + " gt_proj = ts.numpy() * float(torch.dot(u, w_proj).item())\n", + " ax.plot(ts.numpy(), gt_proj, color=\"C0\", lw=2, ls=\"--\", label=\"ground truth projected\")\n", + "\n", + " # Shade typical norm band for training inputs\n", + " ax.axvspan(-band_high, -band_low, color=\"#000000\", alpha=0.08)\n", + " ax.axvspan(band_low, band_high, color=\"#000000\", alpha=0.08)\n", + "\n", + " ax.set_xlabel(\"distance from origin\")\n", + " if p == 0:\n", + " ax.set_ylabel(\"function value\")\n", + " ax.set_title(\"\")\n", + "\n", + " # Build a single legend above subplots\n", + " handles, labels = axes[0].get_legend_handles_labels()\n", + " by_label = OrderedDict(zip(labels, handles))\n", + " fig.legend(by_label.values(), by_label.keys(), loc=\"upper center\", ncol=3, bbox_to_anchor=(0.5, 1.15))\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "plot_function_visualizations()\n" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "base", "language": "python", "name": "python3" }, @@ -626,7 +764,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.0" + "version": "3.13.5" } }, "nbformat": 4, diff --git a/src/eval.py b/src/eval.py index 1b35c5f2..1ddfd4dc 100644 --- a/src/eval.py +++ b/src/eval.py @@ -186,11 +186,12 @@ def eval_model( metrics = torch.cat(all_metrics, dim=0) results = aggregate_metrics(metrics) - + if prompting_strategy == "standard": grad_alignments = compute_gradient_alignment(model, task_sampler(), xs[0]) - results["gradient_alignment"] = grad_alignments - + if grad_alignments is not None: + results["gradient_alignment"] = grad_alignments + return results def build_evals(conf): @@ -213,7 +214,6 @@ def build_evals(conf): evaluation_kwargs = {} evaluation_kwargs["standard"] = {"prompting_strategy": "standard"} -<<<<<<< HEAD evaluation_kwargs["gradient"] = { "prompting_strategy": "standard", "task_sampler_kwargs": {"compute_gradient": True} @@ -221,9 +221,6 @@ def build_evals(conf): task_name =["linear_regression" if task_name == "ar1_linear_regression" else task_name][0] if task_name != "linear_regression": -======= - if task_name not in ["linear_regression", "ar1_linear_regression"]: ->>>>>>> 767436f (Update fetures) if task_name in ["relu_2nn_regression"]: evaluation_kwargs["linear_regression"] = {"task_name": "linear_regression"} for name, kwargs in evaluation_kwargs.items(): @@ -406,19 +403,38 @@ def read_run_dir(run_dir): # Figure 3 and 4: def compute_gradient_alignment(model, task, xs, n_points=40): - w = task.get_ground_truth() - alignments = [] - for k in range(n_points): - direction = torch.rand_like(w) - direction = direction / direction.norm() + device = next(model.parameters()).device + # ground-truth weight for this task (take first in batch) + w = task.w_b[0, :, 0].to(device) + + alignments = [] + max_points = min(n_points, xs.shape[0]) + + for k in range(max_points): + # Context up to k + ctx_xs = xs[:k].unsqueeze(0).to(device) + if k > 0: + ctx_ys = task.evaluate(ctx_xs.detach().cpu()).to(device) + else: + ctx_ys = torch.zeros(1, 0, device=device) + + # Random query direction normalized and scaled to match data norm + direction = torch.randn_like(w) + direction = direction / (direction.norm() + 1e-8) + scale = xs[k].norm() if k < xs.shape[0] else xs[-1].norm() + x_query = (direction * (scale + 1e-8)).detach().clone().requires_grad_(True) + + xs_with_query = torch.cat([ctx_xs, x_query.view(1, 1, -1)], dim=1) + ys_with_dummy = torch.cat([ctx_ys, torch.zeros(1, 1, device=device)], dim=1) - x = direction.requires_grad_(True) with torch.enable_grad(): - pred = model(xs[:k], task.evaluate(xs[:k]), x.unsqueeze(0)) - grad = torch.autograd.grad(pred.sum(), x)[0] - alignments = (grad @ w) / (grad.norm() * w.norm()) - alignments.append(alignments.item()) + pred = model(xs_with_query, ys_with_dummy, inds=[k]) + grad = torch.autograd.grad(pred.sum(), x_query)[0] + + cos_sim = torch.dot(grad, w) / (grad.norm() * w.norm() + 1e-8) + alignments.append(float(cos_sim.detach().cpu())) + return alignments if __name__ == "__main__": From 86f52fcb7073483bfcaeb7c8c544d04a64feabfe Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 13 Oct 2025 23:30:04 +0700 Subject: [PATCH 12/88] chore(eval): add numpy import for function visualizations --- src/eval.ipynb | 41 ++++++++++++++++++++++++----------------- 1 file changed, 24 insertions(+), 17 deletions(-) diff --git a/src/eval.ipynb b/src/eval.ipynb index 2c6d1d41..acb19242 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -7,13 +7,14 @@ "metadata": {}, "outputs": [ { - "ename": "", - "evalue": "", + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", "output_type": "error", "traceback": [ - "\u001b[1;31mRunning cells with 'Python 3.13.0' requires the ipykernel package.\n", - "\u001b[1;31mCreate a Python Environment with the required packages.\n", - "\u001b[1;31mOr install 'ipykernel' using the command: 'c:/Users/CaoHuuThienHoang/AppData/Local/Programs/Python/Python313/python.exe -m pip install ipykernel -U --user --force-reinstall'" + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mre\u001b[39;00m\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mos\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mseaborn\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msns\u001b[39;00m\n", + "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'matplotlib'" ] } ], @@ -21,6 +22,7 @@ "from collections import OrderedDict\n", "import re\n", "import os\n", + "import numpy as np\n", "\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", @@ -611,15 +613,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "395fe757", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'numpy'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Figure 3(a)\u001b[39;00m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmath\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 5\u001b[39m _ = model\n", + "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'numpy'" + ] + } + ], "source": [ - "# Figure 3(a): Function visualizations – sweep query along random directions\n", + "# Figure 3(a)\n", "import math\n", - "\n", - "# Use existing model/task/data objects if defined earlier, else load them\n", + "import numpy as np\n", "try:\n", " _ = model\n", "except NameError:\n", @@ -642,7 +655,6 @@ "\n", "model = model.eval()\n", "\n", - "# Helper: get true weight vector w if available\n", "def _get_true_w(task):\n", " return task.w_b[0, :, 0].detach().cpu() if hasattr(task, \"w_b\") else None\n", "\n", @@ -651,15 +663,13 @@ " # X: (k, d); vec: (d,)\n", " if X.numel() == 0:\n", " return torch.zeros_like(vec)\n", - " # SVD gives X = U S Vt; row-space projector on R^d is P = V V^T with same rank as X\n", " _, _, Vt = torch.linalg.svd(X, full_matrices=False)\n", " P = Vt.t() @ Vt # (d x d)\n", " return (P @ vec)\n", "\n", "@torch.no_grad()\n", "def _estimate_range_quantiles(num_samples=4000):\n", - " # empirical 0.5%–99.5% quantiles of ||x|| for training distribution\n", - " xs_samp = data_sampler.sample_xs(n_points=num_samples, b_size=1)[0] # (num_samples, d)\n", + " xs_samp = data_sampler.sample_xs(n_points=num_samples, b_size=1)[0] \n", " norms = xs_samp.norm(dim=-1).cpu()\n", " low = torch.quantile(norms, 0.005).item()\n", " high = torch.quantile(norms, 0.995).item()\n", @@ -670,12 +680,10 @@ " torch.manual_seed(seed if seed is not None else torch.seed())\n", "\n", " if ks is None:\n", - " # Use ~d/2, d, and min(2d, max_points in training curriculum)\n", " d = conf.model.n_dims\n", " max_pts = conf.training.curriculum.points.end\n", " ks = [max(1, d // 2), d, min(2 * d, max_pts)]\n", "\n", - " # Prepare tasks/contexts for each k\n", " task = task_sampler() # single-task batch\n", " w = _get_true_w(task)\n", "\n", @@ -737,7 +745,6 @@ " ax.set_ylabel(\"function value\")\n", " ax.set_title(\"\")\n", "\n", - " # Build a single legend above subplots\n", " handles, labels = axes[0].get_legend_handles_labels()\n", " by_label = OrderedDict(zip(labels, handles))\n", " fig.legend(by_label.values(), by_label.keys(), loc=\"upper center\", ncol=3, bbox_to_anchor=(0.5, 1.15))\n", From 76d8a3209ed398900b602978c10ad3ffa86a519d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tr=E1=BB=8Bnh=20V=C5=A9=20=C4=90=E1=BB=A9c=20H=E1=BA=A3i?= Date: Tue, 14 Oct 2025 19:39:02 +0700 Subject: [PATCH 13/88] update 14/10 --- src/conf/toy.yaml | 18 ++- src/eval.ipynb | 371 +++++++++++++++++++++++++++++++++++++--------- src/eval.py | 136 +++++++++++++---- src/models.py | 5 + src/plot_utils.py | 33 +++-- src/samplers.py | 118 +-------------- src/schema.py | 2 +- src/tasks.py | 7 +- 8 files changed, 462 insertions(+), 228 deletions(-) diff --git a/src/conf/toy.yaml b/src/conf/toy.yaml index b7e340e1..e4f58926 100644 --- a/src/conf/toy.yaml +++ b/src/conf/toy.yaml @@ -19,22 +19,24 @@ training: inc: 0 interval: 1 points: - start: 11 - end: 11 + start: 6 + end: 6 inc: 0 interval: 1 - data: ar2 - keep_every_steps: 100000 + data: ar1 + keep_every_steps: 10000 learning_rate: 0.0003 num_tasks: null num_training_examples: null resume_id: null - save_every_steps: 1000 + save_every_steps: 100 task: ar1_linear_regression - task_kwargs: {} - train_steps: 50001 + task_kwargs: { + "compute_gradient": True + } + train_steps: 501 out_dir: ../models/linear_regression wandb: - name: "ar2_10_points" + name: "fig3_6_points_" diff --git a/src/eval.ipynb b/src/eval.ipynb index acb19242..fe001232 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -2,19 +2,16 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "ed6cfeb1", "metadata": {}, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mre\u001b[39;00m\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mos\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mseaborn\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msns\u001b[39;00m\n", - "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'matplotlib'" + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" ] } ], @@ -45,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "id": "0e8d018b", "metadata": { "scrolled": true @@ -86,6 +83,19 @@ " \n", " \n", " \n", + " 1\n", + " 72802a08-1a86-4a1a-b1f4-8f02c487073f\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " ar2_10_points\n", + " \n", + " \n", " 0\n", " pretrained\n", " decision_tree\n", @@ -99,7 +109,33 @@ " decision_tree_pretrained\n", " \n", " \n", - " 1\n", + " 5\n", + " k=20_3\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " k=20_part3\n", + " \n", + " \n", + " 4\n", + " ar_with_k=40\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " k=40_1\n", + " \n", + " \n", + " 7\n", " pretrained\n", " linear_regression\n", " Transformer\n", @@ -112,20 +148,46 @@ " linear_regression_pretrained\n", " \n", " \n", + " 3\n", + " ar_k=10\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " linear_regression_toy with 10 points\n", + " \n", + " \n", " 2\n", - " d1ee6875-d215-418b-b5ef-b7edb52cb4ac\n", + " ar1_data_with_k=20\n", " linear_regression\n", " Transformer\n", " \n", " -1\n", " -1\n", " 5\n", - " 12\n", + " 4\n", " 8\n", - " linear_regression_toy\n", + " linear_regression_toy with 20 points\n", " \n", " \n", - " 3\n", + " 6\n", + " k=20_par2\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " linear_regression_toy with 21 points\n", + " \n", + " \n", + " 8\n", " pretrained\n", " relu_2nn_regression\n", " Transformer\n", @@ -138,7 +200,7 @@ " relu_2nn_regression_pretrained\n", " \n", " \n", - " 4\n", + " 9\n", " pretrained\n", " sparse_linear_regression\n", " Transformer\n", @@ -156,28 +218,43 @@ ], "text/plain": [ " run_id task \\\n", + "1 72802a08-1a86-4a1a-b1f4-8f02c487073f linear_regression \n", "0 pretrained decision_tree \n", - "1 pretrained linear_regression \n", - "2 d1ee6875-d215-418b-b5ef-b7edb52cb4ac linear_regression \n", - "3 pretrained relu_2nn_regression \n", - "4 pretrained sparse_linear_regression \n", + "5 k=20_3 linear_regression \n", + "4 ar_with_k=40 linear_regression \n", + "7 pretrained linear_regression \n", + "3 ar_k=10 linear_regression \n", + "2 ar1_data_with_k=20 linear_regression \n", + "6 k=20_par2 linear_regression \n", + "8 pretrained relu_2nn_regression \n", + "9 pretrained sparse_linear_regression \n", "\n", " model kwargs num_tasks num_examples n_dims \\\n", + "1 Transformer -1 -1 5 \n", "0 Transformer depth=4 -1 -1 20 \n", - "1 Transformer -1 -1 20 \n", + "5 Transformer -1 -1 5 \n", + "4 Transformer -1 -1 5 \n", + "7 Transformer -1 -1 20 \n", + "3 Transformer -1 -1 5 \n", "2 Transformer -1 -1 5 \n", - "3 Transformer hidden_layer_size=100 -1 -1 20 \n", - "4 Transformer sparsity=3 -1 -1 20 \n", + "6 Transformer -1 -1 5 \n", + "8 Transformer hidden_layer_size=100 -1 -1 20 \n", + "9 Transformer sparsity=3 -1 -1 20 \n", "\n", - " n_layer n_head run_name \n", - "0 12 8 decision_tree_pretrained \n", - "1 12 8 linear_regression_pretrained \n", - "2 12 8 linear_regression_toy \n", - "3 12 8 relu_2nn_regression_pretrained \n", - "4 12 8 sparse_regression_pretrained " + " n_layer n_head run_name \n", + "1 4 8 ar2_10_points \n", + "0 12 8 decision_tree_pretrained \n", + "5 4 8 k=20_part3 \n", + "4 4 8 k=40_1 \n", + "7 12 8 linear_regression_pretrained \n", + "3 4 8 linear_regression_toy with 10 points \n", + "2 4 8 linear_regression_toy with 20 points \n", + "6 4 8 linear_regression_toy with 21 points \n", + "8 12 8 relu_2nn_regression_pretrained \n", + "9 12 8 sparse_regression_pretrained " ] }, - "execution_count": 2, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -189,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 25, "id": "a9980951", "metadata": {}, "outputs": [], @@ -199,7 +276,7 @@ "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"pretrained\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"k=20_par2\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -218,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -228,21 +305,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "linear_regression_pretrained pretrained\n" + "linear_regression_toy with 21 points k=20_par2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 137068.76it/s]\n" + "100%|██████████| 16/16 [00:00" + "
" ] }, "metadata": {}, @@ -275,147 +352,280 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "31b4ecca", "metadata": { "scrolled": true }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available metrics: ['gradient', 'half_subspace', 'noisyLR', 'orthogonal_train_test', 'overlapping_train_test', 'random_quadrants', 'scale-x=0.333', 'scale-x=0.5', 'scale-x=2', 'scale-x=3', 'scale-y=0.333', 'scale-y=0.5', 'scale-y=2', 'scale-y=3', 'skewed', 'standard']\n", + "Processing: gradient\n", + "Metric keys: []\n", + "Processing: half_subspace\n", + "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + ] + }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: noisyLR\n", + "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + ] + }, { "data": { - "image/png": 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", + "image/png": 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", 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" + "
" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: orthogonal_train_test\n", + "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + ] + }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: overlapping_train_test\n", + "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + ] + }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: random_quadrants\n", + "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + ] + }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: scale-x=0.333\n", + "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + ] + }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: scale-x=0.5\n", + "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + ] + }, { "data": { - "image/png": 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", + "image/png": 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", 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" + "
" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: scale-x=2\n", + "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + ] + }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: scale-x=3\n", + "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + ] + }, { "data": { - "image/png": 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", + "image/png": 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", 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" + "
" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: scale-y=0.333\n", + "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + ] + }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: scale-y=0.5\n", + "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + ] + }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: scale-y=2\n", + "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: scale-y=3\n", + "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + ] + }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: skewed\n", + "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + ] + }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: standard\n", + "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + ] + }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -424,8 +634,11 @@ ], "source": [ "# plot any OOD metrics\n", + "print(\"Available metrics:\", list(metrics.keys()))\n", "for name, metric in metrics.items():\n", - " if name == \"standard\": continue\n", + " print(\"Processing:\", name)\n", + " print(\"Metric keys:\", list(metric.keys()))\n", + " if name == \"gradient\": continue\n", " \n", " if \"scale\" in name:\n", " scale = float(name.split(\"=\")[-1])**2\n", @@ -433,6 +646,16 @@ " scale = 1.0\n", "\n", " trivial = 1.0 if \"noisy\" not in name else (1+1/n_dims)\n", + " \n", + " # only plot models that exist in this metric dict\n", + " models_present = [m for m in models if m in metric]\n", + " if len(models_present) == 0:\n", + " print(f\"Skipping {name}: no matching models in metric keys {list(metric.keys())}\")\n", + " continue\n", + " \n", + " \n", + " \n", + " \n", " fig, ax = basic_plot(metric, models=models, trivial=trivial * scale)\n", " ax.set_title(name)\n", " \n", @@ -440,7 +663,19 @@ " ax.set_xlim(-1, n_dims - 1)\n", " ax.set_ylim(-.1 * scale, 1.5 * scale)\n", "\n", - " plt.show()" + " plt.show()\n", + "std = metrics.get(\"standard\", {})\n", + "for model_name in models:\n", + " mres = std.get(model_name, {})\n", + " if \"gradient_alignment\" in mres:\n", + " print(\"Plotting gradient alignment for\", model_name)\n", + " alignments = mres[\"gradient_alignment\"]\n", + " plt.figure(figsize=(6, 4))\n", + " plt.plot(range(len(alignments)), alignments, label=f\"{model_name} gradient-w alignment\", lw=2)\n", + " plt.xlabel(\"# in-context examples\")\n", + " plt.ylabel(\"normalized inner product\")\n", + " plt.legend()\n", + " plt.show()" ] }, { diff --git a/src/eval.py b/src/eval.py index 1ddfd4dc..ccf0fe1e 100644 --- a/src/eval.py +++ b/src/eval.py @@ -187,11 +187,21 @@ def eval_model( metrics = torch.cat(all_metrics, dim=0) results = aggregate_metrics(metrics) + # if prompting_strategy == "standard": + # grad_alignments = compute_gradient_alignment(model, task_sampler(), xs[0]) + # if grad_alignments is not None: + # results["gradient_alignment"] = grad_alignments if prompting_strategy == "standard": - grad_alignments = compute_gradient_alignment(model, task_sampler(), xs[0]) - if grad_alignments is not None: - results["gradient_alignment"] = grad_alignments - + # sample a single long prefix to compute gradients on (use same data_sampler) + xs_samp = data_sampler.sample_xs(n_points=min(n_points, 40), b_size=1)[0] + task = task_sampler() + try: + grad_alignments = compute_gradient_alignment(model, task, xs_samp, n_points=min(40, n_points)) + if grad_alignments is not None: + results["gradient_alignment"] = grad_alignments + except Exception: + # best-effort: don't fail whole eval if grad computation crashes + pass return results def build_evals(conf): @@ -342,26 +352,50 @@ def conf_to_model_name(conf): else: return conf.wandb.name - def baseline_names(name): + """Map internal model names to display names""" if "OLS" in name: return "Least Squares" + if name == "averaging": return "Averaging" - if "NN" in name: - k = name.split("_")[1].split("=")[1] + + if "NN_n=" in name: + k = name.split("n=")[1].split("_")[0] return f"{k}-Nearest Neighbors" + if "lasso" in name: - alpha = name.split("_")[1].split("=")[1] + alpha = name.split("alpha=")[1].split("_")[0] return f"Lasso (alpha={alpha})" - if "gd" in name: - return "2-layer NN, GD" + + if "gd" in name and "adam" in name: + return "2-layer NN (Adam)" + if "decision_tree" in name: - return "Greedy Tree Learning" + depth = name.split("max_depth=")[1] + return f"Decision Tree ({'unlimited' if depth=='None' else f'max_depth={depth}'})" + if "xgboost" in name: return "XGBoost" - return name + + if "ridge_var_adj" in name: + alpha = name.split("alpha=")[1].split("_")[0] + ar = name.split("ar=")[1] + return f"Ridge Var Adj (alpha={alpha}, ar={ar})" + + if "ridge_alpha" in name: + alpha = name.split("alpha=")[1] + return f"Ridge (alpha={alpha})" + + if "feasible_gls" in name: + ar = name.split("ar=")[1] + return "Feasible GLS" if ar=='est' else f"Feasible GLS (ar={ar})" + + if "gls_ar" in name: + ar = name.split("ar=")[1] + return f"GLS (ar={ar})" + return name def read_run_dir(run_dir): all_runs = {} @@ -402,41 +436,89 @@ def read_run_dir(run_dir): return df # Figure 3 and 4: +# def compute_gradient_alignment(model, task, xs, n_points=40): + +# device = next(model.parameters()).device +# # ground-truth weight for this task (take first in batch) +# w = task.w_b[0, :, 0].to(device) + +# alignments = [] +# max_points = min(n_points, xs.shape[0]) + +# for k in range(max_points): +# # Context up to k +# ctx_xs = xs[:k].unsqueeze(0).to(device) +# if k > 0: +# ctx_ys = task.evaluate(ctx_xs.detach().cpu()).to(device) +# else: +# ctx_ys = torch.zeros(1, 0, device=device) + +# # Random query direction normalized and scaled to match data norm +# direction = torch.randn_like(w) +# direction = direction / (direction.norm() + 1e-8) +# scale = xs[k].norm() if k < xs.shape[0] else xs[-1].norm() +# x_query = (direction * (scale + 1e-8)).detach().clone().requires_grad_(True) +# print("ctx_ys.shape:", ctx_ys.shape) +# print("ys_with_dummy.shape:", ys_with_dummy.shape) +# xs_with_query = torch.cat([ctx_xs, x_query.view(1, 1, -1)], dim=1) +# ys_with_dummy = torch.cat( +# [ctx_ys, torch.zeros(ctx_ys.size(0), 1, device=device)], +# dim=1 +# ) + +# with torch.enable_grad(): +# pred = model(xs_with_query, ys_with_dummy, inds=[k]) +# grad = torch.autograd.grad(pred.sum(), x_query)[0] + +# cos_sim = torch.dot(grad, w) / (grad.norm() * w.norm() + 1e-8) +# alignments.append(float(cos_sim.detach().cpu())) + +# return alignments def compute_gradient_alignment(model, task, xs, n_points=40): + """ + Compute cosine similarity between model gradient (w.r.t. query input) and + the true task weight w. xs: (n_points, d) single sample (no batch dim). + Returns list of length <= n_points with float cosines. + """ + device = "cuda" if torch.cuda.is_available() and next(model.parameters()).is_cuda else "cpu" + model = model.to(device).eval() - device = next(model.parameters()).device - # ground-truth weight for this task (take first in batch) + # get ground-truth weight if available + if not hasattr(task, "w_b"): + return None w = task.w_b[0, :, 0].to(device) alignments = [] - max_points = min(n_points, xs.shape[0]) - - for k in range(max_points): - # Context up to k - ctx_xs = xs[:k].unsqueeze(0).to(device) + max_k = min(n_points, xs.shape[0]) + for k in range(max_k): + # context (0..k-1) + ctx_xs = xs[:k].unsqueeze(0).to(device) # (1, k, d) if k > 0: ctx_ys = task.evaluate(ctx_xs.detach().cpu()).to(device) else: ctx_ys = torch.zeros(1, 0, device=device) - # Random query direction normalized and scaled to match data norm - direction = torch.randn_like(w) + # random direction scaled to typical norm + direction = torch.randn_like(w, device=device) direction = direction / (direction.norm() + 1e-8) scale = xs[k].norm() if k < xs.shape[0] else xs[-1].norm() - x_query = (direction * (scale + 1e-8)).detach().clone().requires_grad_(True) + x_query = (direction * (scale + 1e-8)).detach().clone().requires_grad_(True).view(1, 1, -1).to(device) - xs_with_query = torch.cat([ctx_xs, x_query.view(1, 1, -1)], dim=1) + xs_with_query = torch.cat([ctx_xs, x_query], dim=1) ys_with_dummy = torch.cat([ctx_ys, torch.zeros(1, 1, device=device)], dim=1) with torch.enable_grad(): pred = model(xs_with_query, ys_with_dummy, inds=[k]) - grad = torch.autograd.grad(pred.sum(), x_query)[0] + # pred could be tensor with shape (1, m) or scalar-like; sum to scalar + loss_term = pred.sum() + grad = torch.autograd.grad(loss_term, x_query, retain_graph=False, create_graph=False)[0].view(-1) - cos_sim = torch.dot(grad, w) / (grad.norm() * w.norm() + 1e-8) - alignments.append(float(cos_sim.detach().cpu())) + # cosine similarity between grad and w + denom = (grad.norm() * w.norm() + 1e-8) + cos_sim = float(torch.dot(grad, w).cpu() / denom.cpu()) + alignments.append(cos_sim) return alignments - if __name__ == "__main__": run_dir = sys.argv[1] for task in os.listdir(run_dir): diff --git a/src/models.py b/src/models.py index b190d424..252e8b27 100644 --- a/src/models.py +++ b/src/models.py @@ -30,6 +30,11 @@ def get_relevant_baselines(task_name): task_to_baselines = { "linear_regression": [ (LeastSquaresModel, {}), + (RidgeModel, {"alpha": 0.1}), + (RidgeModel, {"alpha": 1.0}), + (RidgeModelWithVarianceAdjustment, {"alpha": 1.0, "ar_coef": 0.5}), + (FeasibleGLSModel, {"ar_coef": None}), + (GLSModel, {"ar_coef": 0.5}), (NNModel, {"n_neighbors": 3}), (AveragingModel, {}), ], diff --git a/src/plot_utils.py b/src/plot_utils.py index 006ed39b..a2d26874 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -9,40 +9,53 @@ sns.set_theme("notebook", "darkgrid") palette = sns.color_palette("colorblind") - relevant_model_names = { "linear_regression": [ - "Transformer", + "Transformer", "Least Squares", - "3-Nearest Neighbors", - "Averaging", + "Ridge Var Adj (alpha=1.0, ar=0.5)", + "Feasible GLS", + "GLS (ar=0.5)", + # "3-Nearest Neighbors", + # "Averaging" ], "sparse_linear_regression": [ "Transformer", - "Least Squares", + "Least Squares", "3-Nearest Neighbors", "Averaging", + "Lasso (alpha=0.001)", "Lasso (alpha=0.01)", + "Lasso (alpha=0.1)", + "Lasso (alpha=1.0)" ], "decision_tree": [ "Transformer", + "Least Squares", "3-Nearest Neighbors", - "2-layer NN, GD", - "Greedy Tree Learning", + "Decision Tree (max_depth=4)", + "Decision Tree (unlimited)", "XGBoost", + "Averaging" ], "relu_2nn_regression": [ "Transformer", "Least Squares", "3-Nearest Neighbors", - "2-layer NN, GD", + "2-layer NN (Adam)", + "Averaging" ], "ar1_linear_regression": [ "Transformer", "Least Squares", "3-Nearest Neighbors", - "2-layer NN, GD", - ], + "Ridge (alpha=0.1)", + "Ridge (alpha=1.0)", + "Ridge Var Adj (alpha=1.0, ar=0.5)", + "Feasible GLS", + "GLS (ar=0.5)", + "Averaging" + ] } diff --git a/src/samplers.py b/src/samplers.py index 4ed2955c..9656917d 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -18,6 +18,7 @@ def get_data_sampler(data_name, n_dims, **kwargs): # "var1":VAR1Sampler, "ar2":AR2Sampler, "vr2":VR2Sampler, + "nonstation":NonStationarySampler, } if data_name in names_to_classes: sampler_cls = names_to_classes[data_name] @@ -60,124 +61,17 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): if n_dims_truncated is not None: xs_b[:, :, n_dims_truncated:] = 0 return xs_b - - -# class AR1Sampler(DataSampler): -# def __init__(self, n_dims, bias=None, scale=0.9, sigma=0.5, init_state=None): -# super().__init__(n_dims) -# # phi là số thực, có thể lấy trace nếu scale là ma trận -# if torch.is_tensor(scale) and scale.ndim == 2: -# self.phi = torch.trace(scale).item() -# elif isinstance(scale, (int, float)): -# self.phi = float(scale) -# else: -# raise ValueError("scale phải là số hoặc ma trận 2D torch.Tensor") - -# self.sigma = sigma -# self.bias = bias -# self.init_state = init_state if init_state is not None else torch.ones(n_dims) - -# def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): -# xs_b = torch.zeros(b_size, n_points, self.n_dims) - -# for b in range(b_size): -# if seeds is not None: -# torch.manual_seed(seeds[b]) - -# state = self.init_state.clone() -# for t in range(n_points): -# # noise = torch.randn(self.n_dims) * self.sigma -# state = self.phi * state -# if self.bias is not None: -# state += self.bias -# xs_b[b, t] = state - -# if n_dims_truncated is not None: -# xs_b[:, :, n_dims_truncated:] = 0 -# return xs_b - -# class VAR1Sampler(DataSampler): -# def __init__(self, n_dims, bias=None, scale=None, sigma=0.5, init_state=None): -# super().__init__(n_dims) -# if scale is None: -# self.phi = 0.9 * torch.eye(n_dims) -# else: -# assert scale.shape == (n_dims, n_dims), "scale phải có shape (n_dims, n_dims)" -# self.phi = scale - -# self.bias = bias -# self.sigma = sigma -# self.init_state = init_state if init_state is not None else torch.ones(n_dims) - -# def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): -# xs_b = torch.zeros(b_size, n_points, self.n_dims) - -# for b in range(b_size): -# if seeds is not None: -# torch.manual_seed(seeds[b]) - -# state = self.init_state.clone() -# for t in range(n_points): -# noise = torch.randn(self.n_dims) * self.sigma -# state = self.phi @ state + noise -# if self.bias is not None: -# state += self.bias -# xs_b[b, t] = state - -# if n_dims_truncated is not None: -# xs_b[:, :, n_dims_truncated:] = 0 -# return xs_b -# def test_ar1_sampler(): -# n_dims = 3 -# n_points = 5 -# b_size = 2 -# phi = 0.5 -# sigma = 0.0 -# init_state = torch.tensor([1.0, 2.0, 3.0]) - -# seeds = [42, 123] - -# sampler = AR1Sampler(n_dims=n_dims, scale=phi, sigma=sigma, init_state=init_state) -# xs = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) - -# print("Output shape:", xs.shape) # should be (b_size, n_points, n_dims) -# print("First batch:\n", xs[0]) -# print("Second batch:\n", xs[1]) - -# # Test reproducibility -# xs2 = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) -# assert torch.allclose(xs, xs2), "Output not reproducible with same seeds!" -# print("Reproducibility test passed.") - -# # Test AR1 dynamics roughly -# print("\nCheck AR1 dynamics:") -# for t in range(1, n_points): -# expected = phi * xs[0, t-1] -# print(f"t={t}, previous*phi: {expected}, current: {xs[0, t]}") - - -# def test_var1_sampler(): -# n_dims = 2 -# n_points = 4 -# b_size = 2 -# phi = torch.tensor([[0.5, 0.1], [0.0, 0.7]]) -# sigma = 0.1 -# init_state = torch.tensor([1.0, 2.0]) -# seeds = [42, 123] - -# sampler = VAR1Sampler(n_dims=n_dims, scale=phi, sigma=sigma, init_state=init_state) -# xs = sampler.sample_xs(n_points=n_points, b_size=b_size, seeds=seeds) -# print(xs) # code này là thêm: class AR1Sampler(DataSampler): - def __init__(self, n_dims, rho=0.9, noise_std=1.0, bias=None, scale=None): + def __init__(self, n_dims, rho=0.9, noise_std=1.0, bias=None, scale=None,compute_gradient=False): super().__init__(n_dims) assert 0 <= abs(rho) < 1, "|rho| must be < 1 for a stable AR(1)" self.rho = float(rho) self.noise_std = float(noise_std) self.bias = bias self.scale = scale + self.compute_gradient = compute_gradient def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): # Shape: (batch, time, dims) @@ -335,19 +229,19 @@ def __init__(self, n_dims, coef_base=0.5, coef_amplitude=0.4, noise_std = 0.1, super().__init__(n_dims) self.coef_base = float(coef_base) self.coef_amplitude = float(coef_amplitude) - self.coef_noise_std = float(noise_std) + self.noise_std = float(noise_std) self.scale = scale self.bias = bias def get_transition_matrix(self, t, n_points): t_norm = t / (n_points - 1) if n_points > 1 else 0.0 - time_varying_factor = self.coef_base + self.coef_amplitude * torch.sin(2 * math.pi * t_norm) + time_varying_factor = self.coef_base + self.coef_amplitude * math.sin(2 * math.pi * t_norm) A_t = time_varying_factor * torch.eye(self.n_dims) return A_t def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): xs_b = torch.zeros(b_size, n_points, self.n_dims) generators = None - if seeds is None: + if seeds is not None: assert len(seeds) == b_size generators = [torch.Generator().manual_seed(int(seed)) for seed in seeds] if generators is None: diff --git a/src/schema.py b/src/schema.py index 11919181..99bfc0cf 100644 --- a/src/schema.py +++ b/src/schema.py @@ -51,7 +51,7 @@ "task_kwargs": merge(tdict, required), "num_tasks": merge(tinteger, nullable, default(None)), "num_training_examples": merge(tinteger, nullable, default(None)), - "data": merge(tstring, allowed(["gaussian","ar1","var1","ar2"])), + "data": merge(tstring, allowed(["gaussian","ar1","var1","ar2",'vr2',"nonstation"])), "batch_size": merge(tinteger, default(64)), "learning_rate": merge(tfloat, default(3e-4)), "train_steps": merge(tinteger, default(1000)), diff --git a/src/tasks.py b/src/tasks.py index d324413a..384f967b 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -62,12 +62,15 @@ def get_task_sampler( "decision_tree": DecisionTree, "ar1_linear_regression": AR1LinearRegression, } + if task_name in task_names_to_classes: task_cls = task_names_to_classes[task_name] if num_tasks is not None: if pool_dict is not None: raise ValueError("Either pool_dict or num_tasks should be None.") pool_dict = task_cls.generate_pool_dict(n_dims, num_tasks, **kwargs) + + # Simple return for all tasks - no special case needed return lambda **args: task_cls(n_dims, batch_size, pool_dict, **args, **kwargs) else: print("Unknown task") @@ -348,7 +351,7 @@ def get_metric(): def get_training_metric(): return mean_squared_error class AR1LinearRegression(Task): - def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, ar_coef=0.5, noise_std=1.0): + def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, ar_coef=0.5, noise_std=1.0,compute_gradient=False): """ AR(1) Linear Regression: y_t = x_t^T w + epsilon_t where epsilon_t = ar_coef * epsilon_{t-1} + u_t, u_t ~ N(0, noise_std^2) @@ -361,7 +364,7 @@ def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, ar_c self.scale = scale self.ar_coef = ar_coef self.noise_std = noise_std - + self.compute_gradient = compute_gradient if pool_dict is None and seeds is None: self.w_b = torch.randn(self.b_size, self.n_dims, 1) elif seeds is not None: From 2d654bed5621fab702f16a256bf02d9a69b357c3 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 22 Oct 2025 09:54:35 +0700 Subject: [PATCH 14/88] exponential error --- src/conf/toy.yaml | 12 +-- src/eval.ipynb | 198 +++++++++++++++++++++++++++------------------- src/eval.py | 2 +- src/tasks.py | 5 +- src/train.py | 19 +++++ 5 files changed, 145 insertions(+), 91 deletions(-) diff --git a/src/conf/toy.yaml b/src/conf/toy.yaml index e4f58926..9e1f7b5e 100644 --- a/src/conf/toy.yaml +++ b/src/conf/toy.yaml @@ -15,28 +15,28 @@ training: curriculum: dims: start: 5 - end: 5 + end: 10 inc: 0 interval: 1 points: start: 6 - end: 6 + end: 21 inc: 0 interval: 1 - data: ar1 + data: gaussian keep_every_steps: 10000 learning_rate: 0.0003 num_tasks: null num_training_examples: null resume_id: null save_every_steps: 100 - task: ar1_linear_regression + task: noisy_linear_regression task_kwargs: { - "compute_gradient": True + # "compute_gradient": True } train_steps: 501 out_dir: ../models/linear_regression wandb: - name: "fig3_6_points_" + name: "exponential_noise_experiment" diff --git a/src/eval.ipynb b/src/eval.ipynb index fe001232..7d72f5af 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 21, + "execution_count": 3, "id": "ed6cfeb1", "metadata": {}, "outputs": [ @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 5, "id": "0e8d018b", "metadata": { "scrolled": true @@ -83,8 +83,8 @@ " \n", " \n", " \n", - " 1\n", - " 72802a08-1a86-4a1a-b1f4-8f02c487073f\n", + " 3\n", + " ar2_40_points\n", " linear_regression\n", " Transformer\n", " \n", @@ -93,7 +93,7 @@ " 5\n", " 4\n", " 8\n", - " ar2_10_points\n", + " ar2_40_points_\n", " \n", " \n", " 0\n", @@ -109,7 +109,20 @@ " decision_tree_pretrained\n", " \n", " \n", - " 5\n", + " 1\n", + " 61f8e530-b627-471a-af64-2db247bb09ab\n", + " linear_regression\n", + " Transformer\n", + " compute_gradient=True\n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " fig3_6_points_\n", + " \n", + " \n", + " 6\n", " k=20_3\n", " linear_regression\n", " Transformer\n", @@ -122,7 +135,7 @@ " k=20_part3\n", " \n", " \n", - " 4\n", + " 5\n", " ar_with_k=40\n", " linear_regression\n", " Transformer\n", @@ -135,7 +148,7 @@ " k=40_1\n", " \n", " \n", - " 7\n", + " 9\n", " pretrained\n", " linear_regression\n", " Transformer\n", @@ -148,7 +161,7 @@ " linear_regression_pretrained\n", " \n", " \n", - " 3\n", + " 4\n", " ar_k=10\n", " linear_regression\n", " Transformer\n", @@ -174,7 +187,7 @@ " linear_regression_toy with 20 points\n", " \n", " \n", - " 6\n", + " 7\n", " k=20_par2\n", " linear_regression\n", " Transformer\n", @@ -188,6 +201,19 @@ " \n", " \n", " 8\n", + " nonstation\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " nonstation_10_points_\n", + " \n", + " \n", + " 10\n", " pretrained\n", " relu_2nn_regression\n", " Transformer\n", @@ -200,7 +226,7 @@ " relu_2nn_regression_pretrained\n", " \n", " \n", - " 9\n", + " 11\n", " pretrained\n", " sparse_linear_regression\n", " Transformer\n", @@ -217,44 +243,50 @@ "" ], "text/plain": [ - " run_id task \\\n", - "1 72802a08-1a86-4a1a-b1f4-8f02c487073f linear_regression \n", - "0 pretrained decision_tree \n", - "5 k=20_3 linear_regression \n", - "4 ar_with_k=40 linear_regression \n", - "7 pretrained linear_regression \n", - "3 ar_k=10 linear_regression \n", - "2 ar1_data_with_k=20 linear_regression \n", - "6 k=20_par2 linear_regression \n", - "8 pretrained relu_2nn_regression \n", - "9 pretrained sparse_linear_regression \n", + " run_id task \\\n", + "3 ar2_40_points linear_regression \n", + "0 pretrained decision_tree \n", + "1 61f8e530-b627-471a-af64-2db247bb09ab linear_regression \n", + "6 k=20_3 linear_regression \n", + "5 ar_with_k=40 linear_regression \n", + "9 pretrained linear_regression \n", + "4 ar_k=10 linear_regression \n", + "2 ar1_data_with_k=20 linear_regression \n", + "7 k=20_par2 linear_regression \n", + "8 nonstation linear_regression \n", + "10 pretrained relu_2nn_regression \n", + "11 pretrained sparse_linear_regression \n", "\n", - " model kwargs num_tasks num_examples n_dims \\\n", - "1 Transformer -1 -1 5 \n", - "0 Transformer depth=4 -1 -1 20 \n", - "5 Transformer -1 -1 5 \n", - "4 Transformer -1 -1 5 \n", - "7 Transformer -1 -1 20 \n", - "3 Transformer -1 -1 5 \n", - "2 Transformer -1 -1 5 \n", - "6 Transformer -1 -1 5 \n", - "8 Transformer hidden_layer_size=100 -1 -1 20 \n", - "9 Transformer sparsity=3 -1 -1 20 \n", + " model kwargs num_tasks num_examples n_dims \\\n", + "3 Transformer -1 -1 5 \n", + "0 Transformer depth=4 -1 -1 20 \n", + "1 Transformer compute_gradient=True -1 -1 5 \n", + "6 Transformer -1 -1 5 \n", + "5 Transformer -1 -1 5 \n", + "9 Transformer -1 -1 20 \n", + "4 Transformer -1 -1 5 \n", + "2 Transformer -1 -1 5 \n", + "7 Transformer -1 -1 5 \n", + "8 Transformer -1 -1 5 \n", + "10 Transformer hidden_layer_size=100 -1 -1 20 \n", + "11 Transformer sparsity=3 -1 -1 20 \n", "\n", - " n_layer n_head run_name \n", - "1 4 8 ar2_10_points \n", - "0 12 8 decision_tree_pretrained \n", - "5 4 8 k=20_part3 \n", - "4 4 8 k=40_1 \n", - "7 12 8 linear_regression_pretrained \n", - "3 4 8 linear_regression_toy with 10 points \n", - "2 4 8 linear_regression_toy with 20 points \n", - "6 4 8 linear_regression_toy with 21 points \n", - "8 12 8 relu_2nn_regression_pretrained \n", - "9 12 8 sparse_regression_pretrained " + " n_layer n_head run_name \n", + "3 4 8 ar2_40_points_ \n", + "0 12 8 decision_tree_pretrained \n", + "1 4 8 fig3_6_points_ \n", + "6 4 8 k=20_part3 \n", + "5 4 8 k=40_1 \n", + "9 12 8 linear_regression_pretrained \n", + "4 4 8 linear_regression_toy with 10 points \n", + "2 4 8 linear_regression_toy with 20 points \n", + "7 4 8 linear_regression_toy with 21 points \n", + "8 4 8 nonstation_10_points_ \n", + "10 12 8 relu_2nn_regression_pretrained \n", + "11 12 8 sparse_regression_pretrained " ] }, - "execution_count": 22, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -266,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 9, "id": "a9980951", "metadata": {}, "outputs": [], @@ -276,7 +308,7 @@ "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"k=20_par2\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"nonstation\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -295,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 10, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -305,7 +337,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "linear_regression_toy with 21 points k=20_par2\n" + "nonstation_10_points_ nonstation\n" ] }, { @@ -317,7 +349,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -352,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "31b4ecca", "metadata": { "scrolled": true @@ -366,12 +398,12 @@ "Processing: gradient\n", "Metric keys: []\n", "Processing: half_subspace\n", - "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -384,12 +416,12 @@ "output_type": "stream", "text": [ "Processing: noisyLR\n", - "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "data": { - "image/png": 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6q83klNafqEqJNvF0v9ZccWpqrfa7Z88eDh3K4a233sXpdLJp0wbGjBnJ8OEPcu65kygpKWHWrBk88cT/WLjwS0wmEwDvv/8ON954M3Pnvs3OnTsYO3YMTZo0YfDgu/jpp6W8+eZcnnxyMk2aNGHt2jWMHz+WjIxM+va9kOjoaMaMGcncufPIyspi//593Hnn7XTr1oMXX3wZp9PJ9OnPM3ToEN5550Oio2MAWLjwM8aNm0DLlq1o0KABK1eu4JtvvuLqq6/jjTfe4euvv+KVV2bx1VdLGD58BJmZmUycOJ5nnpnEm2++i2EYPPjgcCwWC88++wIxMTEsWbKIu+4axKuvvknr1m0AWL16FddddwPz5r2HpokfrTVFiKq6xIiwlAr+mKoKS5UUJgWVoXJMVcULYakSCOqaB77YRJHTU69jyCl188AXm2pdVAEMGjSEhg0bAbBly2ZGjBjNVVdd499/3XU38OCD95GXl0taWjqAz3rljdfKysqie/eerFnzJwB79+7BZDKTkZFJenoG6ekZpKSkkp6ejtVqJS7OGxOakJBITEwsb7wxF5stinHjJmA2e793J02awsCBl7FkyWKuvvpaAFq1ak2/fhcHjT0+PoHhwx9ElmVuuOEmXnllFn36XMi5554HwCWXXM60ac8C8PvvK1m3bg1ffvk98fHeMQwdeh9r1vzFBx+8x9ixT/j7vfPOuyu5TgVHRoiqOsSIuIzqEt6YqgBLlRJOlqqqs6ob5TFV5eMXCASCEGncOMv//JRTWhMXF8dbb73Bzp072LNnN1u3bgYIsto0adI0qI/o6Bh/rNRFF/Xn888XcO21A2jWrDndunXn/PP7kp6eUeX5t2/fxqmnnuYXVADJyQ3IymrC9u3bAsbZuNKxmZkNkX2l0Gw2GwCNGjXy77dYLP7ElZs3b8IwDAYM6B/Uh8vlxums+EGdmJgkBNUxIERVXRLoi4qI2n+6N/1nUExVmFqqXIFFlb2r/yRJuP8Egrpi2iVtwsb9VxdYrRU/IFet+oMHHriXM888m/btO9Cv38U4nQ5GjXoo6BiTqfL3Y/mq74SERObNe5+1a9ewcuWvLF/+Kx988B533nk3gwffVe1xh6PrOqpacau2WCr/0A3cX0513hFdN4iOjuGNN96utC9Q0Ils+seGEFV1SYSt/gN8tf8qLFVSOMVUWaqOqcIXUyUQCOqOK05N5dLWKSddoHpVvPvuPDp16sLkyc/6t3344fu+ZzX75fbll4spKSnm6quvo337Dtx551AmTnySb775qkpR1bJlK776ajEul8svbnJzc9mzZzdXXXXtcV9TOS1atKC0tASPx0OzZs392ydOfJJWrVpxzTXX19q5/o0IUVWXRJr7z/D9E6aWKkkxgxoDnpJKRZURGdUFgjpHkSUaRIfPd0JdkZaWxv/934/8+edqUlPTWLXqN155ZRYALpfrKEfjbzdjxjSio6Np374T2dkHWb36Dzp06FRl+4EDr2H+/I954on/cfvtg33HP098fAIXXNCv1q6tR48zOeWU1jz22CM89NBIUlPT+OSTj/jii4W88MKLtXaefytCVNUlEZpSwQjTmCoAyZyI4SnBcOcHbdd1LWKMgQKBILy5666h5Obm8vDD9wPegPRHH32cceP+x8aN62natNlR+7j88gEUFhbw2mtzyM4+SGxsHOef34dhw4ZX2T4zM5PZs+cwc+YLDBlyO2aziW7dejBu3ARiY2svtklRFKZPn8WMGdN49NHR2O0OmjVrxuTJz9KlS7daO8+/FcmozpErCELTdPLySkM6pmjOAJybvwcgecJeZGt4B/2pmh3Xwb+x/3YnRslmQMJy1tdI1agVc1I6UkImmhbaW0hVZRITo8nPL8XjqbmFyYSLwkX90AvXAGA5c7F/daI5MQUpMSuil/4e67yc7Ih5qZrjnZekpGgUpeofew6Hg+3b/6ZBg3TMZhFbI/h343I5OXToAC1aNA+KvauKyDGfRCBGUEqFSDCjGL74JF9GddkSduOWzIkVLwJdgCKmSiAQCAT1jBBVdUnEuf8Oq/0XRvFUXqQgURW4AtDQhPtPIBAIBPVLJNzpI5eIE1Ve/CkVwqjuXzlBoipoBaB24gcjEAgEAkEAkXOnj0QirEyNf6WwL1BdOlrdv3owDVUnqgzDOCzZqkAgEAgEJ5YIuNNHMBGXp8rwZSf3WarCqJhyOZKpmpgqw0ASokogEAgE9UhYiaqXX36ZW2655Yhttm7dyl133UX37t3p2bMnw4cPZ9++ff79mqbRrl07WrduHfQ3Y8aMuh5+FUSY+88wMAwNDF9yv3AUVdW6/wyRTl0gEAgE9UrY5Kl65513mDZtGl26dKm2TX5+PoMGDaJTp07MmzcPl8vF5MmTGTJkCJ9++ikWi4WdO3fidDpZsGAByckVGbijoqJOxGUEo0eO+89vSCsPUqcG7r96oNpAdcPwWQaVehiVQCAQCARhIKoOHjzI448/zooVK2jatOkR23777beUlZUxZcoUf66IZ555hl69erFq1Sp69uzJ5s2biYmJoU2bNidg9Ecm8lIqEFxMOcxF1eHuP1FMWSAQCAT1Sb2bT9avX4/JZGLhwoW0b9/+iG179uzJrFmzgpJvlVfmLioqAmDz5s20aNGi7gYcEr6bfKQIKnQMrcJSFY6r/1BjQfJaowKzqhsYiFI1AoFAIKhP6t1S1bt3b3r37l2jto0aNaJRo0ZB21555RWsVitdu3YFYMuWLXg8HgYPHsymTZtIS0vjtttu44orrjjusapqiBq0/CYvyaEfe4KRJJDdEjIVda0kxYpcTcZlAEUGSZGRpNAsROVZnKvL5lwdsi6jqAqSKQHDlQvuAv/4ZElCkYAwn+cjcazzcrIj5qVqxLwIBOFHvYuq42HevHm8/fbbPPbYYyQlJQHeQHZd1xk+fDjp6eksXbqUMWPG4Ha7ufrqq4/5XLIskZgYHdIx+RK4AaTQj60P3FIZutnA7ntttkQRE129tcoUZcEcf+yxanFxtpDaa047apEZuzUJjysXw11AdJTZ61pVVCw2E2pM+M/z0Qh1Xv4tiHmpGjEvR2bAgEu45JLLuPPOu+ttDIWFBSxd+iOXXz6g2jY5OTm88spsli9fRn5+PgkJCXTt2p3Bg++iUaPGJ26wguMiIkWVYRi88MILzJ49m6FDhwatGFy0aBGaphEd7b25tmnThn379vHaa68dl6jSdYOiorKQjvHXoZNk8vNDqxt4opEkkO1OykqL/NvcukpJqbPaYywWJ/bCMnQ9dEtVXJyNoiJ7SLX6FN2Ny+5CV+K9GwyNksJcJFMskqKhlTrQ3OE9z0fiWOflZEfMS9Uc77zExdmElesEMX368+zbt69aUeVyubjnnjvJyspi4sQpNGiQwoED+3nllZe46647eOedD0lMTKzyWEF4EXGiyu12M2bMGBYtWsSYMWO4/fbbg/ZXVezwlFNOYeHChcd97pCLlpZn+ZbksC8EK0kSimaguysC1Q3JjH6EL2tNB0nTQy6o7D9e00OaFwndOx41oaIPRx6yHI1kaGiaB03TIz6zQqjz8m9BzEvVnMh5MXQtaNXtiUYyJyLJkbfC92jfSStXLmf37l289tpbxMXFAZCRkcmUKc9xySUX8PXXX3LddTecgJEKjpeIE1WjRo3im2++YerUqVxyySVB+4qKiujbty+PPPIIAwcO9G9fu3YtrVq1OtFD9a/+k8I8nUIFBugVoiocUyoASKaEihfuAqCxN1BdNwAJsQpQIKh9XDs/w75iFIYjp97GIFlTsHWfgrnpgFrtd9GiBcyb9yYHDuwnPT2DgQOv5pprrvcvhPrzz1XMmfMSGzduxO12kZnZkNtvH8zFF3vvQXl5eTz77GT++ON3HA47p5zShqFDh9GpU2fGj3+cxYs/B6BHj04sX76q0vnLz7Ns2U/+PgFiY2N5++0PSEiosFL93//9yMsvz2LPnt20bn0q/ftfwuTJT/n7rcrdefi2BQs+5cMP32PPnt1IkkTr1m144IGHOfXU0/zte/fuyy+//Ex+fj6TJj1Dx46dePvtN/n000/Izc0lKyuLm266lYsu6u8/zzvvvMX8+R+TnX2QBg1SuOyyKxg0aEjkrH6vBcJaVGmaRl5eHrGxsVitVubPn8/ixYsZNWoU3bp1Iyen4sMdGxtLXFwcPXr04Pnnnyc5OZkmTZrw9ddfs3DhQl5++eUTfwH+1WiR8oYyIHD1X7iKqqAEoL5fzb48VZIkcoAKBHVB2S/3g7vo6A3rEMORQ9kv99eqqPrss0+YNWsmI0c+wmmnnc7mzZuZOvVpsrOzue++B8jOzub++4dxzTXX8cgjj+HxeJg37w0mThxPt249SE5OZsqUibjdLmbPnoPJZOKNN15j1KgH+fzzr3jooYdxOh1kZx9k8uRnqxxD167dOfXU03jiif/x+uuv0rVrNzp06ES3bt3Jymrib/fnn6sYPXoEgwYNoV+/i/nttxVMnz4tpOv98cfvmTr1acaM+R8dOnQkN/cQU6dOYeLE8cyb976/3ccff8Czz75AbGwsLVq05KWXZvL111/x8MOjadKkKX/+uYopUyZRUlLC1Vdfy08/LeXNN+fy5JOTadKkCWvXrmH8+LFkZGQGCcWTnbAWVfv376dPnz5MmjSJgQMHsmjRIgCmTJnClClTgtqWt5k4cSIzZszg8ccfJzc3lxYtWjB9+nTOOeec4xqLYRi43e4q90mShKpWTGV5O0OvSKlQvk2SQFVNldpW3W/N2wKYTMfW1uNxAxK6y43HY/dv1zBjeDRMaoW53aNpftEiuT1IbneQ+y+4X483KedhGIaEy2UK2lZd23JUVfVrU6M8pgrwOPLQPV43q+JyopvcyLLi/2WkaZ4jxnypqhrQVkPXq3ejhNJWURT/r89Q27rdLjyeqsd8rP3quo6mVV90WpZlFEUJm7aGYeDxeHzPve+XwHmprm3V/Uooilrrbav73B9/25p97g1Dwu02B20L5XN/pM/bv4m5c1/ljjuGcMEF/QBo2LARZWUlPPPMZO66aygul5M77/wPN910q//zf+utg1i8eBG7d/9DcnIye/fuoUWLlmRmNsRqtfLQQyPp168/sixjs8VisVhRVRPJyQ2qHIPJZGL27Ff58MP3+e67r5k//2M++eQjFEXlyisH8sADI1BVEx999AFnnNGeu+4aCkCTJk3ZuXMHH3/8YY2vNz4+nv/+d6zfwpSRkclllw3g2WcnB7Xr2fMsunXrDoDdbuf9999l/PiJnHWW917aqFFj9u/fx9tvv8nVV1/L3r17MJnMZGRkkp6eQXp6BikpqaSnp4fwvxH5hJWomjw5+D+1UaNGbN682f967ty5R+0jJiaGMWPGMGbMmFodW3FxEXPmVF3qJiurGZdeeqX/9euvz8bj8dAv7xBxgNPt9h+bmdmIAQOu9bedN+9VHA774V0CkJKSxjXX3OR//f77b1JcXPUvxcTEZG644Tb/648/fpf8/Nwq28bGxnHLLUP8rz/99ENycg4C0Nq8nu6+xUQ/rd/Bvg2/cusFZ/vbLlm5hv15BVX2q6oqd9013P/6yy8/Z9euHVW2BRg+/GH/8+++W8L27VurbXvnnfdh9t0P1u8roTy165+b/uKvv+KD2g4adDc2m3dV4rJlS1m37q9q+7355sHExXmPX7HiZ/78849q215//a0kJXm/FP/4YwW//7682rZXXXUjaWneL5M1a1bx668/Vdv2iiuuoWHDxr5+/2DJkiXVtu3ffwBNmzYHYOvWTXz//VfVtr3wwktp2fIUAP7+extff72o2ra9e/ejTZvTAdi1ayeLF39WbdtzzunNGWd0AGD//r0sWPBRtW179jyHjh296U5ycrL55JN3q23bpUsPunU7E4D8/Fzef/+tatt26NCZM888D/B+Nt9++7Vq27Zt255zz+0DgMNh5/XXX6q2bevWp9Gnz0WAV+hX95kHaNGiFf36XeZ/faS21X1HVEUo3xGZmZlcffWN/tehfEeUlpYQHx9fZduqiDrzhbBx/9UW+fn5ZGcfZPbsmbz88iz/dl03cDqd7Nu3l2bNmnPppZfz4YfvsW3bNvbs2c22bVuAisVIgwffxbhxj/HDD9/Rrl0HevToyYUXXozFUnNrv9Vq5dZbb+fWW2+nsLDA912wiI8//hCr1cawYffz99/b6d69Z9BxnTp1CUlUdezYmR07/mbu3Dns3LmTPXt2sW3b1ko/0Bo3zvI/37Hjb5xOJ2PHPoosV3heNE3D5XLhcDi46KL+fP75Aq69dgDNmjWnW7funH9+X9LTM2o8tpOBsBJVJxuSP7YnUtx/oFLxS9djmMJy6G4p1v/cKkXuaj+BIJIwNx2AKeuykypQvVxI3H//CLp27VZpf3p6Bjt2/M1//nMHrVuf6hMKvUlISOSOOypWnffq1ZtFi77i119/4bffVvLee2/z2muv8Oqrb9K8+dGTUS9Y8Ckej4errroGgPj4BHr37kPv3n34739H8csvPzNs2P1AcKUOCLY+Vkeghfirr5Ywfvzj9Ot3Me3atePKKweyffv2SpaqQEFYPk9PPTWZJk2aVurfbDZjtVqZN+991q5dw8qVv7J8+a988MF73Hnn3QwefNdRx3iyIERVDYmNjePOO++rct/hQXiDBnlNs8VT56EdysVitfmPPTxeL9BiVLnf4NfXX39b1Q2rIPDX69G48sprkSQJuTSHstW7MPZ4t/fu1AEpMfiL5uJu7fzuP3NSOsSlV+teu+iiy6p0MaiqREJCNKWlFQKuT5+L6d37omrH6HWdeH/Zd2zTHo8v1rN1RhSnnXKudzyJKUgJDZGkii/ds846j549zz1Kv166dz+brl3PrFHbzp27+y0wVVHumgJo164Tbdt2qFHbzp0707RpqyO6/8pp1aoNLVqcUqN+mzdvWe37FyoCZQGysprWuG1GRsMat01JSa1x28TEZH/b8vdLQUFpkPuvnCN9Nr1tKz5I1oDP4tHaqqp6xLaHf+5DaVv+HVF12+DX1X1HqKpEYmIMJSUVCXtD+Y6Ijo6pcVv/2GQFyVq1CysSSUpKIjExkb179zBwYEXKnW+++YqlS39g7NjxzJ//MYmJScyYMdu//6eflvqeGbhcLmbNmsHFF1/CBRf044IL+uFwOLjkkgtZtuwnmjdvcdSiGjt2/M1XXy3hoov6+9MBlRMTE+vPw9i6dRvWrl0TtH/Tpg1Br00mE6WlFT82S0tLyMvL879+663XufzyAYwe/V//tv/7P+/1GIZRZVB506ZNURSVAwcOcPbZFd+nH3zwHjt3/s3o0Y/y5ZeLKSkp5uqrr6N9+w7ceedQJk58km+++UqIKkFlJEmq0S8CCPzl4L0BSLJS7bE17bMu26qqCVmWkFUTMi7Kf9MoJhuKGvyrUA24UZtMKphM1YqqQBESvF3GbDYHiarq2laFYk2i3HEieQr9MV8mWUIymYNy9iiKilLDH7aKogQJkfpqazKZkaSjL5EPpV9ZloOESLi3Dfy8lb9fTCZ3lfMSymezrtrCif8sq6rs2+c6atuq+DetyNqzZze//rosaJvFYqVTp87cfPPtvPzyi6Snp9Oz51ls27aVZ56ZxDnnnIfZbCYtLY3s7IP88ssymjVrxqZNG3nuuWcAb34ps9nMxo3r+euv1YwYMYqkpAb8+usy7PYyzjijHQA2WxSHDuWwb99eMjMbVhrfjTfezDfffMXQoXcyePCdtGrVmsLCAlas+JWvvlrMs89OA+Cmm25l8OBbeeGF5xgwYCCbN2/ivfeCXepnnNGO7777mt69+xIbG8srr8xGDfgeT0tLZ82aP9m0aSMxMTH89NNSPv74A//1VOWyjImJ5corr+KVV2YRHR1Nu3btWbXqd1588QVuvXWQ/9gZM6YRHR1N+/adyM4+yOrVf9ChQ6dj/F+LTISoqksCytREDFpgmZowXf0nm0GJAq0sqKiyoWvI/577hEAgqCFffbWEr74KjlVMT8/gs8++4KabbsFisfDRR+/zwgvPkZzcgCuuGOhPP3DttTfwzz87GTfuMTweN40aZTF06DBfioUN9Ox5FhMmPM20ac8ycuSDlJSU0KRJU5544im/oLjkkstYuvQHbrjhGj7+eAEpKSlBY0lNTWPu3Ld47bU5PP/8s+Tl5WI2mzn99LZMmzaTjh07A9Cq1SlMm/Yi06c/x8cff0CLFi25/PIreP/9CmF1993DKCws4L77hhIbG8MNN9xCcXGxf//DD49m0qQJ3HPPnZhMZlq1asXYseP53//GsHHj+mpF0AMPjCAxMZFXXpnNoUM5pKWlceedd3PzzV7r6OWXD6CwsIDXXptDdvZBYmPjOP/8PgwbNrzK/k5WJEMsAakRmqaTlxda/E7exLZoebuQY1NJfnxbHY2sdpBlCbn4IMW/PoR20PvlY+74CnJ082qPMSdnQFxGyBnVVVUmMTGa/PzSkJIWmnDjOrANzenA+fttGI69oMRg7fmZd390HEp6q4hNEHms83KyI+alao53XpKSoqvNqO5wONi+/W8aNEjHbA7PH1cCL4sWLWTChHFV5r8S1A4ul5NDhw7QokXzKhOMBxJBJpQIxJ97IDKm2QAMvcJSFa55qgAoTwCqlWDovhQWhi6WiQsEAoGg3oiMu32kUp5RvYZxJPWOYYBekfwzXDOqw+FZ1Qu9j4aBZAhLhkAgEAjqhwi520cmRsRlVAe0ijI1hGlMFYBkTvA/D86qLixVAoHg38Oll14uXH9hhBBVdUkEuf+85V30kNx/Un2KxQBLleELVve6/oSlSiAQCAT1Q/jf7SOZSFz95y+oLIFU8+XZJ5rKRZXxuf+EpUogEAgE9UME3e0jEL+oihD3n2FAuaVKtoR1HhvJFFBU2VXgfcTAMKqvLScQCAQCQV0iRFVd4q9SEwnTLAFGRUxVGAepA4e5/wJjqupnOAKBQCAQRMLdPmIpD1SXIkJUAYZREVMVxkHq4K0BVo4R4P5DWKoEAoFAUE9EyN0+Qom4mCrDH1MlyUdOcFbfVBVTZRgG6CJQXSAQCAT1Q6Tc7SOTiI6pMtfvWI6GGkv52zfIUoUeMdMtEAjqnry8PMaNe4yLLurN+eefxUMPDWfnzh1HPOaPP36nR49OvPDCc1Xu79GjE4sWLayL4dYqBw7s55tvvqp2/5w5L9GjR6eAAtEVlM/Bvn37anSuffv20aNHJ/744/cata9J/0OH3sn48Y/XqL9wQYiquiSCUioAGJobDF+p4rB1/3kVkyTJYIoHKmKqDAzQDSIqL5hAIKhTRo9+iN27d/HcczOYO3ceFouF++67G4fDftRjP/jgXdas+esEjLJuGD/+cX799Zejtnv66acoKio6rnOlpaXxxRdf065d++PqJ9KJjLt9pBJhliojzLOpHx6D7ncBugq8rj/DqJhzgUDwr6eoqIiMjEz++9+xnHba6TRr1pw77riTnJwc/v7776Men5GRyYQJ43A4HEdtG47UpGxXXFwcLpeL556bclznUhSF5OQGmEzhm4rnRKDW9wBOaiJNVHnKKl6Eoag6HMmciFG2Aww3aKUgxYCh+xKZ1vfoBIKTD83QyXcd3cJTVySabSghWP7j4uIYP36i/3V+fj7vvfcOqalpNGtWfbH4ckaNGsOoUSN46aWZPPDAw9W2W7PmL2bNms7GjRtISEjk7LPP4Z577iM6OgbwuuFmznyBP/74jaKiYpKSkujX72Luuec+ZFlm0aKFvPHGq5x55jl88cXndO7chSlTnmPHjr+ZPv15/vxzFVFR0XTu3JX773+Q5OQGAOzatYvnnnuatWvXYhg6Z5zRjvvue5CWLVsxdOidrF79B6tX/8GqVb/z2WdfVDn2qKho/vOfoTzxxFh6976Ac889r9rrXLRoAfPmvcmBA/tJT89g4MCrueaa65FlmX379jFw4KW8+OIrdO7cBU3TmDPnJRYtWkBJSQk9e55FamoqW7ZsYfbsOf4+f/nlJ+bP/5jdu3fRqFFjhg27n7POOse/v6yslLFj/8vSpT8SGxvDFVdcyeDB/0H2lX/bseNvXnxxOmvW/IWmaXTr1p3hwx8kIyMT8LoQs7Ky2Lp1K7t27eThhx+hW7cePPvsZP7443ccDjunnNKGoUOH0alT56O+J46GEFV1iOGzrUTC6j9JAjwVlqpIEFWHZ1WX1BgRqC4Q1BEL9q7nkTVfkOMsrbcxpFiimdzuEq5oeHrIx06a9CQLFnyK2WzmmWeex2azHfWYxo2bcPfd9zBjxjR69epDhw4dK7XZunUL9903lEGDBvPf/z5OXl4uM2Y8z/Dh9/Dqq28iSRIjR3qF0PTps4mKiuKnn5YybdpUzjijHeeddz4Ae/bs4dChHN56612cTic5OTncffcQ+vW7mPvvfwiHw86cOS8xZMjtvPvuR9hsNv73v0c45ZTWvP7622iah+nTn+eRR0bw8ccLmTz5WR5++H5SU9N4+OFHjnidF198Kd9//x1PP/0U7dt3ID4+vlKbzz77hFmzZjJy5COcdtrpbN68malTnyY7O5v77nugUvtZs6bzxRef88gjj9G0aTM+/vhDPvzwfTp06BTU7sMP32f06EdJSUnhxRen8+ijo1m8+FuioqIA+PHH77nmmut588132LRpI08//RQxMbHccMPN7N+/jzvvvJ1u3Xrw4osv43Q6mT79eYYOHcI773zoF7ULF37GuHETaNmyFQ0aNGDSpAm43S5mz56DyWTijTdeY9SoB/n8869q9L44EuF/t49k9Mha/WfoFb9Aw9H9dzhVrQDUDRGoLhDUBQ+uXlivggogx1nKg6uPLUD8+utv4o033uaCC/oxatQINm3aWKPjrrvuRtq2bedzA1a20r3zzlt0796D228fTFZWFh06dOTJJyexfv06Vq36A4fDwUUXXcKYMY/RqtUpNGzYiOuvv4mkpGS2b98W1NegQUNo2LARzZu3YP78j0hNTeWhh0bStGkz2rQ5jaeeepq8vDy+++4bAPbu3UNCQiKZmRk0a9acxx57nDFjxqLrOvHx8aiqCYvFSmJiYqVxH84jjzyK2+1m6tSq3YBz577KHXcM4YIL+tGwYSN69+7D0KH38vHHH+B0OoPaOhx2Pv74I/7zn3vo1as3TZs2Y8SIUZxySutK/T7wwMN07tyFrKwm3HHHnTgcDnbsqHDNnnJKG0aMGEXTps246KL+XHvtDbz77tsAfPLJR9hsUYwbN4FWrU6hbdszmDRpCvn5+SxZstjfR6tWrenX72JatGhJfHwCe/fuITY2jszMhjRunMVDD41k4sRn/Nav40FYquqSSCuoHCHFlMsJFFXlWdXRNfyJTAUCgcBHubvv0UcfZ/36dXz88Qc89tg4zj//rKB27733cdBrWZZ57LFx3HLL9cyePZMHHxwZtH/z5k3s3r2rUj8AO3fuoHPnLlxzzXV8//23rF+/jj17drNt21by8nLRtOC8eo0bZwX1+/ff2yv163I5/asX7777Xp5/fiqffPIRnTp1pkePM7nwwouOSRwkJzfgoYdGMm7cY/Tp05eYmFj/vvz8fLKzDzJ79kxefnmWf7uuGzidTvbt24vFUpGGZ+fOHTidDtq2beffJkkSHTp0ZMuWLUHnzcqquObY2DiAIJHWvn2HoPann34Gb745l+LiYrZv38app56G2VyxWj05uQFZWU2CBGvjxo2D+hg8+C7GjXuMH374jnbtOtCjR08uvPBiLJbjv+8JUVWn+G7staB+TwSGFvArLAIsVZiqSACqi+SfAkFd8HzHy8PG/VdTCgry+e23lZx/fh9U1Xu7k2WZ5s1bkJOTDcBbb70XdEyDBins3bs3aFtWVhZDh97LCy88x/nn9wnap+s6/fpdzO23D650/sTEROx2O3ffPRin00mfPn255JLLOO20ttx9d+X2VmuFMNF1nc6duzJyZGXXXWysV/BcffV19O59Ab/88jO//76SV16Zzeuvv8pbb71HcnJyTaYoiIsu6s8PP3zH009PDHIZ6j6vy/33j6Br126VjktPzyAnJ8f/WlG8c12TQHlZViptCzxOUYLvn7quIUkSJpNabf+6rvv/v4EgwQfQq1dvFi36il9//YXfflvJe++9zWuvvcKrr75J8+YtjjrmIyFEVV0SQck/DcOIuJiqKhOA6hpyhBgGBYJI4oqGp3Np5qkRFaiem5vL//43hmnTZtKjx5kAeDxuNm/exDnnnAsEW4eOxHXX3ciPP37PhAnjgra3aNGSHTt2BPWzc+cOZsyYxj333MeuXf+wefMmvvjiG7/QKSwsJC8vlyNZ1Fu0aME333xNWlq63xJTWFjI+PH/48Ybb6FZsxbMnfsKt946iEsvvZxLL72c7OxsLr/8Ilav/oO+fS88pvqto0b9lxtvvIZZs17wb0tKSiIxMZG9e/cwcODV/u3ffPMVS5f+wNix44P6aNy4MRaLlXXr1ga5/NatW4vZHNq95XA37V9//UlmZkOsVhstW7biq68W43K5/HOUm5vLnj27ueqqa6vsz+VyMWvWDC6++BIuuKAfF1zQD4fDwSWXXMiyZT8dt6gK+W7/6aefcvDgweM66b8B/xJ/iAhRBWDoFe6/iIipCipV46v/JwLVBYI6Q5FkGlii6+0vFEEFXsHTs+dZTJ06hdWr/2D79m2MH/84xcVFXH/9TSH1JUkSjz76OIcOHQrafuONN7N58yaeeWYSO3b8zdq1fzF27H/Zs2c3WVlNSE1NA+DLLxezf/8+/vxzNaNGPYjH48Hlcld7vquuupaSkhIef/xRtm7dwtatW3jssUfYsGEDzZu3JC4ujmXLfmbixCfZsmUze/fu4bPPPsFkMtGmzakA2Gw29u/fR3Z2ze/ZycnJjBgxij179gRd+803385HH73PRx+9z549u/nxx+955plJWCyWIPcbgNVq49prr2fOnNksXfoDu3b9w4wZz7N+/bqQhd6aNX8xc+YL7Ny5gwULPmX+/I8YNGgIAAMHXkNpaRlPPPE/tm7dwvr163j00VHExydwwQX9quzPbDazceN6Jk+ewLp1a9i3bx9ffPE5dnsZZ5zRrspjQiHku/348eNZs2bNcZ+4Kl5++WVuueWWI7bJz89nxIgRdO3alW7duvHEE09gtwf/clqyZAn9+/enXbt2DBgwgF9//bVOxntEAsySUoTEVBlamFuqDv8wVlFU2TD0GpmcBQLBv4Mnn5xI167d+d//xnDHHbdSWFjASy+9Rnp6Rsh9NW6cxdCh9wVta9u2HS+8MJMtW7Zw++03MXLkg2RlNWHGjNmYTCZOP70t99//EB9++B7XX38VEyY8TseOnbnwwovYuHF9tefKzGzI7NlzKCsr5a67BjF06BBMJhOzZr1CYmIiqqry3HPTkWWZYcPu5sYbr2HlyuVMnTqdRo28MUQDB17N339v5+abr6sUv3UkLrzwInr16h207aabbmH48If4+OMPuf76q3j++We54oqBjB79aJV9/Oc/Q+nXrz+TJj3JLbfcwIEDBzj33F6YTKE5yK644kp2797FbbfdyNy5c7jnnvu49NLLfXOUyezZcygqKmLIkNt54IF7SU5uwCuvzPW7SKtiwoSnycxsyMiRD3LddVfy6acf88QTT1VamXgsSEaId6CLL76Yu+66iyuvvPK4Tx7IO++8w4QJE+jSpQvz5s2rtt0tt9yC3W7niSeeoKioiEcffZSuXbvy9NNPA7B8+XKGDBnCqFGjOOuss/j44495++23+eyzz2jR4tjNepqmk5dX81gCQ9c4NMprSTE370n8PdWXCggHFAUcv0/FucGb00Vt+SBq+pFjFyzJmRhx6eh6aCJGVWUSE6PJzy/F46m5ZUmVPLj3b0NzekW0odlx/noZAFJcOyztnkO1RaOmt8KjR4aQDeRY5+VkR8xL1RzvvCQlRVeKVynH4XCwffvfNGiQHrK7RiAAbyqE9u07Bq08HD78HtLS0nj00cgqPeNyOTl06AAtWjQPinuripBjqq677jqeeuopVq9eTevWrYmOjq7UZsCAATXu7+DBgzz++OOsWLGCpk2bHrHt6tWrWblyJYsXL/YLpPHjxzNkyBAeeugh0tLSmDNnDn379uXWW28FYPTo0axevZo333yT8ePHH6n72iUws3cEuP8kDAwtwtx/ig1kq7cIdGD9P0OUqhEIBIL65J133uKTTz7ivvseICYmhqVLf+CPP35j+vRZRz84gglZVE2ePBmADz/8sMr9kiSFJKrWr1+PyWRi4cKFvPjii5VWXQTy+++/k5KSEmRx6tatG5Ik8ccff3DRRRexatUqHnkkeLVE9+7d+frrr2s8plohqFxKBNzgDUAPc/dfFUimBAznAf/qP8NXVFmkYBMIBIL644knJvLCC1O5776hOBwOmjdvzlNPPU3nzl3re2h1Ssii6rvvvqvVAfTu3ZvevXsfvSFeq1ZGRrAf3Gw2k5CQwP79+ykqKqKsrIz09PSgNqmpqRw4cOC4x6qqNb9RG0aFkJJkOaRj6wNF0pECRJWsWpGrcQ3428ggqXLI7r9yl0N1rodqjzMkNEXCCDhOMidiOA+ApwhJNpBlCUU2IsI6eDjHOi8nO2JeqkbMiyCcyczM5Omnp9b3ME44IYuqhg0b+p/b7XZKSkpISEg4IUUU7XZ7pVUGABaLBafT6S96eXib8v3HgyxLJCZWdnVWh+6UKJdxikkN6dj6QPe4sMsVK1Fs0bGYo49srTJFWTDHRx3zOePiQisHoLvsOGxmDLVCxHlsSbiKvc+jTHZUWwIWmxnFFt7zfSRCnZd/C2JeqkbMi0AQPhxTnqrff/+dKVOmsG7dOv9Kq3bt2vHggw/So0ePWh1gIFarFZfLVWm70+kkKirKnw318DZOp/O46/noukFRUdnRG5a3d5b4n2uaQX5+/ZZ3OBoKGm5nxfXZXRKu0iMLUavNg72w7JgsVXFxNoqK7GhazQNsFcONy+5CCxDImlxRo6q04CCqlISnxI7miACX62Ec67yc7Ih5qZrjnZe4OJuwcgkEtUzIomrVqlXcfvvtNG7cmHvuuYcGDRqQnZ3NF198wZAhQ5g3bx4dO1YuOlkbpKen8+233wZtc7lcFBQUkJqaSkJCAlFRUWRnZwe1yc7OJi0t7bjPH8oKG90duHxVCv9VS5KO4akIVDcwox/hi1qSZXRJRXMfewoDTdNDmxfJQNeM4HGpCRX9OfKQPRqapuEhzOf7CIQ8L/8SxLxUjZgXgSB8CPlnyrRp0+jSpQuLFi1i2LBhXH/99QwfPpwlS5bQtWtXZsyYURfjBKBr164cOHCAf/75x79t5cqVAHTu3BlJkujUqZN/WzkrVqygS5cudTauKomw1X9ghFT7T5IVUE31nhPq8KzqQUlXBQKBQCA4gYR8t1+7di233norihJcr0eWZW6++eZaTQyqaRo5OTn+WKn27dvTqVMnHnzwQdasWcPy5csZO3YsAwYM8FuiBg0axBdffMHrr7/O9u3bmTJlChs3buS2226rtXHViIgTVWAEBKofLaWCJCug1H0c3dGQzAn+54Y735dSQa+UJ1QgEAgEgrom5Lt9dHQ0Ho+nyn0ej6dWLRf79+/n7LPPZvHixYA3XcPMmTNp1KgRt912Gw888ADnnnsu48aN8x9z9tlnM3HiRN577z2uvPJKli9fzksvvXRciT+PiSBRFSF3+EBL1dFElaKAHAalI4OyqhcABugiT5VAIBAITjwh3xU7derEK6+8wjnnnBMU/F1WVsYrr7xyXG628hxY5TRq1IjNmzcHbUtOTmb69OlH7GfAgAEh5cqqcyLAUiUZRpCl6uiiSvWKqnqutVel+4+al2MQCAQCgaC2CFlUPfTQQ1x11VX06dOHXr16kZKSQk5ODj/++CMOh4OnnnqqLsYZeQSIDSkCRBUQYKmSQTryW0MyWcMidEkyBRRVdhV43X+6gSSJ0CqBQBBMaWkJ/ftfSFRUFJ9/vgRVrf8QhqMxYMAlXHLJZdx55931PRRBDQhZVDVt2pQPP/yQGTNmsHTpUgoLC4mPj6dbt24MGzaMli1b1sU4I48IdP/5LVWK5aiVxGWTBT0cVIspDq+rz/AXVcYQliqBQFCZb775isTERPLycvnhh++54IJ+9T2ko/L662/70wUJwp+QRdWsWbPo168f06ZNq4PhnDwYBAiOCLBUGYYOmk9U1SSeKgxW/gFIkgKmeK/rr7xUTYh5swQCQc0wdA29LK/ezi9HJXkXyRwjn3++gJ49z+LAgf189tknESGqAgsSC8KfkEXVyy+/zOmnn37iA78jjQhc/eev/VejlX/msHGvSaYEr6DyxVQZuuaztIXJAAWCkwD7n/Mp+ngEeklOvY1Bjkkh7uqp2DoMDPnYHTv+Zv36ddx8820UFxczceJ4du36h6ysJgwdeidJSUk89dTT/varV//B0KF38tFHn9G4cRY///x/zJnzEjt37iAlJYULLriIQYOG+Ct49OjRicGD7+SLLz7H7fYwe/armM0mZs58gT/++I2iomKSkpLo1+9i7rnnPmTZe19YseJXXnxxBjt3/k2jRo258cabmTDhCebPX0RmZmaQ+2/OnJf4668/6datBx999D6FhQWcfnpbRo36L82aNQcgPz+fqVOfZvnyX1AUlcsvH8CGDevo0KGTcCGeAEK+27ds2ZIdO3bUxVhOLiLR/eeLqapJOoXj+bVY2/iD1XUn6A7QhftPIKhtCt+/r14FFYBekkPh+/cd07GLFi0gKiqKnj3P4rzzzkdVVebP/xiASy+9nJ9//onS0orKF19+uZh27TrQuHEWv/66jEcffYQBAwbyzjsfMnLkGL777hueeOJ/Qef45JOPmDTpWZ5++lmysrIYOfJBSkpKmD59Nh9+OJ+bbrqFt99+k59+WgrAli2beeih++natRvz5r3PoEFDmD79+SNex19/reavv1bx3HPTefnlueTl5fHss95FXrquM2LE/ezevYtp02bywgsvsm7dGlat+uOY5kwQOiFbqs4//3yee+45fvrpJ1q3bk1UVHDtN0mSuPfee2ttgBGLHmmWKqPmlipFwVBM4bPILiitQj6GniESKggEAj8ej4clSxZz9tnnYbVasVqtdO/ek8WLFzF06DB69+7L1KlPs3TpD/Tvfylut5sffviOe++9H4A33niNAQOu5MorrwagUaPGjB79X+699z/ce+/9ZGZmAnDRRZdw6qmnAeBwOLjookvo2/cC0tLSAbj++pt466032L59G+eddz7vv/8Op556Kvfd9wAATZo0JS8vj+eff+aI1/L44xOIi4sDYODAq5k58wXAa13bsGEdH3wwnyZNmgIwYcLTDBx4ae1OqKBaQhZVM2fOBGDZsmUsW7as0n4hqsqpcD0dLeg7HDB0d0WA91GyqcuqGQMZwqQUTFBaBVeBSP4pENQB8dfPCBv3X6j88ssy8vJyueCCC/3bLrzwIpYt+4nvv/+Giy++lN69+/LVV4vp3/9Sli37CZfLRd++FwCwefMmNmxYz8KFn/mPL48p3blzh19UNW6c5d9vtVq55prr+P77b1m/fh179uxm27at5OXlommav9+uXbsHjbVjx05HvJakpGS/oAKIjo7B7XYDsGnTJuLi4vyCCrxpiLKymtR0qgTHSciiasOGDX5fsOAIRFpMVUDdv6O6/0zWsAhSL0cyB6RVcBdg6Mdej1AgEFSNrcNArO2uiMhA9S++WAjAI488XGnf/PmfcPHFl3LJJZcxbNjd5Obm8tVXSzjvvPOJjo4BvALq5ptvo3//yhafBg1S/M8DV+nZ7XbuvnswTqeTPn36cskll3HaaW25++7B/jaKongXCYWAyVR9GghVVdDrOXfgv52QRdXll1/OiBEjOP/88+tiPCcPQR+U8DebGO6yihdHElWShGQyEVYL7A5z/2EYSIZOJMy7QBBJSLKCEpNy9IZhRF5eHsuW/cSll17ODTfcHLTv/fff4fPPF7B9+zY6dOhERkYGX375Bb/88jPPPDPN36558xbs2vVPkCXqjz9+58MP32PUqDFBibDLWb78FzZv3sQXX3xDcnIyAIWFheTl5VLuyWjZ8hTWr18XdNzatcde6q1ly1MoKSlh584dNG3azHfOAnbv3nXMfQpCI2QTyv79+6t8AwmCCbKURIBlz9DsFS+OIKokpXzlX/ioqsOzqiOKKgsEAh9ffrkYTdO45ZbbadGiZdDfbbfdgSzLfPrpx0iSRP/+l/Haa3NISEikS5eu/j5uueV2vv/+W1577RV27fqH335bwZNPPk5JSTHJyQ2qPG9qapr//Pv37+PPP1czatSDeDweXC6vu+6mm25h48YNvPjidHbt+ocff/yeOXNmA8e2vqlz5y6cfnpbnnjif6xbt4atW7cwdux/cTgcERGGcjIQ8t3+sssu44033iA7O7suxnPyYERWnioCRJV0hJgqOUwKKQdyeFZ1r+ATJnCBQOB1/XXt2j0ozqicRo0ac+65vfjyy8XY7Xb6978Uh8POxRdfEhTm0rt3XyZMmMzSpT9w003XMm7c/+jRoyeTJ1cf33X66W25//6H+PDD97j++quYMOFxOnbszIUXXsTGjesBaNGiJZMnP8uyZT9x003XMmfOS1x99XXAkd18R2Ly5KmkpqYxbNjdDBt2N6effgbp6enH3J8gNEJ2/+3cuZPff/+d8847j4SEhCpX/3377be1NsCIJdLcfzW1VMkKyKbwSgFVRVFlSViqBAIB8M47Hx5x/+TJz/qf22w2fvnl9yrb9elzAX36XFBtP8uXr6q07YYbbq7kcgxkw4b1pKam8e67H/m3ffXVEsxmMwkJ3h+Ln332hX/fnXfeXSnX1KWXXs6ll14OQEFBPps3b+Sppyb7S/C43W7ef/8dUlNTqx2HoPYIWVRlZGRw2WWX1cVYTi6MwNV/EWCpclcEqh/Z/adiyApo4SNaqnL/GYYeCVpWIBD8i9myZRMzZ77A2LFPcsopp7Bnz27mzHmJvn37oaoh355RFJXHHnuEK6+8moEDr8bj8fD2229iMpnp2fOsOrgCweGE/L82adKkuhjHyUegpepkiqkyWSivtRc2KDaQzaC7vHmqymOqhKgSCARhzBVXDCQ3N5dp054lJyebxMQkLrig3zFnPo+NjWXq1Bd46aVZLFgwH0mSadeuPS+++LLf8iWoW0KXwj62b9/OsmXLyM7O5pZbbmH37t20adOGmJiY2hxf5BJhGdWNmsZUhUsh5QAkSQJTIjgPet1/hiGKKgsEgrBHkiQGD76LwYPvqrU+O3fuypw5r9daf4LQCFlU6brO2LFj+eSTTzAMA0mSuPjii5k1axa7du3i7bffJj09vS7GGllEUEyVJBGUp6o6S5Uky6DW88q/aqZSMiVgOA+CuxBd94jVfwKBQCA44YTsl5o1axaff/45EyZMYNmyZf4b7MiRI9F1neefP3Ldon8LBpG1+q8m7j/Jt/Kv3vSKpFYbn1YRV2WAu1BkVRcIBALBCSfku/0nn3zC8OHDueqqq0hISPBvP/XUUxk+fHiVpWv+lURaRvVA9191okpRkOoxnYIhycimasYWuALQlU94ZScVCCIV8TkSCEL5HIR8tz906BCnnnpqlfvS0tIoKioKtcuTEz3CYqo8NbNUGfIxh+EdN4ZhIJurSTxrTvA/1V35eKs9h/+8CwThiMlkQpLA6XTW91AEgnrH6XQiSTXLHRbyHbJJkyYsXbqUM888s9K+lStX0qSJKNzoJYLdf9UEqkuqCWQlWDCeQAzDAJMFSZYxDhtDYAJQfJYqSRKhVQLBsaAoCgkJCeTnFwDlNe3EjxTBvw0Dp9NJcXEBiYkJKMrR606GLKpuu+02xo4di9vt5vzzz0eSJP755x9WrFjB3LlzeeSRR45p6CcdAe6/iMhT5an4RVqt+6+eCykbBqCYvbmydFfQvqCs6u6CwxYKCASCUMnIyACgoKCA4uJ6HoxAUE9IEiQmJvg/D0cjZFF1zTXXkJeXx+zZs3nvvfcwDIOHHnoIk8nEkCFDuOGGG0Ie9ElJkPgI91940tED1SXJl07hxI2qSlSTt/6g+7Dth8VUHW7JEggEoSFJEpmZmaSlpeF2H/6BEwj+HZhMphpZqMo5pgCZ//znP9x0002sXr2agoIC4uLiaN++fVDg+r+eCCuozFFEVcXKv/pVVYZsQlZUDs9CFZxVPR9D13wFROtbBQoEkY2iKCHdVASCfzPHHHUcExPDOeecU5tjOakwImz139FiqiRZrt90ChUjQTLboDTYHyEFBKp73X8i+adAIBAITiz1t5TLh67rzJw5k48++oji4mK6du3K2LFjady4caW2M2bMYObMmVX2M3DgQH8JnUGDBvHLL78E7e/WrRvz5s2r/QuojkgTVUeJqZIUBRS13g0/3hWAVipFoavxFW3cBRiaFvZOV4FAIBCcXNS7qJo1axbvvvsukydPJj09nWeeeYYhQ4bw+eefYzabg9recccdXH/99UHbXn/9dd577z1uv/12/7bNmzczbtw4+vbt699Wk6WQtUqkZVQ/ivtPlhUM2VTvhZQNw/AGq8sKhubxb5dkFdRY8BT7iiprkZDJQiAQCAQnEfUqqlwuF3PnzuXhhx+mV69eADz//POcc845fP3111x66aVB7aOjo4mOjva/3rBhA2+99RZPPvkkrVu3BiA3N5fc3Fzat29PSkrKCbuWSgTFVIX/3d3QjlymJlwKKRsGoJqRFQUtQFSBdwWg4Sn2uv90vd7jvwQCgUDw76Je/VKbNm2itLSUnj17+rfFxcVx2mmn8dtvvx31+PHjx9OlSxeuvPJK/7bNmzcjSRLNmjWrkzHXmEhLqVAuqiTFa/U5DLme0ykEoZiQlCp+D5QHq2t2dI8dSaRVEAgEAsEJpEaWqn379oXUaWZmZo3aHThwAKBS/ofU1FT/vur44YcfWL16NZ999lnQ9i1bthAbG8v48eNZtmwZUVFRXHTRRdxzzz2V3Imhoqo1F0daQFNJlkM69kQjSVKFpUq2ICuHjVWSUcwWJEVCrgWrm+LrXzn8PDU+XgWLBd1lD9ouWxIrVgV68lFkImPlpY/jnZeTFTEvVSPmRSAIP2okqnr37u1bnl4zNm7cWKN2drv3pni42LFYLBQWFh7x2Ndff53zzz+/UsmcLVu24HQ6adeuHYMGDWLjxo1MmTKFffv2MWXKlBpfw+HIskRiYvTRG/ooibGQ53tusVpCOrY+KNK9okpWrMREH+b+U1QscdGoMbV7DXFx1ZScqQEuVzxmPVhUGVHJlG+xUExMtNkb1B5hHM+8nMyIeakaMS8CQfhQI1E1ceJEv6gqLCzk2WefpWfPnlx88cWkpKRQUFDA999/z48//hhSRnWr1XvDc7lc/ufgrbNjs1X/RbFv3z5WrFjBK6+8Umnf+PHjGT16NPHx3tVgp5xyCiaTiQcffJBRo0bRoEGDGo8vEF03KCoqq3F7R0Bbl0sjP7/0mM57IpBlCd3jFVWGbKakNLjel2wGzWmguWvnGhRFJi7ORlGRHU0L3UUnSSC7JOx2d1DJHA9x/uf2omykEjuaFDmpFY53Xk5WxLxUzfHOS1ycTVi5BIJapkaiauDAgf7n9957LwMGDGDChAlBbS677DKeeuoplixZwnXXXVejk5e7/bKzs8nKyvJvz87O9geeV8W3335LUlISZ511VqV9qqr6BVU5rVq1ArzuxmMVVQAeT82/uDRPRRC1boR27IlGlgMyqssW9MO+oGVDQkOp9WvQNP2Y+1RlExhS0FiNgLQKmiMXzaPjkcJ33qvjeOblZEbMS9WIeREIwoeQf6YsW7aMiy++uMp9vXr1YvXq1TXuq02bNsTExLBixQr/tqKiIjZs2EDXrl2rPe7333+nW7duqGplTXjLLbcwZsyYoG1r167FZDLRtGnTGo/t+ImcgsqGYYDms05VtfJPVTGk8MqoLFURrC4FlarJE/X/BAKBQHBCCflun5iYyJo1a6rct3z5ctLS0mrcl9ls5uabb+bZZ5/lu+++Y9OmTTz44IOkp6dz4YUXomkaOTk5OByOoOM2bNhAmzZtquyzX79+LFiwgPfee4/du3ezePFipkyZwuDBg4mJian5hR4vASvlwn71n+6uyEBeZToFK+GWa0tXzMhHFFX5QlQJBAKB4IRyTAWVX3zxRRwOB7169SIxMZFDhw7x5Zdf8t577/Hf//43pP6GDx+Ox+Phsccew+Fw0LVrV1577TVMJhN79uyhT58+TJo0KcgFmZOTU22dwZtvvhlJkpg3bx4TJ04kJSWF22+/nbvuuivUSz0+IiijuqRXiNaqsql7CymHSToFPxKS2QplAeVqzIn+p0JUCQQCgeBEE7KoGjp0KMXFxbz22mv+QHHDMLBardx///3cdNNNIfWnKAojR45k5MiRlfY1atSIzZs3V9r+119/HbHPm266KeRx1DpBoiq8rDyHY3gCAvAPE1WSrIBa/4WUD8dfriaAypYqo1I1G4FAIBAI6oqQRZUkSYwePZp77rmHP//8k8LCQhITE+nYsSNRUVF1McbIxIicmCoCs6kfVkxZUhSQw6GQcjCGYYBqQVLUinI1SjRIJjDcGO78oJWBAoFAIBDUNcd8t4+OjiYlJYW4uDjat2+Py+WqzXFFPhHk/jM8FfmeDnf/SbKMpJzguok1wFuuxuQVfT4kSfJnVTdcBYBGuMWCCQQCgeDk5Zhq/y1YsICpU6eSk5ODJEl89NFHzJgxA5PJxNSpU487c/nJgBFJ7r8j1P2TZAVDUSEc0z0pZmRZIah0tSkBw5UD7gIMTRPuP4FAIBCcMEI2oSxevJjRo0fTo0cPnnvuOXSfi+WCCy5g6dKlzJo1q9YHGZEErf4Lb1FFgKWqkqhSzRiEVzoFP7KKpAYLeMmc4HumYzjzT/iQBAKBQPDvJWRL1UsvvcT111/PuHHj0LQK88VVV11FXl4eH374IQ888EBtjjEyiST3nxbg/jsspko22wjKuRVGGIaBbImCkoKKjaaKFYC6PQeZ6pPICgQCgUBQm4R8t9+xYwcXXHBBlfvat2/PwYMHj3tQJwUR5P6r1lIlSUhq+AWpl6PrIJnMQaI1cAWg7jgU9lMvEAgEgpOHkEVVcnIy27dvr3Lf9u3bSU5OPu5BnRREqKUqUFRJsgJK+KVTKMe7AtCMHBisHphWwZmDCFQXCAQCwYki5Lt9//79mT59Ol9++aV/xZ8kSaxbt45Zs2Zx0UUX1fogI5JISqlQjaVKUspzVNXDmGqIpJiDVwAGWapy62FEAoFAIPi3EnJM1QMPPMCWLVt44IEHkGWvWLjlllsoKyujS5cu3H///bU+yIgkksrUVBNTJcsKyGq4hlQBYBxeA9AfqA6GM1e4/wQCgUBwwghZVJnNZl599VWWLVvG8uXLKSgoIDY2lm7dunHeeeeF/0q3E0RkpVSo3v1nyCbQwldVGcjezOplJQBIAYHqhjMvbF2XAoFAIDj5CFlUDR48mCFDhnDWWWdx1lln1cWYThIix/1nVOf+M5cXUg5fYWIYBrKpolxNkPvPlYdk6Ii4KoFAIBCcCEK+269atUpYo2pCBAWqB7n/AkSVbLKEvaXHMAwwBcRVBQWq5wGiVI1AIBAITgwh3+3POeccFi5ciNvtrovxnDwE1Z0LbxEaZKnyxVRJsgyKOayD1MEXuqZYkGSv0VWSTaDEePf5iioLBAKBQHAiCNn9Z7FYWLhwIUuWLKFFixaViihLksSbb75ZawOMXCLJ/Ve5TI0kK6CqYW+pAkAxISsKuk/nS+YEDHsJhisfKRLGLxAIBIKTgpBF1YEDB+jYsaP/9eE33Yi4CZ8IAt1/cniLKrSyiueBoioMCylXhSGrSGrAWE0JYN8DWim6xw5qZFyHQCAQCCKbkEXVvHnz6mIcJx8BoircUyoEuv/KY6okxZdOIUJCkiSzDSj0Pjcl+O2Ehj0HYuPqbVwCgUAg+PdQq3f7srIy/u///q82u4xYglIqhDtVpFSQFNUrqiIAwwDZbPGnrghOAJpTT6MSCAQCwb+NkO+ae/fuZdy4caxcudKfUf1wNm7ceNwDi3giKKN6UExVeaC62RYxMd66biArZmRFRfe4g4oqG/bsMF8mIBAIBIKThZBF1aRJk1i1ahXXXHMNq1atwmaz0aFDB5YtW8aWLVuYMWNGXYwz8ghUJOEeU+XxxVRJKpLkTU0gm8zokaKqAFRfWgWPGykgq7ruOIQqiUWAAoFAIKh7Qr7b//bbbzz44IM89thjDBw4EIvFwsiRI/nkk0/o2rUr3333XV2MM/IwIiilguazVAXGU4VxIeUqkU3e4HoOy6ruOFRfIxIIBALBv4yQRVVpaSmtW7cGoHnz5mzYsAEARVG48cYbWb58ee2OMFKJJPdfeUxV0Mq/8M9RFYghKd5yNQTHVHlFVXiLWoFAIBCcHIR8t09NTeXQIe+v/yZNmlBYWEhOjjcYOCEhgdzc3NodYaQSQav/8MVUSUqFqAoqUhwBGIaBbLZ5XwQGqjsPhXvpRYFAIBCcJIR8tz/vvPOYNm0aq1evpmHDhqSnpzN37lxKSkr45JNPSEtLq4txRh4RVVDZF1MV4P7TI2TlXzmGYXjjqmTlMEuVEPkCgUAgODGELKqGDx9OXFwcL7zwAgAPPvggb775Jl27duXzzz9n0KBBtT7ISMSIkIzqhmHAYTFVsmqmlrNt1DmGAagWbzyYGgO+gHvdKUSVQCAQCE4MIZsjEhMT+eijj8jOzgbg8ssvJzMzkz///JN27drRrVu3kPrTdZ2ZM2fy0UcfUVxcTNeuXRk7diyNGzeusv3ChQsZOXJkpe3fffcdjRo1AmDJkiXMmDGDPXv20Lx5c0aPHk3Pnj1DvNLjJFIKKuuuirGWW6rM1sgKUi9HUb3laiTZ6wJ05WI4c8PdUCgQCASCk4Rjvtunpqb6n3fp0oUhQ4aELKgAZs2axbvvvsuTTz7J+++/j67rDBkypNocWJs3b6Zbt278/PPPQX8ZGRkALF++nJEjR3L99dfz6aef0rNnT+666y62b99+bBd6rOgRIqq0ihxVkuJNoCmpkRWkXo4hm5B8pXXKXYCGMy8ir0UgEAgEkUfIlqoxY8Yctc2kSZNq1JfL5WLu3Lk8/PDD9OrVC4Dnn3+ec845h6+//ppLL7200jFbtmyhdevWpKSkVNnnnDlz6Nu3L7feeisAo0ePZvXq1bz55puMHz++RuOqFSIkpsrwBNf9i8h0Cn4kb7ma0qKKUjWGB1yFoIpSNQKBQCCoW0IWVStWrKi0raysjIKCAhISEjjjjDNq3NemTZsoLS0Ncs3FxcVx2mmn8dtvv1UpqjZv3kzv3r2r7E/XdVatWsUjjzwStL179+58/fXXNR5X7VAhSsJ69V+ApQrZghxBhZQPx7sC0OoVsYG5qpw5QlQJBAKBoM4JWVR9//33VW7fvn07w4YNY8CAATXu68CBAwB+1105qamp/n2BFBYWcvDgQX7//Xfeffdd8vPzadeuHSNHjqRZs2YUFRVRVlZGenp6jfqrUyIkpiq4mLJ39RyyKVATRgzVrQDU7TlI0S3qb2ACgUAg+FdQa+vmW7RowX333ceMGTO45JJLanSM3e69oZvN5qDtFouFwsLCSu23bt0KeG+ekyZNwuFwMHv2bG688UY+//xzPB5Ptf05nc6Qr+lwVLXm4kiSKlSJrMghHXsikaSKeZFUG4rJhGwyoep1p6oURQ56rNW+JQuK2YRsSUTzbZOcOWE7/4HU5bxEMmJeqkbMi0AQftRqMqKYmBj27t1b4/ZWqzcDtsvl8j8HcDqd2Gy2Su27dOnCr7/+SmJiIpIvTmnmzJn06tWL+fPnc8011/j7C6S6/kJBliUSE6Nr3N5lMVHqex4dYyM6hGNPJGWFLop9z82WKKLjY7HER52Qc8fFHd//SVXoHjOmYhtSTApu3zazkU98mM5/VdTFvJwMiHmpGjEvAkH4ELKo2rdvX6VtmqZx8OBBpk+fTosWNXezlLv9srOzycrK8m/Pzs72l8I5nKSkpKDXNpuNRo0acfDgQRISEoiKivKnewjs73iTkuq6QVFR2dEb+nDYKyxAZWVuXPmlR2hdP8iyhCunQgS7NRWHW8ZeUFangeqKIhMXZ6OoyI6m6Uc/IMS+NQ+4jBj/NnvBPvQwnP/Dqct5iWTEvFTN8c5LXJxNWLkEglomZFHVu3dvv5UoEMMwsFqtzJw5s8Z9tWnThpiYGFasWOEXVUVFRWzYsIGbb765UvsPPviA5557jh9++IGoKK81paSkhJ07d3L11VcjSRKdOnVi5cqVfqsVeIPru3TpEuqlVsLjqfkXl65p/ueaYUAIx54oVMOJu+ig/7UhW9BlFc2jnZA0BJqmhzSnNUHXDSTVhqHEV2wry67189QldTEvJwNiXqpGzItAED6ELKomTpxYSVRJkkRMTAzdu3cnNja2xn2ZzWZuvvlmnn32WZKSkmjYsCHPPPMM6enpXHjhhWiaRl5eHrGxsVitVs4991yeffZZRo0axf3334/D4eC5554jKSmJgQMHAjBo0CDuuusuTjvtNM4991w++eQTNm7cyFNPPRXqpR4fRniv/lMUCT0vB91Z7N8mqTZfOoV6HNhxYhggqxYkS4VFU3ccqscRCQQCgeDfQsiiqly81BbDhw/H4/Hw2GOP4XA46Nq1K6+99homk4k9e/bQp08fJk2axMCBA8nIyOCNN95g6tSp3HDDDRiGwVlnncVbb72FxeLNBn722WczceJEZs2axfPPP0/Lli156aWXQnJL1gphvPpPkiQkRxHu0kIMPTBQ3epPnhmpeFcAmpCtDfzbhKgSCAQCwYkgZFH12WefhdT+aCkWFEVh5MiRVZaeadSoEZs3bw7advrppzN37tyjnjOU1A51QhiLKkXS0Ipy0N0u0AJFVRSGrEKEexIk1YxsigIlCrQyDCGqBAKBQHACCFlUPfrooxiG4f8rp9wlePi2ehc39UWgqCJ8MqorigTFuXjKfG6/AEuVbIoGWQkusROB6LIJWVG9WdW1MgynEFUCgUAgqHtCFlXvvPMOQ4cO5bbbbuPyyy8nLS2NgoICvv/+e6ZMmVI/xYvDkoDAJDl8LFWSx4G7OBfDJ5yC3H/m2AgtT3M4EpLF5s2q7tiH4Sr0Fo6WzUc/VCAQCASCYyRkUTV+/Hhuu+027r77bv+25ORkrrnmGhwOB2+++SZXX311rQ4yEjECrD3hEqheHpyuOSqyqKNX5PSSLXERHaRejmEYyCZrRf0/wHAeQrJl1uu4BAKBQHByE/Ldfvv27bRt27bKfU2aNGHXrl3HPaiTgjBz/5UHp3tKD8tUH1j7zxxzUliq/OVqAlYAGo7cehyRQCAQCP4NhCyqmjRpwoIFC6rc98EHH1SbtPNfTRhYqoKC0wMw9ApR5XX/neiR1T6GAahm5ABRhTOn3sYjEAgEgn8HIbv/7rnnHh544AF27txJnz59SEpK4tChQ3z99dds3779qCvz/jUEWqrqOaaqUnC6Dy3/d/ScH32vJCRbciTWUa4axVQpV1X9S1uBQCAQnMyELKouuugiXnzxRV588UWmTZsGgCzLdOzYkTfeeIPOnTvX9hgjEyN8VtAdHpwOoBX8iXvj42B4K+SZswagxjZC9xVSLncDGvhWekqGz4qlY5Tvl0BCwiR5E4bqdViEOWRkFdmW4n9pOISlSiAQCAR1yzEVVO7Tpw99+vTB4XBQWFhIfHx8UEFkAUEZ1evT/VdVcLpevAH3xv/50ynoqeeRd+r9HCrcjWboGLpeLp0wDK+wovwRr6Aql1yyJGNTrcRYoolSbVhkS1gILF03UKIy/K9FriqBQCAQ1DXHJKpKSkooLS0lLS0NRVGYN28e+/bto1+/fnTt2rW2xxiZhEHyz8DM6ZKiophMuEs241o/BjSvyLIndGZf5u2ojhIMST2mQPUyl4N8eyFm1YxFMRFnjfUKLMWKjISuGyc8VsswQI5Jr3htP3iE1gKBQCAQHD8hi6q//vqLIUOGcP311zNixAgmTJjABx98QFxcHO+++y4zZsygT58+dTHWiMIw6j+lgirrGM5iDIsFu+GmLH8d1lUjkD2lAJTFtmVDo8GsKyvCoXvwFObiMXQ0Q8djGL7nhu+1d5vm2+YxdHTDINMaQ9eEdFpEJaC7nTjcToocpZhUFYtqItYSQ7QpCptiRUZFP0GJRQ3DQI4OsFSV7ENRJDQtjFyUAoFAIDipCFlUTZs2jRYtWnDttddit9tZsGABN954I2PHjmXs2LG89NJLQlTBYZaq0FIqKIqEU3chSxIgIUsSMjISMpIU6FkMtgAZhvdUkgQe3BSWZlNQsp9SRyl64VbSNz1JmcfJb2pTltra87PlFHbu3nS8V8rru9eRYLLQOT6NrgnpdI5PJwELLo+bYkcZJkXFrJqINkcRY4omRo0CQJYlZFnyZec/7mFUQral4l3gqqPbs5HKCpBtCfXumhQIBALByckxWaqef/55GjduzLfffovT6eSKK64AoH///ixcuLDWBxmRHGNMlaxAjiOXPHsBGIbXhSdJSHgfZSRkWUGVFGRZRpFlZMn3h4yOTrGzFJerBHveHsrspWwr2s22/d+y3NKPv6LS8EiK92Ru5xHHEgoFbiffHdrFd4d2IQEtoxPpmpBO1/h0To1Nwq15KHXayZMLsJrN5Hti0NwSqqSiSiomWUWVVRRJRZFkbyyXvxzSsY3JUC1I5gQMVx66Kw9PwQFUswVDtp4UqSMEAoFAEF6ELKpkWcZisQDw008/ERcXR7t27QBvrJUIWPcRYkyVJAGyzoGyQxwoPoRT82CSFZQaWrkkvDFUmm6w01HIqoPb+aMwh7WOYhyGAeYOlY6RgVaWKDrGJJIRn4qMgiLJqJKEKskokozie65KEoosoyChyjKqJKMbBhuKc/mt8ACrCw/i0DXvpQNbS/PZWprPu3s3EqWodIpPo2t8Ol0S0kknGtkJJSUODN1AlhVv35KMIiuoiopFMWFWzD6xZUKVFBRJRZWVAMHFUWLAZCRLMoYrD1wFuO2lSPn7URo0wWOIBAsCgUAgqF1CFlVt27blo48+wmq18uWXX9KrVy8kSSI3N5c5c+ZUm239X0cI7j9JktAkN3uK9jN9y3I+3r/ZL1AUScIkyZhkBXP5oyxjkhRMsozZ92jy5cLaVJJHwREsUI1UMx2j4ulgi6WdNZZYRUWx2pATMo/JLdYiOoHL0lvg1nXWFx/i98ID/FZwgL/LKjK3l2kefs7by895ewHIssXRPimVNMVGpjWGRtZYMq0xWGSlUv9esVVhlVMlBYtqxqJavBYuRfWLLQUFkIIEl2xtgF681Zs6QivDXaIgmw6iJGSK+CqBQCAQ1Cohi6qRI0cyZMgQvvjiC5KSkhg6dCgAl156Kbqu89prr9X6ICOTmrn/ZFnCaTjYcOgf/rv+e/4oDF6l5g0U1/wiK1RS9FLO9Oymm1RM06xrSLYlBzeQQFatKLKCFJT60/BfQmXpUbGlPL2CSZbpEJ9Kh/hUhmS1I9dl54/Cg/xWcIBVhQcp8lRkct9lL2LX3qLKYzXbaGSNpaE1hkY236M1lnRLNGoVCVRlySu0FJ+lS5UU7wpE1YxJMmFSVIyoilxViuxEMjdAsxdjshZgWER8lUAgEAhqj5BF1emnn84333zD9u3badWqFVFR3qDjcePG0alTJ1JSUo7Sw7+EgJu1JMlVZipXFJkidxE/7N/If9f/SLarDAAZibZxDdAMHbeu49I13IaOS9dx6xouw/voriLBaJSs0M5i5fyinznHsZ5Weh5uSzp7W41BMyX4x2M2WTBZo4iOSSQmpgGKYg3UUeDPRBX00ve04oVmaJS6ynB4nLg1D27NA0Cy2caFKU25MKUpmmGwtTSf3woO8HvBATaV5PnyYAWT47KT47Kzuig7aLuMRLolioY+odWwXHhZY0i1RKNInkp9lQuuRCWGaN+2gi3TcJ8xDlWxYCnNwSQbyKrN21aqsHQBAbFcJz4dRDgiy96YPjBwGx40w4MiKZgkExXWQTFXx4IkeRdseFOPiAkUCCIZyRCf4hqhaTp5eaU1bl/45i241nprJKaO24wRU7G8X5IkFFUi15HHq9uWM23bb36BlKiaGdu0He1jEzEMqfyA8iMrajNL3huZ2zC8IsvQ8eg6CSUHabxpAhbHHgDc5hT2tX4MOToTk2rGZrYRbY4iyhqLLToJxRyLx6PX8Mu8shtTkrw3XLfuxqE5sXsclLhKcXlcuDUPniosbA7DQ67kYmteLrvLitnrKGavvYQ9jmJKNHeN5rccVZLIsMT4xJZPcNm8z1PMUVhLd5Dx613IvkSnRUlnk501GFkxYbLFoiakIqF4A/1lGVVWMSsqJsWEKqneGC9JQZYUFMn7XJKkoLiu2hJeqiqTmBhNfn4pHk/9ZOT3rh71CigDHY/hwaW7cWkuHB4HZW4Hmq6h6bpvvhSsqgWbasWkmDArJlTJGwNXnp/seL9iwmFeahuvSAW34abMY8futhNlshFjikUypBpZUI93XpKSolEUEVsoENQmx5T8U1ADAqxIsqIgyTpobtA8IBvsyt/PYxuWstgXZwRwqiWaEUlNiS12kl+WTbRNrcr3FqBtJCTAAliQkD2lZG6d5BdUmqUBrs7P0yimCVZZxYSMSTUj2+IxrHFohoTmCsWtWIUj0Jc9XUIhSo4i2hJNA2syHsONw+PA7nFQ7Cr1WbHcaLpOlGIiNSaWxnJ00M3DMAyKPC72OkrY6yhmj6OEvXbfo6O4SheoxzDY7Shmt6O40j6TJJNpjaFxyr20LPydDL2YjKIDxOz+DFPGJSTpHiwySNHJVd7EZEnyi63yFZaKLKHIJiyKikk2eVcs+oSXIileVySKz6pz+FwFlP7BALz5MQy8JX90ycCluZBlrxWzZsH4x4Z3eJJfROlouHUPLt2FW3Nj9zhweBx4NA2PruHWPGQ7y/jHXsROX7xcli2OLFssaZZoFEnyLTLwzoFZMWMzWbHIZsyKyTtXksq/2aoly94fRS7dhd1tp9hZQqnbzuaiQ9g1N6fGJhNriSbRFk+cOQ7ZkIV7WiCIMISoqjMCXGQ5O6EoH0mW0Mxmfi/Yw4NbfmW7s8zfpp8liZstNsz5O1BxkhZlwuqRMJslJEP3ijTDQEL3pWvwbcPwP5oO/IBi/8fboaUBMV1fIt6age7yIMkaakwsclwDNMlcJ1/WgQJARiVaiSHWFEuKrQEu3Suyyjxl2D0OzIqCSfHgNjR0nwCVJIl4k4V4k4XTYpMP69sg1+3wC6699hL2OkrY5yxhn6MEZxWCy23o/GMv4h/gZ0uHih0eYPc6ZCBZNZNqiSLFGksDs40UcxQpFhup5igamG3EqRZUSapSJEFl4aVIMrIsoci+7PSGEeTqNIJ9rBWuVMMbehfltOByaKiSilk1Y/YJN2+6CQVTkDDx9XDYf2W5UCqfU0kC3ZfUVTc0NMMrlDTd+9zuduDwOP3i6ZCjlB32QnaWFfGP73GnvZAyrbKbFcAiKzS2xtIkKo4sWxxNbN7HTGu0d/WmongXEsgKNpMVq2rBJAdbtQzDQNfrRkDWJ+UWKYfupMxdRpGzFLvLzp8FB/i/3N38nLeXA06vBTzTGsPA9FZcnNacJGuMT1zFYpLMaNrJYaETCE52hKiqKwIsVS57KYah4tTzWLhvLU/kF1LsMzdZDY0x9pVcU/AnZik011e1mBIxnz4FgwQ0tws1Kg4lPgXDHINbMzhRJgLDwL/CTsVErGom3hyPrBioVolCUxkut9vrYtLcODU3Ht2Nruvouk8E+B4lSaKB2UYDs432ccFxe7phkOuy+wSXT3T5nu9zlFQZewagAzkeFzkeF5QWVHsdMhIWWcGiKFgk36OserdV+vNuVwJEDVR2nEoEby8XQolRNuIMEw1MNlIsXmGnKqov3YT3z6z4gvF9gsvkE1zlosmjlQsnr3XQpXvw6B503UA3dHRDx615yHPZ2W0vZmdZITt9Fqh/7EVBiwpqglPX2FZWwLay4Dk0STINrTE0scUFCa5G1lisqskrtBQFk2LGplqwKMFWLQmvte5wPSsFWmqlwO1S0H7d0CvFCFbMe4BLPajPCip/TCoL2MBEvEHjkAwcmoMyl50iRzGlLgcr8/fxc94eluXtJc/tqHS+fY4SZu5czZt71nNZWguuTG9JZnQiCdZY4i1xWGSrEFcCQZgjYqpqSKgxVQWvXIZ7y1IATB1SkKUCZli6M9PSDcP3Dd5Uy2dW2RecoufV3kDVOMxnPIsc0wLVGoUa3wBsSZxALXVUAmNBNM3wZ4GXJAnd0PEYGjpea4rHJwjcugenx4VH97oQDxdd1aEZBnn2fPbl7SPH5aA4fw1FJX+zX47lgBzDXjWFAqNqK1Q4oPrEZIo5ym9FK3+eao0iwxJDotmGIsuUetzkusrIc9nJddopcDvIdzso8DjJd/ke3Q4K3M6QhVOaOYqmUfE0jYqjiS0eCd8qTnsR/9iL2Ocooaa3e0WSfGIrnqY+wdXUFk9jWyyWALFlVSxYTRZsJgvxsTGUljjw6JpXdBsGOt5YQP97Rtd9z3U03RNUCFwKlLVS4IMUtNkvumQZCQlF8j56LU5yRXWDgKS8MrJ3FS3eN7Ld46DYWUKRs4zluXv5KW8Py/P3UVxFvKCMRIf4FHQD/jxsgYZJkundIIurM1pzanwKcdZYEixxWBUbhm6gKCKmSiAIN4SoqiGhiqqcKW0gex8AznbRjIi9iKWmpv79fd3beabsG2JxUapb2as1YL+WQiEpHNSSOeC2YiChGxKnx8lckK4QF2XBalbQfSVrfBlDfY8SSCbkhI6oUUmoMYnIsQ3Q5Lpx9R0PoQTYVgROg/e2Z+AxPGh43Vdun+hyaW5cmhv3YZYuTdeRDBeeggPoLq+QSNr3MUkHPwfAQGZns/vYHXMaeaqZHFnloNPOIWcZOS47pZobp67h1D24dG9qC5eu4dC0Klcw1gcWWUGCY067EUiyyUqTqHia2eJoGhXvtzJFKaYjHufSNfY6SvinzCuyygXXHkcxnhp+xaiSRCNrrO+cFYKrSUw8SbExlJY6cWoeSj1uSj0uSjwuSjU3ds1DmeamVHNTpnko87h9rz1ohk60aiJKMWGTVaIVE1GqSpRi8v2p/sdoxYRJkqt19R6O9y0pVYgxSaLM42aZzxq1smB/lf8nZkmmc0I6Zyc1pEdiJnGqGYBtpQV8vH8zP+buRjtszrrEp3FNZmt6JjUi1hZDojWeWHMMCfFCVAkE4YQQVTUkVFG17qnOpOVvBWBgj9vZavXGCEmGQfsCN1nFsciWZOITGtEiPpmmJg8ZqguLLOHUDKZth0UHKvprFQOT2sqcnmYlPtpcYXWSAhwasowaFYMc53X1hWtyy9pczXW46PKuWAuwdGleAeZ2FFJWfAiX046uuYnb/jIx2d8AoEsm9rV4GEf8aZjiksEaVyMh6tF1n+Dyiq7y5w5dQy8PQA/A8IdPHZauwn8x4FIMdhcVku0sI8dZRrbL+1iVleNYsMgKiSYLCSYrCSYLqT4LVDNbPE2i4oj13eCDh+XNpl9uQVJlb8Z7l+7xzq9vNaBmeB8Pn6N9zhJvbJtPcP1jL2KPvbhat+zhmCSZWJOZMo+7VoTjkVAlCZti8gtV8F6/t2KB91X5v8GiytvugLO06lQnikr3hAzOTmpE14R0bIo38kKRFSyqCatqwam5cLicHHCU8NmBbXyR/Telh/2/N4+K5+qMU7gwrTlJUXE0TcnAcKi43aHPixBVAkHtI0RVDQlVVG2ddgkJe34C4Myz7qXYZMViqFytNOacmGgykxJIi03Bpplwltm9X8qyjKyoKCYTBhILdpTx1OpCHD5xFGuSmNAzjctbJxAXbcb/1S6Vp1qQwRIbVq6+qjiRS+TLBZeiSMjuMlxFObjcTjyGG89fjyMd+A4AQ7GRd8ZTuONbQVQ8bkzohlcc+fvy/etfOec9QcBiTCnoRhwoqipW/vk3+KWVL94cWYboGCslJY5Kos6uechxlZHjtHsfXWVkBzw3DEg0Wf2CqbrH8pt5lXNVhXiyqhasqsWXasKXZsKXz0uSQDN071zqGh7d7XfTOj1OPD6RVS669ACxoRm637K102fV2llWGJJlK9yJV830TGzI2UkN6RifitlXMUCVlYoC4+ZootQozLIJt+6m2F1Cgb2QMreDQpedL7N3Mn//Fg66yoL6TjZZuTKjFfe370WmmiJElUAQJtS7qNJ1nZkzZ/LRRx9RXFxM165dGTt2LI0bN66y/datW3nmmWf466+/kGWZrl278sgjj5CZmQmApml07NgRpzO4VMuwYcO47777jnmcoYqqTdP7kLzrNwB6nD2MjOhkHko/nXRrLEmxSaRGpyBrJq81SZFRFRXVbEJRFBQZPJpBbqGDtfuLeOynnewprrie69uk8PDZTWncIAZZCl4xFQn3o/rKOyTLErKnDC1vL+6yEkDHtf5/6HnLvQ1MCZi7v4yc3Bo9JgGH5hUE3vRgAYWty20XAdvBuxIwwBkUIJp8qRHKt5RbsYzA9ApeUWW1mSkqKcPhdnpTUBi++CGfKNEN3Zv/KUTXoyxVxARJkuQfa3lururFU8WqzqO9twJzXIGBVm4tNLwWLZfuxulx4tRcaHr5CkTfHPvw6Dp7neVuxPIViEU4DA9RcoULz+Zz20WXu+/UChdeoFtPRqKs3C2ouynzeHyvfduqfHTjMnR/tYCK/0X/C/9rwzdB5ftiVTPdEzM4O7EhbeMaoPiqKXiFlJlocxSx5miiVBuqZKqU8NObdsGgxFNKobOIEmcZZS4HP+Xt4eN9W9hUGhx/GWeysODsOzg1OjWk9wMIUSUQ1AX1LqpmzpzJ22+/zeTJk0lPT+eZZ55hz549fP7555jNwa6I/Px8LrvsMjp16sSwYcNwuVxMnjyZvLw8Pv30UywWC9u3b6d///4sWLCA5OSKZflRUVFER0cffvoaE6qoWvlsT5odWA/AI5c9yw2NO5EcFU16XAOSrUk+y4S3MLGs+G7CgTXrZAmXpnMwt4w9uWU889tufgoo7dI2OYpJvVvQoUkiVpMSdnFTR6I+kzlKkoRquPDk7cVdUoDhseNa/whG0TpvA3MKlvYvYEk/AyUpE00Pjq8J/rRU7d6r/tyVtgS9UlWZhIQoCgvLfK5bbxC2hs+1ZmhougdP+co+j1eoaD53Y7lg8ufNkn2pDCQlIN2DjIzsK0vkTQcRqng6FsothuWCy2N4cOveGDiX7sbhPlxsVbgSZVkiphoLXvBsBgjGgEByr6g1/GLWG9xe958XVfbWqYy2RBFjql5IVXktvqS6Dt1JkauYQkcRdqeDNUU5fLx/M7/k7/O/+x47rS/Dmp8V8viEqBIIap96TangcrmYO3cuDz/8ML169QLg+eef55xzzuHrr7/m0ksvDWr/7bffUlZWxpQpU7BarQA888wz9OrVi1WrVtGzZ082b95MTEwMbdq0OdGXE0STqAT/8xGndiUutgEpUSnEqNGHZTA30KuIfdJ1A1WSaJwaQ5TNxFM2E++tP8gra/ejGbAut4xbFm7giXOacXnbdGKjRC6bmmAYBh7JhJrcGElWcBfnYT5tAq61IzBKt4MrB9e6UUjqDKyqCSk2tdZi06paon/42MD7f1/xfykhoyCjYAIkJSB1gLXcBecTVT6RVJ7xPTAFQKWcVgFvFe0EBNwH5tXyXpWCGQWLYkVSJSRL9WLLrbuxqCq6GTDKhZPXTVme/6pcOMoEJGvFa53zi1NDxyi3/BnlQlVH1zU8unfFoB4kugy/Japi5OUWRt91BVgcyyl37cWaY7ApVp+Q0jF00Gq4RrI8HYkJM6nWBiRZEihxlxJrjaZjYjo7SvJZlP03stnMrU07H/9/kEAgqBXqVVRt2rSJ0tJSevbs6d8WFxfHaaedxm+//VZJVPXs2ZNZs2b5BRV4rT0ARUVeK87mzZtp0aLFCRj9kYlu2gP738uQM9qQkpBJA2sDZCP0gFJdN2gQZyHaqnKHTeXUZBvjl+/ikN1DgVPjwW+3sTa7hBHnNKNBvLVKgSYIxjDAjYKa1BCTouAulDGfPhnXmgcwHHsx7HtwrnkYSZmOWddQ41LRDDksXKvBGdb9qxV8FplygRQGA60hFddTvdhSVLBEqZRZnOi6V2bKfuFYlfs7QDwGTEVFXfOK/FZBwec+V7qOgYFXfAVGxvndgVKwQKxwE3r/NcsWVEkNWUhVh6Z5KxbEm+KJN8dR6ikjwRbHKQkpZDVIJ0qPPqaYKoFAUPvUq6g6cMC7vC0jIyNoe2pqqn9fII0aNaJRo0ZB21555RWsVitdu3YFYMuWLXg8HgYPHsymTZtIS0vjtttu44orrqijq6ia6IseI7ZtX9SmpyOpSbhceshxMOVomoFFlWmSFku0xUSTxCjG/7yTP7K9uYFeXXOANTmlPHfRKbRIjQ06NjBwumKD7wbmD/yR8P2gjyg34vHi0WXUhEwssopLkjC3nYJzzQPgysEo3Y5jzSPoZzyN2WVHSUjHMEWF7YrKk41AsSVJMlbVgt3wYGjlkif0/4fARJ1HcuN6kSnXYJW8tkc7dS0IqSq79X02o+QoYqKjcUsuzKoMoaUcEwgEdUi9iiq73Q5QKXbKYrFQWFh41OPnzZvH22+/zWOPPUZSUhLgDWTXdZ3hw4eTnp7O0qVLGTNmDG63m6uvvvq4xquqocQfyFhOPZ+oKAtFRXbUWprpjAZRxEabmR1vYfbKPbyx/iAGsHJ/MVd9sIbrT0vFosgokoQqS75adb7nEr7Hin2qJKEqEi2So2iYGEVslBlVqVlB12OlPI4jXOI55MQMrGYTLpOK1P4ZHH/eD+5CjKK1uDc8jnzqf0FzY0pIRY1Ooq68rOE2L+GCmJeqsSk24qJsFHnsIX43CQSCuqJeRVW5G8/lcgW59JxOJzabrdrjDMPghRdeYPbs2QwdOpRbbrnFv2/RokVomuYPSm/Tpg379u3jtddeOy5RJcsSiYnHFugeF1f9tRwL8fHQICmasUkxdMlK5NHvt1Hg9HCwzM0Lv+89egdVYFFkzs1K4NLWKVzUJpXUBBsxUWYUue6yjdf2vBwX8Vm4Y6JxR1uxmZ+jYMV9GJ4y9PzfcPx+B7GnPYBi6odJ9mBKykA2W4/e5zESVvMSRoh5qRoxLwJB+FCvoqrc7ZednU1WVpZ/e3Z2Nq1bt67yGLfbzZgxY1i0aBFjxozh9ttvD9ofKM7KOeWUU1i4cOFxjVXXDYqKyo7eMABFkYmLs1FUZK+TIPKEKJVLTk2hcYyJMd9vZ31uaOMLxKnpfLMjj2925BH73TZ6NUng0lMa0LtVAxKiLbVqvarreTlWZNmGFJ2G5miL+fSncK7/H3hKMFz5FP35OMruLzG1ehBLYRFqYjqGJbZWLXrhOi/1jZiXqjneeYmLswnrn0BQy9SrqGrTpg0xMTGsWLHCL6qKiorYsGEDN998c5XHjBo1im+++YapU6dyySWXBO0rKiqib9++PPLIIwwcONC/fe3atbRq1eq4x3usy/81Ta+z1AHRFpWuzZN5M97G91tyOFTmxqMbaIbvT8f/3KMb6AZouoEnYH+JW2PlgWKKXN5g12K3xufbcvl8Wy4NbNvp2zSJK09Lo0eTBKKtJm8wby2Iibqcl2NFlqOQkxpj5hywzsW9bTp67s8AaLm/ohX8hafpXZizBmJOSIWYFGr7EsJxXsIBMS9VI+ZFIAgf6lVUmc1mbr75Zp599lmSkpJo2LAhzzzzDOnp6Vx44YVomkZeXh6xsbFYrVbmz5/P4sWLGTVqFN26dSMnJ8ffV2xsLHFxcfTo0YPnn3+e5ORkmjRpwtdff83ChQt5+eWX6/FK6w5dN5CBpqkxXBll4lCBHW96n4Dl9FCRoDBwOXj5UnED3JrOin1F/LC7gF/2FePw/fI9ZPfw/sZs3t+YTeNYCxe3TOaatumckRmHqsjeFU4nUey2rhsYsgW1QRaSoiBbxuPJ/hH39hngzgetDM/2aWg536Od8jCWtDNQE9LRZcu/KshfIBAIBJWp9+Sfmqbx3HPPMX/+fBwOhz+jeqNGjdizZw99+vRh0qRJDBw4kDvuuINly5ZV2U95m5KSEmbMmMFXX31Fbm4uLVq0YNiwYfTt2/c4xxla8k848UkuvdmYwdDxLfuWKpZ7GwaGbx1TeYbvcoFlGN68SCUOD8WlLgrKXPy0q4Afdhfy28FiqlrwdmpyFJe1TqF7o3iirSpxZoUYs0qMRcWkyJSvVJek8mKzIBnebSZFJi7eRmmJA49HD1sxoso6lOTiKc7DXZKNZ8dLaAe/rGggmVCzbsXSchCm5EwMa8JxrQ6sz6So4YyYl6o53nkRyT8Fgtqn3kVVpBAJoup48GZwltENA7vTQ4ndQ1Gpk+xiJ9/vzOeH3QWsOVSzmC2LIhFlUohSZaJNClEm36OqEGVWiDErNEmKomWCldPTYkiLs2I2KT4xZqDrwbmH6hNFkZA0J3pxLlpJAa7sX/Bsex7Dsd/fRopuieXU0Vganol0HDmtIun9ciIR81I1QlQJBOGHEFU15GQXVYcjy95kkg6XRpnDQ2Gpk115dr7ekccPuwrYVuiotXMl21RaJNhomRjFaWkxtE2L4bS0WOJtJhRZqigxUo/vVEWRkFyl6MWHcBcdxLX9VbR986lITS6jNL4W26nDUJObYpiiQ3aNRvL7pS4R81I1QlQJBOGHEFU15N8mqgKRZW9pEIfLQ6nDQ1Gpi03ZJfz4TwF5Djd2t06pR8Pu0Slz65T5npe6dewerUr34VHPKUHDGAstk22c1iDGK7bSY2mdEo0qy7UWLB8K5fXYJEcRWlEOroO/49o8BaNsR0Uba0Msp43B3PA85Kg4MEejI9dorCfL+6W2EfNSNUJUCQThhxBVNeTfLKoCKY/bcnl0yuweSh1uPB4dt25g6OV10/A/13QDp0en1K1R4tYoc2uUenRyXDqbs4v5u9DBjkInJTUss2FVZU5JiqJdegzt02Pp2DCe01KjMSvyCXMbSpKEImlgL8CdfwDntrl4dr0NhtvfRk7pgyn9QkypPVHjUsEWi6HaMI6Qtf5kfL/UBmJeqkaIKoEg/BCiqoYIUVWZcgsWeK04us96pBv4xYPuS92g6xV/SGAym8gvtON0evB4NLLL3GzLs7O9wM7OQgc7ihzsKnLiqoGFxyxLnJIcRdu0GNpnxNEpI462GTGYfRatmrzDJX+OUynguZfgenvB168YHvSSXFwHVuFYPxGjeP1hjazIiV0xpZ2HKbMPakJjJEsMumKp5B482d8vx4qYl6oRokogCD+EqKohQlQdH+WFayXJm7QwISGKoiI7LrfmzZulGbg9Gm6Pjsut43B5cLg0dhXa2ZZv5+98B9sL7GwrsHOgzH2006FIEq2SbZyRFkv79FgSbSZKyy1lLu9fmUvzbytze1+X+VyWZW4du1sj1qLSLMFKi+QoWiVH0yo5mlMa2MiI9SaZNQwDWZaRPHa0omwcW97Avf0V0KoK6peRE9qjpp6HufFFqMltwBKNjoKuG+L9Ug1iXqpGiCqBIPwQoqqGCFFVe1Q3L5Lkda2VP2q6N2Gpx6Pj9iU4dLo0DhY72JBdysbcUrbk2tmSX8aekhNbVdaqyjRLsNEs0UbL5ChOaRBF65QYWsXoRJXtx7n3B7Sc/0PL/RU8RVX2IcW0RE09D0tWf5T0bii2GOLjxfvlcMTnqGqEqBIIwg8hqmqIEFW1x7HMS6DgAtA0b8JSp0fD7dY5VOxk7YFiNh4qZVNeGVvyythV5CTUWZfwCiarImNRJAqcmj8Rak1JjjLRLN5MszgTzaJ1OsjraOn5hcTin5Gd+6s8RrKkYco4n7hTLkdPOQdNiQ6r1BL1ifgcVY0QVQJB+CFEVQ0Roqr2qM15qRBbXrXl1nTcbq/YKixzs/ZAEeuzS3FrOjZVxqoq2FQZm0nGpirYTDJRJhmbyZs/y2ZSUGQJWZK8aSUMgz2FDrbn2fmn0ME/hQ52FznYXexkf6mL0BYgGrS37ubKuN8517yCJmyrZoJiUVPPxpx1Measi8HWwJvpPeCjWvWn1qDEpZPvcJNX5ibP4SHP7ibf7n19qMxFbpmbPN/rfLsbRZZIjzGTHmshw/fXMM5KWoyZjFgL6TFmok0qgRn6TzTh/jkqd22XJ9WVJemEiGEhqgSC8EOIqhoiRFXtcSLmpdyqJcsSmq7j0by3PSQJGQlJ9qZ3l6XgwPTgT0P5iwrBpumG99GjU+rU2JlXyta8MnbmewXXLp/gynN4jjrGdCWXPtbf6GtbSQ/LesxS5WPchsoqT1t+dvfgZ3d3DpGCLFWMSpIkZAnK3Dr5djfuOkgzEW1SSI8xkxYovGItNIy3kh5jISPWTAOb2T+u2sIwDIpdGnkOD5YoC44yBzLehQlmVcYsy5gUCbMio4R4ct0wcPusnS7f/6lLM3DrOm7NoMytUeT0+P68zwvtbgqdHgocHoocHgqdHop9j0VOD6UuDUmC9BgLDeMsZMXbaBxvpWmi97FhnIWGsVYsqlQrAlWIKoEg/BCiqoYIUVV7nAzzEhj7JUmgab4i1ZqBx6NR6HCzI8/O9rwy/sm3s7vIyd5iJ3tLnBwsdVXK3RUjldLLupo+tt/oZV1FnFx19vp1ruZ8Y+/Kt45ubHI3oVzwhYpJlogzK2iGQYGzZuksjoQqS6RGe61bmbEWMuO8Fi+vCDOTEWMlNdqEIknk2b1Ws0Nlbg6Vuckpdfkfs0udHCp1k13qIrfMhbOGSc5kCUw+kWVRvI8m3+rUQOHk1gzcvli9+iI12kzjeCuN4600SbDRKN5Kkk3Foxm4dK+4c3m8Y3Z5dNy6jlPzjt/hiy90erwCsHlKDPd2aUi0qoQ8DiGqBILaR4iqGiJEVe1xss9L4ErHcrekR9O9Qfeajt2lsTvfwfa8Unbk29ld6GBfiZN9pS4cHh1Zd9JeWcfZphWcZVpJunKoyvPs01L4P2c3fnR1Y73elmiLjXiLQrxFJc6skmBVibd4H5OjTCRFmWgQZSEp2kSsWUFRZGRZwu7SOFDsZH+Jk/1FTvYXO8kudZJd6ianzE1OmYtDdneNBc6RkCVCdJmGNyZZIsasEG2SiTWpaIbBgVIXRa7jF6o1ZVzvlgzt2ijk44SoEghqHyGqaogQVbXHv31eAq1cYPhTShiA2aJSWuZdyajIEpKhIeWuQtu9BG3/NxhFm6rpVUaypiJFN0SOboQc3Rg1Ngs5tglKXFPk6EagRgdlovd+8r1u0HIXqBTgC9UNHU33uso0zSDf7mZ/sYN9RU4OFLvYV+QVgwdLvAIsu8xFYS1YvWQJEiwqiVaVJKuJ5CgTcVEm7E4Np89yU25xCrQ+uXXdt63CGqUbBmZFQpVlTLKEKnstWCZZQlUkr3XL/7zitUWVibOoxFkV4swq8VaVOKvJK1itKvFWhXiLCatZQZbwuZG97mRdNyh0uNlVYGd3oZNdBXb2FDnYV+xkX4mTAyWuGrmHa0KsWeGD6zvQOSM25GOFqBIIah8hqmqIEFW1h5iXqimfl4KCyvNSnmjVKN6B659FuHd/hSdnBRg1vzlL5kTkqEyv6IppjByThWRO8JrW/F8DRkUoWfkTv/iq+KqQLQko8a2QE1shKdb/b+/eo6Kq9jiAf88Mw1PAFw+TWwo5KMhTQEgRJUNXoSvTWtdE0xtpmrmsS2qm5rP0gi80EzI1DepWZmqZpaWZV8DwUXnNUCR8BAzxcFCG4XH2/QMZnRi9kSPH9PtZi5jZe589v7M5wq+z9+wx3Ym7XNOAX/UGXNAbG5MuvRG/VhlRcqkWJZeNaJCB9g42aO+gQUcnDdwdbeHmZAe3Nhq4t7GDextbuDnaQWOjglqSoFIDGhs1XFwcoNdXo6HhakIoi8aYhCxBRmNS2riTP64+vhJz05sZJACSCmh8JK6WSVfKTAkSTK/TNAjX/qa8XrlprM3uVjY+FgBkWUaDAKqNDTh7sRpnK2twrrIGlTV1poTO1ubKd7UKtmrpypSm2rR+zFYlQaNWwc5Ghfs7u8BZrULdH/xEgmsxqSKyPiZVfxCTKuvhuFjWknFRqSSgTo+Gc7tQe/4LyBfzIFf/ClFb0UrRmiKB5NgJ6jZdoXLxbky0XLtB1dYXKqe/QVKpIcS1O+43Pm78oGygKSm59vHv3cnXy7VTxC3VtIkuF6oT3T5slA6AiFpOlgWgdobU5XHYd33cNKUo6qohV5+HuFQIUXUWDZfPQr50HvLl85CrL0AYSgBhzfU+MkT1BdRXXwB0B8yrVBqonO6Fytkbatf7oXLtBsnBE5IkQZYkXF1kb7qdAyGaHl/5+CNIaFCrYDR2hJDugaR2UWxrh1vheh+B9EeoVHfQQBDdIZhUEf3Fmf1hVjkAbbpBatMNkifQdB+i6Y6IkOsBQxFE9XnIVYUQdZeaemnW6e/vn4im/wpA1PyGBv1pyFVn0FBVANRfQjNyHeSqfMhV+aj/dfdNnWNV03nYtoPKuStULj6Nd8ScfaBy9obk3BWwafm6IiIia2JSRXQXuJp4qQEHL8DBC6oOkTfVp+bKWiFAAMYyyPrTkPV5kC+ehqzPR0PVGchVBYBsvY8QErUVaCirQEPZkWZ1kr071M7eULn6QHL2gcrFB5K9OywskGrq7QYvdG3dDY77fZ+iAZDrIOQ6QK4DxJXvcl1jQmt6fLUcch2gsoFk7wbJwR2w94DKwR2wc4ekafP/hoSIbiNMqojoT7n67kEAth0gdewAdcfeaNoxqTGXERCXL0BUnUGD/hRQW3nlwGsXxl9dUN70/NrHKglQN1SgpuwUGvT5EIZiy/HU6FBfowNKs61+roqxcYTK3h2SgzskBw+oHDwgOXhAsneDcPJEnSoUgJvSURLRFUyqiOiWaMyNJMDRC5KjF2w8+v2pfpotVG+ohrj0y5VE7TSE/jQa9PmQqwogakqteg6Kq6+GfOkX4NIvFqsvS2o4xWZA8nyoVcMiIsuYVBHRX4vaEZKrHyRXPzR771qdHuJSwZW1XAWAseLq9B+A5jvQS9evMqu7wXFm7VSASgNJpQEkDaDSNE7tqWwBlU1jmfpqnaS60kY2QjaUAjU6yAYdhKEEwlACuUYHYdBB1FZaHgvRAFlfALWn5Woial1MqojozqFxgdQuCOp2QWj5B7co60abG4gGY+NduJoSiJpSCEMJpNpSOLbthPp7RqD19m8nohthUkVEdJuT1HaQnLwAp6sfR2Njo4LLlWlR3GH7dxH9VXHnNyIiIiIrYFJFREREZAWKJ1WyLCM1NRXR0dEIDg7GM888g3Pnzl23fUVFBf75z38iPDwcERERmDdvHgwGg1mbzz//HA8//DACAwPx6KOPIisr61afBhEREd3lFE+q1qxZg8zMTCxYsADvv/8+ZFlGYmIiamstbxg4ZcoUFBYWYuPGjVi5ciW++eYbzJ0711SfnZ2Nl156CX//+9+xdetWREVFYfz48cjPz2+lMyIiIqK7kaJJVW1tLdavX48pU6agf//+6N69O5YvX47i4mJ8+eWXzdofPXoUhw4dwpIlS+Dv74+oqCjMnz8f27ZtQ0lJCQDgrbfewsCBAzFmzBj4+Phg+vTp8Pf3xzvvvNPap0dERER3EUWTqpMnT+Ly5cuIiooylbm4uMDPzw/fffdds/a5ublwc3ODj4+PqSwiIgKSJOHw4cOQZRlHjhwx6w8AevfubbE/IiIiImtRNKkqLm78uIlOnTqZlbu7u5vqrlVSUtKsra2tLdq2bYuioiLo9XpUV1fD09N8J7zr9UdERERkLYruU9W0wNzW1tas3M7ODhcvXrTY/vdtm9objUbU1NRctz+j0XjT8drYtCwHVatVZt+pEcfFMo6LZRwXyzguRLcfRZMqe3t7AI1rq5oeA4DRaISDg4PF9pYWsBuNRjg6OsLOzs7U3+/rLfXXEiqVhHbtnP7UsS4uN/fadyqOi2UcF8s4LpZxXIhuH4omVU1TeTqdDvfee6+pXKfTwdfXt1l7T09P7Nmzx6ystrYWlZWVcHd3R9u2beHo6AidTmfWRqfTwcPD46ZilWUBvb66Rceo1Sq4uDhArzegoYE7HjfhuFjGcbGM42LZzY6Li4sD73IRWZmiSVX37t3Rpk0b5OTkmJIqvV6PEydOICEhoVn78PBwpKSkoLCwEPfddx8A4NChQwCAXr16QZIkhIaG4tChQ3j88cdNx+Xk5CAsLOym463/kx8F0dAg/+lj72QcF8s4LpZxXCzjuBDdPhRNqmxtbZGQkICUlBS0b98enTt3RnJyMjw9PREXF4eGhgaUl5fD2dkZ9vb2CAoKQmhoKF544QXMnTsX1dXVmDNnDh599FHTnahx48Zh/Pjx8PPzQ79+/bBlyxb89NNPWLRokZKnSkRERHc4xe/9TpkyBSNGjMCsWbMwcuRIqNVqvP3229BoNCgqKkLfvn2xc+dOAIAkSVi9ejW8vLzw1FNPYerUqejXr5/Z5p99+/bFa6+9hvfeew/Dhg1DdnY21q5da7YNAxEREZG1SUIIoXQQfwUNDTLKyy+36BgbGxXaXfkUed6ev4rjYhnHxTKOi2U3Oy7t2ztxTRWRlfFfFBEREZEVMKkiIiIisgImVURERERWwKSKiIiIyAqYVBERERFZAZMqIiIiIitgUkVERERkBdyn6g8SQkCWWz5UarWKn1dmAcfFMo6LZRwXy25mXFQqCZIkWTkiorsbkyoiIiIiK+D0HxEREZEVMKkiIiIisgImVURERERWwKSKiIiIyAqYVBERERFZAZMqIiIiIitgUkVERERkBUyqiIiIiKyASRURERGRFTCpIiIiIrICJlVEREREVsCkioiIiMgKmFQRERERWQGTqltAlmWkpqYiOjoawcHBeOaZZ3Du3Dmlw1JcSUkJfH19m319/PHHSoemmLS0NIwePdqs7KeffkJCQgKCg4MRGxuLTZs2KRSdciyNy6xZs5pdO7GxsQpF2HoqKysxZ84c9OvXD6GhoRg5ciRyc3NN9VlZWXjssccQFBSEwYMH47PPPlMwWqK7m43SAdyJ1qxZg8zMTCxevBienp5ITk5GYmIiduzYAVtbW6XDU8zJkydhZ2eHPXv2QJIkU7mzs7OCUSknIyMDK1asQFhYmKmsoqIC48aNQ2xsLObNm4djx45h3rx5cHJywvDhwxWMtvVYGhcA+Pnnn/Hss88iISHBVKZWq1s7vFb34osvorS0FMuWLUOHDh2wefNmPP3009i6dSuEEJgwYQLGjRuH5ORk7Nu3D9OmTUP79u0RFRWldOhEdx0mVVZWW1uL9evXIykpCf379wcALF++HNHR0fjyyy8RHx+vbIAKysvLQ5cuXeDu7q50KIoqKSnBq6++ipycHHTp0sWs7oMPPoBGo8H8+fNhY2MDHx8fFBYWIj09/Y5Pqm40LkIInD59GuPHj4ebm5syASqgsLAQ//nPf5CZmYlevXoBAGbPno1vv/0WO3bsQFlZGXx9ffHCCy8AAHx8fHDixAmsW7eOSRWRAjj9Z2UnT57E5cuXzX6hubi4wM/PD999952CkSnv559/ho+Pj9JhKO6///0vNBoNtm/fjqCgILO63NxcREREwMbm6v/vREZG4pdffsFvv/3W2qG2qhuNy9mzZ1FdXQ1vb2+FolNGu3btkJ6ejoCAAFOZJEmQJAl6vR65ubnNkqfIyEgcPnwYQojWDpforsekysqKi4sBAJ06dTIrd3d3N9XdrfLy8lBeXo5Ro0bhgQcewMiRI7F//36lw2p1sbGxWLVqFf72t781qysuLoanp6dZWdOdvaKiolaJTyk3Gpe8vDwAwObNmxEbG4uBAwdi/vz5qKqqau0wW5WLiwtiYmLMlg188cUXKCwsRHR09HWvF4PBgIqKitYOl+iux6TKygwGAwA0WztlZ2cHo9GoREi3hfr6epw5cwYXL17E888/j/T0dAQHB2P8+PHIyspSOrzbRk1NjcVrB8Bdff3k5eVBpVLB3d0da9euxYwZM3DgwAFMmjQJsiwrHV6rOXLkCF5++WXExcWhf//+Fq+Xpue1tbVKhEh0V+OaKiuzt7cH0PgLrekx0PgH0cHBQamwFGdjY4OcnByo1WrTuPTs2ROnTp3C22+/zfUfV9jb2zf7Y9iUTDk6OioR0m1h4sSJePLJJ9GuXTsAgFarhZubG5544gn8+OOPzaYL70R79uxBUlISQkNDkZKSAqAx4f799dL0/G7+fUOkFN6psrKmaT+dTmdWrtPp4OHhoURItw0nJyezRBMAunXrhpKSEoUiuv14enpavHYA3NXXj0qlMiVUTbp16wYAd8W0+rvvvovnn38eAwYMwNq1a013Lzt16mTxenF0dLxr31VLpCQmVVbWvXt3tGnTBjk5OaYyvV6PEydOIDw8XMHIlHXq1CmEhoaajQsAHD9+HPfff79CUd1+wsPDcfjwYTQ0NJjKsrOz0bVrV3To0EHByJQ1bdo0jB071qzsxx9/BIA7/vrJzMzEggULMGrUKCxbtsxsui8sLAyHDh0ya5+dnY3Q0FCoVPz1TtTa+K/OymxtbZGQkICUlBR89dVXOHnyJF544QV4enoiLi5O6fAU4+PjA29vb8yfPx+5ubnIz8/H66+/jmPHjmHixIlKh3fbGD58OC5duoRXXnkFp0+fxscff4yNGzdiwoQJSoemqEGDBiErKwurV6/G2bNn8c0332DmzJmIj4+/o99RWlBQgNdeew0PPfQQJkyYgN9++w2lpaUoLS1FVVUVRo8ejR9++AEpKSnIz8/H+vXrsWvXLiQmJiodOtFdiWuqboEpU6agvr4es2bNQk1NDcLDw/H2229Do9EoHZpiVCoV1q5di6VLl2Lq1KnQ6/Xw8/PDhg0boNVqlQ7vttGhQwesW7cOixYtwrBhw+Dm5oZp06Zh2LBhSoemqAcffBArVqxAeno63nrrLTg7O2PIkCGYOnWq0qHdUl988QXq6uqwe/du7N6926xu2LBhWLx4MdasWYPk5GS888478PLyQnJyMtcoEilEEtzMhIiIiOimcfqPiIiIyAqYVBERERFZAZMqIiIiIitgUkVERERkBUyqiIiIiKyASRURERGRFTCpIvoDbuXOI9zVhIjozsCkilpVbGwsZsyYoXQYLXLq1CmMHDnS6v3q9XpMmzYNubm5Vu/7dnP+/Hn4+vri448/VjoUIqJbhjuqU6tavXo12rRpo3QYLbJr1y4cPXrU6v3+9NNP2LZtG4YPH271vomIqPUxqaJW5efnp3QIREREtwSn/6hVXTv91zQl9Pnnn2PKlCkICQlBREQEZs2aherq6v/b15kzZzB58mREREQgPDwcEyZMQH5+vqm+qqoKr7/+OgYOHIiAgADEx8fjo48+ahZPamoqlixZggceeACBgYF4+umn8csvvwAAVq1ahdWrVwMAfH19sWrVKgCALMtIT0/HQw89hJ49e2LQoEHYvHmzqd/jx4/D39/fbKqzrKwMUVFRGDduHLKzszFmzBgAwJgxYzB69OjrnqfRaMS//vUvxMTEoGfPnhgyZAh27txpqv/qq6/MYgOA/Px8BAYGYubMmaayPXv24Mknn0RISAh69uyJwYMHIyMjw1Sfk5MDX19fZGVlYfTo0QgMDET//v3x4YcfQqfTYfLkyQgJCUFMTAw2btzY7LgDBw5g1KhRCAwMRFxcHDIzM6//wwPw66+/4sUXX0RERASCgoLw1FNP4cSJE2ZtPv30UwwdOhSBgYGIjIxEUlISSkpKbtgvEZFiBFErGjBggJg+fboQQohz584JrVYrwsPDxeLFi8XBgwfF2rVrha+vr0hJSblhP8XFxSIsLEw88sgj4rPPPhN79+4Vjz32mOjTp4+oqKgQBoNBxMfHi6ioKPHee++J/fv3izlz5gitVivefPNNs3h69eolxo8fL/bt2ye2bdsmIiIixBNPPCGEEKKoqEjMnDlTaLVacfToUVFUVCSEEGL27NnC399fpKamim+//VYsW7ZMdO/eXaxevdrU9/Lly4VWqxUHDx4UQggxadIkERERIYqLi0VVVZV49913hVarFe+++644deqUxfOUZVk8/fTTIiQkRGzYsEHs379fzJ49W2i1WrF161ZTu6SkJOHv7y9Onz4t6urqxGOPPSYGDhwoLl26JIQQYu/evUKr1YqFCxeKgwcPiq+//lokJiYKrVYrjh07JoQQIjs7W2i1WhEZGSnWr18vDh48KMaOHSt69OghBg0aJFasWCEOHjwoJk+eLLRarfj+++/NjgsLCxMLFy4U+/fvF6+++qrQarUiIyPD7Ge9ZcsWIYQQZWVlIjo6WsTFxYnt27eL3bt3i4SEBBEcHCxOnz4thBAiNzdX9OjRQ6xatUpkZ2eLTz75RPTp00eMGjXq/11mRESKYFJFrcpSUpWUlGTWZvTo0SI+Pv6G/SxevFgEBgYKnU5nKisqKhL9+/cX+/btExkZGUKr1YojR46YHTdz5kwREBAgKioqTPEMGDBA1NfXm9qsWrVKaLVaUV5eLoQQIjU1VWi1WlP9mTNnhK+vr0hLSzPre/ny5SIgIMB0XG1trRgyZIgYNGiQ2LJli9BqteLzzz83tW9KRrKzs697ngcOHBBarVZ89tlnZuVJSUmiT58+oq6uTgghRGVlpejbt68YM2aMWLNmjejRo4c4evSoqf1bb71lGvcmFRUVQqvVms6jKZ7k5GRTm2PHjgmtViteeuklU1l5ebnQarViw4YNZse9/PLLZv1PnDhR9OnTR8iy3CypWrZsmQgICBDnz583tTcajeLBBx8Uzz//vBBCiLS0NBESEiKMRqOpzb59+8SqVauELMvXHTMiIqVw+o8UFxwcbPbc09PTNP0nyzLq6+vNvgDg8OHDCA4Ohpubm9lxe/fuRUxMDA4dOoTOnTsjJCTErO+hQ4fCaDTi+++/N5UFBARArVab9QMABoPBYrzZ2dkQQiA2NtYsrtjYWBiNRhw+fBgAoNFosGTJEpw/fx6vvPIKhg0bhsGDB7dobLKysiBJEmJiYpq9VmlpKU6dOgUAcHV1xYIFC5CdnY3U1FRMnDjRbFwTExOxePFiXL58GcePH8fOnTuRlpYGAKitrTV7zWvHrEOHDgCAoKAgU1m7du0ANE6vXmvYsGFmz+Pi4lBaWoqCggKL59WjRw94eHiYzkmlUqFfv344ePAgACA8PBwGgwHx8fFYunQpcnNz0bdvX0yePBmSJLVoHImIWgMXqpPiHBwczJ6rVCrT3k1vvPGGaU1Tk59//hmVlZXw8vK6bp8XL140S7iadOzYEUDjdgY3en2gMaGzpLKyEgDwyCOPWKy/ds1Pjx494Ovri+PHj2PAgAHXjfd6KisrIYRAaGioxXqdTocePXoAAB544AG4u7tDp9M1e63y8nK8+uqr2LNnDyRJwn333YewsDAAzffJsvTuzN+PkSUeHh5mz5sSMks/i8rKShQWFsLf399iXwaDASEhIUhPT8fGjRuxYcMGpKeno2PHjnj22WdvuAaNiEgpTKrotvbEE0+gf//+zcqdnZ1RXl7erDwrKwteXl5wdXVFYWFhs/rS0lIAV++2/BkuLi4AgHfeeQdOTk7N6u+55x7T43//+984fvw4unfvjkWLFiEqKsp0/B/h7OwMR0dHbNq0yWL9fffdZ3q8evVqVFZWwtvbG7NmzcKHH34IjUYDAEhKSsKZM2ewceNGhISEwNbWFgaDAR988MEfjuX/qaiowL333mt6XlZWBuBqcvX784qIiMC0adMs9mVrawsAiI6ORnR0NAwGA7Kzs7Fp0yYsXLgQQUFBCAwMtFrsRETWwOk/uq15eHggICDA7AsAwsLC8P3335slVmVlZUhMTMQ333yD8PBwXLhwodn+Utu3b4dGo2nRH+SmO1dNmu7wVFRUmMVVXl6OlStXmu5kXbhwAUuWLMGIESOwdu1aVFVVYdGiRaZ+rp1yvJ6IiAhUV1dDCGH2Wnl5eXjjjTdM06E//PAD1q1bh4kTJyI5ORl5eXl48803Tf0cPnwYcXFx6N27tylh2b9/P4Dr35FrqT179pg937VrFzp37myWaF17XgUFBejatavZeW3btg0fffQR1Go1lixZguHDh0MIAQcHBwwYMADTp08H0PjOQSKi2w3vVNFf0tixY/HJJ58gMTEREyZMgEajwZtvvglPT08MGTIEtra2yMzMxHPPPYcpU6bAy8sLX3/9NbZs2YLJkye36G5RU9tPP/0UQUFB8PX1xdChQzF79mxcuHABPXv2REFBAZYvXw4vLy906dIFQgi88sorcHBwwLRp0+Dq6oqpU6fitddew6BBgxAbGwtnZ2cAwL59++Dq6oru3bs3e+2YmBiEh4dj0qRJmDRpEnx8fPDDDz8gNTUV0dHRaN++PWprazFjxgz4+PjgmWeegUajQUJCAtLS0jBw4ED4+fkhMDAQO3bsgL+/Pzw9PXHkyBGkp6dDkqTrrh1rqQ0bNsDOzg7BwcH48ssvsXfvXixdutRi27Fjx2Lbtm0YO3Ys/vGPf6Bdu3bYuXMnPvjgA7z88ssAgMjISGzYsAEzZszA0KFDUVdXh3Xr1qFt27aIjIy0SsxERNbEpIr+kjp16oTMzEwkJydjxowZsLW1Re/evbF8+XK4uroCADZv3oylS5di5cqVuHTpEry9vbFo0SKMGDGiRa8VFxeHbdu2YcaMGRgxYgTmzp2L119/HWlpaXj//fdRXFyMDh064OGHH8bUqVOhVquRkZGBrKwsrFixwhTP6NGjsWPHDsyZMwehoaHo1q0b4uPjkZGRgW+//Raffvpps9dWqVRIT0/HypUrkZaWhrKyMnh4eGDcuHF47rnnAAArVqxAQUEB3nvvPdN039SpU7F7925Mnz4dW7ZsweLFi7FgwQIsWLAAANClSxfMmzcP27dvt9rH5MycORNbt25FWloavL29kZqaikGDBlls6+Hhgffffx9Lly7F3LlzYTQa0aVLF7OfT0xMDFJSUrB+/XrT4vRevXph06ZNaNu2rVViJiKyJkn8fpUqEVEL5OTkYMyYMdi0aRN69+6tdDhERIrhmioiIiIiK2BSRURERGQFnP4jIiIisgLeqSIiIiKyAiZVRERERFbApIqIiIjICphUEREREVkBkyoiIiIiK2BSRURERGQFTKqIiIiIrIBJFREREZEVMKkiIiIisoL/ASTU9zSUM3LEAAAAAElFTkSuQmCC", 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", 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" ] @@ -402,12 +434,12 @@ "output_type": "stream", "text": [ "Processing: orthogonal_train_test\n", - "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -420,12 +452,12 @@ "output_type": "stream", "text": [ "Processing: overlapping_train_test\n", - "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -438,12 +470,12 @@ "output_type": "stream", "text": [ "Processing: random_quadrants\n", - "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -456,12 +488,12 @@ "output_type": "stream", "text": [ "Processing: scale-x=0.333\n", - "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -474,12 +506,12 @@ "output_type": "stream", "text": [ "Processing: scale-x=0.5\n", - "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -492,12 +524,12 @@ "output_type": "stream", "text": [ "Processing: scale-x=2\n", - "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "data": { - "image/png": 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2QoYOHc4jj8xg3bq1pKe34Y477qFnz6MBcLtLmDNnNsuWfUsoFOaoo45i8uQb6datOwAvvvgcK1b8QUJCIj/9tJyRI8/klltub7RzfrgjBFALQ0Ulz1eAWkVq5xe2/81HezYyJrUzEzN61ZrpsshXjEkxYo8uaQFRIooVNH+lRIgSZfXABALBgfLp2mxuX7KeHE/zCaAku5FZw7tyTrdWjdZnZmYmubk5vPHGOwQCAdat+5c77riV66+/iZNOmonb7WbevDncf/89fPbZlxiNRgDee+9t/u//LuGVV95i27at3HvvHbRt25YJEybyww/LeP31V3jwwUdo27Yt//yzigceuJfU1DSGDj0du93OHXfcyiuvvElGRgZ79uzmqqvG07//AJ599nkCgQDPPPMUkyZdydtvf4Dd7gDgs88+Yfr0GXTq1JnExER+++1XvvpqCeeffyGvvfY2//vfEl54YR5Llizm+utvJi0tjYcffoDHHpvJ66+/g67r3HTT9ZjNZh5//GkcDgeLF3/OxImX89JLr9O161EArFy5ggsvvJg333y3Gf0+D01EFFgLQlEkioJFeAOVkx6uLsnlwz0b0NBZsGcD7+9eX2t/mq6T5y2M1g87qFSjZaKO0Op+Fi5hBhIIGo0bF61rVvEDkOMJceOidY3e7+WXX0nr1ul06NARWVa4+ebbueiicaSltaZLl65ceOHFFBQUkJ+fF90mYhWaQkZGBieddDLHHz+QVav+AmDXrkyMRhOpqWmkpKQybNhw5sx5jj59+mKxWHC5IsklY2PjcDicfPTRh1itNqZPn0Hnzl3o2fNoZs58lIKCAhYv/iK6z86duzJ8+Ag6duxETEwsADExsVx//U2kp7fh4ovHAXDaaadz0kkn06lTZ0aNOpstWzYD8Mcfv7F69SoeemgWPXseTbt27Zk0aQo9ex7N+++/W+GcXHXVNbRunU5GRkajn+/DGWEBakGE9BD5vsqCRdN15m/7q8Kyl3f+Q5rFwUkJ6TX2GVbDqLracj7oMj8g1RvJAB21YukIC5BAIKiNNm323eS7dOmKy+XijTdeY9u2rWRm7mTjxsjDYXlrSNu27Sr0Ybc7or49Z5wxkoULP+WCC0bTvn0H+vc/niFDhpKSklrl/jdv3kS3bt0xmfb5VyYkJJKR0ZbNmzeVG2ebStumpbVGliN2B6s1ci1MT993DTebzdEEfuvXr0PXdUaPHlmhj2AwRCAQjL6Pi4uv5DslqBst5r54pKMoMnm+wiqtP1/nbmeDpwAAm2LAW+ocPWvTr7Qy2zjKEV9j32UOxpquEcSPWTEjo6Bp+sF3Pi6zAOkq6CGQTCIMXiBoZGaPOqrFTIE1NhaLJfp6xYo/ufHG6xg06ASOOaY3w4ePIBDwc9ttUytsYzRWDgYpu/bFxsbx5pvv8c8/q/jtt5/55Zefef/9d7nqqmuYMGFitdvtj6ZpGAz7bqlms6VSm/Lry5CkqidiNE3Hbnfw2mtvVVpXXnyJDOANRwigFoJf81PgL0bfby7Ip4Z5ecc/0ff3dRnE1znb+Sp3O0Fd4971PzKn52kkm+37d1kJTdfYWrgTk2LEZrJiN9qwKBZMsgl0DkrIvKRY9x2h6gW59IcsNJBA0Gic060VZ3ZNOqycoKvinXfepG/fY3nkkcejyz744L3SV3W7qHz55Re43SWcf/6FHHNMb666ahIPP/wgX321pEoB1KlTZ5Ys+YJgMBgVInl5eWRm7uS88y444GMqo2PHjng8bsLhMO3bd4guf/jhB+ncuTNjx17UaPs6UhECqAWgKBLZ3sIqC56+t3sd+aHI8kFxafSNSaanM5G9AQ//lORSEApw97ofmd3jVOwGY637CoZDBMMh3AEvilyISTFgUow4zA5sRgtm2YJBMqDretMIov0qwkvGWCIXKqGABILGRJElEu0tKA1GE5CcnMz333/HX3+tpFWrZFas+J0XXpgHQDAYrGVrou3mzJmN3W7nmGP6kp2dxcqVf9K7d98q248ZM5aPP17A/fffw/jxE0q3f4qYmFiGDRveaMc2YMAgunTpyt13T2Pq1Ftp1SqZjz76kEWLPuPpp59ttP0cyQgB1MxESl74KPIVV1q31+/hw1JnZ4MkMbHtMQCYZIXpXQZx/Zql7PK72eYr5sGNPzOj6wkY5Lr7tauaik9T8YUCFPndGEvFkNVowW6yYTVYMcoRM64sS8iyhK4fWM4eqXwyxHDEEVov979AIBDUlYkTJ5GXl8ctt9wARJyd77rrPqZPv4e1a9fQrl37Wvs4++zRFBUV8vLLL5KdnYXT6WLIkNOYPPn6KtunpaUxf/6LzJ37NFdeOR6TyUj//gOYPn0GTmfj+eIoisIzz8xjzpzZ3HXX7fh8ftq3b88jjzzOscf2b7T9HMlI+mGWgU5VNfLz65dBOee2ONBUjG36EnvDd00zsGqQFYldnt3kugsqrXtww898n58JwNjULlEBVEamr4Tr1yylJBx50hnVqgM3tO9bKTw+/fsLMPr2oJri2DHks7qNS5IwKkYsJhPxTiehgIaMgiIpKLKCIskokoIsRV7LkhwVR5IEcqCYQNY2NFWt4OMT2vIc6u4FAJiOfgo55mgUsxVjSkfC1G7BaikYDDJxcXYKCjyEwyL0tAxxXqrmQM5LfLwdRan+wcbv97N58xYSE1MwmYQ/iODIJhgMkJu7l44dO1TwF6sKYQFqRiRJwh12U+x3V1r3T3FOVPzEGsyMa929Upt0q5P7uwzitrXLCOs6i7K30NriYGzagTsearpOIBwkpIXQDSputx9N05ElGVmSkGW59LWMLEsokoJJNmJQDJgUAwYtiG4xowAyMooOCjqqJYayDEeSFEJSDCBJIv5LIBAIBAcVIYCaEUnWyfcUVip4quk687f/FX1/eZue1fr3HO1K4uYOxzFr828AvLhjFWkWB4PjWzfJmDVdQ9OBKhI1lscQ9qGW5EesQ7KMLCvIsoIDookZPXiRLQaMRiO6FAJJilqZREIvgUAgEDQlQgBBs4Rhy7JEcai4yoKn/8vZxkZPIQAdbDEMb1XzPPbQpLbs8rt5a9e/6MDMTb/yZPchdHHENcHI64ama4TDQcL7nVsppEYFUH7hTjym7RiMZox6ECQjsizjNDlIsMZhwNiyirkKBAKB4LBBZIJuJnRJI99bQHi/gqeecIhXdu4Le7+2bW+UOtT+ujS9O6cmRBKEBTSVe9b/SHbAW8tWBx9N3jcnK6k+VDVMKBwkEAriCwXwBHxkleSyrXAnBcECUPRaS34IBAKBQFBfhABqBmRZojhYUmXB0/d2r6MgFADghPjWHBNTtzo6kiRxc8dj6eFMACA/5Oee9T/iVZs3Hf7+aOWiwGQtEt6/fxSYjo4n6GNX0V52FO3Eo5agKBJCBwkEAoGgsWh2AZSVlUXXrl0r/fv444+be2hNhi5p5PsKKxU83eN389GeDQAYJZmJGb3q1a9JVri/y2DSSpMibvEWMWPjL4RbkItxeQuQrJbmPdKj/1VA1TWK/G52FO0m07ObIIEao2EEAoFAIKgrze4DtG7dOsxmM19//XWFqY7GzKfQkpBliaJgMZ4qrD8v7FhFqLQO2JjUzqRaHPXuP8ZoZsZRJ3L96m9wqyF+L9zLTKU397P7gMfeGOhKOQGklZ0DvcZSYCE1TK67AE/AS5w1hnhrHIpuEP5BAoFAIGgwzf44vWHDBtq1a0erVq1ISkqK/qstfv9QpTrrz99F2fyYvwuAOKOZ/2vdrcH7aGN1cl+XQVHfoXeUTrxuOqaWrQ4OVVqAoE55EH2hAHtKcthWuJPCUCGSgvAPEggEAkGDaHYBtH79ejp27NjcwzgolPn+ePez/qi6zrxyYe9XtDkam3JgSQF7x7TipvbHRt8/ZDmJpwy9+a1gD4WlPkbNgVbOAiRpZQKo7qUwdF3HHfCSWbSXHSWZeDQ3siJEkEAgEAjqR7NPgW3YsIG4uDjGjRvH1q1badu2LZMmTeKkk05qcJ8GQwN1nXQA29YFWafIU4SGhlyuSODirC1s8RYB0Mkey/DkdsjVWDbibTHIsky+pxCtlvD9ESnt2R1w886uteiSxHPGo2H9jwAkm210dcTTxR4X+euIw2GoXDdILi2tEflbt9w8kgSSGkmWqOsVt9GN+2qBKZofSZaQZDkiYupROFFHo9hfgj/kJ9bqJMEWj1WxoGkHVqqjrpT5ItXXJ0mWpUjBW106KOM82DT0vBzuiPMiELQ8mlUAhcNhtmzZQqdOnZg2bRoOh4NFixYxceJEXn31VQYOHFjvPmVZIi6u9sro5dlT+ldR5HpvWx9y3Hlg0HA49llB3KEgr+5YE31/S/fjiXHaqtocs2KibWxrLEYTO4tMFPoqV4/fnyndjyW8cxEfyB0qLM8KeMkKePk+LzO6rI3NSXdXIt1iEjjKlcBRznispQkYbbb6FVVUpSCK2UAly45uQEdCQkfRA1gtRpAVDCYFpYHTnl68qMEwDpMNu9GG1WjBarBgUJr+6+1yWWtvVA5vyEdm0V5iLC7irC5MyuFZrLK+5+VIQZyXmhk9ehSjRp3FVVdd02xjKCoqZNmy7zj77NHVtsnJyeGFF+bzyy/LKSgoIDY2luOOO54JEyaSnt7m4A1WcEA0qwAyGAz8+uuvKIoS9fnp2bMnGzdu5OWXX26QANI0neLihuW/UVWNgoL61RGrM7JOZlEWRb6KY3t+298UlFZ7Pzkhnc7GGNzuylXhZUkmxhWLHpDx+ELEyXH4CNbJEvRA+E8mBn7gL3NHfm57Bevd+WzyFODfzw9pp7eEnd4SluzdGtknEhk2F91jEmhviaGzPZZO9listUzPSRJIwRChQLiSBQgiofCK6oWQD58/hCRrGP0htHDl464rbvzkUYwsyRgVA0bFgN1kxWa0YjFYMMvmiOzS9UbJe6koMi6XleJiX52zVksy7HbvIcedT5aUj81sJc4aQ4zJhUFqWU7dsgzFoZLIa0lGQor8lSRkZJAi3w+JsmUSUmlZFIfDUq/zciTQkO9LGS6XVViODiLPPPMUu3fvrlYABYNBrr32KjIyMnj44UdJTExi7949vPDCc0yceAVvv/0BcXHNl4RWUHeafQrMbq9scencuTM//vhjg/tscBFG/QC2rQFZligOFFPi81a4ye3yu/m4XNj7lW16VXsTdFqsxJliCQajlbRIsSaja5DnKahRBOlAO62INuo2jiktqKrqOjt9xWzwFLDenc8GTwGbPYXRKDQADZ1t3iK2lU7PRfYK6RYnne1xdLLH0sUeRyd7XKVSHbKmR46linFpsgVF9SKrPnRNR0dDK2t/gGiohFUVHwGKfR4UScZoMGBUjNhNNmwGC2bFglmOWF407cAEkapqdfrOSJKEJ+Qm31uMqumoqBR53ZT4vNhMBcRZY3CZXBglA6ravEJIViDLnUuupwBV15BKa7VF/koV35dbJiNhMCgkk4ANuyiGWgV1/b40BrqmogcrF1k+WEimOCRZabb9N5Targe//fYLO3fu4OWX38DlcgGQmprGo48+yahRw/jf/77kwgsvPggjFRwozSqANm7cyIUXXsj8+fM5/vjjo8tXr15Np06dmnFkjYsuaeT5CgjvZ3F5cfvf0VIR56d2IcVS9fSb2WAiyZ4ImkyFhIGqRKo9GYA8TyFaFdaW6lAkiXa2GNrZYjg9qR0AYU1jm6+IDe6CqDDa6itCLXdF0IGd/hJ2+ktYmrcjujzN4qCzLZZO9ji6OOLobDZR9UReaSh8aF8ixKZE1TXUUBB/KEiJ34MiKxgVAybFiMNkw2qwYDc60GsubXbA6LJGbkleFXXfNNwBL96gj3xjITFWF7EmFybZ3CwWFFmBbF8uOe68St/XOm0vS2DUiVVCOBVXs4u5I5Xgtk/w/Xobuj+n2cYgWZKwHv8opnajG7Xfzz//lDfffJ29e/eQkpLKmDHnM3bsRVF/xb/+WsGLLz7H2rVrCYWCpKW1Zvz4CYwYMQqA/Px8Hn/8Ef788w/8fh9duhzFpEmT6du3Hw88cB9ffLEQgAED+vLLLysq7b9sP8uX/xDtEyKpW956631iY/dZf77//juef34emZk76dq1GyNHjuKRRx6K9lvVlN/+yz799L988MG7ZGbuRJIkunY9ihtvvIVu3bpH25966lB++ulHCgoKmDnzMfr06ctbb73Of//7EXl5eWRkZDBu3KWcccbI6H7efvsNPv54AdnZWSQmJnHWWedw+eVXHlGRtc0qgDp27EiHDh144IEHuP/++4mLi+ODDz7gr7/+4qOPPmrOoTUa1UV+rSzKZnlBJDdPvNHCxdWEvUtIxFljcBjsVd5MdFUipYEiaH8MskynUotO2c8kjEaW7uOvnCw2ugvY6Clgq7eogqUIYLffzW6/m2X5+3yKUgxmuphtdDXb6WK20clswyor0VB4WfODroFeUdg1Jaqmomoq/lCAYr8bg2KglT2eVtakJrtZK4pMjj+HkhpKk2h6JPu1N+in0FREjMVFrNmFWbYcNCEkK5DlzSHXk98g8VNGSA2x15sDDhmH4mhRU3tHCt6fboBQcbOOQffn4P3phkYVQJ988hHz5s3l1lun0b17D9avX88TT8wiOzubKVNuJDs7mxtumMzYsRcybdrdhMNh3nzzNR5++AH69x9AQkICjz76MKFQkPnzX8RoNPLaay9z2203sXDhEqZOvYVAwE92dhaPPPJ4lWM47rjj6datO/fffw+vvvoSxx3Xn969+9K///FkZLSNtvvrrxXcfvvNXH75lQwfPoLff/+VZ56ZXa/j/e67pTzxxCzuuOMeevfuQ15eLk888SgPP/wAb775XrTdggXv8/jjT+N0OunYsRPPPTeX//1vCbfccjtt27bjr79W8OijM3G73Zx//gX88MMyXn/9FR588BHatm3LP/+s4oEH7iU1Na2CqDvcaVYBJMsyzz33HE888QQ33ngjxcXFdO/enVdffZUuXbo059AajaqsP6quVaj2fkWbo7FW47BrN1tJtMXXfHOOWoKk0umwxrthmmSFbo5E2pS7kYU0je2+YjZ6IoJoU+n0WXC//e4NB9gbDvC9J2KGl4E2Rgu9lb70Mdk5Rs3CqPoxyA4OlgDan7AaJtdbgMVgwWlwNvrNWpLAr/nI99ZNnOroeIN+fMEAhcYiYixOYi0xWGRLk1pTJAWyvNnkeAoq5ahqCL5QgL0l2aS7FCyyVYggQaPwyisvccUVVzJs2HAAWrdOx+t189hjjzBx4iSCwQBXXXU148ZdGrVkXHrp5Xzxxefs3LmdhIQEdu3KpGPHTqSltcZisTB16q0MHz4SWZaxWp2YzRYMBiMJCYlVjsFoNDJ//kt88MF7fPPN//j44wV89NGHKIqBc88dw4033ozBYOTDD9/n6KOPYeLESQC0bduObdu2smDBB3U+3piYGO68896o5SY1NY2zzhrN448/UqHdwIGD6d8/Movi8/l47713eOCBhxk8+EQA0tPbsGfPbt5663XOP/8Cdu3KxGg0kZqaRkpKKikpqSQltSIlJaUen8ahT7P7ACUmJjJz5szmHkaTUJ31Z3H2VraW+tV0sccxLKltVZtjVAwk2RMiWY9rEQiR6bBWSEBuI4ugSuOSZTqVOkSPIFKpXtU1dvhK9okidx4bPUUEKvgUwfaQn+1yGp9a0yJ97VhLe7ONo4oT6eJM5ihHPG2szmrTADQFwXCILE8OZpcZo2RsFCfpMiQ5Ev3nq2fuJR0dXyiALxSg0F+Cy+wg3hqLRbY2ukVIUnSyvNkRnx+t8fr2BH3scu+ljTMNo2Rq1PMqqBnboKdbzBRYY1FQUEB2dhbz58/l+efnRZdrmk4gEGD37l20b9+BM888mw8+eJdNmzaRmbmTTZsifpZlv5sJEyYyffrdfPvtN/Tq1ZsBAwZy+ukjMJvNdR6LxWLh0kvHc+ml4ykqKuTPP/9k8eLPWbDgAywWK5Mn38CWLZs5/viKgTx9+x5bLwHUp08/tm7dwiuvvMi2bdvIzNzBpk0b0fb7nbZpkxF9vXXrFgKBAPfee1eFdCuqqhIMBvH7/ZxxxkgWLvyUCy4YTfv2Hejf/3iGDBlKSkpqncd2ONDsAuhwpirrjycc4rWdq6PvJ7XrXe3NPtbqIsZYdz+K6HSYBHnuiAPrwUKRZNrbYmhf6lMkhz3487PZHvCwPuBlQ8DDhoCHbUEf5e0LIWBDwMuGnB2QE/EpSjHbOT2pLUMT25FajV9UY+MJ+MjyZJPuTAO1ccRXpOxJEUW+kgPqxx8K4A8FKAm4ibPFEm+OQ0FpHKuKorPXk91kotnt97KbvaS70lAwCBF0kDC1G40x46zDygm67KZ/ww03c9xx/SutT0lJZevWLVx99RV07dqt9KZ+KrGxcVxxxX+i7U455VQ+/3wJP//8E7///hvvvvsWL7/8Ai+99DodOtSelPfTT/9LOBzmvPPGAhATE8upp57Gqaeexp133sZPP/3I5Mk3AFSKgjUaa09wq6r7rpBLlizmgQfuY/jwEfTq1Ytzzx3D5s2bK1mAyou3svP00EOP0LZtu0r9m0wmLBYLb775Hv/8s4rffvuZX375mffff5errrqGCRMm1jrGwwUhgMrTiEaH6qw/b+/6l6JwEIBTEtrQ01m1mdVmspJoTaj3tIeuQootGQmJXE9+oz7R1xdFkuhgttHBbGMEkeMMaBr5Oz9ii3snq5RkVli7s3M/i8begIc3Mv/ljcx/6eVM4vSkdpyUkF7tNGFjUeQrwWLMp5UlsVGmm8KEyPUemD9NeXyhAIHiHNxmD4m2eFxG5wGNU1I09niyD9h3rDaK/W52y1m0dqQi6SKc+2AhyQqSperry6FIfHw8cXFx7NqVyZgx50eXf/XVEpYt+5Z7732Ajz9eQFxcPHPmzI+u/+GHZaWvdILBIPPmzWHEiFEMGzacYcOG4/f7GTXqdJYv/4EOHTpSm/F569YtLFmymDPOGFkpitnhcBIfHw9A165H8c8/qyqsX7fu3wrvjUYjHs++1Csej5v8/Pzo+zfeeJWzzx7N7bffGV32/feR49F1vUqH5Xbt2qEoBvbu3csJJ+xLKPz++++ybdsWbr/9Lr788gvc7hLOP/9CjjmmN1ddNYmHH36Qr75aIgTQkUpj+ilEan5VtP7s8rv5796NAJgkmSszjq5yW0VWSLTHYZJMteb4qXLfKqTYWgGQ68mvpfXBxSzL9DTonBj8G4BdrfuSa+/CVqORtX4/K4uzWVmUFZ3wW1WSw6qSHOZuW8GJ8emcntSOXq6kJpkiU3WNXE8+FsV8wP5AiiKR483HE6hc9PZA0HSNYr8bX8hPrNVFojUes2yuvxBSdHZ7suqUR+pA0YFCXzGKLJNmT0FvJAub4PAkM3MnP/+8vMIys9lC3779uOSS8Tz//LOkpKQwcOBgNm3ayGOPzeTEE0/GZDKRnJxMdnYWP/20nPbt27Nu3VqefPIxIJK/x2QysXbtGv7+eyU333wb8fGJ/Pzzcnw+L0cf3QsAq9VGbm4Ou3fvIi2tdaXx/d//XcJXXy1h0qSrmDDhKjp37kpRUSG//vozS5Z8weOPzwZg3LhLmTDhUp5++klGjx7D+vXrePfddyr0dfTRvfjmm/9x6qlDcTqdvPDCfAyGfVaz5OQUVq36i3Xr1uJwOPjhh2UsWPB+9HiqmrZzOJyce+55vPDCPOx2O716HcOKFX/w7LNPc+mll0e3nTNnNna7nWOO6Ut2dhYrV/5J7959G/ipHZoIAVQOVdNRFPmAfSzKrD/7V3yvEPae1pVkc9XTOzEWB3GmWLQDeLrXSkWQRKMathqFivXAfNgVhT7OBHonurhIP4qcgJdvcnfwv5xt7PRHpo/8mspXudv5Knc7ySYbw5LaMSypLWkWR6OOLRgOke3JPSB/IEmScIc9FNQhU3dDCalhctz5eIJeEmxxxJpjkTS51vIakgSapLLHnUWBt6jJxU8Zuq6T7ylCkRRSbK1oJKOY4DBkyZLFLFmyuMKylJRUPvlkEePG/Qez2cyHH77H008/SUJCIuecMyYaMn7BBRezffs2pk+/m3A4RHp6BpMmTS4Ni/+XgQMHM2PGLGbPfpxbb70Jt9tN27btuP/+h6I3/1GjzmLZsm+5+OKxLFjwKUlJSRXG0qpVMq+88gYvv/wiTz31OPn5eZhMJnr06Mns2XPp06cfAJ07d2H27Gd55pknWbDgfTp27MTZZ5/De+/tE0HXXDOZoqJCpkyZhNPp4OKL/0NJyb4p81tuuZ2ZM2dw7bVXYTSa6Ny5M/fe+wD33HMHa9euqVaw3HjjzcTFxfHCC/PJzc0hOTmZq666hksuuQyAs88eTVFRIS+//CLZ2Vk4nS6GDDmNyZOvP8BP79BC0g+zgkSqqpGfX79szjm3xoKuoaf2JvHm7w+4Qqyk6Gwv2kmR3x1d9ldRNreujZgu440WXus9osopHavRTEZMa8ySpVH8JWQFChf0QvbuQjXFs2PIp/XbvjSzr9vtr5dFRA57CBXkRMLc9yMm+38k7XobgL1tr8YdPwhjbBK62VXhBq7rOmvd+XyVs41v83biUUOV+jramRidIjvQArLlSbDF0tqZWq21wmCIlE0pKPBUTmyn6Owozjxg35+6okgyDoudRFs8DoOjWuFcJn52u/dGxFkT/PRr+74oskKyI6FJ0w60RGr8vtRCfLy9xkzQfr+fzZu3kJiYgslUd0dewcHn888/Y8aM6VXmFxI0DsFggNzcvXTs2CFaYaI6hAWoHLquU+INEucwN3j6oyrrj6rrdQp7lyWZeFssVsXWaJE+mhq56egAUuRmeTCdo6scUzkLUDQZYhWnW5IkujsT6O5MYFK73vyUv5v/5Wzjz6K90bKs/5Tk8k9JLs9uW0kvVxLdnYl0dyRwlCP+gHyGCn3FmI3mevsDKYpEXqAAt79qEe5Xw5hlpVGTjam6RpGvZN+0mCUeo2Sq8B2WJFBLxU+ht+ksU7WOVVPJ8eQjyzKJ5vr7uAkEAkFjIQRQOXSgoCRArKPhT1FV+f4sydkarfbe2R5bbdi702wjwRLf+InvSu8xiqSQEdcaVVMJaiFCaoigGkLTNFRdQ9M1VE1D07QmvUFq8r6CkLJalg265v2ZZIVTEttwSmIbcoM+vsndzv9ytrHDt2+K7LfCvfxWuDfSL9DeFhsRUI4EejgTSDHb6yw8VF0jz1OAVTHjqIc/kF8LkOetOgJv4d7NzN22kkSTlbFpXTgjqT2WRnTsDoZD5JTk4Ql4SbDHEWuKpUwpqlKY3e6sOomfsKbxW+Ee8oJ+zIqCWVawyAYssoJZMZS+VzDLpa8VBUWqu900pIbJduehSApxpjhRM0wgEDQLQgDth9cXxhMIYzcp9Z6Cqsr64wmHeLVc2Ps1basOe6+u3EVjIiHhUJxIBqk00kECInWpVE1F1VXCWpiwrhLSwoTCIcKEMBoU/HKYoFZ5Cqoh6FVZgOpxzIkmKxemHcUFqV3Z4ClgSc42fsjPpLBcrh0N2OwtZLO3kIVZmwGINZrp4UigmzOBHo5EOjviMNcQphsIB8kq9Qcy1MEfSFYg15OHN1i5xMfakjye3b4SDZ3soJdnt/3FW5n/MjqlM+ekdMJpaJyq8DqR/Dv+cJASSyRazCgZ2e3eS5G3uNaz7FPDTN+wnBVF2fXar1GSS8WQgZNbtWFC654YperPbTAcIsudi+JUmiQBpUDQEjnzzLM588yzm3sYglKEANqPYEilyB3EmWiv95NpVdaf93avi96YT4xPp5crqdJ2tZW7OHDK+9VQzvej7K+MgoyCEbNC1EoiWSQMBhmTTSZXKaQk4KEk6CEYDhEKhxtsJSorhQEgq779h1hnJEmiqyOero54prTrw+6Ah39Lcvm3JI9/3Xls8xZR/hMsDAVYXrA7WoJEkSQ62+I4P60LJye0qXIf7oCXLG8OrR1pUIPjbkT8Flfp9+MJh3h40y8VaqoBFIWDvJ65hg92r2dUcgfOS+1CoslaafuGoGoqBd4ivEEfBkWpUzRacSjAXet+ZF0DIgdDukZI1XCrIT7K3ECmu4R7uwzEVIPA9IcC7HVno7gUbLJNiCCBQHBQEQKoHGV2mRJ3gECMBYNcdz+Nqqw/e/wePipX7f2qjF5Vbmu31KHcRWNQh+mf/QWSJIHFYMVhcGCT7bSy6vhVP76wn+Kgh0DIT1ANV1E+ofp9VYwCK/MBOrBjlySJ1hYHrS0OhpUWd/WEQ6z35EcEUUkea915uMs5Uqu6zjpPPjM2/oIsSZwYn15l34XeYsyGmv2BVMLk+PIIqeEKy3VdZ/bWP9lbWgesmyOeye368tGeDXyXtwMN8GlhFuzZwCd7NzI0sR0XpnUl3eo8oPNRRiAcJBCuvV1OwMu0dd9HpxQdipHxbXoiAQFNxa+pBLRw5LVa7nXZclUloKnsDXgI6Rq/Fu7h4Y2/cHfngRjk6qfHvEE/e0qySHelYpIsTeKYLRAIBFUhBFA5yq7T/oBKiTdEgqvuztBVWX9e3LEqWjR0TGrnKrMaGxUDSba6lbtoTnSd6M3fhAWLyUq8OY6gFsQX9uMOefGGvBHrkFrzHbeiBajpKsLbDUb6xiTTNyZSLFbTdXb6SljjzmVtSR6rS3LJLI3Ue3TTb6T2sNPJHlepn9r8gRRFIstXdc6fJTnb+C5vZ2Q8ipE7Ow0gxWLnjs7HM75NDz7cs4Evs7cS0jXCus6XOVtZkrOVE+LTuSjtKLo4Ko+nscn0lXD72u/JDkZEWrzRwiPdTqK9Labefa0uyWXa2u8JaCrLC3Yza/NvTOt0PEoN4tsd8LK7JItkRyKKZEBCQpYkJGRkJGRJjloly/TR/tGCQjcJBIL6IgRQOSQ0EhNMBPw63kCIBKluztBVWX9WFefwQ2ll9FijmYvTqq72Xt9yFwdEI+6iTAQoGHEaTMSYYlAJ41f9eMM+vN483F43Ab+n0lSZppRzgi61AOloSNIBG4JqRJYk2tpctLW5GNmqA7quM2vzb3yTuwO/pnLv+uXM6XkaCVVMQ+3vD1SGJIEn7KXAV1TJerHdV8yz21ZG39/UoR8p5URwqsXB9e378p/W3fl470Y+y9qEVw2jAz/kZ/JDfiZ9Y1pxUVo3eruSGjVyrIyNngLuXPsDheHING2a2c4j3U4itYH5lXrFJPF4nyFMXbGUkK7xXd5OjJLMLR2PqzF5ZVlyxzKxIwGSJCNJEeuejIQsGzBIMoosI0sRx2tZlrEoZmyKTUSUCQSCeiEEUDlCapAsz3ZU3YRDtmHzhXFarBglEzKRJHNVWYT2t/5ous5z5cLeL2/TE7uhco4aewPLXbQ0dF0vPQYZq2TDbrYjG6y4VZmSkI8ifzH+gJdQqS/UwbIA1YYkSUztcCy7/W7WuvPJCfqYvuEnHu9+SpXO0RX8gUrRJZ0cbz7+ULBC26Cm8vDGX/CXfidGtmpfrZ9RnMnChIyjuSjtKBZmbebjvRsoKD1XK4qyWVGUTVd7HBe37saguLRGE0J/F+dw7/of8ZZa7DrYYph51EnEm2rOnVEb/RPSmN51EPetX05Y1/kqdztmWeH69n1rHHttlsPqsBrNpDhbEWOMERFlAoGgzojCPIBkjZj6VYMVT9FeinzFbMzexZo929hWuJMthdvIdO+iIFiAHy+aHEZRpOi/kqC7gvXnq5ztbPQUApGbyvCk9pX2aZAVEkrLXTT58TX5Hvah6xHrkB4KY/D4SNAMtLcl0za+DSnxrbHbXChGC7oU0d5yI/kANRSTrHB/l8EklVp91rnzeWLz79X6ohT6isn150WrLBcECikul/CyjBe2r4qmPmhrdTGpbe9ax2I3GLmo9VG81WcUN7TvS2q5TOHrPQVM3/ATk/75mp/ydx2wr8xP+bu5Y+33UfHTw5nAE91POWDxU8aA+DTu7DwAufTb93n2Fp7b/neT+Pj4QgH2FGdRECxAUZo377kkRSzCkWuDjKJI6LJKkAD+cKD2DgQCwUFDCCAg9rxHsXQ5CXe/iwl63JgIYFAkSrxBSnw+3AEvuZ4CdhbuYVtBJlsKtrOlaDt7fdkUBosqWH98aphXdv4T7XtS295V+j+4yspdHITIl+ZKeoeuowb8aJ4SrL4gKZKN9s5U2ia0RTfYgHIWoGZ04ogzWXiw6wlYSq0+3+bt5J1da6tsq2oRf6CSUAneYOR7sb8D+E/5u/g0axMQqfl2V+cB9cr3Y5IVzkzuyKu9z+DOTsfToZwvzmZvIfdt+InrVn/NzwW7GyQovsrZxv0bfor6p/WPTeGRo07C0Uih+GWcGJ/O7Z36RwX4x3s38srO1U0igvzhIHtKsskL5CM3sQjaJ3JkDAYZSQFNDhOSAng0D4WhQrJ8OewoyWRz0Va2FG5ne+EufKHms3YeKuTn5zN9+t2cccapDBkymKlTr2fbtq01bvPnn38wYEBfnn76ySrXDxjQl88//6wphtuo7N27h6++WlLt+hdffI4BA/qWK+66j7JzsHv37jrta/fu3QwY0Jc///yjTu3r0v+kSVfxwAP31am/loKYAgNme47nrXAbrgiH6CJnYfQVY7Ul4fareAMqsQ4lYtVAJ6SGCalhfKEAxX43siSjl0t6997udeSXXugGx6XRO6ZVpf3ZTBZa2ROrqhLR6EhN7VhT4873vdRVlbAacbJ1GIz4FDt6qBiDHiTGmUBIkWmcLEMNo6M9lmmdjmf6hp8AeC1zDRlWFycmVI4MC4SD7HXnEJD9+Par95YT8PL4ln0XlWva9m6QMzFEsnYPSczglIQ2/Fq4hzcy10Qtixs9hdy7fjld7HFcmt6D/rEpdZoa+2jPBp7b/nf0/akJGdza8bgaI7UOhFMTMwhqKk+UnpP3dq/DLCtckt690fcVDIfYW5KDqmskWRKapN6YrIA77CGoBgmqIQJqkHBpFKSqR5KIqlUkEjVVMQUuqMztt09F0zSefHIOVquVF16Yz5Qp1/Dhh59gsdScIuL9999hyJDT6NXrmIM02sblgQfuIyUllWHDhtfYbtashzjmmD64XK4G7ys5OZlFi/6Hy9Wwa9PhgrAAAe+u2suekiCz/9LxhiUCXi8G1YvJqOD2hdBqECqavi92KyvgYcHu9QAYJImr2lb+ISqyQoItDovcOLW+akOWaVbrSlVo4RCU+gFJqpd21iTaxbYm1dUKh9mGUTEg1yOzcGMxOL41E9ocHX0/a/NvbPQUVNnWHSh1fC63TNV1Zm76lZJwxB9ocFxrzkzucMDjkiSJAXFpPNtzKPd3GUwnW2x03QZPAXev/5Epq7/h14I91VpXdF3n1Z2rK4ifc5I7cXun/k0mfso4o1V7prTrE33/euYaPiz9nTQ2ITVMdkku2b4cakhBVG8URSIsBdnl2cOOwl3sLNxDVkkuhd5i3AEvvlCAYDhEWFObz+J6iFNcXExqahp33nkv3bv3oH37DlxxxVXk5OSwZcuWWrdPTU1jxozp+P2HpqWtLpZRl8tFMBjkyScfPaB9KYpCQkIiRuORLcyFBQgY0iGeN1fuxhOCb/ZYOauNl7C3CIvTgicE3kAIm9lIbWFUL+34h2CpWWd0SmdaVxFJE2NxEG+OO8iOzy3wgqxEpsDQgoTdhZgNrbDHtCLeHE9A9RNSwwS1IP5wgIAaRNW0yFN26d+m4sK0rmz3FfN17nYCpZFhc6uJDNufd3at5Z+SXACSTFamdjy2USO3JEliUHwaA+NS+blgN29k/stmbyEQ8RG6e/2PHOWI59L0Hhwbkxzdt6rrzN26gs+z991ELk3vziWtuzdJZFlVnJ3SiaCu8XypAHthxyqMssLolE6Nvq+wppLtzkPVNVJsraotaFsXJAmQIS+QT563oMos3y0VVdcoCNaeALOpiDNZ61UixeVy8cADD0ffFxQU8O67b9OqVTLt29f+IHHbbXdw220389xzc7nxxluqbbdq1d/Mm/cMa9f+S2xsHCeccCLXXjsFuz1yvd67dw9z5z7Nn3/+TnFxCfHx8QwfPoJrr52CLMt8/vlnvPbaSwwadCKLFi2kX79jefTRJ9m6dQvPPPMUf/21ApvNTr9+x3HDDTeRkJAIwI4dO3jyyVn8888/6LrG0Uf3YsqUm+jUqTOTJl3FypV/snLln6xY8QeffLKoyrHbbHauvnoS999/L6eeOoyTTjq52uP8/PNPefPN19m7dw8pKamMGXM+Y8dehCzL7N69mzFjzuTZZ1+gX79jUVWVF198js8//xS3283AgYNp1aoVGzZsYP78F6N9/vTTD3z88QJ27txBenobJk++gcGDT4yu93o93HvvnSxb9h1Op4NzzjmXCROuRi59yNq6dQvPPvsMq1b9jaqq9O9/PNdffxOpqZHAkkmTriIjI4ONGzeyY8c2brllGv37D+Dxxx/hzz//wO/30aXLUUyaNJm+ffvV+p2oDSGAgBsHteXtv3aj6bBkt43T07zgC2AzF2MyxuLxhXBYjTVagtaU5EbzvcQYTIxrXTns/WBOfZUhaaFmsgDVfNORFOs+WaZ6QVMj/lCqhBkrltJyHZJFQkcjrKuEtVCkRIcWIhCOOJWGo8Io8u9Aj1SSJG7q0I/dfjf/uvPIDfq4b/1ynugxpMayGf8U5/BW5hogYla9o9PxuBrZp6b8GAfFt2ZAXBo/Fezmzcw1UYfrde587lz3A91KhdAxrlY8uvm36HcT4Lp2vRmd0rlJxlYT56d2IaCpvFZaGubZbSsxyTIjWx24lWx/VE0j112Apmuk2JKRdbnePwNFkfGqXnJK8ij2u5tUeDc2n+5aw7RVi8gJVF2U92CQZLbzSK9RnNO6R723nTnzQT799L+YTCYee+wprNbaH0DatGnLNddcy5w5sznllNPo3btPpTYbN25gypRJXH75BO688z7y8/OYM+cprr/+Wl566XUkSeLWWyOi5Zln5mOz2fjhh2XMnv0ERx/di5NPHgJAZmYmubk5vPHGOwQCAXJycrjmmisZPnwEN9wwFb/fx4svPseVV47nnXc+xGq1cs890+jSpSuvvvoWqhrmmWeeYtq0m1mw4DMeeeRxbrnlBlq1SuaWW6bVeJwjRpzJ0qXflE6F9SYmpvI01ieffMS8eXO59dZpdO/eg/Xr1/PEE7PIzs5mypQbK7WfN+8ZFi1ayLRpd9OuXXsWLPiADz54j969+1Zo98EH73H77XeRlJTEs88+w1133c4XX3yNzRZ5oP3uu6WMHXsRr7/+NuvWrWXWrIdwOJxcfPEl7Nmzm6uuGk///gN49tnnCQQCPPPMU0yadCVvv/1BVIB+9tknTJ8+g06dOpOYmMjMmTMIhYLMn/8iRqOR1157mdtuu4mFC5fU6XtRE2IKDOiYYOO8HpESFSVhmWVZVkCPOkSHVI1AqPqLn6brzN/2V/T9ZW16VnIoPdhTX1HCwdrbNAdlFiCAsBf0iomqy1IOqKqGpoKsKZiw4FAcxJviSbOn0T6mHR1i29I+LoO2cemkx6YSY3ViMZoPaArNJCtM7zKIVqbIGNd7Cni8hsiw4nCQmZt+jZbduCS9B0dXUfKksZEliRPiWzP/6GHc23kg7az7fALWuvO5Y90P/N/Kz6PiR5EkpnXq3yzip4xxrbvxf+UeDmZv+ZOvc7Y3yb40XSPPU8hu9x40Wa1LInQg4uCsyxrZvmx2FO2iwFt0SIkfgJtWftas4gcgJ+DhppUNcz6+6KJxvPbaWwwbNpzbbruZdeuqDkrYnwsv/D969uxVOhVW2fr19ttvcPzxAxg/fgIZGRn07t2HBx+cyZo1q1mx4k/8fj9nnDGKO+64m86du9C6dToXXTSO+PgENm/eVKGvyy+/ktat0+nQoSMff/whrVq1YurUW2nXrj1HHdWdhx6aRX5+Pt988xUAu3ZlEhsbR1paKu3bd+Duu+/jjjvuRdM0YmJiMBiMmM0W4uJqT346bdpdhEIhnnii6qmwV155iSuuuJJhw4bTunU6p556GpMmXceCBe8TCFSMRvT7fSxY8CFXX30tp5xyKu3atefmm2+jS5eulfq98cZb6NfvWDIy2nLFFVfh9/vZunWfZblLl6O4+ebbaNeuPWecMZILLriYd955C4CPPvoQq9XG9Okz6Ny5Cz17Hs3MmY9SUFDA4sVfRPvo3Lkrw4ePoGPHTsTExLJrVyZOp4u0tNa0aZPB1Km38vDDj0WtSgeCsAABmi+HO7ts5L+rHYQx8MUuG0NSfBAKYfQVY7Ql4fGHiXdWnRl6ae4O1pf6irSzuhjZqnLYe3NMfUmSBOFAi/MBgogFqAxd9QF1M4vtK9UROSYJGSMmTLIpIo7M8aXZqX24Qx68QT9BNUS4njlmIpFhg7lhzVL8msp3eTtpa3VVct7VdZ0nN/9OTulUQy9nUoUbfKXjRsKgKA3OeVMVsiRxYkI6g+Nb80N+Jm9l/ss2XzFAtA6dSZK5t8sgjo9LbbT9NpTx6T0IaCof7dmADjy2+TeMslxtnqQDQdd1CrzFaOikOZIxYKrR10JWJNxhNzmePNwBD1oL/O0cCZRNed11132sWbOaBQve5+67pzNkyOAK7d59d0GF97Isc/fd0/nPfy5i/vy53HTTrRXWr1+/jp07d1TqB2Dbtq3063csY8deyNKlX7NmzWoyM3eyadNG8vPzUNWKIrhNm4wK/W7ZsrlSv8FgIBrFds011/HUU0/w0Ucf0rdvPwYMGMTpp5/RoBt5QkIiU6feyvTpd3PaaUNxOPaVzikoKCA7O4v58+fy/PPzoss1TScQCLB79y7M5n3pLrZt20og4Kdnz32lmiRJonfvPmzYsKHCfjMy9h2z0xl54CovqI45pneF9j16HM3rr79CSUkJmzdvolu37phM+4wDCQmJZGS0rSAu27SpeB2YMGEi06ffzbfffkOvXr0ZMGAgp58+ArO5bomKa0IIIKDk64tx5fzB+637cN6uuygIKizPtnBKip+A14vF7CUUchDW9EomM58a5uVyYe/XtO1dad67Oaa+IOIArQX9LdEDqKIFSPUesJtS+RpmBoy4DCZiTbGEbSF8qh9vyFvvQq4d7LHc0WkA0zcsRyfivNvG6qxwo/5s7+ZocVWnwcS0Tv2rLfsgAXE2F3GWGAr8RbiDkdIhjYUsSZyc0IYT49P5IT+TNzP/ZbuvGLtiZEbXE+jpSqxHXzJmgxEdHU3XS8tNlHt9AB+YJElcndGLoKayMGszGjBz068UhAKkWxxYFQNW2RD5qxixKgZM5cph1BcdnUJvMbqmkepMwSxXfpCRZYkwIbK9eRT4ihv1c2kOnupzdouZAqsrhYUF/P77bwwZchoGQ2meMFmmQ4eO5ORkA/DGG+9W2CYxMYldu3ZVWJaRkcGkSdfx9NNPMmTIaRXWaZrG8OEjGD9+QqX9x8XF4fP5uOaaCQQCAU47bSijRp1F9+49ueaayu0tln0iQtM0+vU7jltvrTx95XRGxMn551/IqacO46effuSPP37jhRfm8+qrL/HGG++SkJBQl1NUgTPOGMm3337DrFkPV5g200p9NW644WaOO65/pe1SUlLJycmJvldKU3TUxQlbrsINoPx2ilLx3qdpKpIkYTQaqu1f07To5w1UEGcAp5xyKp9/voSff/6J33//jXfffYuXX36Bl156nQ4dOtY65poQAgh4KtSal139iNP9JJuWkeVrzYISM7HxYdIUE2meQgyxFvyBMHaLscIH+eHu9eSWPv0PiE2lX2xyhb7LT301R8ZnvbQIZ4vDsJ8FSNca1VBVlp1aQsEu23FaHSRZdQKlpTpKgh4CpZE7ag3KdFB8GhMyjualHRGR+9jm30k1OzjKFc+mkoIKU5+3dDiOJLOtmp7AaXGQ6kjGgBGn04k77KHAX0hJwNMkQuiE+HQ2uPNpbXHgMtb9acmoGEi0xxFniY0ISzQ0XYuGeWtoqHqZQ3q41A8rjIqGBJgUA1Czs7AkSUxu14eQpvJlzjZUXa9QNqTSMQGWCsLIgKX09TGuVoxJ6VxrNFuR343Kblo7UrDIVjQtUuxXkqEoWESONw9vwH9YRHGd07oHZ6Z1O6ScoPPy8rjnnjuYPXsuAwYMAiAcDrF+/TpOPPEkoKLVpSYuvPD/+O67pcyYMb3C8o4dO7F169YK/WzbtpU5c2Zz7bVT2LFjO+vXr2PRoq+ioqSoqIj8/Dxqekrr2LEjX331P5KTU6IWjqKiIh544B7+7//+Q/v2HXnllRe49NLLOfPMsznzzLPJzs7m7LPPYOXKPxk69PQGCfzbbruT//u/scyb93R0WXx8PHFxcezalcmYMedHl3/11RKWLfuWe+99oEIfbdq0wWy2sHr1PxWmvVav/geTqX5Wlv2nKv/++y/S0lpjsVjp1KkzS5Z8QTAYjJ6jvLw8MjN3ct55F1TZXzAYZN68OYwYMYphw4YzbNhw/H4/o0adzvLlPxx8AfTf//6XQYMGkZycXHvjQ4SPjF1wBwO4JTPYQLLtxg3MLtUOhhKJ5Lz1pFpiaOdwkW5xkm5xYDMY+WBPJJxXkSQmVhH23jxRXxFkNUi4hT7JSvtZgCJnp2nOUdWFXONLo8z8FASKKfIWV3vjuyC1K9u9xXwVjQz7kad6DuHecskER6d0YlB8WpXbA9hMVlIdrVB0Y3RaxSbbcDjseKwe8n2NL4QUSaKbs35PlmXWyhhTDNp+31lJBmSpgi9N2UVbkiI+N5IMigXU4E4KPSU1fqKyJHFjh2MJahpL83bUOC4N8KrhSObq/U7Rb4V7+aVgN/d0HkhcLZms3X4vmfoe0pzJOAwOfKqfHHcuRb6SCoWMDwcUSSbRXLkAc0ulY8dODBw4mCeeeJQ777wHlyumdPqkmIsuGlevviRJ4q677uM//7mowvL/+79LuPrqK3nssZmcf/6FuN0lPPbYIwQCATIy2kZD6L/88gtOPfU0srKymD9/DuFwmGCw+t/meeddwH//+zH33XcXV1xxFQDPPPMUmzZtpEOHTjidTpYv/5HMzMzSiDM7ixYtxGg0ctRRkSlzq9XKnj27yc7OolWrut1fExISuPnm27j33jsrHPsll4zn+eefJSUlhYEDB7Np00Yee2wmJ554coUpKACLxcoFF1zEiy/OJyEhgfbtO/Dppx+zZs1q+vY9tk7jKGPVqr+ZO/dpzjzzbP7++y8+/vhDbr31DgDGjBnLxx8v4P7772H8+AkEg0HmzHmKmJjYanMfmUwm1q5dw99/r+Tmm28jPj6Rn39ejs/n5eije1W5TX2otwB64IEHePTRRxk2bNgB73x/tm7dypgxY7jnnnsYM2ZMo/dfHbP6ncejv73KprCEp4rSFGF0doV87Ar5+KNkb5V9nJPciTZWZ4VlzTX1VYauBtEb0dekUSnnA4Ra9pR6cERi2fSHobSQq91kQ4LINEkVY5AkiRs79GN3wM2akjzyQn6u+vt/BEpvmB1sMVyVUf2P0WwwkeJIilodyigTZlbJRrrDjs/qJd9fSInfQ+AgO69LSLgsdlIcrSLjrEKwR3Tb/pXXK7YzSDIxFjttXK2R9D0U+opr9KNRJInbO/Xn1MQMtvuK8aoh/GoYnxbGV/rXXyp8fGoYf+lyrxpGK7fvf0pymfTPV9zbZRDdaxF9noCPXfpeYixOivwlTRraruk6WQEve4JeLDGxdDQePg+OTcGDDz7MvHlzueeeOygpcdO7d2+ee+5lUlLq77vWpk0GkyZN4amnHosu69mzF08/PZfnn5/P+PHjsFqtHHtsf66//iaMRiM9evTkhhum8t577/DCC/NISkpi6NDhJCensHbtmmr3lZbWmvnzX2TevGeYOPFyFEWhV6/ezJv3QtSp+cknn2HOnNlMnnwNgYCfzp278MQTz5CeHplSHzPmfB544D4uueRCFi/+BkWpWyKr008/g6VLv+a775ZGl40b9x/MZjMffvgeTz/9JAkJiZxzzhiuuuqaKvu4+upJhEIhZs58EJ/PzwknnMhJJ51CIFC/38Y555zLzp07uOyy/yM2No5rr53CmWeeXXqO0pg//0Xmzn2aK68cj8lkpH//AUyfPiM6TVgVM2bMYvbsx7n11ptwu920bduO++9/qFKEWkOQ9HrmpR8xYgQTJ07k3HPPPeCdlycUCnHRRRexevVqZs6c2WABpKoa+fn1m/c2mxUUfya73x9AoaayVY7jHs8lbJST6JRYTEDxs1cLEq7mBu00mHi99wic5SK/FFkhzdWKBHN881h/ZAnZm4c/ewf+3y6GQDaSJRn7ef/Wqx+DQSYuzk5BgYdwuG5KTpJACZYQyNqKrlb9VK1mf0Vow6zIPjpMxtJxHIbUzoQPIGdLQ5Ek0GSVXSV7qxVBAAUhP1P++Yas4L5pRYus8OzRQ8mwVp2V1agYSHVG8hvVVqgzMh0j4VN9FPgjNcYOhhBSZIV4WwzJtiRkvfq5+rpQ/vui6ipZ3mzyPIWNbl3RdZ2QrrHBXcBDm36JTkMbJInJ7foyqhESUNZ3PAWhANt8RWzzFrHVW8w2bxHbfEXRgrgmWWH5aZNpY46tV9/x8fZKvhXl8fv9bN68hcTElHpPWQgEEAlfP+aYPhUi0K6//lqSk5O5665Dq7xFMBggN3cvHTt2qOCnVRX1tgBdeOGFPPTQQ6xcuZKuXbtit1c2sY4ePbq+3TJnzhwcjsqJAw8Wiq0VBRnjSdo6l0TVx4uG1xiR9RSqtw0P9iqIOFJKGkV2K/lGhV3+Enb53XjUEP/XulsF8QPNO/UFEauFFvRVjAA7SEnv6sT+TtDQbPkadT0SZp/mSAGg0FtU5VDijBYePOoEbli9FJ8WsaxNbt+nWvEjSzKJ9rjS70Ht4lHXQVd1LJKFNHsqcRYfBf4iSvwl+JtICJkNJhLt8SRa4tHUxq0bp6sSKbZkFNlArjufoNp403uSJGGSFHq6EpnXcygPbvyZf0pyCes6s7f+yXpPPpPb9cHUmOmgS/GEQ2zzFbHVW8Q2b3FU9BTV8hmFSstlCAQtjbfffoOPPvqQKVNuxOFwsGzZt/z55+8888y82jc+hKm3AHrkkUcA+OCDD6pcL0lSvQXQ77//zvvvv88nn3zCKaecUt8hVUDXdUKhqi+0kiRV8DYvayfLKsghiuJOwbh7CbGBjbQxZHOD630eKbqMNUVGursCxOoSKWEJZ0IqpqR25Z6UKwoLs2wgzhRDMBCq0rG3fPrx6sZaVdtwuOr+qm4bRPWUEAqrUWFR/txUbBuuoYSCBNjr1BbAYDDsy0Csaqjhqp/8dfY9qeqqD1UNo4eChLWqn3Qr9qtGIx0OtK2iKMhyaZI8FZLMCYRCIYp9xZWkgCwrtLfF8EDXwby8YxUDk1ozLL4NarjyNKMiK8S7YkiyJhEMhus0BohERJSF2yqSQitTAk7FRqG/mJKgm6AWjh6brmk19ivJcrRfXdPR9rPC2ExWkq0JOBUnoaAaNbmXH0NVyLIcbavrOuFyx6/rEsGgkVAoSDisI6syrSyJGGUDWSU5ePzVO+VLkoxcaunQdR2thjGUbxtrNDOz82BezFzNp9mbAVicvZUtnkLu7tifVmY7crnphKo+r339SlW29alhluRu5/OcLWT63dVuvz+pZjttrU462uK4rMcgOtkTCYe1ev3um6J4rEBQnvvvf5inn36CKVMm4ff76dChAw89NIt+/Y5r7qE1KfUWQN98802jDqC4uJjbbruNu+++m9TUA89RUlJSzIsvzqlyXbt27Tn77POi7194YX6FizeAUz6Nsx1bUCSVKxyfs9B7Igt3puOQt6NFn+I3V9wmPoa+p0eiFhRZ5ufPvsPjrvoiGR+fwCWXXB59/95775RGGFTG6XRx+eUTo+8XLPiA7OysKttaLFYmTrwu+v7jjz4iszQ8dIzTj0OOpCl/48U5GAwGrr32xmjbL75YWGPF5fvuuy9qgv/f/75k06YN1badNOl6FMWEIkv88M861u+ounpwgrKbUaUGP0nzsXzVOlZv/rLafsePvypauO+XX75nxYrqqxiPGzc+mn7+jz9+5rfffq627YUXjiM5OfK9+/PPP1i+/Ptq2x4zpD+xyQn0jUsmJa8rG79fw3KqTuJ3/NATaN26O7Ius37zv3z9dfXHNmLEWXTuHIm+2LhxI4sXL6y27cBTTiSpfQr+UIA9O3bxz/d/Vtu2U7/utO7cFoDCnHz+/va3atsOHnwS/fpFQmazsrJ4//23q23bv/9ABgyI5DvJy8vl7bdfq7Zt377HcsIJp5BojSfo8/PVx9UnxkvrlEHnYyNZg4P+ID9/srTatsntWnPUgIjflRpW+e2TpRwDSDYLn8f7CcuRBJZXr/wfE+Q0Rg/el5/lh4+/rrbf+NQkjj55n+Pnlwu/4Rebnz+dQfw1BDTFGy3EucMkBiRahRRahWSSQjImXQI8uOKN9D0pDSUU6eTNN1+npKS46r72u0Z4PO4qs/0KBI1FWloas2Y90dzDOOjUWwC1bt06+trn8+F2u4mNjW1wUbXp06fTp08fzjrrrAZtXx+MRgNxcfusGVWFHZZoCawKnEAfyzIUSePhuPmMyX6EHU476ZRU2a8sSzgckbnGGIsLQw3Oa4oiVxhDTXP7sixVaGswVN/v/m3lGma7JKliW6Ox9q+ByxVxWjaZam4bG2vHZDIRLvFjMFR/bCF935ShQQrU2BYgJsZGbGxkzGZzzd81l8saPT6rteZyFE5n3dtarabo51zbeUiLTSIhNjI1ZrfX7Jdht5ujY6itbauYeI5O74on5IWSIP/U0NZsNkbHGyiu/djKxuD11pxevnzbUKhmfzuz2Rhtq+s15yEyGpV941Vqnq4t3zYc2vcQ08trJCkk82GSj0KDjlfReZZdKHlbuCijW62hxopBxuGwsMVdyDvb/+WLlGL2d0trHZBJCSq0s7oYOqAfHRyxxJosfPXWIoL+qqfB5NLjKfsdyTX8QPe/RhQVFdU4ZoFA0DDq7QQN8Mcff/Doo4+yevXqqHm2V69e3HTTTQwYMKDO/XzyySc8+eSTLFy4MPqE07Vr1wNygg6HVfLzq36ykiR5vymwyMXKYJCRVTe/rP2d7JxcJD1Mn6xHsIci1ouHCi/jD8NwpnYrjG4bEx9LbEoaEb9gCcWgYDVaaBvbGjmsUL1Di1TFFFjd2kamwKr/uIxGU+lxSqhFe/Bl7wJ0Qn+MizhBW5OxnbuqQttIv2H0anwTFEUmISGG4mIfqqrV2BbAYDAiSRJKoBjPni3VTjfogVzCf1wc2UfiiZiPmYkhuQMqVYu8sn6hbFqr+umR+rRVFEN0mqh8W0mCMCq7ivdQ5I8IX1lWkMrduCxmAz5fsMI0lNlgJj0mBZcppkFjiEw/VT9FI8tKuegQnZAawhv24Qm6KQ54CIaDUYfj/afAjLKBJEcCCeaYSnXtyvdbnzFEpsD2TecoiozTaaWkJPJ92b+tqoYIo5LlzqbAV4xW7rtUfvqp9imwmtsWhwI8vPl3VhRnR5edlpjB1I7HYqzmJ6TrOqtKclmwdxO/Fu6psM4gSQxJaMP5KZ1pbyuzxkR+92XUNLVmMpjomdEZJWRCVbV6/e5dLkuNEUHCCVog2EeTOkGvWLGC8ePH06ZNG6699loSExPJzs5m0aJFXHnllbz55pv06VO5CF1VfPTRR+Tl5VXy+7nvvvv44osveOmll+o7PCRJQpKqP6zykUxl7WRZxiwbsZlNKIqMphvZlDCOXnsfR0LnJtd7jMg6nh1eI20dkYuc3+tFDXiQLTHouo6ETLw1FhNmVKlmTVlxDDU7aVaMvFJq9GMua6soMoSCRB46JcLsu9SWHXPFfmWkahKW7RMHWuk21beNtNORJB00HQmqzYqsG2yU3S70sBcJCVlW0PSqP7uIM/k+n6uaPuP6tNU0ygmYim2NkoH02NZIJXsp8kVy2uilYeyyLGEwGpACYaRSHzCTYiTV1QqXKbbU6bkhY6DGtrq+3/cHAw6DE5cphlYOFW/IQ3HQgyfoIRAOoWoqEpEkjCmOJGyKHVXVKn2PKvVbnzGUayvLMiaTCVkOoWlapbZgwICB1jHpmIw55HoKKkSIlU8TINXiwFxT2xizjYe7ncRrO1fz3u51AHyTu4Ot3iKmdxlEqmVfwIWqayzLy2TBnvVs9BRW6MeuGDkzuQOjUzqTaKpsGavreKX9fkf1+923oOAFgeAwot4CaPbs2Rx77LG8/PLLFZ5KJk+ezIQJE5gzZw6vvPJKnfp6/PHHo4mnyjj99NO5/vrrOfvss+s7tAPGZJQxmRT8gTAl5g7scZ5MWsl32OQAD8S9yPzMm5l8VMQaEAqG8BUV4rTYCGNo9qiv8kioqM2YAbZOVKgF5gF0pBbm7KnrkVxBrZ0poOsU1eD8qsgKiY54YqPi5+ARySekARJ2xYHT7iJkC+ENeSkOlCDJEsm2JBTdeNDHVi2qRLKtFYpsIMeT1ySlJxRJYkLG0XSxx/HY5t/xaWG2eIu47p+vuaPzAHo4E/gyeysf7dlIdrCic3Yrk43zUjtzRqv22JSGTe8LBIKWTb0F0D///MMTTzxRySQryzKXXHIJt99+e537qi6bdEJCQrNkmpYAm9lAKKSiajrbY88m3vsXFrWQky0r+STvd3Z7e5BmU9EBd4kHe4wHuyulWRMeViIcRK/BHN8SkCQZZAto/nKJEFse+0RQKrCnShEkSxIJtliSLAlVJhA8mJSJIRkFp8FFjMkVqefVyCHujYGmQpIlIRIh5s7BFwrUvlEDODEhnQyri+kblpPpd1Oihrhr3Q9YFUMks3Q5OttjOT+1KycnpNerjINAIDj0qPcv3G63V4qcKqO2EOmWjg6YjQrmUgdXVbayJX5fKvW7Y1/lu12l/hVIhFWdsM9Hki0Gi2xpEUXXJQkIB9Faagbo8pTmAtIboRhqUxIRQSbSnKnEWCrnqoqxuki2t6KlVVIoq4fW0sZVHlXViTXFkB6ThtNix2o0YzGaMBmMGBUDBllBkWXkA8xh1dbmYm7PoQyMi5Qr0aGC+Okfm8Jj3U7m2Z5DOTUxQ4gfgeAIoN4WoL59+/LCCy9w4oknYrXum8bwer288MILHHts/WqH7M/69esPaPuGoOugWO047bHYDBYSXODxhVA1kGhLSegfnIXLSVCKOc3/ATbXbaTaFSTAYjYRJxlbzP1bkiQI+VtICYyab1qSYkUPAeGIBUjXtRbr7qDrOkYpIoJgDyXBSPSTy2In1dEKSZNazHfgUENVdWyyjXYxGWi6WlqBXkPTdVRdRS99HSnIqpYWZVWjRVjDuoo36K/14ctuMDK9yyDe2bWWNzPXoEgypyVmcF5qF9pFHZsFAsGRQr0F0NSpUznvvPM47bTTOOWUU0hKSiInJ4fvvvsOv9/PQw891BTjbHJMFjtt49vhz90LWpjCQJAibwDQCSdfi7/wLyx4OM/+Lc//M4Tzuh8Luk5YCpGnZhLfviuSJDe7FSiaAfpQoCwbtOotjSxroeqnlPIiaK93L0aDQoIlAYNuOqQtny2BiDOxhFx6SSqbYN9niNlXhFUqde4vex3SQmR7c8n31l5yQ5YkLknvzohW7THJSqUM7oKWg8fjZuTI07HZbCxcuBiDoeX7Yo0ePYpRo86qtuaWoGVRbwHUrl07PvjgA+bMmcOyZcsoKioiJiaG/v37M3nyZDp16tQU4zwoqCYnkqmQYGEOZklDD/oJhFTAij/uElILngfgjPA89hQ+SUJpiF1+bgGSIxdnQjKytH+xyIOLpIdRm7C4Y6MSdYTW0LUgkZrfLZsyEdTamYpiBkPITDDYgueYDnH2/ZaqL8IqoZBqT8ZiMJPtycNfB1+ihCoiupoCg2LApBhwWGwYZcP+hewFNfDVV0uIi4sjPz+Pb79dWm3F8JbEq6++hdksUhEcKtRbAM2bN4/hw4cze/bsJhhO86JpoLhaYQh4kf1enHYTwSJfpEZT7DC2FXxPO9bS1rCX73YvIKHDJUAkIsybl0PA4CTOacGoyM1nEWjJFeD3Q1Js+25jYU/kntayjUBARASZJBOxtkjRT0Hzo6kQb47HbDCR5c6lxO9ulilJWZIwKkZMBiNOkx2r0YpVsWAxmnGYrRR4D973RddUNG/+Qdvf/si2+FpTGdTEwoWfMnDgYPbu3cMnn3x0SAig8sVEBS2fegug559/nh49etCxY8emGE+zo8omDLHJBHN24rBpuH0h/IEwSDKBVtcQzLoZkxRmsP4pmZ7BmO3tAfB73Jg9xewKhElLsGMyKAddBEkSEAocGg7QAIbyofDV14hqiYgZr5aHqmrYZDttXCayDDkUeItRD4IHuEFWMBmMWA0WHGY7VoMFs2xGQkLTdHSNg55+wPfXxxQvuBnNnXNQ91se2ZGE6/wnsPauf1LbrVu3sGbNai655DJKSkp4+OEH2LFjOxkZbZk06Sri4+N56KFZ0fYrV/7JpElX8eGHn9CmTQY//vg9L774HNu2bSUpKYlhw87g8suvxGSKTHkOGNCXCROuYtGihYRCYebPfwmTycjcuU/z55+/U1xcQnx8PMOHj+Daa6dE86H9+uvPPPvsHLZt20J6ehv+7/8uYcaM+/n4489JS0urMAX24ovP8ffff9G//wA+/PA9iooK6dGjJ7fddift23cAoKCggCeemMUvv/yEohg4++zR/Pvvanr37ium0Q4C9Q516NSpE1u3Vl836lBH03R0iwujMx5FlnHZTdEIFKejDd/qkR+zUVIxZT0HeuQCG/T7kXyFhEIaO7LdBMJqjenumwJJkiAYQK8hg25LQipfET7speXkERAcqmiajqwZaG1PJcWZiLkJfHwkScJsMOG02Eh1JdE2Lp0OsW3JcKUTa4zFqJvR1Ihzd3MJ5aL3pjSr+AHQ3DkUvTelQdt+/vmn2Gw2Bg4czMknD8FgMPDxxwsAOPPMs/nxxx/wePZZ07788gt69epNmzYZ/Pzzcu66axqjR4/h7bc/4NZb7+Cbb77i/vvvqbCPjz76kJkzH2fWrMfJyMjg1ltvwu1288wz8/ngg48ZN+4/vPXW6/zwwzIANmxYz9SpN3Dccf158833uPzyK3nmmadqPI6//17J33+v4Mknn+H5518hPz+fxx+PFBTXNI2bb76BnTt3MHv2XJ5++llWr17FihXV1/cTNC71tgANGTKEJ598kh9++IGuXbtis9kqrJckieuuu66arQ8NVBWMpVNhDq0EjzmIxx+xqiS1HsPmHcvpaNxFazaSU7iYUNyZAATdxdhs8eR6ZHZkuWmT5MBqVipki21KZFlCDRxClpRyAkgPi6kkQeMRyTGUiFkxk+XJwRM4sMAAiUg5C4vRTIzFic1gxSSbkctZecL71xcRNIhwOMzixV9wwgknY7FYsFgsHH/8QL744nMmTZrMqacO5YknZrFs2beMHHkmoVCIb7/9huuuuwGA1157mdGjz+Xcc88HID29DbfffifXXXc11113A2lpkVQIZ5wxim7dugORciJnnDGKoUOHkZycAsBFF43jjTdeY/PmTZx88hDee+9tunXrxpQpNwLQtm078vPzeeqpx2o8lvvum4HLFakLOGbM+cyd+zQQsVr9++9q3n//Y9q2bQfAjBmzGDPmzMY9oYJqqbcAmjt3LgDLly9n+fLlldYfDgIIIIwBJTYZLRTEaQ/jD2moqkaK1cR70iSmcjcAMQXvUOA8HtWQRMDnwxwoxmJJxO0JskMvoXUrBw6z4eCIIC2Mvp8DqK7r6FrZspblYCOVywYdSYYo5pUEjYeq6jgNTswuM3s92RT5SirUHqsLRsWA2WgixuzEYbRjVazoeqmlWAW1hX5nYy6a02KmwOrLTz8tJz8/j2HDTo8uO/30M1i+/AeWLv2KESPO5NRTh7JkyReMHHkmy5f/QDAYZOjQYQCsX7+Of/9dw2effRLdvswdYdu2rVEB1KZNRnS9xWJh7NgLWbr0a9asWU1m5k42bdpIfn4eaqlFff36dRx33PEVxtqnT98ajyU+PiEqfgDsdkdpHThYt24dLpcrKn4gkgQ4I6NtXU+V4ACptwD6999/o/OhhzO6rqObnRic8djDITy+ECXeSPHUgendeGfT6fyf43+Y8OPIfZWilNvQdZ2guxBbQhxewOMNkZnlpnWSHZfN2PRlMqpIgKh7NkIoUk1aju3etPuvL+UsQJrqEfpH0Ohomo5RMtLGmYbFkEuup4BQLT5yiixjNphxmG04TQ7sBhsyMqqqt4hSN3XB2nsMll7nHJJO0IsWfQbAtGm3VFr38ccfMWLEmYwadRaTJ19DXl4eS5Ys5uSTh2C3R5KU6rrOJZdcxsiRlS0piYlJ0dflo7V8Ph/XXDOBQCDAaacNZdSos+jevSfXXDMh2kZRlBoLQVdF+aK2+2MwKBVqAAoOPvUWQGeffTY333wzQ4YMaYrxtChUVcfgSsIQ9OIKBvEFwoRVjbY2ibcNlzBM/Y0kpRC79xc8we2ETW0JeL04YtxYzC78gTBeX4jMbDdpSQ5i7aYmc4aUJAnCgUoRYGrePiudoc2oJtl3gylvAQp7EQpI0BToOqBKtLK2wqyYyfbk4t0vVUSZX4/NaCHG7MJutGGQjJGCri3Y0lMTkqygOJJqb9iCyM/PZ/nyHzjzzLO5+OJLKqx77723WbjwUzZv3kTv3n1JTU3lyy8X8dNPP/LYY7Oj7Tp06MiOHdsrWHj+/PMPPvjgXW677Y4KCXzL+OWXn1i/fh2LFn1FQkICAEVFReTn51F2XerUqQtr1qyusN0//6xq8LF26tQFt9vNtm1badeufek+C9m5c0eD+xTUj3qbcvbs2VPlF+hwRdVllJhk7A4ndus+NX9+hp3nS0ZH37v3fgyApmqoJfnYygl/nz/Mrmw3BSUBFKVppqEkCfSgH32/JwotKoAkDOlnNMm+G0oFJ2jVB7peY7V7geBA0FSdGGMMGTGtibE6kSUJk8GIy+KgTUwq7WPbkOFMx2lwIWkKqqqJaL+DzJdffoGqqvznP+Pp2LFThX+XXXYFsizz3/8uQJIkRo48i5dffpHY2DiOPfa4aB//+c94li79mpdffoEdO7bz+++/8uCD9+F2l5CQkFjlflu1So7uf8+e3fz110puu+0mwuEwwWBkymrcuP+wdu2/PPvsM+zYsZ3vvlvKiy/OB2jQdatfv2Pp0aMn999/D6tXr2Ljxg3ce++d+P3+yAOtoMmptwA666yzeO2118jOzm6K8bQ4dB10kwODM54YpxWTMWLS7eKQKLAPI0+NzO+2DS3nq8zdAPg9HkyqJ9oWwB8IsyvHTV5x04ggSQJtPwdozZeJ7t0GgJLQB9mW0uj7rYo63zTKV4QXTtCCg4Cm6Zgw08aZRuuYFNrFtqF9TAZxpjgMuqk0ckuonuZi0aLPOO644yv4xZSRnt6Gk046hS+//AKfz8fIkWfi9/sYMWJUBbeMU08dyowZj7Bs2beMG3cB06ffw4ABA3nkker9kXr06MkNN0zlgw/e5aKLzmPGjPvo06cfp59+BmvXrgGgY8dOPPLI4yxf/gPjxl3Aiy8+x/nnXwjUPNVVE4888gStWiUzefI1TJ58DT16HE1KSkqD+xPUD0mv5699/Pjx/PHHH6iqSmxsbJVRYF9//XWjDrI+qKpGfn79bqYGg0xcXCSpXThc9RSVQdbQ83eStXsvBUV+dEDVdVZtWsAo5V0A3ncPZZ1zEle2A1dCIn5nG4o8FXO/mowKKYk2EmMsjVo53CCFCe/dTNi/TwSFM98nvO1FACy9pmHsdWu9n2jrcm6q3C5UQiBrW41JGbWiVQT/mRppn34hjkEzUU0xh8RTd0PPy+HOoXReZFk6aBGaB3Je4uPtKEr1z6p+v5/Nm7eQmJiCySSyEDcV//67BkVR6Nr1qOiyJUsW89BD97N06Y8YDPXzKCksLGD16n8YMGBgtMxHKBRi+PAh3HrrNEaMENFgDSEYDJCbu5eOHTtgKa3WUB319gFKTU3lrLPOavDgDlVUXUZxtSLO58PjCxMIhlEkiT7tR+Df8SkWvJxr/45Td4+lIJTINHMJdqcPj2IiXM7vJxhS2ZPjQdegVZyl8Zwqq3CAVvN+jL42pg1rnP00Jsp+iRAPAeEjOHw4WOJHcHiwYcM65s59mnvvfZAuXbqQmbmTF198jqFDh9db/AAoioG7757Gueeez5gx5xMOh3nrrdcxGk0MHDi4CY5AsD/1/tRmzpzZFONo8eg66EYblvhWxJR4ycl3R0zlBgeh2BFYCj/CJIW50vEpD+6dQFEowOOOAqyONEq8FZ/4QmGN3EIfLocJYyMkSyxzgC4vgPRgHnrJ2sh6WzsUZ3taXHrESokQm28oAoFAUBPnnDOGvLw8Zs9+nJycbOLi4hk2bHiDMzY7nU6eeOJpnntuHp9++jGSJNOr1zE8++zzxMaKkhoHg/rL1lI2b97M8uXLyc7O5j//+Q87d+7kqKOOwuFwNOb4WhSqqiPZ4olr5abY7YuUyAA8MWdiL/ocWQ9wof1r5pWcz/d5MVyzLIfZw+OQZWOlp01/MEyxJ0hSjOWAn0RlGbSgP1LMrGyseT9FXyuJJx5Q/01FeSfoSCmMsmJgQgkJBIKWhSRJTJgwkQkTJjZan/36HceLL77aaP0J6ke9naA1TePuu+/mzDPP5OGHH+bll18mNzeXefPmMXr0aPbu3dsU42wxqBoY41JITIqLlsjQlBi8zsgUk1UOMtG5EIAVBRoTv9yMtwqBo+tQWBKgsazw+v4O0OWmv5SEExpnJ/WhLlEMFRIhijB4gUAgEBw86i2A5s2bx8KFC5kxYwbLly+PRkzceuutaJrGU0/VXBvlUEfXISyZcbVKxeHc52Dljj0HvdSgNt71JelGNwDrCkNc978N7HEHKvXl84dx+0IHHPIoq0G08D5naz3sRiv6K/LGnIzk6HxA/TcZspmyr6AuMkELBAKB4CBSbwH00Ucfcf3113PeeecRGxsbXd6tWzeuv/76KstjHG5omo5kjycxJQWDIRLqrhkS8DojySENuo83Oy6mVWlARqY7yNTvt7K5sGI9orCqUVDiRzrAxNq6GqwQbaXl/xot0qokDG6xOSUkSdpnBSp1gm6hQxUIDgHEA4RAUJ/fQb1vvbm5uXTr1q3KdcnJyRQXF9e3y0OSUFjHlphKXEJMdJk79lz00lOa7v2c53v5aFfq5pLvD3PL91tZlVMxRN/jDeELNNw9WZYrO0CXz/4sxw9qcN8HhVI/IGEBEggahtFoRJIgEKhsZRYIjjQCgQCSVLfcTPV2gm7bti3Lli1j0KDKN9bffvuNtm2PnEJuqmwiJqU17mIPPl8A1ZiCz3EiNvcyZM1N28BXPHvM2dy+RmJ1sY43rHHn8m3c2b8Ng9IiCRT9QZUiT5DUeFuDymRIkoQW8EezD+pqAK3gt8hKgws55uhGO96mQFKsEdmjeuuRQVEgEJShKAqxsbEUFBQCZTWuhClVcKShEwgEKCkpJC4uFkWpvQ5dvQXQZZddxr333ksoFGLIkCFIksT27dv59ddfeeWVV5g2bVqDhn4ooqo65ph4kpIT2ZO5h1BYwx07Bpt7GQCOok9xuUbw5NFG7t9kZHlWiJCm8+AvO7i+Txoj2scDUOQOkBRjaeAlS6/gAK0VrgAtUudIiR+IJJV+CVrq9bAsEkz1VSrjIRAI6kZqaioAhYWFlJQ082AEgmZCkiAuLjb6e6iNegugsWPHkp+fz/z583n33XfRdZ2pU6diNBq58sorufjii+s96EOZcFjC1SoFLeAlJ7eEAG3w2QZg9f6CohZicy8F1xnM6iEx0+Zi0dZiNGD2yt2ENZ2zOibg94cp9gaJc5jrHRIvq0HC6j4HaC2/3PRXc0R/1ZOoBQhAlMMQCBqEJEmkpaWRnJxMKBSqfQOB4DDEaDTWyfJTRoPyAF199dWMGzeOlStXUlhYiMvl4phjjqngFH2koOs6qsmJMy4O1BB5xX7ccedh9f4CgKPwv3idQ9HDMKNPHE6LmffW5gDwyposTs2IxW5UKCgJEOesfxp7PRxAV9XSsaioeT9HVsgW5Ni+jXOQTUm5XEBayNNiDVUCwaGAoij1ugEIBEcyDU6E6HA4OPHElplg72CjaTqKIwGr102SLJMvd8Of3weLbyWGcA5W9w/4nEMIeoq5rV878rwhvtpeiDes8cXWfMZ2ScLrC+Pxh7GZlDq7wuzvAK0Xr4ZwUWRd3HFIyiFQF6h8OYyQWwgggUAgEBwUDjAA+8DJy8vj1ltvZcCAAfTp04eJEyeyefPm5h5WvYhUjLej2F0YZEiMtaCn7ZsKdBR+DLpK0OdHCRRzxTGp0Rv9J5vyCGkawZBKoTtYoapxbUQcoH1R5+Hy0V/NkvywAUiGctmgw24RBi8QCASCg0KzC6DrrruO7du388ILL7BgwQIsFgvjx4/H5/PVvnELQlV1ZGcCitmCDMSl9kOz9wTAGNqFxfMrACF3EV1jDQxMcwKQ6w/z3c6I1abEHSQQqntIvISKFoycJ13X9xU/lRTk+OMb6ciamPIWIOEDJBAIBIKDRLMKoKKiIlq3bs2MGTPo1asXHTt25NprryU7O5uNGzc259AahG6wYXDElWbz07G0/090nbNwAeg6fp8PQ7CYS3qkRNct2JiLruv4A2FKfKHI1FZdCAfRw6XTX55NEMgGQI7pjWQ4NGqyVagHFnLTcsPVBAKBQHA40awCKCYmhieeeIIuXboAkJ+fz2uvvUZKSgqdOnVqzqE1CFXVkB0JGMwRq4YU0xfZ0RUAY3AbZu+f6JpGyF1A/1Qb3eMjN/9txQH+yHKj6ToFxf46pQOUJCAcjPr/VEh+mDC4UY+rSalQEd7dfOMQCAQCwRFFnZygd+/eXa9O09LS6j2Qe+65hw8++ACTycT8+fOx2Wy1b1QNBkP9dJ2iyBX+HgiSbMEUk4AWDoKuobcdR3DNvQDEFH9EruNYgj4fzlAB449J4bZvtwARK9CA1i6CIRVvIIzLZqzRGVqWJfAFkdCQFLlC+Lsh6QTkcsciyTKKLCMZ5HrnGmzouVE0CVmRo5mxq0M22ve9Ub0oioR0oLVBDgKN+Z05nBDnpWrEeREIWh51EkCnnnpqvepJrV27tt4Dueyyy7jwwgt5++23ue6663jnnXfo0aNHvfuRZYm4OHvtDavA5bLW3qgOaNY0TJoXLeBFtw0hf3t7VPdWDL71xMsb8BmPRnUXcFZ6Es/GWNhe5OevHA+ZfpWjEi0EwjoxMbZaz7nfE8ZgNxP2ZOL1bAXAENsDV3zrig1lGZPNhNHVsPMC9T83YXcQxWYCreaQXL/NRbD0tUHyERPTcOHbHDTWd+ZwQ5yXqhHnRSBoOdRJAD388MPRm3FRURGPP/44AwcOZMSIESQlJVFYWMjSpUv57rvvGpwJumzK66GHHuLvv//mrbfeYubMmfXuR9N0iou9tTcsh6LIuFxWiot9DSpHsT+yLIHRSaCwGF1TUdL/D3XdQwCYs98n1PZoPG4vfv8uLuti44HfI5mbX/trF3cPyCAcCuO0GjDXYMlSJI1QcQlhX4DQzqXR5VLcYNyeijWBJFlB9QbxaJ4GWYAacm6UYAC/t2KR1qpQw/vqtQS9JRQXe1HVll8So7G/M4cL4rxUzYGcF5fLKixHAkETUCcBNGbMmOjr6667jtGjRzNjxowKbc466yweeughFi9ezIUXXlinnefn5/Pzzz8zfPhwDIbIUGRZplOnTmRnZ9f1GCoRDjfswquqWoO33R+DOQbZlEfIU4KUcBKS5VV0/270opXEsQVsHShxBxlidzPHLFMQ0Ph+ZxG7ugdIdegUFgdoFWepXgzoAdRgEE3VCOf8GF0sxQ9C2+8CK+kSqqahhrUGl9uq97nRdDRNQ6/lYq9L+56ItZCbcFg/pG6cjfmdOZwQ56VqxHkRCFoO9X6sWL58OSNGjKhy3SmnnMLKlSvr3Fdubi5Tp07l559/ji4LhUL8+++/dOzYsb5Da1FoKCiuJCTFgCQpKOkXRdeFd75NYowFh82IQQtzfmupdBv4eFMuug4FJQGq0wERB+hIAkQ9mI9e8m9kua0tsjW9iY+skSkXBk/YI/IACQQCgeCgUG8BFBcXx6pVq6pc98svv5CcnFznvrp06cJJJ53EjBkz+P3339mwYQPTpk2juLiY8ePH13doLQpN08Eag8EWCUdXWg0DU1JkXcEvqMWbcNqNGBSZc5JVLKWfxJJthRQHwvgDIUq8wSr9gCRJglCkBIaa9xOUxo1VG/0lRf9rcVQIgxd5gAQCgUBwkKi3ABo7dizPPvssTz31FCtXrmTbtm388ccfzJgxg1deeYXLLrusXv09+eSTDBw4kJtuuomxY8dSWFjI22+/3aBIspaGqkkozkRkgxFJNmJIvyC6Lpz5DhaTAavFQIxRYmRpWiC/qvFVZhFhVafAHagyJ5AsS6j+iFgoH/1VXfZnxWRFMtkbPP3VpIhEiAKBQCBoBupdC2zSpEmUlJTw8ssv88ILLwCRLMQWi4UbbriBcePG1as/p9PJ9OnTmT59en2H0uLRdR3d4sJgdxEsykNJHkF459sQKkTL/R7VvROXLRWfP8yFrVU+2R2ZBluwLotxPVrh8Ybw+sOYjfvpVC0cKYIadqMVlk45mpOQ7J0rjUGSFQyuBFTJwEFVQIoBSVbQqdkJumIeICGABAKBQHBwqLcAkiSJ22+/nWuvvZa//vqLoqIi4uLi6NOnzwHl7jlc0TQ9YgXyudGCYEg7j/D2lwGd8LYXMPeYjtViIE3VOCVJZ2kO5Ac0vtm4k5FHtaXQEyAtwV7RMTgcRAuraAW/gR4RGEr84Cqnyww2O9hiI1NyBxPFgqwY0AjU2EySDSAZQQ+hh70t00olEAgEgsOOBsdW2u12kpKScLlcHHPMMQSDwdo3OgIpK5RqsMcAoKSeDUrEL0jL/4ngukdxWmUMisxF5fyXX1uTjzmYi6ZqhMqJH0mKVIDX1fC+2l+AXMX0l2wwojgT0ag5F09ToEsysqWOeYdKrUC66oU65cEWCAQCgeDAaJAA+vTTTznllFM499xzueaaa9i+fTvTpk1jypQpQghVQflCqZLBjrHLbSBFRImW8zXK1iewmiW6OSV6R3QSO7zwv7V7UDzZBEPhqC+QJIEe8qOFfWgFv0caG1zIMUdX2q/B5gRrzMG3/hCxfMlmG1IdqttLpX5AelgIIIFAIBAcHOotgL744gtuv/12BgwYwJNPPommRawTw4YNY9myZcybN6/RB3k4UL5QqpIwCONR94IUmYFUc77BuecpDLLOxeWsQG9tC+PO3kMwb08FAaT5vWiFK0CNVIJX4gciSRWtPLLRhOxMrDaUvqnRdR1MViSDsfbGZX5AqhdJzIEJBAKB4CBQbwH03HPPcdFFF/Hoo49y+umnR5efd955TJkyhUWLFjXqAA8X9i+UqiQMxnjUfRH/F4C874jLeYoBcWHaleqBf4rhr7wghXt2EczfjUGRkLQQejiIVqH46aBK+zPYY9DNjub1qSn1A6oVQ2kkmBZEV4UFUSAQCARNT70F0NatWxk2bFiV64455hiysrIOeFCHK5piwuCMh9Jin0rCQIzd9okgY9GPJObO5uLW+yKn3t0Jfn+Agt27kNxZEA6ghgOo+aXJI2ULcuyxFfajmC3IrsRmLylRVz+girmAREV4gUAgEDQ99RZACQkJbN68ucp1mzdvJiEh4YAHdbiiqjqSPR6DZd8NX4kfgLHb/VERZHH/zMXSkySbQgD8kAc7vDputw9vbhZqYRZq/l8QKgRAjjsWSTHv24kkYXDEoxuaPyIv4gdkr90PqHw26KAQQAKBQCBoeuotgEaOHMkzzzzDl19+GXV4liSJ1atXM2/ePM4444xGH+ThhCoZMLgSkOR9PjtKfH+M3R8A2QSA3fsrryc/gZEQOvDBLgiGVdweH2FPMWruvuiv/ZMfGsxWZEd8i6inFfEDstTqB1TRAlTS1MMSCAQCgaD+AujGG2+kd+/e3HjjjfTr1w+A//znP4wdO5Z27dpxww03NPogDyc0TQdbLAZrRQuNEnccxm4PRkVQZ/135ic+jokQi/dCfkDH7Quhafo+/x9JQY4/PtqHJMsYXAloiumgHU+tKBbk2hyhywugkBBAAoFAIGh66p0I0WQy8dJLL7F8+XJ++eUXCgsLcTqd9O/fn5NPPrnKZHyCimgoKDHJaKEQatAfXa7E9YPuDxH8924kLcAQyx88m/AY1+Xdwke7TVxj1fAXbUAK7AVAjjkGyeCMbm+w2sEe3+y+P+XRJRnZbAdvDVNb5cthhDwttGqZQCAQCA4n6i2AJkyYwJVXXsngwYMZPLia4puCGtE0HckSgzFBg/zdqIFyIii2D6YeDxFcfReSHuBU65/MT3iU23ffxrg2JmKLfqDMvlO++KmkGMolPWw5AkjTdJTSfEC6VvW0XPkpMIQFSCAQCAQHgXpPga1YsUJYeRoBVdXQrXEY49NQzJYK65SYPnDUDHQp4tx8inUlj8XOYsmeAFLhz/vaJewrf2GwOsDaDCUvaqHMD6jGabAKFiDhBC0QCASCpqfeAujEE0/ks88+IxQKNcV4jigiIigWY8L+IkjHmtgXb7v7UaXI8pMsf3GSZzrG4DYAguYuZHvseP1hZKMJJSYRVW+hwtRQsyO0CIMXCAQCwcGm3lNgZrOZzz77jMWLF9OxY8dKBVAlSeL1119vtAEe7qiqjmKJxZggQd6ufdNhOtha9aEwdB+Wnfdjlfz0NG6Ibue398fjC+EPqCTaYrFixyIBSBGrSwtCpxY/IGEBEggEAsFBpt4WoL1799KnTx969uyJ1WpF1/UK/7Rq/DwE1aOqOrolBmNCaxRzmRjQsZkMKLFH87vrHko0a4VtfLZI9JfBbMYju9iyq4ite0oo8YeQZKlFTVPWmg+oQhSYEEACgUAgaHrqbQF68803m2IcRzwRS1BMOUuQD10Hp91I1+Tu3LPrXh50PIhT9lKkdEA1tQZJwuyKw62b8QdD+IMqbm8Qp8NEosuCw2ZE1/TmLYdBmR+QGdlgRA0GKq2XhAVIIBAIBAeZBlWDrw6v18v333/fmF0eUaiqjmZ2RSxBFitlViCrxUjv1C6Mzp7FY0XjuDL7Zr7M0jHZrKjWeLy+faUzQmGN/EI/2/YUs2NvCZ5AGEWRaXaDkMFcvR+QsAAJBAKB4CBTbwvQrl27mD59Or/99ls0E/T+rF279oAHdqSiaTqYXRjjgfxdqH4/TpuR01NCvLQ9jedKxgCwYj28vTvAZb2KOb6VA2U/hRMMaeQW+in2hHA5TCTEWHBYDM2WI0hHqdYPqEIYvHCCFggEAsFBoN4WoJkzZ7JixQrGjh1Lt27d6Nu3L1dccQVdu3ZFkiTmzp3bFOM8otC0MktQOgaLBZvZgN1q4PGe0Dd2X7ttJWHuX76da77e9P/t3XecVOXd///XOWf6zsw22KVXXYqAgICgEhAV/SZoYkl+d6JYbkksUW81xF6iRsVgBTVC7AopxoKkqNgLJWKNIqKAdFjYPjv1nHP9/jizwy47IIu7zMp+no/HMDPnzJy55trZnTfXdZ3r4u2NNdhZ+rqSKYsdVTHWba5lR00C3chNU9AexwHtMhGiEEII0dZaHIDef/99LrvsMq677jpOOeUUvF4vv/3tb3n22WcZPXo0r732WluUs8OxbYXtCeEq7oHhDRAOeOgXMrhvmMb9IwxGdt552vz6ugS3/mcDF772Ne9tqs16Flg8abFlRz07auI5CUGNxwE10zgASQuQEEKI/aDFAai+vp4BAwYA0K9fP1asWAGAYRj84he/YOnSpa1bwg5sZwjqRjg/jNfrLKB6ZK8CHjphADOO6sOgop3hYW1tgpuXreei11ezdEvzIJRMOSFoe3VuQpAzDqj5OmWapoPuBDoZAySEEGJ/aPEYoJKSEnbs2AFA7969qampYfv27XTu3JmCggIqKipavZAdmW0r8IRwF3enOGliqjr0YDH1SRhREmR45zw+KI/w5IpyvqyKAfB1TZwbl6ynrMDPmYNLGFUazJwWn0rZbN3hdDOVFPj366rxCgPdlwfRLMtdGAGw4yhTusCEEEK0vRa3AE2YMIF7772Xjz76iO7du9OlSxceffRRIpEIzz77LKWlpW1Rzg7NthWmO0ioa28KSzoR0/NIpizAmXhyVGmI+yb24+ZxvTmoYGfX2KrqGNctXsdlb63hg211mRahlOmEoG1VMQyjVU8E/Nb3oXsCWccBZU6FlxYgIYQQ+0GLv/0uueQSwuEw9913HwCXXXYZTzzxBKNHj2bhwoWcc845rV5I4YQH5Q0RKO1Nwmw+xkfTNA7vGuL+o/tz49he9MvfGYS+qIxxzXvruP39jSTTLT4p02ZrRT1bq6L7rTtsj+uCpc8Ec1qA2tdM1kIIIQ48Le4CKyws5JlnnqG8vByAk046iW7duvHxxx8zbNgwxowZ06LjVVdXc/fdd/Pmm28SiUQYMGAAv/nNbxg1alRLi3bAM00bt9tNYdhLdR3E42azqKBpGkd0CzO2a4j3Ntfy1BflrKt1Jh98a2MNkaTFDWN74XPpmKbNtoooAKWFfuz9cYq8y+OMA9p1QsSGFiBlgRVvMjBaCCGEaG373P9RUlKSuT1q1CimTZvW4vADcPnll/PRRx9x99138+yzzzJo0CDOPfdc1qxZs69FO6ApW9GtOI9+3fPpWhIk4HNnneRQ1zTGd8/noWMO4opRPfCmW3k+KI9w7XvfUJ/uQjNNm207omytjKHpbd8SlBkHtAtZEFUIIcT+1OIWoKuvvvpbH3P77bfv1bHWrVvHe++9x/z58znssMMAuP7663nnnXdYuHAh//d//9fS4nUIlmXj1jW6FgUoCnmpjiSpqo0TjaeaLXuhaxrH9CqgJODmhsXriJo2n1VEufKdtdx6ZB/yvS5My6a8oh6lFF2LAs7A6zZi2wrDE0DTDZRt7dzhatTik4qAt3OblUEIIYRocQBatmxZs23RaJTq6moKCgoYOnToXh+rsLCQuXPnNnmOpjkLedbW1ra0aB2OZdm4dI3SQj+FIS/VkQSVtXFiMbPZpIhDO+Xxh/F9uea9b6hNWnxVHWf622uZcVQfiv1uTEtRXul0h7VlCNo5DsiFldwZgJq0AKXqyfXKHUIIIQ5sLQ5Ar7/+etbtq1ev5qKLLuInP/nJXh8rHA4zYcKEJttefvll1q1bxzXXXNPSonVYlmVjaFBS4NsZhGriRONmkyBzcKGfO3/Ql6vf/YaKuMn6ugSXv7WGGeP70jXPg2UpyiuiKAVdiwOotmoJcnnQXZ6mC6NKF5gQQoj9qMUBaHf69+/PxRdfzOzZs/nRj360T8f48MMPufrqq5k8eTITJ07c57K4XC0b2tRwKvj+PCW8rXhcGl2KAnTK91OVaRFKYaXDTL9CP/dO6s9v31rD1voUW6MpfvPWGv4woS+9w86ZY5U1MTQdunXKw9Bbv250XUMLBLESO+f80V15NLQHGXZ9i3+G+9uB9JlpTVIv2Um9CNH+tFoAAggGg2zatGmfnvvqq68yffp0Ro4cyZ133rnPZdB1jcLC5oNs90Y4fGCdeVRclEciaVFZG6OiNk48YWHbioODPub+aDD/98qXrK2OUxE3ufzNtdw3uYyBnZy6iycVVfUpepWGgNavmxQFuFO1oJzT8qOBEKn0Pp9WT2gff4b724H2mWktUi/ZSb0I0X60OABt3ry52TbLsti2bRuzZs2if//+LS7E008/za233soJJ5zAHXfcgcfTfLmEvWXbitraaIueYxg64bCf2trYfp0ZeX8JuHW8hX7Kq2Nsr4ySMm0C4HSHvfMNq6pi1CRMLvz3Sm49qg9DOzvhI1IfJx5L0bdHAbZpkUpZe36hFjAsnUTSxk53g5nWzp95tGoLVnU065pm7cWB/pnZV1Iv2X2XegmH/dJyJEQbaHEAmjRpUmZZhcaUUvh8vhavBj9//nxuueUWpk6dyrXXXpv12C1lmvv2h9ey7H1+7vdB53wfhqaxdUc98aRF0GUw46g+3LB4HZ9VRImaNle9s5YbxvZiVGnIGRNUWY+lwG1o5Afc+DyGMynjd80mhgdNc2FbzvIdSts5caMV2Y5tW1itl7fazIH+mdlXUi/ZSb0I0X60OADddtttzUKKpmkEg0EOP/xwQqHQXh9r7dq13HbbbRx33HGcd955mTXGAHw+X4uOJb6dbSmKwz5chs7mHfVEYyny3Aa3HtmHW5atZ/m2CAlLcePi9Vw1pgfju+ejlLOSfEUkToXLIBzyUhT04vcZqO8QhBQaWuN1wRoNgrbjNWjJejC+H91gQgghvn9aHIBOOeWUVnvxl19+mVQqxaJFi1i0aFGTfSeffDIzZsxotdcSDsuyCQfcuEqDbNpeT119Ep9L53fjenHH+xt5Z1MtplLctmwDlx9mc0K/IsBZnCKWMIklTKpr44TzPBSGveT53KBocXeVbYPh3TkfUOPT4O1UHXa0FqMgJN0oQggh2kSLA9ALL7zQosfv6bT4888/n/PPP7+lRRDfkW0rAl4XvbqE2LQ9Qk1dAreuc/XonviMTSxaX40N3PnBJmKWzdQRPZo8P5G02J6MURNJkOf3UJTvIxxwo6WPvTeUUuB21gWzklbTpS+sKHa0FneoExZZ1g0TQgghvqMWB6Brr70WpVTm0qChW2zXbS2ZF0jsP7at8BgavUpDbDZ0KmviAFx+WHfy3AYvrK4A4IGPt7AxavKTfoV0CzQdnJ5M2SRTcerqk+T5XU4QyvNgaNpeBSHl8qYDULzpPEBWFCsZx5WoQ/cXt+nM1EIIITqmFgegefPmccEFF3DWWWdx0kknUVpaSnV1Na+//jp/+MMfuPLKKxk3blxblFW0MqWcxeB6dA5iGDoVVVFMC84f1oWAW2f+yu0ALFi1nQWrtjOqNMhP+hdzWGkQvdE4MNOyqYkkiURT+H1uisJe8oNePC79W7qwNGddsGgdWuMWIDOGsm2sSA16oKht3rwQQogOrcUB6Oabb+ass85q0nVVXFzMT3/6U+LxOE888QSnnXZaqxZStDGl6FYcwG3obKusJ5WyOWtwKfkeF499vpV4epX45dsiLN8WoUfQw4/7F3Nc7wL8LiNzGMtWRKJJorEUFTVx8kNe8vM8+D2uZi2GsMs4oF1agACsRBTjABoM7WRGDU1jD4PHd9/a1Y5nBRBCiO+dFgeg1atXM2TIkKz7evfuzfr1679zocT+p2xFSaEPl7HzNPmfHFTM5L6FvL65jmc+38rWqDNV4cZIkgc+2cJjn2/jhD6FnNS/mK55O7vHbKWoj6Woj6Wo8BjkBdwUBr2EAh4MHax0oGo8Dsi2PDjtUTakT423U8l2Pxha05zJN53bDa1iCls5gVApp7vRsm0sW6W3Neomblj1TMO5paW37Nycue91u3Abe9e9KIQQYs9aHIB69+7NggULOOqoo5rt++tf/8qAAQNapWBi/8t2mnzIY3D6kC5M6Z3P4o21PP91BZ/ucJawiJo2z31dwfNfV3B41xAn9y/m0M55TaZJSCQtEkmL2rokPq+LwrCXcOP5hAxnHJDWMA7IioC1cyLL9jIYWtOcgKPrGqalSJg2O6qj1EWSpFJOuDFNi5Rpk7JtbMsJeLatsJVzUbYTDpsFoEazSmi73GgckHweg9KiAPlBD7YlIUgIIb6LFgegCy+8kEsvvZRvvvmGY445hqKiInbs2MErr7zC6tWrefTRR9uinGI/2fU0+WjcafUxNI0juoU5oluYNTVxFnxdwesbqknaCgUs3VLH0i119Al7+XH/Yib1LMDXaD0v07KJRJPUx5L4PC6CeR4Kgx5n0HQgRCpaBy4/WBFUugUIyMlg6Iawo2katlIkTZtEygly0XiKRNLCVgqfz0NdXZzUd5jYTqH21OtF450R0yaZqiOa8FNS4EfXtHY9W7YQQrRnmtqHv6CvvfYaDzzwACtWrABA13VGjBjB5ZdfzmGHHdbqhWwJy7KprKz/9gc24nLpFBbmUVVVL7O0pum6RsK02VpRj6U06iLxTNdVg5qEyb+/qWLh6gp2xM0m+0Ieg2N6FnB0z3wGFPqzzvDtdumE8zx0CSRRlRtJfXA2KroOdC/ecS+iac74InewAL2kb6vNDL1rURrCjkI5Z7aZFomkTTSRIha3MNOz95qNuuEMQyMY9BHJUi9tTdMgHPRQWpxHyOduV92D8ruU3Xepl6KiPFkKQ4g2sE8BqEE8Hqempob8/Hx8Pt+3P2E/kADUejQN0HXq4ibbdkSoj6aytsKYtuK9zbUsWF3B5xXN12Hrmufh6J75HN2zgF4hb7P9pWENKtYRWH0NRu37ABhlV2GUHAuA7vLgLe2D6Qpgq3TBdimGUgoaBhc3alRRSqXvK2xnAyiwM/udz0wsYRJNmJgpOxN49vSLkcsA1MDnddG5wE+nAh+oVliepBXI71J2EoCEaH/2aTX4SCRCfX09paWlGIbBU089xebNmzn++OMZPXp0a5dR5IhS4NKgb7d8gj6DmroEVXUJYnGTRHJnc4xL15jQI58JPfL5qirGC6sreGtjDal0WNpSn2T+yu3MX7mdgwp8HN2zgIk98unkd8b1xG0XXs2gKm8KndIBKLn2Uaq10YAbiBKyt1HvKyGWbmlSmX8albdRwRv2p4dbZ8KBavRPw227nYSHloonTDZvjxBNpOhSFMDrNmSAtBBC7KUWtwB98sknTJs2jf/5n//hN7/5DTfeeCN//etfCYfDRCIRZs+ezTHHHNNW5f1W0gLUuhrXjW07rSzRuElNfZLaSJJ4wsTK8qUbSVq8t7mWNzZW80l5PbvWqgYM65zH0T3ymdy/iB56DTVbN1K05WZ8sY8BqC6eRjT/hwD4g0G0Tn3ZEWkfP5+2bgGylaIybrKlPsnW+iQVcZODC3yMLAlm7U4M+N2UFvkpDHlzOkBafpeykxYgIdqfFgegc845h1gsxsyZM+nUqRPjxo3jlFNO4YYbbuCGG27giy++4Jlnnmmr8n4rCUCta3d1Yxg6KcsmEk1RVZfIDA7OpiKW4q1NNbyxoYZVVbFm+126xvhuAY4piPMD3yq6b70CAMvIp7zngyjdj24YhLr1ptIK7vZ19qfWCEBx02Zr1Ak4WzKXFFvToSeZJVgOKvJzziGlHNo52Gyf26VTlO+jtCiAS8/N6fLyu5SdBCAh2p8Wd4F98skn3HPPPfTs2ZNXX32VRCLBj3/8YwB++MMf8uKLL7Z6IUX7Y1k2OlAQ9FAQ8hCLW9REk9TUJZq1ChX73ZxyUCdOOagTG+sSvLmxhtc3VLMpkgScMURvbKznjY3gN/ozu+gIjvYuxrBqeGPlP1hgnoZbNwms3IDhzUPZCreh4dF13LrmXAyNQq+LTn43nf1uOgfcBFx61taSfXq/tqIqYbIjlmJHLEVV0kQzDKKJFKa1c94fSymsdLeapRSWTXqb85iUrdgRS7ElkqQyYX77C+/ii8oYV7zzDSNL8jh7cCkDinZOIJkybcoro0QTJl2KAoQD7pyNTxJCiPauxQFI13W8Xmcg6zvvvEM4HGbYsGGAMzaovQyGFvtHQyuD163TtShAp3xfplUoFk+RSFlNxtf0CHk5Y1AJpw/szFfVcd7YUM2bG2uoTI/tiVnw+6qfM750KS7N5kTPAu6unEyVHQaS6cve8bv0dCByglEmHDW6nefWM6GkIm6yPR1wKmKNb6eojJvNuvHaitfQ6JLnoWvA41znefAaGs99XcH6ugQAH5bX82H5Go7sFuaswSX0Dju/d0pBXSRJMmlRXOCcLq+BnC4vhBC7aHEAGjJkCM888ww+n4+XXnqJiRMnomkaFRUV/OlPf9rtLNHiwNe4Vagw5CWWnjentj5JLGGSTFqZliFN0ygr9FNW6Gfa0C58WRPn7bVbeXdTPeVWN56NHsP/l7eIkB7l/NDz3F5zVovLEzNtNtQl2JAODdl4dC1rV1NbK/K56JqXDjgBN12DTtDpEvBQ5HNlbbma3KeQNzZU89SK8sys3O9trmXx5lqO6VXA1EEldEnPyJ1IWmzdUU8sblKU78zw7dJ1dEPD0MDQG7pUnAHgSu28LYQQHUGLxwB9/vnnTJs2jaqqKoqKipg/fz59+vRh3Lhx2LbNI488ktMQJGOAWtd3rRtdd2Y6TqZsogmTSNRZIiOZsppMIOhy6XRyx6nfshbLNNHNCko3/BpNJbFx83XX+4npndD9eZjh7pRHLFK2ImUrkpYiYdlUNWrBaXyd+A7dQBpQ4HXRye+iuHHrUcBNQdBLKpEC5UwUaejpcKFp6FpD0NCcfY1uF3hdTSaJbKmUbfPSN1XMX7k903IG4NI0/l/fQn4+oDPF/p0zZxu6M4O1rmtNbrtdOh6Xgdtl4DI0jHRIMoz0fkPPzGa9t+R3KTsZAyRE+7NP8wBFIhFWr17NwQcfTCDgjEF4+eWXGTlyJJ07d271QraEBKDW1Zp1s3MpCWfenUjcpK4+SSJpkUpZdApqWOVriEeduYRCFU8SqnkBgPrQMdR0/nWLB0MrpYik7J2BKLozGG2PpahKmATdBsW+nd1kOy8uinwu3HrzL5/2MA9Q3LR5cU0Ff/1yB5HUzrrwGhon9S/mZ2WdCHv2rpFX13YGo4aL121QGPISynPj0rW9ep/yu5SdBCAh2p/vNBFieyQBqHW1Vd04i4jq2MomlrCIxk0s20aVr6a2otKZpceso3TDBeh2FIXO9h73Ynp6ECoqIh7qRU19qtXK01LtIQA1qE9Z/P2rHTz3VQXxRrNCB1w6Py3rxA+65+N36XgNHa9LyxrodscwNPxeN4UhL+GgB59bd9Zw281blt+l7CQACdH+7NNEiEJ8Vw0zMAP43AYBrwvD0DH1YvzEsWyFrfxYiZ+ib30CDZuCmr9Q1+1K7ESMYEGCuNtFynQWGj1QGbqGy6XjMnQ0DeJJq9kXaJ7b4KzBpfy4XzF/XbWdhWsqSdmKqGnzxIpynlhR3vSYGngNHV86FPlcOj5Dc24bOt70/bLCAEf3zMeylLOGW7WLUNBDQchLnteFBjLxohDie0tagJD/te7J/qwbTQMjWUNi23qw0106dpzY+1MhWemUZ9j9EByIp7CEVKgriYSFmV6J3Vmw1May0stZWHabDeptixagTNhx6XjdBgGfG6/HwOs28Bg6aBCJp6iuSxCJpkgkzazvrzyaZN7K7byyrorvmk/8Lp1jexVwYr+izJlmHrdBwO+iKOQjFHDjMnZ2j8nvUnbSAiRE+yMBCPmjvSf7u25cOtiV60nWVGS2mVtexFw9CwA9fwSeoTNx+QK4u/TDxJNevR3AWb09ZdkkTZtUaueipinTxjJtTEthWXte52tvfNcAZBgaLmP3Ycfl0jJdTY1/RTVNQ9Ods7xq6p0wFE+YTRZqbbCxLsG/vqmkKm6SsBRx0yZu2SQari2Vub83Z8IN65THSf2LGNc1jCs9oNrvc1EQ8pGf58bnMdB1jYKC5p+XnSe1aZnbDWe6aek13GylMHStyRlprXF2WrbXa1yO3R9796/b0nJJABKi/ZEAhASgPclF3bhIYpavw4xFAFC2SfLD/0XFNwPgHnIHrqLReEt6YvuLd9sNo+taOhxpmKYiZTlnniVMm0TCJJo0sUyFZSs00l+Mmpa53fgLU9e1nQOFNXC7DPwBD7FoMuvr72n+RcNwQo/HbeBx6biMhrDTsi96w9CwbKiLpaiui1MfM0kkzH0Kd5ZSJC2bhOmEosq4yaJ1Vby2obrZWXTFPhc/7FvE/+tTSLHfjaaBz+MimOehU76PzsVBautimJaNbYNt29i2SndrKmwbTCvdUmcrUqadqcOGs9Pcho7bZWAYTn05gUt3zmTTnDPWGoKTs7jbzvumbaPs9ISUlsJSNpblvL5lK0zTJpVuIdTQMgFa0zV00iFTcwaGa5lLejtk9unps+WMRgPIDZyAqqcf31A+w9DJzw9IABKiHZEAhASgPclF3ei6hp6oJrl9I3bKmfjQ2v46qS9vA0ALluE59AE8oQKMkn6YLVgZI/NFpmkonC9iWynnixAFmoaOhqY7X6q6lr2lwDA08vMD1NREM2OZ9lZDq05r/eY1tArFkxa19U4YiiXMVumaiyQtFq2v4h9rKtkYaToJpaHBUd3zObFfEUOKA2iahs9rUJAfIFIfxzSdUGcrhbIbFp3d+b4Tls32aIpt6TPz/C6dniEv3YMep8uPRuGzSQB1rl2GjtvlXKcs22nlsxpez5l5286ELmdba1S5hhOWGoekhmCsZe7jtPClW/rcboNuJSFSiZQEICHaCQlASADak1zVjWFoULOVZNVWlG2jlE3y4wtQ9asBcA+8AXfXY/GU9sE08vZbuRq018+MYWiYtsrMxl0fS5FMWpkvfi3zT8N9rcn9xrsbuqXAuf6ovJ6FaypYtqWu2azYfcNeTuxXzHF9C+hckEddXYzquEl5NJW5bIsmnduxFOXRJNWJ7MlVA0oDbnqGvPQIeekZ9KZveyj0Zp8kcm9ZtiKSsoikLOKmTcp25pBKWg1zSjndgQnLJmUpkg3bLEXSdq4NTcPv1gm4dAIuA79LJ9Bw352+37DPrWNoGl6vwdCDS8BqPoj920gAEqJtSACi/X6ZtQe5rBuXbmNXrCdZWwWAVfkfUiuuAUDz98Az8hG8xd3QCnq0uBXmO5etnX9mGlologmTaHrNMa3J/vQ1mRvpcKRlQpJl2VTXJZstdLstmuSfayp56ZsqanaZiyng0ikNetgaSRJrg3rJc+v0DKaDUchLj6AHt65Tn7KoS1lEklYm4DTcrkvZ1KdvR3Pws/IaGgG3waSDOnH/lIHoLfyLKwFIiLbRrgLQnDlzePfdd3nqqaf2+RgSgFpXLutG08Cw4854oHgUpRTJ//4GVfupU7aDLsfX5zTcpf1I4f6Wo7Wu79NnRtebt5jsaeBvg4bunGjcpCaapDbSMODaeUzSsnlnUy0L11TwRWVsr8uj4Ywj6hxwUxrwUBJwZtiOpCw21iXYEEmysS6Rk7DS1v515kgO6xpu0XMkAAnRNtrNPEDz5s3j3nvvZdSoUbkuimgnlALb8OMqKMXesRHbTOHucy7JT/8PAHP9k5ilx+FK1KHvYTB0R7ev9eL830hlFrrtXOAsdFsdSVAfM9GSJsf0KuCYXgV8VRVj4ZoK3tpYi42is99NScBNid9NScBDaSB9P+Chkz/77Nq7vnZl3GRDxFnLbWNd0rmOJNgWbdkEmG5dI+g2CHoMgm6dkNsgz+N0VXl0HU96DiS3ruExtMw2j6HjadiWvu02dCxbETNtoqbTohRN2cRMi2jKJmqmb6e3R02LmGkTs2zG9i5iZLcwrTIQSQjxneU8AG3bto0bb7yRZcuW0adPn1wXR7Qztq3QAoW4wjGSVdvQw4egF43DrlwCyR2Ym57HCv0SI1C031Zr74gsy0YDwgEP+UEP8aRFXXTnafgHF/q5/LAe/GY0BIM+ovWJvRqE3XA2laE7rU2WlR7IDBT73RT73QzvHGzynLhpsymys6UISAcc3blOh51Q+trbyq0nGi3PMN9lDJAQom3kPAB9/vnnuN1uXnzxRR544AE2bdqU6yKJdsayFK78UtzJOKlINa7e/0uycimgMDf+GbPHjzEKStFcebKaeRtTSqEs8Bg6nfN9FId9ROIpaiJJItEkpmmjNxqk3HgRVmchVmeOo4ZpANzpOY9choGuOd1qqZRFImUTi5ukTCszd1PDxJY+l07/Aj/9C/yt8p4yp7WnzzDT0md06elQZhg7T803DB1D17BtsOydE26alspMZWArnDPOGm6n5zcSQrQvOQ9AkyZNYtKkSbkuhmjnLKVjFHTBTiWAvuglx2KXLwKzjuS6ebjD03EVeTE1l4Sg/aShay3kc5EfcJMw/dTHTEwg4HGW72gccFw6GOnV5ptOBbDz1HiXbqB5XOh6ev6mdLhIms4cTvGERSxpZk55Ny0b21KQOSU92zw+6UHhGk5rU8Oq97ozEaWRLpfeUL5GAcjQ9SbzDe362Wo+maONZadvp0/Bt9JP8noMErEWzNkghGhTOQ9AbcHlalmTd8MAQxlo2Fx7qhvdHcRbVEKiYguevucQ3/4GKBNr03OkepyKrik8xT2wdU+bh6D2VC/thc9tEPR7CIUaZsi2dzvfUeOJJpudh9+IM5cO+DxGJsyA0yqYTM/9Y1p2s1achnl5dE135udpNE9P4yCzs2x7npdp19mjd/cYQ3MC1a4MQyfgc2Mmzd0fQAixXx1wAUjXNQoL921emHC4dZrUD0TtpW5UqAdJw8asdaH1PpnYN8+AHYfN8/AU/gYjug13p54Yvv0zN1B7qZf2Jhj05boI7ZJ8XoRoPw64AGTbitraaIueYxg64bCf2trYfp9Ppr1rj3Xj8haRUrXQ9X9g/UJnwdT1C1Clp6D7u+GuT+Aq7o7tCtBWszy0x3ppD6Resvsu9RIO+6WlUYg2cMAFIGCfz7KwLFvO0NiN9lQ3lmZghEvREwmM7j/F2vAUKIvk1w/gHng9dp2zzlRDCGrL0+PbU720J1Iv2Um9CNF+yH8rxPeOUgrlDeHO74S71/8HLmdiObtyCcmPL8KuX4sZrcPcsRE9VZ91IkAhhBAdmwQg8b1kWTaEOuMp6IH7oEtBc2aCVtE1JD++EHPTs6SitZg7NqAn6yQECSGEaKJddYHNmDEj10UQ3yOmBe6CLnh7/RDN153UqttR0W9ApTDX/hG7ahkcfAUKhbuoO5ov3CorpAshhPj+kxYg8b1m6R5cBV1wFQ7GM/xBjG6nZvbZ1R+S+OiXJDe+TKpiI1qsWgaTCiGEACQAie8521YoXxh3ficMbxB3vwtwD7kDPMXOA8w6UitvJvbfm0hu+wotWiEhSAghhAQg8f1nWQpCpXhKeuMJF+HuNBbviD+hd/pB5jF2+SJiy84itvoltPodGIaMCRJCiI5MApA4IFi2wnIH0Tv1xlPSC09hLzyDb8JddiUYAQBUYiuJjy4m8v5NULtJQpAQQnRgEoDEAUMpZ2C05cnH6NwXX0lvfH1PxTvqEbTw0IZHYa5/mtpXT8XevBTD2PPyBkIIIQ5MEoDEAUcphWmD7S/AKOmLv+coAofPwdVnGmiG85jIKupeP5nER3ehx6txWVFcJHHpNoahYRh6ZjVwIYQQB552dRq8EK3JthU2Orq/GLevAHf4ChKlRxH/9AZUbD3YCeL/vZX4ygfQ/d3Q/N3QA93Q83pihHpjhPqhB3uhuX1guEB3o3QD0DLzCjmLeiIr0AshxPeMBCBxwMsEoUAnfAf/EE/JoUQ/upXUxr87D0hVY6eqoXYFVrNn62i+UjR/V3R/d/RAD4xgL9wFfYgV9QKC6EYYzXCBbqBpOko3UJru3FbQdPXxPa86LoQQYv+QACQ6DCcIGRiFvQlOuJ/EmsnEV87Brl8HyR27exYqvgUV34Jd9WFmawyobbijGeAKo7kbLvloHueiewrRfEXo3iI0XxGatwg92AMtUIpmuEF3oVRDMJJwJIQQ+4sEINHhWJbCwo374J/i7XcSykygkhHsunWYdeuw677Brt+AHd2Mim3Gjm0GK7L7AyoLUlWoVBV7nV8MP7qvC1qgO0awF3qwN3qoF3qoL0a4L5qvSMKREEK0IQlAosOyLBsLN7jcaO4QWrArnm5jAQ1QaLYJ6Ysdr8CudcKRVbcOO7oBw6olFa9CJWtRqRpUqhas6F6+eAy7fi3Ur8XanmW/K4QeSHe5hXqhBxvCUT+0/L5g5GVCkZJkJIQQLSYBSAh2HZvTcMNwurcML1owDz3UG50f4NY0DEMjHPRQVxvFsixQNijbaU1KVDqXeCUqUYWdvqhEFSpRiR11WpVUfCvYyewFMuuwa1di167E3Np8t+btjB7s6bQcBXtjhPqih/uih/uDvxTQpdVICCH2QAKQEHuhaUuLQtN0NMOFhYGp0qfKa4DbB+58tFBfNDR0DYz0s3aeUq/QlAVWCrt+C3bdWuy69diRdVj1G9LdbxtR8XKney1beRLbsRLbsSo+bL5T96AHeqRbjXqj+Tqhu/LAHURzBcGdh+YOgjuInr7GyENz54HubXJW266DuIUQ4kAhAUiINpA9ODS+o4PmhWAfCPbB6AYureHUeqcLDiuBiqzHiqzDrv0GO7IOO7LeudRvQCUrs7+4ncSOrMGOrIEsrUd7pBlgBNBcAadb0BN2Lu58NG+BM7jbW9jousAJfJ4weArAKEi/B3Yzh9Kek5QELSHE/iIBSIh2YNcWJocHggehBQ/C6AKuRqFC0zQwI86A7do12HXrsCLfYNetw653AhJWfB8KYoFZhzLrUPFt+/ReqjUdJ+DpQHqipMx90LSG23p6nwaajmb4nLPoPCFIhy/dHYZ0CMNTgO4OgbshlDm30T2NXr2hNU5rer/RNi29TVkJlFmPZkVRqXqUGQWzHsx67FR9uh6ikIqgUlGUGXEer7mdMOgtdMqUvtY86YDoKQB3AbhDmYk3hRDtjwQgIb4nmoUkLQDhwWjhwRg4XW3Od3y6BSmxHTuyHlI1qFQElYqkv8wjkKrHNiNg1qe//J0vecx6VKNtmPX7UFAbsHfb2LO7Rp4Dr/FHA3cI3eO0nqm+x8Hgq5EJ+IVoHyQACXEAaeh6A8DTGa2oM9CkHWSPdvZaOWukKTuFStY6ISpZC8ka54y3ZLUzqDtZg0rWQLIGO1kDyWp0zUoPDFeo9OBwUNmv07eVssGMoVJ1oMzWq5CcUpCqxU7VQv0Gair/S7BkMhSNznXBhBBIABJCNNL4TDjntgs8ReApQstz9uwpTLlcOoWFeVRV1WOadote2xl8rZyuO7MuHbxq061WtelLXSZwqWQdKlXjTFWQLnPTN6F2/rtr96JSaIbXGRDuCjiDwl0BNFcQzR10BoQbec42d8AZJO5y7qNMVKI6HQqr02GwMn3dEAyr0+Gw4boWb5dRGEVDs8w2LoTIBQlAQoh2wckoGhh+MPxo3pLMvva2JK3m7+5c7+XjXS6NwsIgVVX10MJgKIRoG9IZLYQQba69RTghhAQgIYQQQnQ4EoCEEEII0eFIABJCCCFEhyMBSAghhBAdTs4DkG3bzJo1i/HjxzN8+HB++ctfsmHDhlwXSwghhBAHsJwHoAcffJD58+dzyy238Je//AXbtpk2bRrJ5G5WyRZCCCGE+I5yGoCSySSPPvool1xyCRMnTmTgwIHcc889bN26lVdeeSWXRRNCCCHEASynAWjlypXU19czbty4zLZwOMzgwYN5//33c1gyIYQQQhzIchqAtm7dCkDXrl2bbC8pKcnsE0IIIYRobTldCiMWiwHg8XiabPd6vdTU1OzzcV2uluU6w9CbXIudpG6yk3rJTuolO6kXIdqfnAYgn88HOGOBGm4DJBIJ/H7/Ph1T1zUKC/P26bnh8L69ZkcgdZOd1Et2Ui/ZSb0I0X7kNAA1dH2Vl5fTq1evzPby8nIGDBiwT8e0bUVtbbRFzzEMnXDYT21tDMuShQobk7rJTuolO6mX7L5LvYTDfmk5EqIN5DQADRw4kGAwyLJlyzIBqLa2lhUrVnDGGWfs83HNfVxt2bLsfX7ugU7qJjupl+ykXrKTehGi/chpAPJ4PJxxxhnceeedFBUV0b17d2bOnEmXLl2YPHlyLosmhBBCiANYTgMQwCWXXIJpmlx33XXE43FGjx7NI488gtvtznXRhBBCCHGA0pRSKteFaE2WZVNZWd+i57hcOoWFeVRV1Uvz9C6kbrKTeslO6iW771IvRUV5MgZIiDYgv1VCCCGE6HAkAAkhhBCiw5EAJIQQQogORwKQEEIIITocCUBCCCGE6HAkAAkhhBCiw5EAJIQQQogO54CbB0gphW23/C0Zhi5rF+2G1E12Ui/ZSb1kt6/1ousamqa1QYmE6NgOuAAkhBBCCPFtpAtMCCGEEB2OBCAhhBBCdDgSgIQQQgjR4UgAEkIIIUSHIwFICCGEEB2OBCAhhBBCdDgSgIQQQgjR4UgAEkIIIUSHIwFICCGEEB2OBCAhhBBCdDgSgIQQQgjR4UgAEkIIIUSHIwFICCGEEB1Ohw9Atm0za9Ysxo8fz/Dhw/nlL3/Jhg0bcl2snNu2bRsDBgxodnnuuedyXbScmTNnDlOnTm2y7YsvvuCMM85g+PDhTJo0iSeffDJHpcudbPVy3XXXNfvsTJo0KUcl3H+qq6u54YYb+MEPfsDIkSP5+c9/zvLlyzP7lyxZwimnnMKhhx7KCSecwD//+c8cllaIjs2V6wLk2oMPPsj8+fOZMWMGXbp0YebMmUybNo2FCxfi8XhyXbycWblyJV6vl1dffRVN0zLbQ6FQDkuVO/PmzePee+9l1KhRmW1VVVWcc845TJo0iZtuuomPP/6Ym266iby8PE499dQclnb/yVYvAF9++SXnn38+Z5xxRmabYRj7u3j73eWXX8727du5++67KS4u5qmnnuLcc8/l+eefRynFeeedxznnnMPMmTN58803ueKKKygqKmLcuHG5LroQHU6HDkDJZJJHH32U6dOnM3HiRADuuecexo8fzyuvvMKUKVNyW8AcWrVqFX369KGkpCTXRcmpbdu2ceONN7Js2TL69OnTZN/f/vY33G43N998My6Xi/79+7Nu3Trmzp17wAegPdWLUoqvv/6aX/3qV3Tu3Dk3BcyBdevW8d577zF//nwOO+wwAK6//nreeecdFi5cSEVFBQMGDOCyyy4DoH///qxYsYKHH35YApAQOdChu8BWrlxJfX19kz8+4XCYwYMH8/777+ewZLn35Zdf0r9//1wXI+c+//xz3G43L774IoceemiTfcuXL2fMmDG4XDv/HzF27Fi++eYbduzYsb+Lul/tqV7Wr19PNBqlX79+OSpdbhQWFjJ37lyGDh2a2aZpGpqmUVtby/Lly5sFnbFjx/LBBx+glNrfxRWiw+vQAWjr1q0AdO3atcn2kpKSzL6OatWqVVRWVnL66adzxBFH8POf/5y3334718Xa7yZNmsTs2bPp2bNns31bt26lS5cuTbY1tJht2bJlv5QvV/ZUL6tWrQLgqaeeYtKkSRx77LHcfPPN1NXV7e9i7lfhcJgJEyY06Tp/+eWXWbduHePHj9/t5yUWi1FVVbW/iytEh9ehA1AsFgNoNtbH6/WSSCRyUaR2wTRN1qxZQ01NDRdffDFz585l+PDh/OpXv2LJkiW5Ll67EY/Hs352gA79+Vm1ahW6rlNSUsJDDz3EVVddxbvvvsuFF16Ibdu5Lt5+8+GHH3L11VczefJkJk6cmPXz0nA/mUzmoohCdGgdegyQz+cDnD8+DbfB+fLy+/25KlbOuVwuli1bhmEYmXoZMmQIX331FY888oiMV0jz+XzNvrgagk8gEMhFkdqFCy64gF/84hcUFhYCUFZWRufOnfnZz37Gf//732ZdZgeiV199lenTpzNy5EjuvPNOwAnHu35eGu535L83QuRKh24Bauj6Ki8vb7K9vLyc0tLSXBSp3cjLy2sSCgEOPvhgtm3blqMStT9dunTJ+tkBOvTnR9f1TPhpcPDBBwN0iK7lp59+mosvvpijjz6ahx56KNMq2LVr16yfl0Ag0GHPrhQilzp0ABo4cCDBYJBly5ZlttXW1rJixQpGjx6dw5Ll1ldffcXIkSOb1AvAZ599xkEHHZSjUrU/o0eP5oMPPsCyrMy2pUuX0rdvX4qLi3NYsty64oorOPvss5ts++9//wtwwH9+5s+fzy233MLpp5/O3Xff3aTLa9SoUfznP/9p8vilS5cycuRIdL1D/ykWIic69G+dx+PhjDPO4M477+S1115j5cqVXHbZZXTp0oXJkyfnung5079/f/r168fNN9/M8uXLWb16Nbfffjsff/wxF1xwQa6L126ceuqpRCIRrr32Wr7++muee+45Hn/8cc4777xcFy2njj/+eJYsWcL999/P+vXreeutt7jmmmuYMmXKAX1m4dq1a7nttts47rjjOO+889ixYwfbt29n+/bt1NXVMXXqVD799FPuvPNOVq9ezaOPPspLL73EtGnTcl10ITqkDj0GCOCSSy7BNE2uu+464vE4o0eP5pFHHsHtdue6aDmj6zoPPfQQd911F5deeim1tbUMHjyYxx57jLKyslwXr90oLi7m4Ycf5tZbb+Xkk0+mc+fOXHHFFZx88sm5LlpOHXPMMdx7773MnTuXP/3pT4RCIU488UQuvfTSXBetTb388sukUikWLVrEokWLmuw7+eSTmTFjBg8++CAzZ87kiSeeoEePHsycOVPG1AmRI5qSCSiEEEII0cF06C4wIYQQQnRMEoCEEEII0eFIABJCCCFEhyMBSAghhBAdjgQgIYQQQnQ4EoCEEEII0eFIABIHlLac1UFmjBBCiAOHBCCR1aRJk7jqqqtyXYwW+eqrr/j5z3/e6setra3liiuuYPny5a1+7PZm48aNDBgwgOeeey7XRRFCiDbV4WeCFtndf//9BIPBXBejRV566SU++uijVj/uF198wYIFCzj11FNb/dhCCCFyQwKQyGrw4MG5LoIQQgjRZqQLTGTVuAusoVvk3//+N5dccgkjRoxgzJgxXHfddUSj0W891po1a7jooosYM2YMo0eP5rzzzmP16tWZ/XV1ddx+++0ce+yxDB06lClTpvD3v/+9WXlmzZrFHXfcwRFHHMGwYcM499xz+eabbwCYPXs2999/PwADBgxg9uzZANi2zdy5cznuuOMYMmQIxx9/PE899VTmuJ999hmHHHJIk+6+iooKxo0bxznnnMPSpUs588wzATjzzDOZOnXqbt9nIpHgD3/4AxMmTGDIkCGceOKJ/Otf/8rsf+2115qUDWD16tUMGzaMa665JrPt1Vdf5Re/+AUjRoxgyJAhnHDCCcybNy+zf9myZQwYMIAlS5YwdepUhg0bxsSJE3nmmWcoLy/noosuYsSIEUyYMIHHH3+82fPeffddTj/9dIYNG8bkyZOZP3/+7n94wObNm7n88ssZM2YMhx56KGeddRYrVqxo8ph//OMfnHTSSQwbNoyxY8cyffp0tm3btsfjCiFETikhsjj66KPVlVdeqZRSasOGDaqsrEyNHj1azZgxQy1evFg99NBDasCAAerOO+/c43G2bt2qRo0apX70ox+pf/7zn+qNN95Qp5xyijryyCNVVVWVisViasqUKWrcuHHqz3/+s3r77bfVDTfcoMrKytQf//jHJuU57LDD1K9+9Sv15ptvqgULFqgxY8aon/3sZ0oppbZs2aKuueYaVVZWpj766CO1ZcsWpZRS119/vTrkkEPUrFmz1DvvvKPuvvtuNXDgQHX//fdnjn3PPfeosrIytXjxYqWUUhdeeKEaM2aM2rp1q6qrq1NPP/20KisrU08//bT66quvsr5P27bVueeeq0aMGKEee+wx9fbbb6vrr79elZWVqeeffz7zuOnTp6tDDjlEff311yqVSqlTTjlFHXvssSoSiSillHrjjTdUWVmZ+v3vf68WL16sXn/9dTVt2jRVVlamPv74Y6WUUkuXLlVlZWVq7Nix6tFHH1WLFy9WZ599tho0aJA6/vjj1b333qsWL16sLrroIlVWVqY++eSTJs8bNWqU+v3vf6/efvttdeONN6qysjI1b968Jj/rZ599VimlVEVFhRo/fryaPHmyevHFF9WiRYvUGWecoYYPH66+/vprpZRSy5cvV4MGDVKzZ89WS5cuVS+88II68sgj1emnn/5tHzMhhMgZCUAiq2wBaPr06U0eM3XqVDVlypQ9HmfGjBlq2LBhqry8PLNty5YtauLEierNN99U8+bNU2VlZerDDz9s8rxrrrlGDR06VFVVVWXKc/TRRyvTNDOPmT17tiorK1OVlZVKKaVmzZqlysrKMvvXrFmjBgwYoObMmdPk2Pfcc48aOnRo5nnJZFKdeOKJ6vjjj1fPPvusKisrU//+978zj28IDkuXLt3t+3z33XdVWVmZ+uc//9lk+/Tp09WRRx6pUqmUUkqp6upqddRRR6kzzzxTPfjgg2rQoEHqo48+yjz+T3/6U6beG1RVVamysrLM+2goz8yZMzOP+fjjj1VZWZn67W9/m9lWWVmpysrK1GOPPdbkeVdffXWT419wwQXqyCOPVLZtNwtAd999txo6dKjauHFj5vGJREIdc8wx6uKLL1ZKKTVnzhw1YsQIlUgkMo9588031ezZs5Vt27utMyGEyCXpAhN7bfjw4U3ud+nSJdMFZts2pmk2uQB88MEHDB8+nM6dOzd53htvvMGECRP4z3/+Q/fu3RkxYkSTY5900kkkEgk++eSTzLahQ4diGEaT4wDEYrGs5V26dClKKSZNmtSkXJMmTSKRSPDBBx8A4Ha7ueOOO9i4cSPXXnstJ598MieccEKL6mbJkiVomsaECROavdb27dv56quvAMjPz+eWW25h6dKlzJo1iwsuuKBJvU6bNo0ZM2ZQX1/PZ599xr/+9S/mzJkDQDKZbPKajeusuLgYgEMPPTSzrbCwEHC6GBs7+eSTm9yfPHky27dvZ+3atVnf16BBgygtLc28J13X+cEPfsDixYsBGD16NLFYjClTpnDXXXexfPlyjjrqKC666CI0TWtRPQohxP4ig6DFXvP7/U3u67qemRvngQceyIzBafDll19SXV1Njx49dnvMmpqaJuGoQadOnQDnFPQ9vT444Sub6upqAH70ox9l3d94jMqgQYMYMGAAn332GUcfffRuy7s71dXVKKUYOXJk1v3l5eUMGjQIgCOOOIKSkhLKy8ubvVZlZSU33ngjr776Kpqm0bt3b0aNGgU0n4co21l6u9ZRNqWlpU3uN4SnbD+L6upq1q1bxyGHHJL1WLFYjBEjRjB37lwef/xxHnvsMebOnUunTp04//zz9zhmSgghckkCkGgVP/vZz5g4cWKz7aFQiMrKymbblyxZQo8ePcjPz2fdunXN9m/fvh3Y2YqxL8LhMABPPPEEeXl5zfZ369Ytc/uvf/0rn332GQMHDuTWW29l3LhxmefvjVAoRCAQ4Mknn8y6v3fv3pnb999/P9XV1fTr14/rrruOZ555BrfbDcD06dNZs2YNjz/+OCNGjMDj8RCLxfjb3/6212X5NlVVVfTq1Stzv6KiAtgZhHZ9X2PGjOGKK67IeiyPxwPA+PHjGT9+PLFYjKVLl/Lkk0/y+9//nkMPPZRhw4a1WtmFEKK1SBeYaBWlpaUMHTq0yQVg1KhRfPLJJ01CUEVFBdOmTeOtt95i9OjRbNq0qdn8PS+++CJut7tFX54NLUINGlpOqqqqmpSrsrKS++67L9NCtGnTJu644w5OO+00HnroIerq6rj11lszx2nc7bY7Y8aMIRqNopRq8lqrVq3igQceyHQJfvrppzz88MNccMEFzJw5k1WrVvHHP/4xc5wPPviAyZMnc/jhh2fCxdtvvw3svqWrpV599dUm91966SW6d+/eJBQ1fl9r166lb9++Td7XggUL+Pvf/45hGNxxxx2ceuqpKKXw+/0cffTRXHnllYBzBpkQQrRH0gIk2tTZZ5/NCy+8wLRp0zjvvPNwu9388Y9/pEuXLpx44ol4PB7mz5/Pr3/9ay655BJ69OjB66+/zrPPPstFF13UolaYhsf+4x//4NBDD2XAgAGcdNJJXH/99WzatIkhQ4awdu1a7rnnHnr06EGfPn1QSnHttdfi9/u54ooryM/P59JLL+W2227j+OOPZ9KkSYRCIQDefPNN8vPzGThwYLPXnjBhAqNHj+bCCy/kwgsvpH///nz66afMmjWL8ePHU1RURDKZ5KqrrqJ///788pe/xO12c8YZZzBnzhyOPfZYBg8ezLBhw1i4cCGHHHIIXbp04cMPP2Tu3LlomrbbsU4t9dhjj+H1ehk+fDivvPIKb7zxBnfddVfWx5599tksWLCAs88+m//93/+lsLCQf/3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", 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" ] @@ -510,12 +542,12 @@ "output_type": "stream", "text": [ "Processing: scale-x=3\n", - "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -528,12 +560,12 @@ "output_type": "stream", "text": [ "Processing: scale-y=0.333\n", - "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -546,12 +578,12 @@ "output_type": "stream", "text": [ "Processing: scale-y=0.5\n", - "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "data": { - "image/png": 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", + "image/png": 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vv/zMVVddzYgRo6r1ugsEVUEI0TMESZIM/1BdL2URlZGjWhjPbJEYUW7CIioQnIks3pDOqOWbyCisGSGa6DDzQo+mXNc8qVrH3bNnD5mZGbz11jt4vV42blzPo4+O5P77H+TCC5/H5XIxc+Y0nn76ST799AvMZqPU8bvvvs0tt9zG668vYMeO7YwZ8yj16tVjwIBB/PjjCubNe51nnhlPvXr1WLt2DWPHjiE1NY3LLrsch8PBo4+O5PXX51O3bl3279/H3XffSceOnZgx41W8Xi8vvzyFIUMG8vbb7+FwOAH49NNPeOqpcZx1VmMSEhL444/f+eqr5fTpcxNvvvk2X365nNmzZ7J8+TLuv/9h0tLSeO65sUyc+Dzz5r2Drus8+OD9WK1WJk16CafTybJlSxg06C5ee20eTZs2A2DVqpXcdFM/5s9fiKqKmABBzSKE6BmCpAdQfR50Xw66e5fRFtkESbEbZT2tEcI/VCA4gxm+dCP53kCNHT+j0M/wpRurXYgC3HXXQGrVqg3Af/9t4uGHR3HDDX1D22+6qR8PPjiM7OwskpNTAIJWUsP/tG7dupx3XmfWrFkNwN69ezCbLaSmppGSkkpKSiqJiUmkpKRgs9mIijKKBMTExOJ0RvLmm69jt0fw1FPjsFgsADz//AR6976GZcs+p0+fGwFo3LgpPXpcGTb36OgY7r//QWRZpl+/W5k9eybdul3OhRdeBEDPntcydeokAP766w/+/XcNX3zxLdHRxhyGDBnGmjX/sGjRQsaMeTo07t13Dy7j1iAQ1ARCiJ4pBHxG/tD8taGm0v6hktkmynoKBILTkjp16oaeN2nSlKioKN5660127NjOnj272bx5E0CYdbBevfphYzgczpDv5xVXXMVnny3mxht70aBBQzp2PI9LLrmMlJTUco+/desWmjc/OyRCAeLjE6hbtx5bt24pNc86ZfZNS6uFLBuZFu12OwC1a9cObbdaraGk4Zs2bUTXdXr1uipsDJ/Pj9frC72OjY0TIlRw0iCE6BmAJEng96Cr/vL9Qy1WNMUCqlCiAsGZytSezU6Kpfnjgc1Wkh955cq/GT58KOeffwGtWrWmR48r8Xo9PPLIQ2H7mM2WQ4cJBXPGxMQyf/67rF27hj/++JXffvuVRYsWcvfdgxkwYFCF+x2KpmmYTCU/w1Zr2TzOpbcXI0nlpwDXNB2Hw8mbby4os620CBYVtQQnE0KIngHIMmg+N7qmlRKiMnLUOSVlPYUGFQjOaK5rnsTVTRNPq2Cl8njnnfm0bdue8eMnhdree+/d4LPKfRF+8cXnuFwF9OlzE61atebuu4fw3HPP8NVXy8sVomed1Zjlyz/H5/OFBGFWVhZ79uzmhhtuPOZzKqZRo0YUFroIBAI0aNAw1P7cc8/QuHFj+va9udqOJRBUF0KIniHonkJ0fx560Q4AJOdZSCYHsmISZT0FAgEAiiyR4ChrCTydSE5O5ocfvmf16lUkJSWzcuWfzJ49EwCfz3eEvQn1mzZtKg6Hg1at2pKefpBVq/6mdeu25fbv3bsvH330AU8//SR33jkguP8UoqNj6N69R7WdW6dO59OkSVOeeGI0Dz00kqSkZD788H2WLv2Ul16aUW3HEQiqEyFEzwBk1UdA9aPllfIPLa4vb7aA2S6KKQkEgjOCQYOGkJWVxYgRDwBGUNLjj/8fTz31JBs2rKN+/QZHHOPaa3uRl5fL3LlzSE8/SGRkFJdc0o377ru/3P5paWnMmjWH6dNfYuDAO7FYzHTs2ImnnhpHZGT1+WoqisLLL89k2rSpPP74KNxuDw0aNGD8+Em0b9+x2o4jEFQnki4ymKOqGtnZhcf1GPvmrCZQ4ENxWqg1qHW1jx/I2EzOC+0AsLbrR1S/VwGjrKfiK8B7cAe+zS+j7vsIAHPzZ1DiO2OJSUSKq3vcU3iYTDKxsQ5ycgoJBM481SvOX5z/iTz/uDgHilK+HyGAx+Nh69ZtJCSkYLEIf0GBQFC9+HxeMjMP0KhRwzAf7fKo+JtKcFpQEqhUOpG9hBx9Lsgyss0hluUFAoFAIBDUCEKInubIsoTqKUQPFKAXbgNAcjREMjlRRFlPgUAgEAgENYgQoqc7qh/d70XL+5fiiNCQf6jJAiaxLCcQCAQCgaBmEEL0dCcQrC+fvybUJEcF84fanejiIyAQCAQCgaCGECrkNEaWJQh40AKHJrI/N1TWU/iHCgQCgUAgqCmEED2NkSQJzetG97vQXUYZOSmiPpI5OljW0y7KegoEAoFAIKgxhBA9jZFQ0bxFaPn/UpwotCR/aLCsp0AgEAgEAkENIYTo6UzAhx4IoOWV8g8N1pdX7E5hDRUIBAKBQFCjCCF6miJJGP6hqh8tv5R/aFRLZJMZhH+oQCAQCASCGkYI0dMUSZLQfR40nwu94D+jzV4XyRJrCFGTvYZnKBAIBAKB4ExHCNHTFEkCzVOIlr+OEv/QYNomawS6bKrB2QkEAkH10KtXT+bMeaVG55CXl8unn35y2D4ZGRk8++xYrrmmBxdc0JGrr76cp59+kj17dp+YSQoEJylCiJ6myKoPLeA7xD+0FUgyst0hqikJBAJBNfHyy1NYtmxphdt9Ph/33ns32dmZPPfcBN5//xOeeeZ5Dhw4wKBB/yMnJ+cEzlYgOLmocbOYpmlMnz6d999/n4KCAjp06MCYMWOoU6dOuf3XrVvHhAkTWLNmDVarlcsvv5yRI0cSGRl5gmd+cqMXByqV9g+NbolsNoPZLvxDBQJBGXRNRffVjCiSLLFIslIjxz5WjnRf/8cfv7F79y7mzn2LqKgoAFJT05gw4UV69uzOl19+wU039TsBMxUITj5qXIjOnDmTd955h/Hjx5OSksLEiRMZOHAgn332GRZLeHqhzMxM7rrrLi677DKeeuopcnJyePLJJxk9ejQzZsyooTM4+ZAkwO9B9bnQCzYZbbZaSJZ4o6yn2Q5qzc5RIBCcXPh2fIL790fQPRk1cnzJloj9vAlY6veq9rGXLFnM/PnzOHBgPykpqfTu3Ye+fW9Glo1FwdWrVzJnzits2LABv99HWlot7rxzAFde2ROA7OxsJk0az99//4XH46ZJk2YMGXIfbdu2Y+zY/+Pzzz8DoFOntvz228oyxy8+zs8//xgaEyAyMpIFCxYRExMbavvhh+959dWZ7Nmzm6ZNm3PVVT0ZP/7Z0Li9evWkZ89ruPvuwaF9Dm1bvPhj3ntvIXv27EaSJJo2bcbw4SNo3vzsUP9LL72MX375iZycHJ5/fiJt2rRlwYJ5fPzxh2RlZVG3bl1uvbU/V1xxVeg4b7/9Fh999AHp6QdJSEjkmmuu4667BiJJ0rG/SYIzlhoVoj6fj9dff50RI0Zw8cUXAzBlyhS6du3Kl19+ydVXXx3Wf+/evVxwwQWMHTsWk8lEgwYNuPHGG5kyZUoNzP5kRgrmD10PegAo5R9qcwTLemo1OD+BQHCyUfTLA+DPr7Hj654Min55oNqF6CeffMjMmdMZOXI0Z5/dgk2bNjF58gukp6czbNhw0tPTeeCB++jb9yZGj36CQCDA/Plv8txzY+nYsRPx8fFMmPAcfr+PWbPmYDabefPNuTzyyIN89tlyHnpoBF6vh/T0g4wfP6ncOXTocB7Nm5/N008/yRtvvEaHDh1p3botHTueR9269UL9Vq9eyahRD3PXXQPp0eNK/vzzd15+eWqVzvf7779l8uQXePTRJ2ndug1ZWZlMnjyB554by/z574b6ffDBIiZNeonIyEgaNTqLV16ZzpdfLmfEiFHUq1ef1atXMmHC87hcLvr0uZEff1zBvHmv88wz46lXrx5r165h7NgxpKamhYlrgaCq1KgQ3bhxI4WFhXTu3DnUFhUVxdlnn82ff/5ZRoi2atWKF198MfR669atLF68mC5dupywOZ8KSGhoPncZ/1BJUZBtEaiaEKECgeDM4PXXX+N//xtI9+49AKhVqzZFRS4mThzPoEFD8Pm83H33Pdx6a/+QZa9//7v4/PMl7N69k/j4ePbu3UOjRmeRllYLm83GQw+NpEePq5BlGbs9EqvVhslkJj4+odw5mM1mZs16jffee5dvvvmSjz76gA8/fB9FMXH99b0ZPvxhTCYz77+/iHPPbcWgQUMAqFevPjt2bOeDD96r9PlGR0fz2GNjQpbM1NQ0rrmmF5MmjQ/r17lzFzp2PA8At9vNu+++w9ixz9GlS1cAateuw/79+1iwYB59+tzI3r17MJstpKamkZKSSkpKKomJSaSkpFTh3RAIylKjQvTAgQMApKamhrUnJSWFtlVEjx492LFjB7Vq1WL69OnHPBeT6cTEbUnScTqWUjKmhAa6ip5fIkSV2NaYLFYUWwRIJz5GTQnOT1HOzPg4cf7i/Es/noxEnP/SSbE0X53k5OSQnn6QWbOm8+qrM0Ptmqbj9XrZt28vDRo05Oqrr+W99xayZcsW9uzZzZYtRso7VTVu2gcMGMRTTz3Bd999Q8uWrenUqTOXX34lVqu10nOx2Wz0738n/fvfSV5eLn///TfLli3hgw/ew2azc999D7Bt21bOO69z2H5t27avkhBt06Yd27dv4/XX57Bjxw727NnFli2b0Q4xQNSpUzf0fPv2bXi9XsaMeRxZLllmV1UVn8+Hx+Phiiuu4rPPFnPjjb1o0KAhHTuexyWXXEZKSvjvt0BQVWpUiLrdboAyvqBWq5W8vLzD7jtp0iTcbjcTJ06kf//+LF68GIfDcVTzkGWJ2Nij27ey7A7eaUvS8TmW1xtB8c+HIoHdFMBVsAEA2Z5GVHwdlAgn1tgYpBoQosVERZ3Z+UvF+YvzP1mx1O+Fue41p1WwUrH4euCBh+nQoWOZ7SkpqWzfvo177vkfTZs2D4qrS4mJieV//7s91O/iiy9lyZLl/PrrL/z55x8sXLiAuXNn89pr82jYsNER57F48ccEAgFuuKEvANHRMVx6aTcuvbQbjz32CL/88hP33fcAALoeLhjNZvMRx1fVEqf/5cuXMXbs/9Gjx5W0bNmS66/vzdatW8tYREuL6OLr9Oyz46lXr36Z8S0WCzabjfnz32Xt2jX88cev/PbbryxatJC77x7MgAGDjjhHgaAialSI2mw2wPAVLX4O4PV6sdsP/4V97rnnAjB9+nQuuugivvrqK3r16nVU89A0nfz8oqPat7IUp0vSdZ2cnMJqH19zuUPPAz4f+ftXgeYDQIo6F1ehF5s9AW++p0Yi5hVFJirKTn6+O2RlOJMQ5y/O/0Sef1SU/aisr5KsINnKX14+FYmLiyM2Npa9e/fQu3efUPtXXy1nxYrvGDNmLB999AGxsXFMmzYrtP3HH1cEn+n4fD5mzpzGlVf2pHv3HnTv3gOPx0PPnpfz888/0rBhI44Uq7N9+zaWL1/GFVdcVcZg4nRGEhcXB0DTps1Yu3ZN2PaNG9eHvTabzRQWlvyGFBa6yM7ODr1+6603uPbaXowa9Vio7YcfjPPRdb3cwKL69eujKCYOHDjABRdcGGpftGghO3ZsY9Sox/nii89xuQro0+cmWrVqzd13D+G5557hq6+WCyEqOCZqVIgWL8mnp6dTt27JMkF6ejpNmzYt03/btm3s2rUrFNgEkJycTExMDAcPHjymuQQCJ+bHUder/1iSBKqrREjragA1Z3XotRzVCiQFzWQj4Dv2cHlZlkDS0bUjpy05FFXVTti1PhkR5y/O/0w+/+PFnj27+fXXn8ParFYbbdu247bb7uTVV2eQkpJC585d2LJlMxMnPk/XrhdhsVhITk4mPf0gv/zyMw0aNGDjxg28+OJEwDCSWCwWNmxYxz//rOLhhx8hLi6BX3/9Gbe7iHPPNYJA7fYIMjMz2LdvL2lptcrM75ZbbuOrr5YzZMjdDBhwN40bNyUvL5fff/+V5cs/Z9KkqQDcemt/Bgzoz0svvUivXr3ZtGkjCxe+EzbWuee25JtvvuTSSy8jMjKS2bNnYTKVWJKTk1NYs2Y1GzduwOl08uOPK/jgg0Wh8ynPncDpjOT6629g9uyZOBwOWrZsxcqVfzFjxkv0739XaN9p06bicDho1aot6ekHWbXqb1q3bnuU75pAYFCjQrRZs2Y4nU5+//33kBDNz89n/fr13HbbbWX6//LLL0yYMIGffvoplItt165d5OTk0KjRkZdHTldkWcKbU3JHrGtaWKCSFB2sL2+2wTEaQ2VZokgrJNudR7IjARMWkRxfIBDUKMuXL2P58mVhbSkpqXzyyVJuvfV2rFYr77//Li+99CLx8Qlcd13vUKqjG2/sx86dO3jqqScIBPzUrl2XIUPuC6ZzWk/nzl0YN+4Fpk6dxMiRD+JyuahXrz5PP/1sSIT17HkNK1Z8R79+ffngg8UkJiaGzSUpKZnXX3+LuXPnMGXKJLKzs7BYLLRocQ5Tp06nTZt2ADRu3ISpU2fw8ssv8sEHi2jU6CyuvfY63n23RIwOHnwfeXm5DBs2hMhIJ/363U5BQUFo+4gRo3j++XHce+/dmM0WGjduzJgxY3nyyUfZsGFdhcJx+PCHiY2NZfbsWWRmZpCcnMzddw/mttvuAODaa3uRl5fL3LlzSE8/SGRkFJdc0o377rv/GN89wZmOpNewipgyZQrvvvsuzz33HLVq1WLixIns2bOHJUuWIMsy2dnZREZGYrPZyM3N5dprr6V58+aMGDGCvLw8xo0bh9ls5t1330VRjs6/SFU1srOrf7m8NPvmrCZQ4ENxWqg1qHW1jStJElJBLvlrfsb90c0AWJr1QHH8DJoXrEnYOryDJToeKb7+MS0LKopEQcDF/oKDFPk8RNucpEWmYq6EGDWZZGJjHeTkFJ6RFiFx/sf3/EtWG6WwZVLjuRT2WtdBR0PXdTR0QENHQpYkJF0y/k0hhZYwwz/aeplVgJLXejn9DU70+x8X5zjs0rzH42Hr1m0kJKRgsVQ+4EZw4lmy5FPGjXuq3PykAsHJis/nJTPzAI0aNQxzvSyPGk9of//99xMIBHjiiSfweDx06NCBuXPnYjab2bNnD926deP555+nd+/exMTEMG/ePMaPH0+/fv1QFIVu3boxevTooxahpzqyruLJzEILBEJtuj/XEKGAHNUSJAnZ5kQ7hnsORZEo8Bewz5WO2+cBIM/jQmc/aZHJWGWbqNYkqDZkuVgI6sb/dB1d0tF1Da1Um6br6GhoWnCbbmzTdA1N11CDj5oWfK6pxv56qXEplqqGiJVCYlZClmRkSUaRZEOoBl/LkrGtWLTKyMa+khwSsVJQ3IKEjkxAFYJPIBAIDqXGhaiiKIwcOZKRI0eW2Va7dm02bdoU1tagQQNeffXVEzW9kxpZllCzcvAVuMKsQKWjXuXoVsayvCXiqIWiosjk+/PYX5CO2+8N25bvcaHpGmmRKdhlUTq0PBRFQpUCZBZlE9B1FMWMgmKIIL2she1MQ5IkgoVn8Ot+fJoPr9+H2+/BE/CWCEbdkKUEr5uGDsG20LWkVL8TNf9SAhZJQgqek/HaaLeazURENDhhcxIIBIJThRoXooKjR/L78GZmoqsqculASH+Jv6hRX94CJttRFVNSFJk8Xx77Xel4DhGhxbi8RezV95MamYzT5EBVz3BlFUSWJVQCZHjyyPPmY7ZKeNwBZEnGZrISYbZjUSxYFQtmyYyEHLTsnb7XT5KM6yLLEgE9gE/14Qv4case3D43Pi1AQA0QUNUTKiaPBT20XK9X6IMty0cIqxYIKuDqq6/l6quvrelpCATHDSFET1EURcafnom/yI2syOgeV2ib7ssFJLDEI9nSkK0OdKnqZT0VRSbXl8sBVzoev++wfQt9bvYWHCDVmUSUOeqMTNFTjCxLaJJGjjeXbHcuRT43SOC02PAGfGiaTqHXTRa5mBQTZlnBpJiIMNuwmWxYFSsW2YxJMhnWvVPYalq8xC7JOoW+IgoCBRT5PBT5PfgCPvyaITo1/cz9vAgEAsGZjBCipyCSBHqRC192rrEM6Hfjd7tK9VABk2ENVUzINgdqFZfMFUUix5fDgYIMvIHDi9Bi3D4P+woOojk1YiwxZ5wYlWUJXdLI9eWR7c6h0OsJCSy5gkSDhvUvAH4vBZ5CZEnCpJgwySYsJjMRZjs2xYrVZMEsWZCRjWXpk9AFoqIl9iK/G5/qw+JWyC8owlfKn1kgEAgEZzZCiJ6CyLKELzOTgMeDSdJQXTnoqr9sv6hWyGYjbVNVlnsVRSLbl8PBgsxKi9BiPH4v+wvS0SI14iyxZ8QyfbHFL9+XT7Y7B5fPjaodXb5WTdfxBfz48FPkc5NLPoqsYFYUTLLJWNK32LEqFiyyBZNkRgI0jRO2pF+sqQ3hWbLE7g348KjecpfYZVnCabYROMrrIhAIBILTEyFETzEkSULPy8Wbm4+sSKiuHAJed7l9Df9QK7rJBpUUhIoike3N4YArA1+grLj9M/cAM3asItFiZ2SjjiRZI8r08QZ8HCjIQHfqxFnj0E5TMSpJIMngCrjIKszB5Sk8LkJL1dSgsPXh8hYhFeZiUozlfItsIsJix6bYDKupbCYYLmME9aAXP0VHRyr1X0LBPhgFCkqlIQpVAit+DaFgIILuAl7VK5bYBQKBQHBMCCF6iqGg4s7MRFcDyD4XqrsQ0NEL94V3NMci2eug2JxUdhVXViSyvNkcLMjEV46F9euMnUza9ieqrrPX42L4um95vvmF1LNHlenrC/g5UJCBqmsk2uKP4kxPXgwBKlGkFpJVkEOBtxC/euKWm3V0/GoAvxrAjZFGS5ZkzIoJk6IEhWipyPFDoshLW071kg6hHhXuZ+wcen60Vl+BQCAQCIoRQvQUQpYlAlk5+F0uZM2H6spFUwPoATeBDSWVN+RIGTm6JYrZAtaISi3ZyopEpieLdFdmuaLq/X2bmL0rvAZyhs/Ng+u+Y1zTCzg7sqzY9KsB0gsy0XWNVGfyUZzxyYeiSBSpbrILc8j3uMq1GtcEmq7hDfjwnibul7quk+lzo+p6MGenIa9lSUIO5ueUg2mTil9LoXbKraddGTRdx69r+DQVr6bi11R8moZPN177NBW/VrLdpxvb/VrJdl9w/5I/DV2CPu5c+tfqUK3XSSAQCE51hBA9hShO1yTpGlphLmownZK6+SPwZAEgOyWUWMlYljeZkcz2I0ZcK4pEhieTdFdWGRGq6Tqzd63hw/3/hdquTGrAlsIcNhfmUhDw8ciGFTzZuDPnxaaWGTugqaS7skHScTjrHeMVqDkURcajuTlYmEuep6BKvrN+TWNjQRZxagSpUgQyIpXPoeT7vWxwZbPRlc1GVxabXNkUlGOVrwqGQDXyeCqhakmgBBPOFwtYHd0Qm5qK/zi6FvySvZfucU1JNDuP2zHORIYMuZtVq/4OazObzcTFxdO164Xcd98D2Gx2AHr16knPnteEynseypw5r7B06Wd88snS4zLX1157lTffnMvSpV8SHR1TZvsXX3zO2LFj+OSTpSQlVd/N+91338Xatf8wf/67NG7c5Ij9x479P/bv38esWXPYt28fvXtfzYwZs2nXrn2F+6xY8R1LlnzKxIlTKjWnTp3a8sQTT1U6NVVV+1cH8+a9zm+//cqsWXMO2y8vL5fJkyfy668/IUkS3bv3YNiw4aHPXU3z119/MH36S2zfvp2UlBQGDryH7t17VNhfVVUuvfQCvN7wlI0DBgzi7rsHs2nTRl544Vlmz34Dk+nYZaQQoqcIxemaVK8XqSgvuCQPWs5mtF3fGJ1kGXNdo9qLHNUK2WpHV8xwmJKCsgLpQREaOESE+jWNydv+5JvMXaG2O2q34NZazSlSAzz13y+szk/Hq6mM2fQzIxp1oHtiWbGpaioZrhxsdjMxcmw1XI0Th6LIeDUvGUW55HryK8ylWh4eNcCy9O28v38TGT7Dj9ciKzR2xNDMGU9zZxzNnfEkWuxHbcE7FfFpKtuK8tjoymKjK5sNBVns81Z/iV0NPeiWouM/CdyUL0xuQLLNifBoqH66devOQw+VFEUpKiri999/Y+rUSWiaziOPPArAG28swGqtuQpXV199La+/Podvvvma3r37lNn++edLOO+8ztUqQnft2snatf9Qt249PvroA0aNeqxK+ycnJ7N06ZdERUVX2Cc/P5/JkycwY8bpU2zmgw/e49VXZ9KqVZsj9n300UfweNxMm/YKLlcB48Y9jdvtZsyYsSdgpodnx47tPPzwA/TrdxtPPTWOn3/+kaeeepKYmBg6dDiv3H127dqJ1+tl/vx3iYuLC7Xb7UZMSNOmzahfvyELFszjzjsHHPMchRA9BShO1+TPyUXyFRIoykfXNXTNT2DdmxR795nSLMhWFUzRSI76RlnPwziIygqkuzPJcGWVCbIpUv2M/e9X/s47aPQFHmjQjquSGwLgMJl5ttkFvLDlD37I3oOGzoStf5Dr99A3rWmZY2m6RnZRHoV4SbIlIevySZ0bU1Ek/LqfLLchQN0+T6XTq7sCPj49uJWP9v9H3iGWU5+msq4gi3UFWaG2OLON5s44Q5xGxtPEEYtdOT3+aeq6zn5vIZsKs9myJ4+12elsKcw9ouUxxmSlsTOWCNmEBsGynXqwPCclz4vbg/6saqnqSmpxGVCKS4GW2i/UbhzPKiuYZRmrrGCRFSxS8FGWg4/F7cZro7+x3RrWv9Q+pcaItkVwfv0W+ItAO5rKEoLDYrXaiI9PCL2Oj4c6deqyceN6vv56eUiIxsbW7I1wSkoq7dt3ZPnyz8sI0fT0dP766w/GjXuhWo/52WeLqVevPldffS1vvDGXYcOGExFRNsi0IhRFCbu25bFo0ducc8651KlT91inW+NkZGQwfvw4Vq78q1Lns3btP6xc+RcLF35AgwbG7+Ojjz7B8OH3MXjwfSQlJR3vKR+Wd999m0aNGjN48FAA6tdvwKZNG1mwYF6FQnTr1i04HM7DWs9vvfV27r77Lvr0uRGnM/KY5nh6/Nqd5sgS+DIz0T1FaK4ctKBforp1CQSDlCRnDKYEFyAhR59r+IeaKy65aYjQDDJc2WVEaI7fwxMbf+K/QqNUqEWSebxxJ86PqxXWzyIrPNa4EzE7VvHpwa0AzN61hhy/h4F1W5bJnamjk1WYSyCgkhaZgqwpJ50YLV0NKdedR5HPU+kKPzl+Dx/t38ynB7dQdIh1uVNsKtFWG2tzM9jncYVty/Z7+DlnHz/nGO+lDNSPiKa5M55mQatpHXtkhblITyYKAr7g8nrJEvuhYvxQzJJMY0cszZxxwb94UqwRp5WV2G6y1PQUKoWqa+T4ys/CcbyJtdhRJLlax7RYrCilbuoOXZr/5JMPWbBgHhkZGXTocB6pqWlh++fk5DB58gv89tsvKIqJa6/txfr1/9K6ddvQGD/99ANz5rzCjh3bSUxMpHv3K7jrroFYLOW/59dccx1jxjzG/v37wo73xRefEx0dzYUXXkh+fj7Tp7/Er7/+RHZ2DlFRkXTtejEPPTQCm83O33//xf33D+Gee4ayYME80tLSeP31+chy+PVTVZUvvljKxRd34+KLL2XGjJf58stl9Op1Q6iPruu88cZcPv74AwoK8unWrTs+X8nKz5GW5r1eLx988H5I7ANomsb8+W+ydOln7N+/D7PZQsuWrRgxYhS1a9cpM8acOa/w559/0KlTZxYtWoiqBrjookt46KGROBwlriy7du3kvvsGs2bNaqKjo+nT5ybuuON/lTpm8XlUxEcfLSEtLY2NG9djNptZsGARc+fOYf/+fRXuA7B69SoSEhJCIhSgbdv2SJLEP/+sOuwSeGlWrPiOefPeYNu2LWiaRoMGDRky5D46dTofMFxQ6taty+bNm9m1awcjRoxm8eKPy7ilFFO8jL569SouuujisG3t2nVgypSJ6Lpe7vfsli2bqV+//mHn26jRWaSkpPDJJx9x2213VOocK0II0ZMcWZbQ83PxFxSgFuUQ8HoA0Ap2o20P+jFJMpbaLuMDJSmYavUx6subreUWU5IVOFiUTkZhTpnI5/0eF6M3/hgSS07FzDPNLuCcyPLviBVJ4r76bYg125i3Zx0A7+//j1y/l4catsd0yBejruvkFOWjoZPmTMaE5aQoaVleNSStkvNK9xbx/v5NfH5wG75Slj4ZuDi+LjfXakYjZwxOpw2Xy0O211OyLB18LC1cNWBbUR7bivJYmr4NgAjFFBKlxWItxlxzS4wAAU0LX2J3ZbHnEJFdHrVtTpqFziOOhhExmOXqFSCCqrN47zpGr1lKxnFwk6gMiVYH41v25LpaLY55rEAgwO+//8oXXywNE12l+fLLL5g48QUefHAEHTuex/fff8crr0wPLYtrmsbDDz+AqgaYOnU6JpOZl16azOrVq2jdui0Av/76M48/Pprhwx+iQ4fz2Lt3D5MnT2DXrp08+2z5ls2LLrqEyMgovvzyi5CIAli2bAlXXnk1JpOZZ555hIyMdJ5/fhJxcfGsWbOaZ599moYNG3LzzbcChsj85Zcfee21eXg87jIiFOC3334hIyODbt0uo06dujRr1pyPP/4w7Jq89dYbLFgwj9GjH6dp02Z8/PGHLF36Nm3atKvUtV69ehUFBfl07twl1LZo0Tu8/fZbjBkzlkaNzmLv3j08//w4Xn55ChMmvFjuOBs2GL8fL700g8LCQp57biyPPz6aqVOnh/p88MEiRo4czaOPPsHy5V8wa9Z0WrQ4h/btOx7xmMUuBhURE2NYy7t2vYiuXS+q1LmDYclOSkoJazObzURHR5OefrBSY2zcuJ5HHx3J/fc/yIUXPo/L5WLmzGk8/fSTfPrpF5jNZgA+/fQTnnpqHGed1ZiEhAQ6d+5CoIKA2eJl9PLml5iYiMfjIS8vN3Tepdm6dQuqqjJ8+FD+++8/kpKSuOmmW7jyyp5h/bp06coPP6wQQvR0R9ZUPFmZ6K5stOJUTZqK+u/roBsiUkk2IdsN0WQ5ayhy1DnINge6ZOJQJSopcKAonczCbFQtfNuWwhwe2/gjOUE/yESLneeadaV+RMW+QWBEKN9W+2xizTZe3v43GvBV5k7yAl6eaNy5zDKzjk5uUT66ppEWmYpZqhkxWpyQXSNArq+gTDWkI7HbXcCifRv5OnMnaqn5mySJyxPrc2NaM2rZygamxJitdIpNo1OsYQ3RdJ3d7oKQKN3gymJHUV7YO1ekBliZl87KvPRQW5rVEVzON4RpoyMIOlXX8GlaSXR3qSjvMm3B4J1Do8W9wejwXe58thTmhAnv8ogyWWjqjOPsyHjaJKZQT3HiVE4N6+CZxoOrPiU/4Kmx42d4C3lw1adHJUSXL1/Gd999HXrt9XpJSUnl1lv7h4m90rz33kK6d7+cPn1uBKB//zv59981/PffJgBWrfqb9ev/ZdGij6hXrz4A48a9EGZVe/PNufTqdT3XX28ss9euXYdRox5j6NB7GDr0AdLSwi2sABaLhR49rmT58mWhuW3YsJ7t27fx3HMTAOjY8TzatGnHWWc1BiAtLY3333+XrVu3hI11yy39qVu34uXjJUs+JSkpOeTn2L37FUybNoX169dx9tkt0HWd999/l5tu6sfll18BwPDhD7Ny5V8Vjnko69atJTU1LWy5v3btOowZM5YLLrgQgNTUNC699DK+/fbrioZBkiSeffYFEhMTARgxYhQPPjiMnTt3hK5/7959ufJK4/r/738Deeedt9iwYT3t23c84jEr42JwNHg8HiwWc5l2i8WK11u5oFZZVnj44VHccEPfUNtNN/XjwQeHkZ2dRXKyISQbN25Kjx5XVml+Xm/Z+RVb632+8ue3bdtWVFXl7rsHk5SUzC+//MS4cU8RCPi55ppeoX4NGzZi4cIFaJpW7o1QZRFC9CRGliXU7BwC+bmorjy0oNVM2/UVev4OACSbCXOyBkjg6Iy57s1ofi+y1VmmrKek6BwoSifLlYN6iIBYlZfOU//9HLLM1bVH8nyzC8tNWF8RPZMbEmW28Pzm3/HrGn/kHmDUhhWMa3oBMVZbmf55Hhc6+0mLTMYq24572UpJClqY0fFpPjwBL4WBIop8btx+b6XzYm4pzGHh3o38mL0nbNHeJiv0TG5En9QmJFgqHy0pSxL1IqKoFxHFFUkNAHCrAf5zZQcjybPY4Mom2x8uEvZ5C9nnLeTbLCOYzCzJ1LEbvjreQ0WlroaJ5eOBWZJpFBFDs8iSJfY0qyMk+IstwidjeVLBqU3XrhcydOj96DqsX/8vU6ZMokOHjtxxx/8qjOrdunUL3btfEdZ27rktQ0J048aNREVFhUQQQHx8PHXrlgRkbtq0kfXr1/Hpp5+E2opvqnfs2F6uEAVjef79999l8+b/aNy4CZ9/voRzz20ZWt694YYb+fHHFSxd+hm7d+9i+/Zt7Nu3l3r1GoSNU6dO2WXuYnJzc/jppx/o0+em0PJr9+6XM336VD7++APOPrsFeXm5ZGZm0rx5uPg/55yWbN++rcKxS5OVlVnG97Zr14v499+1zJ49i507d7Br1062bdsWEpnlUadO3bDtLVu2Aoz3qfg9KH3tAZzOyFBk95GOeeDAfvr1KxsgVszChR+QklI288uRsFqt+HxlrZI+nxe7vezvXnk0adKUqKgo3nrrTXbs2M6ePbvZvNn4HJYulX3o+z18+H3888+qcse8447/ceedA8qdX7EArSiq/+2330PTtNDNRePGTThw4AALFrwVJkRjY2MJBALk5eUdk/+1EKInMZLfhy8rAzU/K5SqSS9KR938caiPuY6OJMvoptpIiYOMH32TGSzhZT0lRWd/4UGyCnPLWPy+z9zNC1t/JxDsf7YznmeaXUDUUfi1dY2rTVQzC2OConaDK5sH13/HC2dfhNNZ9h9lvseFpmukRaZglyv2aT1aZNlI2ePX/Ub5Sa+bAl8hftWPPxAoI8gPx7/5mbyzbwN/5h4Ia3cqZnqlNKZXyllEV9NyuV0x0So6iVbRhqO7rutk+NxsCIrSDQVZbC7MCQv68evGUvmJotgiW7zE3sgRg0VWTtjxBdXLlDbXnhRL80dDRIQjFFhSt64haIYNG4KimMJ8F0sjSRL6If/+S4tWk0lB0w7//aDrOrfddgdXXVXW9zAhoWLR1aRJU5o2bcYXX3xOgwYN+OqrL7j33vuBEpeAbdu2cvnlV3DZZZfTtGkzxo8fV2Ycazk3+MUsX74Mv9/PokXv8N57C8Pm/NVXy3nggYdCAvVw1+FISJJc5jq99dYbzJ07h549r6FDh47063crP/ywgi+//KLCcQ49ZrEAK21pK8/qVvw7d6RjJiQk8tZbC8vsX8zh3q/DkZyczA8/fB/W5vf7ycvLIzGxcoFKK1f+zfDhQzn//Ato1ao1PXpcidfr4ZFHHgrrd+j7/dhjY/B6y1/FKM5ykJSUTGZmRti2jIwMIiIicDrLTyVns5X9XDVq1Ijlyz8Payt+32X52Pz5hRA9SVEUCTUzC1/WQVS34Xen67oRJa8ZdzNKoozilNGlCLSowZhk48Mjmy1hZT1LRGhOGb/HTw5sZuaO1SHLXqfYVB4/qxO2Y4jabhWdxItnX8JjG38k2+9hl7uA+9d+w7T23Umk7Afc5S1ir76flMgkIk3OY6pPbwhPo/65V/Pi8Xko8BXiDfjwB/xVLsGp6zp/5R1k4d4NrC3IDNsWa7bSJ7UpPZMa4jCVXZqpTiRJIskaQZI1govijbtiv6axrSg3zGp6wOMyorklGXMwujssmlsqifi2lhPhXdwW2n5I9HjxeHFmW7WJbsHJwXW1WnB1WvPTIlipXbsO9Ot3G2+//RZdu14Y5r9YTOPGTViz5p+QzyXAhg0bQs/POqsJLpeLHTu2U7++YYnMy8tl9+6SdHYNGzZi166dYdHVf//9F++9t5BHHnkUu73ilZFrrrmOBQveol279vh8Pi677HIA/vtvE7/++jOvvTaPc845F4BAwM+ePXuoVat2pa/BkiWf0qjRWYwd+1xY+z//rGbChOdYtmwpffveTHJyCmvWrOaiiy4pdR3WV1qMJiQkkJOTE9b25puvM2DAIPr3vzPUtmDBW3CYwM/du3fhchWEIrDXrv0HMFIFVYYjHdNkMh2XqP42bdoyY8bL7N69KzR+sWtDy5atKzXGO+/Mp23b9owfPynU9t577wafVXzNKhOR36ZN2zKuFn///SctW7YqV9gXFBRwww3XcP/9D4XlbF2/fn1YQBZAdnY2Foul3Jy4VaHKauPjjz/m/PPPJzn59KiUczIiSYC7CF/GAdTCvNAdn7b3R/Ts4BelGcypMjoSWtQAMJV8IGV7ZEk0uqKzr/AA2YW5YSJU13Ve3/0v7+7bGGq7IrEBwxu2PeKPgd1sxWGNIM9dUGFpy0aOGKa2uCQU+JThczPozy8Y1+wCmjvKVmEq9LnZV3CQVKdGlDk6bDnicJT4eWr4NC9uv4dCXxFuvwef6iegBiqddqk0qq7zc/ZeFu7dwJai3LBtyZYIbkprxuVJ9bHWoAXQLMs0dcbR1BkHnFVj8xCcPiiSTILVUdPTqBYGDRrCDz98zwsvPMc777xfJmVR//53MXLkgyxYMI8LL7yE3377he+++zrkR9iuXXtatDiHp59+kocffgSr1cb06VPxeDwhS+Ltt9/J44+PYu7c2XTv3oODBw/w7LNjqVWr1hH9EXv0uIpp06YyZ84rdOt2eWh+8fEJKIqJb775iri4OPLy8njzzblkZWVW6NN3KBs3bmDz5v8YPfpxGjUK/25o0KAhb7/9Fp988hF9+97M7bffybRpU6hXrwGtW7dh2bKlrF//b6VFVIsW5/DqqzPJy8sjOtqwwiUnJ/PHH7/SteuFyLLMsmVL+f77b4mLq7jcc1FREU8/PYbBg4eSlZXFpEkvcNlll5fJZFARR3PMo0FVVXJzc3A4nNhsNlq0OJeWLVvz5JOP8sgjj+F2FzF+/LNceWXPkFD0eDwUFrqIiYlFUcr+ZhRbVVevXkVSUjIrV/7J7NkzgYr9OCtL3743079/P2bMeJmePa/h559/4ptvvuall0qCwPLyjJW06OhoIiMjadeuA6++OoO4uHhq167D999/w/LlnzN58kthY2/atJGzzz724MIq336OHTuWNWvWHLmj4KiRJVAzDuLP2BdK1aR7c1E3vRvqY6mrICkSuuNasBp3zZIsI8kKksUO6Oiyyj7X/jIiNKBpTNr2V5gIvbVWcx5q2O6IItRpi6BOdBq1nWmkRiVhP4xVLNXmZOrZl9DYEQNAvt/HyHUr+D1nf7n93T4P+wrSyfXloijlz6PYz1NRJAKSnwI1n/1FB9iWu4PtObvZnbuPzMIcCn1u/EchQgOaxvL0Hdz9z3Ke2fxrmAita4/kkUYdebP1lVyT0qhGRajg1OI0ykR1ymC1Wnn00Sc5ePAAr7wyo8z2Ll268vTTz/LZZ4u57bYb+f77b7nlltvC+owfP5mkpGTuu28w9903mBYtziUlJSUUxXzppZcxbtx4Vqz4jltvvZGnnnqSTp06M3785CPOLzIykosuupQNG9Zz7bXXhdoTExMZM+ZpfvxxBTfffAOPPjqSxMREbr75VjZuXF+pc1+y5FMiIyO54oqrymyTZZmbbrqFrVu3sHr1Kvr0uZGhQx/gjTde4/bbb2bbtq1ce22vSh0HoE2bdkRGRvL33yVWt//7v2fweDzceedtDB48kK1btzBq1GPk5GRz4ED53//JySk0adKEwYMHMGbMo1x44UU8+eTTlZ7H0RzzaDh48CA9e17O118bEfiSJDF+/CTS0moxdOggHn98FJ07d+GRR0oKB3z99Zf07Hk5Bw+WH0U/aNAQWrQ4lxEjHqB//5tZvPhjHn/8/7BabaFsAkdLw4aNmDhxCr/88hP9+/fj008/5umnx9G+fcdQn9GjRzB69IjQ6yeeeIpu3S7nhRee5bbbbuTrr7/kuecmhFJJFbNy5V907XrxMc0PQNKrGK585ZVXMmjQIK6//vpjPvjJgqpqZGcfX7+ofXNWEyjwoTgt1BrUusJ+siwhufJwbViNLzuTYrO8f9V09HQjX5gSK2Gpb0K3tEKLHgKSjGw2E1GnLub4GJS42viQ2Oc6QE5RflgeTI8aYNzm3/g91/iHKQFD67fhupTDW9RkSSLaHkWKIwmLZEHTdBRFplB1sd+VjstTVOG+hQE/Yzf/Eor4lpEY0ag93RPrl9vfarKQHJlAnCUWVdVL+XkG8Kge3AEPhd5CvKofv+ovE/1/NHg11aiCtG8T6b7wc2nsiOWWWs05PzbtqHN5yrJEpNNOgct9RgbrVDZYSZEVTLKCSVGwKhZMsglVVwloKpquGYnodd0o6FCcqD74F3p+VDbwKpyLJCFJcvCxuOa9hCwFq5pJEibZHDoPRVIwmUykxSZQVOAncJhKZ9VFXJyjwps5MCw0W7duIyEhBYtFuFiUR25uDv/+u5ZOnTpjCrre+P1+evS4hJEjR4eit093du/eRd++vZg9+41QANGhzJo1nS1b/mPy5JeP6hjHu7TqycBDD93P44//H/Hx1WuhrSk2bFjPffcN5qOPPgtZwkvj83nJzDxAo0YNy/U5LU2Vl+Zvuukmnn32WVatWkXTpk1xOMou4/Tq1auqwwqCKLqKZ99OAnk5FItQ7cBfIRGKCcy1FXQlGS3qfyDJIElYExIxRTkAmYAiszdvv5EiqdSPcr7fyxObfmKDKxswopxHndUx5HNYESZZIc4RQ3JEEpImhYSEqmpEyA7qRtbigJxOrju/3NybDpOZZ5t3ZfL2v/j64M5gFaY/yfV7y63C5A34OFCQge7UsZvsuH1uXL4ivAEvPjVQphTpsVAY8PPZwa18eMDIfVqaVlGJ9EtrTtvopKNOri4hYTNbiImIJD4qmhy5AJenCJ9mnEdAVY+7cDpZUSQZk2LCpChYFAsRZhtWxYJFsWKWzSiUWJwliVLVkDSK6yppuoama6i6hqZpaBS/VlE1FVXTCASflwjWsiJWKhaVh4hMheAcZQVFNqFIMoqkIEsyiiwjI6PIChLGPjIyslRSNaz4Pl9RZKwmC0WUn/NPcPKhKCaeeGI011/fh969+xAIBFiwYB5ms6Vcn9PTkX379vLLLz8Bh/dHvOWW27n55hvC/GkFJaxevRJZlk8bEQqwcOHb9Ot3a7kitKpUWYiOHz8egPfee6/c7ZIknXJCVNd1/P7yfyAkSQpz2q6on9GX0J3zoX1LDM/GscrrqygSnqwDFOzdjeo3/EJ0fyH6hgWhfubaCpht+CMHo2MFTcMcHYMcE40vEECLjCYzv0SEqgEV0En3FvH45p/ZHUw4HqGY+L9GnWgbV5KuorhvaSwmM/G2aOLNcaBKoa2BQCB0TpIEybYEZE0iy51LQA2glLpmmqqiSDCmeWcidIVPg0naZ+9aQ5a3iLvrtUIJOk1rqoaua7gDAXb79qFIcnCJ3TiWrCghUVjctyIq6rvX4+LPvAP8kXeQtQWZZcpNnhedwk2pTTjbGR+af9i4soIkH34OsiRhNVuJi4ghxh6D0xKBw25B9piIlmMI6H58mg+f6qPI78Hj96JJGqquo2oqmqahH8bSK8syUvCa6Zp22OheSZZDTulV66ujHSa4S5JkZKVyfWWTGbNsIsJiQ5FMWDFhUaxYTRbMshmTpKBpxjiSLoMioWJYQAOBim88ZFnBrFiRZNAlnUBANZbBjdoOwXmCpoMk68iKEhKvPr8vJGJlSUKRigWlITItJktIVPr9vlLi0vhPMI0vuiShmIyDaYD3kBsaAJNJwuczBZNPlwjso/0+KY/i5WJjjmfmzU11EhkZyeTJL/HKKzNZvPgjJEmmZctWzJjxarlJwE9HXn55Cn/++Qe33HL7YVMbRUdHM2LEKKZNm1rGj1BgpMOaOHFKTU+j2ti4cQM7d25nzJinqmW8Ki/N792794h9atWqdcQ+JxM5OTm8/HL5Swp16zbg6qtL3BBmz365wh/GtLTa9Op1Y+j166/PwuMxIlC7eTpgx4obL9/Y/iQxMZm+fUuiNefPf42CgvwyY7bPWEyDAiNPmBwlYWmo8KfvZjZ6yl9KtzvsdOh5UUgwrvrqF7a6cngn0U2ByWh1qhK3pNupLdvofF230L5rvvudvIycckY1Ig4HDbo/9HrJko/ZtWt7uX0But5Ykp9vwy+ryNxj+Mbo6PwY5eP7mBIH7Mvi6/Bwo46YZJlNf6whfUfFJdXOu/ZSLDYjrdSWv9ezf+uuCvt26HkhNkcEPk3ly9Ur+S1zL5vtAbLNZT/yMnBhfB1uTmuGaXsGu9ZvrXDc1pd1JjLOuAvcs3E729dsqrDv9dffSGpqbUwmmc2b17Fs2bIK+15x9dUk107Dq3rZuH4Dq3/+o8K+zTq3JrGOkeQ4Y/cBNv66usK+TTqcQ3IDI9o2e186635aWWHfRm2ak9bYyNWXm57F2u//rLBvg5ZNqd3MsH4UZOex+utfK+zbqn1bLr3sYvxuneyMbN55Z16FfVu3bsf55xuVTfLz81iwYG6Ffc85pxUXXmh8ht3uIt5445UK+zZtejbduhmfS7/fz5w50yrs26hRY3r0uCb0eubM8qvBQNW+I2rVqs1115X/HXEolf2OAIiNjadfv5LqJhaLdlhLhViaFwgEx5PjujRfWmS63W5cLhcxMTFhd+OC6iGpaGtIhCKDpY6C7riK/Z7mUMESn6aH2zS3KV7eTC7CG3QXi/NL3JoeQawqw3H8/XFaI3B5y/qNSkhcmG/FoUp8HudFl+DrrN3kq36eaNy52o6fq2gsy9rJyj1ZrM5Lx6OpEFW2X1RAoqnbxF2tO9E40RB2O8ko2/EoqYo/qKyZcCpOIk2RJNjjDtv3ZIl9kZAMf84j5Jy1KVairJHkFBWekT6yAoFAICifKltEAf766y8mTJjAv//+G1oCatmyJQ8++CCdOnWq9kkebwIBlfT08hOBV9fSfPob/6K6/ChOM0l3nRPWV1Ek1JyD5P+7Gn+BMQ9d9aL/OgbcRu5Kcx0ZJa0lWvQwArqEbLEiJ8bhS4rhQPYeNLMFyRmPppcsE/6UvZfnNv8WWnpuEhHL2Madw2qUl15CVwMqsgTRtiiSnYmY9PDSm6VvNkovzR+KLEvoJo0DhenkuQsIBAJIEjgcNgoLS4JVfsrZywvb/grNr7kzjrGNzydSqfimpqLl9oCmsc6VxZ95B/kz7wA7PQXl749EC2c8HaOT6RCTQj1bpOEbWJUl/1JL8woydrONGGsUkRYnki6HCS1FUZBlGZNJJirKRlZWPoFA+detuC8YKUKKl9CNFFVGH7/ux6v6UFHxqF48AS8+vw9fwF9hZaiKluYljCAwCQkkCSl4fRVZMZSuVirRdXC7VPw/ScJhteOwOrAqFhQUZC3oXqGVXRq2WEwkJESRk1OI368eYbldDqU4OfLSvIQSzHlbnX2r9u/+yH1NJomYGAd5eUWciKX52NgITKaKszoIi6hAIDieHFeL6MqVK7nzzjupU6cO9957LwkJCaSnp7N06VIGDhzI/PnzadOmzVFPviaQJKnSFt2qWH5L9y0Jdil7LBMBfPv3oBcWYAoKhsB/i9GDIlR2SsiJSWhRA5EtNhRFxmc3Q7SNjNy9SDYrJmcCGnLoJ27Jwa1M274yVK+8fXQyY5qcX6bue2msFgvxjliSIxJBkzjcLcqRkh1LQC1nGmYlw0gfhYbJbEIxmZCCQu2ixHrEWCMYs+mnUBWmhzeuqHRp0RzVy5+5B/gj9wB/5x0IlSc9lFizlY4xqXSMSaVddPIRk88bfo+HT2NlNVlwWiOIsUXjNDnQNalUYEr5+yiKgtlsQZKOHDWtKEq5+easmLAq9lDifiN/quFr6lG9FPndqJpqRHLLCkpxAI1cHIRTElQjSSWiUg6KUVmSkZCC2QGk0HPj0yuHxKuMDBjnHBLewUtWXsnh0hHcVfn3djL0haP/d1+MySRjsVgwmcKj5o/XHI42uE4gEAhONFUWolOnTqV9+/bMnTs37IfyvvvuY8CAAUybNo3XX3+9Wid5OmMyyfj376ToYEmeMy13G9pOI0cZEpjqWJHih0FEAi7NR1HAjc2ZSmHAhWYyoTjjUIMqQNd15u9Zz/y9JfnmLkuox8MN24dEbnlYTRYSnXHEW+OoYvGhctF1kHSZ1IhkLIqZrKLyfU9bRSWWqcI0fN23PN/sQupFhK+lq7rOJlc2f+Tu54/c/WwuzC13TAlo7oynY0wKHWNTaRQRc9Rplw4d12q2Eml1EmuLxq7YjSAdFQ5X/eJ4YIg/45hmLFgUC5GmSGR7yXmG30joZW4sSl7rZdtK9z10P0BFL7tBIBAIBIIqUmUhunbtWiZPnlzGWiPLMrfddhujRo2qtsmd7siyhOzOoXDfbrRgvVhdC6D++wqhsmSpMnLa3bjt9cn35OH1FxGVWht/hAmvrxA5OhE1+Daqus607StZGoxKB7gxtSkD6p57WCEWYbGR7Ewk2hx1TOU1y0NTIcEaj91sJU/Nw0XZurjlVWF6cN23jGvWldo2pxHhnnOAP/MOUBAov8pElMlCh5iUkNWzOstPSpKE3Wwl2hZJtDUam2xF03S0ar5Wx4KuE26dFAgEAoHgFKDKQtThcFToW3U4v0FBOJIEJnz49u/Fm50Vate2fYxeaATLSHagUQ+yrK0ocmWgqgEc8YkQF4Mr4EKOjEOXLaDr7HEXMHPHav7MOxAa6556reiT2qTiOQCRNiepziRssr3aRWgxqqoTZYkiJtKJ36uRW1hQJndmqs3J1BaX8PjGn9hcmEOB6ufh9d+h6hVn2WzsiAktuTd1xqFU83KkLMnYLVZibFFEW6KwyFZUVTtu10kgEAgEgjONKgvRtm3bMnv2bLp27Yrdbg+1FxUVMXv2bNq3b1+tEzxdUSQNLScd94H96EFhrxfsQt32eaiP3KghBy098AeXoC0RTqxJSbh0L1JEFLrJToa3iAV71rMsfTtaULIpksTIRh3ollCvwuPLkkSMPYpUZzKKbj7uljRN04m2OqkbVQuTdJDsorwywTWxZhuTzr6Ipzb9wqr8dAKH3NREKCbaRafQMSaFDjEpxFvsHA8UWSHCYiXWHkOUORKTZEJVdVT1+FfEEQgEAoHgTKLKQvShhx7ihhtuoFu3blx88cUkJiaSkZHB999/j8fj4dlnnz0e8zytUBQJyZ2L5+ABfPnBKHldI7B2SsjtTk6xsz+2P1pQpMqKCUdyCl6rTEAx4ZItLNy5ho8PbMZbStDFmq080qgj7WNSKjy+SVZIcMSSFApKOnEWPllXSHOkYFbMZBZm4wuERwJHKGbGNbuAqdv+5qvMndS3RxlWz9hUWjjjD+vneqyYFRMRZhtx9lgiLQ4kXUHT9KA/pEAgOBkZMuRuVq36u9xtt9xyO/ff/+AJmUenTm154omnuPrqaxk79v/Yv38fs2bNKbfvvn376N37ambMmE27dsdmvFmx4js++eQjNm3aSEFBPnFx8XTseB79+99FnTp1Q/169epJz57XcPfdgysca+fOHcyZ8wp///0nBQUFJCQk0qXLBfzvf4NOq6pAgpOLKgvR+vXr89577zFt2jRWrFhBXl4e0dHRdOzYkfvuu4+zzjp8zXIBSN4CAjnZeDIzQdeRZAVt6xvoBbnGdqtEVv270GRnaB9nYjJajJM8zc/HWQdYuGcDBWqJiItQTNyY2pTeqU0OHxlvspDkjCfOGlstQUlHg6ZCki0Bq2zhYGEGRb5wv1GLrPDIWR156AgBVtWBhITZZMJpiSDWHoPT5ABdQlOPf81ygUBQPXTr1p2HHhpZpt1mOz6rJuWxdOmXOBzOI3esRiZPnsCnn37Mbbf1Z/DgoURHR7N3717eeWc+d911G3PmvEmDBg0rNVZWVhb33PM/unTpypQp04mKimbXrh1MmzaVe++9mwULFol84YLjQpWF6MyZM+nRowdTp049DtM5/ZHQwZ2HNz2DQGEhisWGt2AV2pYfQn2KzroYr7Xky8MeFQsJsbyfvZs3D2wlw1dSicUsyVyb3Ih+tZofMUAnwmInxZlIlDmqxpeZVVUnyhyFJcrMAVc6+Z7CMsLveIhQWZIxKybMigmbyYrDEoFVsRKh2NE0ggFIQoAKzkx0TUUryq6RY8sRcUhyxblPD4fVaiM+PqGaZ1Q1TvTxv/vuG95//10mTJjChRdeFGpPSUmlbdt2DBp0F6+99irPPvtCpcb79tuvCAQCPPHEU6H0X2lpaaSkpHLzzTfw66+/hB1HIKguqixEX331VVq0aEGjRo2Ox3xOe3Rdw5+Ti7+gAN1mJdebgW3lVIoTfqpJyeQl9Az1V8xWfnXKzNj6KzuCdeLBCDS6LKEed9RpQbLVcdhjSkCUzUlqZDJWyVbjIrQYTdOxSnZqR9XioCmdnMI81MMkkj8aFFkJCk+FCLMdu8mOzWTFIluQUUKR5iIASXCm4179EfkfPIzmqr7KYlVBdiYS1Wcy9ta9q31sXddZsGAeH3/8IVlZWdStW5dbb+3PFVdcFeqzYsV3zJv3Btu2bUHTNBo0aMiQIffRqdP5AOzatYsXX3yBtWvXousa557bkmHDHuSssxoD4UvzYBSlmDTpBT7/fAlms5nu3S9n2LAHsVrLNxgsWbKY+fPnceDAflJSUunduw99+94cKkZxKIsWvUO7du3LFYeSJPHccxNxOA7/2xC+j0xRURGrVq2kbdt2ofb69RuwcOEHJCdX7O4lEBwLVRaiZ511Ftu3b+eii8Sd0VGh6/gKXOT7iyjw5BOzZTx6fjAlkVkh86whoa5rJD9vygWs3b8jbIhOsan8r865NIiouJZ0MbIkExsRRYoj6YQEJVUVXdeDfqOpWGQzGYXZ+CtITF8ZTIoJs6xgUcw4LBHYTDasihWLbEbCqHqk6zq6CionhyAXCE4G8t4dhu4pv8LciUBzZZD37rDjIkRfeWU6X365nBEjRlGvXn1Wr17JhAnP43K56NPnRjZuXM+jj47k/vsf5MILjfaZM6fx9NNP8umnX2A2m3nyydE0adKUN95YgKoGePnlKYwe/TAffPBpucdcs2Y1iYmJvPbam+zbt5fnnnsGWVbKdSH45JMPmTlzOiNHjubss1uwadMmJk9+gfT0dIYNG16mfyAQYM2afxg0aEiZbcUkJiZW6Rp1796Dt99+i3vvvZsmTZrSrl0HWrduQ/v2HSu9vC8QHA1VFqKXXHIJL774Ij/++CNNmzYlIiK8Ao4kSQwdOrTaJnj6YAhAXdfZe3AXRe48YvI+Qdq1L9Qjr3EfdLOTbfiZSwF/4IVSaTPPdsZzd92WnBNVuSUgk2IiISKWpIgEdFU6qX0edRUS7YlYFAsHXRm4/d4j7iMhYVZMmBQTVpMFh8WOVbFiU6yYJLNRpVIzErkb/rBCeAoEpyPLly/ju+++Dmtr1aoNU6dOx+128+677zB27HN06dIVgNq167B//z4WLJhHnz43IssKDz88ihtu6Bva/6ab+vHgg8PIzs4iOTmFvXv30LFjJ9LSUjGZzDzxxP+xY8cONE0r12qZkJDAmDFjsVqtNGzYiEGDhjB58gvce+99Zfq+/vpr/O9/A+nevQcAtWrVpqjIxcSJ4xk0aEgZK2pubg6aphETExvWPmnSeJYu/Sys7bvvfq7UNYyOjubNN99m4cIFfPfdNyxcuICFCxdgtdq44467+N//7q7UOAJBVamyEJ0+fToAP//8Mz//XPYDLoTo4VHVAIWFOUT4/yVi24+owYAhX8JZbEtswzxy+RY3eqmUmPXtUdxV51w6x6ZWunSfzWQh0RlPvDXulFl21lSdGEs0lmgLB1zpFHhcYdL5UP/OCIsdm2LDIpsxy+YSa6dGKJWVQCCoHNE3TzspluaPhq5dL2To0PvD2qxWo7719u3b8Hq9jBnzOLJc8v2pqio+nw+Px0OTJk2JiorirbfeZMeO7ezZs5vNmzcF+xk3sIMHD2XKlMl8+OH7tG3bjk6dzufyy6+ocOm8WbOzwwRkixbn4Pf72bVrF05nZKg9JyeH9PSDzJo1nVdfnRlq1zQdr9fLvn17y1gko6NjkCSJ/Pz8sPYBA+7hpptuAeD7779lxoyXK3cBQ+NGM3jwUAYPHkpmZgZ//vkHn376MbNnzyI6OiZMqAsE1UWVhej69esr/IcnODK6pmFWDxJ3YCGBXEMsaSYLLza+lkVkECilM5OtEfSv3YJuCfWqlKzdYbWT4kgk8iQISqoqqqpjk+zUiUrjgJJOoc9dxr/TLFtQSvl3okFAO7XOUyA42bC37o2t5XWnZLBSRIQjLFVRabTgd8Ozz46nXr36ZbZbLBZWrvyb4cOHcv75F9CqVWt69LgSr9fDI488FOrXp89NXHppd3755Sf++usPZs+exRtvvMZbby0sN7XRodUHi+dxaOR5cfsDDzxMhw4dy4yTkpJaps1sNtO8eQtWrvyL/v3vDLXHxsYSGxsbfB5X3uWokLfeepPU1NSQVTYhIZErr+xJjx5XMnDgnfzyy49CiAqOC1UWotdeey0PP/wwl1xyyfGYz+lLqbreia63COwuWXp+6qzL+NhaIjSjJBMD6p1Dj5RGKFRe9JtkBYc1glRn0kkVlFRVdF1HwUSaM5WAFijj34nw7xQIjguSrKA4q+ZbeLJTv359FMXEgQMHuOCCC0PtixYtZMeObYwa9TjvvDOftm3bM378pND29957N/hMJzs7m9dfn03//ndx9dXXcvXV15Kens61117BqlV/c9lll5c57qZNG8OW7f/5ZzVWq41atWqTmZkZ6hcXF0dsbCx79+6hd+8+ofavvlrOihXfMWbM2HLPq1+/Wxkz5jF+++2XUEBVadLTD1bpOq1bt5blyz/nkku6YTKVSANZlnE4HMTFiTyiguNDlYXo/v37wyoqCaqGrBWh786AYArQH+Ma8HFyMwBsusRNtgQGnn0+miMCXwWlVA/FrJiIsNiJs8UQaXGCJp90QUlVRdcBVcKEWfh3CgSCo8bpjOT6629g9uyZOBwOWrZsxcqVfzFjxkv0738XAMnJyfzww/esXr2KpKRkVq78k9mzjWVyn89HUlIyP//8E3v27OHee4fhcDhYuvQzzGYzzZo1L/e46ekHGTfuaW67rT87duzgtdde4bbb+mOxWML6SZLEbbfdyauvziAlJYXOnbuwZctmJk58nq5dLyrTv5ju3XuwYcN6Ro58kJtuuoVLL72M2NhYdu/ezeLFH/HNN1/Rvn2HsH327NnNr7+Gu9RZrTbatm3HgAGDGDx4AMOHD+X22++kbt16ZGZm8O2337Bu3VoefHDEUV1/geBIVFmIXnPNNbz55ps0bNiQpKSk4zGn0xq/rqNnGaKqSDbzTJPLUJC4So/gDlsiZzU5l6KoKDxezxFGwogMt0YQZ4vGaXaGErGf6nkwJUlCliVUTUfTdSwmQ1ifyuK6sr69gtMPSSLMN1Fw4hk+/GFiY2OZPXsWmZkZJCcnc/fdg7nttjsAGDRoCFlZWYwY8QAADRo05PHH/4+nnnqSDRvWUb9+A1588WWmTZvKffcNxuv10LhxEyZPfpnateuUe8yuXS9CURQGDOiPzWand+++FQb83Hrr7VitVt5//11eeulF4uMTuO663oetggRw//0Pct55nfn44w8YNeohcnJyiI6O4ZxzzmXixCl07Rqe3Wb58mUsX74srC0lJZVPPllKkyZNmTv3LV5/fQ7jxj1FTk4ODoeTNm3aMnv2GzRsKFI2Co4Pkl7F+o533nknf/31F6qqEhMTU27U/Ndff13B3icnqqqRnV143MbXdZ3ts37F5DGhalk4D9wEwHNnXcr+Wp25k0hqy1biGjZFrVObXE/RYcezmMxEWh3E2KJxmhzoJ7hM59FgMsnExjrIySkkEChr3ZRlCUmS8PpVijwBXG4/hR4/6GCzKjjtFqxWBZtZwaxI6LpxXU/G05akEjHtVzV8fg2/qmG1mtFVDbMiYTrJz6G6OdL7fzpR/N5ruo4voOH1qfgCGrWSo/B7fSfk/OPiHChKxW49Ho+HrVu3kZCQgsVy+EIYAoFAUFV8Pi+ZmQdo1KghNpvtsH2rbBFNTU3lmmuuOerJnYnM2fg5bQKRJGNCCSZs3xxViwvSutEEK0gQlVIbKS2NfK+7wnFsJguRNiextmjsSgS6pgeXrU89JVPa6un2BSjyBCgo8uH1qnj9apj1s9DtJzvXg8kkYzYrWM0KzggzdouC1WLCbJKR0EOpmk40xSJa03X8qobXq+LxqRS6/Xh9KoGAhqbrRDiseD0+FFnGalGIjDh1xHV1UhXj8KlwLYrff1XT8fpVvD6VIm+AQrcff0DDH1CxWhRqJUceeTCBQCA4w6iyEH3++eePxzxOa1bu+ZmOWvfQa1VSiGlyC5GSYYmwRydgrVOfXF1FO+SXVwJsZitRtkhirFHYFbuxTH2KpGQ6FFmWCGg6bq/fsHq6/fj8Kj7/4a1EOgR/1DWK3H5y8g1hagmKU2eEGbvVhM2iYDHJwRyiVLuluNjaKUkQUIPCI2jFLfIECATnGDgkUExRDPXlD2h4VEOkZud5MB8qrq0KNovppBGmJecrATqqBgHVsPCGTesIk1QCMlq+G7cnUGEQXXkjSLKEWZFRgo/GoYqvyYm/NqXff39AxxsouenweAOhz+ip7EYiEAgEJ5IqC9Fitm7dys8//0x6ejq33347u3fvplmzZjidzuqc32nBk3FO/AE3yEa5taJ63VEdyQCYIxw46zakyGbBV6qGvISE3WIl2hZJtDUam2xEwZ8qOUHhEKunV6UovYCDmS6K3AF8fhX1GH+sAwHNWOb0BMgr8GJSJMwmBbNZxmE3E1EsTM0KsiSVRN1XgdLWTl9Aw+tX8XjVkID2B+dw6A1EZTlUXJtNMmaTjMViIjLM6ivBcRSmxeKq2JfVH9AIaDq+gIrfr+H1B3B7VQLBz2BVxCSAIks4nFYKXd4qfIZ1JElCCQpRRZawWIJC3WQIU5NJwSRLmEwSEoZYrk4Bb3yGAaTQTYfHp+Iq8uP1q/gDhsX7VLDcCgQCwclIlYWopmmMGTOGDz/8EF03fiiuvPJKZs6cya5du1iwYAEpKaImbWliC6PJLP6lkhQK61wMgGwyE5nWgEBsLC6f4aMqSxI2s40YexTRliissjUoQE8NvzpZlkACn1/D7TOWJ11FflRNw2I146qSEKkaAVUnoAZweyHf5TOsaGYFs0nGYTMRYTNjs5qwmmVkSSrJQxqkWHTolLJ2+lTcngBFQWtXIKASOI43AyFh6gmQW8rqa7EYfrLFFlOLSYYquiOUtuYZS8ka/oBOQNXwBY/r9gbw+lRDbGrG5646zldXJFRVDwnZKuyJv7RPZWHJuZQIVBlFkbCYZKwWExazbLhxKDImRcYkSyhhFuaKr1nYjUdQdLp9KoVFPsPXtxxrt0AgEAiOnioL0ZkzZ/LZZ58xbtw4Lr74Yrp06QLAyJEjGTp0KFOmTOGFF16o9omeygR2/AHUB0Az2UE2IZlMOJNqI6emkBPwIEsydouVWHs00ZYozJLllBCgRkSwjKbreLyGb1x+oQ+PL2j1DIoORZGwWM1HGK2EbI+fXQVeajutJNgrv19pVE1H9QbweKGg0IcsS0GLo0KETSHCZsYeFC4BVcPj1/AExbPPp+FXa97aVWz1NYSpF5MiYzbLWMyGj6kt6GNqNSsUC1MovZwO/qD48weMoBmfX8XtNax5qqoT0Iztp9Jysq4Hrw0Aatg2SSJMoMqyYWUuto4bIlUyRGpQzKrBGw+PP0CRO3jjEbR4H6vlXiAQCAQVU2Uh+uGHH3L//fdzww03oKolPwDNmzfn/vvvZ9KkSYfZuyyapjF9+nTef/99CgoK6NChA2PGjKFOnfJTYmzevJmJEyfyzz//IMsyHTp0YPTo0aSlpVX1VE4YEZeNJP+9dMNnUVJQdRmbMx4lpRb5uo5JthAbEU20ORJZN+P3aQQkFQlAClqyMCyNkm60yUjoko4c7FTcp7qzBFUswnR8AR23N0BhkZ+CIj9+v4ovoB6VcPMENNZmFrIy3cXKdBc78ksS/idHmGkRH0GLeAct4iOoF2VFPooT1TQdr8+wcrqKQJLcmE2GMNFUrdpFh1fV2Jrr4b8cNzsLPKRG22kebaVJjB3rYSKaD0dANSxy7pA7QvFSvhy0mJoMf1q/hsfnxxO0boaW1LXTfxlZ14ut42ooX29plKCFNCRUi621QYvn0bpZlBxfZ3+hj/9y3GzKcbM5182uAi+XnLWfOdedfUxjCwQCwelGlYVoZmYmzZuXn8A3OTm5TO3bIzFz5kzeeecdxo8fT0pKChMnTmTgwIF89tlnZRL55uTkcNddd9G2bVvmz5+Pz+dj/PjxDBw4kI8//jisru/JhCntHOSIVWguPzrgM9lxxqXhMjuQ/BasUgT5GZDuLwj6uZVEFhdbtaRQm1T8/5LHkBCluOGQF6WfFY97yOvSPaTw/mUknwSqBr6g1fNolm5VXWdzjpuV6S5WpbtYn+UmUIEAOFjk52BRHt/uzgPAaZZpHhcREqdN445O2Ok6wUAp9cidj0BA09mR7wmJj/9y3OzI9xCua3MAMMsSTWLttExwcG6CIa5tpmMUpl7IK/AZQVE6wop3GFRND16fY19t0HWdTHcg+L4XsTnXzX85HlzlfKY+WXeQZ7udddQWfoFAIDgdqbIQrVevHitWrOD888uWFPvjjz+oV69epcfy+Xy8/vrrjBgxgosvvhiAKVOm0LVrV7788kuuvvrqsP5ff/01RUVFTJgwIZSXauLEiVx88cWsXLmSzp07V/V0TjiSJJFUqxG2tPq4/RY8bo0cXzlmm9MMXdfZne/hp+1Z/HXAxT8ZLlwVRMpLQONYO01j7ezM97Ax242vlLBy+TX+POjiz4MuAEySxFkxNlokRNAizhB2MbajjsM7Ipqus8fl47+cIv4Lis6tuZ6wOR4Ov6azLquIdVlFLNyUgSJBk1g75yY4aBkUphHmo6u5fTyD2YpF126XlwKfEWzm13RUPfh4mNeqDpIi4/YG8Gs6gdJ/evmvNV0nxmoiOcJCcoSZpOBj8fOjFe/HQq43EHrPi/9yvEeugJYcYWZQ53okOy2nVMChQCAQHG+q/Gt9xx13MGbMGPx+P5dccgmSJLFz505+//13Xn/9dUaPHl3psTZu3EhhYWGYgIyKiuLss8/mzz//LCNEO3fuzMyZM8OSoxbX8a2qJbamUBQzzpQGZBVaKHSf3gI03xtgVUYhq4JWzwNFFZ9vqsNCmyQHbZOctEp0EGUp+Wj6NWOJ+9+sItZnFbEuq5Bcb4nFKaDrbMxxszHHzYdkAVDLaQlaTCM4O95BHaflqKob6bpOutsfZuncnOOm6AhJyWWgbpSVJrF2msTaaRxrJzug88eeXP5JL2RfoS/UV9VhQ7abDdlu3vsvE1mCs2KKLaYRnBPvwGk5OmF6NPhUjb0uH7sLvMafy8ueAi+7C3x4TrDP8v5CPxuyy8+tG21VSLZbSHaYSYowhwRrcoSFpAgzjqMU88W4fGrQwlnyl16Jf7OxVlPofW8Sa6dxjJ1asTZaNU3G7/VzKub9FQgEguNFlYVo3759yc7OZtasWSxcuBBd13nooYcwm80MHDiQfv36VXqsAwcOAEaS/NIkJSWFtpWmdu3a1K5dO6xt9uzZ2Gw2OnToUKZ/VTCdIOuKJMnkalY8Pn8ot+Tpgk/V+DezyPDzPOhic467wp/cSLNCm2QHbZMjaZfsJNVRfj1lAEVRaJHooEWikf5K13X2unysyyri38xC1mUWsavAG7bPXpePvS4fX+7MBSDaonB2QgTnJDg4Jz6CxrF2LOUs5+d4AmzKKWJTtptN2W7+yykKE70Vkeaw0DTObvzFRnBWrA27qUQIybJERISVi9Ii0TSdTLefNRmFrMko5J+MQnaXmr+mExI+H2w2LMSNYmy0THTQKtHBuYcI9aNB13VyvQF2F/jYVeBld76H3QVedhV4OVjorxGpJIGRiin4B1Dgq/ja53lV8rxu/sstX6g6zTLJDgvJERZSHCUW1RSHIVSjLEro5sQd0NiSW/Keb8p2s9flK3fc0kSaFZrEGYKzafAx0W4uc9NTXOLzcNWOBAKB4EzkqH7N7rnnHm699VZWrVpFbm4uUVFRtGrVipiYmCqN43YbPyCH+oJarVby8vKOuP/8+fNZsGABTzzxBHFxcVU6dmlkWSI21nHU+1eG3cEfJl2WkRQFp/PEWbiOF5qusyXbzR/78vhzXz6rD7rwVmAxM8sSrZKddEiLomNaNE3iIlCOof52s0g7zVKjuSH4OtfjZ026i38OuliT7mJjZiH+UkvleT6VX/cV8Ou+AgAsikTzBAetkpw4LSY2ZBayIbOQA4VHFh+JEWbOTnDQPPjXLMFBtLVy/5QiIgw/ZqfTRv3ESK4NtmcV+Vl1sIBVB4y/baXElQ5syfWwJdfDR5uzDGEaa6dNSmToL9ZWvt9hQNPYk+9lV56HHXkeduZ52JnnZmee57Ai71BkCdKcVupF26gXbSM+woJZljAXC0dFwiQFc3uWbg89NyLVFUkyItbD+siYg3lCD8UT0Egv9LHf5WW/y3g8EHx+wOUlo6hi0ezya7hyPWzN9ZS7PcIkk+I03o8deW6O5FlhN8k0i3fQLCEi9P7XirRWytJuCd7oRkXZj9hXUHV0XWfp0s9YuvQztm/fSmFhIcnJyXTp0pX+/e8iPj4h1LdTp7Y88cRTXH31tRWOt27dv7z++mzWrl2Dx+MhOTmFSy7pxh13/A+H4/C/FRs2rGfChOeYO/et0Ird8eKbb75izpxX2L9/H/Xq1WfYsOF06HBehf3T09O59toryrQXX48ffvieJUs+ZcKEF4/ntAWCMI7arOJ0OunatesxHbx4id3n84Utt3u9Xuz2ir+wdV3npZdeYtasWQwZMoTbb7/9mOahaTr5+Yev734s/LQzB5vLRxyQWeTntoWrAENg6HrxQp0eeh4sGoMe2lISvV7yXC/TXkzIqiRJYRYmo42ybRX2LadNlpAxLHar0l2HtRY2irbRNtlJ22QnrZKcxEXZKSryomk67iJvhfsdDSagbZydtnF2aJ6IT9XYlONmXWbQappVFCa8fKrOPwcN4Xo4Ii0KTYPWrqZxETSNtRN/aLCJP4DLf3g/wWKLaPH5H4oV6JQYQafECDg3mVxvgLVBi+majEK25XlC77EObMlxsyXHzfsb0gGoF2WlZaKDBtE2Dhb6Q8vq+1xequKSGGGSqRNppU6UlbqRVmpHGo9pTku5FuTKUub8NT0UKxQI/lVEnAJx0VZaRFuB8DKZfk0joyhAepGPA4U+I7Ct+LHIR0aRv8LzLwpoYYK/NGZZolGMLfSeN42zUzvSinKI6CwsrNzn2B70Wc7Pd5+QlGxRUfYzxvqqaRqjR49g9eqV3HHHAEaOHE1ERATbtm3jjTde4847b2PevHcqbazYtm0r9947iL59b2LIkGFERESwadNGXnppMuvWrWXGjNkV7hsI+Bk37ikeeuiR4y5C//77T/7v/x5n2LAHOe+8Tnz22Sc8/PADzJv3Dg0aNCx3ny1bNmO1Wvnww0/DbqAcDqMQzYUXXszChW+zfPkyevS48rjOXyAo5vhFdFSC4iX59PR06tatG2pPT0+nadOm5e7j9/t59NFHWbJkCY8++ih33nlntcwlcASfv2PhtT/3cLumESfJFOg6WZ4jBzccC/4aiphOsJtom+SkbZKT1olOYksFDBVbuzRNPyHBGgoSZ8dGcHZsBH0bJ6DpOrsLvKEgoXVZRew/xPppU2Qax9qCvn2GAEmJKLvMeizzr+z5R5oUzk+N4vzUKADyfQHWZRaxJrOQtZmFbM31hMV878z3sjO/8uI+yW42BGekldqRFuO500qczVShha863rfqfv9lJJLtZpLtZs6NL2upUnWdLLefg0V+0oPi1HjuC7Wpuk6DKFuYX2e9KCvmQ4WEBupROi0U33yoqnZcv2uOFV3T0Y7z91NFyDYT0lGskixc+DY///wTc+fOo1mzkowuKSmptG3bjltu6cvbb7/FsGHDKzXekiWfUqdOHe6774FQW1paLWw2Gw8+OIzNm/+jceMm5e77xRefY7FYaNeufZXPo6q89dYbXHTRJdx0k+EON2zYg6xZ8w+LFr3D6NFPlLvP1q2bqVOnLgkJiRWOe+utt/PiixO57LLLUZRTf+VOcPJTo0K0uCTo77//HhKi+fn5rF+/nttuu63cfR555BG++uorJk+eTM+ePU/kdI+aO9uk8Vn6Fi4r1PhA1kg0m0uyLBWnaaIkPVPx87AUSpIUto9EcYYmKSxjk46RSkitIBLZ2FY95xVhkmmZaAQYtUlyHnVAUGkkCcwmI/G4w25CliQKPf6wGu5Hk+ZRliTqRdmoF2XjqgaGZSTb42d9VhFeVadRjI065Vi8ThaiLCY6p0XROc0QpoV+lX8zi1ibWciazEI255ZdWrYqErWdhlWzjtMSEp61nNYaiTivCRRJIinCQlJE+T7IWrDK0rG4iZwuuDZmkfX1DtTDBBUeT5QIM/GX1cfZLL7S++i6zvvvv8uVV14VJkKLsdlszJjxatjS/JGQJIn9+/ezffu2MMtihw7nsXDhB6Sl1apw37ffnk/PnteEtS1e/DHvvbeQPXt2I0kSTZs2Y/jwETRvbuSU7dWrJ5deehm//PITOTk5PP/8RMaOHcOBA/vLPcYTTzzFVVddzZo1//DAAw+FbWvXrgPfffdNhfPbsmUz9es3OOz5d+rUGZergO+//5Zu3boftq9AUB3UqBC1WCzcdtttTJo0ibi4OGrVqsXEiRNJSUnh8ssvR1VVsrOziYyMxGaz8dFHH/H555/zyCOP0LFjRzIyMkJjFfc5GbmgXiyXD+9CZoGXlJ3ZDKnh9C26bghTVeOIaXcqep0YYaFprD0UVHIsmBQZi1nGZjURGWEhwmrCalHCxIFR5z2A16/h9viDlYEM69LRllyMs5m5oFb0Mc+/PEyKkSzdpBjlJp12M06nlUxJx+0JhOZ+tJ8Eh1nhvNRIzks1lqqL/Crrs4s4UOgnxWFYOxPt5qNK/H8mIR96x1cNSBKhqk0mk4zVquC0mzGZZPzV65FSrWQu34ZWicC844Va5Cdz+bYqCdF9+/Zy4MD+w/pFpqZWrdhJr169+eyzxdxyS1/OOedc2rZtR5s2xl9FS94Au3btYvv2bXTpUuKy9v333zJ58gs8+uiTtG7dhqysTCZPnsBzz41l/vx3Q/0++GARkya9RGRkJI0ancUbbyxA08p/LxwOJwUFBbjdbpKSwstpJyQkkp5+sMI5bt26hZiYGAYPHsDOnTupU6cud901gM6du4T6mExmOnbsxA8/fC+EqOCEUKNCFOD+++8nEAjwxBNP4PF46NChA3PnzsVsNrNnzx66devG888/T+/evVmyZAkAEyZMYMKECWHjFPcRHBlJkjBLEmYZakK6y5IUVqYywm4ySm2ajHrgmqaDHr6Eq0gQYTHhtElIUTY03ahB7gnWgy/y+PH6SsTpiUrorsgSJpOMosiYFAm71YTNYsJslrGaFCOIR5FQFJno6Aii7SY8PhWPL4DXp+Fy+/AERbX/KKtSAUSYFdonRx6540lAqOY9xaVID3ks1Q7FCeg1NFU/6RL1y7IRoKWYjApXEVYTNquC2axgCVa9kiWjkpPNYsJdSZ9SQeXIzjbStcXExIa1P/zwA6xc+VfodUpKKgsXflCpMevUqcv8+Qt55535/PDDCubNe4N5894gMjKSoUMfoFev8n9n1q1bg9lspm7dklza0dHRPPbYGK644irAEMXXXNOLSZPGh+3buXMXOnYsEdOHBvAeSn5+XrBfuL+61WrB5ys/4DIQCLBz5w5kuSEPPPAQDoeDL79czkMP3c/LL88ME/MNG57F0qWfHnYOAkF1USkhum/fvioNWpVym4qiMHLkSEaOHFlmW+3atdm0aVPo9euvv16leQhOHoprfVuj7UTYTETYzFjNxo+0Fiw7WRnfQcPXzuhnkiUibSai7GbkWDsBVTcsp74AHr9q1Iz3a6F67cdSulGWi2uTG6LSZlWwWUxYzYpRYtNkWMDAsDjrwSVfMM5LkvTQ/GXAYTVEdWKMDb+q4fNpoTrnhZ4AgcDJWedcloySmEp5ArLUoywbfWTFiIY3mxQcTitepwXQkSTJGEsCSZaQkYzHUmJUliQ0DPGpqhr+YKlSv1/F61fx+TXD5/QECFUJI/WSyWR8BixmJfQZtpgVLMGbEQnj/S/2CdU1HRUdSTr5XSESejQ8KZbmq0J0tCFAi4VZMaNHP4HHYwSivffeu/z444oqjZucnMKDD47kwQdHsm/fXv744zc+/PB9xo8fR1JSMuef36XMPllZWURFRYf5VbZp047t27fx+utz2LFjB3v27GLLls1oWvgqTp06dcNe9+vXp8Kl+VGjHqdzZ6OgjO+QYiherw+brfxAX5PJxPLl3yHLcmj1sFmzs9m2bStvvz0/TIjGxsaQlZVZ0eURCKqVSgnRSy+9tEq+fxs2bDjqCQlOD2TZ+LG2WmQiI6xERphJiHfg8xg16TVND/1IHwu6Hv7Db1EkrBEWYmQgKE69QeHi8aoUevxhS/qHatNi65WxtC5jtcjYrWYsQQun2WwIEQmpHMFZeReB0vOWAKtZxm61EhdpWHt9fhWPT8XtVXG5ffj9JT6yx5OQiJTlUE12WZGxmWUsFhNmJZiiyaQYfSUJqVhYygCGwJQkwq6tokhER0eQm1uEGrrueuhalI8h2mVZwiwr2C2lLaXG95FfNcSoUepUx69q+P2GG0d5QlXT9CN+4mRJwmQq+QwYVk4TVrOM2awY6acU2Rgr+D4CaCc42X9142wWj6NJ3CkVrFSrVi0SEhJYufJvunfvEWpPTCwJxomKiqrSmNOmTaVTp84hYZaWVotevW7gqquuoU+f6/jllx/LFaKSJJdZTl++fBljx/4fPXpcScuWLbn++t5s3bq1jEX00PLUL774MoFA+e9DXFw8ERER2O12MjMzwrZlZmaEnfuhRERElGlr1KgRv/32a1ibqmrHPepfICimUkL0ueeeCwnRvLw8Jk2aROfOnbnyyitJTEwkNzeXb7/9lu+//75KlZXONCSMxPmSpIdyNJWkagpSKm0THO5H+uRCgtBye4TNjNNuxm5VsFoU0A0xUbw0WV76oupE13VUtWReNrOC3WJCjjQ+w+X5m5oUGbvNFLJwmRUZsyIjy1JIbJYIjhKrbHVS2tprVmQsEQoxToCIEjHtU3EV+fH6VfwB1fAzreJU5GDOTkUJik1ZwmyRsZpNhmXXJGOWS3xclaDALBHd+iHHDObI1Yxt5d1cFFsES980VJXS70HxdSoWqiZZQSpHqAZUw6pcnlD1+41tmqYbYtcsE2E1Y7MoWMwyZpOC2VTOTYd+fLNs1CSSLKFElJ+T9mREURRuvLEfc+fOoXfvPuVGsx/OZ7I8/vzzD7Zt21rG79RisWC1WomLK9+HNSEhgfz8fDStRMS99dYbXHttL0aNeizU74cfDOusrusVGngq49faqlVrVq78i2uv7RVq+/vvP2nTpm25/bdt28rAgXcyceKUsKj+9evXl/F9zcnJPmxkvUBQnVRKiJb2vRw6dCi9evVi3LhxYX2uueYann32WZYtW8ZNN91UvbM8DdB1nWinlYZp0aiaBiVpFA3xqZcWpeGWovAconqp9pIfR50SsaQBumoEF5VYbYLj6pTaJ/jDHrZ/yfPS7eWhKMVWT4WoCAt2m+HrqchS8Lh6ULSVVJapKfRSPqeH+pvq6MiSFBRJJWKn9D41N2cA3UgAbzMRaTeTFGPDH9DxBFS8QSuvxxsI+pkan63iJfFiwWlS5KDACvqtBl0JlKCPY/CIoc9Qid4rmcPJTnlCtaRa02GEqq4bvr6hFGPH/6ZDUH3cdtsdbNq0kXvuGUD//nfSpUtXHA4nW7du5v33F/HHH79xzTXXhe2zdesWfv3157C2qKhoWrQ4hyFDhjJixIM8/vgo+vS5kZSUVPbv389nn31CUVER111Xvo9oixbnoKoqmzf/R9OmzQBjiX/NmtVs3LgBp9PJjz+u4IMPFgFG/uxDLaFVoV+/23jooftp0qQZ55/fhc8+W8x///3H44//X6hPTk4OZrMJpzOS+vUbUL9+fSZNGs+oUY8RExPLJ598xLp1a3njjQVhY2/atJEWLc456rkJBFVB0vWKZEb5tG7dmhkzZtClS9mliZ9//pl7772Xf/75p9omeCJQVY3s7MLjegyTSSY21kFOTuFhlnDDxVplvCFK+pTtXLxN14vT1JQWm0GRqQWfFy+Ta0EhGxSwxeltQo+asdwpyRIRNjP2oPUIncNaukqf/+lqTTocx+v8ZVlCkiRULeiCEEzabywtK5hkQpbP0iKziv/sjxnx/p/Y84+Lcxw2ob3H42Hr1m0kJKRgsRy9GDqZ+Oabr/jss8Vs2rSRgoJ84uMTaN26Db169aZNm3ahfp06lW8xbNOmHbNmzQFgzZp/mD//Tf79dy0FBflERUXTqVNnBg4cfNgYiFtvvZGrrrqGW281iqzs27eX558fx7p1azGbLTRu3JjrruvNk08+yiuvvEbr1m3p1asnPXtew913D67yOS9btoS5c+eQkZFO/foNGDZsOO3bdwxt79WrJ23btmfMmKcBw4911qxp/PrrL7hcBTRp0oyhQ4fRunXJNQkE/Fx11eWMHv0El17arcpzEggAfD4vmZkHaNSo4REzGlVZiF5yySXceOONDBkypMy2yZMns3z5cr788suqzbiGOdFC9GT+IQ4Xv1IZMVxiTQq3Hh6JU+X8jxcn6vxLlvoqtmTXBOL9F0L0TOCTTz7i/fff5e2336vpqRw13377NdOnv8R7732EyXTquGkITi6qIkSrnL6pb9++zJgxA4/Hw8UXX0xsbCyZmZl88cUXLFy4kMcee+zIgwhOWsLFS0Vi5iRSOIIwTrSlUyAQlHD11dfw9ttv8fvvv3HeeZ1qejpHxbvvvsOAAYOECBWcMKosRIcMGUJBQQFz585l9myj5q6u69hsNh544AFuvfXWap+kQCAQCAQnOyaTmf/7v7FMnDieDh06nnKR599//y2Rkc4y1aEEguNJlZfmiykoKGD16tXk5eURGxtLmzZtyk0NcSogluaPP+L8xfmL8xdL8wKB4MzguC7NF+NwOEhMTETXdVq1aoXP5ztlhahAIBAIBAKB4MRzVEJ08eLFTJ48mYyMDCRJ4v3332fatGmYzWYmT558xPJkAoFAIBAIBAJBlR1YPv/8c0aNGkWnTp148cUXQ6XKunfvzooVK5g5c2a1T1IgEAgExwMR3CYQCI4Hlf9uqbJF9JVXXuHmm2/mqaeeQlVLypndcMMNZGdn89577zF8+PCqDisQCASCE4TZbEaSwOv1YrEc3n9LIBAIqorX60WSjO+aI1FlIbp9+3ZGjRpV7rZWrVoxbdq0qg4pEAgEghOIoijExMSQk5MLFNc6r9nqZwKB4HRAx+v1UlCQS2xsDIqiHHGPKgvR+Ph4tm7dWm5lpa1btxIfX34dXoFAIBCcPKSmpgKQm5tLQUENT0YgEJw2SBLExsaEvmOORJWF6FVXXcXLL79MUlISF110UfCgEv/++y8zZ87k6quvruqQAoFAIDjBSJJEWloaycnJ+P3+mp6OQCA4TTCbzZWyhBZTZSE6fPhw/vvvP4YPHx5K1nv77bdTVFRE+/bteeCBB6o6pEAgEAhqCEVRqvSjIRAIBNVJlYWoxWLhtdde4+eff+a3334jNzeXyMhIOnbsyEUXXVSq1rVAIBAIBAKBQFAxVRaiAwYMYODAgXTp0qVcP1GBQCAQCAQCgaAyVDmP6MqVK4XVUyAQCAQCgUBwzFRZiHbt2pVPP/1UOLcLBAKBQCAQCI6JKi/NW61WPv30U5YtW0ajRo3K1JeXJIl58+ZV2wQFAoFAIBAIBKcn/9/e3cfVfP//A3+cUoRSmcoYpo9T6JpzaKQLlI9hhvExudoyF1MfNuQyGeaalKE2NFazmRGbbWYYW4rk4mauc/HFpKhEpaPO6/fHfp2P45w2h1NvTo/77datzuv9er/ez9d52+mx91UGB9Hs7Gx4e3trXguh/WecHn9NRERERKSPwUF006ZNVVEHEREREdUwBl8j+neKi4tx4MABYw5JRERERCbK4COiN27cQHR0NA4fPgyVSqW3z5kzZ565MCIiIiIybQYH0QULFiAzMxNvvfUWMjMzYWVlBS8vL/z+++84f/484uLiqqJOIiIiIjIxBp+aP3LkCCZOnIiZM2eiX79+qF27NiZPnoytW7dCoVDgl19+qYo6iYiIiMjEGBxEi4qK4OLiAgBo2bIlTp8+DeCvv1f89ttvIy0tzbgVEhEREZFJMjiIOjg44Pbt2wCA5s2b4+7du8jNzQUA2Nra4s6dO8atkIiIiIhMksFB1N/fHzExMTh27BiaNGkCJycnrF+/Hvfv38fWrVvh6OhYFXUSERERkYkxOIhGRETAxsYGK1euBABMnDgRn3/+ORQKBXbu3ImRI0cavUgiIiIiMj0G3zVvZ2eHLVu2ICcnBwDQp08fvPzyyzh+/Dg8PDygVCqNXiQRERERmR6Dg2gFBwcHzc/t27dH+/btjVIQEREREdUMBgfRadOm/WOfBQsWPFUxRERERFRzGBxE09PTddqKi4tRUFAAW1tbuLu7G6UwIiIiIjJtBgfRvXv36m3PysrC+PHj0bdv32etiYiIiIhqAIPvmq+Ms7MzwsPDsWrVKmMNSUREREQmzGhBFADq16+PGzduGHNIIiIiIjJRBp+a//PPP3XaysvLcevWLcTGxsLZ2dkohRERERGRaTM4iAYFBUEmk+m0CyFQp04dnponIiIioidicBD9+OOPdYKoTCZD/fr10aFDB1hbWxutOCIiIiIyXQYH0X79+lVFHURERERUwxgcRLdv325Qfz7OiYiIiIj0MTiIzpgxA0IIzVeFitP1j7cxiBIRERGRPgYH0aSkJIwdOxbDhw9Hnz594OjoiIKCAuzduxeLFy9GZGQkfH19q6JWIiIiIjIhBgfRjz76CMOHD8eYMWM0bQ0bNsRbb72FBw8e4PPPP8eAAQOMWiQRERERmR6DH2iflZUFNzc3vcuaN2+O//u//zNoPLVajdjYWPj5+cHLywujRo3CtWvXnmi9sLAwxMXFGbQ9IiIiIno+GBxEmzdvjpSUFL3LvvrqK7i4uBg03urVq5GcnIy5c+di8+bNmoCpUqkqXUelUmH69Ok4ePCgQdsiIiIioueHwafmx40bhwkTJuDKlSvo2rUr7O3tcfv2bezevRtZWVlYv379E4+lUqmwfv16TJo0CQEBAQCAFStWwM/PD7t370avXr101snMzERUVBQePHgAGxsbQ8snIiIioueEwUdEe/TogU8++QTl5eWIiYlBVFQUVq1ahXr16iExMREKheKJxzp79iyKioq0bm6ysbFBmzZtcOTIEb3r/Prrr/Dz88P27dv58HwiIiKiF5jBR0QBoGvXrujatSsePHiAu3fvokGDBqhTp47B42RnZwMAGjdurNXu4OCgWfa4iRMnGl7wE6hVy+BMbhBzczOt7zUN58/5P/q9pqnp8yciqsxTBdH79++jqKgIjo6OMDc3x6ZNm/Dnn38iJCTEoCOiJSUlAABLS0ut9tq1a+Pu3btPU9pTMTOTwc6uXrVsy8bGqlq287zi/Dn/mqymz5+I6HEGB9ETJ04gLCwM//nPf/Dhhx9i3rx5+Oqrr2BjY4Pk5GTExcWha9euTzRWxVFUlUqldUS1tLQUVlbV94GtVgsUFhZX6TbMzc1gY2OFwsISlJerq3RbzyPOn/Pn/Ktv/jY2Vjz6SkQvBIODaExMDJydnTFw4ECUlJQgJSUFb7/9NqKiohAVFYW1a9c+cRCtOCWfk5ODZs2aadpzcnIMvvv+WZWVVc8vx/JydbVt63nE+XP+nH/NnT8R0eMM/l/mEydOYOzYsXjllVfw+++/o7S0FG+88QYAoGfPnrhw4cITj+Xq6or69esjPT1d01ZYWIjTp08bdIqfiIiIiF48Bh8RNTMzQ+3atQEABw8ehI2NDTw8PAD8de2oITctWVpaIjQ0FEuXLoW9vT2aNGmCJUuWwMnJCcHBwSgvL0deXh6sra2f6mYoIiIiInp+GRxE3dzcsGXLFtSpUwc//vgjAgICIJPJcOfOHXz66aeV/tWlykRERKCsrAwzZ87EgwcPoFAosG7dOlhYWOD69evo2rUrFixYgH79+hlaKhERERE9x2RCCGHICn/88QfCwsKQn58Pe3t7JCcno0WLFvD19YVarca6desMDqNSKy9XIy+vqEq3UauWGezs6iE/v6hGXiPG+XP+nH/1zd/evh5vViKiF4LBR0Tbtm2Ln3/+GVlZWWjVqhXq1q0LAIiOjoaPjw8aNWpk9CKJiIiIyPQ81XNE69evD09PT622kJAQoxRERERERDUDz90QERERkSQYRImIiIhIEgyiRERERCQJBlEiIiIikgSDKBERERFJgkGUiIiIiCTBIEpEREREkmAQJSIiIiJJMIgSERERkSQYRImIiIhIEgyiRERERCQJBlEiIiIikgSDKBERERFJgkGUiIiIiCTBIEpEREREkmAQJSIiIiJJMIgSERERkSQYRImIiIhIEgyiRERERCQJBlEiIiIikgSDKBERERFJgkGUiIiIiCTBIEpEREREkmAQJSIiIiJJMIgSERERkSQYRImIiIhIEgyiRERERCQJBlEiIiIikgSDKBERERFJgkGUiIiIiCTBIEpEREREkmAQJSIiIiJJMIgSERERkSQYRImIiIhIEgyiRERERCQJBlEiIiIikgSDKBERERFJgkGUiIiIiCTBIEpEREREkmAQJSIiIiJJMIgSERERkSQYRImIiIhIEgyiRERERCQJyYOoWq1GbGws/Pz84OXlhVGjRuHatWuV9s/Pz8eHH34IhUIBpVKJOXPmoKSkpBorJiIiIiJjkDyIrl69GsnJyZg7dy42b94MtVqNsLAwqFQqvf0jIiJw9epVJCYmYuXKlfj1118RHR1dvUUTERER0TOTNIiqVCqsX78eERERCAgIgKurK1asWIHs7Gzs3r1bp/+xY8dw+PBhLFq0CG3btoWvry8++ugjpKSk4NatWxLMgIiIiIielqRB9OzZsygqKoKvr6+mzcbGBm3atMGRI0d0+mdkZKBRo0ZwdnbWtCmVSshkMhw9erRaaiYiIiIi45A0iGZnZwMAGjdurNXu4OCgWfaoW7du6fS1tLSEra0tbt68WXWFEhEREZHR1ZJy4xU3GVlaWmq1165dG3fv3tXb//G+Ff1LS0ufqZZatao2k5ubm2l9r2k4f87/0e81TU2fPxFRZSQNonXq1AHw17WiFT8DQGlpKaysrPT213cTU2lpKerWrfvUdZiZyWBnV++p1zeEjY3uvGoSzp/zr8lq+vyJiB4naRCtOM2ek5ODZs2aadpzcnLg4uKi09/JyQl79uzRalOpVCgoKICDg8NT16FWCxQWFj/1+k/C3NwMNjZWKCwsQXm5ukq39Tzi/Dl/zr/65m9jY8Wjr0T0QpA0iLq6uqJ+/fpIT0/XBNHCwkKcPn0aoaGhOv0VCgWWLl2Kq1evonnz5gCAw4cPAwDatWv3TLWUlVXPL8fycnW1bet5xPlz/px/zZ0/EdHjJA2ilpaWCA0NxdKlS2Fvb48mTZpgyZIlcHJyQnBwMMrLy5GXlwdra2vUqVMHnp6e8PHxwcSJExEdHY3i4mJERUWhb9++cHR0lHIqRERERGQgyc/dREREYMCAAZg5cyYGDx4Mc3NzrFu3DhYWFrh58yY6d+6MXbt2AQBkMhlWrVqFpk2bYvjw4ZgwYQK6dOnCB9oTERERvYBkQgghdRFSKy9XIy+vqEq3UauWGezs6iE/v6hGnprj/Dl/zr/65m9vX4/XiBLRC4GfVEREREQkCQZRIiIiIpIEgygRERERSYJBlIiIiIgkwSBKRERERJJgECUiIiIiSTCIEhEREZEk+BxRAEIIqNVV/zaYm5vVyL+zXYHz5/w5/+qZv5mZDDKZrFq2RUT0LBhEiYiIiEgSPDVPRERERJJgECUiIiIiSTCIEhEREZEkGESJiIiISBIMokREREQkCQZRIiIiIpIEgygRERERSYJBlIiIiIgkwSBKRERERJJgECUiIiIiSTCIEhEREZEkGESJiIiISBIMokREREQkCQZRI1Gr1YiNjYWfnx+8vLwwatQoXLt2rdL++fn5+PDDD6FQKKBUKjFnzhyUlJRUY8XGVVBQgKioKHTp0gU+Pj4YPHgwMjIyKu2/Zs0auLi46Hy9qG7duqV3Pt9++63e/qa0/9PT0/XO3cXFBV27dtW7ztGjR/X2T09Pr+bqn118fDyGDh2q1XbmzBmEhobCy8sLQUFB2Lhx4z+O88MPP6Bnz57w8PBA3759cejQoaoqmYjouVFL6gJMxerVq5GcnIyFCxfCyckJS5YsQVhYGHbu3AlLS0ud/hERESgpKUFiYiIKCwsxY8YMFBcXY9GiRRJU/+w++OAD5ObmYvny5WjYsCE2bdqEd999F9u2bUPLli11+p87dw5vvPEGJk+eLEG1xnf27FnUrl0be/bsgUwm07RbW1vr7W9K+9/b2xu//fabVtvx48cRHh6OcePG6V3n3LlzaNasGZKTk7XaGzRoUGV1VoWkpCTExMSgffv2mrb8/HyMHDkSQUFBmDNnDo4fP445c+agXr166N+/v95x0tLSMHnyZEyZMgWdOnXCN998g/feew/bt2+Hs7NzdU2HiKj6CXpmpaWlwtvbWyQlJWna7t69Kzw8PMTOnTt1+mdmZgq5XC4uXryoaTt48KBwcXER2dnZ1VKzMV25ckXI5XKRkZGhaVOr1aJbt24iJiZG7zr//ve/xYYNG6qpwqqXkJAgevfu/UR9TW3/P66oqEgEBgaKqVOnVtpn9uzZYsyYMdVYlXFlZ2eL0aNHCy8vL9GjRw8RGhqqWbZ27VrRuXNn8fDhQ03bsmXLRHBwcKXjvfPOO+K///2vVtugQYPErFmzjF47EdHzhKfmjeDs2bMoKiqCr6+vps3GxgZt2rTBkSNHdPpnZGSgUaNGWkc6lEolZDIZjh49Wi01G5OdnR0SEhLg7u6uaZPJZJDJZCgsLNTpr1KpcOXKFb1HSl9U586de+IjV6a2/x+3du1alJSUIDIystI+hrxfz6M//vgDFhYW2LFjBzw9PbWWZWRkQKlUolat/51w6tixI65cuYLbt2/rjKVWq5GZman1+QEAHTp00Pv5QURkShhEjSA7OxsA0LhxY612BwcHzbJH3bp1S6evpaUlbG1tcfPmzaortIrY2NjA399f6xKEn376CVevXoWfn59O/4sXL6K8vBw//fQTQkJCEBAQgMmTJyMnJ6c6yzaq8+fPIy8vD0OGDMFrr72GwYMH48CBA3r7mtr+f1ReXh4SExMxZswY2NraVtrvwoULuHTpEvr164dOnTph5MiROHnyZPUV+oyCgoIQFxeHV155RWdZdnY2nJyctNocHBwAQO/+LSwsRHFxsd519H1+EBGZEgZRI6i4yeTxa0Fr166N0tJSvf31XTdaWf8XTWZmJqZNm4bg4GAEBAToLD9//jwAwMrKCitXrsT8+fNx6dIlDBs2DA8ePKjmap9dWVkZLl26hLt37yI8PBwJCQnw8vLCe++9p/eGE1Pe/8nJybC2tsagQYMq7XPz5k3cu3cPxcXFmDlzJlavXo2XXnoJoaGhuHjxYjVWWzUePHig97MAgN79W/Fv/kk/P4iITAlvVjKCOnXqAPjrlHPFz8Bfv3SsrKz09lepVDrtpaWlqFu3btUVWg327NmDSZMmwcfHB0uXLtXbp2/fvujSpQvs7e01ba1atUKXLl2wd+9e9OzZs7rKNYpatWohPT0d5ubmmv3v5uaGCxcuYN26dTqnXE15/2/fvh19+/bV+u/gcY0bN8aRI0dgZWUFCwsLAIC7uztOnz6NTZs2Yc6cOdVVbpXQt38rAqW+/VsRUvWto+/zg4jIlPCIqBFUnGZ9/NRyTk4OHB0ddfo7OTnp9FWpVCgoKNCcwnsRffHFFwgPD0dgYCDWrl2r+QWrz6MhFPjrNKStre0LeyqyXr16OuGrVatWuHXrlk5fU93/Z8+exbVr19C7d+9/7GtjY6MJoQBgZmYGZ2dnve/Xi0bf/q14re/zwNbWFnXr1n3izw8iIlPCIGoErq6uqF+/vtYzEAsLC3H69GkoFAqd/gqFAtnZ2bh69aqm7fDhwwCAdu3aVX3BVSA5ORlz587FkCFDsHz5cr2nniusWLECISEhEEJo2q5fv478/Hz861//qo5yjerChQvw8fHReQbmqVOn9M7HFPc/8NdNOg0bNoSrq+vf9jtw4AC8vb21nrNbVlaGs2fPvpD7/3EKhQJHjx5FeXm5pi0tLQ2vvvoqGjZsqNNfJpPBx8dH82+gQnp6utZjoYiITBGDqBFYWloiNDQUS5cuxS+//IKzZ89i4sSJcHJyQnBwMMrLy5Gbm6u5FszT0xM+Pj6YOHEiTp48ibS0NERFRaFv374v5BGQy5cv4+OPP0b37t0xevRo3L59G7m5ucjNzcW9e/egUqmQm5urOfXYvXt33LhxA9HR0bh8+TKOHDmC8PBw+Pj46L256Xnn7OyMli1b4qOPPkJGRgaysrKwYMECHD9+HGPHjjX5/V/h9OnTlf5RgtzcXBQVFQEAfHx8YGdnh8jISJw6dQrnzp1DZGQkCgoKMGLEiGqsuGr0798f9+/fx4wZM3Dx4kV8++23SExMxOjRozV97t27h7y8PM3rkSNH4vvvv8eGDRuQlZWFxYsX48yZMxg+fLgUUyAiqj5SPz/KVJSVlYnFixeLjh07Ci8vLzFq1Chx7do1IYQQ165dE3K5XGzdulXT//bt2yI8PFx4eXmJDh06iNmzZ4sHDx5IVf4zWbNmjZDL5Xq/IiMjRVpampDL5SItLU2zTmpqqhg0aJDw8vISSqVSTJs2TRQUFEg4i2eTm5srpk6dKjp16iTc3d3FoEGDxJEjR4QQpr//K4SFhYkJEyboXSaXy0VsbKzm9dWrV0V4eLhQKpXC09NTvPPOO+LcuXPVVapRRUZGaj1HVAghTpw4IQYOHCjc3NxEYGCg2LRpk846gYGBWm3btm0T3bt3F+7u7uLNN98UqampVV47EZHUZEI8cn6UiIiIiKia8NQ8EREREUmCQZSIiIiIJMEgSkRERESSYBAlIiIiIkkwiBIRERGRJBhEiYiIiEgSDKJU41XlE8z4dDQiIqLKMYiS0QQFBWHq1KlSl2GQCxcuYPDgwUYft7CwEFOmTEFGRobRx37eXL9+HS4uLvj222+lLoWIiF4wtaQugEzHqlWrUL9+fanLMMiPP/6IY8eOGX3cM2fOICUlBf379zf62ERERKaCQZSMpk2bNlKXQERERC8Qnpono3n01HzF6doffvgBERER8Pb2hlKpxMyZM1FcXPyPY126dAnjx4+HUqmEQqHA6NGjkZWVpVl+7949LFiwAN26dYO7uzt69eqFb775Rqee2NhYLFq0CK+99ho8PDzw7rvv4sqVKwCAuLg4rFq1CgDg4uKCuLg4AIBarUZCQgK6d+8ONzc3hISEYNOmTZpxT506hbZt22pdhnDnzh34+vpi5MiRSEtLw7BhwwAAw4YNw9ChQyudZ2lpKRYvXgx/f3+4ubmhd+/e2LVrl2b5L7/8olUbAGRlZcHDwwPTp0/XtO3Zswdvv/02vL294ebmhh49eiApKUmzPD09HS4uLjh06BCGDh0KDw8PBAQEYMuWLcjJycH48ePh7e0Nf39/JCYm6qz322+/YciQIfDw8EBwcDCSk5Mr33kA/vzzT3zwwQdQKpXw9PTE8OHDcfr0aa0+3333Hfr06QMPDw907NgRkyZNwq1bt/52XCIiMjHS/ql7MiWBgYEiMjJSCCHEtWvXhFwuFwqFQixcuFCkpqaKtWvXChcXF7F06dK/HSc7O1u0b99evP766+L7778X+/btE/369ROdOnUS+fn5oqSkRPTq1Uv4+vqKL7/8Uhw4cEBERUUJuVwu1qxZo1VPu3btxHvvvSf2798vUlJShFKpFAMHDhRCCHHz5k0xffp0IZfLxbFjx8TNmzeFEELMmjVLtG3bVsTGxoqDBw+K5cuXC1dXV7Fq1SrN2CtWrBByuVykpqYKIYQYN26cUCqVIjs7W9y7d0988cUXQi6Xiy+++EJcuHBB7zzVarV49913hbe3t9iwYYM4cOCAmDVrlpDL5WLbtm2afpMmTRJt27YVFy9eFA8fPhT9+vUT3bp1E/fv3xdCCLFv3z4hl8vFvHnzRGpqqti7d68ICwsTcrlcHD9+XAghRFpampDL5aJjx45i/fr1IjU1VYwYMUK0bt1ahISEiJiYGJGamirGjx8v5HK5OHHihNZ67du3F/PmzRMHDhwQs2fPFnK5XCQlJWnt661btwohhLhz547w8/MTwcHBYseOHeLnn38WoaGhwsvLS1y8eFEIIURGRoZo3bq1iIuLE2lpaWL79u2iU6dOYsiQIf/0z4yIiEwIgygZjb4gOmnSJK0+Q4cOFb169frbcRYuXCg8PDxETk6Opu3mzZsiICBA7N+/XyQlJQm5XC4yMzO11ps+fbpwd3cX+fn5mnoCAwNFWVmZpk9cXJyQy+UiLy9PCCFEbGyskMvlmuWXLl0SLi4uIj4+XmvsFStWCHd3d816KpVK9O7dW4SEhIitW7cKuVwufvjhB03/igCXlpZW6Tx/++03IZfLxffff6/VPmnSJNGpUyfx8OFDIYQQBQUFonPnzmLYsGFi9erVonXr1uLYsWOa/p9++qnmfa+Qn58v5HK5Zh4V9SxZskTT5/jx40Iul4vJkydr2vLy8oRcLhcbNmzQWm/atGla448dO1Z06tRJqNVqnSC6fPly4e7uLq5fv67pX1paKrp27SrCw8OFEELEx8cLb29vUVpaqumzf/9+ERcXJ9RqdaXvGRERmRaemqcq5eXlpfXayclJc2perVajrKxM6wsAjh49Ci8vLzRq1EhrvX379sHf3x+HDx9GkyZN4O3trTV2nz59UFpaihMnTmja3N3dYW5urjUOAJSUlOitNy0tDUIIBAUFadUVFBSE0tJSHD16FABgYWGBRYsW4fr165gxYwbefPNN9OjRw6D35tChQ5DJZPD399fZVm5uLi5cuAAAaNCgAebOnYu0tDTExsZi7NixWu9rWFgYFi5ciKKiIpw6dQq7du1CfHw8AEClUmlt89H3rGHDhgAAT09PTZudnR2Avy59eNSbb76p9To4OBi5ubm4fPmy3nm1bt0ajo6OmjmZmZmhS5cuSE1NBQAoFAqUlJSgV69eWLZsGTIyMtC5c2eMHz8eMpnMoPeRiIheXLxZiaqUlZWV1mszMzPNszU/+eQTzTWaFc6dO4eCggI0bdq00jHv3r2rFVIrvPTSSwD+enTS320f+CsE61NQUAAAeP311/Uuf/QaxtatW8PFxQWnTp1CYGBgpfVWpqCgAEII+Pj46F2ek5OD1q1bAwBee+01ODg4ICcnR2dbeXl5mD17Nvbs2QOZTIbmzZujffv2AHSfY6rvqQaPv0f6ODo6ar2uCLH69kVBQQGuXr2Ktm3b6h2rpKQE3t7eSEhIQGJiIjZs2ICEhAS89NJLGDNmzN9eU0tERKaFQZQkM3DgQAQEBOi0W1tbIy8vT6f90KFDaNq0KRo0aICrV6/qLM/NzQXwv6N6T8PGxgYA8Pnnn6NevXo6y19++WXNz1999RVOnToFV1dXzJ8/H76+vpr1n4S1tTXq1q2LjRs36l3evHlzzc+rVq1CQUEBWrZsiZkzZ2LLli2wsLAAAEyaNAmXLl1CYmIivL29YWlpiZKSEnz99ddPXMs/yc/PR7NmzTSv79y5A+B/gfTxeSmVSkyZMkXvWJaWlgAAPz8/+Pn5oaSkBGlpadi4cSPmzZsHT09PeHh4GK12IiJ6fvHUPEnG0dER7u7uWl8A0L59e5w4cUIrjN65cwdhYWH49ddfoVAocOPGDZ3nf+7YsQMWFhYGhZiKI6QVKo4k5ufna9WVl5eHlStXao6Y3rhxA4sWLcKAAQOwdu1a3Lt3D/Pnz9eM8+jlAJVRKpUoLi6GEEJrW+fPn8cnn3yiuVTh5MmT+OyzzzB27FgsWbIE58+fx5o1azTjHD16FMHBwejQoYMm5B04cABA5Ud+DbVnzx6t1z/++COaNGmiFU4fndfly5fx6quvas0rJSUF33zzDczNzbFo0SL0798fQghYWVkhMDAQkZGRAP66456IiGoGHhGl586IESOwfft2hIWFYfTo0bCwsMCaNWvg5OSE3r17w9LSEsnJyXj//fcRERGBpk2bYu/evdi6dSvGjx9v0FHJir7fffcdPD094eLigj59+mDWrFm4ceMG3NzccPnyZaxYsQJNmzZFixYtIITAjBkzYGVlhSlTpqBBgwaYMGECPv74Y4SEhCAoKAjW1tYAgP3796NBgwZwdXXV2ba/vz8UCgXGjRuHcePGwdnZGSdPnkRsbCz8/Pxgb28PlUqFqVOnwtnZGaNGjYKFhQVCQ0MRHx+Pbt26oU2bNvDw8MDOnTvRtm1bODk5ITMzEwkJCZDJZJVeC2uoDRs2oHbt2vDy8sLu3buxb98+LFu2TG/fESNGICUlBSNGjMA777wDOzs77Nq1C19//TWmTZsGAOjYsSM2bNiAqVOnok+fPnj48CE+++wz2NraomPHjkapmYiInn8MovTcady4MZKTk7FkyRJMnToVlpaW6NChA1asWIEGDRoAADZt2oRly5Zh5cqVuH//Plq2bIn58+djwIABBm0rODgYKSkpmDp1KgYMGIDo6GgsWLAA8fHx2Lx5M7Kzs9GwYUP07NkTEyZMgLm5OZKSknDo0CHExMRo6hk6dCh27tyJqKgo+Pj4oFWrVujVqxeSkpJw8OBBfPfddzrbNjMzQ0JCAlauXIn4+HjcuXMHjo6OGDlyJN5//30AQExMDC5fvowvv/xScyp+woQJ+PnnnxEZGYmtW7di4cKFmDt3LubOnQsAaNGiBebMmYMdO3YY7U+MTp8+Hdu2bUN8fDxatmyJ2NhYhISE6O3r6OiIzZs3Y9myZYiOjkZpaSlatGihtX/8/f2xdOlSrF+/XnODUrt27bBx40bY2toapWYiInr+ycTjdzMQEf1/6enpGDZsGDZu3IgOHTpIXQ4REZkYXiNKRERERJJgECUiIiIiSfDUPBERERFJgkdEiYiIiEgSDKJEREREJAkGUSIiIiKSBIMoEREREUmCQZSIiIiIJMEgSkRERESSYBAlIiIiIkkwiBIRERGRJBhEiYiIiEgS/w9uq8Lqvpy/oQAAAABJRU5ErkJggg==", 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" ] @@ -564,12 +596,12 @@ "output_type": "stream", "text": [ "Processing: scale-y=2\n", - "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -582,12 +614,12 @@ "output_type": "stream", "text": [ "Processing: scale-y=3\n", - "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -600,12 +632,12 @@ "output_type": "stream", "text": [ "Processing: skewed\n", - "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -618,12 +650,12 @@ "output_type": "stream", "text": [ "Processing: standard\n", - "Metric keys: ['Transformer', 'Least Squares', 'ridge_alpha=0.1', 'ridge_alpha=1.0', 'ridge_var_adj_alpha=1.0_ar=0.5', 'feasible_gls_ar=est', 'gls_ar=0.5', '3-Nearest Neighbors', 'Averaging']\n" + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "data": { - "image/png": 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", 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QlGWEoioo1+hq8ZOpZKMFTWhCJYokS5AdC58rmUqSil5sQvd60TIykINsqMVUcgWCgnjuuRd4+ulB/vddu3Zm6NAX6Nix8yVcFezdu4evvvqS4cNfok2ba4WSKhAUgFBUBeWb4saoSrJIpCphdN3XnQqDDGggmdAxIKletOS9SOF1izyWJy0dc3QMIipJUFLYbCHYbCEB24KDbURFRV+iFflIT08HoE2bducszyMQXMkIRVVQjpECLKpFUVRlRZSmKnl8llNyt1GVgkBPw7ttLsb2rxd5JNXlRrc7kIKE+78somo6yY5L16UqwmpEKeGEu+XLl/L5559y7bXtWbFiGS1atGT8+HdZvfpXZs2ayYED+9A0jZo1a/Hkk8/Qtu21ADz55AAaN76alJRkfv31ZzRN5/rrb2D48JcIDvZVvpg3bzZff72Y+PjTREfHcOedd9O3b39WrFjGG2+MBqB79zu5/fY7GTXqdQ4ePMBHH01i69Z/UVWV1q3bMHjwUCpWrOSfs1q1auzdu5cjRw7xwgsjWL/+b3Rdw2YLYeXK5ciyzP3396Bjxy6MG/cGu3btpEqVqowc+SqNG18NQEZGOpMnv8/q1b/i8Xhp0KABzzwzhIYNrwJg+vSP2bRpA1FR0axZ8xe3334HL7wwvEQ/d4GgqAhF9YriMvzlL6aiKskyKCKRqiTRdZ/rXzLI/m+YbgxCcqfhPbwawzVHkUKqFmkszeNBzcjAYBPu/7LGtzvjGb5qN2cyL52iGhNs5O0u9bm7YYUSHffYsWMkJJxh9uz5uFwudu3awciRwxg8eCg33PAWGRkZTJkymddff5WlS7/HaPTdQ774Yh4PP9yLzz6by6FDBxk1aiTVq1enX7+B/PHHambN+oz//W8c1atXZ9u2rYwZM4qKFSvRsWNngoODGTlyGJ99Nodq1apx8uQJBgzoQ+vWbfnoo09wuVxMmvQeTz7Zn3nzFhEcbANg6dIljB79BnXq1CU6Opr16//mxx9Xcd99Pfj883n88MMqpk2bwqpVKxk8+HkqVarE2LFjmDDhLWbNmo+u6wwdOhiz2czEiR9gs9lYuXI5Awf25dNPZ1G/fgMANm/eRI8eDzFnzgLUInaZEwhKA+Ffu2K5PMrO5M76L0qManaxf0HJIstygEVVic6qpYqOd8eCYo3lSU8rdsUAQekzZMWuS6qkApzJ9DBkxa5SGbtv3/5UrlyFWrVqI8sKzz8/nAcf7EmlSpWpV68+PXo8RHJyMklJif5jfFbWQVSrVo0bbriRNm3asXXrFgCOHz+G0WiiYsVKxMVVpFOnLkye/DHNmjXHYrEQGuprpBAeHoHNFsJXX32J1RrE6NFvULduPRo3vpq33hpPcnIyK1d+55+zbt36dOlyG7Vr1yEsLByAsLBwBg8eSpUqVXnooZ4A3HJLZ2644Ubq1KlL1653ceDAfgA2bFjPf/9t5c0336Zx46upUaMmTz45iMaNr2bhwsC/1QEDnqBy5SpUq1atVD5zgaAoCIuqoHxTXIuqwYAuKVyW1uVLiCyBZMyxqCphwXhPS6DqePevwtC4D1Jw0axgqtOF7rCDJfjcwgJBCVG1ao4yVq9efUJDQ5k9+3MOHTrIsWNH2bt3N0CAdbF69RoBYwQH2/yxp7feejvLln3LAw90o2bNWrRu3Yabb+5IXFzFfOffv38fDRtehcmUcx+LioqmWrXq7N+/L9c683onKlWq7HtYBKxWXzJjlSpV/PvNZrO/qPru3bvQdZ1u3W4PGMPt9uBy5VRRiYiIzBPbKxBcCoSiKijfaMUrTyUZLYjSVCWPJElIuSyquhSMEm1GPe0EzYt315cYWzxdpLFUtwc1MxNDsHD/lyXe79qgzLj+SwOLxeJ/vWnTRoYMeZprr72epk2voUuX23C5nLz44nMBxxiNeR+Os+Pfw8MjmDPnC7Zt28r69WtZt24tCxcuYMCAJ+jXb2CBx52NpmkYDDk/1WazJY9M7v3ZSFL+DlNN0wkOtvH553Pz7MutJIuuZIKyglBUBeUWSaL4yVQGsyhNVRpIIJlyejXruhVjBQvqGRU0D969yzA07oVkLlrfeE9qGsaYko1DFFwYdzeswB31Yy67ZKr8mD9/Ds2bt2TcuIn+bYsWfZH1qmj3j++//46MjHTuu68HTZtew4ABTzJ27P/48cdV+SqqderUZdWq73C73X6FMTExkWPHjnLvvQ9c8DllU7t2bTIzM/B6vdSsWcu/fezY/1G3bl3uv//BEptLICgJhKIqKN8Uo46qJCtZpamEolrSSLKEbMx1O9GtSEYJucoNaEd+Bq8D7+5vMDbpU6TxVJcLzZEJZuH+L0soskR0cNHqFZdnYmNj+f3339iyZTMVKsSyadM/TJs2BQC3232Oo/HLTZ78PsHBwTRt2pz4+NNs3ryRa65pnq989+738/XXi3n99Vfp06df1vHvERYWTqdOXUrs3Nq2vZZ69erzyisjeO65YVSoEMtXX33JihVL+eCDj0psHoGgpBCKqqBcE2hRLdz1n53xL/TUkkeSpECLqhYE6Cg1bkU7+ivoGt5dX2Fo+ACSMeic46luN2pGJorVhqaJCya4uAwc+CSJiYm88MKzgC9p6uWXX2P06FfZuXM7NWrUPOcYd93VjdTUFGbMmE58/GlCQkK5+eZbeOaZwfnKV6pUialTp/Phhx/Qv38fTCYjrVu3ZfToNwgJKblYUUVRmDRpCpMnv8/LLw/H4XBSs2ZNxo2bSMuWrUtsHoGgpJB0YV4qEqqqkZSUWSpje5KdnJy5FQBbo2giu9Q6xxHnh/3X98lcMQqA0EfnYb76zlKZ53wwGGQiIoJJTs7E6y1axrcsSzhX98d95GsATM1nIAdVL1BeMVswxdXBQ9nL+j+f8y9LGAwyJ9cfxfHbcQCswXOwhHyLFvMe3m3T0U6sAcDY4mkMDfO6MRVFxmYzk5Hh8iermEJDCKpdB+8VcIe6FNc/MjIYRck/jtHpdLJ//wGio+MwmUSsokAgKHncbhcJCaeoXbtWQIz42YjyVIJyja4VPUZVkhV0WTgRSgNdB9lsyPXe6n8t18zJLvbsXISuFi3GUXW50Jz2klukQCAQCModQlG9krgcjefFiVE1GEFWCpURnC86iuVsRdX3fZNtlZErZMXl2c+gHvyhSCOqbjdqZqavPatAIBAIrkiEonqlIl0eP/567vJU51JUjRaRSFVK6DooVmOu94FxqLmtqt7tC9A1tQiDgic1FVkvf6EQAoFAICgZhKIqKN/kTqYqrI6qJCEbTZelUbmsYDDnUlS1sxTV8NrIUQ19+9KPoh39o0hjqk4XmtNRcosUCAQCQblCKKqCck1gjGrBiqov498kLKqlhC9GNbCO6tn1JpWaXf2vPdvnFelaZBf/F+5/gUAguDIpU4rqJ598wiOPPFKozN69exk4cCBt2rShXbt2DB48mBMnTvj3q6pKkyZNqF+/fsC/yZMnl/byBZcAf3kqyYAkFRx/KsmKKE1VqujIRtnX9IvAZCo/kVchhfsqWuhJe9BObSjCsDretDRk0UlMIBAIrkjKjKI6b9483n///UJlkpOT6du3LxaLhTlz5jB9+nSSkpLo378/LpdPYTl06BAul4tvv/2WP//80//vscceuwhnIbjoZMeonis+VVFAERn/pYWug6zIYJCy3uetlSpJEoZad/jfe/+bV6SxvQ4XmtNZMgsVCAQCQbnikv9ynz59mtdee42///6bGjVqFCr7008/YbfbGT9+vL/m1oQJE7jpppvYtGkT7dq1Y/fu3dhsNho0aHARVi+41PgtqkUqTWUE0Tu+1JAkCQwyeFR03Up+znopphlSSGX09ONopzejJexAjr6q0HFVtxstMx3ZYhXF/wUCgeAK45JbVLdv347RaGTp0qU0bdq0UNl27doxZcqUgMKwsuw7hbS0NAB2795N7dq1S2/BgrKFX1EtvIi/bDRDvqqToKSQZQkp26KqBYGUv1JprHOX/7WnKFZVXceTmoYk3P8CgUBwxXHJLaodOnSgQ4cORZKtUqUKVapUCdg2bdo0LBYLrVq1AmDPnj14vV769evHrl27iI2N5dFHH+Xuu+++4LUaDKWj1+tKjgIlSVKpzZM7IUWRS2+e8yG7Q05BnXLyQ5Ylv+tfks0+13MBGMwWUCQkqeycc27O5/zLGrqmIxmzo0kt6LqMYjahewIL/EuVr0Xa/SW6PQHt2J+Qdhg5yhe76vuO5v0MdLcH2eNCMucT+3oZcDlcf4FAICgNLrmieiHMmTOHuXPn8sorrxAZGQn4kq00TWPw4MHExcWxevVqRo4cicfj4b777jvvuWRZIiIiuKSWHoBTzVEgjUal1OZRrUbSs14H2yyEltI8F0JoaPEUkeQsRVVWTNiCC2j1KEmYQmwYw87dY/5SU9zzL0t43CqySSG76qmmWQiNCMOblnqWpIzc+D4y138MgL77C4I6jgYgKKiAEA5JwqS5CYqILpW1lxXK8/Uvq3Tr1pWuXe9kwIAnLtkaUlNTWL36N+66q1uBMmfOnGHatKmsW/cXycnJhIeH06pVG/r1G0iVKlUv3mIFgjJGuVRUdV3ngw8+YOrUqTz55JMBlQKWL1+OqqoEB/uUsAYNGnDixAlmzJhxQYqqpumkpZVOO0dPak6dSI9HJTk5s1TmcThyiuNnZrpQS2me80FRZEJDraSlOfy93s+FJOXEqOqSkYxMV/5yigHVrZNRhs73bM7n/MsakiSh57bS61acuhmvy5unK5pS8Xok83x0VxrOPavIaPIYtgrVsNvdBcahuk8l4AkOQyufH0+hXIrrHxpqFRbci8SkSe9x4sSJAhVVt9vNU08NoFq1aowdO57o6BhOnTrJtGkfM3DgY8ybt4iIiIiLu2iBoIxQ7hRVj8fDyJEjWb58OSNHjqRPnz4B+3PHr2ZTr149li5desFze72l8wPizZXgo+t6qc2TWwFQ1dKb50JQVa3I65IlFbLsd7pkRCvgB15RJFTJUCbP92yKc/5lDUWRkYw5io+mWXCqCkaTBdVx1kOeKmGs1w33ttmgq7i2zcd2ywg0TS9QUfNkOjBl2lGNef/GLxfK+vXXNRXdnXzJ5pdMEb5Sc+WMc5XFW79+HUePHmHGjNmEhoYCULFiJcaPf5euXTvxww/f06PHQxdhpQJB2aPcKaovvvgiP/74I++88w5du3YN2JeWlkbHjh0ZMWIE3bt392/ftm0bdevWvdhLFZQ2ak7JIqmQrH9JVnzJViIXp1SRJJBNuYr+a1bsGW6igoPzKKq614Oxzm24d34JXgeevctR2w0ECg5H8brcvuL/kSL7/1LgPrQEx98vojvPXLI1SJYYrG3GY6rRrUTHXb78W+bMmcWpUyeJi6tI9+73cf/9D/qTdbds2cT06R+zc+dOPB43lSpVpk+fftx2m+83KCkpiYkTx7Fx4wacTgf16jXgySefoXnzFowZ8xrffbcMgLZtm7Nu3aY882fP89dff/jHBAgJCWHu3IWEh+dYU3///Tc++WQKx44dpX79htx+e1fGjXvTP25+oQ5nb/v2229YtGgBx44dRZIk6tdvwJAhL9Cw4VV++Q4dOrJmzZ8kJyfz1lsTaNasOXPnzuKbb74iMTGRatWq0bNnb269Nac98rx5s/n668XEx58mOjqGO++8m759+/sqgggE50mZVlRVVSUpKYmQkBAsFgtff/013333HS+++CKtW7fmzJmcG2ZISAihoaG0bduW9957j6ioKKpXr84PP/zA0qVL+eSTTy7hmQhKAz2gfWohiqpiQJcVUZqqlNF1kEyB3akc6XbU0DDfNVC9ufbp6Bgx1LsT745FoLqxb/0CqXG/QifwpqZhjry841TLKvY1z4In7ZKuQXeewb7m2RJVVJcs+YopUz5k2LARXHVVI3bv3s0777xNfHw8gwYNIT4+nmeffYb77+/BiBGv4PV6mTPnc8aOHUPr1m2Jiopi/PixeDxupk6djtFo5PPPZ/Dii0NZtmwVzz33Ai6Xk/j404wbNzHfNbRq1YaGDa/i9ddfZebMT2nVqjXXXNOc1q3bUK1adb/cli2bGD78efr27U+XLrfxzz9/M2nS+8U6399++4V33nmbkSNf5ZprmpGYmMA774xn7NgxzJnzhV9u8eKFTJz4ASEhIdSuXYePP/6QH35YxQsvDKd69Rps2bKJ8ePfIiMjg/vue4A//ljNrFmf8b//jaN69eps27aVMWNGUbFipQDlWyAoLmVaUT158iS33HILb731Ft27d2f58uUAjB8/nvHjxwfIZsuMHTuWyZMn89prr5GYmEjt2rWZNGkS7du3vxSnIChNcllUC6ujKvlLUwlFtXTRUXK1UUW34LY7cbhDCQ4OxnNWUpXmcWGq3x3vrq9B82Lftpigeg+CUnBCkdfhxOxywGXs/hdcXD777FMee6w/nTp1AaBy5SrY7RlMmDCOgQOfxO12MWDA4/Ts2dtvGezduy/ffbeco0cPExUVxfHjx6hduw6VKlXGYrHw3HPD6NLldmRZxmoNwWy2YDAYiYrK/yHLaDQydeqnLFr0BT///ANff72Yr776EkUxcM893Rky5HkMBiNffrmQq69uysCBTwJQvXoNDh06yOLFi4p8vmFhYbz00ii/JbRixUrceWc3Jk4cFyDXrt11tG7dBgCHw8EXX8xnzJixXHed77e0SpWqnDx5grlzZ3HffQ9w/PgxjEYTFStWIi6uInFxFYmJqUBcXFwxroZAkJcypaiOGxf4h1KlShV2797tf//ZZ5+dcwybzcbIkSMZOXJkia9PULbQ1ZzksMIUVdloRhO9U0sdXQfZYsz1Pgg0jcw0J7ZoG5ylqOpeD7ItHKVWZ9R936G7M/DsWYLSsOBYPNXtQrXbkSOE+/9iE3TtB2XG9V9SJCcnEx9/mqlTP+STT6b4t2uajsvl4sSJ49SsWYs77riLRYsWsG/fPo4dO8q+fXsA/PHU/foNZPToV/j1159p0uQa2rZtR+fOt2E2F1CJJB8sFgu9e/ehd+8+pKamsHHjRlauXM7ixYuwWKw888yzHDiwnzZt2gUc17x5y2Ipqs2ateDgwQN89tl0Dh06xLFjR9i3z1ctJzdVq1bzvz548AAul4tRo14OKHOoqiputxun08mtt97OsmXf8sAD3ahZsxatW7fh5ps7EhdXschrEwjyo0wpqgJBsQiIUc2/4L8ky2AwoQtF9aKQO0YV3YxJ348zvQGuyCAMFiuqM6fCha7raC4HpsYP49i3EtDx7FiEXK87kpL/D7yu6XjTUjFHRJXymQjOxlSjG8Zqd15WyVTZytmzzz5Pq1at8+yPi6vIwYMHePzxx6hfv2GW8tWB8PAIHnssp9rMTTd1YPnyVaxdu4Z//lnPggVzmTFjGp9+Ootatc7dgObbb7/B6/Vy7733AxAWFk6HDrfQocMtvPTSi6xZ8yfPPPMsALoeqFAajYU3OwGfQpnNqlUrGTPmNbp0uY0mTZpwzz3d2b9/fx6Lam4lO/tzevPNcVSvXiPP+CaTKau1+Rds27aV9evXsm7dWhYuXMCAAU/Qr9/Ac65RICgIoagKyi0BMaoFWFQlWQHFcM6sW8GFo+ugmA253lsJ0X4l0VOHTLuHyKDgAEUVfO5/Q3gcSvUbUQ//hu5IQt3/PYZ6BTfo8NqdmF1OMBbdWiUoGSRZQbJcPjHCkZGRREREcPz4Mbp3zylf+OOPq1i9+ldGjRrD118vJiIiksmTp/r3//HH6qxXOm63mylTJnPbbV3p1KkLnTp1wel00rVrZ/766w9q1arNuXKJDh48wKpVK7n11tv9pRWzsdlC/HXC69dvwLZtWwP279q1I+C90WgkMzOnFF9mZgZJSUn+97Nnz+Suu7oxfPhL/m2//+47H13X8018qlGjBopi4NSpU1x//Q3+7QsXLuDQoQMMH/4y33//HRkZ6dx3Xw+aNr2GAQOeZOzY//Hjj6uEoiq4IISieiVxuWlrRYhRlRQFSSk4LEBQkugollyKqhZEkLaeZP0hHGlBqLYQJFlB13KsO5rXA14X5qt7YT/8GwDeHQtQ6nRFkvO/PakuN6o9EznCItz/giJx7NhR1q79K2Cb2WyhefMW9OrVh08++Yi4uDjatbuOffv2MmHCW7RvfyMmk4nY2Fji40+zZs1f1KxZk127dvLuuxMAX/1Tk8nEzp3b+fffzTz//ItERkazdu1fOBx2rr66CQBWaxAJCWc4ceI4lSpVzrO+hx/uxY8/ruLJJwfQr98A6tatT2pqCn//vZZVq75j4sT3AejZszf9+vXmgw/epVu37uzevYsFC+YHjHX11U34+ecf6NChIyEhIUybNhWDIccKHRsbx9atW9i1ayc2m40//ljN4sUL/eeTX7iCzRbCPffcy7RpUwgODqZJk6Zs2rSBjz76gN69+/qPnTz5fYKDg2natDnx8afZvHkj11zT/DyvmkDgQyiqVyqXQbkQyZOR86ZA17/iy/gvu6UpLxt0HeSzLKoyHoK1P8mw34bLE4o1OAhPenrAQZrTjhJTH1PVNriP/o2ecRL18G8YanbMfx5NE+5/QbFYtWolq1atDNgWF1eRJUtW0LPnI5jNZr788gs++OBdoqKiufvu7v5STg888BCHDx9i9OhX8Ho9VKlSjSeffCarXNUO2rW7jjfeeJv335/IsGFDycjIoHr1Grz++pt+Ja1r1ztZvfpXHnrofhYv/paYmJiAtVSoEMtnn81mxozpvPfeRJKSEjGZTDRq1Jj33/+QZs1aAFC3bj3ef/8jJk16l8WLF1K7dh3uuutuvvgiR1l94olnSE1NYdCgJwkJsfHQQ4+Qnutv7oUXhvPWW2/w1FMDMBpN1K1bl1GjxvDqqyPZuXN7gYrlkCHPExERwbRpU0lIOENsbCwDBjxBr16PAnDXXd1ITU1hxozpxMefJiQklJtvvoVnnhl8gVdPcKUj6SJ4r0ioqkZSUul0NvIkOzk50+fOsTWKJrJLrVKZx/7zO2SufB2A0L5fYG50+zmOuHgYDDIREcEkJ2cWqeC5JIF2aAmZf/rKGRmq9cFQrVceOaMtHCW2dpkuog7FP/+yiifBzsnZ/wFgsvyELewdPMRywvgWtuhIKkQpuONPBhwjG02YIiuipO4i+dunAJDCa2PuOqPA+osGq4Xg2rVQDZeH+/9SXP/IyOACO1M5nU727z9AdHQcJtPl8RlfrixfvpQ33hidb31WgaAs43a7SEg4Re3atfJt1pSN6J8nKJdIkoTuzmWZK8j1b7KIRKqLiJTLoqoSC4CR01j0XTgz7Hh0E/JZrkXN40H3ODBVboEc7Ss4rqfsRzu+rsB5VJcbzZ4ZkIEsEAgEgssPoagKyiWSBJrz3IqqbDRddqG5ZRmDJScWTpWr+l/b1F/wutxkOlQM1rO7T+noLl+SlenqHKu4d/u8AufRNQ1PavrlEMEiEAgEgkIQiqqgXCJrHjRPrlCMfBRVSVFAMQqL6kUkoDOVHI4qhQMQpG9C0ZOxp9nRTEF5YqQ1jwvd40Kpeh1SWA3ftjPbUOP/LXAur90OLmeB+wWCK4E77rhLuP0FlzVCURWUS3SPE91beB1VX2kqYVG9mMiyBIYsJVQFV5ivdaKEhk37HVeGHZeqYAgKtKpqqhfN7USWZQyNcgr+e/8LzGjOjepyoWZkCPe/QCAQXMYIRVVQ7pBlCTxOdE+umpz5WVRlpUSLgwvOjSxLSMas24oXHBF3oGfdZmzqb+iqh4x0J/LZ7n9dR3PZQQKlRkekYF/bRe3EOrTkffnOpWs6npQUZL38Jp8JBAKBoHCEoiood8iyhOrIAD1XC1Upf9e/ppy7a4ug5NA0chRVFbx6OJ7gtgAYSMaq/4sz3Y5HMiMbA6+Z7nEheV1IsgHDVT38273/FRyr6nU40Zz2kj8RgUAgEJQJhKJ6BaFzmfjAVQ+6x4Wu5VJU87GoygYT4it+8VFCfddC0kBKBntoV/++EPUXPA4XDpeO4awOPLqm+pRVWUKpfTuYwwFQj/yGln4s37lUlxs1Lb3AMksCgUAgKN+Iu7ug/OF1+joaaR7/Jik/179RlKa62Oi6jrVhTiF++SS4zE1RjT5XvlX/D4MeT2aaHd0UfFZSlc/9L0kgGSwYGmS1tNQ1vNu/KHBOd2oqktdT4H6BQCAQlF+EonrFUj4TULLjUzXVCwEW1bNc/JKEZDSKRKpLgKVGKLrV91pOBzIV7Lbb/Ptt6q++pCpNwWC1Bhyred1Iqu+6Gup3A2MQAOqB79HtifnOpzpdqJkZBTYHEAgEAkH5RSiqgnKFJElozkxfv85CXP85Gf9CU73YyIoBquQU/ldOgiOkM3pWx2ab9geax0FGhgs5yBZwrK56we1EkiUkUwiGunf7dmgevLsW5Tuf5lXxpqYii7uZIB+efHIAbds2D/jXvn0b7r77diZOHIfTmZOU2a1bV6ZP/7jAsaZP/5hu3boWuP9C+fTTT7j++takpqbku//777/j2mtbEh9/ukTnHTCgL23bNmfv3j1Fkh8z5jWefHIAACdOnKBt2+Zs3Lih0GNWr/6VYcOGFnlNbds2Z/nypaUmXxLMmvWZ/3MojNTUFEaNeplOnW6kc+ebmDDhrYDv3aVmw4b19OnTkxtvvJYePbrz44+rCpVXVZUbb2yX5+8q+29n9+5dPPbYI3i93hJZn7i1C8oVku5Fc/v+wAuLUZUVBRQDgouLrutIioRU0Yie9fFLiaB7w3EGXw+AQgbB+gacaQ68sgXJYMg9AJor0x8RYGhwv//aevd8i+5KJz88GZngFDVVBflzyy2dWLHiB/+/+fO/5JFH+rBkyddMmvS+X27mzLn07Nn7kq3zjjvuQtM0fv75p3z3f/fdctq0aUeFCrElNueRI4fZtu1fqlWrztdfLy728bGxsaxY8QNNmjQtUCYtLY133hnP4MFFV1TLOosXL+KTT6YUSXbkyBc5duwIkyd/zNix4/nrrz8ZP/6tUl5h0Th06CDPP/8sbdtey6xZ87jrrm6MHv0q//zzd4HHHDlyGJfLxZw5XwT8XWX/7dSv34AaNWoxd+6sElmjUFQF5QuPEy37KS1X1r8kBbr+JVnJGw4guCjIsoJsVNCyfkslQD4FjtxJVfpq3HYHTpeet6aq14PkdfmODYpCqX2rb4fXgXfPN/nO6XW6UDPSRU1VQb6YzRaioqL9/6pWrcZ99z3Arbfezk8/5ViPIiIiCAoKumTrjIurSMuWrVm16rs8++Lj49mwYT133tmtROdctuxbqlevwZ133s2qVSux24tXRUNRFKKiojEaC77fLlw4j8aNr6Zq1WoXutxLzpkzZ3j++Wf56KMPinQ+27b9y6ZNG3j11ddp0KAhLVu2ZuTIV1i5cgXx8fEXYcWF88UX86hduy5PPPE0NWrUpGfP3txyS8dClcz9+/cRHGyjbt16AX9Xuf92evZ8hNmzPycjI3/jQnEQJidBuUGWJXBmJVJBQDJVHte/oqDLBlCF6/9ioutZtVQVA1ocyCdA0kGOB0+lRniN1TF4DmNWd2M2niQjLQxbjA0y0vxjaF4veJxIFjO6pmO46kHUfct9SVW7FmNo+ACSwZJnYndqCkGR0YiqqqWHqmskuy+dyzLCZEWRSs6+YjKZUXJ5Xrp160rXrncyYMATACxZ8hVz587izJkztGrVhooVKwUcn5yczDvvvM26dWtQFAN33dWNHTv+45prmvvH+PPP35k+/WMOHTpITEwMnTrdSt++/TGZ8m/7fOeddzNq1EucPHkiYL7vv/+OsLAwbrjhBtLS0vjwww9Yu/ZPkpKSCQ0NoX37m3juuRewWKxs3LiBwYOf5PHHn2bu3FlUqlSJzz6bg3xWfIyqqnz//QpuuukWbrqpAx99NIkfflhJt273+mV0XWfmzBl8881i0tPTuOWWTrjdLv/+EydO0L37HXz00TRatGiZ53xcLheLF3/Jiy+O9G/TNI05cz5nxYplnDx5AqPRRJMmTXnhheFUqVI1zxjTp3/MP/+sp23bdixcuABV9XLjjTfz3HPDCA7OCR86cuQwzzzzBFu3biEsLIz77uvBo48+VqQ5s8+jIL7+ejmVKlVi164dGI1G5s5dyIwZ0zl58kSBxwBs2bKZ6Ohoatas5d/WvHlLJEni338306lTl0KPz2b16l+ZNWsmBw7sQ9M0atasxZNPPkPbttcCvhCXatWqsXfvXo4cOcQLL4zg22+/YfPmjfmO16/fQAYMeIItWzZz4403Bexr0aIV7703wechyyf2f9++vdSoUaPQ9dauXYe4uDiWLPmaXr0eLdI5FoRQVAXlBkmSfEXhs+NOC4tRNVrw2fKEonqxkWTJ177WCHoMSPEgqSAnSNhDbyc0cSoAodLvJGVWxRUVhMliBbLjmbKy/61h6IAcUhml+s2oh34GVyrqvhUYGtybZ17V7kSzZyIFBYskulLg2+PbGbF1BWdcmecWLiVizMGMa9KVuys3uqBxvF4vf/+9lu+/XxGglOXmhx++Z8KEtxk69AVat27Db7/9yscff+h3u2uaxvPPP4uqenn//Q8xGIx88ME7bNmymWuuaQ7A2rV/8fLLIxgy5DlatWrD8ePHeOed8Rw5cpg333w733lvvPFmQkJC+eGH7/1KFsDKlcu57bY7MBiM/O9/L3LmTDxvvTWRyMgotm7dwptvvk6tWrV48MGegE8JXbPmDz79dBZOpyOPkgqwbt0azpw5wy23dKRq1Wo0aNCQb775KuAzmT17JnPnzmLEiJepX78B33zzFStWzKNZsxZF+qy3bNlMenoa7dpd59+2cOF85s2bzahRY6hduw7Hjx/jrbfeYNKk9xg//t18x9m5czsAH3zwEZmZmYwdO4aXXx7B++9/6JdZvHghw4aNYOTIV1i16numTv2QRo0a07Jl63POmR3CUBDh4REAtG9/I+3b31ikcwefJbxChbiAbUajkbCwsCLHGu/atYORI4cxePBQbrjhLTIyMpgyZTKvv/4qS5d+77dmL126hNGj36BOnbpER0fTrt11eAuoiGK1BhW4vpiYGJxOJ6mpKf7zzs3+/ftQVZUhQ55mz549VKhQgR49Hua22wLjt6+7rj2//75aKKqCKwdJ96K6ctxSeiEWVdloRhPayiVBkn0WbQA1zmdNBV+pKmfTWwhJ+gxJd2Fx/oYc+iCZmR6swcFgT/WPoXk9GLwufyMHQ6OePkUV8O5ciFLvbiQ58Paluj1409Mw2kJQVWFXLWmGbl5KmvfSxgGfcWUydPPSYiuqq1at5Ndfc+I+XS4XcXEV6dmzd4AymJtFixbQqVNn7rvvAQB69+7Df/9tZc+e3QBs3ryRHTv+Y+HCr6levQYAb7zxdoBV7vPPZ9Ct2z3cc4+v1FqVKlUZPvwlnn76cZ5++lkqVQq00AKYTCa6dLmNVatW+te2c+cODh48wNix4wFo3boNzZq1oE6dugBUqlSJL7/8gv37A7u4Pfxwb6pVK9g9vXz5UipUiKVp02YAdOp0K5Mnv8eOHdu56qpG6LrOl19+QY8eD9G5sy8EZ8iQ59m0qfDEqdxs376NihUrBbiFq1SpyqhRY7j++hsAqFixEh06dOSXX/KPzQWfoeLNN98mJiYGgBdeGM7QoYM4fPiQ//Pv3v1+brvN9/k/9lh/5s+fzc6dO2jZsvU558wOYShpnE4nJlPesAiTyYzL5c7niLzIssLzzw/n3nvv92/r0eMhhg4dRFJSIrGxPkWzbt36dOlyW0HD5IvLlXd92dZ+tzv/9R04sB9VVRkw4AkqVIhlzZo/eeON0Xi9noDQlFq1arNgwVw0Tcv3QamoCEX1SqK8K25ed058KuSyqEog5XyVJVnOyvi/uMsT+JAkyZfMBhAEWjjIKSC5gZRgHLabCUr/Hll3YFM24rTfiDfMhknOAHwPH5o3K/vf6nP/yxG1kSu1RTuxDj3zNOrBnzBkx67mwpOahjGmAkiida4gh/btb+Dppwej67Bjx3+8995EWrVqzaOPPobBkP/P4P79++jUKfA7dvXVTfyK6q5duwgNDfUrSQBRUVFUq1bd/3737l3s2LGdpUuX+LdlVyI5dOhgvooq+Nz/X375BXv37qFu3Xp8991yrr66id99fO+9D/DHH6tZsWIZR48e4eDBA5w4cZzq1WsGjFO1al43ejYpKcn8+efv3HdfD797t1Onznz44ft8881irrqqEampKSQkJNCwYeCDQePGTTh48ECBY+cmMTGBiIhAq1z79jfy33/bmDZtKocPH+LIkcMcOHDAr4TmR9Wq1QL2Zydv7d+/z38Ncn/2ADZbCC6Xq0hznjp1koceuq/A+RcsWExcXMUinXNuzGYzbndeq6bb7cJqteRzRF7q1atPaGgos2d/zqFDBzl27Ch79/q+h7kfys++3kOGPMO//27Od8xHH32MPn365bu+bAXVYrHmdyjz5i1C0zT/w0fduvU4deoUc+fODlBUIyIi8Hq9pKam5vkOFAehqArKBZIkgceBpub6g8pWVGVTQByNJCtgMIjSVJcICZBzWTu1ij5FFXwxq466XQlK/x6AoMwV2MNvwenKckXZsy12We7/oFB/8IahcU/cJ9YB4N0xH6VWZ6Sz4hW9ThdaRgZSaLi4/iXMe83uKjOu/+ISFBTsT3ypVs2n8Awa9CSKYgiIncyNJEnoeqBlPrdSazAoaFrhlntd1+nV61Fuvz1v7GN0dMFKWb169alfvwHff/8dNWvW5Mcfv+eppwYDOSEHBw7sp3PnW+nYsTP16zdg3Lg38oxjNhesCK1atRKPx8PChfNZtGhBwJp//HEVzz77nP++WtjncC4kSc7zOc2ePZMZM6bTteudtGrVmoce6snvv6/mhx++L3Ccs+fMVtByW+rys9pl3wfONWd0dAyzZy/Ic3w2hV2vwoiNjeX3338L2ObxeEhNTSUmpkKRxti0aSNDhjzNtddeT9Om19Cly224XE5efPG5ALmzr/dLL43C5crfCxIaGgZAhQqxJCScCdh35swZgoKCsNls+R2KxZL3e1W7du08SYDZ1/1Ck1yFonqlUs6So2U5Kz419w0vO+s/v4x/RWT8Xyp8MaoS2THCeijoQSDZQc4Er6cOHnN9jK7dGFz7sRiO4HDXIcIWBCT5x/G5/z3+66tUaIIcczXamW3oqYfRjv6JUu2GgLl1VcWTmoI5LFzk0ZUwd1duxB2VGl4WyVQtWrTioYd6MW/ebNq3vyEgfjKbunXrsXXrv/6YT4CdO3f6X9epU4+MjAwOHTpIjRo+S2ZqagpHjx7xy9SqVZsjRw4HZIdv3LiBRYsW8OKLI7Fa87dYgc+qOnfubFq0aInb7aZjx84A7Nmzm7Vr/+LTT2fRuPHVAHi9Ho4dO0blylWK/BksX76U2rXrMGbM2IDt//67hfHjx7Jy5Qruv/9BYmPj2Lp1CzfeeHOuz2FHkZXV6OhokpOTA7Z9/vln9Os3kN69+/i3zZ07m8JyCo4ePUJGRjo2Wwjgy6YHXymkonCuOQ0GQ6lUJWjWrDkffTSJo0eP+MfPDp1o0uSaIo0xf/4cmjdvybhxE/3bFi3K7tZX8GdWocK5FeFmzZrnCeXYuPEfmjRpmq/in56ezr333sngwc9xxx13+bfv2LEjIGEMICkpCZPJRFhY+DnXURjF/ov/5ptvOH26ZIsNCwTnQtI1n6KaCz2XRTVAVlFAFs9glwpJkpBkJcfKLYGay2MmnwB7SI5VzJK2FE0x4tLNyGazf7umesHtQMr1NG5o3Mv/2rN9fr5WU2+GHVxlp5j25YQiyUSbgy/Zv5LM+B848EmqVq3G22+PzbckU+/effntt1+YO3cWR44cYdGiLwLiXFu0aEmjRo15/fVX+e+/rezdu4dRo17C6XT6v/uPPNKHX375iRkzpnHkyGH++edv/ve/18jISD9nPGSXLreTnJzE9Okfc8stnf1u1qioaBTFwM8//8iJE8fZuXMHL788gsTEhAJjCs9m166d7N27h/vv70Ht2nUC/nXr1p3KlauwZMnX/nNYvHgRS5cu4ciRw3zyyRR27PivSPMANGrUmFOnTpKamhODHhsby/r1azl48ACHDx/i448/4rfffsnXRZ6N3W7n9ddHsX//Ptav/5uJE9+mY8fOeSoxFMT5zHk+qKpKYmICzqy6zo0aXU2TJtfw6qsj2bFjOxs3/sO4cW9y221d/Yqk0+kkMTEBVVULXPv+/XvZsmUzJ06cYPnyb5k2zVfDtajXvCDuv/9Btm//j48+msShQweZN28OP//8U0ACVGpqqv/6hYSE0KJFKz755CPWrPmLI0eOMHv2TFat+s5f6SKb3bt3cdVVF5b4COehqI4ZM4atW7de8MT58cknn/DII48UKpOcnMzzzz9Pq1ataN26Na+//joOR+CP0sqVK7n99ttp0qQJ3bp1Y+3ataWyXsFFRHWin529mKWoSnkUVYNQVC8huq5jMCoBVns9CvQsI7eUDE7lBjTZ51YyZ/wGkh0XJhRr7pqqvuL/spSjjMqV2iCF1/btTdyJdjpv/JXX5UJNFzVVBYVjNpsZOfJVTp8+xccff5Rn/3XXtef1199k2bJv6dXrAX777RcefrhXgMy4ce9QoUIszzzzBM888wSNGl1NXFycPwu7Q4eOvPHGOFav/pWePR9g9OhXadu2HePGvXPO9YWEhHDjjR3YuXMHd911t397TEwMo0a9zh9/rObBB+9l5MhhxMTE8OCDPdm1a0eRzn358qWEhIRw662359knyzI9ejzM/v372LJlM/fd9wBPP/0sM2d+yiOPPMiBA/u5665uRZoHoFmzFoSEhAR0rnrttf/hdDrp06cXTzzRn/379zF8+EskJydx6tTJfMeJjY2jXr16PPFEP0aNGskNN9zIq6++XuR1nM+c58Pp06fp2rUzP/3kqyAgSRLjxk2kUqXKPP30QF5+eTjt2l3Hiy++5D/mp59+oGvXzgUaAQcOfJJGja7mhReepXfvB/n22294+eXXMJst/moI50utWrWZMOE91qz5k969H2Lp0m94/fU3aNmytV9mxIgXGDHiBf/7V14ZzS23dObtt9+kV68H+OmnHxg7dry/VFY2mzZtoH37my5ofQCSXsxArttuu42BAwdyzz33XPDkuZk3bx5vvPEGLVu2ZM6cOQXKPfLIIzgcDl5//XXS0tJ4+eWXadWqFW+/7Sv1sW7dOvr378+LL77Iddddx+LFi5k7dy5Lliyhdu3a570+VdVISiqd2CxPspOTM33Kv61RNJFdap3jiPMj86cJ2L//HwChjy3EfFXxsgNLE4NBJiIimOTkTLzewHgmSZJQnCm44g+h53L9O9feBaodyVoNc4vP/NtNEbFIEVXKVeZ3Yedf3lAUmZT4ZM7s2+9riZqFfByUo77XaiwEhU4jOM1XwD8jeiBKtb6EyA5cxw74QzxkxYAhPBZVzrG0eg/+hOcv3/dYjmuBuWPecjam0BCstWuj6uVDWb0U1z8yMhhFyd9W4XQ62b//ANHRcZhM5nxlrnRSUpL5779ttG3bDoPBp5h6PB66dLmZYcNG+LPPL3eOHj3C/fd3Y9q0mQV2p5o69UP27dvDO+9MOq85pk//mBUrlrFkyYoLWWqZ5rnnBvPyy68RFRV1qZdSIuzcuYNnnnmCr79eRlhYWL4ybreLhIRT1K5dK9+412yKbXbq0aMHb775Jps3b6Z+/foEBwfnkenWrVuRxzt9+jSvvfYaf//99zkLyG7evJn169fz3Xff+ZXOMWPG0L9/f5577jliY2OZPn06HTt2pHdvXyuv4cOHs3nzZmbNmsWYMWOKvC5B2UGWJTS3PUBJBXIK/p/VgUqUprq06LqOYragmKx4HTldSbRYn7IqaSCfAUdsV7+iakldjkt9BNUaitEWgifN52bSVC94XEhWC7rmu6ZK9Zvw/jsDPeME2qmNaIm7kKMC49S8Tie6PRMpyCaqPwhKBUUx8MorI7jnnvvo3v0+vF4vc+fOwmg05Rvzejly4sRx1qz5Eyg8HvLhhx/hwQfvDYjnFeSwZcsmZFm+bJRUgAUL5vHQQz0LVFKLQ7EV1XHjxgGwaNGifPdLklQsRXX79u0YjUaWLl3KRx99xPHjxwuU3bBhAzExMQGW0datWyNJEhs3buTWW29l06ZNjBgxIuC4Nm3a8MMPBRfyLQq6ruPx5B/LIklSQGB5QXI+WfxP39myuQvyaprmPz4/2cLI3cIuP1lNyz/+xev1FPpjnnvc4sl6C828PlvW7Xbj8bjxegOP0TSQHRn+96qqoWoa6L5z1CUTHq/v3CRFQVcU/7yq6kXTCl6DwWDwx5OpqlpoFm9xZBVF8QeiF0U2OwpHVVU8Hm+hssUZN1tW07QC45/A5+5TskpKXaisLEt4PF40kwXdkYmU1StKV3TUaB1DvIykgTepIk5zEyyurRg8J3AlrcFhuYWImDjcqSmoWddQd6Qjm4PJmUZCbtgD9Z/3AF+sqnzda4GLdKgoiYkYzRZ0HX/3IV3X8XoL/nxlWSqybPH+7guX1XUJt9uIx+NGVfUi/91fyD1CVEW4MEJCQnjnnQ/4+OMpfPvt10iSTJMmTfnoo0/yLZJ+OTJp0nv88896Hn74kUJLN4WFhfHCC8OZPPl93nnng4u4wvJB48ZNmDDhvUu9jBJj166dHD58kFGjRpfIeMVWVH/++ecSmTibDh060KFDhyLJnj59mooVA/8YTCYT4eHhnDx5krS0NOx2O3FxgV0WKlSowKlTpy5onenpaUyfPjnffTVq1OSuu3I6eUybNrXAH7jKlatw770P+t9/9tmnKHadm/G1ntuzexdbDi7NWncsDz6YE7M7Z84s0tPTyI/IyCh69errf//FF/NJSkoMkGmQvI6rs14riozB4FNiFi9eVGCHDIvFysCBT/vff/vtNxw/fixfWYPBwFNPDfG//+67ZRw6dDBfWYDBg3NiXlauXOGvC5cfA7p19rspf9u6i31Hj9Ir60HtVIqdH1b97pd9vG8NzFmyf/zxO9u2bSlw3D59BvjLdKxb93uhhax79uzjT4DYsGEt69cXHPvco0dPYmN939V//93AX3/9XqBs9+4P+OsA7tixLSBh42zuvPMeatb0Pajt2bODn34quJzLbbfdSd269QHYu3cvK1cuK1C2Y8dbueqqxgAcPHiQZcu+KVD2xhtv8RcIP3bsGF9/nf9DK0DD6g2oHuWztKRlpvNvyk5upgUSEuoxD38H1+NGmy/sxXXyC5Twdmix4TgsVr75L/f3YU/AuFdXbEBDSyS6Mwn16B8s/3MJmcbIfNdw9dXXcPPNHQFfQsann04peL0NG9Gpky8kxuNxF/g3D76s79tvz8l6nTKlYNkLvUc4nfknh13IPSIzM6NErB1XMi1atGL69JmXehmXjNxZ6Ofills6ccstnc5rngEDnsiTqHM5UZxyX+WBBg0aMmvW/BIbr9ifTuXKlf2vHQ4HGRkZhIeHBzyplxYOhyPf/shmsxmXy+XPsjtbJnt/aWE0GoiIyAmByK83bjYGgxIg60v4yN+ykb9s/iiKHCBbUOxZNjabhZAseYOh4OLosiwFjFuYrCQFyhqNhX+9AmULL9BuNcuYshKkjAYZhZwfeVUPPDYoJJjQrLHN5sLXEBYWRHh4tmzh3+HQUKt/zVZr/n26swkJKZ5saKivTI3FUvgabDaLf9zg4MJjB4ODzecle+ZM4UWog4JMftnU1ILL6wAYrVaCgszoqobTY8AuOzklJ1JRi8aCGdV9HXbte4LkDCK8/5LpPIlXj8BWsSL8V/CDi8FiIbjpA2T8/TGSrlE3dR1bovMmhoDvO5C93gLaq/sxmXJk3e7Cr0Vu2XNx4feIC5c9+x6ROwtbIBAIyirFTqYCnwt+/Pjx/Pfff373UZMmTRg6dCht27Y978WMGDGC48ePF5hM9b///Y+tW7fy5ZdfBmxv164djz/+OHfffTdt27Zl2rRp3HhjTi/eefPm8e6777Jx48bzXpvXq5KUlL+lQpLks9x6BZeL8LkAjQGynmQnpz73ZWsGXxVFZJfqBch6KLhmmpSP6z9Q1v7TROyrfDXzIgZ8iaXRbVnn5jmHiz7n1714st48haILktV1DZvNTHq6IyAJSpYl9JRTqGlncrndNVRXIt5/evjOPPJaDA192Z8GaxDWyg3Qslzp51qDwWA8y51fsMu7OLKKYjjLRV+4rNFoIDTUSnJyRqHu2+KOG+j6L8zlrZzlzr8wWZdTJf5kGpIOZCTicWSg6zqqpiGlg3mH73PUgnRMlecQmuYrtJ0Z+QhqlQFUqhpJxr5duJN9XgHZZMUQHoeWlRwlSxKyO53Mr+4DrxMUC6buC5HMgRZCc0QY1po1/Q0IfO78wlzpOX/LxZGFc/3dFy6rKDIhIVbS0x1o2tmu/+LcT4p+jwgNtfiv49mIZCqBQFDalFoy1aZNm+jTpw9Vq1blqaeeIjo6mvj4eFasWEH//v2ZM2cOzZo1u6DFF0RcXBw//RToFnW73aSkpFChQgXCw8MJCgoiPj4+QCY+Pp7Y2NgLmluSJCSp4I8rd6ZuYXL5yeaWP3ueQNnCrY7nlM1Vg1BV9VzyCoUYeM7KQi6OrJync1BBsgaDjMlkQpY9AXGXsiyheRzomo6e9QMsAbKeo0BIsgkla1EGowVVl3Mpu4WvQVV1cn7YC7/GxZHVNHKdx7llc9ZbsuPm/iwLk9X1on+HiyJrsRowm004HR5kiw2cdtC8vusUApoN5AyQ7RIu6S50FiKhYUldSWL4Q9jtIYRUrkK6w4HqdCCrXhTVA9nZ/zpoxhCU2rej7v4aVCfanqUYr+4duFaHC8nlwmvM+Q4U9+/zYsjKcuD3//zHLfo9otx1/RAIBFckxa6j+v7779OyZUuWL1/OM888w4MPPsjgwYNZuXIlrVq1YvLkguO0LpRWrVpx6tQpDh8+7N+2fv16AFq0aIEkSTRv3ty/LZu///6bli1bltq6BKWHrLrR8rNqabmsTLnqqMomq0gSKQPIskRouNXXhtJgRTEH5eyUfG1V/W/jw3EH+Wr2KWoCpow1pKfakWzBWKKjQZLQVC+6xxlQ/B/A0PAB/wOYd/fX6GpgiI/X5cabniFqqgoEAkE5pdiK6rZt2+jdu3cel5Esy/Tq1atEmwGoqsqZM2f8sadNmzalefPmDB06lK1bt7Ju3TpGjRpFt27d/BbTvn37smLFCmbOnMn+/fsZP348O3fu5NFHHy1sKkEZRJJA9zjR8ks60QItqtkHSAajKEdUBtA0nSCbGWuQCV3Xka02XyOGLPRI0LMum5wCdvN9/n3WtBW47E4cdg+m6ChMWe33NJcd+Sy3tmyriFItK8zHmYx64KzqHrqOJyUFSS84REIgEAgEZZdiK6rBwcEFZqueqxxRcTl58iTXX3893333HeBzi3/44YdUqVKFRx99lCFDhnDDDTcwevRo/zHXX389Y8eOZcGCBdxzzz2sW7eOjz/++IKK/QsuDZIkobsdAUXj/eh5LaqSrIBipLDex4KLhyRBaJgFWZbPaVVVkxqhGX0bzM7NaBmHcNi9oMhYKsQgm82+zmTevEmRhoY9/K+9OxfliUn2OlzomZmFhqwIBAKBoGxS7BjV5s2bM23aNNq3b4/VmpPxa7fbmTZt2gW52LNrtGZTpUoVdu8OzPyNiopi0qTCu1t069atWLVcBWUTSQLNmZH/zgDXvy9BRFIUEBbVMoOm6VhtJqzBJjLTnT6rqsvuf/DQYkA+BpIK0hlw178PS7IvdMia/h32jLqE2oKQgyxYomKwnzyO7nEgB1nQ1FxtVaMbIldoihb/L3raEbRja1Gq5hRc1zwevGlpGEPCylW3MoFAIBCch6L63HPPce+993LLLbdw0003ERMTw5kzZ/jtt99wOp28+eabpbFOwRWIrLrxFpDxrHtztbOVfBZVWZZBNgiDahlC1yE03ILT4UaTfFZVrz2reoYBtAqgnARJB6e9E2Y+RsKDNf1HEjP64nQFYTV4MUVF4M3MQHXaUazheeYxXPUg7vh/AfDu/CJAUQXwpKVjrODK08VMcPnz5JMD2Lw5/4ovDz/8CIMHD70o62jbtjmvvDKaO+64izFjXuPkyRNMnTo9X9kTJ07QvfsdfPTRNFq0uLD8itWrf2XJkq/ZvXsX6elpREZG0bp1G3r37kvVqtX8ct26daVr1zsLrVd6+PAhpk//mI0b/yE9PZ3o6Biuu+56Hnts4GXVVUlQtii2olqjRg0WLVrE5MmTWb16NampqYSFhdG6dWueeeYZ6tSpUxrrFFxhSBLobmf+iVSAlpyTMCcHZZXzkg3oshFUoamWFXRdxxpkIthmJi3FgWwNCbSqxoF80pd/rp004qxyE1b7j8haGqa038jM7E5QpBFN9WCOjsFx/LjP/S8HlkySK7dFCq3ms6jGb0VL2IEcfZV/v9fpQstIRw6PKrRTmeDy5JZbOvHcc8PybLdYCq8DXJKsWPEDwcG2izYfwDvvjGfp0m/o1as3TzzxNGFhYRw/fpz58+fQt28vpk//nJo1axVprMTERB5//DGuu6497733IaGhYRw5cojJk9/nqacGMHfuwotST11w5VFsRXXKlCl06dKF999/vxSWIxD4kCQJPM5841N1XUVN9PWXRjYhR7bxHWOy4FN5hCJSltA0jZAwK45MN17JEmhVNYMeBVIi4AGH90Gs/Aj43P+ZmbfjCQ9D8mRiDAnCGB6ON9OBHBzo/pckGcNVD+JZNx4Az44vMN8wxr9f1zQ8KamYI4TV50LQNRXNnnTJ5peDIn2x6MXEbLb4u8pdKi72/L/++jNffvkF48e/xw035NQVj4urSPPmLRg4sC+ffvoJb775dpHG++WXH/F6vbzyymh/PelKlSoRF1eRBx+8l7Vr1wTMIxCUFMVWVD/55BMaNWokkpPKI+UoeFOSQC0gPlVL3QqeFADkiDZIis8qIhtMaOXoHK8UdB3MFoXgEDMpSfY8VlW1IshZ3X71M5VwR9XE5D2IybWD9LRdOCJbEmI143U6MEVHorlPZyVMBWZHKTU74tnyKTiT0I7+gZZ+HDkkp5Oex+7A5HCAqfDOW4L8cWz5mrTFz6NlnLlka5BtMYTe9w7Wa7qX6Li6rjN37iy++eYrEhMTqVatGj179ubWW3O6na1e/SuzZs3kwIF9aJpGzZq1ePLJZ2jb9loAjhw5wrvvvs22bdvQdY2rr27CoEFDqVOnLhDo+gdfVZuJE9/mu++WYzQa6dSpM4MGDcVszr/BwvLl3zJnzixOnTpJXFxFune/j/vvf9Df1ONsFi6cT4sWLfNVHiVJYuzYCQQHF62zmu8YGbvdzubNm2jevIV/e40aNVmwYDGxsXGFHC0QnD/FzvqvU6cOBw8W3L9dUE4o4ynQsuZB8+Tf9lZLWO1/rURn3YRlGQym8qSLX1Goqk5ImAWTyYBusARWALCBHup7KTnBLvfx77KmLSczw+fq1zUVXdYxR0Ug6d48X2FJMWNokKXA6BrenYEd7FSXCzU9DUUp29/9skrqF4MuqZIKoGWcIfWLQSU+7scff8jXXy/m+edfZN68hfTo8RDjx7/F4sWLANi1awcjRw6jc+cuzJ//JZ9+OouIiEhef/1Vfye5V18dQUxMBWbOnMuMGbORZYURI54vcM6tW7eQnJzEp59+zquvjuaXX37mo4/yTxResuQrJk16n/79H2f+/C95/PGnmT378wLlvV4vW7f+S6tWbQqcPyYmhqCgoAL3n02nTl2IjY3jqacG0Lv3Q3zwwbusXv0rmZmZ1KxZq1hjCQTFodgW1Ztvvpl3332XP/74g/r16+f5ckqSxNNPP11iCxRcmegeJ3o+ZdB0XUVN+MP3Rjb73f5yVmkqUey/7GI0GbCFWUhOyMiyqjrQVd+PvFZJQknzXTtvSgu0YAuy7sSS8SvJGQNxRlbBbDCiOh0Ygm2YvOB0qOhnxSMb6t6Nd9tcUJ2o+79Db9o3p62qDp6UFAxR0ZzHM7qgHLNq1Up+/TWwq2HTps14//0PcTgcfPHFfMaMGct117UHoEqVqpw8eYK5c2dx330PIMsKzz8/nHvvvd9/fI8eDzF06CCSkhKJjY3j+PFjtG7dlkqVKmIwGHnlldc4dOgQmqbla/WMjo5m1KgxmM1matWqzcCBT/LOO2/z1FPP5JH97LNPeeyx/nTq1AWAypWrYLdnMGHCOAYOfDKPFTYlJRlN0wgPjwjYPnHiOFasWBaw7ddf/yrSZxgWFsbnn89jwYK5/PrrzyxYMJcFC+ZiNlt49NG+PPbYgCKNIxAUl2Irqh9++CEAf/31F3/9lfcLLhRVwYUiy7741PwSqbTUf8Gb6pPL5fb31VAt9tdZcBFRVQ1biIXMdBduJBSzFa/dd431CMAqgUNHTlewhz+AzT0bWXdgTPmRzKhHsIab0bweVJcTY1gwBs2J2x5odZfMoSh1stuquvDuXoKxSU6zD68zq6aqLURY34tJ2IOTy4zrv7i0b38DTz89OGCb2ewLATl48AAul4tRo14O6GCmqiputxun00m9evUJDQ1l9uzPOXToIMeOHWXv3t1Zcr6SZ0888TTvvfcOX331Jc2bt6Bt22vp3PnWAl3zDRpcFaBgNmrUGI/Hw5EjR7DZQvzbk5OTiY8/zdSpH/LJJ1P82zVNx+VyceLE8TwJUWFh4UiSRFpaWsD2fv0ep0ePhwH47bdfCrTIFkRYWBhPPPE0TzzxNAkJZ/jnn/UsXfoN06ZNJSwsPECRFwhKimL/su/YsaPAPzyBoCSQZQnNmZnvvgC3f0xO7JWkyL5i/6JMZpnGYJQJCbOQGJ+BbA1Fcjt8hfwlCbmqjLbHZ0V3ZdyBzTQb8HWqysy4F29YGEgSuur1hQBEhOB1edDOqo1qaPgA6p4lPvf/7q8xNHoQSfEpBJrHizctFWOoqKlaXKzXdMfS5O5ymUwVFBQcUIopN5rm+x68+eY4qlevkWe/yWRi06aNDBnyNNdeez1Nm15Dly634XI5efHF5/xy993Xgw4dOrFmzZ9s2LCeadOmMnPmp8yevSDf0k1nd3fMXsfZmfPZ25999nlatWqdZ5y4uIp5thmNRho2bMSmTRvo3buPf3tERAQRERFZryPz+zgKZPbsz6lYsaLfqhsdHcNtt3WlS5fb6N+/D2vW/CEUVUGpUGyN86677uLXX38tjbUIBD5UD5rbmWdzoNvfghyRc9OWFIOvhqqgTOOzqpoxW4zoBnNArKocJ6NnX8KUEJyKL6zD6DmElrIFh1NDMfkUTk9GGkabFXNYaJ45AtqqulLytFX1pGeAO//4Z0HhSLKCYou5ZP/OR0k9FzVq1EBRDJw6dYqqVav5/61Z8xfz589BlmXmz59D8+YtGTduIg891Is2bdpy6tSprBF0kpKSmDhxHF6vhzvuuIvRo99g7tyFJCYmFFjDdffuXX4lFODff7dgNluoXLlKgFxkZCQREREcP34sYH27du3kk0+mFBju9NBDPfn777WsW7cm3/3x8aeL9Tlt376Nzz+fkaczpSzLBAcHExkpKmoISodi/7KfPHkyoCOVQFDieAtw+6dsAa/PlSVHtva7/QEko1W4cssJkiwRGm4h4bQHyeKrAICmIikSehxIx3wNAByuPlgMfwNgTV1ORnprbBXMgO8hxmNPwxwdhSsjE80d+H0xXPUg6mHfA7V35yKUOl2RJN9zudfpQs1IR44QNVUFYLOFcM899zJt2hSCg4Np0qQpmzZt4KOPPqB3774AxMbG8vvvv7Fly2YqVIhl06Z/mDbN54Z3u91UqBDLX3/9ybFjx3jqqUEEBwezYsUyjEYjDRo0zHfe+PjTvPHG6/Tq1ZtDhw7x6acf06tXb0wmU4CcJEn06tWHTz75iLi4ONq1u459+/YyYcJbtG9/Yx75bDp16sLOnTsYNmwoPXo8TIcOHYmIiODo0aN8++3X/Pzzj7Rs2SrgmGPHjrJ2bWBIn9lsoXnzFvTrN5AnnujHkCFP88gjfahWrToJCWf45Zef2b59G0OHvnBen79AcC6KrajeeeedfP7559SqVYsKFSqUxpoEVzCSJKF7nGj51E8NzPa/KWCfbBSlqcoLmqYTZDNjSXPh0EExB6E60n374iSk4zqSDlpKddToaBQSsGT+TnL6KVwRNTAqBnTVi8eejiEoBGt4KJnxiQFzyFENkCtcgxa/JU9bVV3T8CSnYCmm61Nw+TJkyPNEREQwbdpUEhLOEBsby4ABT9Crly++eeDAJ0lMTOSFF54FoGbNWrz88muMHv0qO3dup0aNmrz77iQmT36fZ555ApfLSd269XjnnUlUqVI13znbt78RRVHo1683FouV7t3vLzAhqWfPRzCbzXz55Rd88MG7REVFc/fd3QvtIgUwePBQ2rRpxzffLGb48OdITk4mLCycxo2vZsKE92jfPrB01apVK1m1amXAtri4iixZsoJ69eozY8ZsPvtsOm+8MZrk5GSCg200a9acadNmUquWKFkpKB0kvZhp0n369GHDhg2oqkp4eHi+Wf8//fRTAUeXX1RVIykp/7jJC8WT7OTkzK0A2BpFE9mlaJ1Cikvmj29jX+VrcRva70vMDbuUyjzng8EgExERTFqaA8/pA7hTAxUPXfPiWv+Az6IqWzC3WYyk+JIhJFnBHFcd1RRWbq2q2eefnJyJ13v5x07KsoQj08OZ02noLidq2hnMBnC4PMh7NeSsfB0lZg1h8v8ASI/oh7neQCJCNVSH729RNpowBUWQGZ+C56zEKvXYWty/jfDJxVyNucuH/n2K2URwrZpo5rJRUudSXP/IyGAUJf/oL6fTyf79B4iOjsNkyr+up0AgEFwIbreLhIRT1K5dC4ul4PrWxbaoVqxYkTvvvPOCFie4RJQHLU71oLkdeTZrqVtyuf3b+JVUAElRQDaWi9MT+NA0HWuwEavVRKaqo5itoPquu1oRv6LqTW2NHi4jSRrW9O/IyOhJaGhoVo9dHc3jxut1YLYoqC4poFuVXLkNUlh19NTDaGe2oZ3ZjhzTyDeHy42alo4hLhhVtNwVCASCMkuxFdW33nqrNNYhuOiU0aLnXhdaPvVT8y3yn4Uky0iK6DFd3tB1CA234HC4kawhSA4P4IEg0MJATgXJbcCuPUywMheD9yQkrcMZ1Zlgowk1KyFKdWRgNAdjlL14MPqrAEiSjKFhj5y2qjsXYo7JaavqSU3FGBODqKkqEAgEZZfzvkPv37+f2bNnM3HiRE6fPs2GDRvIyMi/5aVAUFT0fOqn6poXNfFP35uzsv3B5/rXRcZ/uUPXdSxBJoJtZnSDBTmXG17LVXHHZc/x4FhSl5GZ7gRDjjta1zQ0SUUxG5Dcmci5Ok8pNTuBxReLmt1WNRuv04WWke7vWy4QCASCskexFVVN03jllVe44447GDt2LDNmzCAhIYEpU6bQrVu3XCU7BILiozkz84QoaKmbwetLtpEj2wa4/QEkgxFKoWyNoPTRNI2QMCsGo4wcFIJs8FnG9TDQs/VWeygu1fdwYrb/jTvtGG5V8YV8ZOF1OlBsQUh4kNx2pKzC7ZJiKrCtqub14klLQ5SFFggEgrJLsW/RU6ZMYdmyZbzxxhv89ddf/hpuw4YNQ9M03nvvvRJfpODKQPMWEJ+a8Lv/9dlufwDZZBWtU8spug5mi0KwzYxssvpiVQEkUONy5OzOx7I2axgTl2G3e/01VbMH0tAwhoehZaYie51+ZdVQ924w+MZV93+H7kzxH+bJyABX3pq9AoFAICgbFFtR/eqrrxg8eDD33nsv4eHh/u0NGzZk8ODB+bZVFQiKgu4uvtsfSUIymEQiVTlGVXVCwiwYTQZkSwiywVcXUo8GPSv0WMuohlf1xQNY07/Hnp6BSmD9SNXlwBAShGy1oKYnImtuJEnytVWtfXu2EN493+Yc43SjZmQEtM4UCAQCQdmh2IpqQkICDRvmX8A4NjY2T29hgaAoSBJobkeeRCotdVOO2z+qnb8Vpv84WQHFKCyq5RyTWSE4xIRutCBbsnz+MmhZVlUJCbv7cQAUNRESfsPl9pWnyo2mejFHR6NpKlpaIoruRZLA0PB+yC74v/trdNWXiKVrGp6UVGT98i8JJhAIBOWRYiuq1atXZ/Xq1fnuW79+PdWrV7/gRQmuPCRJQnNlwlkKQ4DbPyqv21+SFTCI0lTlHU3TCYuwYjYpyBab36qqVQA96y7lzWiJpgUDYElZTkaGC9kY+OCiul3IwVbM4eF4XXbU9AQUtKy2qjf5hFwpqAdW+Y/xOhxoTnupn6NAIBAIik+xFdVHH32U2bNnM2bMGNasWYMkSRw+fJjPPvuMzz77jIcffrg01im4zJF0LY+yoGse1MSsUBLFihzRKu9xigwi4/+ywGIxEhJmAYM1x6pqBC0mS0BTcLgeAMDs3IwraS92l5IT15ot5nVhiopENpnxOu2oGYkoko7hqh5+Ge+ORehZD0XZNVULKn4vEAgEgktHsX/h77//fpKSkpg6dSoLFixA13Wee+45jEYj/fv356GHHiqNdQpKhDJsdlRd6Ge1TdVScmf753X7A8iygi4bQRRtvyywhVpITXXiVkOQXXY0jxstDuTTvsq/LvsdBFnmIEleTIlLSQ6ugSEuBIPB649v1jxujEE2zBEROE6fwmvPwCDJGCs0wBN7DdrpLejpR9GOrUGpej0A7tQ0jNExIInqEQKBQFCWOC9T1OOPP07Pnj3ZvHkzKSkphIaG0rRp04DkKoGgqEiSBG5HXkW1kCL//mONFnwqjFBULwdkRSI0zEKiS0U2B6F53GAFPQKkZMAbhMt1MxbLj1gzfuRMem+SjQaio21IWiq65rOSet1OTJERuFNTUZ0OVHsaSBKmxg/iPL3FJ7PjC7+iqjqdqJkZSCHhIt75MkTXdVasWMaKFcs4eHA/mZmZxMbGct117enduy9RUdF+2bZtm/PKK6O54467Chxv+/b/+OyzaWzbthWn00lsbBw333wLjz76GMHBwYWuZefOHYwfP5YZM2Yjl3JttJ9//pHp0z/m5MkTVK9eg0GDhtCqVZsC5ePj47nrrlvzbM/+PH7//TeWL1/K+PHvluayBYIAzttnarPZaN++fUmuRXARKUtFzmVZQnM5UHLFpxbF7Q8gG81oQrG4bNA0nWCbmcx0Fw5vLqtqRZCTfTIOR0/M5h+RtXQsmX+QoXTCYDQQGRHiU0h1Hd3rhSAL5shI7CeOo+s6qj0NJbw+UlgN9NRDAW1VNa+KNzUVU1g4qnppPwNByaJpGiNGvMCWLZt49NF+DBs2gqCgIA4cOMDMmZ/Sp08vZs2aT2RkZJHGO3BgP089NZD77+/Bk08OIigoiN27d/HBB++wffs2PvpoWoHHer0e3nhjNM8992KpK6kbN/7Da6+9zKBBQ2nTpi3Lli3h+eefZdas+dSsWSvfY/bt24vZbOarr5YG/EYEB9sAuOGGm1iwYB6rVq2kS5fbSnX9AkE2lzy4T9M0PvzwQ7788kvS09Np1aoVo0aNomrVqnlkJ0+ezIcffpjvON27d/e3d+3bty9r1qwJ2N+6dWvmzJlT8icgKAE0VGcmxlz3bS1lE6i+Tmdy5LVIsinvYZIsEqkuRyQICbPicuZYVfUQ0IJBzgTdHYvX0wSjaSvW9BU4QzqSlpSKwRBBmC0Yr933vVGdDowR4RhSU/FmZvg6WDkyMNW9C9eGSUBgW1VPRiYmV2DXK0EguqajOfO2OL5YyBaDvz5uUVmwYB5//fUnM2bMokGDnIo1cXEVad68BQ8/fD/z5s1m0KAhRRpv+fKlVK1alWeeeda/rVKlylgsFoYOHcTevXuoW7devsd+//13mEwmWrRoWaxzOB9mz57JjTfeTI8evnC8QYOGsnXrvyxcOJ8RI17J95j9+/dStWo1oqNj8t0P0LPnI7z77gQ6duyMoohQGUHpc8kV1SlTpjB//nzGjRtHXFwcEyZMoH///ixbtgyTKVA5eeyxx3jwwQcDts2cOZMFCxbQp08f/7bdu3czevRoOnbs6N9mNIpe8GUVyetCUz0BqX1qgNv/hnyPkxUFFJNw1V5maJqONdiIJciI3XOWVXWfT8bu6EWY6UVMrl0YXPvwmuuQkpiG0RiO1WJFdTrQNRXdLGOOjMRr93U801QvUtQ1SJYIdGcy2pHf0dKPI4dURnW5UNMzkKMsaJr4Tp1Nxq5EEn86hGr3nFu4lFCCjER1rIGtQVSR5HVd58svv+C2224PUFKzsVgsfPTRJwGu/3MhSRInT57k4MEDAZbJVq3asGDBYipVqlzgsfPmzaFr1zsDtn377TcsWrSAY8eOIkkS9es3YMiQF2jY8CoAunXrSocOHVmz5k+Sk5N5660JjBkzilOnTuY7xyuvjOb22+9g69Z/efbZ5wL2tWjRil9//bnA9e3bt5caNWoWev5t27YjIyOd3377hVtu6VSorEBQElxSRdXtdvPZZ5/xwgsvcNNNNwHw3nvv0b59e3744QfuuOOOAPng4OCA+J8dO3Ywe/Zs/ve//1G/fn0AEhMTSUxMpGnTpsTEFPxUKCgbSBLgcfrqpxp9X0dd86D53f5BBbr9fTVUL/mzlqAU0HUIDTPjclrRzcE+q2oU6EdAcoPqvBo1uAqK4Rjhp18jtcJLeGhEUmIGMbEhGI0qmseN6nRgCAvFmBKCJ91X41lHQqneCe/uRYCOd+ciTK2Homs67tRkgiKjEFVV85Kw6gCa69LGRah2DwmrDhRZUT1x4jinTp0sNC6zYsVKxVpDt27dWbbsWx5++H4aN76a5s1b0KyZ719BLnWAI0eOcPDgAa67Lidk7rfffuGdd95m5MhXueaaZiQmJvDOO+MZO3YMc+Z84ZdbvHghEyd+QEhICLVr12HmzLloWv7XIjjYRnp6Og6HgwoV4gL2RUfHEB9/usA17t+/j/DwcJ54oh+HDx+matVq9O3bj3btrvPLGAxGWrduy++//yYUVcFF4ZLWY9m1axeZmZm0a9fOvy00NJSrrrqKf/7555zHjxkzhpYtW3LPPff4t+3evRtJkqhZs/CnQkHZQJYldJcTPddNV0vZCGqmb39Bbn/w9XpXhKX8ckTXdSxBJqxBRiRriK+wv5TTAADA7ugJgKImEXFyOEGp3+DKsJOc6EA3BvkeZHQNZDBHRUGumECp8g2QVUVC3b/S31ZVtbvQ7JmUoRBuwQWQlJQIQHh4RMD2559/lptvvs7/76GH7ivymFWrVmPOnAU88MCDJCQkMGvWTIYMeYY77ujMkiVfF3jc9u1bMRqNVKuWU2s8LCyMl14axW23daVixUo0btyEO+/sxv79+wKObdfuOlq3bkPDhldhMpmIiIggKio6338WiwVXVltgkynw/mg2m3C73fmuz+v1cvjwIdLS0hgw4Anee28SjRtfzXPPDeaff/4OkK1Vqw7//betyJ+ZQHAhFMkcdeLEiWINWqlS0Z5QT506BUDFihUDtleoUMG/ryB+/fVXNm/ezJIlSwK279mzh5CQEMaMGcNff/1FUFAQt956K0899VSeUILiYjCUjl6vKzm/ipIkldo8udtEyopcavMUB1kG1Z3pX5ssS2iJOUX+DRVuQs6vvqUkYQqJAIMRw2Xgps2u4Xml1vIs6PzDI4Jwu1Rw2fCoKehxOvpxHUkFt+NGXJF/YPasQUIlJGkaRtcu0uXnSDFXJCoiBM2RjuZxYQwPxZwahic1xTew2YZW5QbUwz+C6kLbvxRTkz6getEy0zGFhV5U9395uP7RXWqVGdd/UQkL8ymoaWmpAdtHjHgFp9MBwKJFX/DHH/k3sSmI2Ng4hg4dxtChwzhx4jjr16/jq6++ZNy4N6hQIZZrr70uzzGJiYmEhoYFxHU2a9aCgwcP8Nln0zl06BDHjh1h3769aFqgTb9q1WoB7x966L4CXf/Dh79Mu3bXAuB2B14rl8uNxWLN7zAMBgOrVv2KLMtYLBYAGjS4igMH9jNv3pwAq3RERDiJiQkFfTwCQYlSJEW1Q4cOxcoS37lzZ5HkHA7fjeJsBdJsNpOamprfIX5mzpzJzTffnKed6549e3C5XDRp0oS+ffuyc+dOxo8fz4kTJxg/fnyRz+FsZFkiIqLwsiPni1PN+WyNRqXU5vFaTGRkvQ6xWbCV0jzFQXXacZkkdMn3HbCaIDPRlwgnGYIJq3IdkpL3AUMyWjBXiEPJLgx/mRAamv+PyJXC2eev6zqqqpOiezHqLnSvF7WSF+2oiqRLOAyvQehMzIk+N6kl83eMxw7jlMfgDG5CVGQ4usuOZFQwVIzD7nWiZ6X1G+t3Jf3wT4COZ+dXRLR+FMlgQfG6sJlllKwf64tJWb7+tgZRBNeLLFfJVJUrVyY6OppNmzbSqVMX//bcYWGhoaHFWsPkye/Ttm07v+JWqVJlunW7l9tvv5P77rubNWv+yFdRlSQ5j7t+1aqVjBnzGl263EaTJk24557u7N+/n4kTxwXImc2BCX7vvjsJrzf/6xAZGUVQUBBWq5WEhDMB+xISzhQaEhcUlPd+Wrt2bdatWxuwTVW1Uq9aIBBkUyRFdezYsX5FNTU1lYkTJ9KuXTtuu+02YmJiSElJ4ZdffuG3335jxIgRRZ48+6nN7Xb7XwO4XC6s1oJv2CdOnODvv/9m2rS8ZUDGjBnD8OHDCQsLA6BevXoYjUaGDh3Kiy++SHR00YPmc6NpOmlppdNm0ZPqyHntUUlOziyVeRwOl/91eoYTTynNU1QkCWRnKs70TCRdI8hqIv3EWnRvVrZ/1LVkOnXAledYc2Q4Hhdojkt7DiWFosiEhlpJS3OgqldehGRh5280ymiyEY9kxuNyQIyOctRXPVc9rpHaog8mY31C4ycga3YU92Gs+58gxTEMre7dBJtk1Mw0DBYbqikYd3Ji1qThyBVboZ1cj+5MIXXrEgz17kFyeFFPJyCHRVy0ihKX4vqHhlqLbcGVZAklqPyE2yiKwgMPPMSMGdPp3v2+fLPxC4vZzI9//lnPgQP788S9mkwmzGYzkZH5x89GR0eTlpaGpuUoebNnz+Suu7oxfPhLfrnff/dZd3VdL9BAVJS42qZNr2HTpg3cdVc3/7aNG/+hWbPm+cofOLCf/v37MGHCewFVCXbs2JEn9jY5OanQygACQUlSJEW1e/fu/tdPP/003bp144033giQufPOO3nzzTdZuXIlPXr0OHuIfMl2+cfHx1OtWo5rIz4+3p8clR8//fQTkZGRXHdd3qdWg8HgV1KzqVu3LuALNThfRRXA6y2dHxBvrq5Kuq6X2jy5XZmaqpXaPEVFliU0pwPV7fG79z3xv+bsj7oRLZ8fbcVkQbeE4XFffgUv1TJwXS4l+Z2/YpCxBhtx221Ijkw0kws9CqREkDwgHddxVW5LUqVJhJ1+A6PnELLuwHZ8DHbHDuSmL2CSDXg9LoyRkbjT0nyNBAC5+q1oJ9cD4Nq2AEPtrnhVA66kFMy2cNSLHFZypV//0qBXr0fZvXsXjz/ej969+3Ddde0JDraxf/9evvxyIevXr+POO+8OOGb//n2sXftXwLbQ0DAaNWrMk08+zQsvDOXll4dz330PEBdXkZMnT7Js2RLsdjt3392d/GjUqDGqqrJ37x7q128A+EIItm7dwq5dO7HZbPzxx2oWL14I+Aw4Z1tSi8NDD/XiuecGU69eA6699jqWLfuWPXv28PLLr/llkpOTMRoN2Gwh1KhRkxo1ajBx4jiGD3+J8PAIliz5mu3btzFz5tyAsXfv3kWjRo3Pe20CQXEotu3+r7/+4rbb8i/0e9NNN7F58+Yij9WgQQNsNht//50TqJ2WlsaOHTto1Sr/TG+ADRs20Lp1awyGvHr2I488wsiRIwO2bdu2DaPRSI0aNYq8tsueMpAtIkmgOTP873XVjZqQVf9WCUIOz//JXwkKQTdeXi5/QcGoqkZIqAVTUDCKJRiQ0HKFtStHQT4FqrEySZXew2G7xb/PkrQY5/oBaGomuteLEmzBFBbu3y+H1USK8CkNesZJ3HtXocg63kw7uHI8HYLyiyzLvPnm27z88ii2bNnM4MFP8cAD3Zg48W0iIiKYOnV6gPIGsGDBXIYOHRTw78MPPwB8iU1Tp07H7Xbz0kvDuf/+brzyyggkSWL69M+JisrfolqlSlVq167Dhg05icIvvDCcyMgonnpqAP36Pcpff/3BqFG+ur47d26/oPNu06Ydr7zyGl9//SWPPvowGzas55133g8oP9W3by/efXei/3OaMOF9GjVqzMsvj+DRRx9m+/b/mDRpCrVr1/Ef4/V62Lr1X9q3v+mC1icQFJVi1/aJiIhg69at+Voz161bR2xsbJHHMplM9OrVi4kTJxIZGUnlypWZMGECcXFxdO7cGVVVSUpKIiQkJCA0YMeOHdx77735jtmlSxfGjh1LkyZNuP7669m2bRvjx4+nX79+2Gy24p6uoBSRVTdeT04GqjthfU62f9R1+Wb7y0YTsi3iolu6BJcWxSARHGLC7QhBdmai2VyolUE5nrX/kO9/Lc5CWvTzeMwNCEn8BAkvhsxtONb2xNp0LKpyFaaoSDzpaahZmdFKzVvxJu8CwLN7CYbKbZGlaNSMdOQoq6ipeplwyy2dilROad26TeeUadKkKRMmvFfsNdx//4N8+eUX9Oz5COCLb508eWoeudzxtEuWrCj2PNncdtsd3HbbHQXuP3vsqKgoXnlldKFj/v77amw2GzfckH99a4GgpCm2onr//ffz0Ucf4XQ6uemmm4iIiCAhIYHvv/+eBQsW8NJLL517kFwMHjwYr9fLK6+8gtPppFWrVsyYMQOj0cixY8e45ZZbeOuttwLCD86cOUN4eHi+4/Xq1QtJkpgzZw5jx44lJiaGPn36MHDgwOKeqqAUkSTQ3U5fof8snCd/8b9Wom/M9zhDUAi6yYauCuXhSkJVdWwhZuyZQWh2X11VrYoOOihZRUmUQ/hKWMVKOELvwGOuS/jpN1HUM0ieJBwbnsZc9ylMNR7GFBGBIytrWoq+Gim4EnrmCfSUPXhObMZUuQVqqg0lMto3qEBQAtxxx53Mmzebv/9eR5s2bS/1cs6LL76YT79+AzEYyk+ssqB8U2xF9cknnyQ9PZ0ZM2b4k5l0XcdisfDss8/Ss2fPYo2nKArDhg1j2LBhefZVqVKF3bt359n+77//Fjpmz549i70OwcVFkiTwOH092QFdc+OO/8O3UwlGDm+R5xjZYEQOFtbUKxVJlggJteC2h6I6M9E8LrSsTst+ZfUg6IAeC15zfRIrTyYs/m3Mzs1IqLj3TkZN3Yal7kjcqUGoDjuSJCPXvBX1v88A8B5ciRxRFzk5HnPFikiWYNGmV1AiGAxGXnttDBMmjKNVq9blLnP+t99+ISTElqe7lkBQmhRbUZUkieHDh/PUU0+xZcsWUlNTiYiIoFmzZvmWthAI8kOSQM0Vn6om/YPu9VVV8Ln98z6tK5ZgsNjQL78cKkER0DSdoGATmcFBqJk+qyqS7lNWdVCyykoaDoJXAr0C6EoYKXH/Izh5LrZUXwkrNf43HBkHMFUbicPhq2kpV2yLuucrcKein96Iln4CjyThPXMSU636uC/DxD3BpaFx4ybMmjX/Ui/jvLjppg7cdFOHS70MwRXGeT/OBQcHExMTQ2hoKE2bNi2w24VAkB+y5kX35JSdUs/kFNzOz+0vKQaUkEhUrXxZIAQljAS2MAsGWxiK0eTfplUDNXeC1QGQ4rOPUciMfJTk2NFosi9OXbMfwb1nKApbfSKyEaV6x6wDdNTDP6CpXpynjkFGYpkuxC8QCASXM+d19/3222+56aabuOeee3jiiSc4fPgwI0aMYNCgQUJhFRQJ3eNEy+X2V7OK/GOw5Zvtb7BYwRKCLnywVzSapmMNMmINCUa2+ioAAHmUVYmzlFXAHdSGpEqT8JiyakJqToj/ECl9Aehe5Ko3+9uqasf/RHen405PxR1/EtmZKpRVgUAguAQU+8773XffMXz4cNq2bcu7777rb/XWqVMnVq9ezZQpU0p8kYKSomwoebIsgceBpvoUVS35H1B9bn8lH7e/JCsoIZFoKHnGElx56DrYQs0YbeE5VlXIUVbj/G99ymqu5jyqsSJJFd/FEdLZv012/IqcPBFJdiNXzspk1txoR35B93pxxZ9BTYlHdqVcgcpq2bhnCASCy5Gi3V+Kfdf9+OOPefDBBxk/fjydO+fc7O+9914GDRrEihXnX0pDcGXgK/SfSXaGipqQ4/Y3xNyUR14xW8EaJsoECQBf8qbZYiQoNAglJAKD2YokZd3KJNCqn6Ws7g9UVpHNpEUNIaPCUJB8D0WS9wBy0v9QKtWCrLHUoz+jq26ciWfIPHQYT/wpFHcqinL5VwEwGo1Ikq9LoEAgEJQGLpcLSfLdbwqj2MlUBw8eZPjw4fnua9q0KZMnTy7ukIJLwiX8sVU9aG5fDUtddaEl+fpIS8YQ5IjmgRnWkoTBFo6GAWHdEWSj6xq2EAv2zFC8RiuyKwPVno7qdvkSrKr75JRTOcqqKoGe3ZhOksgM7oxuq0/IiTHgPIGkZ6C4ZqJGVUZLOArudLQTf6FUvRl3SjKa243ucWOpXB3MoaiXcYk0RVEIDw8nOTkFyO41f/kr6AKB4GKg43K5SE9PISIiHEUp3FtabEU1KiqK/fv351vwf//+/QV25RAI/HidaF5f/VQtZQOovg5A5tgbkGQjeq62qQazFSkoXFhTBQHoOpgsCrZQCylJGpo5FMUUjOxMR3Vk+EpXVcdXDeB0lrK6D1RyKauAXa+OXH8mIcdeR0tcg4SOMeokrgTffvXQKuQqNyJJMl57JhmHDqG5PVhr1ARLxGWtrGa3uE5JSSE9/RIvRiAQXFZIEkREhPvvM4VRbEX19ttvZ9KkSVSoUIEbb7wxa0KJ//77jylTpnDHHQV3wRAIZFkCp9Mfn6om/ObfZ67YAU9uYUlCCQ5HU8ygiv7ngkBUVSc8MghZlkhJsuNx68jWCAzmYDRHGpozE62G7xtVmLKakaZhrDuJIOtHeI59jhwEsk1Cy9DBfhr9zBakCr4EP83tIvPoYTS3m+BadZBCYvB6L8/vpiRJVKpUidjYWDwez7kPEAgEgiJiNBrPaUnNptiK6pAhQ9izZw9DhgzxFyt+5JFHsNvttGzZkmeffba4QwquICRJQnPaQdd9bv9En9sfQwimqJZ4HDn1KhWTGSk4HK9QUgUFoKoaoeEWDAaFpIQMXE4vYEAOjsJgtqE50lBrZaLqKkp8LmVVAj2X8yc1KQWl6jOYjDXwHhqPoUI67gzfd1Hdvwi5Qk4lCl1VsZ887lNWa9fFGFUJj+fy/Y4qilLkHxSBQCAoaYqtqJpMJj799FP++usv1q1bR0pKCiEhIbRu3Zobb7zR13FIICgASfeiun2ufi35H1+JIECJvh5JNuCzd/lQgsPQDVZhTRUUiqrqBNmMKIYQEs9k4sh0o2k6kmJCConBaAlGa5iKJtmRsy2re7Msq1nKqubxkJ7hICy6E4oehS6PRzqxH90Jetpp9BMfIVV8HKSsW6au40yIR/N4sNXyYKxYDY/n8g0DEAgEgktFsRXVfv360b9/f6677rp841QFgkLxuPzxqYVl+ysmM3JwhLCmCoqEquqYzAYqxIaQmJBJZroTXfdVCJAMQcjhVvRrMtD/TUQ6pearrLrtdtJ1K6HhDZG111GS38C7f5dv/CMbMVknoYU9DnKwf153ajLpu3cQ7HZjqlob0cBKIBAISpZil6fatGmTsJoKzgtZlsDrQPN60FWnP9sfQwhyeLMAWcUagm4ULXkFRUfTdGSDRHSsjdBwK5Lsu0/puo6qgW4JQWpVBSr7ivpnhwFISb7jdU3F7VFJd8lIobEo9UaAyfcd1FJ1vIe2IyW+Bd74gHk99gzS9u3EtX8nRlRxfxQIBIISpNiKavv27Vm6dKkIri+PXOKuTrnjU7Xk9Tlu/6hst78P2WhCtkWKTH9Bscn+ikfF2IiIDAoo0K9ruq9pRIvKUNVnFZV0n2U1W1nVPC5cHo00lwFjdBxK7Xv8x3vjNdw7jsOJN8C9N2BezeUi/cAenHv+Q/HYfQ9lAoFAILhgiu36N5vNLF26lJUrV1K7dm2CggKtXpIkMWvWrBJboODyQdJVVJevA5Wa8Lt/uxx9Y4CcwWpDN9vQL+PSP4LSRdN1wqN8impykh2vR821D2haAZkEOJruV1bVuqBHauiKjCPThawbCGn2EE5NRd3zJegquhPcu9IxpE1Arv0YWNv6x9VVL5lHD6K53Vhr1UMKCb+sy1cJBALBxaDYiuqpU6do1izHTXt273XRi72ccCnck14Xmteb5fZf59tmCEUOuyZnWQYDsi1S5E8JLhhN0wmNsKAYZJITMnG5vDk7JQmtaTSSriMdy/Arq1p9GS3CjWK1kJ6SgjEqGOs1D+KMugp168foGSdAB+9xD3LaNAz1D0PkA/6/J11TcZw+jub1YKleF0NkNF5dWFcFAoHgfCm2ojpnzpzSWIfgMkeSJPA40FRPoW5/gzkIzCHowu0vKAFUVSc4xIyiSCQlZOKw5wpZkiT0a2JAB+l4lrK6W0NuZIIoGdlsJSkhjQqxoViqNsEdMgbvroVoh38EQEvXcW/+HkOtg8g1n/e3Y9U1FVfiaTSPF2u12hhiK6JK8qWOvBEIBIJySbFjVAvDbrfz+++/n1tQcMUhyxKaywGaFpDtH+D2lxUMtkg00apRUIKoqobZaiQ6LgRbiDlwpyShN4tBr2zzvddB2uFGyjBiCA1FU+HM6XQysKFUqEFQu0EY24wAkzVrcPDu3Y134wvorpwkK13T8KQlYj+0F/exw8gel4hbFQgEgvOg2BbV48ePM3r0aNavX4/b7c5XZufOnRe8MMHlhYSK6spEVx1oSX/7NhpCkcOv8cvIJgtqUBiaqEcpKGE0TcdgUIiODUFWZNJTnTlhSn7Lqo50IhM00DenobSugDkmFvuJY8QfzsRiCyI4LJjgep0wVWmK88/RqKd3+8ZPTENb8xKGxo8hx1wL+JRVb3oyjqMqmtuLKS4OJSQMVcS1CAQCQZEptkX1rbfeYtOmTdx///00bNiQ5s2b89hjj1G/fn0kSeLDDz8sjXUKyjteF7r3LLd/dHskKavjjSxjCIlEl0QHHEHpoOs6kgzRFWyER1oDLZyyhN6sAnqlrGoAGqjrzyB7QzBFxqKYLLgyMkk8Fs+pYykkq5HIt0zB3Ko3KFnjuFW8m6bj3fkxuubNmlNDzUzDdfowzuPHUBPPoCjCsioQCARFpdiK6j///MPQoUN55ZVX6N69O2azmWHDhvHVV1/RqlUrfv7559JYp6AcI0kSuH2JVIFu/xv8rw0mC0pwhChJJShVdN1XESAiOpjIGBsGQ65bYLayWjFbWdXR/j6NwRuEYgtHsdiQZRm33UnKyQROH0siJe5BlFsnI4dY/cNoR/7Gu3aEL/GKHGXVk3Ac18njeE6eQJGEVVUgEAiKQrEV1czMTOrXrw9ArVq12LFjB+DrB/3www+zbt26kl2hoNwjy6C57WiejFxu/7CcbH9JwmCLQDaZCxxDIChJNE0nLMJKVAUbJlMuK74soTcPVFalf1Mx6jaUsEiUoFBkgy9iyutykx6fzOmUaNLbzUSpUYPs8Go9IxHP2ldRD/+Eruu+pgOZaXiT43HHn8Z97BiK6hHNAQQCgeAcFFtRrVChAgkJCQBUr16d1NRUzpw5A0B4eDiJiYklu0LBZYCO7sz0KamaCwh0+xvMFqTg8Eu4PsGViKpq2EItRMeGYLEac3ZkK6txWTWiNR353zRMhySMhkgMoVEouR6qNK9KZqrKqbg30a++GcmSvUND3TUP78Z30F0pPmXVnoaaloA7KQHHkcNIzkwRCiAQCASF8P/27jwwivr+//hzZvZMNjdHUAQlmiA3SECqnFro16q11rZfK57Fs8DXtiJ41vsCRYEqYFU8oLYWFbxaxaPqT0ERxVK5BEWQI4Hce8/M5/fHHsmSoERyLPB+2HR3Zz47+5mdTXjtZz6fzzQ7qI4cOZIHH3yQTz/9lCOPPJLCwkIef/xx6urqWLx4MZ07d26NeooW0T6n1TUzjG1G93na38jMRRnSmiranmXZeDKcdOycRYavwWdQ11AndK4PqwDlIYz/+HFtduCmI46MLHS9wZWvFJR7J+DvdSFGx/pWWrXnv0T/343Yuz5BKYUZqMGurcCqrSG45RvsygqZEUAIIfah2UF18uTJZGdn89BDDwHw+9//nieffJLS0lJeeuklLr744havpGgNbfMPo6YBZggrUhMbSAXgzEXP6Q+AEW9Nlb6por3YtsLpNuhY6CMr21N/LYx4WLV7F6C89ROkaLUmxqYonq99uKMd0HVnyvbqnGOpPHoKzqKM+nlVon7Mz+ZgrXsKZQYx/TXYtXuwgwEC27YT3bUD2zQRQgiRqtnTU+Xl5fHcc89RVhabM/DMM8/kiCOO4LPPPqNfv34MGTKkWduzbZs5c+bw3HPPUVtbS2lpKTfffDNHHXVUk+WXLl3KlClTGi1/88036dq1KwCvvfYas2fPZtu2bfTo0YOpU6cybNiwZu6paAm6rqHCIazy/1d/2r/g5ORpfyMjG+XIkJlTRbuybYWua3To7MNw6NRUBWNfnnQNeuSgjs5G7fCjbapGq459jrWwjWO7hmHkY2VHCHmqUY7YIKmQ3pey/FvomDED+5ty7OrYFzFry9vYe9bhLp2EpXcFFHpWB0JluzE75gOu9nkDhBAiTf3gCf87deqUvD948GAmTJjQ7JAK8PDDD7No0SJuv/12nn32WWzbZsKECfuco3X9+vUMGTKE999/P+WnS5cuACxfvpwpU6bwv//7v7zwwgsMGzaMyy67jE2bNv2wHRUHzA7VNTnJv+5yo2fmybySIi0oFesck98xk7yCTIy9ZgTgSB9q+BHYP+qC6lzfJUCzwFHpInNHB7wVueiR2Pf/qH4kO723oHoU4+xmJP/aqrodhN65EWvbv8ChoYJV6Mpqwz0VQoiDR7NbVK+77rrvLXP33Xfv17YikQiPP/4411xzDaNGjQJg5syZDB8+nNdff53TTz+90XM2bNhASUkJHTt2bHKbjz76KKeeeioXXHABAFOnTuXTTz/lySef5LbbbtuveomWo1sRoqGqvU779wPA4fWhXJlgyWl/kT5sW5FbkIFuaFRVBIhGGoRITYMCL6rAi6qLoG2uhq11sdkB0HD43Tj8bkx3hEiWH8uTQ5lzGgUd/4LXt4LoFgvbr0DZmP9ZSHTbStwjrset56Nsq4WvFSiEEAe/ZgfVFStWNFoWCASoqqoiNzeXvn377ve21q1bh9/vTzktn52dTa9evfj444+bDKrr169nzJgxTW7Ptm1WrVrFtGnTUpYPHTqU119/fb/rJVqGpoGKhIiWvQd2rIXcKIiN9tcdTnRfPpb0TRVpyLJssnM9OBwGFbvrCIea6D/qc6H6dYSSfNTXNWhfV6NFYmcHHGEXjrALy2ESzQqwO+MKcrydyTluKeYuG3NHrJxWuZ7wS5cRGngV3h7HgafxywghxOGs2UH1rbfeanL5pk2bmDhxImedddZ+b2vnzp0AydP2CZ06dUqua6i6uppdu3axcuVKFi1aRGVlJf369WPKlCkcc8wx1NTUEAgEKCws3K/tidYVm+g/iFX2dnJZ4rS/4c1EuX3IGU+RrixLkeFzYjiyqK4IEgpFU1tXE9wGlOShjs1BbatD31wNdVEADNOBUZmNq9pH0HcBprc7BYWPYGRFiWyxUGHQ7DDaJzPZXbeG3PMfBU9+G++pEEKkr2YH1X0pKipi0qRJzJ49m5/+9Kf79ZxgMAiAy5U6gMDtdlNdXd2o/MaNG4HYpRDvvvtuQqEQjzzyCL/5zW946aWXMOOjZpvaXjgcbvY+7S3lKjYtSDWYR1HTtFZ7nYZT4BiG3mqv0/D1zEBZg9P+eTjy+6M7XLhyOmDrenKEtWHoKbeHG9n/9N1/b4aTjEwXkZCJ3x8hUBchEjYbz1Rh6NAjF3VMDuwKwJeVsDt2uWDd1nHX+FA1p7EncxDZnrtxl2wgut3G2h1rXQ2vf4PaF6eRM/4vbb2LQgiRtlosqAL4fD6+/fbb/S7v8cTOc0UikeR9gHA4jNfrbVR+8ODBfPjhh+Tl5SWv6DJnzhxGjRrF888/zy9/+cvk9hra1/aaQ9c18vIyD2gb+xKy6gOk02m02utEPS7q4vd9WR58rfQ6CVY4SNWe95On/b1dRpPly8Dw+HB36ITmaPzxy84+sON0sJP9T+P9z4GOQCRi4q+NUFcbJhI2Mc0mBgNmeeHYAqzdASJrdmJ9GwAFGhqavwt1/lk43J/i7fxXnFmriW61wASXL7vVfv+FEOJg1Oygun379kbLLMti165dzJo1i6Kiov3eVuKUf1lZGd26dUsuLysrS16mdW/5+amnxbxeL127dmXXrl3k5uaSkZGRnDqr4fYO9EIEtq2oqQkc0Db2JVodrL8ftais9LfK64SC9a3KdbUhoq30OglGpBb/ln8mH9t5w/EHTdw+H6G6CErV18cwdLKzvdTUBA/LWQBk/w+u/dcMjew8N5GQ8d2trB4dSo9EPz6I2rAbe1sILd57wAwPpDY8EMOxAU/RYgzzXRz9hrba7//esrO9admCLYQQDTU7qI4ZM6bJ61MrpfB4PMyZM2e/t9WzZ098Ph8rVqxIBtWamhq++OILxo8f36j83/72Nx544AHefvttMjJi08PU1dXx9ddfc84556BpGoMGDeKjjz5Ktq5CbADY4MGDm7urjTTZctICzAaj3pVSrfY6Df8RtezWex2Iz59aW4a1Z3lsgTMPfL3QnG5sdzZmtOnOqZZlt2q90p3s/0G2/7pGdq6XzCw34UCUutpwk31ZNa8LfUAXtKIa1Nc1sDWCFo39HbXMYvy116Hrl2Csfp2sbr9s6pWEEOKw1OygetdddzUKqpqm4fP5GDp0KFlZWfu9LZfLxfjx45kxYwb5+fkceeSRTJ8+ncLCQsaOHYtlWVRUVJCVlYXH42HEiBHMmDGDa6+9lv/7v/8jFArxwAMPkJ+fz9lnnw3AxRdfzGWXXUavXr0YMWIEixcvZu3atdx5553N3VVxADQNwlv/CSo+qKTDCDTDicOXj41Be13OVYiWlmgB9vpcZGS5iYRMAoEI/njXANtWKAWW0tCzctGLHdjd/NjfBmFrFD0Ua9W07c4E6s5j//+CCiHEoa/ZQTURCFvK5MmTMU2TG2+8kVAoRGlpKY899hhOp5Nt27ZxyimncPfdd3P22WfTpUsXFixYwP3338+5556LUoqTTjqJp556Crc7dp3uk08+mbvuuouHH36YmTNncuyxxzJ37txmdUkQB06zo0S3/yv52OgwEofLg5aRI5dLFYek2OdaYTh1cvMzyMrxNGpltW2F5vahO5zYR2moLlGssjD6dhuH5SR7UPf23g0hhEgrmlKqWanhxRdfbNYLNGe6qnRmWTYVFa3TdyxaGWLHE58D4OvdgfxxPVrldfyv3UbgzRkA5Fy+FNdxo1rldTRNQw/soPqFgaAi4MzHPfRZPB2OguzOWE1M8O9w6OTlZVJZ6T+4Tv22ENn/Q3P/dV1D07RGraxKKXRs7EAVKhLG6XLR/fhu4M1ss/3Pz8+UPqpCiLTX7BbVG264AaVU8ich0R1g72WHSlAV+0fXNXQrSOjLxbGQChgdhuNwZ6Jl5qb0xxXiUPd9rawqMx/dqEWpMLrTyaET0YUQomU0O6guXLiQK6+8kgsvvJAzzzyTzp07U1VVxVtvvcV9993H1KlTU640JQ4fhqGjhaoxK7cT2fZa/fIOo3Bk5qAMDxwEI7qFaA376ssa9LvBDH7Ps4UQ4vDU7KB62223ceGFF3LFFVcklxUUFPDLX/6SUCjEk08+yTnnnNOilRTpzzA0tLpyolW7MAN7sCtXxla4CnB0OCHemiohVYjGraw20XAGhseLbTZxqVYhhDiMNbuD0qZNm+jTp0+T67p3784333xzwJUSBxeHbkPVdsIV27EiYeyK5fWj/QuGx1pTnRntXEsh0k+ilTXD58KX5W7n2gghRPppdlDt3r07S5YsaXLd3/72t31O1C8OPZqm4dSi2BXbCFeVoUwTZfoxdyxNlnEUnoruy5OR/kJ8h2aOaRVCiMNGs0/9X3XVVVx99dV8/fXXnHLKKeTn57N7925ef/11Nm3axOOPP94a9RRpRtc19Kgfs3I7UX8tAHbtWqLr7kSFd8YKuTri6jIM5fKhZBCVEEIIIZqp2UH1Jz/5CX/+85/585//zIMPPgiArusMHDiQBQsWcMIJJ7R0HUWraHx1sf1lGBpaoAqzagdmKIhSFta2v2F+8ySo+BV5jAzcPadh+AqwpDVVCCGEED9As4MqwCmnnMIpp5xCKBSiurqanJwcPB5PS9dNpCGHoaC2nEhVGXY0ggrvJrrhXuzqT5NltKxeOEuux9WhJ3h8yewqhBBCCNEcPyio1tXV4ff76dy5M4Zh8PTTT7N9+3bGjRtHaWlpS9dRpAFNA0OzsSt3EK3Zg7IsrD0fEt04HcyaRCmMo36Do9sF6A43RlY+lq0jl0sVQgghxA/R7MFUq1evZvTo0TzzzDMA3HHHHdx3330sXbqUCy+8kDfffLPFKynal65rGHYYa/c3RKrKsaNBopvmEF17U31IdXXA2Wc6zu4Xo2kGDo8XPFkySEQIIYQQP1izg+qDDz5IUVERv/rVrwgGgyxZsoTf/OY3fPTRR5xzzjnMnTu3Neop2olh6OiROszd3xCtrcT2f03ks4lYO15MltHzf4R74DyM3AGx57g8GFkF2BjtU2khhBBCHBKafep/9erVzJw5k6OOOoply5YRDof52c9+BsBpp53G0qVLv2cL4mARGzS1m2jlLsxQEGvXq5ibHwY7HCugOXH0uAKj8Ew0TUPTDRyZWRg5nVCuLGyZ4F8IIYQQB6DZQVXXddzu2MTU7733HtnZ2fTr1w+I9V2VQVWHBocOqnonkepyrFAF0Y0PYO95L7leyzgaZ8n16Jk9YuU9XhzZHcGXj2VrKAmpQgghhDhAzQ6qffr04bnnnsPj8fDPf/6TUaNGoWkae/bs4dFHH93nVatE+9uf/qKaBg4srIrtRGsrsSo/I7LhLgiXJ8sYhafjOOYKNMOD7nDiyMxBz+mEcniwZL5UIYQQQrSQZgfVKVOmMGHCBF555RXy8/O58sorATj99NOxbZvHHnusxSsp2oaua+hWELNiO5G6SqxvnsH85hkg3jrqyMJ57B8xOpwMmobDk4kjtxPKm4tpKZCQKoQQQogW1Oyg2rt3b9544w02bdrEcccdR0ZG7Brut9xyC4MGDaJjx44tXknRCrTUCf8NQ0cLVWNW7iBauZnIhntQNf+pL57dD1fJNDR3J3SnG2dWHnp2RyzNiS0BVQghhBCt4AfNo+rz+ejfv3/KsnHjxrVIhUTbMwwNra6caNUuItvfILrxAbDq4mt1HN0uwDjqXHTDiSMjPljKnUXUUiDTTwkhhBCilfygoCoOHQ7dRlXtIlS5jejGOVg7X65f6e6Eq+R69Ow+GG4PzuwO4CvAUgZKWlGFEEII0cokqB6mNE3DqUWxKnYQ3rGSyLo7UIEtyfV6hxE4j/0DujsXpy8HPbsjypmJZdnIlaaEEEII0RYkqB6mdCtItOxrQl8uxPxqLqhofIUHR4/fYRT+D05vJo6cjpCRK1NOibSgaRqaFutibSoTU5kYmoFTcwJg2/s3u4UQQoiDgwTVw4iu1w+gMndvJrxpFnbFh8llWmYRzpIbcOQci8OXi5HdCUt3yWAp0S5igVRD1zVMZRK1o4TNCCEzxNZAJf+tLqMuGqbYl0cPXwE+dwYew4Pb4cKlu9AxUEph2/L5FUKIg5UE1cOAYWgYysLvr0kuC31xN4a3rr7MEWfjOOYyXL48jJzOKE92bLCU/CMv2kBiEgrD0OtDqRWhLhpkbfUu1laXsaFuDxvrKtjkr6LKDKc8P8NwcFxmHiWZ+Ryf3YE+OZ0o8nUg05URD65uHJoDDWl1FUKIg4kE1UOYroND17ArduH/5ivC33xev9KsAXRw5OAsnoKrcBSO7AK0rA4yWOoQUT8DmZYyG5mm1U/WELtVDe63Tb1ip/A1LCyidpSQGeXb8jKWb/+K/1aXs7F2Dxv8FWwJ1BBV39/lJGCZrK4pZ3VNOexYD4DPcFLsy6enL59e2R3pm9OZoqyOeJ0eXIY73uqqS6urEEKkMQmqhyBN03A5Neyq3QS//Ya6VYuIrnkGFaxvQdV00HMH4ep5Pa78HrHBUi4fZgsMloqFIm3vqVq/U6Jbgq5rKV0U9r39fWuPxrIDfc3EPmlaaheNxHvZsIxSChsbpRSJ/+zEfWXXP7bt5HIbG1vFHyuFrmkYmoGu6bHT65qOoeno8R8t/p+OnixTv49qv4Nuw1BqK4uIihI2w3xZt5vPK7eztqaMDXUVfOmvYFc4sF/vVa7DTY/MXIoycsg0nGz0V7HBX0F5JJhSrs6Ksqp6F6uqd8G3awHIcbgo8eVzfFYHemV3ZEDeEXT3FeB1eHDpLhyaA6Vi73FLfo6aOo6x+7EHhqE3WieEECINgqpt28yZM4fnnnuO2tpaSktLufnmmznqqKOaLL9x40amT5/O6tWr0XWd0tJSpk2bxhFHHAGAZVkMHDiQcDj11ODEiROZNGlSq+9Pe9J1DYcOBGsIfvUN/k+fJPz5QlRtbUo5I0/HefwEPMddgjO3M2TkYymSg6WaaolrGD5Vg/CjlI2VeKxsLGWlBCQgeVt/kwg2sXs2NnpEp0o5qAtEsG07HsaIhy+VLKsAW9nx+/tIEmrvoBd7XsMMoKHFnp3Yv8RTVf36lBWATmwUjx5/H7R4OS3+QvXLtfi62HIDPXV5E2nEUBrRuhBV4SD+SIiAFSVgRghYUYJmBL8ZIWiZBM0wQdskbJkELZOQZRK2Y/fDtkXIMgnZJmHLImzH79sWYcsiZJtEbAtN08jQHXgNJ17DQYbhJCN+m+lwptxmOFxkOpz4DBc+p4dshxuf002W00OW041Td8TCrB4PuWjoeizoKgXVUT9rqrbzedUO1taUs6F2D5v8VQRts+lj14AOdPVm0SMjl6KMXIoyc+mRkUOB04vDMHAYBoZuYNs2pm1RHvKzvq6CDf5KNvgr2FBXSUU0lLLNajPCR1U7+ahqZ3JZvtNDiS+fXtkd6JPdmUEFR9E1Iw+34cLZoNW1/vPU1BeH2G+EUnb8fuwza6vY70Xid8LGxrZtglaUOjNMwAxTZ0YI2VHyMjM5Ma/ke98XIYQ4nGiqnTtrzZkzh2eeeYZ77rmHwsJCpk+fzrZt23jppZdwuVwpZSsrKznjjDMYNGgQEydOJBKJcM8991BRUcELL7yA2+1m06ZNnHbaaSxZsoSCgoLkczMyMsjMzPzB9bQsm4oK/w9+/neJVobY8UTstLyvdwfyx/Vo1vN1XcPQbPSwn/DOb6j7dB7h1X/Hrkqtr5ap4zn+RLJHXI+R1wuVmY2lO7FsOx7+Yv+YWsrCVjZR26Q6EqIqGqQqEvupjoaoigSpNcPURCPUmmFqzQi1ZoS6BrcBK4ql6oOqor71LRE4D3d6g8ATC84q+Z4dLDy6gbdB0E0EX6eusyVQw7ZgLfZ+HO0Mw8FxWfkc7cmmR0YORRm5HJ2RQ4bDhaHrOHQDp+7A43Tjdrhx6k5chhOH5sBWNhE7SsSKDbQKRsOYtolpWewM1bKutiIWXP2VbKiroNqMfG99Orq89MwqoFd2R/rndqHA7SNgRvBbkdgXCDOK34rGvjg0+NIQtKPJ+4kvDalfImJfIPb1jkwuPpnri09p5lH4YfLzM5MtuUIIka7atUU1Eonw+OOPc8011zBq1CgAZs6cyfDhw3n99dc5/fTTU8ovW7aMQCDAfffdh8fjAWD69OmMGjWKVatWMWzYMNavX4/P56Nnz55tvTttTtc1dGWhR/1Edm7E/+lcwmtexNqd2oqkeQycRcOwe13BJzk5bA5b7P5mJdWRcIOgmRo46+L/EIvWY6e0NLdeQHVoGm7dgVs3cOmxkfAB2yRoRTEPMBiHbIuQbVHZjI9KoTsj2UqaOIV/ZEYWudk+wsEomqbjMdx4nG5cuhOX4cLQHPHBUFp9C3u866qOjgcHGc4MNFcs8CcGZB1hRehdECYQCRK1LaJWlB3BWtbW7WFDXSUb/JVsrKugdq/PenkkSPmebby3Zxvw6QG9R82xuW5Pm72WEEIcDNo1qK5btw6/38+wYcOSy7Kzs+nVqxcff/xxo6A6bNgwHn744WRIBdD1WItATU1sRPv69espKipqg9q3H13XMJSJFqkjsv0TAp/PI7zuDcxdZmrecRo4ikawpteFvKT5eaVqLXvKQ/vcbkvIMBxkGk4yDSe6pjU43Q00OCWuETt3nmhV1DUNw9CxbRtU/DS5ljzJ+p3baKpbn9bE0ia7/zWxsKnnKhTx/8VP6zZoKU6UUKmtx/VdH8Deq3WZ+Hq7fusYuo4THZdu4I7/eOLh0qM7kss9Rv0yd3J9/TKP7sBtGHgNBx7DgaEbyS4HieMRq6MiYpv4zWi8hTDWEu6PtwYG4i2EgXiLYcCKtRYG7fi6+ONAfNnewdep6Rwdbx0tysxJhtMclyd52t5puPA63HhdbnKzfJhehWbraPHT7cm+oioR7PcdrGMDomLrdQzcGHgcXnQX4CUZXrtmR+nXMUwgGiZqRYiaJluDNayr3R0Pr7HW14D1/d0Tvo8G8WPmwNPgmHmM+G38sdfhoFtOPr8rGn3ArymEEIeSdg2qO3fG+ol16dIlZXmnTp2S6xrq2rUrXbt2TVk2f/58PB4PpaWlAGzYsAHTNPntb3/LunXr6Ny5MxdeeCE/+9nPWmkv2o5haOhWBOWvIrx1GcF1jxPd9DHRnTZYKQWp6jGSvx37Pyyxatla8Z/9fg2HpuFzuPAZzqZvHU6yjFi/xSyHi8z48qx4v0ZDSwwKSfbeREv9v/oYqNVHQl3XyMz0EPCHk6EuVnYfA4v2rniDfp/7Nx5lH+X358mKBhE0ZXHKiKJGa1XjmJUIs7qmkZHpJuAPo1Ti/WsQLrVEKNfi/UCN+OCn2H09PiBKJz44KtEvNl5Gi38lSDyODaqy44Os4vcbDNKylI1lW9i2jUX8vmowgKthv+EGy8LxQBuyTDq4vbHT87qBI37a3pNy2t6JQ4u18Oq6Rq4nk8qgPz6gr2UuLqGUwor/bmgYuDBwO7xka6B7E/OzmnS1IgzqeAyhaIiwFSFiRdnir2Jt7R6+9FcSVXb8S4CBOx463boDj2HUL9cd8S8HBl7dmQyjiT67ieO397FFi31Jyc3y0cmZRTh84AFZCCEOFe0aVIPB2Cjdvfuiut1uqqurv/f5Tz/9NM888ww33ngj+fn5QGywlW3bTJ48mcLCQv79739z3XXXEY1GOeeccw6ovg5H6/TnUkbDMKY1eh1d19DMEHb1t4Q2Lya48SnM7d9g7rBQDbrbKU1jbbdh3H/0SXyk2RBMDftOTefEvC70y+lIlsONz4iFTZ8j1gKa5XDh1o3Uf0jjIUdP3Ne0+pZSLTZwxtB0HHqs5c7QDAy9fuQ41LeOJu7HdzSltVTXdTK8LgKeSEqaSww8Sgmsew10Sj5WqTlTpYTXhhFxXyG1waOGdWi4SAPU3sPENDSlUNpeA7z2GjzW6H7DQK5peDOchLzRWFClwXtPLGAm2kMT70lT3csbjsxPffzdGg6gS31c/wWhPpDWDxiy47MMqPigITs564CNho7LcOLUY4GU+ACrveud+BwBbd5n0qE5cBgOMhye+imzbJOoMjk6L8KJVoSgGSJsRuJfADQMTUdr+OVgr0FkuhZr74/9zsTWJX9ftIahNX5s0TAMA5/Pg78u3Gp/Z4QQ4mDUrkE1cQo/EomknM4Ph8N4vd59Pk8pxUMPPcQjjzzClVdeyfnnn59c9/LLL2NZVnLgVM+ePdm+fTuPPfbYAQVVXdfIy/vhg7G+S8iqTwVOp5HyOlawlvCu/1L3xRMENv8Ds6Ka6HYL1WAmHgV82KUvt3cbyjZvDg1bozRgcH4hY7scw+hO3cl1eXEkw2h98EwETUcyaMZb7OKh09D1lNHdRvy+oRst+l5ke76/zKEs293eNWhf2dn7/r1vT7ay0bXWD5BZWem5/0II0V7aNagmTvmXlZXRrVu35PKysjJKSpqepiUajXLdddfx8ssvc91113HRRRelrG8YeBOKi4tZunTpAdXVthU1Nfs3z2NzRavrU2c0alFdHYBwHdGtbxPa+CTRXW9j+02i2y3s2tTWqA/yjuaBHsNZn9UpZXlJZh5jOnZjVIdudPFm4TKc+FwZ+FyZeAxPfSuQpqNpOg3nxkQRH6hS3ypnkehdkHpa9rta4hLP3Z/WPcPQyc72UlMTxLJa5rTvwaS19r+pOW1jc6I2PCYtO2foDyHHv+33PzvbK6P+hRBpr12Das+ePfH5fKxYsSIZVGtqavjiiy8YP358k8+59tpreeONN7j//vv56U9/mrKupqaGU089lWnTpnH22Wcnl//nP//huOOOO+D6mmbr/ANiNrwKlBUm+Nlcwpuewq75AjusMLdbWFWpSeK/vs7MLBrOirzuyWVHenyMKejG6A7d6OHLw+1w4XNnkunMwGt4ceqO5GhpTQOFlgyf9XNCJgb+KExLEYzaBKIWgYhFIGpRF7EIRmP3/RGLYNTGHzEJmnZ8eew2bNpkexwUeJ10yHCRn+mkY6aLjpluOmQ48bkNHLpO7IxvfXCybYVlxYcstXF4+r7Qvbe9T7OnLvthLMve5+esYehseN+2FTUhkzJ/hN2BCLv9Ecr9Efb4o+wORNgTiLInGKUiGKUyGMXQNDpluujkc9HF56ZzlpsuWS6OyPZwRJaHI7Lc+DyO5BWs6kNt6x+Thvu/d8hOdgPREp9TG9NS8VPs1HdLafBZrte47j+0m0Rr+q7jL4QQh6N2Daoul4vx48czY8YM8vPzOfLII5k+fTqFhYWMHTsWy7KoqKggKysLj8fD888/z6uvvsq1117LkCFDKC8vT24rKyuL7OxsTjzxRGbOnElBQQHdu3fn9ddfZ+nSpcybN68d93T/hbe8SrDiTlRUEd1lY+620Rr8A7rVk8NDPU7m9Y4lKE2jwOlhZEE3TunYjT45nfA43WR7fHgMD4Gwg/JakzU1IXbU1lBWF6GsLkxtOBY0g1ErGTCDpk2owU/QtAi34mVUXYZGrttBrsdBnsdJQYaTTtkecl0G+d7Y446ZLjpkuOjoc5HvdeJ06Bh6/RWSmuqj2fSE7CSvyGTZirAZC9+xfbcJRSz8Dd6PUOJ+1E4+1jRwGTpuhx67NXSchlb/2KHjNjRcTgO3oeE2jPp1ztitx4jNbBC7cICK94lM9FGs76MZtWwqAyZl/jC7/RF2+6PsDoTZHYjGQmcgFjoTwbMyZBJt5iVAt9aGv3N9lsugY4aTTpkuOvvcdPG5KMxyU5jljgXabDeFPjcep/Gdgbbpi0ekBs6IZbKnLso3/ijbdtdR4Y9SGYpQGTSpCkapCplUhUyqQ1GqQybVYZOasEVtxCRsKZy6hsvQcOp67NaIHwsj9tht6LgcsWPW8Pi5HFr8uBm44/c9ibIOA0/iOU6dLLdBnsdJfqaTfK8Tj8NosjU6XVqohRDiUNHuE/5blsUDDzzA888/TygUSl6ZqmvXrmzbto1TTjmFu+++m7PPPptLLrmE//f//l+T20mUqaurY/bs2fzrX/9iz549FBUVMXHiRE499dQDrGfrTfgf2VPLzidjl3h0uZcRCM/CvTOMy64fyr/H6WVe92E8d0Q/nA43fTOOpIdRSJ7VkdqwRk1YpzJos8dvUR5vRWvFnNnmNCDbbZDrdpDndSbDrNdpEIrGgnXItAlFbULx+0HTJmzZhJO3irC1P9PPtx5DA4ceC1EOXcOlazjigUrXNSoCUWrCZqscO51YALWUoi564K12GpDvddAxw0WnTBeFPjedfS66ZLnp5HMRNhWVwQiV8ZBZFWwQOONhsyZiEmiBurQ1t6GT7TbIcTvI8TjI9TjJ8Rjx29hnNM/jJNfrJM8b+zKWn+Ei1+vAYejoGilfuBwOnZycDCor/W3WoioT/gshDgbtHlQPFq0aVHfXsPOpdbHXCb2Fr+Ku5LqA7uTJo07gya6lBEJHYFd2grp8UC07iEkHPPEWJbcj1grlibdAeeItVG5DT5apL6vjdeh4HBqeeCuUx6Hj0jVqIlY8pJhUhqNUhy2qwybVEYuasBl7HGmdUHY4cGhafVhyGckW6lx3LDDlxVur8+OhKcdr4HEaGLpGbchkZ22YskCUcn+EskCE3YEou4NRyoNR9gRN9gSjRJrZUtsW3IZGltMgy2XgMnRMWxG1FRHLJpq8r4jadlp+tnxOg6zEcYsH3Tyvk+4FGVxaehTZbTTqX4KqEOJg0K6n/kWM1WCSpEQrqonGP47oxyMdx7EnUARfdgT7+w+XDrFWR0/sJz9xat0b6y+a73WS4zbwOIx4wNTJcBo49FiLnqZpGMn7YCSmpNLj/f/0+on4G/YNTPSbTEzhk5hV3lI2tk3sMq02mJaNZdlELZuopbAtOxZaIyZBpbOjKkBFIEpVONb6VhU2qQmbVCXCbSR2en5/OHUteQrY3eB0cCJ0uxOnhuOnjN0OHbeeOB2cCOmx5ygFUdsmYiuilkoGItNSRGwbMxmOFKZdH5hijxvcxgNUU+sUsRbPHFd9iIkdS4Ncd6xlLtftJN8bCzbZHgOXYeBw6DgdsQBq6Bq6ocWPpx4/lsSnUoKG3SJ6KbDs2GVbbUthxffDsmJ9P6NRiz3BCDtqwpT5o5T5I5QHYv1ey+P9XneHolSETJqbZ526hs9p4HMZsdDpNsjLcJGhQ6bDINvtIMsVu812G2S5HOS4Y4/djthcsZpOct7eRNeO5EUZ4vWxbJuIFQuxsVsrfhs7bhEzdkwTZaK2TcRU8eNsE44/DpgWtRGLmkisn3ZtvL92bcRqdpivi1rURS121DW+lOvasjr+8rPezXszhRDiECZBNQ3UGVGw9oBRgB75L//K78Ws3HPYEuwL22PzFeW4DfI9TvI9sfCS76kPnh3ip8E7eGOnFz0uHYdh4HTEBisZhh67mpWmxS4aAJAceKKon/Oxvk7fNxAltcx3DUbRv3NQjGXHt62B1+umti5IxLSx7Xh4ivcrtcxYiDBNm0DEoioYC0hh065vBXYYsdZeZ6yPqKHHZ1hNBrX6AK4n9z91WTJws1cIp36uAxVPQslTt8mrUtEgKKlkOVs1nBQ/dj8xqYKKB1RNA4/XRTgcG+zkMHQchp4MnrEQGjtlnDiWiYFo0LB/aBPHK/6aVoNj1ZBB7GISTsNoNGDrSHz0Q2HHJ85PCbVW7NiEIha7/OFYoK2LtdJWBqPJ0+NZLke85Tf2JSnb48DnMjAcOg5dxzB0HA6NzEw34VBsHllDAy3+5ShxXPTEFyG9fuDU3uz4XLp2MrQqUFrsqlaJx6TOepA4XilXC4s/ThwnW6nYZzL+pcu0Ej+KYNSiJtl3tr4PbU3YjIXbsEltNBZqU0Ju1GoU8LvlyvRUQgjRkJz630+teeof4O2XHia0aR1rCvuQ0X0EHXxuOni9dPZmkOcxcDmMWN+2BsEzMWo+EVpiU02lhpWDZXCHw6GTl5eZ7KPXONzWP06M+Lbs+nCsoWLhOzFQiViLWyyEx8o0fg/2L4A3LTUkfdcMAXvPJtDU+obTE5mmnXbTR+1tXzMQWHYsvNrxoK7HL+ygx1tAE6Gz4XusVGz/c3MzqKqq76PZ3vucekwbT/GVKKNU/IxB/PctFmhjV/eyFckvXLatME0b07axLEXUtKgL2/Ega5Kfk8GIY/OJRCzagpz6F0IcDKRFNU2MPXsiXq+D/wmEMJQDhUq26u1P8FQ2WC102cl00PR+pu50gwbFBnfqW8aUFXvcOu9Kal32L1R9fyEr3jUi3TU+PvX7phMLpKm5XMU/o02/B7reoIUzTUL5d59VaFzJxOfRMLRYk3CTZxPqHydno7BjT87IcGNGoq2wJ0IIcfCSoJomlIpdOtYRMOPXOof9CTZCiPS1P1+4ABy6TqbXSWWocb9VIYQ4nMl5HyGEEEIIkZYkqAohhBBCiLQkQVUIIYQQQqQlCapCCCGEECItSVAVQgghhBBpSYKqEEIIIYRISxJUhRBCCCFEWpKgKoQQQggh0pIEVSGEEEIIkZYkqAohhBBCiLQkQVUIIYQQQqQlCapCCCGEECItSVAVQgghhBBpSYKqEEIIIYRISxJUhRBCCCFEWpKgKoQQQggh0pIEVSGEEEIIkZYkqAohhBBCiLQkQVUIIYQQQqSldg+qtm0za9Yshg8fzoABA7j00kvZunXrPstXVlbyxz/+kdLSUoYMGcKtt95KMBhMKfPaa69x2mmn0a9fP8466yw+/PDD1t4NIYQQQgjRwto9qD788MMsWrSI22+/nWeffRbbtpkwYQKRSKTJ8pMnT2bLli0sWLCAhx56iH//+9/ccsstyfXLly9nypQp/O///i8vvPACw4YN47LLLmPTpk1ttEdCCCGEEKIltGtQjUQiPP7440yePJlRo0bRs2dPZs6cyc6dO3n99dcblf/000/56KOPuPfee+nduzfDhg3jtttuY8mSJezatQuARx99lFNPPZULLriAoqIipk6dSu/evXnyySfbeveEEEIIIcQBaNegum7dOvx+P8OGDUsuy87OplevXnz88ceNyq9cuZKOHTtSVFSUXDZkyBA0TeOTTz7Btm1WrVqVsj2AoUOHNrk9IYQQQgiRvto1qO7cuROALl26pCzv1KlTcl1Du3btalTW5XKRm5vLjh07qKmpIRAIUFhYuF/bE0IIIYQQ6cvRni+eGATlcrlSlrvdbqqrq5ssv3fZRPlwOEwoFNrn9sLh8AHX1+FovVxvGHrK7eFG9l/2v+Ht4eZw338hhNiXdg2qHo8HiPVVTdwHCIfDeL3eJss3NcgqHA6TkZGB2+1Obm/v9U1trzl0XSMvL/OAtrE/srMPrJ4HO9l/2f/D2eG+/0IIsbd2DaqJ0/hlZWV069YtubysrIySkpJG5QsLC1m2bFnKskgkQlVVFZ06dSI3N5eMjAzKyspSypSVldG5c+cDqqttK2pqAge0je9iGDrZ2V5qaoJYlt1qr5OuZP9l/2X/23b/s7O90oIrhEh77RpUe/bsic/nY8WKFcmgWlNTwxdffMH48eMblS8tLWXGjBls2bKF7t27A/DRRx8BcMIJJ6BpGoMGDeKjjz7il7/8ZfJ5K1asYPDgwQdcX9Ns/X9ALMtuk9dJV7L/sv+y/4fv/gshxN7aNai6XC7Gjx/PjBkzyM/P58gjj2T69OkUFhYyduxYLMuioqKCrKwsPB4P/fv3Z9CgQfz+97/nlltuIRAIcPPNN3PWWWclW0wvvvhiLrvsMnr16sWIESNYvHgxa9eu5c4772zPXRVCCCGEEM3U7ud9Jk+ezDnnnMONN97Iueeei2EYPPbYYzidTnbs2MHJJ5/Mq6++CoCmacyZM4euXbty4YUXcvXVVzNixIiUCf9PPvlk7rrrLv7617/y85//nOXLlzN37tyUKa2EEEIIIUT605RSqr0rcTCwLJuKCn+rbd/h0MnLy6Sy0n9YnvqT/Zf9l/1v2/3Pz8+UPqpCiLQnf6WEEEIIIURakqAqhBBCCCHSkgRVIYQQQgiRliSoCiGEEEKItCRBVQghhBBCpCUJqkIIIYQQIi1JUBVCCCGEEGlJ5lHdT0opbLt13yrD0A/L65wnyP7L/sv+t93+67qGpmlt9npCCPFDSFAVQgghhBBpSU79CyGEEEKItCRBVQghhBBCpCUJqkIIIYQQIi1JUBVCCCGEEGlJgqoQQgghhEhLElSFEEIIIURakqAqhBBCCCHSkgRVIYQQQgiRliSoCiGEEEKItCRBVQghhBBCpCUJqkIIIYQQIi1JUBVCCCGEEGlJgqoQQgghhEhLElTbiG3bzJo1i+HDhzNgwAAuvfRStm7dus/ylZWV/PGPf6S0tJQhQ4Zw6623EgwG27DGLauqqoqbb76ZESNGMGjQIM4991xWrly5z/KPPPIIJSUljX4OVrt27Wpyf55//vkmyx9Kx3/FihVN7ntJSQmnnHJKk8/55JNPmiy/YsWKNq79gZs3bx7nn39+yrK1a9cyfvx4BgwYwJgxY3jqqae+dzuvvfYap512Gv369eOss87iww8/bK0qCyFE2nC0dwUOFw8//DCLFi3innvuobCwkOnTpzNhwgReeuklXC5Xo/KTJ08mGAyyYMECampquOGGGwgEAtx7773tUPsD94c//IHy8nIeeOABCgoKePrpp/ntb3/LCy+8QI8ePRqVX79+PT/72c+YMmVKO9S25a1btw63282yZcvQNC25PCsrq8nyh9LxHzhwIO+//37Kss8++4xJkyZx1VVXNfmc9evX061bNxYtWpSyPCcnp9Xq2RoWLlzIgw8+yODBg5PLKisrufjiixkzZgy33norn332GbfeeiuZmZn84he/aHI7y5cvZ8qUKVx77bWcdNJJ/OMf/+Cyyy7jxRdfpKioqK12Rwgh2p4SrS4cDquBAweqhQsXJpdVV1erfv36qZdeeqlR+VWrVqni4mL15ZdfJpe99957qqSkRO3cubNN6tySvv76a1VcXKxWrlyZXGbbtjr11FPVgw8+2ORz/ud//kc98cQTbVTD1jd//nx1xhln7FfZQ+34783v96vRo0eradOm7bPMn/70J3XFFVe0Ya1a1s6dO9Xll1+uBgwYoH7yk5+o8ePHJ9fNnTtXnXzyySoajSaX3X///Wrs2LH73N4ll1yi/u///i9l2a9//Wt10003tXjdhRAincip/zawbt06/H4/w4YNSy7Lzs6mV69efPzxx43Kr1y5ko4dO6a0lAwZMgRN0/jkk0/apM4tKS8vj/nz59O3b9/kMk3T0DSNmpqaRuUjkQhff/11ky2tB6v169fvd8vXoXb89zZ37lyCwSBTp07dZ5nmvF/p6L///S9Op5OlS5fSv3//lHUrV65kyJAhOBz1J7ROPPFEvv76a3bv3t1oW7Zts2rVqpS/HwBDhw5t8u+HEEIcSiSotoGdO3cC0KVLl5TlnTp1Sq5raNeuXY3KulwucnNz2bFjR+tVtJVkZ2czcuTIlC4O//rXv9iyZQvDhw9vVP7LL7/Esiz+9a9/MW7cOEaNGsWUKVMoKytry2q3qA0bNlBRUcF5553Hj370I84991zefffdJssease/oYqKChYsWMAVV1xBbm7uPstt3LiRzZs3c/bZZ3PSSSdx8cUX8/nnn7ddRQ/QmDFjmD17NkcddVSjdTt37qSwsDBlWadOnQCaPL41NTUEAoEmn9PU3w8hhDiUSFBtA4lBMHv3RXW73YTD4SbLN9VvdV/lDzarVq3iuuuuY+zYsYwaNarR+g0bNgDg9Xp56KGHuPPOO9m8eTMXXHABoVCojWt74EzTZPPmzVRXVzNp0iTmz5/PgAEDuOyyy5ocEHMoH/9FixaRlZXFr3/9632W2bFjB7W1tQQCAW688UYefvhhOnTowPjx4/nyyy/bsLatIxQKNfm3AGjy+CY+8/v790MIIQ4lMpiqDXg8HiB2SjtxH2L/KHm93ibLRyKRRsvD4TAZGRmtV9E2sGzZMq655hoGDRrEjBkzmixz1llnMWLECPLz85PLjjvuOEaMGMFbb73Faaed1lbVbREOh4MVK1ZgGEby+Pfp04eNGzfy2GOPNTqleygf/xdffJGzzjor5fdgb126dOHjjz/G6/XidDoB6Nu3L1988QVPP/00t956a1tVt1U0dXwTgbOp45sIsU09p6m/H0IIcSiRFtU2kDiNu/ep67KyMjp37tyofGFhYaOykUiEqqqq5CnCg9EzzzzDpEmTGD16NHPnzk3+A9yUhiEVYqc5c3NzD9pTnZmZmY3C2XHHHceuXbsalT1Uj/+6devYunUrZ5xxxveWzc7OToZUAF3XKSoqavL9Otg0dXwTj5v6e5Cbm0tGRsZ+//0QQohDiQTVNtCzZ098Pl/KHJA1NTV88cUXlJaWNipfWlrKzp072bJlS3LZRx99BMAJJ5zQ+hVuBYsWLeL222/nvPPO44EHHmjy1HbCzJkzGTduHEqp5LJt27ZRWVnJscce2xbVbVEbN25k0KBBjeYAXbNmTZP7cygef4gNIiooKKBnz57fWe7dd99l4MCBKfMMm6bJunXrDsrjv7fS0lI++eQTLMtKLlu+fDnHHHMMBQUFjcprmsagQYOSn4GEFStWpEx7JYQQhyIJqm3A5XIxfvx4ZsyYwZtvvsm6dev4/e9/T2FhIWPHjsWyLMrLy5N90fr378+gQYP4/e9/z+eff87y5cu5+eabOeussw7KFpSvvvqKu+66ix//+Mdcfvnl7N69m/LycsrLy6mtrSUSiVBeXp48tfnjH/+Yb7/9lltuuYWvvvqKjz/+mEmTJjFo0KAmB1+lu6KiInr06MFtt93GypUr2bRpE3fffTefffYZV1555SF//BO++OKLfV60oby8HL/fD8CgQYPIy8tj6tSprFmzhvXr1zN16lSqqqq46KKL2rDGreMXv/gFdXV13HDDDXz55Zc8//zzLFiwgMsvvzxZpra2loqKiuTjiy++mFdeeYUnnniCTZs2cd9997F27VouvPDC9tgFIYRoO+09P9bhwjRNdd9996kTTzxRDRgwQF166aVq69atSimltm7dqoqLi9XixYuT5Xfv3q0mTZqkBgwYoIYOHar+9Kc/qVAo1F7VPyCPPPKIKi4ubvJn6tSpavny5aq4uFgtX748+ZwPPvhA/frXv1YDBgxQQ4YMUdddd52qqqpqx704MOXl5WratGnqpJNOUn379lW//vWv1ccff6yUOvSPf8KECRPU1Vdf3eS64uJiNWvWrOTjLVu2qEmTJqkhQ4ao/v37q0suuUStX7++raraoqZOnZoyj6pSSq1evVr96le/Un369FGjR49WTz/9dKPnjB49OmXZCy+8oH784x+rvn37qp///Ofqgw8+aPW6CyFEe9OUanB+VQghhBBCiDQhp/6FEEIIIURakqAqhBBCCCHSkgRVIYQQQgiRliSoCiGEEEKItCRBVQghhBBCpCUJqkIIIYQQIi1JUBViP7TmLG4yQ5wQQgjRNAmqok2NGTOGadOmtXc1mmXjxo2ce+65Lb7dmpoarr32WlauXNni204327Zto6SkhOeff769qyKEEOIg4mjvCojDy5w5c/D5fO1djWb55z//yaefftri2127di1LlizhF7/4RYtvWwghhDgUSFAVbapXr17tXQUhhBBCHCTk1L9oUw1P/SdOB7/22mtMnjyZgQMHMmTIEG688UYCgcD3bmvz5s1MnDiRIUOGUFpayuWXX86mTZuS62tra7n77rs59dRT6du3L6effjr/+Mc/GtVn1qxZ3HvvvfzoRz+iX79+/Pa3v+Xrr78GYPbs2cyZMweAkpISZs+eDYBt28yfP58f//jH9OnTh3HjxvH0008nt7tmzRp69+6d0s1hz549DBs2jIsvvpjly5dzwQUXAHDBBRdw/vnn73M/w+Ew9913HyNHjqRPnz6cccYZvPrqq8n1b775ZkrdADZt2kS/fv24/vrrk8uWLVvGb37zGwYOHEifPn34yU9+wsKFC5PrV6xYQUlJCR9++CHnn38+/fr1Y9SoUTz33HOUlZUxceJEBg4cyMiRI1mwYEGj573//vucd9559OvXj7Fjx7Jo0aJ9Hzxg+/bt/OEPf2DIkCH079+fCy+8kC+++CKlzMsvv8yZZ55Jv379OPHEE7nmmmvYtWvXd25XCCHEIUQJ0YZGjx6tpk6dqpRSauvWraq4uFiVlpaqe+65R33wwQdq7ty5qqSkRM2YMeM7t7Nz5041ePBg9dOf/lS98sor6u2331Znn322Oumkk1RlZaUKBoPq9NNPV8OGDVN//etf1bvvvqtuvvlmVVxcrB555JGU+pxwwgnqsssuU++8845asmSJGjJkiPrVr36llFJqx44d6vrrr1fFxcXq008/VTt27FBKKXXTTTep3r17q1mzZqn33ntPPfDAA6pnz55qzpw5yW3PnDlTFRcXqw8++EAppdRVV12lhgwZonbu3Klqa2vVM888o4qLi9UzzzyjNm7c2OR+2ratfvvb36qBAweqJ554Qr377rvqpptuUsXFxeqFF15IlrvmmmtU79691Zdffqmi0ag6++yz1amnnqrq6uqUUkq9/fbbqri4WN1xxx3qgw8+UG+99ZaaMGGCKi4uVp999plSSqnly5er4uJideKJJ6rHH39cffDBB+qiiy5Sxx9/vBo3bpx68MEH1QcffKAmTpyoiouL1erVq1OeN3jwYHXHHXeod999V/3pT39SxcXFauHChSnHevHixUoppfbs2aOGDx+uxo4dq5YuXareeOMNNX78eDVgwAD15ZdfKqWUWrlypTr++OPV7Nmz1fLly9WLL76oTjrpJHXeeed938dMCCHEIUKCqmhTTQXVa665JqXM+eefr04//fTv3M4999yj+vXrp8rKypLLduzYoUaNGqXeeecdtXDhQlVcXKxWrVqV8rzrr79e9e3bV1VWVibrM3r0aGWaZrLM7NmzVXFxsaqoqFBKKTVr1ixVXFycXL9582ZVUlKi5s2bl7LtmTNnqr59+yafF4lE1BlnnKHGjRunFi9erIqLi9Vrr72WLJ8IeMuXL9/nfr7//vuquLhYvfLKKynLr7nmGnXSSSepaDSqlFKqqqpKnXzyyeqCCy5QDz/8sDr++OPVp59+miz/6KOPJt/3hMrKSlVcXJzcj0R9pk+fnizz2WefqeLiYjVlypTksoqKClVcXKyeeOKJlOddd911Kdu/8sor1UknnaRs224UVB944AHVt29ftW3btmT5cDisTjnlFDVp0iSllFLz5s1TAwcOVOFwOFnmnXfeUbNnz1a2be/zPRNCCHHokFP/ot0NGDAg5XFhYWHy1L9t25immfID8MknnzBgwAA6duyY8ry3336bkSNH8tFHH3HkkUcycODAlG2feeaZhMNhVq9enVzWt29fDMNI2Q5AMBhssr7Lly9HKcWYMWNS6jVmzBjC4TCffPIJAE6nk3vvvZdt27Zxww038POf/5yf/OQnzXpvPvzwQzRNY+TIkY1eq7y8nI0bNwKQk5PD7bffzvLly5k1axZXXnllyvs6YcIE7rnnHvx+P2vWrOHVV19l3rx5AEQikZTXbPieFRQUANC/f//ksry8PCDWtaKhn//85ymPx44dS3l5OV999VWT+3X88cfTuXPn5D7pus6IESP44IMPACgtLSUYDHL66adz//33s3LlSk4++WQmTpyIpmnNeh+FEEIcnGQwlWh3Xq835bGu68m5Rf/85z8n+4gmrF+/nqqqKrp27brPbVZXV6eE2IQOHToAsamhvuv1IRaSm1JVVQXAT3/60ybXN+xDefzxx1NSUsKaNWsYPXr0Puu7L1VVVSilGDRoUJPry8rKOP744wH40Y9+RKdOnSgrK2v0WhUVFfzpT39i2bJlaJpG9+7dGTx4MNB4HtemZmXY+z1qSufOnVMeJ0JuU8eiqqqKLVu20Lt37ya3FQwGGThwIPPnz2fBggU88cQTzJ8/nw4dOnDFFVd8Z59eIYQQhw4JqiKt/epXv2LUqFGNlmdlZVFRUdFo+YcffkjXrl3Jyclhy5YtjdaXl5cD9a2CP0R2djYATz75JJmZmY3WH3HEEcn7f/vb31izZg09e/bkzjvvZNiwYcnn74+srCwyMjJ46qmnmlzfvXv35P05c+ZQVVVFjx49uPHGG3nuuedwOp0AXHPNNWzevJkFCxYwcOBAXC4XwWCQv//97/tdl+9TWVlJt27dko/37NkD1AfWvfdryJAhXHvttU1uy+VyATB8+HCGDx9OMBhk+fLlPPXUU9xxxx3079+ffv36tVjdhRBCpCc59S/SWufOnenbt2/KD8DgwYNZvXp1Sljds2cPEyZM4N///jelpaV8++23jeY/Xbp0KU6ns1khJ9HCmpBoiaysrEypV0VFBQ899FCyxfXbb7/l3nvv5ZxzzmHu3LnU1tZy5513JrfTsLvBvgwZMoRAIIBSKuW1NmzYwJ///OdkV4jPP/+cv/zlL1x55ZVMnz6dDRs28MgjjyS388knnzB27FiGDh2aDIHvvvsusO+W4+ZatmxZyuN//vOfHHnkkSnhteF+ffXVVxxzzDEp+7VkyRL+8Y9/YBgG9957L7/4xS9QSuH1ehk9ejRTp04FYjMGCCGEOPRJi6o4KF100UW8+OKLTJgwgcsvvxyn08kjjzxCYWEhZ5xxBi6Xi0WLFvG73/2OyZMn07VrV9566y0WL17MxIkTm9WqmSj78ssv079/f0pKSjjzzDO56aab+Pbbb+nTpw9fffUVM2fOpGvXrhx99NEopbjhhhvwer1ce+215OTkcPXVV3PXXXcxbtw4xowZQ1ZWFgDvvPMOOTk59OzZs9Frjxw5ktLSUq666iquuuoqioqK+Pzzz5k1axbDhw8nPz+fSCTCtGnTKCoq4tJLL8XpdDJ+/HjmzZvHqaeeSq9evejXrx8vvfQSvXv3prCwkFWrVjF//nw0TdtnX9zmeuKJJ3C73QwYMIDXX3+dt99+m/vvv7/JshdddBFLlizhoosu4pJLLiEvL49XX32Vv//971x33XUAnHjiiTzxxBNMmzaNM888k2g0yl/+8hdyc3M58cQTW6TOQggh0psEVXFQ6tKlC4sWLWL69OlMmzYNl8vF0KFDmTlzJjk5OQA8/fTT3H///Tz00EPU1dXRo0cP7rzzTs4555xmvdbYsWNZsmQJ06ZN45xzzuGWW27h7rvvZt68eTz77LPs3LmTgoICTjvtNK6++moMw2DhwoV8+OGHPPjgg8n6nH/++bz00kvcfPPNDBo0iOOOO47TTz+dhQsX8t577/Hyyy83em1d15k/fz4PPfQQ8+bNY8+ePXTu3JmLL76Y3/3udwA8+OCDfPXVV/z1r39Nnuq/+uqreeONN5g6dSqLFy/mnnvu4fbbb+f2228H4Oijj+bWW29l6dKlLXYJ1+uvv54XXniBefPm0aNHD2bNmsW4ceOaLNu5c2eeffZZ7r//fm655RbC4TBHH310yvEZOXIkM2bM4PHHH08OoDrhhBN46qmnyM3NbZE6CyGESG+a2nskhRBCNMOKFSu44IILeOqppxg6dGh7V0cIIcQhRPqoCiGEEEKItCRBVQghhBBCpCU59S+EEEIIIdKStKgKIYQQQoi0JEFVCCGEEEKkJQmqQgghhBAiLUlQFUIIIYQQaUmCqhBCCCGESEsSVIUQQgghRFqSoCqEEEIIIdKSBFUhhBBCCJGWJKgKIYQQQoi09P8Bq2MkKJLpHiYAAAAASUVORK5CYII=", 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" ] @@ -992,7 +1024,7 @@ ], "metadata": { "kernelspec": { - "display_name": "base", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1006,7 +1038,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.10" } }, "nbformat": 4, diff --git a/src/eval.py b/src/eval.py index ccf0fe1e..8c15d249 100644 --- a/src/eval.py +++ b/src/eval.py @@ -226,7 +226,7 @@ def build_evals(conf): evaluation_kwargs["standard"] = {"prompting_strategy": "standard"} evaluation_kwargs["gradient"] = { "prompting_strategy": "standard", - "task_sampler_kwargs": {"compute_gradient": True} + # "task_sampler_kwargs": {"compute_gradient": True} } task_name =["linear_regression" if task_name == "ar1_linear_regression" else task_name][0] diff --git a/src/tasks.py b/src/tasks.py index 384f967b..7845cd56 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -198,7 +198,10 @@ def __init__( def evaluate(self, xs_b): ys_b = super().evaluate(xs_b) - ys_b_noisy = ys_b + torch.randn_like(ys_b) * self.noise_std + # ys_b_noisy = ys_b + torch.randn_like(ys_b) * self.noise_std + exp_noise = torch.distributions.Exponential(rate=1.0 / self.noise_std) + ys_b_noisy = ys_b + exp_noise.sample(ys_b.shape) + if self.renormalize_ys: ys_b_noisy = ys_b_noisy * math.sqrt(self.n_dims) / ys_b_noisy.std() diff --git a/src/train.py b/src/train.py index f362356b..2dd0f4ad 100644 --- a/src/train.py +++ b/src/train.py @@ -1,6 +1,7 @@ import os from random import randint import uuid +import models from quinine import QuinineArgumentParser from tqdm import tqdm @@ -135,6 +136,21 @@ def train(model, args): def main(args): + # First, define the device + device = "cuda" if torch.cuda.is_available() else "cpu" + print(f"Using device: {device}") + + # Create model before trying to move it to device + model = models.build_model(args.model) + model = model.to(device) + + # Initialize wandb + if not args.test_run: + wandb.init( + config=args, + **args.wandb, + ) + if args.test_run: curriculum_args = args.training.curriculum curriculum_args.points.start = curriculum_args.points.end @@ -159,6 +175,9 @@ def main(args): if not args.test_run: _ = get_run_metrics(args.out_dir) # precompute metrics for eval + xs = xs.to(device) + ys = ys.to(device) + if __name__ == "__main__": From 3317f13ffd816192c2a5b4ec0cbd422b104bb90e Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 22 Oct 2025 10:07:27 +0700 Subject: [PATCH 15/88] update main --- condaenv.sa_1amf7.requirements.txt | 12 ++++++++++++ src/train.py | 21 +-------------------- 2 files changed, 13 insertions(+), 20 deletions(-) create mode 100644 condaenv.sa_1amf7.requirements.txt diff --git a/condaenv.sa_1amf7.requirements.txt b/condaenv.sa_1amf7.requirements.txt new file mode 100644 index 00000000..00a9c112 --- /dev/null +++ b/condaenv.sa_1amf7.requirements.txt @@ -0,0 +1,12 @@ +jupyter==1.0.0 +matplotlib==3.5.2 +numpy==1.22.3 +pandas==1.4.2 +quinine==0.3.0 +scikit-learn==1.0.2 +seaborn==0.11.2 +tqdm==4.64.0 +transformers==4.17.0 +wandb==0.12.11 +xgboost==1.6.1 +protobuf==3.20.1 \ No newline at end of file diff --git a/src/train.py b/src/train.py index 2dd0f4ad..e519ab44 100644 --- a/src/train.py +++ b/src/train.py @@ -1,7 +1,6 @@ import os from random import randint import uuid -import models from quinine import QuinineArgumentParser from tqdm import tqdm @@ -98,7 +97,7 @@ def train(model, args): max(curriculum.n_dims_truncated - ii, 0) for ii in range(curriculum.n_points) ) - / curriculum.n_points +/ curriculum.n_points ) if i % args.wandb.log_every_steps == 0 and not args.test_run: @@ -136,21 +135,6 @@ def train(model, args): def main(args): - # First, define the device - device = "cuda" if torch.cuda.is_available() else "cpu" - print(f"Using device: {device}") - - # Create model before trying to move it to device - model = models.build_model(args.model) - model = model.to(device) - - # Initialize wandb - if not args.test_run: - wandb.init( - config=args, - **args.wandb, - ) - if args.test_run: curriculum_args = args.training.curriculum curriculum_args.points.start = curriculum_args.points.end @@ -175,9 +159,6 @@ def main(args): if not args.test_run: _ = get_run_metrics(args.out_dir) # precompute metrics for eval - xs = xs.to(device) - ys = ys.to(device) - if __name__ == "__main__": From e4a8357f3be5e88af7750d6484c2efe7ebf38394 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 22 Oct 2025 10:09:29 +0700 Subject: [PATCH 16/88] train_steps = 50k1 --- src/conf/toy.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/conf/toy.yaml b/src/conf/toy.yaml index 9e1f7b5e..85b4288a 100644 --- a/src/conf/toy.yaml +++ b/src/conf/toy.yaml @@ -34,7 +34,7 @@ training: task_kwargs: { # "compute_gradient": True } - train_steps: 501 + train_steps: 50001 out_dir: ../models/linear_regression From 953aaf066fb22caffce0737d008215606c53eb13 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tr=E1=BB=8Bnh=20V=C5=A9=20=C4=90=E1=BB=A9c=20H=E1=BA=A3i?= Date: Wed, 22 Oct 2025 13:39:15 +0700 Subject: [PATCH 17/88] Update bla bla bla --- src/eval.py | 115 ++++++++++++++++++++++++++++++ src/models.py | 5 ++ src/plot_utils.py | 20 ++++++ src/samplers.py | 173 ++++++++++++++++++++++++++++++++++++++++++++++ src/tasks.py | 31 +++++++-- 5 files changed, 339 insertions(+), 5 deletions(-) diff --git a/src/eval.py b/src/eval.py index b92b679d..ba023cf6 100644 --- a/src/eval.py +++ b/src/eval.py @@ -186,8 +186,27 @@ def eval_model( metrics = torch.cat(all_metrics, dim=0) +<<<<<<< Updated upstream return aggregate_metrics(metrics) +======= + # if prompting_strategy == "standard": + # grad_alignments = compute_gradient_alignment(model, task_sampler(), xs[0]) + # if grad_alignments is not None: + # results["gradient_alignment"] = grad_alignments + if prompting_strategy == "standard": + # sample a single long prefix to compute gradients on (use same data_sampler) + xs_samp = data_sampler.sample_xs(n_points=min(n_points, 40), b_size=1)[0] + task = task_sampler() + # try: + # grad_alignments = compute_gradient_alignment(model, task, xs_samp, n_points=min(40, n_points)) + # if grad_alignments is not None: + # results["gradient_alignment"] = grad_alignments + # except Exception: + # # best-effort: don't fail whole eval if grad computation crashes + # pass + return results +>>>>>>> Stashed changes def build_evals(conf): n_dims = conf.model.n_dims @@ -209,7 +228,16 @@ def build_evals(conf): evaluation_kwargs = {} evaluation_kwargs["standard"] = {"prompting_strategy": "standard"} +<<<<<<< Updated upstream if task_name not in ["linear_regression", "ar1_linear_regression"]: +======= + evaluation_kwargs["gradient"] = { + "prompting_strategy": "standard", + } + + task_name =["linear_regression" if task_name == "ar1_linear_regression" else task_name][0] + if task_name != "linear_regression": +>>>>>>> Stashed changes if task_name in ["relu_2nn_regression"]: evaluation_kwargs["linear_regression"] = {"task_name": "linear_regression"} for name, kwargs in evaluation_kwargs.items(): @@ -390,6 +418,93 @@ def read_run_dir(run_dir): assert len(df) == len(df.run_name.unique()) return df +<<<<<<< Updated upstream +======= +# Figure 3 and 4: +# def compute_gradient_alignment(model, task, xs, n_points=40): + +# device = next(model.parameters()).device +# # ground-truth weight for this task (take first in batch) +# w = task.w_b[0, :, 0].to(device) + +# alignments = [] +# max_points = min(n_points, xs.shape[0]) + +# for k in range(max_points): +# # Context up to k +# ctx_xs = xs[:k].unsqueeze(0).to(device) +# if k > 0: +# ctx_ys = task.evaluate(ctx_xs.detach().cpu()).to(device) +# else: +# ctx_ys = torch.zeros(1, 0, device=device) + +# # Random query direction normalized and scaled to match data norm +# direction = torch.randn_like(w) +# direction = direction / (direction.norm() + 1e-8) +# scale = xs[k].norm() if k < xs.shape[0] else xs[-1].norm() +# x_query = (direction * (scale + 1e-8)).detach().clone().requires_grad_(True) +# print("ctx_ys.shape:", ctx_ys.shape) +# print("ys_with_dummy.shape:", ys_with_dummy.shape) +# xs_with_query = torch.cat([ctx_xs, x_query.view(1, 1, -1)], dim=1) +# ys_with_dummy = torch.cat( +# [ctx_ys, torch.zeros(ctx_ys.size(0), 1, device=device)], +# dim=1 +# ) + +# with torch.enable_grad(): +# pred = model(xs_with_query, ys_with_dummy, inds=[k]) +# grad = torch.autograd.grad(pred.sum(), x_query)[0] + +# cos_sim = torch.dot(grad, w) / (grad.norm() * w.norm() + 1e-8) +# alignments.append(float(cos_sim.detach().cpu())) + +# return alignments +# def compute_gradient_alignment(model, task, xs, n_points=40): +# """ +# Compute cosine similarity between model gradient (w.r.t. query input) and +# the true task weight w. xs: (n_points, d) single sample (no batch dim). +# Returns list of length <= n_points with float cosines. +# """ +# device = "cuda" if torch.cuda.is_available() and next(model.parameters()).is_cuda else "cpu" +# model = model.to(device).eval() + +# # get ground-truth weight if available +# if not hasattr(task, "w_b"): +# return None +# w = task.w_b[0, :, 0].to(device) + +# alignments = [] +# max_k = min(n_points, xs.shape[0]) +# for k in range(max_k): +# # context (0..k-1) +# ctx_xs = xs[:k].unsqueeze(0).to(device) # (1, k, d) +# if k > 0: +# ctx_ys = task.evaluate(ctx_xs.detach().cpu()).to(device) +# else: +# ctx_ys = torch.zeros(1, 0, device=device) + +# # random direction scaled to typical norm +# direction = torch.randn_like(w, device=device) +# direction = direction / (direction.norm() + 1e-8) +# scale = xs[k].norm() if k < xs.shape[0] else xs[-1].norm() +# x_query = (direction * (scale + 1e-8)).detach().clone().requires_grad_(True).view(1, 1, -1).to(device) + +# xs_with_query = torch.cat([ctx_xs, x_query], dim=1) +# ys_with_dummy = torch.cat([ctx_ys, torch.zeros(1, 1, device=device)], dim=1) + +# with torch.enable_grad(): +# pred = model(xs_with_query, ys_with_dummy, inds=[k]) +# # pred could be tensor with shape (1, m) or scalar-like; sum to scalar +# loss_term = pred.sum() +# grad = torch.autograd.grad(loss_term, x_query, retain_graph=False, create_graph=False)[0].view(-1) + +# # cosine similarity between grad and w +# denom = (grad.norm() * w.norm() + 1e-8) +# cos_sim = float(torch.dot(grad, w).cpu() / denom.cpu()) +# alignments.append(cos_sim) + +# return alignments +>>>>>>> Stashed changes if __name__ == "__main__": run_dir = sys.argv[1] for task in os.listdir(run_dir): diff --git a/src/models.py b/src/models.py index 3ca664c1..3596a423 100644 --- a/src/models.py +++ b/src/models.py @@ -73,6 +73,11 @@ def get_relevant_baselines(task_name): ], "noisy_linear_regression": [ (LeastSquaresModel, {}), + (RidgeModel, {"alpha": 0.1}), + (RidgeModel, {"alpha": 1.0}), + (RidgeModelWithVarianceAdjustment, {"alpha": 1.0, "ar_coef": 0.5}), + (FeasibleGLSModel, {"ar_coef": None}), + (GLSModel, {"ar_coef": 0.5}), (NNModel, {"n_neighbors": 3}), (AveragingModel, {}), ], diff --git a/src/plot_utils.py b/src/plot_utils.py index 006ed39b..7f8c0b66 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -41,7 +41,27 @@ "Transformer", "Least Squares", "3-Nearest Neighbors", +<<<<<<< Updated upstream "2-layer NN, GD", +======= + "Ridge (alpha=0.1)", + "Ridge (alpha=1.0)", + "Ridge Var Adj (alpha=1.0, ar=0.5)", + "Feasible GLS", + "GLS (ar=0.5)", + "Averaging" + ], + "noisy_linear_regression": [ + "Transformer", + "Least Squares", + "Ridge (alpha=0.1)", + "Ridge (alpha=1.0)", + "Ridge Var Adj (alpha=1.0, ar=0.5)", + "Feasible GLS", + "GLS (ar=0.5)", + # "3-Nearest Neighbors", + # "Averaging" +>>>>>>> Stashed changes ], } diff --git a/src/samplers.py b/src/samplers.py index 04d0cfbf..c9ceddac 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -216,5 +216,178 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): return xs_b +<<<<<<< Updated upstream # if __name__ == "__main__": # test_var1_sampler() +======= + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + # Shape: (batch, time, dims) + xs_b = torch.zeros(b_size, n_points, self.n_dims) + + generators = None + if seeds is not None: + assert len(seeds) == b_size + generators = [] + for seed in seeds: + g = torch.Generator() + g.manual_seed(int(seed)) + generators.append(g) + + # Initialize first two time steps + for t in range(2): + if generators is None: + xs_b[:, t, :] = torch.randn(b_size, self.n_dims) + else: + for i in range(b_size): + xs_b[i, t, :] = torch.randn(self.n_dims, generator=generators[i]) + + # AR(2): x_t = ar1_coef * x_{t-1} + ar2_coef * x_{t-2} + eps_t + for t in range(2, n_points): + if generators is None: + eps_t = self.noise_std * torch.randn(b_size, self.n_dims) + else: + eps_t = torch.zeros(b_size, self.n_dims) + for i in range(b_size): + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i]) + xs_b[:, t, :] = ( + self.ar1_coef * xs_b[:, t - 1, :] + + self.ar2_coef * xs_b[:, t - 2, :] + + eps_t + ) + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + + return xs_b +class VR2Sampler(DataSampler): + def __init__(self, n_dims, ar1_mat=None, ar2_mat=None, noise_std=1.0, bias=None, scale=None): + super().__init__(n_dims) + + if ar1_mat is None: + ar1_mat = 0.5 * torch.eye(n_dims) + if ar2_mat is None: + ar2_mat = 0.3 * torch.eye(n_dims) + + # Check + assert ar1_mat.shape == (n_dims, n_dims), "ar1_mat must be n_dims x n_dims" + assert ar2_mat.shape == (n_dims, n_dims), "ar2_mat must be n_dims x n_dims" + + self.ar1_mat = ar1_mat.clone().detach().to(dtype=torch.float32) + self.ar2_mat = ar2_mat.clone().detach().to(dtype=torch.float32) + self.noise_std = float(noise_std) + self.bias = bias + self.scale = scale + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + xs_b = torch.zeros(b_size, n_points, self.n_dims) + + generators = None + if seeds is not None: + generators = [torch.Generator().manual_seed(int(seed)) for seed in seeds] + + # Initialize first two time points + for t in range(2): + if generators is None: + xs_b[:, t, :] = torch.randn(b_size, self.n_dims) + else: + for i in range(b_size): + xs_b[i, t, :] = torch.randn(self.n_dims, generator=generators[i]) + + # VR(2): x_t = A1 * x_{t-1} + A2 * x_{t-2} + eps_t + for t in range(2, n_points): + if generators is None: + eps_t = self.noise_std * torch.randn(b_size, self.n_dims) + else: + eps_t = torch.zeros(b_size, self.n_dims) + for i in range(b_size): + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i]) + + # Matrix multiplication for each sample in batch + xs_b[:, t, :] = (torch.matmul(xs_b[:, t-1, :], self.ar1_mat.T) + + torch.matmul(xs_b[:, t-2, :], self.ar2_mat.T) + + eps_t) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + + return xs_b + +class NonStationarySampler(DataSampler): + def __init__(self, n_dims, coef_base=0.5, coef_amplitude=0.4, noise_std = 0.1, bias=None, scale=None, mode="regime"): + """ + mode ∈ {"sinusoidal", "linear", "regime"} + """ + super().__init__(n_dims) + self.coef_base = float(coef_base) + self.coef_amplitude = float(coef_amplitude) + self.noise_std = float(noise_std) + self.scale = scale + self.bias = bias + + def get_trainsition_matrix_linear(self, t, n_points): + t_norm = t / (n_points - 1) if n_points > 1 else 0.0 + time_varying_factor = self.coef_base + self.coef_amplitude * t_norm + A_t = time_varying_factor * torch.eye(self.n_dims) + return A_t + def get_transition_matrix_regime(self, t, n_points): + if t < n_points // 3: + factor = self.coef_base + elif t < 2 * n_points // 3: + factor = self.coef_base + self.coef_amplitude + else: + factor = self.coef_base - self.coef_amplitude + A_t = factor * torch.eye(self.n_dims) + def get_transition_matrix_sinusoidal(self, t, n_points): + t_norm = t / (n_points - 1) if n_points > 1 else 0.0 + time_varying_factor = self.coef_base + self.coef_amplitude * math.sin(2 * math.pi * t_norm) + A_t = time_varying_factor * torch.eye(self.n_dims) + return A_t + + def get_transition_matrix(self, t, n_points): + if self.mode == "sinusoidal": + return self.get_transition_matrix_sinusoidal(t, n_points) + elif self.mode == "linear": + return self.get_trainsition_matrix_linear(t, n_points) + elif self.mode == "regime": + return self.get_transition_matrix_regime(t, n_points) + else: + raise ValueError(f"Unknown mode: {self.mode}") + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + xs_b = torch.zeros(b_size, n_points, self.n_dims) + generators = None + if seeds is not None: + assert len(seeds) == b_size + generators = [torch.Generator().manual_seed(int(seed)) for seed in seeds] + if generators is None: + xs_b[:,0,:] = torch.randn(b_size, self.n_dims) * self.noise_std + else: + for i in range(b_size): + xs_b[i, 0, :] = torch.randn(self.n_dims, generator=generators[i]) * self.noise_std + for t in range(1, n_points): + A_t = self.get_transition_matrix(t, n_points) + + if generators is None: + eps_t = self.noise_std * torch.randn(b_size, self.n_dims) + else: + eps_t = torch.zeros(b_size, self.n_dims) + for i in range(b_size): + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i]) + xs_b[:, t, :] = (torch.matmul(xs_b[:, t-1, :], A_t) + eps_t) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + + return xs_b +>>>>>>> Stashed changes diff --git a/src/tasks.py b/src/tasks.py index 2da4cb57..ea78d901 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -182,23 +182,44 @@ def __init__( pool_dict=None, seeds=None, scale=1, - noise_std=0.01, + noise_std=0.9, renormalize_ys=False, + noise_type="poisson", uniform=False, ): """noise_std: standard deviation of noise added to the prediction.""" super(NoisyLinearRegression, self).__init__( n_dims, batch_size, pool_dict, seeds, scale, uniform ) - self.noise_std = noise_std + self.noise_std = float(noise_std) self.renormalize_ys = renormalize_ys - + self.noise_type = noise_type.lower() + def sample_noise(self, shape): + if self.noise_type == "normal": + noise = torch.randn(shape) * self.noise_std + elif self.noise_type == "uniform": + a = math.sqrt(3) * self.noise_std + noise = torch.empty(shape).uniform_(-a, a) + elif self.noise_type == "exponential": + exp_noise = torch.distributions.Exponential(rate=1.0 / self.noise_std) + noise = exp_noise.sample(shape) - self.noise_std + elif self.noise_type == "beta": + alpha, beta = 2.0, 5.0 + beta_dist = torch.distributions.Beta(alpha, beta) + noise = beta_dist.sample(shape) - beta_dist.mean + elif self.noise_type == "poisson": + lam = max(self.noise_std, 1e-3) + poisson_dist = torch.distributions.Poisson(lam) + noise = (poisson_dist.sample(shape) - lam) / math.sqrt(lam) * self.noise_std + else: + raise ValueError(f"Unknown noise type: {self.noise_type}") + return noise def evaluate(self, xs_b): ys_b = super().evaluate(xs_b) - ys_b_noisy = ys_b + torch.randn_like(ys_b) * self.noise_std + noise = self.sample_noise(ys_b.shape).to(xs_b.device) + ys_b_noisy = ys_b + noise if self.renormalize_ys: ys_b_noisy = ys_b_noisy * math.sqrt(self.n_dims) / ys_b_noisy.std() - return ys_b_noisy From e03541cdc6b75e7dca873400220dc21c87947dec Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tr=E1=BB=8Bnh=20V=C5=A9=20=C4=90=E1=BB=A9c=20H=E1=BA=A3i?= Date: Tue, 28 Oct 2025 20:17:18 +0700 Subject: [PATCH 18/88] Update tasks.py --- src/tasks.py | 36 ++++++++++++++++++++++++++++++------ 1 file changed, 30 insertions(+), 6 deletions(-) diff --git a/src/tasks.py b/src/tasks.py index 7845cd56..377f5e3d 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -185,26 +185,50 @@ def __init__( pool_dict=None, seeds=None, scale=1, - noise_std=0.01, + noise_std=0.9, renormalize_ys=False, + noise_type="uniform", # "normal", "uniform", "exponential", "beta", "poisson" uniform=False, ): - """noise_std: standard deviation of noise added to the prediction.""" super(NoisyLinearRegression, self).__init__( n_dims, batch_size, pool_dict, seeds, scale, uniform ) self.noise_std = noise_std self.renormalize_ys = renormalize_ys + self.noise_type = noise_type.lower() + + def sample_noise(self, shape): + if self.noise_type == "normal": + noise = torch.randn(shape) * self.noise_std + + elif self.noise_type == "uniform": + a = math.sqrt(3) * self.noise_std + noise = torch.empty(shape).uniform_(-a, a) + + elif self.noise_type == "exponential": + exp_noise = torch.distributions.Exponential(rate=1.0 / self.noise_std) + noise = exp_noise.sample(shape) - self.noise_std + + elif self.noise_type == "beta": + alpha, beta = 2.0, 5.0 + beta_dist = torch.distributions.Beta(alpha, beta) + noise = (beta_dist.sample(shape) - 0.5) * 2.0 * self.noise_std + + elif self.noise_type == "poisson": + lam = max(self.noise_std, 1e-3) + poisson_noise = torch.distributions.Poisson(lam) + noise = (poisson_noise.sample(shape) - lam) / math.sqrt(lam) * self.noise_std + else: + raise ValueError(f"Unsupported noise type: {self.noise_type}") + return noise def evaluate(self, xs_b): ys_b = super().evaluate(xs_b) - # ys_b_noisy = ys_b + torch.randn_like(ys_b) * self.noise_std - exp_noise = torch.distributions.Exponential(rate=1.0 / self.noise_std) - ys_b_noisy = ys_b + exp_noise.sample(ys_b.shape) + noise = self.sample_noise(ys_b.shape) + ys_b_noisy = ys_b + noise if self.renormalize_ys: ys_b_noisy = ys_b_noisy * math.sqrt(self.n_dims) / ys_b_noisy.std() - return ys_b_noisy From 70e269c6abeff5dc539d1f46de91c1cb4266b83e Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Fri, 31 Oct 2025 09:16:26 +0700 Subject: [PATCH 19/88] current status --- condaenv.sa_1amf7.requirements.txt | 12 ------------ src/train.py | 2 +- 2 files changed, 1 insertion(+), 13 deletions(-) delete mode 100644 condaenv.sa_1amf7.requirements.txt diff --git a/condaenv.sa_1amf7.requirements.txt b/condaenv.sa_1amf7.requirements.txt deleted file mode 100644 index 00a9c112..00000000 --- a/condaenv.sa_1amf7.requirements.txt +++ /dev/null @@ -1,12 +0,0 @@ -jupyter==1.0.0 -matplotlib==3.5.2 -numpy==1.22.3 -pandas==1.4.2 -quinine==0.3.0 -scikit-learn==1.0.2 -seaborn==0.11.2 -tqdm==4.64.0 -transformers==4.17.0 -wandb==0.12.11 -xgboost==1.6.1 -protobuf==3.20.1 \ No newline at end of file diff --git a/src/train.py b/src/train.py index e519ab44..736a1b2f 100644 --- a/src/train.py +++ b/src/train.py @@ -180,4 +180,4 @@ def main(args): with open(os.path.join(out_dir, "config.yaml"), "w") as yaml_file: yaml.dump(args.__dict__, yaml_file, default_flow_style=False) - main(args) + main(args) \ No newline at end of file From 319e9e9f34d1af8d2a67edd2fce55172250fd610 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Fri, 31 Oct 2025 11:44:27 +0700 Subject: [PATCH 20/88] ad vr1 --- src/samplers.py | 48 +++++++++++++++++++++++++++++++++++++++++++++++- src/schema.py | 2 +- 2 files changed, 48 insertions(+), 2 deletions(-) diff --git a/src/samplers.py b/src/samplers.py index 9656917d..0c0dffe2 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -15,7 +15,7 @@ def get_data_sampler(data_name, n_dims, **kwargs): names_to_classes = { "gaussian": GaussianSampler, "ar1":AR1Sampler, - # "var1":VAR1Sampler, + "vr1":VAR1Sampler, "ar2":AR2Sampler, "vr2":VR2Sampler, "nonstation":NonStationarySampler, @@ -266,3 +266,49 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): xs_b += self.bias return xs_b +class VAR1Sampler(DataSampler): + def __init__(self, n_dims, ar1_mat=None, noise_std=1.0, bias=None, scale=None): + super().__init__(n_dims) + + if ar1_mat is None: + ar1_mat = 0.9 * torch.eye(n_dims) + + assert ar1_mat.shape == (n_dims, n_dims), "ar1_mat must be n_dims x n_dims" + + if isinstance(ar1_mat, torch.Tensor): + self.ar1_mat = ar1_mat.float() + else: + self.ar1_mat = torch.tensor(ar1_mat, dtype=torch.float32) + + self.noise_std = float(noise_std) + self.bias = bias + self.scale = scale + def sample(self, n_points, b_size, n_dims_truncated=None, seeds=None): + xs_b = torch.zeros(b_size, n_points, self.n_dims) + + generators = None + if seeds is not None: + assert len(seeds) == b_size + generators = [torch.Generator().manual_seed(int(seed)) for seed in seeds] + + if generators is None: + xs_b[:, 0, :] = torch.randn(b_size, self.n_dims) + else: + for i in range(b_size): + xs_b[i, 0, i] = torch.randn(self.n_dims, generator=generators[i]) + for t in range(1, n_points): + if generators is None: + eps_t = self.noise_std * torch.randn(b_size, self.n_dims) + else: + eps_t = torch.zeros(b_size, self.n_dims) + for i in range(b_size): + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i]) + xs_b[:, t, :] = torch.matmul(xs_b[:, t - 1, :], self.ar1_mat.T) + eps_t + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b diff --git a/src/schema.py b/src/schema.py index 99bfc0cf..e2a3523a 100644 --- a/src/schema.py +++ b/src/schema.py @@ -51,7 +51,7 @@ "task_kwargs": merge(tdict, required), "num_tasks": merge(tinteger, nullable, default(None)), "num_training_examples": merge(tinteger, nullable, default(None)), - "data": merge(tstring, allowed(["gaussian","ar1","var1","ar2",'vr2',"nonstation"])), + "data": merge(tstring, allowed(["gaussian","ar1","vr1","ar2",'vr2',"nonstation"])), "batch_size": merge(tinteger, default(64)), "learning_rate": merge(tfloat, default(3e-4)), "train_steps": merge(tinteger, default(1000)), From 6334a5df5c87be6b2aff0e4f446afa901627804d Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Sat, 1 Nov 2025 10:01:25 +0700 Subject: [PATCH 21/88] Plot --- src/eval.ipynb | 721 ++++++++++++++++++++++++++++++++++++++++------ src/eval.py | 2 +- src/plot_utils.py | 13 +- src/tasks.py | 12 + 4 files changed, 659 insertions(+), 89 deletions(-) diff --git a/src/eval.ipynb b/src/eval.ipynb index 7d72f5af..3b8eb899 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -2,19 +2,10 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "id": "ed6cfeb1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "from collections import OrderedDict\n", "import re\n", @@ -42,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "id": "0e8d018b", "metadata": { "scrolled": true @@ -83,8 +74,60 @@ " \n", " \n", " \n", + " 6\n", + " beta_noise_ar1_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " beta_noise_ar1_data_experiment\n", + " \n", + " \n", + " 7\n", + " beta_noise_ar2_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " beta_noise_ar2_data_experiment\n", + " \n", + " \n", + " 8\n", + " beta_noise_gaussian_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " beta_noise_gaussian_data_experiment\n", + " \n", + " \n", + " 9\n", + " beta_noise_nonstation_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " beta_noise_nonstation_data_experiment\n", + " \n", + " \n", " 3\n", - " ar2_40_points\n", + " 8430062f-1e62-476d-8440-291e30ea2bf3\n", " linear_regression\n", " Transformer\n", " \n", @@ -93,7 +136,124 @@ " 5\n", " 4\n", " 8\n", - " ar2_40_points_\n", + " beta_noise_vr1_data_experiment\n", + " \n", + " \n", + " 5\n", + " b1913a31-abc6-42f1-bf82-15cbaba90bcc\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " beta_noise_vr1_data_experiment\n", + " \n", + " \n", + " 10\n", + " beta_noise_vr1_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " beta_noise_vr1_data_experiment\n", + " \n", + " \n", + " 11\n", + " beta_noise_vr2_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " beta_noise_vr2_data_experiment\n", + " \n", + " \n", + " 13\n", + " cauchy_noise_ar1_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " cauchy_noise_ar1_data_experiment\n", + " \n", + " \n", + " 14\n", + " cauchy_noise_ar2_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " cauchy_noise_ar2_data_experiment\n", + " \n", + " \n", + " 15\n", + " cauchy_noise_gaussian_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " cauchy_noise_gaussian_data_experiment\n", + " \n", + " \n", + " 2\n", + " 6cafdb71-62b3-405f-a7f3-764bb9da4023\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " cauchy_noise_gaussian_data_experiment\n", + " \n", + " \n", + " 16\n", + " cauchy_noise_vr1_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " cauchy_noise_vr1_data_experiment\n", + " \n", + " \n", + " 17\n", + " cauchy_noise_vr2_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " cauchy_noise_vr2_data_experiment\n", " \n", " \n", " 0\n", @@ -109,21 +269,21 @@ " decision_tree_pretrained\n", " \n", " \n", - " 1\n", - " 61f8e530-b627-471a-af64-2db247bb09ab\n", + " 19\n", + " exponential_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", - " compute_gradient=True\n", + " \n", " -1\n", " -1\n", " 5\n", " 4\n", " 8\n", - " fig3_6_points_\n", + " exponential_noise_ar1_data_experiment\n", " \n", " \n", - " 6\n", - " k=20_3\n", + " 20\n", + " exponential_noise_ar2_data_experiment\n", " linear_regression\n", " Transformer\n", " \n", @@ -132,11 +292,11 @@ " 5\n", " 4\n", " 8\n", - " k=20_part3\n", + " exponential_noise_ar2_data_experiment\n", " \n", " \n", - " 5\n", - " ar_with_k=40\n", + " 21\n", + " exponential_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", " \n", @@ -145,24 +305,24 @@ " 5\n", " 4\n", " 8\n", - " k=40_1\n", + " exponential_noise_gaussian_data_experiment\n", " \n", " \n", - " 9\n", - " pretrained\n", + " 22\n", + " exponential_noise_nonstation_data_experiment\n", " linear_regression\n", " Transformer\n", " \n", " -1\n", " -1\n", - " 20\n", - " 12\n", + " 5\n", + " 4\n", " 8\n", - " linear_regression_pretrained\n", + " exponential_noise_nonstation_data_experiment\n", " \n", " \n", " 4\n", - " ar_k=10\n", + " 8d320e10-ce68-4b9a-81e4-ad0fa864b406\n", " linear_regression\n", " Transformer\n", " \n", @@ -171,11 +331,11 @@ " 5\n", " 4\n", " 8\n", - " linear_regression_toy with 10 points\n", + " exponential_noise_nonstation_data_experiment\n", " \n", " \n", - " 2\n", - " ar1_data_with_k=20\n", + " 23\n", + " exponential_noise_vr1_data_experiment\n", " linear_regression\n", " Transformer\n", " \n", @@ -184,11 +344,11 @@ " 5\n", " 4\n", " 8\n", - " linear_regression_toy with 20 points\n", + " exponential_noise_vr1_data_experiment\n", " \n", " \n", - " 7\n", - " k=20_par2\n", + " 24\n", + " exponential_noise_vr2_data_experiment\n", " linear_regression\n", " Transformer\n", " \n", @@ -197,11 +357,11 @@ " 5\n", " 4\n", " 8\n", - " linear_regression_toy with 21 points\n", + " exponential_noise_vr2_data_experiment\n", " \n", " \n", - " 8\n", - " nonstation\n", + " 25\n", + " laplace_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", " \n", @@ -210,10 +370,179 @@ " 5\n", " 4\n", " 8\n", - " nonstation_10_points_\n", + " laplace_noise_ar1_data_experiment\n", " \n", " \n", - " 10\n", + " 26\n", + " laplace_noise_ar2_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " laplace_noise_ar2_data_experiment\n", + " \n", + " \n", + " 27\n", + " laplace_noise_gaussian_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " laplace_noise_gaussian_data_experiment\n", + " \n", + " \n", + " 28\n", + " laplace_noise_nonstation_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " laplace_noise_nonstation_data_experiment\n", + " \n", + " \n", + " 29\n", + " laplace_noise_vr1_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " laplace_noise_vr1_data_experiment\n", + " \n", + " \n", + " 30\n", + " laplace_noise_vr2_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " laplace_noise_vr2_data_experiment\n", + " \n", + " \n", + " 31\n", + " nonstation_noise_vr2_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " nonstation_noise_vr2_data_experiment\n", + " \n", + " \n", + " 32\n", + " poison_noise_ar2_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " poison_noise_ar2_data_experiment\n", + " \n", + " \n", + " 33\n", + " poison_noise_nonstation_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " poison_noise_nonstation_data_experiment\n", + " \n", + " \n", + " 34\n", + " poison_noise_vr2_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " poison_noise_vr2_data_experiment\n", + " \n", + " \n", + " 35\n", + " poisson_noise_ar1_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " poisson_noise_ar1_data_experiment\n", + " \n", + " \n", + " 36\n", + " poisson_noise_gaussian_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " poisson_noise_gaussian_data_experiment\n", + " \n", + " \n", + " 1\n", + " 518d818d-4f1b-4306-8f8e-361a73a1743f\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " poisson_noise_vr1_data_experiment\n", + " \n", + " \n", + " 37\n", + " poisson_noise_vr1_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " poisson_noise_vr1_data_experiment\n", + " \n", + " \n", + " 44\n", " pretrained\n", " relu_2nn_regression\n", " Transformer\n", @@ -226,7 +555,7 @@ " relu_2nn_regression_pretrained\n", " \n", " \n", - " 11\n", + " 45\n", " pretrained\n", " sparse_linear_regression\n", " Transformer\n", @@ -238,55 +567,261 @@ " 8\n", " sparse_regression_pretrained\n", " \n", + " \n", + " 38\n", + " uniform_noise_ar1_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " uniform_noise_ar1_data_experiment\n", + " \n", + " \n", + " 12\n", + " bf1f8f5f-4ee4-4f06-adec-115572528bdd\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " uniform_noise_ar1_data_experiment\n", + " \n", + " \n", + " 39\n", + " uniform_noise_ar2_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " uniform_noise_ar2_data_experiment\n", + " \n", + " \n", + " 40\n", + " uniform_noise_gaussian_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " uniform_noise_gaussian_data_experiment\n", + " \n", + " \n", + " 41\n", + " uniform_noise_nonstation_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " uniform_noise_nonstation_data_experiment\n", + " \n", + " \n", + " 42\n", + " uniform_noise_vr1_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " uniform_noise_vr1_data_experiment\n", + " \n", + " \n", + " 18\n", + " eb5bb96a-12f8-45cf-a178-78815c005f49\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " uniform_noise_vr1_data_experiment\n", + " \n", + " \n", + " 43\n", + " uniform_noise_vr2_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " uniform_noise_vr2_data_experiment\n", + " \n", " \n", "\n", "" ], "text/plain": [ - " run_id task \\\n", - "3 ar2_40_points linear_regression \n", - "0 pretrained decision_tree \n", - "1 61f8e530-b627-471a-af64-2db247bb09ab linear_regression \n", - "6 k=20_3 linear_regression \n", - "5 ar_with_k=40 linear_regression \n", - "9 pretrained linear_regression \n", - "4 ar_k=10 linear_regression \n", - "2 ar1_data_with_k=20 linear_regression \n", - "7 k=20_par2 linear_regression \n", - "8 nonstation linear_regression \n", - "10 pretrained relu_2nn_regression \n", - "11 pretrained sparse_linear_regression \n", + " run_id task \\\n", + "6 beta_noise_ar1_data_experiment linear_regression \n", + "7 beta_noise_ar2_data_experiment linear_regression \n", + "8 beta_noise_gaussian_data_experiment linear_regression \n", + "9 beta_noise_nonstation_data_experiment linear_regression \n", + "3 8430062f-1e62-476d-8440-291e30ea2bf3 linear_regression \n", + "5 b1913a31-abc6-42f1-bf82-15cbaba90bcc linear_regression \n", + "10 beta_noise_vr1_data_experiment linear_regression \n", + "11 beta_noise_vr2_data_experiment linear_regression \n", + "13 cauchy_noise_ar1_data_experiment linear_regression \n", + "14 cauchy_noise_ar2_data_experiment linear_regression \n", + "15 cauchy_noise_gaussian_data_experiment linear_regression \n", + "2 6cafdb71-62b3-405f-a7f3-764bb9da4023 linear_regression \n", + "16 cauchy_noise_vr1_data_experiment linear_regression \n", + "17 cauchy_noise_vr2_data_experiment linear_regression \n", + "0 pretrained decision_tree \n", + "19 exponential_noise_ar1_data_experiment linear_regression \n", + "20 exponential_noise_ar2_data_experiment linear_regression \n", + "21 exponential_noise_gaussian_data_experiment linear_regression \n", + "22 exponential_noise_nonstation_data_experiment linear_regression \n", + "4 8d320e10-ce68-4b9a-81e4-ad0fa864b406 linear_regression \n", + "23 exponential_noise_vr1_data_experiment linear_regression \n", + "24 exponential_noise_vr2_data_experiment linear_regression \n", + "25 laplace_noise_ar1_data_experiment linear_regression \n", + "26 laplace_noise_ar2_data_experiment linear_regression \n", + "27 laplace_noise_gaussian_data_experiment linear_regression \n", + "28 laplace_noise_nonstation_data_experiment linear_regression \n", + "29 laplace_noise_vr1_data_experiment linear_regression \n", + "30 laplace_noise_vr2_data_experiment linear_regression \n", + "31 nonstation_noise_vr2_data_experiment linear_regression \n", + "32 poison_noise_ar2_data_experiment linear_regression \n", + "33 poison_noise_nonstation_data_experiment linear_regression \n", + "34 poison_noise_vr2_data_experiment linear_regression \n", + "35 poisson_noise_ar1_data_experiment linear_regression \n", + "36 poisson_noise_gaussian_data_experiment linear_regression \n", + "1 518d818d-4f1b-4306-8f8e-361a73a1743f linear_regression \n", + "37 poisson_noise_vr1_data_experiment linear_regression \n", + "44 pretrained relu_2nn_regression \n", + "45 pretrained sparse_linear_regression \n", + "38 uniform_noise_ar1_data_experiment linear_regression \n", + "12 bf1f8f5f-4ee4-4f06-adec-115572528bdd linear_regression \n", + "39 uniform_noise_ar2_data_experiment linear_regression \n", + "40 uniform_noise_gaussian_data_experiment linear_regression \n", + "41 uniform_noise_nonstation_data_experiment linear_regression \n", + "42 uniform_noise_vr1_data_experiment linear_regression \n", + "18 eb5bb96a-12f8-45cf-a178-78815c005f49 linear_regression \n", + "43 uniform_noise_vr2_data_experiment linear_regression \n", "\n", " model kwargs num_tasks num_examples n_dims \\\n", - "3 Transformer -1 -1 5 \n", - "0 Transformer depth=4 -1 -1 20 \n", - "1 Transformer compute_gradient=True -1 -1 5 \n", "6 Transformer -1 -1 5 \n", - "5 Transformer -1 -1 5 \n", - "9 Transformer -1 -1 20 \n", - "4 Transformer -1 -1 5 \n", - "2 Transformer -1 -1 5 \n", "7 Transformer -1 -1 5 \n", "8 Transformer -1 -1 5 \n", - "10 Transformer hidden_layer_size=100 -1 -1 20 \n", - "11 Transformer sparsity=3 -1 -1 20 \n", + "9 Transformer -1 -1 5 \n", + "3 Transformer -1 -1 5 \n", + "5 Transformer -1 -1 5 \n", + "10 Transformer -1 -1 5 \n", + "11 Transformer -1 -1 5 \n", + "13 Transformer -1 -1 5 \n", + "14 Transformer -1 -1 5 \n", + "15 Transformer -1 -1 5 \n", + "2 Transformer -1 -1 5 \n", + "16 Transformer -1 -1 5 \n", + "17 Transformer -1 -1 5 \n", + "0 Transformer depth=4 -1 -1 20 \n", + "19 Transformer -1 -1 5 \n", + "20 Transformer -1 -1 5 \n", + "21 Transformer -1 -1 5 \n", + "22 Transformer -1 -1 5 \n", + "4 Transformer -1 -1 5 \n", + "23 Transformer -1 -1 5 \n", + "24 Transformer -1 -1 5 \n", + "25 Transformer -1 -1 5 \n", + "26 Transformer -1 -1 5 \n", + "27 Transformer -1 -1 5 \n", + "28 Transformer -1 -1 5 \n", + "29 Transformer -1 -1 5 \n", + "30 Transformer -1 -1 5 \n", + "31 Transformer -1 -1 5 \n", + "32 Transformer -1 -1 5 \n", + "33 Transformer -1 -1 5 \n", + "34 Transformer -1 -1 5 \n", + "35 Transformer -1 -1 5 \n", + "36 Transformer -1 -1 5 \n", + "1 Transformer -1 -1 5 \n", + "37 Transformer -1 -1 5 \n", + "44 Transformer hidden_layer_size=100 -1 -1 20 \n", + "45 Transformer sparsity=3 -1 -1 20 \n", + "38 Transformer -1 -1 5 \n", + "12 Transformer -1 -1 5 \n", + "39 Transformer -1 -1 5 \n", + "40 Transformer -1 -1 5 \n", + "41 Transformer -1 -1 5 \n", + "42 Transformer -1 -1 5 \n", + "18 Transformer -1 -1 5 \n", + "43 Transformer -1 -1 5 \n", "\n", - " n_layer n_head run_name \n", - "3 4 8 ar2_40_points_ \n", - "0 12 8 decision_tree_pretrained \n", - "1 4 8 fig3_6_points_ \n", - "6 4 8 k=20_part3 \n", - "5 4 8 k=40_1 \n", - "9 12 8 linear_regression_pretrained \n", - "4 4 8 linear_regression_toy with 10 points \n", - "2 4 8 linear_regression_toy with 20 points \n", - "7 4 8 linear_regression_toy with 21 points \n", - "8 4 8 nonstation_10_points_ \n", - "10 12 8 relu_2nn_regression_pretrained \n", - "11 12 8 sparse_regression_pretrained " + " n_layer n_head run_name \n", + "6 4 8 beta_noise_ar1_data_experiment \n", + "7 4 8 beta_noise_ar2_data_experiment \n", + "8 4 8 beta_noise_gaussian_data_experiment \n", + "9 4 8 beta_noise_nonstation_data_experiment \n", + "3 4 8 beta_noise_vr1_data_experiment \n", + "5 4 8 beta_noise_vr1_data_experiment \n", + "10 4 8 beta_noise_vr1_data_experiment \n", + "11 4 8 beta_noise_vr2_data_experiment \n", + "13 4 8 cauchy_noise_ar1_data_experiment \n", + "14 4 8 cauchy_noise_ar2_data_experiment \n", + "15 4 8 cauchy_noise_gaussian_data_experiment \n", + "2 4 8 cauchy_noise_gaussian_data_experiment \n", + "16 4 8 cauchy_noise_vr1_data_experiment \n", + "17 4 8 cauchy_noise_vr2_data_experiment \n", + "0 12 8 decision_tree_pretrained \n", + "19 4 8 exponential_noise_ar1_data_experiment \n", + "20 4 8 exponential_noise_ar2_data_experiment \n", + "21 4 8 exponential_noise_gaussian_data_experiment \n", + "22 4 8 exponential_noise_nonstation_data_experiment \n", + "4 4 8 exponential_noise_nonstation_data_experiment \n", + "23 4 8 exponential_noise_vr1_data_experiment \n", + "24 4 8 exponential_noise_vr2_data_experiment \n", + "25 4 8 laplace_noise_ar1_data_experiment \n", + "26 4 8 laplace_noise_ar2_data_experiment \n", + "27 4 8 laplace_noise_gaussian_data_experiment \n", + "28 4 8 laplace_noise_nonstation_data_experiment \n", + "29 4 8 laplace_noise_vr1_data_experiment \n", + "30 4 8 laplace_noise_vr2_data_experiment \n", + "31 4 8 nonstation_noise_vr2_data_experiment \n", + "32 4 8 poison_noise_ar2_data_experiment \n", + "33 4 8 poison_noise_nonstation_data_experiment \n", + "34 4 8 poison_noise_vr2_data_experiment \n", + "35 4 8 poisson_noise_ar1_data_experiment \n", + "36 4 8 poisson_noise_gaussian_data_experiment \n", + "1 4 8 poisson_noise_vr1_data_experiment \n", + "37 4 8 poisson_noise_vr1_data_experiment \n", + "44 12 8 relu_2nn_regression_pretrained \n", + "45 12 8 sparse_regression_pretrained \n", + "38 4 8 uniform_noise_ar1_data_experiment \n", + "12 4 8 uniform_noise_ar1_data_experiment \n", + "39 4 8 uniform_noise_ar2_data_experiment \n", + "40 4 8 uniform_noise_gaussian_data_experiment \n", + "41 4 8 uniform_noise_nonstation_data_experiment \n", + "42 4 8 uniform_noise_vr1_data_experiment \n", + "18 4 8 uniform_noise_vr1_data_experiment \n", + "43 4 8 uniform_noise_vr2_data_experiment " ] }, - "execution_count": 5, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -298,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 19, "id": "a9980951", "metadata": {}, "outputs": [], @@ -308,7 +843,7 @@ "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"nonstation\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"poisson_noise_vr1_data_experiment\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -327,7 +862,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 21, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -337,21 +872,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "nonstation_10_points_ nonstation\n" + "poisson_noise_vr1_data_experiment poisson_noise_vr1_data_experiment\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00 \u001b[39m\u001b[32m9\u001b[39m \u001b[43mbasic_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstandard\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 10\u001b[39m plt.show()\n\u001b[32m 12\u001b[39m \u001b[38;5;66;03m# Figure 3 and 4\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32md:\\MyBK\\Semester_5\\Programming_Intergration_Project\\in-context-learning\\src\\plot_utils.py:66\u001b[39m, in \u001b[36mbasic_plot\u001b[39m\u001b[34m(metrics, models, trivial)\u001b[39m\n\u001b[32m 63\u001b[39m fig, ax = plt.subplots(\u001b[32m1\u001b[39m, \u001b[32m1\u001b[39m)\n\u001b[32m 65\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m models \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m66\u001b[39m metrics = {k: \u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[43mk\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m models}\n\u001b[32m 68\u001b[39m color = \u001b[32m0\u001b[39m\n\u001b[32m 69\u001b[39m ax.axhline(trivial, ls=\u001b[33m\"\u001b[39m\u001b[33m--\u001b[39m\u001b[33m\"\u001b[39m, color=\u001b[33m\"\u001b[39m\u001b[33mgray\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[31mKeyError\u001b[39m: 'Transformer'" ] }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -1024,7 +1571,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "base", "language": "python", "name": "python3" }, @@ -1038,7 +1585,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.13.5" } }, "nbformat": 4, diff --git a/src/eval.py b/src/eval.py index 8c15d249..8cdbcc29 100644 --- a/src/eval.py +++ b/src/eval.py @@ -432,7 +432,7 @@ def read_run_dir(run_dir): all_runs[k].append(v) df = pd.DataFrame(all_runs).sort_values("run_name") - assert len(df) == len(df.run_name.unique()) + # assert len(df) == len(df.run_name.unique()) return df # Figure 3 and 4: diff --git a/src/plot_utils.py b/src/plot_utils.py index a2d26874..df8e1e27 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -10,8 +10,19 @@ palette = sns.color_palette("colorblind") relevant_model_names = { + "noisy_linear_regression": [ + "Transformer", + "Least Squares", + "Ridge (alpha=0.1)", + "Ridge (alpha=1.0)", + "Ridge Var Adj (alpha=1.0, ar=0.5)", + "Feasible GLS", + "GLS (ar=0.5)", + # "3-Nearest Neighbors", + # "Averaging" + ], "linear_regression": [ - "Transformer", + "gpt2_embd=128_layer=4_head=8", "Least Squares", "Ridge Var Adj (alpha=1.0, ar=0.5)", "Feasible GLS", diff --git a/src/tasks.py b/src/tasks.py index 377f5e3d..fe1c7bdc 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -218,6 +218,18 @@ def sample_noise(self, shape): lam = max(self.noise_std, 1e-3) poisson_noise = torch.distributions.Poisson(lam) noise = (poisson_noise.sample(shape) - lam) / math.sqrt(lam) * self.noise_std + elif self.noise_type == "cauchy": + # 6. Nhiễu Cauchy - Đuôi dày, không có Mean/Variance hữu hạn. + # Dùng scale parameter để kiểm soát độ trải (như FWHM). + scale_param = self.noise_std * 0.5 + cauchy_dist = torch.distributions.StudentT(df=1, loc=0, scale=scale_param) + noise = cauchy_dist.sample(shape) + elif self.noise_type == "laplace": + # 7. Nhiễu Laplace (Double Exponential) - Zero-mean, Var = 2*b^2 + # Để có Var = noise_std^2, ta cần b = noise_std / sqrt(2) + scale_param = self.noise_std / math.sqrt(2.0) + laplace_dist = torch.distributions.Laplace(loc=0, scale=scale_param) + noise = laplace_dist.sample(shape) else: raise ValueError(f"Unsupported noise type: {self.noise_type}") return noise From ba71f7529d9e7418f27f664767a09fc4b68d38a6 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 3 Nov 2025 15:50:44 +0700 Subject: [PATCH 22/88] models update metrics --- src/conf/toy.yaml | 2 +- src/eval.ipynb | 618 +++++++++++----------------------------------- src/models.py | 5 + 3 files changed, 144 insertions(+), 481 deletions(-) diff --git a/src/conf/toy.yaml b/src/conf/toy.yaml index 85b4288a..7c826ded 100644 --- a/src/conf/toy.yaml +++ b/src/conf/toy.yaml @@ -39,4 +39,4 @@ training: out_dir: ../models/linear_regression wandb: - name: "exponential_noise_experiment" + name: "uniform_noise_gaussian_data_experiment" diff --git a/src/eval.ipynb b/src/eval.ipynb index 3b8eb899..916073c7 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -2,10 +2,19 @@ "cells": [ { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "id": "ed6cfeb1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\CaoHuuThienHoang\\miniconda3\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], "source": [ "from collections import OrderedDict\n", "import re\n", @@ -33,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "id": "0e8d018b", "metadata": { "scrolled": true @@ -74,72 +83,7 @@ " \n", " \n", " \n", - " 6\n", - " beta_noise_ar1_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " beta_noise_ar1_data_experiment\n", - " \n", - " \n", - " 7\n", - " beta_noise_ar2_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " beta_noise_ar2_data_experiment\n", - " \n", - " \n", - " 8\n", - " beta_noise_gaussian_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " beta_noise_gaussian_data_experiment\n", - " \n", - " \n", - " 9\n", - " beta_noise_nonstation_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " beta_noise_nonstation_data_experiment\n", - " \n", - " \n", - " 3\n", - " 8430062f-1e62-476d-8440-291e30ea2bf3\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " beta_noise_vr1_data_experiment\n", - " \n", - " \n", - " 5\n", + " 10\n", " b1913a31-abc6-42f1-bf82-15cbaba90bcc\n", " linear_regression\n", " Transformer\n", @@ -152,8 +96,8 @@ " beta_noise_vr1_data_experiment\n", " \n", " \n", - " 10\n", - " beta_noise_vr1_data_experiment\n", + " 7\n", + " 8430062f-1e62-476d-8440-291e30ea2bf3\n", " linear_regression\n", " Transformer\n", " \n", @@ -178,46 +122,7 @@ " beta_noise_vr2_data_experiment\n", " \n", " \n", - " 13\n", - " cauchy_noise_ar1_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " cauchy_noise_ar1_data_experiment\n", - " \n", - " \n", - " 14\n", - " cauchy_noise_ar2_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " cauchy_noise_ar2_data_experiment\n", - " \n", - " \n", - " 15\n", - " cauchy_noise_gaussian_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " cauchy_noise_gaussian_data_experiment\n", - " \n", - " \n", - " 2\n", + " 5\n", " 6cafdb71-62b3-405f-a7f3-764bb9da4023\n", " linear_regression\n", " Transformer\n", @@ -230,20 +135,7 @@ " cauchy_noise_gaussian_data_experiment\n", " \n", " \n", - " 16\n", - " cauchy_noise_vr1_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " cauchy_noise_vr1_data_experiment\n", - " \n", - " \n", - " 17\n", + " 13\n", " cauchy_noise_vr2_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -269,7 +161,7 @@ " decision_tree_pretrained\n", " \n", " \n", - " 19\n", + " 15\n", " exponential_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -282,8 +174,8 @@ " exponential_noise_ar1_data_experiment\n", " \n", " \n", - " 20\n", - " exponential_noise_ar2_data_experiment\n", + " 9\n", + " 91bfd5cf-de01-49ea-93ed-1d646a6b6c31\n", " linear_regression\n", " Transformer\n", " \n", @@ -292,11 +184,11 @@ " 5\n", " 4\n", " 8\n", - " exponential_noise_ar2_data_experiment\n", + " exponential_noise_experiment\n", " \n", " \n", - " 21\n", - " exponential_noise_gaussian_data_experiment\n", + " 2\n", + " 60ac1b2d-f9c1-4fdd-85d1-ec3074701880\n", " linear_regression\n", " Transformer\n", " \n", @@ -305,11 +197,11 @@ " 5\n", " 4\n", " 8\n", - " exponential_noise_gaussian_data_experiment\n", + " exponential_noise_experiment\n", " \n", " \n", - " 22\n", - " exponential_noise_nonstation_data_experiment\n", + " 4\n", + " 6418d745-d2fe-4494-8923-71f9627b66e4\n", " linear_regression\n", " Transformer\n", " \n", @@ -318,10 +210,10 @@ " 5\n", " 4\n", " 8\n", - " exponential_noise_nonstation_data_experiment\n", + " exponential_noise_experiment\n", " \n", " \n", - " 4\n", + " 8\n", " 8d320e10-ce68-4b9a-81e4-ad0fa864b406\n", " linear_regression\n", " Transformer\n", @@ -334,98 +226,20 @@ " exponential_noise_nonstation_data_experiment\n", " \n", " \n", - " 23\n", - " exponential_noise_vr1_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " exponential_noise_vr1_data_experiment\n", - " \n", - " \n", - " 24\n", - " exponential_noise_vr2_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " exponential_noise_vr2_data_experiment\n", - " \n", - " \n", - " 25\n", - " laplace_noise_ar1_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " laplace_noise_ar1_data_experiment\n", - " \n", - " \n", - " 26\n", - " laplace_noise_ar2_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " laplace_noise_ar2_data_experiment\n", - " \n", - " \n", - " 27\n", - " laplace_noise_gaussian_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " laplace_noise_gaussian_data_experiment\n", - " \n", - " \n", - " 28\n", - " laplace_noise_nonstation_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " laplace_noise_nonstation_data_experiment\n", - " \n", - " \n", - " 29\n", - " laplace_noise_vr1_data_experiment\n", + " 3\n", + " 61f8e530-b627-471a-af64-2db247bb09ab\n", " linear_regression\n", " Transformer\n", - " \n", + " compute_gradient=True\n", " -1\n", " -1\n", " 5\n", " 4\n", " 8\n", - " laplace_noise_vr1_data_experiment\n", + " fig3_6_points_\n", " \n", " \n", - " 30\n", + " 16\n", " laplace_noise_vr2_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -438,7 +252,7 @@ " laplace_noise_vr2_data_experiment\n", " \n", " \n", - " 31\n", + " 17\n", " nonstation_noise_vr2_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -451,7 +265,7 @@ " nonstation_noise_vr2_data_experiment\n", " \n", " \n", - " 32\n", + " 18\n", " poison_noise_ar2_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -464,58 +278,6 @@ " poison_noise_ar2_data_experiment\n", " \n", " \n", - " 33\n", - " poison_noise_nonstation_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " poison_noise_nonstation_data_experiment\n", - " \n", - " \n", - " 34\n", - " poison_noise_vr2_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " poison_noise_vr2_data_experiment\n", - " \n", - " \n", - " 35\n", - " poisson_noise_ar1_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " poisson_noise_ar1_data_experiment\n", - " \n", - " \n", - " 36\n", - " poisson_noise_gaussian_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " poisson_noise_gaussian_data_experiment\n", - " \n", - " \n", " 1\n", " 518d818d-4f1b-4306-8f8e-361a73a1743f\n", " linear_regression\n", @@ -529,20 +291,7 @@ " poisson_noise_vr1_data_experiment\n", " \n", " \n", - " 37\n", - " poisson_noise_vr1_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " poisson_noise_vr1_data_experiment\n", - " \n", - " \n", - " 44\n", + " 20\n", " pretrained\n", " relu_2nn_regression\n", " Transformer\n", @@ -555,7 +304,7 @@ " relu_2nn_regression_pretrained\n", " \n", " \n", - " 45\n", + " 21\n", " pretrained\n", " sparse_linear_regression\n", " Transformer\n", @@ -568,19 +317,6 @@ " sparse_regression_pretrained\n", " \n", " \n", - " 38\n", - " uniform_noise_ar1_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " uniform_noise_ar1_data_experiment\n", - " \n", - " \n", " 12\n", " bf1f8f5f-4ee4-4f06-adec-115572528bdd\n", " linear_regression\n", @@ -594,8 +330,8 @@ " uniform_noise_ar1_data_experiment\n", " \n", " \n", - " 39\n", - " uniform_noise_ar2_data_experiment\n", + " 19\n", + " uniform_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", " \n", @@ -604,11 +340,11 @@ " 5\n", " 4\n", " 8\n", - " uniform_noise_ar2_data_experiment\n", + " uniform_noise_ar1_data_experiment\n", " \n", " \n", - " 40\n", - " uniform_noise_gaussian_data_experiment\n", + " 6\n", + " 70152b8e-2195-4da8-8329-b43fe3146907\n", " linear_regression\n", " Transformer\n", " \n", @@ -620,33 +356,7 @@ " uniform_noise_gaussian_data_experiment\n", " \n", " \n", - " 41\n", - " uniform_noise_nonstation_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " uniform_noise_nonstation_data_experiment\n", - " \n", - " \n", - " 42\n", - " uniform_noise_vr1_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " uniform_noise_vr1_data_experiment\n", - " \n", - " \n", - " 18\n", + " 14\n", " eb5bb96a-12f8-45cf-a178-78815c005f49\n", " linear_regression\n", " Transformer\n", @@ -658,170 +368,85 @@ " 8\n", " uniform_noise_vr1_data_experiment\n", " \n", - " \n", - " 43\n", - " uniform_noise_vr2_data_experiment\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 5\n", - " 4\n", - " 8\n", - " uniform_noise_vr2_data_experiment\n", - " \n", " \n", "\n", "" ], "text/plain": [ - " run_id task \\\n", - "6 beta_noise_ar1_data_experiment linear_regression \n", - "7 beta_noise_ar2_data_experiment linear_regression \n", - "8 beta_noise_gaussian_data_experiment linear_regression \n", - "9 beta_noise_nonstation_data_experiment linear_regression \n", - "3 8430062f-1e62-476d-8440-291e30ea2bf3 linear_regression \n", - "5 b1913a31-abc6-42f1-bf82-15cbaba90bcc linear_regression \n", - "10 beta_noise_vr1_data_experiment linear_regression \n", - "11 beta_noise_vr2_data_experiment linear_regression \n", - "13 cauchy_noise_ar1_data_experiment linear_regression \n", - "14 cauchy_noise_ar2_data_experiment linear_regression \n", - "15 cauchy_noise_gaussian_data_experiment linear_regression \n", - "2 6cafdb71-62b3-405f-a7f3-764bb9da4023 linear_regression \n", - "16 cauchy_noise_vr1_data_experiment linear_regression \n", - "17 cauchy_noise_vr2_data_experiment linear_regression \n", - "0 pretrained decision_tree \n", - "19 exponential_noise_ar1_data_experiment linear_regression \n", - "20 exponential_noise_ar2_data_experiment linear_regression \n", - "21 exponential_noise_gaussian_data_experiment linear_regression \n", - "22 exponential_noise_nonstation_data_experiment linear_regression \n", - "4 8d320e10-ce68-4b9a-81e4-ad0fa864b406 linear_regression \n", - "23 exponential_noise_vr1_data_experiment linear_regression \n", - "24 exponential_noise_vr2_data_experiment linear_regression \n", - "25 laplace_noise_ar1_data_experiment linear_regression \n", - "26 laplace_noise_ar2_data_experiment linear_regression \n", - "27 laplace_noise_gaussian_data_experiment linear_regression \n", - "28 laplace_noise_nonstation_data_experiment linear_regression \n", - "29 laplace_noise_vr1_data_experiment linear_regression \n", - "30 laplace_noise_vr2_data_experiment linear_regression \n", - "31 nonstation_noise_vr2_data_experiment linear_regression \n", - "32 poison_noise_ar2_data_experiment linear_regression \n", - "33 poison_noise_nonstation_data_experiment linear_regression \n", - "34 poison_noise_vr2_data_experiment linear_regression \n", - "35 poisson_noise_ar1_data_experiment linear_regression \n", - "36 poisson_noise_gaussian_data_experiment linear_regression \n", - "1 518d818d-4f1b-4306-8f8e-361a73a1743f linear_regression \n", - "37 poisson_noise_vr1_data_experiment linear_regression \n", - "44 pretrained relu_2nn_regression \n", - "45 pretrained sparse_linear_regression \n", - "38 uniform_noise_ar1_data_experiment linear_regression \n", - "12 bf1f8f5f-4ee4-4f06-adec-115572528bdd linear_regression \n", - "39 uniform_noise_ar2_data_experiment linear_regression \n", - "40 uniform_noise_gaussian_data_experiment linear_regression \n", - "41 uniform_noise_nonstation_data_experiment linear_regression \n", - "42 uniform_noise_vr1_data_experiment linear_regression \n", - "18 eb5bb96a-12f8-45cf-a178-78815c005f49 linear_regression \n", - "43 uniform_noise_vr2_data_experiment linear_regression \n", + " run_id task \\\n", + "10 b1913a31-abc6-42f1-bf82-15cbaba90bcc linear_regression \n", + "7 8430062f-1e62-476d-8440-291e30ea2bf3 linear_regression \n", + "11 beta_noise_vr2_data_experiment linear_regression \n", + "5 6cafdb71-62b3-405f-a7f3-764bb9da4023 linear_regression \n", + "13 cauchy_noise_vr2_data_experiment linear_regression \n", + "0 pretrained decision_tree \n", + "15 exponential_noise_ar1_data_experiment linear_regression \n", + "9 91bfd5cf-de01-49ea-93ed-1d646a6b6c31 linear_regression \n", + "2 60ac1b2d-f9c1-4fdd-85d1-ec3074701880 linear_regression \n", + "4 6418d745-d2fe-4494-8923-71f9627b66e4 linear_regression \n", + "8 8d320e10-ce68-4b9a-81e4-ad0fa864b406 linear_regression \n", + "3 61f8e530-b627-471a-af64-2db247bb09ab linear_regression \n", + "16 laplace_noise_vr2_data_experiment linear_regression \n", + "17 nonstation_noise_vr2_data_experiment linear_regression \n", + "18 poison_noise_ar2_data_experiment linear_regression \n", + "1 518d818d-4f1b-4306-8f8e-361a73a1743f linear_regression \n", + "20 pretrained relu_2nn_regression \n", + "21 pretrained sparse_linear_regression \n", + "12 bf1f8f5f-4ee4-4f06-adec-115572528bdd linear_regression \n", + "19 uniform_noise_ar1_data_experiment linear_regression \n", + "6 70152b8e-2195-4da8-8329-b43fe3146907 linear_regression \n", + "14 eb5bb96a-12f8-45cf-a178-78815c005f49 linear_regression \n", "\n", " model kwargs num_tasks num_examples n_dims \\\n", - "6 Transformer -1 -1 5 \n", - "7 Transformer -1 -1 5 \n", - "8 Transformer -1 -1 5 \n", - "9 Transformer -1 -1 5 \n", - "3 Transformer -1 -1 5 \n", - "5 Transformer -1 -1 5 \n", "10 Transformer -1 -1 5 \n", + "7 Transformer -1 -1 5 \n", "11 Transformer -1 -1 5 \n", + "5 Transformer -1 -1 5 \n", "13 Transformer -1 -1 5 \n", - "14 Transformer -1 -1 5 \n", + "0 Transformer depth=4 -1 -1 20 \n", "15 Transformer -1 -1 5 \n", + "9 Transformer -1 -1 5 \n", "2 Transformer -1 -1 5 \n", + "4 Transformer -1 -1 5 \n", + "8 Transformer -1 -1 5 \n", + "3 Transformer compute_gradient=True -1 -1 5 \n", "16 Transformer -1 -1 5 \n", "17 Transformer -1 -1 5 \n", - "0 Transformer depth=4 -1 -1 20 \n", - "19 Transformer -1 -1 5 \n", - "20 Transformer -1 -1 5 \n", - "21 Transformer -1 -1 5 \n", - "22 Transformer -1 -1 5 \n", - "4 Transformer -1 -1 5 \n", - "23 Transformer -1 -1 5 \n", - "24 Transformer -1 -1 5 \n", - "25 Transformer -1 -1 5 \n", - "26 Transformer -1 -1 5 \n", - "27 Transformer -1 -1 5 \n", - "28 Transformer -1 -1 5 \n", - "29 Transformer -1 -1 5 \n", - "30 Transformer -1 -1 5 \n", - "31 Transformer -1 -1 5 \n", - "32 Transformer -1 -1 5 \n", - "33 Transformer -1 -1 5 \n", - "34 Transformer -1 -1 5 \n", - "35 Transformer -1 -1 5 \n", - "36 Transformer -1 -1 5 \n", + "18 Transformer -1 -1 5 \n", "1 Transformer -1 -1 5 \n", - "37 Transformer -1 -1 5 \n", - "44 Transformer hidden_layer_size=100 -1 -1 20 \n", - "45 Transformer sparsity=3 -1 -1 20 \n", - "38 Transformer -1 -1 5 \n", + "20 Transformer hidden_layer_size=100 -1 -1 20 \n", + "21 Transformer sparsity=3 -1 -1 20 \n", "12 Transformer -1 -1 5 \n", - "39 Transformer -1 -1 5 \n", - "40 Transformer -1 -1 5 \n", - "41 Transformer -1 -1 5 \n", - "42 Transformer -1 -1 5 \n", - "18 Transformer -1 -1 5 \n", - "43 Transformer -1 -1 5 \n", + "19 Transformer -1 -1 5 \n", + "6 Transformer -1 -1 5 \n", + "14 Transformer -1 -1 5 \n", "\n", " n_layer n_head run_name \n", - "6 4 8 beta_noise_ar1_data_experiment \n", - "7 4 8 beta_noise_ar2_data_experiment \n", - "8 4 8 beta_noise_gaussian_data_experiment \n", - "9 4 8 beta_noise_nonstation_data_experiment \n", - "3 4 8 beta_noise_vr1_data_experiment \n", - "5 4 8 beta_noise_vr1_data_experiment \n", "10 4 8 beta_noise_vr1_data_experiment \n", + "7 4 8 beta_noise_vr1_data_experiment \n", "11 4 8 beta_noise_vr2_data_experiment \n", - "13 4 8 cauchy_noise_ar1_data_experiment \n", - "14 4 8 cauchy_noise_ar2_data_experiment \n", - "15 4 8 cauchy_noise_gaussian_data_experiment \n", - "2 4 8 cauchy_noise_gaussian_data_experiment \n", - "16 4 8 cauchy_noise_vr1_data_experiment \n", - "17 4 8 cauchy_noise_vr2_data_experiment \n", + "5 4 8 cauchy_noise_gaussian_data_experiment \n", + "13 4 8 cauchy_noise_vr2_data_experiment \n", "0 12 8 decision_tree_pretrained \n", - "19 4 8 exponential_noise_ar1_data_experiment \n", - "20 4 8 exponential_noise_ar2_data_experiment \n", - "21 4 8 exponential_noise_gaussian_data_experiment \n", - "22 4 8 exponential_noise_nonstation_data_experiment \n", - "4 4 8 exponential_noise_nonstation_data_experiment \n", - "23 4 8 exponential_noise_vr1_data_experiment \n", - "24 4 8 exponential_noise_vr2_data_experiment \n", - "25 4 8 laplace_noise_ar1_data_experiment \n", - "26 4 8 laplace_noise_ar2_data_experiment \n", - "27 4 8 laplace_noise_gaussian_data_experiment \n", - "28 4 8 laplace_noise_nonstation_data_experiment \n", - "29 4 8 laplace_noise_vr1_data_experiment \n", - "30 4 8 laplace_noise_vr2_data_experiment \n", - "31 4 8 nonstation_noise_vr2_data_experiment \n", - "32 4 8 poison_noise_ar2_data_experiment \n", - "33 4 8 poison_noise_nonstation_data_experiment \n", - "34 4 8 poison_noise_vr2_data_experiment \n", - "35 4 8 poisson_noise_ar1_data_experiment \n", - "36 4 8 poisson_noise_gaussian_data_experiment \n", + "15 4 8 exponential_noise_ar1_data_experiment \n", + "9 4 8 exponential_noise_experiment \n", + "2 4 8 exponential_noise_experiment \n", + "4 4 8 exponential_noise_experiment \n", + "8 4 8 exponential_noise_nonstation_data_experiment \n", + "3 4 8 fig3_6_points_ \n", + "16 4 8 laplace_noise_vr2_data_experiment \n", + "17 4 8 nonstation_noise_vr2_data_experiment \n", + "18 4 8 poison_noise_ar2_data_experiment \n", "1 4 8 poisson_noise_vr1_data_experiment \n", - "37 4 8 poisson_noise_vr1_data_experiment \n", - "44 12 8 relu_2nn_regression_pretrained \n", - "45 12 8 sparse_regression_pretrained \n", - "38 4 8 uniform_noise_ar1_data_experiment \n", + "20 12 8 relu_2nn_regression_pretrained \n", + "21 12 8 sparse_regression_pretrained \n", "12 4 8 uniform_noise_ar1_data_experiment \n", - "39 4 8 uniform_noise_ar2_data_experiment \n", - "40 4 8 uniform_noise_gaussian_data_experiment \n", - "41 4 8 uniform_noise_nonstation_data_experiment \n", - "42 4 8 uniform_noise_vr1_data_experiment \n", - "18 4 8 uniform_noise_vr1_data_experiment \n", - "43 4 8 uniform_noise_vr2_data_experiment " + "19 4 8 uniform_noise_ar1_data_experiment \n", + "6 4 8 uniform_noise_gaussian_data_experiment \n", + "14 4 8 uniform_noise_vr1_data_experiment " ] }, - "execution_count": 10, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -833,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 38, "id": "a9980951", "metadata": {}, "outputs": [], @@ -843,7 +468,7 @@ "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"poisson_noise_vr1_data_experiment\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"exponential_noise_nonstation_data_experiment\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -852,6 +477,39 @@ " get_run_metrics(run_path) # these are normally precomputed at the end of training" ] }, + { + "cell_type": "code", + "execution_count": 39, + "id": "937f1b23", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Tên mô hình CÓ TRONG relevant_model_names (models) ---\n", + "['gpt2_embd=128_layer=4_head=8', 'Least Squares', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)']\n", + "\n", + "--- Các khóa (Tên mô hình) THỰC TẾ trong metrics['standard'] ---\n", + "dict_keys([])\n" + ] + } + ], + "source": [ + "# Cell In[26], trước dòng 9\n", + "\n", + "import pprint # Dùng để in dictionary đẹp hơn\n", + "# ...\n", + "models = relevant_model_names[task]\n", + "print(\"--- Tên mô hình CÓ TRONG relevant_model_names (models) ---\")\n", + "print(models)\n", + "print(\"\\n--- Các khóa (Tên mô hình) THỰC TẾ trong metrics['standard'] ---\")\n", + "pprint.pprint(metrics[\"standard\"].keys())\n", + "\n", + "# basic_plot(metrics[\"standard\"], models=models) \n", + "# ..." + ] + }, { "cell_type": "markdown", "id": "f6d09964", @@ -862,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 43, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -872,26 +530,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "poisson_noise_vr1_data_experiment poisson_noise_vr1_data_experiment\n" + "exponential_noise_nonstation_data_experiment exponential_noise_nonstation_data_experiment\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 2/2 [00:00<00:00, 2276.42it/s]\n" + "100%|██████████| 2/2 [00:00<00:00, 10852.02it/s]\n" ] }, { "ename": "KeyError", - "evalue": "'Transformer'", + "evalue": "'gpt2_embd=128_layer=4_head=8'", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[21]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 6\u001b[39m n_dims = conf.model.n_dims\n\u001b[32m 8\u001b[39m models = relevant_model_names[task]\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m \u001b[43mbasic_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstandard\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 10\u001b[39m plt.show()\n\u001b[32m 12\u001b[39m \u001b[38;5;66;03m# Figure 3 and 4\u001b[39;00m\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[43]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 6\u001b[39m n_dims = conf.model.n_dims\n\u001b[32m 8\u001b[39m models = relevant_model_names[task]\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m \u001b[43mbasic_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstandard\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 10\u001b[39m plt.show()\n\u001b[32m 12\u001b[39m \u001b[38;5;66;03m# Figure 3 and 4\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32md:\\MyBK\\Semester_5\\Programming_Intergration_Project\\in-context-learning\\src\\plot_utils.py:66\u001b[39m, in \u001b[36mbasic_plot\u001b[39m\u001b[34m(metrics, models, trivial)\u001b[39m\n\u001b[32m 63\u001b[39m fig, ax = plt.subplots(\u001b[32m1\u001b[39m, \u001b[32m1\u001b[39m)\n\u001b[32m 65\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m models \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m66\u001b[39m metrics = {k: \u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[43mk\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m models}\n\u001b[32m 68\u001b[39m color = \u001b[32m0\u001b[39m\n\u001b[32m 69\u001b[39m ax.axhline(trivial, ls=\u001b[33m\"\u001b[39m\u001b[33m--\u001b[39m\u001b[33m\"\u001b[39m, color=\u001b[33m\"\u001b[39m\u001b[33mgray\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[31mKeyError\u001b[39m: 'Transformer'" + "\u001b[31mKeyError\u001b[39m: 'gpt2_embd=128_layer=4_head=8'" ] }, { diff --git a/src/models.py b/src/models.py index 252e8b27..b7a45812 100644 --- a/src/models.py +++ b/src/models.py @@ -78,6 +78,11 @@ def get_relevant_baselines(task_name): ], "noisy_linear_regression": [ (LeastSquaresModel, {}), + (RidgeModel, {"alpha": 0.1}), + (RidgeModel, {"alpha": 1.0}), + (RidgeModelWithVarianceAdjustment, {"alpha": 1.0, "ar_coef": 0.5}), + (FeasibleGLSModel, {"ar_coef": None}), + (GLSModel, {"ar_coef": 0.5}), (NNModel, {"n_neighbors": 3}), (AveragingModel, {}), ], From 433b64f5f9f0ba4b42ce230685ee5ba1e2e37033 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 3 Nov 2025 19:07:55 +0700 Subject: [PATCH 23/88] return original code --- src/eval.ipynb | 776 +++++----------------------------------------- src/eval.py | 76 +++-- src/plot_utils.py | 13 +- 3 files changed, 121 insertions(+), 744 deletions(-) diff --git a/src/eval.ipynb b/src/eval.ipynb index 916073c7..7c56c416 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -7,11 +7,11 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "c:\\Users\\CaoHuuThienHoang\\miniconda3\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" ] } ], @@ -49,406 +49,16 @@ }, "outputs": [ { - "data": { - "text/html": [ - "
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run_idtaskmodelkwargsnum_tasksnum_examplesn_dimsn_layern_headrun_name
10b1913a31-abc6-42f1-bf82-15cbaba90bcclinear_regressionTransformer-1-1548beta_noise_vr1_data_experiment
78430062f-1e62-476d-8440-291e30ea2bf3linear_regressionTransformer-1-1548beta_noise_vr1_data_experiment
11beta_noise_vr2_data_experimentlinear_regressionTransformer-1-1548beta_noise_vr2_data_experiment
56cafdb71-62b3-405f-a7f3-764bb9da4023linear_regressionTransformer-1-1548cauchy_noise_gaussian_data_experiment
13cauchy_noise_vr2_data_experimentlinear_regressionTransformer-1-1548cauchy_noise_vr2_data_experiment
0pretraineddecision_treeTransformerdepth=4-1-120128decision_tree_pretrained
15exponential_noise_ar1_data_experimentlinear_regressionTransformer-1-1548exponential_noise_ar1_data_experiment
991bfd5cf-de01-49ea-93ed-1d646a6b6c31linear_regressionTransformer-1-1548exponential_noise_experiment
260ac1b2d-f9c1-4fdd-85d1-ec3074701880linear_regressionTransformer-1-1548exponential_noise_experiment
46418d745-d2fe-4494-8923-71f9627b66e4linear_regressionTransformer-1-1548exponential_noise_experiment
88d320e10-ce68-4b9a-81e4-ad0fa864b406linear_regressionTransformer-1-1548exponential_noise_nonstation_data_experiment
361f8e530-b627-471a-af64-2db247bb09ablinear_regressionTransformercompute_gradient=True-1-1548fig3_6_points_
16laplace_noise_vr2_data_experimentlinear_regressionTransformer-1-1548laplace_noise_vr2_data_experiment
17nonstation_noise_vr2_data_experimentlinear_regressionTransformer-1-1548nonstation_noise_vr2_data_experiment
18poison_noise_ar2_data_experimentlinear_regressionTransformer-1-1548poison_noise_ar2_data_experiment
1518d818d-4f1b-4306-8f8e-361a73a1743flinear_regressionTransformer-1-1548poisson_noise_vr1_data_experiment
20pretrainedrelu_2nn_regressionTransformerhidden_layer_size=100-1-120128relu_2nn_regression_pretrained
21pretrainedsparse_linear_regressionTransformersparsity=3-1-120128sparse_regression_pretrained
12bf1f8f5f-4ee4-4f06-adec-115572528bddlinear_regressionTransformer-1-1548uniform_noise_ar1_data_experiment
19uniform_noise_ar1_data_experimentlinear_regressionTransformer-1-1548uniform_noise_ar1_data_experiment
670152b8e-2195-4da8-8329-b43fe3146907linear_regressionTransformer-1-1548uniform_noise_gaussian_data_experiment
14eb5bb96a-12f8-45cf-a178-78815c005f49linear_regressionTransformer-1-1548uniform_noise_vr1_data_experiment
\n", - "
" - ], - "text/plain": [ - " run_id task \\\n", - "10 b1913a31-abc6-42f1-bf82-15cbaba90bcc linear_regression \n", - "7 8430062f-1e62-476d-8440-291e30ea2bf3 linear_regression \n", - "11 beta_noise_vr2_data_experiment linear_regression \n", - "5 6cafdb71-62b3-405f-a7f3-764bb9da4023 linear_regression \n", - "13 cauchy_noise_vr2_data_experiment linear_regression \n", - "0 pretrained decision_tree \n", - "15 exponential_noise_ar1_data_experiment linear_regression \n", - "9 91bfd5cf-de01-49ea-93ed-1d646a6b6c31 linear_regression \n", - "2 60ac1b2d-f9c1-4fdd-85d1-ec3074701880 linear_regression \n", - "4 6418d745-d2fe-4494-8923-71f9627b66e4 linear_regression \n", - "8 8d320e10-ce68-4b9a-81e4-ad0fa864b406 linear_regression \n", - "3 61f8e530-b627-471a-af64-2db247bb09ab linear_regression \n", - "16 laplace_noise_vr2_data_experiment linear_regression \n", - "17 nonstation_noise_vr2_data_experiment linear_regression \n", - "18 poison_noise_ar2_data_experiment linear_regression \n", - "1 518d818d-4f1b-4306-8f8e-361a73a1743f linear_regression \n", - "20 pretrained relu_2nn_regression \n", - "21 pretrained sparse_linear_regression \n", - "12 bf1f8f5f-4ee4-4f06-adec-115572528bdd linear_regression \n", - "19 uniform_noise_ar1_data_experiment linear_regression \n", - "6 70152b8e-2195-4da8-8329-b43fe3146907 linear_regression \n", - "14 eb5bb96a-12f8-45cf-a178-78815c005f49 linear_regression \n", - "\n", - " model kwargs num_tasks num_examples n_dims \\\n", - "10 Transformer -1 -1 5 \n", - "7 Transformer -1 -1 5 \n", - "11 Transformer -1 -1 5 \n", - "5 Transformer -1 -1 5 \n", - "13 Transformer -1 -1 5 \n", - "0 Transformer depth=4 -1 -1 20 \n", - "15 Transformer -1 -1 5 \n", - "9 Transformer -1 -1 5 \n", - "2 Transformer -1 -1 5 \n", - "4 Transformer -1 -1 5 \n", - "8 Transformer -1 -1 5 \n", - "3 Transformer compute_gradient=True -1 -1 5 \n", - "16 Transformer -1 -1 5 \n", - "17 Transformer -1 -1 5 \n", - "18 Transformer -1 -1 5 \n", - "1 Transformer -1 -1 5 \n", - "20 Transformer hidden_layer_size=100 -1 -1 20 \n", - "21 Transformer sparsity=3 -1 -1 20 \n", - "12 Transformer -1 -1 5 \n", - "19 Transformer -1 -1 5 \n", - "6 Transformer -1 -1 5 \n", - "14 Transformer -1 -1 5 \n", - "\n", - " n_layer n_head run_name \n", - "10 4 8 beta_noise_vr1_data_experiment \n", - "7 4 8 beta_noise_vr1_data_experiment \n", - "11 4 8 beta_noise_vr2_data_experiment \n", - "5 4 8 cauchy_noise_gaussian_data_experiment \n", - "13 4 8 cauchy_noise_vr2_data_experiment \n", - "0 12 8 decision_tree_pretrained \n", - "15 4 8 exponential_noise_ar1_data_experiment \n", - "9 4 8 exponential_noise_experiment \n", - "2 4 8 exponential_noise_experiment \n", - "4 4 8 exponential_noise_experiment \n", - "8 4 8 exponential_noise_nonstation_data_experiment \n", - "3 4 8 fig3_6_points_ \n", - "16 4 8 laplace_noise_vr2_data_experiment \n", - "17 4 8 nonstation_noise_vr2_data_experiment \n", - "18 4 8 poison_noise_ar2_data_experiment \n", - "1 4 8 poisson_noise_vr1_data_experiment \n", - "20 12 8 relu_2nn_regression_pretrained \n", - "21 12 8 sparse_regression_pretrained \n", - "12 4 8 uniform_noise_ar1_data_experiment \n", - "19 4 8 uniform_noise_ar1_data_experiment \n", - "6 4 8 uniform_noise_gaussian_data_experiment \n", - "14 4 8 uniform_noise_vr1_data_experiment " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" + "ename": "AssertionError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAssertionError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m df = \u001b[43mread_run_dir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2\u001b[39m df \u001b[38;5;66;03m# list all the runs in our run_dir\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32md:\\MyBK\\Semester_5\\Programming_Intergration_Project\\in-context-learning\\src\\eval.py:445\u001b[39m, in \u001b[36mread_run_dir\u001b[39m\u001b[34m(run_dir)\u001b[39m\n\u001b[32m 442\u001b[39m all_runs[k].append(v)\n\u001b[32m 444\u001b[39m df = pd.DataFrame(all_runs).sort_values(\u001b[33m\"\u001b[39m\u001b[33mrun_name\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m445\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(df) == \u001b[38;5;28mlen\u001b[39m(df.run_name.unique())\n\u001b[32m 446\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m df\n", + "\u001b[31mAssertionError\u001b[39m: " + ] } ], "source": [ @@ -458,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 17, "id": "a9980951", "metadata": {}, "outputs": [], @@ -468,7 +78,7 @@ "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"exponential_noise_nonstation_data_experiment\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"08f273f7-0f91-46d0-9ebf-35e2a4467653\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -479,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "937f1b23", "metadata": {}, "outputs": [ @@ -491,7 +101,7 @@ "['gpt2_embd=128_layer=4_head=8', 'Least Squares', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)']\n", "\n", "--- Các khóa (Tên mô hình) THỰC TẾ trong metrics['standard'] ---\n", - "dict_keys([])\n" + "dict_keys(['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)'])\n" ] } ], @@ -504,7 +114,7 @@ "print(\"--- Tên mô hình CÓ TRONG relevant_model_names (models) ---\")\n", "print(models)\n", "print(\"\\n--- Các khóa (Tên mô hình) THỰC TẾ trong metrics['standard'] ---\")\n", - "pprint.pprint(metrics[\"standard\"].keys())\n", + "pprint.pprint(metrics[\"standard\"].keys()) \n", "\n", "# basic_plot(metrics[\"standard\"], models=models) \n", "# ..." @@ -520,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -530,14 +140,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "exponential_noise_nonstation_data_experiment exponential_noise_nonstation_data_experiment\n" + "uniform_noise_gaussian_data_experiment 08f273f7-0f91-46d0-9ebf-35e2a4467653\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 2/2 [00:00<00:00, 10852.02it/s]\n" + "100%|██████████| 2/2 [00:00<00:00, 30066.70it/s]\n" ] }, { @@ -547,8 +157,8 @@ "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[43]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 6\u001b[39m n_dims = conf.model.n_dims\n\u001b[32m 8\u001b[39m models = relevant_model_names[task]\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m \u001b[43mbasic_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstandard\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 10\u001b[39m plt.show()\n\u001b[32m 12\u001b[39m \u001b[38;5;66;03m# Figure 3 and 4\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32md:\\MyBK\\Semester_5\\Programming_Intergration_Project\\in-context-learning\\src\\plot_utils.py:66\u001b[39m, in \u001b[36mbasic_plot\u001b[39m\u001b[34m(metrics, models, trivial)\u001b[39m\n\u001b[32m 63\u001b[39m fig, ax = plt.subplots(\u001b[32m1\u001b[39m, \u001b[32m1\u001b[39m)\n\u001b[32m 65\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m models \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m66\u001b[39m metrics = {k: \u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[43mk\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m models}\n\u001b[32m 68\u001b[39m color = \u001b[32m0\u001b[39m\n\u001b[32m 69\u001b[39m ax.axhline(trivial, ls=\u001b[33m\"\u001b[39m\u001b[33m--\u001b[39m\u001b[33m\"\u001b[39m, color=\u001b[33m\"\u001b[39m\u001b[33mgray\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[21]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 6\u001b[39m n_dims = conf.model.n_dims\n\u001b[32m 8\u001b[39m models = relevant_model_names[task]\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m \u001b[43mbasic_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstandard\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 10\u001b[39m plt.show()\n\u001b[32m 12\u001b[39m \u001b[38;5;66;03m# Figure 3 and 4\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32md:\\MyBK\\Semester_5\\Programming_Intergration_Project\\in-context-learning\\src\\plot_utils.py:77\u001b[39m, in \u001b[36mbasic_plot\u001b[39m\u001b[34m(metrics, models, trivial)\u001b[39m\n\u001b[32m 74\u001b[39m fig, ax = plt.subplots(\u001b[32m1\u001b[39m, \u001b[32m1\u001b[39m)\n\u001b[32m 76\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m models \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m77\u001b[39m metrics = {k: \u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[43mk\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m models}\n\u001b[32m 79\u001b[39m color = \u001b[32m0\u001b[39m\n\u001b[32m 80\u001b[39m ax.axhline(trivial, ls=\u001b[33m\"\u001b[39m\u001b[33m--\u001b[39m\u001b[33m\"\u001b[39m, color=\u001b[33m\"\u001b[39m\u001b[33mgray\u001b[39m\u001b[33m\"\u001b[39m)\n", "\u001b[31mKeyError\u001b[39m: 'gpt2_embd=128_layer=4_head=8'" ] }, @@ -575,21 +185,21 @@ "basic_plot(metrics[\"standard\"], models=models)\n", "plt.show()\n", "\n", - "# Figure 3 and 4\n", - "for model_name in models: \n", - " if \"gradient_alignment\" in metrics[\"standard\"][model_name]: \n", - " alignments = metrics[\"standard\"][model_name][\"gradient_alignment\"]\n", - " plt.figure(figsize=(6,4))\n", - " plt.plot(range(len(alignments)), alignments, label=f\"{model_name} gradient-w alignment\")\n", - " plt.xlabel(\"# in-context examples\")\n", - " plt.ylabel(\"normalized inner product\") \n", - " plt.legend()\n", - " plt.show()" + "# # Figure 3 and 4\n", + "# for model_name in models: \n", + "# if \"gradient_alignment\" in metrics[\"standard\"][model_name]: \n", + "# alignments = metrics[\"standard\"][model_name][\"gradient_alignment\"]\n", + "# plt.figure(figsize=(6,4))\n", + "# plt.plot(range(len(alignments)), alignments, label=f\"{model_name} gradient-w alignment\")\n", + "# plt.xlabel(\"# in-context examples\")\n", + "# plt.ylabel(\"normalized inner product\") \n", + "# plt.legend()\n", + "# plt.show()" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "31b4ecca", "metadata": { "scrolled": true @@ -599,270 +209,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "Available metrics: ['gradient', 'half_subspace', 'noisyLR', 'orthogonal_train_test', 'overlapping_train_test', 'random_quadrants', 'scale-x=0.333', 'scale-x=0.5', 'scale-x=2', 'scale-x=3', 'scale-y=0.333', 'scale-y=0.5', 'scale-y=2', 'scale-y=3', 'skewed', 'standard']\n", + "Available metrics: ['gradient', 'standard']\n", "Processing: gradient\n", - "Metric keys: []\n", - "Processing: half_subspace\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: noisyLR\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: orthogonal_train_test\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: overlapping_train_test\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: random_quadrants\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: scale-x=0.333\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: scale-x=0.5\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: scale-x=2\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: scale-x=3\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" - ] - }, - { - "data": { - "image/png": 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", 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SePz4MV68eIaRI8fg2rWrEAQJdHR0cfToIaioqKBTpy5o3NgV/v5TERb2ACVKlMTYsRNQpUpVAMDHjx8QGDgf586dwadP6ahQoQIGDx6KihUrAQBWrlyG0NAQFClijMuXL6FFi1YYOdI3z95zIkUVaKIqFouxZs0ajBw5EvXr1wcAzJs3Dy4uLjh+/DhatWqV7ZzQ0FD4+fkhNTUVenp62Y4/eZL5jcXe3h76+tz/j4goL+x/EA3foIeISSq4RLWotjpmulqjbcViedZmZGQk3r2LwYYNW5CWloawsPsYO3YUfHyGoW7dGfj48SOWLAnEpEkTcODAMairqwMAtm3bjD/+cMeaNZvw7NlT+PmNRenSpdGnjxcuXDiH9evXYMoUf5QuXRp3797B5Ml+MDMzR+PGTaGtrY2xY0dhzZqNKFWqFN68eY2+fXvByckZixcvR1paGhYunIcBAzyxefMOaGvrAAAOHNiHiROnoly58jA2Nsa1a1dx4kQQOnbsgnXrNuP48SCsWLEEQUFH4eMzAubm5pg+fTICAmZg/fotEAQBw4b5QFNTE7NnL4COjg6OHj0EL6/eWLVqPaytKwAAbt4MRZcu3bBx41ZkZEjy7L0m+h4Fetd/WFgYkpKSULNmTWmZnp4eKlWqhOvXr8s959y5c3BxccG+ffugq6ub7fjDhw9hbGzMJJWIKA8NPRxWoEkqAMQkfcLQw2F53m7v3p4oXrwELCwsoaKiihEjfNG1a3eYmxeHlZU1unTphri4OMTGvpeekznK6o1SpUqhbt16qFGjJu7cuQUAePUqEurqGjAzM4epqRmaNHFFYOAy2Ns7QEtLC3p6mT+fDAwMoaOji927d6JQocKYOHEqype3QpUqVTFjxizExcXh6NEj0muWL28NV9fmsLQsB319AwCAvr4BfHyGoUSJkujWrTsAoFGjpqhbtx7KlSuPli3bICIiHAAQEnIN9+7dwbRpM1GlSlWUKVMWAwZ4o0qVqti+favMe9K3b38UL14CpUqVyvP3m0gRBTqiGhUVBQAwMzOTKS9WrJj02JeGDRv21TYfPnyIwoULw8fHB6GhoTA0NESHDh3g4eEBFZUfy8vV1PIvr1dVVZH5+3fD/rP/n//9u/nd+1/QSpb8NxmzsrKGnp4eNmxYh2fPniIy8iUeP34IADKji6VLl5FpQ1tbR7r2tFmzFjh4cD86d3ZD2bIWcHKqgQYNGsPUVPZnXZbw8CeoWLGSzHK3IkWMUapUaYSHP/kszuz3bpibF5f+bCtUqBAAoESJEtLjmpqa0g3VHz4MgyAIcHNrIdOGWPwJaWli6WtDQ6Nsa3uJCkqBJqopKSkAkG0tqqamJhISEr6rzcePHyMxMRGurq4YNGgQbty4gYCAACQkJGDIkCHfHauKigiGhtrffX5u6ekVyvdrKDP2n/3/nSlz/+e3rKA0U/957fN7GEJDb2Do0EGoVasObG3t4OraHGlpqRg9erjMOerq2e+hEAQBQOZI6caN23D37h1cu3YFwcFXsH37VvTt2x99+njleN6XJBIJ1NT+/TGtqZn9XovPj2cRieT/wiORCNDW1sG6dZuyHfv85zCfSEbKpEAT1axvDmKxWOYbRVpamvQ3Q0WtXLkSaWlp0mUB1tbW+PjxI5YuXQpvb+/vHlWVSAQkJiZ/17m5oaqqAj29QkhMTPkt1wSx/+w/+/9z+6+nV0ihEdy2FYuhlXXRX+pmKnm2bNkIB4fq8PefLS3bsWPb//8lP6H80rFjR/Dx4wd07NgFtrZ26Nt3AKZPn4ITJ4LkJqrlypVHUNARiMViacL4/v17REa+RIcOnX+4T1ksLS2RlPQR6enpKFvWQlo+ffoUlC9fHp06dc2zaxHllQJNVLOm/KOjo2XWwURHR8Pa+vt+a9bQ0Mg2QmtlZYXk5GQkJCTA0NDwu+NNT8//HyAZGZKfch1lxf6z/+y/8vZfVUUEY235u7H8KkxMTHD+/FncunUTxYqZIDT0OlasWAIgc1AlN8RiMQID50NbWxu2tg6Ijn6LmzdvwM7OQW799u07Yc+eXZg0aQJ69erz//PnQV/fAE2auOZZ35yda8HKyhrjx4/B8OGjUKyYCXbv3onDhw9gwYLFeXYdorxUoIlqhQoVoKOjg6tXr0oT1cTERNy/fx/u7u4KtycIApo0aQI3NzcMHjxYWn737l0ULVr0h5JUIiL69Xl5DcD79+8xcmTmUrGyZS0wbtzfmDhxAh48+AdlypT9Zhtt2rghISEeq1evRHT0W+jq6qFBg0YYPNhHbn1zc3MsXboSixYtgKdnL2hoqMPJyRkTJ06Ve9Pw91JVVcXChUsQGDgf48b5IiUlFWXLloW//2xUr+6UZ9chyksiIafFMT/JvHnzsG3bNkyfPh3FixdHQEAAIiMjcehQ5p5wsbGx0NXVlbsPasOGDdGuXTuZDf9nzpyJbdu2Ydq0aahSpQquXLmC6dOnY9y4cejc+funUDIyJIiNTfru879FTU0FhobaiItLUuoRlfzC/rP/7P/P7b+RkXaOU/+pqakID4+AsbEpNDS4XpGI8p5YnIZ376JgaWnx1b3uC3zDfx8fH6Snp2P8+PFITU2Fo6MjVq9eDXV1dURGRqJRo0aYMWMG2rdvn6v2RowYAR0dHcydOxdRUVEoUaLEDyepRERERPTzFfiI6n8FR1TzF/vP/rP/HFElot9HbkdUuWkfERERESklJqpEREREpJSYqBIRERGRUmKiSkRERERKiYkqERERESklJqpEREREpJSYqBIRERGRUmKiSkRERERKiYkqERH9stzcWmLlymUFGkNCQjwOHNj31ToxMTGYNm0yWrd2RZ06TmjVqikmTZqAyMiXPydIIiXFRJWIiCgfLVw4D0ePHs7xuFgsxsCBfREb+w7Tp8/Czp37MGXKDERFRcHL60/ExcX9xGiJlItaQQdARET/DYIkA4K44JImkYYhRCqqBXb97/WtB5VfuxaMly9fYPXqDdDT0wMAmJmZY9asuWjZsgmOHz+GLl26/YRIiZQPE1UiIvom8bN9SLk6GkJqTIHFINIqikI1ZkGjjFuetnvo0H5s3LgeUVFvYGpqhvbtO6JTp65QUcmcdLx1KxQrVy7DgwcP8OmTGObmxdGrVx80b94SABAbG4vZs/1x40YIUlNTYGVVAQMGDIaDQzVMnvw3jhw5CABwdnZAcHBotutnXefSpQvSNgFAV1cXmzZth4GBobTs/PmzWL58CSIjX8LauiJatGgJf/9p0nbd3FqiZcvW6Nu3v/ScL8v279+LHTu2IjLyJUQiEaytK2Do0JGoWLGStH7Dho1x+fJFxMXFYcaMANjbO2DTpvXYu3c33r9/j1KlSqF7dw80a9ZCep3Nmzdgz55diI5+C2Pjomjdui169/aESCT68Q+JfltMVImI6JuSLw8BPiUWaAxCagySLw/J00R1377dWLJkEUaNGoNKlSrj4cOHmDNnJqKjo+HtPRTR0dEYMmQwOnXqgjFjxiM9PR0bN67D9OmT4eTkjCJFimDWrOn49EmMpUtXQl1dHevWrcbo0cNw8GAQhg8fibS0VERHv4W//2y5MTg61kDFipUwadIErF27Co6OTrCzc4CTUw2UKlVaWu/WrVD4+o5A796ecHVtjuvXr2LhwvkK9ffs2dOYM2cmxo6dADs7e7x//w5z5szC9OmTsXHjNmm9Xbu2Y/bsBdDV1YWlZTksW7YIx48HYeRIX5QuXQa3boVi1qwZ+PjxIzp27IwLF85h/fo1mDLFH6VLl8bdu3cwebIfzMzMZZJvIkUxUSUiot/WmjWr8OefnmjSxBUAULx4CSQnf0RAgD+8vAZALE5D37790L27h3Rk0MOjN44cOYSXL5+jSJEiePUqEpaW5WBuXhxaWloYPnwUXF1bQEVFBYUK6UJTUwtqauooUsRYbgzq6upYunQVduzYhlOnjmPPnl3YvXsnVFXV0K5dewwdOgJqaurYuXM7qla1hZfXAABA6dJl8OzZU+zatSPX/dXX18dff/lJR0LNzMzRurUbZs/2l6lXs2ZtODnVAACkpKRg27YtmDx5OmrXdgEAlChREm/evMamTevRsWNnvHoVCXV1DZiZmcPU1AympmYoWrQYTE1NFfg0iLJjokpERN9UuNYCpZn6zytxcXGIjn6LpUsXYfnyJdJyiURAWloaXr9+hbJlLdCqVRvs2LEVT548QWTkSzx58ggAkJEhAQD06eOFiRPH48yZU7CxsYOzc000bdocmpqauY5FS0sLHh694OHRCwkJ8bhx4waOHj2EXbt2QEurEAYPHoKIiHDUqFFT5jwHh+oKJar29tXw9GkE1qxZiWfPniEy8gWePHkMiUQiU69kyVLSfz99GoG0tDT4+Y2Disq/0/gZGRkQi8VITU1Fs2YtcPDgfnTu7IayZS3g5FQDDRo0hqmpWa5jI5KHiSoREX2TRhk3qJdq/UvdTJWVnA0ZMgKOjk7ZjpuamuHp0wj06/cnrK0r/j/5aggDA0P8+WcPab369Rvi0KEgXLlyGdevX8PWrZuwevUKrFq1HhYWlt+MY//+vUhPT0eHDp0AAPr6BmjYsBEaNmyEv/4ajcuXL2Lw4CEAAEGQTSjV1dW/2X5GRob030FBRzF58t9wdW0OGxsbtGvXHuHh4dlGVD9PsrPep2nT/FG6dJls7WtoaEBLSwsbN27D3bt3cO3aFQQHX8H27VvRt29/9Onj9c0YiXLCRJWIiHJFpKIKkZb86ev/IiMjIxgaGuLVq0i0b99RWn7iRBDOnTsDP7/J2LNnFwwNjRAYuFR6/MKFc///lwCxWIwlSwLRvHlLNGniiiZNXJGamoqWLZvi0qULsLCwxLfuJXr6NAJBQUfRrFkLaGtryxzT0dGFkZERAMDaugLu3r0jczws7L7Ma3V1dSQlJUlfJyV9RGxsrPT1hg1r0aaNG3x9/5KWnT+f2R9BEOTe+FSmTBmoqqohKioKderUlZZv374Vz55FwNd3HI4dO4KPHz+gY8cusLW1Q9++AzB9+hScOBHERJV+CBNVIiL6pUVGvsSVK5dkyjQ1teDgUA3u7r2wfPlimJqaombN2njy5DECAmbAxaUeNDQ0YGJigujot7h8+RLKli2LsLAHmDs3AEDm/qcaGhp48OAf3L59EyNGjIaRkTGuXLmElJRkVK1qAwAoVKgw3r2LwevXr2BuXjxbfH/84Y4TJ4IwYEBf9OnTF+XLWyMhIR5Xr15BUNARzJ49HwDQvbsH+vTxwIIFc+Hm1h4PH4Zh69YtMm1VrWqDU6eOo2HDxtDV1cWKFUuhpvbvKLSJiSnu3LmFsLAH0NHRwYUL57Br13Zpf+QtV9DR0UW7dh2wYsUSaGtrw8bGFqGhIVi8eAE8PHpLzw0MnA9tbW3Y2jogOvotbt68ATs7h+/81IgyiQThWzu8EZC5Fik2NunbFb+TmpoKDA21EReXhPR0ybdP+MWw/+w/+/9z+29kpA1VVfnPfElNTUV4eASMjU2hoZH7dZbKyM2tJaKi3mQrNzU1w759mZvw79q1Azt3bsOrV5EoUsQYTZs2Q9++/aGhoQGxWIyAgBk4d+4s0tM/oUSJUuja9Q+sXLkMrVu3xZ9/9kVMTAzmz5+N0NAQfPz4EaVLl0HPnn9Kb9B68OA+Ro8ejsTEROzatR9FixbNFs/bt1FYvXolrl0LRmzse2hoaKBy5Sro1asP7O2rSeuFht7AwoVzER7+BJaW5WBnZ49t27ZIt6eKjo7GzJlTERISAl1dHXTr1gNXrlz6/yhnf7x+/QozZkzFP//chbq6BsqXL4+2bdtjwoSxWLZsFezsHORucZWeno7169fg0KEDePcuBiYmJmjbtj3c3XtKR2E3blyH/fv3Ijr6LXR19dCgQSMMHuwDLa1CefeB0i9DLE7Du3dRsLS0gJaWVo71mKjmEhPV/MX+s//sPxNVUtyhQwcwdepEufuzEimz3CaqfIQqERERESklJqpEREREpJSYqBIREf1HtWrVhtP+9EtjokpERERESomJKhEREREpJSaqRERERKSUmKgSERERkVJiokpERERESomJKhEREREpJSaqRET0yxowoC+cnR1k/ri41EDbti0we7Y/UlNTpHXd3Fpi5cplOba1cuUyuLm1zLdYV61ajjp1nJCQEC/3+LFjR1CrVnVER7/N0+v27dsbzs4OePz4Ua7qT578NwYM6AsAeP36NZydHXDjRshXzzl37gxGjRqW65icnR1w6NCBfKufF9avXyN9H74mISEefn7j0KRJPTRtWh8BATNk/t8VtJCQa+jVqzvq1auFLl3a48SJoK/Wz8jIQL16NbN9XWV97Tx8GIY//+yB9PT0PIlPLU9aISIiUlKNGjXB8OGjpK+Tk5Nx9Wow5s+fDYlEwOjRYwEAa9dugqZmwT0ytlWrNlizZiVOnTqJ9u07Zjt+5Mgh1KhRE8WKmeTZNV+8eI67d2+jVKnS2LNnF3x9/1LofBMTExw+fBx6evo51klMTMScObOwePHyHw1XaezatQPLly+Bra39N+uOHTsaqakpCAxcho8fP2Dq1ElISUmBn9/knxDp1z179hQjRgxBt27umDhxKi5duoCJEyfAwMAAjo415J7z4sVzpKWlYePGbTAyMpKWFypUGABgbV0BZcpYYNOm9ejVq88Px8gRVSIi+qVpamqhSBFj6Z+SJUuhY8fOaNasBU6e/Hf0yNDQEIULFy6wOE1NzVC9uhOCgo5kOxYdHY2QkGto3dotT6958OB+lC5dBq1bt0VQ0FEkJycrdL6qqiqKFDGGurp6jnW2b9+MKlWqomTJUj8aboGLiYnBiBFDsHjxglz15+7d2wgNDcGECZNQoUJFVK/uhLFjx+Po0cOIjo7+CRF/3bZtm2FpWR79+w9CmTJl0b27Bxo1aoxNm9bneE54+BNoa+ugfHkrma+rz792unfvgQ0b1uHjxw8/HCNHVImIKFcyBAnixAU3ZWmoUQiqorwbX9HQ0ISq6r8/Bt3cWqJly9bo27c/AGDfvt3YtGk9YmJi4OhYA2Zm5jLnx8XFYc6cmQgOvgxVVTW0aeOG+/fvwc7OQdrGxYvnsXLlMjx79hRFixZFkybN0Lu3JzQ0NOTG1Lp1W/j5/YU3b17LXO/YsSPQ19dH3bp1kZiYiEWLFuDKlYuIjY2Dnp4uXFzqY/jwkdDSKoQbN0Lg4zMA/foNwqZN62Fubo41azZCRUX2vcvIyMCxY4dRv34j1K/fEIsXL8Tx40fh5tZBWkcQBKxduxp79+7Chw+JaNSoCcTiNOnx169fo337Vli8eAWqVauerT9paWnYtWundNQaACQSCTZuXIfDhw/izZvXUFfXgI2NLUaO9EWJEiWztbFy5TJcv34Nzs41sX37VmRkpKNevQYYPnwUtLV1pPVevHiOwYP7486dW9DX10fHjl3Qs+efubpmVj9ysmfPIZibmyMs7D7U1dWxadN2rF69Em/evM7xHAC4desmjI2NUbashbTMwaE6RCIRbt++iSZNXL96fpZz585g/fq1iIh4AolEgrJlLTBgwGA4O9cCkLnEpVSpUnj8+DFevHiGkSPHYP/+vbh584bc9vr08ULfvv1x69ZN1KtXX+ZYtWqOmDcvAIIgQCQSZTv3yZPHKFOmzFfjtbQsB1NTU+zbtwfu7j1z1cecMFElIqJv2v/qH4y5cxgxaUkFFkNRTW3427RE2+KVf6id9PR0XL16BceOHZZJyj53/PgxBATMxLBhI+HkVANnz57BsmWLpNPuEokEI0YMQUZGOubPXwQ1NXUsWDAHt27dhJ2dAwDgypVLGDduDIYOHQ5Hxxp49SoSc+bMwosXzzFt2ky5161XrwF0dfVw/PgxaZIFAEePHkLz5q2gpqaOKVNGIyYmGjNmzIaRURHcuXML06ZNgoWFBbp27Q4gMwm9fPkCVq1aj9TUlGxJKgAEB19GTEwMGjVqjJIlS6FChYrYu3e3zHuyYcNabNq0HmPGjIO1dQXs3bsbhw9vhr19tVy917du3cSHD4moWbO2tGz79i3YvHkD/Pwmw9KyHF69isSMGVOxcOE8zJo1V247Dx78AwBYsGAxkpKSMH36ZIwbNwbz5y+S1tm1aztGjRqDsWPHIyjoGJYuXYTKlaugenWnb14zawlDTgwMDAEALi714OJSL1d9BzJHwosVM5UpU1dXh76+fq7XGoeF3cfYsaPg4zMMdevOwMePH7FkSSAmTZqAAweOSUezDxzYh4kTp6JcufIwNjZGzZq1kZ7+SW6bWdP08uIrWrQoUlNTkZAQL+3358LDnyAjIwNDhw7Co0ePUKxYMXTp8geaN5ddv127tgvOnz/HRJWIiPLfsJsHkJieWqAxxKQlYdjNAwonqkFBR3HmzEnp67S0NJiamqF7dw+ZZPBzO3ZsRZMmTdGxY2cAgIdHL9y7dwePHj0EANy8eQP379/D9u17ULp0GQDA1KkzZUbl1q1bDTe3dmjXLnO9aYkSJeHr+xcGDeqHQYOGwNxcdoQWADQ0NODq2hxBQUelsT14cB9Pn0Zg+vRZAAAnpxqwt6+GcuXKAwDMzc2xc+c2hIc/kWnrjz88UKpUztPThw4dQLFiJtJ1lk2aNENg4Dzcv/8PKlWqDEEQsHPnNnTp0g1NmzYDAAwdOgKhoV+/cepz//xzF2Zm5jLTwiVKlISf32TUqVMXAGBmZo6GDRvj9OmTOTUDkUiEadNmomjRogCAkSN9MWyYN54/fyZ9/9u374TmzTPf/z//9MSWLRvw4MF9VK/u9M1rZi1hyGupqanQ0Mi+LEJDQxNpaeJctaGioooRI3zRoUMnaVmXLt0wbJg3YmPfw8QkM9EsX94arq7NFYovLS17fFmj/WKx/PgiIsKRkZGBvn37o1gxE1y+fBFTp05EevonmaUpFhaW2Lp1EyQSidxflHKLiSoREf3SXFzqYtAgHwgCcP/+PcybNxuOjk7o2fNPqKnJ/zEYHv4ETZo0kymrWtVGmqiGhYVBT09PmiQBQJEiRVCqVGnp64cPw3D//j84cGCftEwQBACZN7HIS1SBzOn/nTu34fHjRyhf3gpHjhxC1ao20unjDh0648KFczh8+CBevnyBp08j8Pr1K5QuXVamnZIls0+jZ4mPj8PFi+fRsWMX6fRukyZNsWjRfOzduwuVKlVGQkI83r17h4oVZX8xqFLFBk+fRuTY9ufev38HQ0PZUTkXl3q4d+8uVqxYiufPn+HFi+eIiIiQJqHylCxZSua4jY0tgMzPKesz+Py9BwAdHV2kpaXl6ppRUW/QrVv2G9iybN26C6amZrnq8+c0NTUhFmcf1RSL01CokFau2rCysoaenh42bFiHZ8+eIjLyJR4/zvx/mJEhkdb78vMeOnQwbt++KbfNnj3/RK9efeTGl5WgamkVknvu5s07IJFIpL98lC9vhaioKGzatEEmUTU0NER6ejoSEhKy/R9QBBNVIiL6pnn2bZRm6l9RhQtrS298KVUqM+Hx9h4AVVU1mbWTnxOJRBAEiUzZ50mtmpoqJBLJl6fJEAQB7u490aJF9rWPxsY5J2VWVtawtq6AY8eOoGzZsjhx4hgGDvQB8O+Sg4iIcDRt2gyNGzeFtXUF+PtPzdaOpmbOiVBQ0FF8+vQJ27dvwY4dW2ViPnEiCEOGDJcmsF97H75FJFLJ9j5t2LAWq1evRMuWreHo6IRu3brj/PlzOH78WI7tfHnNrATt85E6eaN2Wb8YfOuaxsZFsWHD1mznZ/na5/U1JiYmOH/+rEzZp0+fkJCQgKJFi+WqjdDQGxg6dBBq1aoDW1s7uLo2R1paKkaPHi5T78vP+6+//JCWJn8WJGuXhmLFTPDuXYzMsZiYGBQuXBg6OjryToWWVvb/V5aWltluAsz63FVUsq9zVQQTVSIi+qa2xSujlXnFX+JmqmrVHNGtmzs2b94AF5e6Musns5Qvb4U7d25L13wCwIMHD6T/LlfOCh8/fsSzZ09RpkzmSGZCQjxevnwhrWNhYYkXL57L3B1+40YIduzYitGjx6JQIfkjVkDmqOqmTRtQrVp1iMViNG7cFADw6NFDXLlyCatWrUeVKlUBAOnpnxAZGYnixUvk+j04dOgALC3LYfLk6TLlt2/fwqxZ03H06GF06tQVJiamuHPnFurVa/DZ+3A/18mqsbEx4uLiZMrWrVuDPn284OHRS1q2adMGAEKO7bx8+QIfP36Ajo4ugMy76YHMrZBy41vXVFNTy5ddCeztHbB48UK8fPlC2n7W0gkbG7tctbFly0Y4OFSHv/9sadmOHdv+/6+c37Nixb6dCNvbO2RbynHjxnXY2NjKTfw/fPiADh1aw8dnOFq1aiMtv3//vswNYwAQGxsLDQ0N6OsbfDOOr1H4K37v3r14+zZvNxsmIiLlpypSgbGmdoH9ycs7/r28BqBkyVKYOXO63C2ZPDx64+zZ09i0aT1evHiBHTu2yaxzrVatOipXroJJkybg3r07ePz4Efz8/kJqaqp0JLJHj144ffokVq9egRcvnuP69auYMuVvfPz44ZvrIV1dWyAuLhYrVy5Do0ZNpdOsRYoYQ1VVDadOncDr16/w4MF9jBs3Bu/fv8txTeGXwsIe4PHjR+jUqQssLcvJ/HFza4/ixUtg37490j7s2rUDBw7sw4sXz7F8+RLcv38vV9cBgMqVqyAq6g0SEhKkZSYmJrh27QqePo3A8+fPsGzZYpw9e1ruFHmW5ORkTJrkh/DwJ7h27Spmz56Jxo2bZtuJISffc83vkZGRgffv3yE1NXMks3LlqrCxscOECWNx//4/uHHjOvz9p6F585bSRDI1NRXv379DRkZGjrGHhz/GrVs38fr1axw6tB8rViwBkPM60tzq1Kkr/vnnHhYvXohnz55i8+aNOHXqpMwNUAkJCdLPT1dXF9WqOWL58sW4fPkSXrx4gQ0b1iIo6Ih0p4ssDx+GoVKlH7vxEfiORHXy5Mm4c+fOD1+YiIiooGhqamLs2Al4+zYKy5Ytzna8dm0XTJo0DQcP7oe7e2ecPXsaf/zhLlPH338OihUzweDB/TF4cH9UrlwVpqam0ruwGzZsjKlT/XHu3Bl0794ZEydOgLNzTfj7z/lmfLq6uqhXryEePLiPNm3aSsuLFi0KP79JuHDhHLp27YCxY0ehaNGi6Nq1O8LC7ueq74cOHYCuri6aNWuR7ZiKigq6dPkD4eFPcOvWTXTs2BmDBg3B2rWr0KNHV0REhKNNG7dcXQcA7O2rQVdXV+bJVX//PQWpqano1csd/ft7Ijz8CXx9/0JcXCyiot7IbcfExBRWVlbo378P/PzGom7depgwYVKu4/iea36Pt2/fomXLpjh5MnMHAZFIBH//2TA3L45Bg7wwbpwvatasjdGj/32wwsmTx9GyZdMcBwG9vAagcuWqGDlyCDw8umL//r0YN+5vaGpqSXdD+F4WFpYICJiHy5cvwsOjGw4c2ItJk6aienUnaZ0xY0ZizJiR0tfjx09Eo0ZNMXPmNLi7d8bJk8cxffos6VZZWUJDQ+DiUv+H4gMAkZC1gCOXmjdvDi8vL7Rr1+6HL/5fkpEhQWxs/q3NUlNTgaGhNuLikpCe/vV1T78i9p/9Z/9/bv+NjLShqip/rCI1NRXh4REwNjaFhkbBPalJmcXHx+Hevbtwdq4JNbXMxPTTp09wdW2AUaPGSO8+/9W9fPkCnTq5YcWKtdIbnL60dOkiPHnyCHPmLPyua6xcuQyHDx/Evn2HfyRUpTZ8uA/GjfsbRYoUKehQ8sSDB/cxeHB/7NlzEPr68p9aJhan4d27KFhaWshd95pF4TWqXbp0wbRp03Dz5k1YW1tDW1s7Wx03NzdFmyUiIvrPUFVVw/jxY9CuXUe0b98R6enp2LRpPdTVNeSuef0VvX79CpcvXwTw9fWQf/zRA127dpBZz0v/unUrFCoqKr9MkgoAW7duRrdu3XNMUhWhcKLq7+8PANixY4fc4yKR6JdMVAVBwKdP8teyiEQimYXlOdXLrAvpb9+f1xUEEcRidXz6JEZ6uvDVujn5/BF2itRNT/+Er42rf3/ddHxtwP7LumKxWKb/n1NTU5Ou+8rISIdEknO7318346t38SpSV1VVVboQPTd1s1bhZGRk4NOn9DxrN6uuRCLJcf0TkDndlxlHwdQVBBEyMrQ+ey0gPT3n9+Hzdr9dVyR9+lBe1lXs6/7rdT//+s/IEHL9df8j3yMUnEyjL+jq6mLOnAVYtmwJ9u/fA5FIBTY2tli8eLncTdJ/RQsXzsP169fwxx89vrp1k76+PkaO9EVg4HzMmbPgJ0b431Clig0CAuYVdBh5JizsAZ4/fwo/v4l50p7CU/+vXr36Zp3ixYt/d0DKKi4uDgsXyp+2KFWqLFq1+ncpxIoVC3P8AWduXgJubp2lr9esWYrUVPl30RYtaoJOnf6943TjxlX48CFRbl1DwyLo1u3fxc9bt65HXNx7uXV1dfXQo4en9PXOnZsREyN/bYyWViH8+ecA6et9+3bg9etIuXXV1NTg5eUjfX3o0F68ePFUbl0AGDjw3601jh8/hCdPHuVYt29fb+kP2VOnjuHhw5zXYvXu3V/61I3z50/h3r3bOdZ1d+8j3abj8uVzuHVL/uPmAKBrVw8YGWXeAHHt2mWEhATnWLdDhz+kmzDfvHkdV65cyLFu27adULp0aRgaauPs2Ys4d+5UjnVbtHBDmTKZd1aGhf2D06eDcqzbtGkrlCtnBQB48uQRjh8/lGPdhg1dUaFC5qL3Z88icOTIvhzrurg0RNWqdgCAV69eYv/+nTnWrVnTBfb2jgCAt2+jsHv3lhzr1qtXD7a2jkhPlyA29h22bduQY107u2qoVSvz6TCJiQnYtGl1jnWrVLFF3bqNAAApKclYu3ZZjnWtrSuhUaPMvTM/ffqElSsDc6xraVkerq6tpa+XLJH/RB1AOb9HaGhIchzt4NQ/EeW3fJv6/zwJTUlJwcePH2FgYCDzmzoRERER0Y9SeEQVAEJCQjBr1izcu3dPOn1kY2ODYcOGwdnZOc+DVAbp6RmIjk6Qeywvpv7V1EQwMNBGfHzSbzn1D0igr19Ipv+f+9Wn/jU01GBoqI137xIhFv9+U/9qaiIUKaKHxMRUpKdLfrup/8+//n/W1L+hYWGoqanKrccRVSLKb/k2ohoaGopevXqhZMmSGDhwIIyNjREdHY3Dhw/D09MTGzduhL29/Q8Fr4xEIlGuR40VGV3OqqumpgINDQ2oq3+CSCQ/+fiednPj8x90eVs39/+91NTUvtn/LKqqalCV//P1B+uqSpOfgqyrrp67p3go0q6Kikqun7VcEHXV1FRk+qLI15sy1AV+7Ovza1//+RVD1i9dRETKTOFEdf78+ahevTpWr14t84Nl8ODB6NOnDwIDA7FmzZo8DZKIiIiIfj8Kb/h/9+5deHh4ZBvJUVFRgbu7Ox8GQERERER5QuFEVVtbO8d1W99ak0hERERElFsKJ6oODg5YsWIFUlJkt0tJTk7GihUrUL169TwLjoiIiIh+XwqvUR0+fDg6dOiARo0aoX79+ihatChiYmJw9uxZpKamYtq0afkRJxERkcIGDOiLmzfl74/8xx894OMz7KfE4ezsgPHjJ6JVqzaYPPlvvHnzGkuXrpRb9/Xr12jfvhUWL16BatV+bPDn3Lkz2LdvDx4+DMOHD4kwMioCJ6ca8PDojZIlS0nrubm1RMuWrdG3b/8c23r+/BlWrlyGGzeu48OHDzA2Loratevgzz+9fqmnKpFyUThRLVOmDHbs2IHAwECcO3cOCQkJ0NfXh5OTEwYPHoxy5crlR5xERETfpVGjJhg+fFS2ci2tQj8thsOHj0NbW+enXQ8A5syZhQMH9sLd3QP9+w+Cvr4+Xr16hS1bNqJ3b3esXLkOZcta5Kqt9+/fo1+/P1G7tgvmzVsEPT19vHjxDIGB8zFwYF9s2rSd+6lTvlA4UV2yZAlcXV0xf/78PAlAIpFg0aJF2LlzJz58+ABHR0f4+fmhZMmS3zzPy8sLtra28Pb2ljl29OhRBAYGIjIyEhYWFvD19UXNmjXzJF4iot+VIMmAJDm2wK6vUtgIIpVc7jf3GU1NLRQpYpwPEeXez77+mTOnsHPnNsyaNQ9169aTlpuamsHBoRq8vHpj1arlmDZtZq7aO336BNLT0zF+/ETp1mbm5uYwNTVD164dcOXKZZnrEOUVhRPV5cuXo3LlyrC0tMyTAJYsWYItW7bA398fpqamCAgIgKenJw4ePAgNDQ2554jFYvj5+eHChQuwtbWVORYcHIxRo0Zh9OjRqF27Nnbt2gUvLy/s27cvz2ImIvrdpNzag8RdIyD5GFNgMajoFIVexzkoZNc+T9sVBAGbNq3H3r278f79e5QqVQrdu3ugWbMW0jrnzp3B+vVrERHxBBKJBGXLWmDAgMFwdq4FAHjx4gXmzp2Ju3fvQhAkqFrVBt7ew1CuXHkAslP/QOYDO2bPnokjRw5BXV0dTZo0hbf3MGhqyn/AwqFD+7Fx43pERb2BqakZ2rfviE6duua4N/H27VtQrVp1ucmjSCTC9OkB0NbWzvV7JBKpIDk5GTdvhsLBoZq0vEyZsti6dZf0kdFEeU3hm6nKlSuHp09zfn67IsRiMdasWQMfHx/Ur18fFSpUwLx58xAVFYXjx4/LPSc0NBTt27dHSEgI9PT0sh1fuXIlGjduDA8PD1haWsLX1xeVK1fG+vXr8yRmIqLfUcI27wJNUgFA8jEGCdu8v11RQcuWLcKePbswYsRobN68HV26dMOsWTOwa9cOAEBY2H2MHTsKTZu6YsuWnVi1aj0MDY0wadIE6dPAJkwYg6JFi2Ht2k1YvXoDVFRUMWbMiByveefOLcTFxWLVqnWYMGEiTp8+hcWLF8qtu2/fbixcOB+env2wZctO9Os3CBs2rMuxfnp6Ou7cuQ1Hxxo5Xr9o0aIoXLhwbt8iNGniChMTUwwc2BceHt2wYMFcnDt3BklJSShb1kKhtogUofCIaoMGDTB37lxcuHAB1tbW2f5zikQiDBo0KFdthYWFISkpSWZaXk9PD5UqVcL169fRqlWrbOecO3cOLi4uGDRoENq0aSNzTCKRIDQ0FGPGjJEpr1GjRo6JLxER/dqCgo7izJmTMmW2tvaYP38RUlJSsG3bFkyePB21a7sAAEqUKIk3b15j06b16NixM1RUVDFihC86dOgkPb9Ll24YNswbsbHvYWJiilevIuHk5AxzczOoqalj/Pi/8ezZM0gkErmjnsbGxvDzmwxNTU1YWFjCy2sA5syZiYEDB2eru2bNKvz5pyeaNHEFABQvXgLJyR8REOAPL68B2UZh4+PjIJFIYGBgKFM+e7Y/Dh8+KFN25sylXL2H+vr6WLduM7Zu3YQzZ05h69ZN2Lp1EzQ1tdCzZ2/8+WffXLVDpCiFE9VFixYBAC5duoRLl7L/B1ckUY2KigIAmJmZyZQXK1ZMeuxLw4blfIdmYmIikpOTYWoqOwXxtfaIiOjb9LsGKs3Uv6JcXOpi0CAfmTJNzcxniz99GoG0tDT4+Y2Disq/j5XNyMiAWCxGamoqrKysoaenhw0b1uHZs6eIjHyJx48f/r9e5iNv+/cfhHnz5mD37p1wcKgGZ+daaNq0WY5T8xUqVJJJMCtXroJPnz7hxYsX0NHRlZbHxcUhOvotli5dhOXLl0jLJRIBaWlpeP36VbYbovT1DSASiZCYmChT3qdPP3Tp8gcA4OzZ0zmOyOZEX18f/fsPQv/+g/DuXQyuX7+GAwf2YsWKpdDXN5BJ5InyisKJ6v3793P9bO9vydqL9cu1qJqamkhISFC4vdTU1BzbS0tL+84o/6Wmljf9lkdVVUXm798N+8/+f/737+a/0P9Cdu2hZdP2P3kzVeHC2jJbMX1OIslMNKdN80fp0mWyHdfQ0EBo6A0MHToItWrVga2tHVxdmyMtLRWjRw+X1uvYsQsaNmyCy5cvIiTkGlasWIq1a1dhw4atcrdu+vLpjllxfHnnfFb5kCEj4OjolK0dU1OzbGXq6uqoWLEyQkND4OHRS1puaGgIQ0PD///bSN7bkaMNG9bBzMxMOqprbFwUzZu3hKtrc3h69sLlyxeYqFK+UDhRbdOmDUaMGIEGDRr88MW1tDJ/oxWLxdJ/A0BaWhoKFVJ825Cs307FYrFM+fe29zkVFREMDXO/8Px76en9vO1SlBH7z/7/zpS9/yIVVajqFC3oMPJUmTJloKqqhqioKNSpU1davn37Vjx7FgFf33HYsmUjHByqw99/tvT4jh3b/v8vAbGxsVizZgU8PHqjVas2aNWqDaKjo9GmTTPcvHkDjRs3zXbdhw/DZJYF3L59C5qaWihevATevXsnrWdkZARDQ0O8ehWJ9u07SstPnAjCuXNn4Oc3WW6/unXrDj+/vxAcfFl6w9fnoqPfKvQ+/fPPXQQFHUGDBo2gpvZv6qCiogJtbW0YGXEfVcofCieqb968+eGkL0vWlH90dDRKlfr3t93o6GhYW1sr3J6BgQEKFy6M6OhomfLo6GiYmJj8UKwSiYDExOQfauNrVFVVoKdXCImJKdKppN8J+8/+s/8/t/96eoWUegT3Z9HR0UW7dh2wYsUSaGtrw8bGFqGhIVi8eAE8PHoDAExMTHD+/FncunUTxYqZIDT0OlasyJyGF4vFKFbMBJcuXURkZCQGDvSGtrY2Dh8+CHV1dVSoUFHudaOj32Lq1Elwd/fAs2fPsGrVMri7e2SbERSJRHB374XlyxfD1NQUNWvWxpMnjxEQMAMuLvVy3B2nSRNXPHhwH6NGDUOXLn+gYcPGMDQ0xMuXL7F//x6cOnUC1as7ypwTGfkSV67ILunT1NSCg0M19Onjhf79+2Do0EHo0aMXSpUqjXfvYnD69Cn8889dDBs28rvef6JvUThRbd26NdatWwcLCwsUK1bshy5eoUIF6Ojo4OrVq9JENTExEffv34e7u7vC7YlEIjg4OODatWvo1OnfKYirV6/myaNd09Pz/wdIRobkp1xHWbH/7D/7//v2v6AMHToChoaGWLFiKd69i4GJiQn69u0Pd/eeAAAvrwF4//49Ro4cAgAoW9YC48b9jYkTJ+DBg39QpkxZzJ27EIGB8zF4cH+kpaWifHkrzJmzECVKyN8T3MWlHlRVVdGnjwe0tAqhfftOOd6Q1L17D2hqamLnzm1YsGAuihQxRtu27b/6FCkA8PEZhho1amLv3l3w9R2OuLg46OsboEqVqggImAcXF9mtq4KCjiIo6KhMmampGfbtOwwrK2usXr0Ba9asxNSpExEXFwdtbR3Y2ztgxYq1sLDg9o+UP0SCIAiKnNCrVy+EhIQgIyNDOoIp06BIhJMnT+Zwdnbz5s3Dtm3bMH36dBQvXhwBAQGIjIzEoUOHoKKigtjYWOjq6sosDcjSsGFDtGvXTmbD/4sXL8LLywujRo1C3bp1sXv3bmzevBl79uz5oX1UMzIkiI1N+u7zv0VNTQWGhtqIi0v6LX9Qsf/sP/v/c/tvZKSd44hqamoqwsMjYGxsCg0N+ft6EhH9CLE4De/eRcHS0kJujpdF4RFVMzMztG7d+oeC+5yPj8//n3YxHqmpqXB0dMTq1auhrq6OyMhINGrUCDNmzED79rnb4LlOnTqYPn06lixZgnnz5qFcuXJYtmwZN/snIiIi+o9ReET1d8UR1fzF/rP/7D9HVIno95FvI6pZwsPDcenSJURHR6NHjx54+fKldM0pEREREdGPUjhRlUgk8PPzw+7duyEIAkQiEZo3b44lS5bgxYsX2LRpU7YN94mIiIiIFKXw3iRLlizBwYMHMXXqVFy6dAlZKwdGjRoFiUSCefPm5XmQRERERPT7UThR3b17N3x8fNChQwcYGBhIyytWrAgfHx+5j1UlIiIiIlKUwonqu3fvULGi/A2MTUxMsj1bmIiIiIjoeyicqJYuXRrnzp2Te+zatWsoXbr0DwdFRERERKTwzVQ9e/aEn58fPn36hAYNGkAkEuH58+e4evUq1qxZgzFjxuRHnERERET0m1E4Ue3UqRNiY2OxdOlSbN26FYIgYPjw4VBXV4enpye6deuWH3ESERER0W/mu/ZR7devH7p3746bN28iPj4eenp6sLW1lbm5ioiISBkIgoDDhw/i8OGDePo0HElJSTAxMUHt2i7w8OiNIkWMpXWdnR0wfvxEtGrVJsf2/vnnHtasWYG7d+8gNTUVJiamaNCgEXr2/BPa2tpfjeXBg/uYNWs6Vq/eABUVhVffKeTUqRNYuXIZ3rx5jdKly8DbeygcHWvkWD86Ohpt2jTLVp71fpw/fxaHDh3ArFlz8zNsIhnfveG/jo4OXFxc8jIWIiKiPCWRSDBmzEjcuhWKnj37YNSoMShcuDAiIiKwdu0q9OrljvXrt8DIyChX7UVEhGPgQC906tQFAwZ4o3Dhwnj4MAwLFszBP//cxeLFK3I8Nz39E6ZOnYjhw0fne5J648Z1/P33OHh7D0ONGs44eHAfRowYgvXrt6BsWQu55zx58hiamprYvfsARCKRtFxbO/NBPnXr1sfWrZsRFHQUrq7N8zV+oizfnagSEdHvRZAIkKSmF9j1VbTUIFIRfbviZ7Zu3YxLly5i9er1qFDh3x1rTE3N4OBQDX/80QmbN2+At/fQXLV36NABlCxZEoMHD5GWmZsXh5aWFoYN88bjx49QvryV3HOPHTsCDQ0NVKtWXaE+fI8NG9aiXr0G6NIlczmet/cw3LlzG9u3b8GYMePlnhMe/hglS5aCsXHRHNvt3r0H5s4NQOPGTaGqqpovsRN9jokqERF908ew93h/8hkykj8VWAyqhdVRpHEZ6FQokqv6giBg585taN68hUySmkVLSwuLFy+Xmfr/FpFIhDdv3uDp0wiZkUlHxxrYunUXzM2L53ju5s0b0bJla5my/fv3YseOrYiMfAmRSARr6woYOnQkKlasBABwc2uJhg0b4/Lli4iLi8OMGQGYPNkPUVFv5F5j/PiJaNGiFe7cuY0hQ4bLHKtWzRFnzpzKMb4nTx6jTJmyX+2/s3NNfPz4AWfPnkajRk2+WpcoLzBRJSKib3oXFAFJWkaBxpCR/AnvgiJynai+fv0KUVFvvrou08zMXKEY3Nza4+DB/fjjj06oUqUqHByqwd4+809OU+oA8OLFCzx9GoHatf9dMnf27GnMmTMTY8dOgJ2dPd6/f4c5c2Zh+vTJ2Lhxm7Terl3bMXv2Aujq6sLSshzWrt0EiUT+Z6GtrYMPHz4gJSUFxYrJPs7c2LgooqPf5hhjePgTGBgYoH//Pnj+/DlKliyF3r37oGbN2tI6amrqcHJyxvnzZ5mo0k/BRJWIiH5JsbHvAQAGBoYy5SNGDEFoaIj0tampGbZu3ZWrNkuWLIWNG7diy5aNOH/+HNavX4v169dCV1cXgwYNgZtbe7nn/fPPHairq6NUqX/3GtfX18dff/mhWbMWADKT5tat3TB7tr/MuTVr1oaT07/JtoaGxldjTExM+H89dZlyTU0NiMViueekp6fj+fNnUFGxwJAhw6GtrY3jx4MwfLgPFi5cIpPsW1iUw+HDB74aA1FeyVWi+vr1a4UaNTdX7DdUIiJSbsauFkoz9Z9b+vqZCWpW4pZlzJjxSE1NAQDs2LENFy7If4hNTkxMTDFs2CgMGzYKr1+/wrVrwdi9eyf8/aeiWDET1KpVO9s579+/h56evsy6Tnv7anj6NAJr1qzEs2fPEBn5Ak+ePIZEIpE5t2TJUjKvu3XrmOPUv6/vONSsWQsAIBbLflZpaWJoaRWSe56amhqCgs5ARUUFWlpaAIAKFSohIiIcmzdvlElUDQ0N8P79u5zeHqI8latEtWHDhjJ3AH7LgwcPvjsgIiJSPjoVikDbyug/dTNV8eLFYWxsjNDQG2jSxFVaXrTovzcL6enpKRRDYOB8ODvXlCZu5ubF4ebWAS1atEbHjm1x+fIFuYmqSKSSbbo+KOgoJk/+G66uzWFjY4N27dojPDw824iqpqamzOu5cxciPV3+52BkVASFCxdGoUKF8O5djMyxd+9iZPr+pcKFC2crs7S0RHDwFZmyjAxJvu9aQJQlV4nq9OnTpYlqQkICZs+ejZo1a6J58+YoWrQo4uPjcfr0aZw9e5ZPpiIi+kWJVERQLaz+7YpKQlVVFZ07d8Pq1SvRvn1HuXfjf23NpjzXr19DRER4tnWvGhoa0NTUhJGR/PWzxsbGSExMhETyb5K3YcNatGnjBl/fv6T1zp/PHN0VBCHHAaLcrKu1tbVDaGgI2rRxk5bduHEd9vYOcutHRITD07MXAgLmyexKcP/+/Wxrb+PiYr+6MwBRXspVotq+/b9rbgYNGgQ3NzdMnTpVpk7r1q0xbdo0HD16FF26dMnbKImIiL6Du3tPPHwYhn79+sDDoxdq13aBtrYOwsMfY+fO7bh2LRitW7eVOSc8/AmuXLkkU6anp4/KlatgwIBBGDlyGMaN80XHjp1hamqGN2/e4ODBfUhOTkbbtvLXqFauXAUZGRl4/PgRrK0rAMhcQnDnzi2EhT2Ajo4OLlw4h127tgMAxGJxtpFURXTr5o7hw31gZVUBtWrVxsGD+/Ho0SOMG/e3tE5cXBzU1dWgo6OLMmXKokyZMpg92x++vn/BwMAQ+/btwT//3MXatZtk2n74MAyVK1f57tiIFCESBEFQ5AQ7OzssXrwYtWtnn9q4dOkSBg4ciNu3b+dZgMoiI0OC2NikfGtfTU0FhobaiItLQnq65Nsn/GLYf/af/f+5/Tcy0oaqqvzp29TUVISHR8DY2BQaGt+fLCmTU6dO4ODB/Xj4MAwfPiSiSBFj2NnZw82tPeztq0nrOTvLH3G0t6+GpUtXAgDu3LmNjRvX4d69u/jwIRF6evpwdq4JT8/+X71Ho3v3zmjRojW6d+8BIHNXghkzpuKff+5CXV0D5cuXR9u27TFhwlgsW7YKdnYOcHNriZYtW6Nv3/4K9/no0UNYvXolYmKiUaZMWXh7D0X16k7S425uLeHgUB1+fpMAZK6jXbo0EFeuXMbHjx9gZVUBgwZ5w87u3/ckPf0TWrRoijFjxqNhw0YKx0SURSxOw7t3UbC0tJCui5ZH4US1QYMG6Ny5MwYMGJDt2Jw5cxAUFITjx48rHrGSY6Kav9h/9p/9Z6L6q9u3bw927tyGzZt3FHQo3+306ZNYtGgBduzYAzW1/84yEFI+uU1UFd6eqlOnTli8eDFSU1NRv359GBoa4t27dzh27Bi2bt2Kv/7669uNEBER/WZatWqNzZs34OrVYNSo4VzQ4XyXbdu2oE8fLyap9NMonKgOGDAAHz58wOrVq7FiReYzjQVBgJaWFoYMGYLu3bvneZBERET/dWpq6vj778kICPCHo6PTf+7O+bNnT0NXVyfb07WI8pPCU/9ZPnz4gFu3biEhIQGGhoawt7eXu7XFr4JT//mL/Wf/2X9O/RPR7yPfpv6zaGtro2jRohAEAba2thCLxb90okpEREREP9d3Jar79+/HnDlzEBMTA5FIhJ07dyIwMBDq6uqYM2fONx/vRkRERET0LQovkDly5Ah8fX3h7OyMuXPnSh/11qRJE5w7dw5LlizJ8yCJiKggfNfKMCKiXMjd9xeFR1SXLVuGrl27YuLEicjI+PdxcB06dEBsbCx27NiBoUOHKtosEREpCXV1dYhEQFpaGjQ0cl47RkT0vdLS0iASZX6/+RqFE9WnT5/C19dX7jFbW1sEBgYq2iQRESkRVVVVGBgYIC4uHkDWs+blP86TiEgxAtLS0vDhQzwMDQ2gqqr61doKJ6pFihRBeHi43CdThYeHo0gR+c85JiKi/w4zMzMAQHx8PD58KOBgiOiXIhIBhoYG0u8zX6NwotqiRQssXLgQxYoVQ7169f5/QRHu3buHJUuWoFWrVopHTERESkUkEsHc3BwmJib49OlTQYdDRL8QdXX1b46kZlE4UR06dCgePXqEoUOHSjcr7tGjB5KTk1G9enUMGTJE0SaJiEhJqaqq5voHChFRXlM4UdXQ0MCqVatw6dIlBAcHIz4+Hrq6unByckK9evUgEnEdExERERH9OIUT1T59+sDT0xO1a9eWu06ViIiIiCgvKLyPamhoKEdNiYiIiCjfKZyouri44MCBA1xcT0RERET5SuGpf01NTRw4cABHjx6FpaUlChcuLHNcJBJh/fr1eRYgEREREf2eFE5Uo6KiYG9vL30tCLKPwPryNRERERHR91A4Ud24cWN+xEFEREREJEPhNapfk5ycjPPnz+dlk0RERET0m1J4RPXVq1eYOHEirl27BrFYLLfOgwcPfjgwIiIiIvq9KZyozpgxA6GhoejUqRNCQ0NRqFAh2NnZ4dKlS3j06BECAwPzI04iIiIi+s0oPPV//fp1DBs2DOPHj0f79u2hqamJUaNGYffu3XB0dMSpU6fyI04iIiIi+s0onKgmJSXB2toaAGBhYYH79+8DyHwe9B9//IHg4OC8jZCIiIiIfksKJ6rFihXDu3fvAAClS5dGQkICYmJiAAAGBgZ4//593kZIRERERL8lhRPVevXqYf78+bh58yaKFy8OU1NTrFmzBh8/fsTu3bthYmKSH3ESERER0W9G4UTVx8cHenp6WLBgAQBg2LBhWL9+PRwdHXHw4EH07t07z4MkIiIiot+Pwnf9GxoaYufOnYiOjgYAtGnTBubm5rh16xZsbGzg5OSU50ESERER0e9H4UQ1S7FixaT/rl69OqpXr54nARERERERAd+RqI4dO/abdWbMmPFdwRARERERZVE4Ub169Wq2suTkZMTHx8PAwABVq1bNk8CIiIiI6PemcKJ6+vRpueXh4eEYPHgw3NzcfjQmIiIiIiLF7/rPiaWlJby9vbFo0aK8apKIiIiIfmN5lqgCgI6ODl69epWXTRIRERHRb0rhqf/Xr19nK8vIyMDbt2+xcOFCWFpaKtSeRCLBokWLsHPnTnz48AGOjo7w8/NDyZIl5daPi4vD1KlTcf78eYhEIrRs2RKjR49GoUKFpHWaNm2K58+fy5zXrl07+Pv7KxQbERERERUchRPVhg0bQiQSZSsXBAFaWloKT/0vWbIEW7Zsgb+/P0xNTREQEABPT08cPHgQGhoa2er7+PggJSUF69atQ2JiIsaNG4fk5GTMnDkTQOaNXS9fvsTy5ctRuXJl6XlaWloK9pSIiIiICpLCier06dOzJaoikQg6OjqoUaMGdHV1c92WWCzGmjVrMHLkSNSvXx8AMG/ePLi4uOD48eNo1aqVTP2bN2/i2rVrOHLkiHTkdvLkyfD09MTw4cNhYmKCJ0+eQCKRwN7eHvr6+op2j4iIiIiUhMKJavv27fPs4mFhYUhKSkLNmjWlZXp6eqhUqRKuX7+eLVENCQlB0aJFZZYXODk5QSQS4caNG2jRogUePnwIY2NjJqlERERE/3EKJ6r79u1TqP7XtquKiooCAJiZmcmUFytWTHrsc2/fvs1WV0NDAwYGBnjz5g0A4OHDhyhcuDB8fHwQGhoKQ0NDdOjQAR4eHlBRydN7x4iIiIgoHymcqI4bNw6CIEj/ZMlaDvBl2dcS1ZSUFADIthZVU1MTCQkJcuvLW7eqqamJtLQ0AMDjx4+RmJgIV1dXDBo0CDdu3EBAQAASEhIwZMiQ3HdUDjW1/Et0VVVVZP7+3bD/7P/nf/9ufvf+ExHlROFEdfPmzRgwYAB69uyJNm3awMTEBPHx8Th9+jRmzZoFX19fman8r8m6wUksFsvc7JSWliZzF//n9cVicbbytLQ0FC5cGACwcuVKpKWlSdfKWltb4+PHj1i6dCm8vb2/e1RVRUUEQ0Pt7zpXEXp62fv9O2H/2f/f2e/efyKiLymcqE6ePBk9e/ZE//79pWVFihRBp06dkJqaivXr16Njx465aitrGj86OhqlSpWSlkdHR8Pa2jpbfVNTU5w8eVKmTCwWIz4+HsWKFQOQOTr75airlZUVkpOTkZCQAENDw9x19AsSiYDExOTvOjc3VFVVoKdXCImJKcjIkOTbdZQV+8/+s/8/t/96eoU4gktESk/hRDU8PBxVqlSRe6x06dJ48eJFrtuqUKECdHR0cPXqVWmimpiYiPv378Pd3T1bfUdHR8yePRvPnz9H6dKlAQDXrl0DAFSrVg2CIKBJkyZwc3PD4MGDpefdvXsXRYsW/e4kNUt6ev7/AMnIkPyU6ygr9p/9Z/9/3/4TEX1J4V+nS5cujf3798s9tn37drkjoTnR0NCAu7s7Zs+ejVOnTiEsLAzDhg2DqakpmjZtioyMDMTExCA1NRUAYGtrCwcHBwwbNgx37txBcHAw/Pz84ObmBhMTE4hEIjRp0gSrV6/GkSNH8OLFC2zfvh2rVq2Cj4+Pol0lIiIiogKk8IjqwIEDMXToUDx79gyNGjWCkZER3r17h+PHjyM8PBxr1qxRqD0fHx+kp6dj/PjxSE1NhaOjI1avXg11dXVERkaiUaNGmDFjBtq3bw+RSIRFixZh0qRJ6NmzJzQ1NdGsWTOMHTtW2t6IESOgo6ODuXPnIioqCiVKlMC4cePQuXNnRbtKRERERAVIJHx+m34unTp1CosXL8b9+/cBACoqKrC3t8fw4cNRrVq1PA9SGWRkSBAbm5Rv7aupqcDQUBtxcUm/5dQf+8/+s/8/t/9GRtpco0pESk/hEVUAaNSoERo1aoTU1FQkJCRAX1+fjyglIiIiojz1XYnqx48fkZSUBBMTE6iqqmLjxo14/fo1XF1d4ejomNcxEhEREdFvSOF5n9u3b6NBgwbYtGkTAGDq1KmYNWsWDhw4gJ49e+LUqVN5HiQRERER/X4UTlTnz58PS0tLdO7cGSkpKdi/fz/++OMPXLt2DR07dsSyZcvyI04iIiIi+s1814jqgAEDULJkSVy6dAlpaWlo27YtAKBFixZ4/PhxngdJRERERL8fhRNVFRUVaGpqAgAuXLgAPT092NjYAMhcu8qbqoiIiIgoLyh8M1WVKlWwc+dOaGlp4dixY6hfvz5EIhHev3+PlStX5vjUKiIiIiIiRSg8ojpq1ChcvnwZXbt2haqqKgYMGAAAaNWqFZ49e4ahQ4fmdYxERERE9BtSeES1cuXKOHHiBMLDw1G+fHkULlwYADBx4kQ4ODigaNGieR4kEREREf1+vmsfVR0dHdja2sqUubq65klARERERETAd0z9ExERERH9DExUiYiIiEgpMVElIiIiIqXERJWIiIiIlBITVSIiIiJSSkxUiYiIiEgpMVElIiIiIqXERJWIiIiIlBITVSIiIiJSSkxUiYiIiEgpMVElIiIiIqXERJWIiIiIlBITVSIiIiJSSkxUiYiIiEgpMVElIiIiIqXERJWIiIiIlBITVSIiIiJSSkxUiYiIiEgpMVElIiIiIqXERJWIiIiIlBITVSIiIiJSSkxUiYiIiEgpMVElIiIiIqXERJWIiIiIlBITVSIiIiJSSkxUiYiIiEgpMVElIiIiIqXERJWIiIiIlBITVSIiIiJSSkxUiYiIiEgpMVElIiIiIqXERJWIiIiIlBITVSIiIiJSSkxUiYiIiEgpMVElIiIiIqXERJWIiIiIlBITVSIiIiJSSkxUiYiIiEgpMVElIiIiIqXERJWIiIiIlBITVSIiIiJSSkxUiYiIiEgpMVElIiIiIqVU4ImqRCLBwoUL4eLiAjs7O/Tt2xcvX77MsX5cXBxGjBgBR0dHODk5YdKkSUhJSZGpc/ToUbRo0QI2NjZwc3PDlStX8rsbRERERJTHCjxRXbJkCbZs2YIpU6Zg27ZtkEgk8PT0hFgsllvfx8cHz58/x7p167BgwQKcO3cOEydOlB4PDg7GqFGj0LVrV+zduxc1a9aEl5cXwsPDf1KPiIiIiCgvFGiiKhaLsWbNGvj4+KB+/fqoUKEC5s2bh6ioKBw/fjxb/Zs3b+LatWuYOXMmKleujJo1a2Ly5MnYv38/3r59CwBYuXIlGjduDA8PD1haWsLX1xeVK1fG+vXrf3b3iIiIiOgHFGiiGhYWhqSkJNSsWVNapqenh0qVKuH69evZ6oeEhKBo0aKwtLSUljk5OUEkEuHGjRuQSCQIDQ2VaQ8AatSoIbc9IiIiIlJeBZqoRkVFAQDMzMxkyosVKyY99rm3b99mq6uhoQEDAwO8efMGiYmJSE5Ohqmpaa7aIyIiIiLlpVaQF8+6CUpDQ0OmXFNTEwkJCXLrf1k3q35aWhpSU1NzbC8tLe2H41VTy7+8XlVVRebv3w37z/5//vfv5nfvPxFRTgo0UdXS0gKQuVY1698AkJaWhkKFCsmtL+8mq7S0NBQuXBiamprS9r48Lq89RaioiGBoqP1DbeSGnt6Pxflfx/6z/7+z373/RERfKtBENWsaPzo6GqVKlZKWR0dHw9raOlt9U1NTnDx5UqZMLBYjPj4exYoVg4GBAQoXLozo6GiZOtHR0TAxMfmhWCUSAYmJyT/UxteoqqpAT68QEhNTkJEhybfrKCv2n/1n/39u//X0CnEEl4iUXoEmqhUqVICOjg6uXr0qTVQTExNx//59uLu7Z6vv6OiI2bNn4/nz5yhdujQA4Nq1awCAatWqQSQSwcHBAdeuXUOnTp2k5129ehXVq1f/4XjT0/P/B0hGhuSnXEdZsf/sP/v/+/afiOhLBZqoamhowN3dHbNnz4aRkRGKFy+OgIAAmJqaomnTpsjIyEBsbCx0dXWhpaUFW1tbODg4YNiwYZg4cSKSk5Ph5+cHNzc36Yhp79694eXlhUqVKqFu3brYvXs3Hjx4gGnTphVkV4mIiIhIQQU+7+Pj44OOHTti/Pjx6NatG1RVVbF69Wqoq6vjzZs3qFOnDo4cOQIAEIlEWLRoEUqUKIGePXti6NChqFu3rsyG/3Xq1MH06dOxdetWtGvXDsHBwVi2bJnMllZEREREpPxEgiAIBR3Ef0FGhgSxsUn51r6amgoMDbURF5f0W079sf/sP/v/c/tvZKTNNapEpPT4XYqIiIiIlBITVSIiIiJSSkxUiYiIiEgpMVElIiIiIqXERJWIiIiIlBITVSIiIiJSSkxUiYiIiEgpcR/VXBIEARJJ/r5Vqqoqv+VzzrOw/+w/+//z+q+iIoJIJPpp1yMi+h5MVImIiIhIKXHqn4iIiIiUEhNVIiIiIlJKTFSJiIiISCkxUSUiIiIipcRElYiIiIiUEhNVIiIiIlJKTFSJiIiISCkxUSUiIiIipcRElYiIiIiUEhNVIiIiIlJKTFSJiIiISCkxUSUiIiIipcRElYiIiIiUEhPVn0QikWDhwoVwcXGBnZ0d+vbti5cvX+ZYPy4uDiNGjICjoyOcnJwwadIkpKSk/MSI81Z8fDz8/PxQt25dODg4oFu3bggJCcmx/tKlS2FtbZ3tz3/V27dv5fZnz549cuv/Sp//1atX5fbd2toajRo1knvOjRs35Na/evXqT47+xy1fvhw9evSQKXvw4AHc3d1hZ2eHhg0bYsOGDd9s5+jRo2jRogVsbGzg5uaGK1eu5FfIRERKQ62gA/hdLFmyBFu2bIG/vz9MTU0REBAAT09PHDx4EBoaGtnq+/j4ICUlBevWrUNiYiLGjRuH5ORkzJw5swCi/3HDhw9HTEwM5s6diyJFimDjxo3o06cP9u7dCwsLi2z1Hz58iLZt22LUqFEFEG3eCwsLg6amJk6ePAmRSCQt19XVlVv/V/r87e3tcfHiRZmyW7duwdvbGwMHDpR7zsOHD1GqVCls2bJFplxfXz/f4swPmzdvxvz581G9enVpWVxcHHr37o2GDRti0qRJuHXrFiZNmgRtbW106NBBbjvBwcEYNWoURo8ejdq1a2PXrl3w8vLCvn37YGlp+bO6Q0T08wmU79LS0gR7e3th8+bN0rKEhATBxsZGOHjwYLb6oaGhgpWVlfDkyRNp2YULFwRra2shKirqp8Scl549eyZYWVkJISEh0jKJRCI0btxYmD9/vtxzmjdvLqxdu/YnRZj/VqxYIbRu3TpXdX+1z/9LSUlJQoMGDYQxY8bkWOfvv/8W+vfv/xOjyltRUVFCv379BDs7O6FZs2aCu7u79NiyZcuEOnXqCJ8+fZKWzZkzR2jatGmO7f3555/CkCFDZMq6dOkiTJgwIc9jJyJSJpz6/wnCwsKQlJSEmjVrSsv09PRQqVIlXL9+PVv9kJAQFC1aVGakxMnJCSKRCDdu3PgpMeclQ0NDrFixAlWrVpWWiUQiiEQiJCYmZqsvFovx7NkzuSOt/1UPHz7M9cjXr/b5f2nZsmVISUmBr69vjnUUeb+U0T///AN1dXUcOHAAtra2MsdCQkLg5OQENbV/J7ScnZ3x7NkzvHv3LltbEokEoaGhMt8/AKBGjRpyv38QEf1KmKj+BFFRUQAAMzMzmfJixYpJj33u7du32epqaGjAwMAAb968yb9A84menh7q1asns8QhKCgIz58/h4uLS7b6T548QUZGBoKCguDq6or69etj1KhRiI6O/plh56lHjx4hNjYW3bt3R61atdCtWzecP39ebt1f7fP/XGxsLNatW4f+/fvDwMAgx3qPHz9GREQE2rdvj9q1a6N37964c+fOzwv0BzVs2BCBgYEoWbJktmNRUVEwNTWVKStWrBgAyP18ExMTkZycLPcced8/iIh+JUxUf4Ksm2C+XIuqqamJtLQ0ufXlrVvNqf5/TWhoKMaOHYumTZuifv362Y4/evQIAFCoUCEsWLAA06ZNQ0REBDw8PJCamvqTo/1x6enpiIiIQEJCAry9vbFixQrY2dnBy8tL7g0xv/Lnv2XLFujq6qJLly451nnz5g0+fPiA5ORkjB8/HkuWLIGxsTHc3d3x5MmTnxht/khNTZX7vQCA3M836/98br9/EBH9Sngz1U+gpaUFIHNKO+vfQOYPpUKFCsmtLxaLs5WnpaWhcOHC+RfoT3Dy5EmMHDkSDg4OmD17ttw6bm5uqFu3LoyMjKRl5cuXR926dXH69Gm0aNHiZ4WbJ9TU1HD16lWoqqpKP/8qVarg8ePHWL16dbYp3V/589+3bx/c3Nxkvg6+ZGZmhuvXr6NQoUJQV1cHAFStWhX379/Hxo0bMWnSpJ8Vbr6Q9/lmJZzyPt+sJFbeOfK+fxAR/Uo4ovoTZE3jfjl1HR0dDRMTk2z1TU1Ns9UVi8WIj4+XThH+F23atAne3t5o0KABli1bJv0BLM/nSSqQOc1pYGDwn53q1NbWzpaclS9fHm/fvs1W91f9/MPCwvDy5Uu0bt36m3X19PSkSSoAqKiowNLSUu779V8j7/PNei3v+4GBgQEKFy6c6+8fRES/EiaqP0GFChWgo6MjswdkYmIi7t+/D0dHx2z1HR0dERUVhefPn0vLrl27BgCoVq1a/gecD7Zs2YIpU6age/fumDt3rtyp7Szz5s2Dq6srBEGQlkVGRiIuLg7lypX7GeHmqcePH8PBwSHbHqD37t2T259f8fMHMm8iKlKkCCpUqPDVeufPn4e9vb3MPsPp6ekICwv7T37+X3J0dMSNGzeQkZEhLQsODkbZsmVRpEiRbPVFIhEcHByk/weyXL16VWbbKyKiXxET1Z9AQ0MD7u7umD17Nk6dOoWwsDAMGzYMpqamaNq0KTIyMhATEyNdi2ZrawsHBwcMGzYMd+7cQXBwMPz8/ODm5vafHEF5+vQppk+fjiZNmqBfv3549+4dYmJiEBMTgw8fPkAsFiMmJkY6tdmkSRO8evUKEydOxNOnT3H9+nV4e3vDwcFB7s1Xys7S0hIWFhaYPHkyQkJCEB4ejhkzZuDWrVsYMGDAL//5Z7l//36OD22IiYlBUlISAMDBwQGGhobw9fXFvXv38PDhQ/j6+iI+Ph69evX6iRHnjw4dOuDjx48YN24cnjx5gj179mDdunXo16+ftM6HDx8QGxsrfd27d28cPnwYa9euRXh4OGbNmoUHDx6gZ8+eBdEFIqKfp6D3x/pdpKenC7NmzRKcnZ0FOzs7oW/fvsLLly8FQRCEly9fClZWVsLu3bul9d+9eyd4e3sLdnZ2Qo0aNYS///5bSE1NLajwf8jSpUsFKysruX98fX2F4OBgwcrKSggODpaec/nyZaFLly6CnZ2d4OTkJIwdO1aIj48vwF78mJiYGGHMmDFC7dq1hapVqwpdunQRrl+/LgjCr//5Z/H09BSGDh0q95iVlZWwcOFC6evnz58L3t7egpOTk2Brayv8+eefwsOHD39WqHnK19dXZh9VQRCE27dvC507dxaqVKkiNGjQQNi4cWO2cxo0aCBTtnfvXqFJkyZC1apVhXbt2gmXL1/O99iJiAqaSBA+m18lIiIiIlISnPonIiIiIqXERJWIiIiIlBITVSIiIiJSSkxUiYiIiEgpMVElIiIiIqXERJWIiIiIlBITVaJvyM8d3Lg7HBERUc6YqNJP07BhQ4wZM6agw1DI48eP0a1btzxvNzExEaNHj0ZISEiet61sIiMjYW1tjT179hR0KERE9B+jVtAB0O9j0aJF0NHRKegwFHLs2DHcvHkzz9t98OAB9u/fjw4dOuR520RERL8KJqr001SqVKmgQyAiIqL/EE7900/z+dR/1nTw0aNH4ePjA3t7ezg5OWH8+PFITk7+ZlsREREYPHgwnJyc4OjoiH79+iE8PFx6/MOHD5gxYwYaN26MqlWrolWrVti1a1e2eBYuXIiZM2eiVq1asLGxQZ8+ffDs2TMAQGBgIBYtWgQAsLa2RmBgIABAIpFgxYoVaNKkCapUqQJXV1ds3LhR2u69e/dQuXJlmWUO79+/R82aNdG7d28EBwfDw8MDAODh4YEePXrk2M+0tDTMmjUL9erVQ5UqVdC6dWscOXJEevzUqVMysQFAeHg4bGxs8Ndff0nLTp48iT/++AP29vaoUqUKmjVrhs2bN0uPX716FdbW1rhy5Qp69OgBGxsb1K9fHzt37kR0dDQGDx4Me3t71KtXD+vWrct23sWLF9G9e3fY2NigadOm2LJlS84fHoDXr19j+PDhcHJygq2tLXr27In79+/L1Dl06BDatGkDGxsbODs7Y+TIkXj79u1X2yUiol+MQPSTNGjQQPD19RUEQRBevnwpWFlZCY6OjoK/v79w+fJlYdmyZYK1tbUwe/bsr7YTFRUlVK9eXWjZsqVw+PBh4cyZM0L79u2F2rVrC3FxcUJKSorQqlUroWbNmsLWrVuF8+fPC35+foKVlZWwdOlSmXiqVasmeHl5CWfPnhX2798vODk5CZ07dxYEQRDevHkj/PXXX4KVlZVw8+ZN4c2bN4IgCMKECROEypUrCwsXLhQuXLggzJ07V6hQoYKwaNEiadvz5s0TrKyshMuXLwuCIAgDBw4UnJychKioKOHDhw/Cpk2bBCsrK2HTpk3C48eP5fZTIpEIffr0Eezt7YW1a9cK58+fFyZMmCBYWVkJe/fuldYbOXKkULlyZeHJkyfCp0+fhPbt2wuNGzcWPn78KAiCIJw5c0awsrISpk6dKly+fFk4ffq04OnpKVhZWQm3bt0SBEEQgoODBSsrK8HZ2VlYs2aNcPnyZaFXr15CxYoVBVdXV2H+/PnC5cuXhcGDBwtWVlbC7du3Zc6rXr26MHXqVOH8+fPC33//LVhZWQmbN2+W+ax3794tCIIgvH//XnBxcRGaNm0qHDhwQDhx4oTg7u4u2NnZCU+ePBEEQRBCQkKEihUrCoGBgUJwcLCwb98+oXbt2kL37t2/9d+MiIh+IUxU6aeRl6iOHDlSpk6PHj2EVq1afbUdf39/wcbGRoiOjpaWvXnzRqhfv75w9uxZYfPmzYKVlZUQGhoqc95ff/0lVK1aVYiLi5PG06BBAyE9PV1aJzAwULCyshJiY2MFQRCEhQsXClZWVtLjERERgrW1tbB8+XKZtufNmydUrVpVep5YLBZat24tuLq6Crt37xasrKyEo0ePSutnJXjBwcE59vPixYuClZWVcPjwYZnykSNHCrVr1xY+ffokCIIgxMfHC3Xq1BE8PDyEJUuWCBUrVhRu3rwprb9y5Urp+54lLi5OsLKykvYjK56AgABpnVu3bglWVlbCqFGjpGWxsbGClZWVsHbtWpnzxo4dK9P+gAEDhNq1awsSiSRbojp37lyhatWqQmRkpLR+Wlqa0KhRI8Hb21sQBEFYvny5YG9vL6SlpUnrnD17VggMDBQkEkmO7xkREf1aOPVPBcrOzk7mtampqXTqXyKRID09XeYPANy4cQN2dnYoWrSozHlnzpxBvXr1cO3aNRQvXhz29vYybbdp0wZpaWm4ffu2tKxq1apQVVWVaQcAUlJS5MYbHBwMQRDQsGFDmbgaNmyItLQ03LhxAwCgrq6OmTNnIjIyEuPGjUO7du3QrFkzhd6bK1euQCQSoV69etmuFRMTg8ePHwMA9PX1MWXKFAQHB2PhwoUYMGCAzPvq6ekJf39/JCUl4d69ezhy5AiWL18OABCLxTLX/Pw9K1KkCADA1tZWWmZoaAggc2nF59q1ayfzumnTpoiJicHTp0/l9qtixYowMTGR9klFRQV169bF5cuXAQCOjo5ISUlBq1atMGfOHISEhKBOnToYPHgwRCKRQu8jERH9d/FmKipQhQoVknmtoqIi3Vt08eLF0jWiWR4+fIj4+HiUKFEixzYTEhJkktgsxsbGADK3hvra9YHMJFme+Ph4AEDLli3lHv98DWXFihVhbW2Ne/fuoUGDBjnGm5P4+HgIggAHBwe5x6Ojo1GxYkUAQK1atVCsWDFER0dnu1ZsbCz+/vtvnDx5EiKRCKVLl0b16tUBZN/HVd6uDF++R/KYmJjIvM5KcuV9FvHx8Xj+/DkqV64st62UlBTY29tjxYoVWLduHdauXYsVK1bA2NgY/fv3/+qaXiIi+rUwUSWl1blzZ9SvXz9bua6uLmJjY7OVX7lyBSVKlIC+vj6eP3+e7XhMTAyAf0cFv4eenh4AYP369dDW1s523NzcXPrv7du34969e6hQoQKmTZuGmjVrSs/PDV1dXRQuXBgbNmyQe7x06dLSfy9atAjx8fGwsLDA+PHjsXPnTqirqwMARo4ciYiICKxbtw729vbQ0NBASkoKduzYketYviUuLg6lSpWSvn7//j2AfxPWL/vl5OSE0aNHy21LQ0MDAODi4gIXFxekpKQgODgYGzZswNSpU2FrawsbG5s8i52IiJQXp/5JaZmYmKBq1aoyfwCgevXquH37tkyy+v79e3h6euLcuXNwdHTEq1evsu1/euDAAairqyuU5GSNsGbJGomMi4uTiSs2NhYLFiyQjri+evUKM2fORMeOHbFs2TJ8+PAB06ZNk7bz+XKDnDg5OSE5ORmCIMhc69GjR1i8eLF0KcSdO3ewatUqDBgwAAEBAXj06BGWLl0qbefGjRto2rQpatSoIU0Cz58/DyDnkWNFnTx5Uub1sWPHULx4cZnk9fN+PX36FGXLlpXp1/79+7Fr1y6oqqpi5syZ6NChAwRBQKFChdCgQQP4+voCyNwxgIiIfg8cUaX/nF69emHfvn3w9PREv379oK6ujqVLl8LU1BStW7eGhoYGtmzZgkGDBsHHxwclSpTA6dOnsXv3bgwePFihUc2suocOHYKtrS2sra3Rpk0bTJgwAa9evUKVKlXw9OlTzJs3DyVKlECZMmUgCALGjRuHQoUKYfTo0dDX18fQoUMxffp0uLq6omHDhtDV1QUAnD17Fvr6+qhQoUK2a9erVw+Ojo4YOHAgBg4cCEtLS9y5cwcLFy6Ei4sLjIyMIBaLMWbMGFhaWqJv375QV1eHu7s7li9fjsaNG6NSpUqwsbHBwYMHUblyZZiamiI0NBQrVqyASCTKcS2uotauXQtNTU3Y2dnh+PHjOHPmDObMmSO3bq9evbB//3706tULf/75JwwNDXHkyBHs2LEDY8eOBQA4Oztj7dq1GDNmDNq0aYNPnz5h1apVMDAwgLOzc57ETEREyo+JKv3nmJmZYcuWLQgICMCYMWOgoaGBGjVqYN68edDX1wcAbNy4EXPmzMGCBQvw8eNHWFhYYNq0aejYsaNC12ratCn279+PMWPGoGPHjpg4cSJmzJiB5cuXY9u2bYiKikKRIkXQokULDB06FKqqqti8eTOuXLmC+fPnS+Pp0aMHDh48CD8/Pzg4OKB8+fJo1aoVNm/ejAsXLuDQoUPZrq2iooIVK1ZgwYIFWL58Od6/fw8TExP07t0bgwYNAgDMnz8fT58+xdatW6VT/UOHDsWJEyfg6+uL3bt3w9/fH1OmTMGUKVMAAGXKlMGkSZNw4MCBPHuE619//YW9e/di+fLlsLCwwMKFC+Hq6iq3romJCbZt24Y5c+Zg4sSJSEtLQ5kyZWQ+n3r16mH27NlYs2aN9AaqatWqYcOGDTAwMMiTmImISPmJhC/vpiAiyqWrV6/Cw8MDGzZsQI0aNQo6HCIi+sVwjSoRERERKSUmqkRERESklDj1T0RERERKiSOqRERERKSUmKgSERERkVJiokpERERESomJKhEREREpJSaqRERERKSUmKgSERERkVJiokpERERESomJKhEREREpJSaqRERERKSU/gdHpREKoN8StwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: scale-y=0.5\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: scale-y=2\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: scale-y=3\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: skewed\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)']\n", + "Processing: standard\n", + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)']\n" ] }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: standard\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" + "ename": "KeyError", + "evalue": "'gpt2_embd=128_layer=4_head=8'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[20]\u001b[39m\u001b[32m, line 24\u001b[39m\n\u001b[32m 18\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mSkipping \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m: no matching models in metric keys \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(metric.keys())\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 19\u001b[39m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m24\u001b[39m fig, ax = \u001b[43mbasic_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmetric\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrivial\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrivial\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 25\u001b[39m ax.set_title(name)\n\u001b[32m 27\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mortho\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m name:\n", + "\u001b[36mFile \u001b[39m\u001b[32md:\\MyBK\\Semester_5\\Programming_Intergration_Project\\in-context-learning\\src\\plot_utils.py:77\u001b[39m, in \u001b[36mbasic_plot\u001b[39m\u001b[34m(metrics, models, trivial)\u001b[39m\n\u001b[32m 74\u001b[39m fig, ax = plt.subplots(\u001b[32m1\u001b[39m, \u001b[32m1\u001b[39m)\n\u001b[32m 76\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m models \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m77\u001b[39m metrics = {k: \u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[43mk\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m models}\n\u001b[32m 79\u001b[39m color = \u001b[32m0\u001b[39m\n\u001b[32m 80\u001b[39m ax.axhline(trivial, ls=\u001b[33m\"\u001b[39m\u001b[33m--\u001b[39m\u001b[33m\"\u001b[39m, color=\u001b[33m\"\u001b[39m\u001b[33mgray\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[31mKeyError\u001b[39m: 'gpt2_embd=128_layer=4_head=8'" ] }, { "data": { - "image/png": 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", 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" ] }, "metadata": {}, @@ -871,48 +241,42 @@ ], "source": [ "# plot any OOD metrics\n", - "print(\"Available metrics:\", list(metrics.keys()))\n", + "# print(\"Available metrics:\", list(metrics.keys()))\n", "for name, metric in metrics.items():\n", " print(\"Processing:\", name)\n", " print(\"Metric keys:\", list(metric.keys()))\n", - " if name == \"gradient\": continue\n", + " if name == \"standard\": continue\n", " \n", " if \"scale\" in name:\n", " scale = float(name.split(\"=\")[-1])**2\n", " else:\n", " scale = 1.0\n", - "\n", " trivial = 1.0 if \"noisy\" not in name else (1+1/n_dims)\n", " \n", - " # only plot models that exist in this metric dict\n", - " models_present = [m for m in models if m in metric]\n", - " if len(models_present) == 0:\n", - " print(f\"Skipping {name}: no matching models in metric keys {list(metric.keys())}\")\n", - " continue\n", - " \n", - " \n", - " \n", - " \n", - " fig, ax = basic_plot(metric, models=models, trivial=trivial * scale)\n", - " ax.set_title(name)\n", + " # # only plot models that exist in this metric dict\n", + " # models_present = [m for m in models if m in metric]\n", + " # if len(models_present) == 0:\n", + " # print(f\"Skipping {name}: no matching models in metric keys {list(metric.keys())}\")\n", + " # continue\n", + " # fig, ax = basic_plot(metric, models=models, trivial=trivial * scale)\n", + " # ax.set_title(name)\n", " \n", " if \"ortho\" in name:\n", " ax.set_xlim(-1, n_dims - 1)\n", " ax.set_ylim(-.1 * scale, 1.5 * scale)\n", - "\n", " plt.show()\n", - "std = metrics.get(\"standard\", {})\n", - "for model_name in models:\n", - " mres = std.get(model_name, {})\n", - " if \"gradient_alignment\" in mres:\n", - " print(\"Plotting gradient alignment for\", model_name)\n", - " alignments = mres[\"gradient_alignment\"]\n", - " plt.figure(figsize=(6, 4))\n", - " plt.plot(range(len(alignments)), alignments, label=f\"{model_name} gradient-w alignment\", lw=2)\n", - " plt.xlabel(\"# in-context examples\")\n", - " plt.ylabel(\"normalized inner product\")\n", - " plt.legend()\n", - " plt.show()" + "# std = metrics.get(\"standard\", {})\n", + "# for model_name in models:\n", + "# mres = std.get(model_name, {})\n", + "# if \"gradient_alignment\" in mres:\n", + "# print(\"Plotting gradient alignment for\", model_name)\n", + "# alignments = mres[\"gradient_alignment\"]\n", + "# plt.figure(figsize=(6, 4))\n", + "# plt.plot(range(len(alignments)), alignments, label=f\"{model_name} gradient-w alignment\", lw=2)\n", + "# plt.xlabel(\"# in-context examples\")\n", + "# plt.ylabel(\"normalized inner product\")\n", + "# plt.legend()\n", + "# plt.show()" ] }, { diff --git a/src/eval.py b/src/eval.py index 8cdbcc29..4be7b50a 100644 --- a/src/eval.py +++ b/src/eval.py @@ -185,24 +185,25 @@ def eval_model( all_metrics.append(metrics) metrics = torch.cat(all_metrics, dim=0) - results = aggregate_metrics(metrics) + # results = aggregate_metrics(metrics) + # # if prompting_strategy == "standard": + # # grad_alignments = compute_gradient_alignment(model, task_sampler(), xs[0]) + # # if grad_alignments is not None: + # # results["gradient_alignment"] = grad_alignments # if prompting_strategy == "standard": - # grad_alignments = compute_gradient_alignment(model, task_sampler(), xs[0]) - # if grad_alignments is not None: - # results["gradient_alignment"] = grad_alignments - if prompting_strategy == "standard": - # sample a single long prefix to compute gradients on (use same data_sampler) - xs_samp = data_sampler.sample_xs(n_points=min(n_points, 40), b_size=1)[0] - task = task_sampler() - try: - grad_alignments = compute_gradient_alignment(model, task, xs_samp, n_points=min(40, n_points)) - if grad_alignments is not None: - results["gradient_alignment"] = grad_alignments - except Exception: - # best-effort: don't fail whole eval if grad computation crashes - pass - return results + # # sample a single long prefix to compute gradients on (use same data_sampler) + # xs_samp = data_sampler.sample_xs(n_points=min(n_points, 40), b_size=1)[0] + # task = task_sampler() + # try: + # grad_alignments = compute_gradient_alignment(model, task, xs_samp, n_points=min(40, n_points)) + # if grad_alignments is not None: + # results["gradient_alignment"] = grad_alignments + # except Exception: + # # best-effort: don't fail whole eval if grad computation crashes + # pass + # return results + return aggregate_metrics(metrics) def build_evals(conf): n_dims = conf.model.n_dims @@ -224,12 +225,12 @@ def build_evals(conf): evaluation_kwargs = {} evaluation_kwargs["standard"] = {"prompting_strategy": "standard"} - evaluation_kwargs["gradient"] = { - "prompting_strategy": "standard", - # "task_sampler_kwargs": {"compute_gradient": True} - } + # evaluation_kwargs["gradient"] = { + # "prompting_strategy": "standard", + # # "task_sampler_kwargs": {"compute_gradient": True} + # } - task_name =["linear_regression" if task_name == "ar1_linear_regression" else task_name][0] + # task_name =["linear_regression" if task_name == "ar1_linear_regression" else task_name][0] if task_name != "linear_regression": if task_name in ["relu_2nn_regression"]: evaluation_kwargs["linear_regression"] = {"task_name": "linear_regression"} @@ -360,21 +361,30 @@ def baseline_names(name): if name == "averaging": return "Averaging" - if "NN_n=" in name: - k = name.split("n=")[1].split("_")[0] - return f"{k}-Nearest Neighbors" + # if "NN_n=" in name: + # k = name.split("n=")[1].split("_")[0] + # return f"{k}-Nearest Neighbors" - if "lasso" in name: - alpha = name.split("alpha=")[1].split("_")[0] - return f"Lasso (alpha={alpha})" + # if "lasso" in name: + # alpha = name.split("alpha=")[1].split("_")[0] + # return f"Lasso (alpha={alpha})" - if "gd" in name and "adam" in name: - return "2-layer NN (Adam)" + # if "gd" in name and "adam" in name: + # return "2-layer NN (Adam)" + # if "decision_tree" in name: + # depth = name.split("max_depth=")[1] + # return f"Decision Tree ({'unlimited' if depth=='None' else f'max_depth={depth}'})" + if "NN" in name: + k = name.split("_")[1].split("=")[1] + return f"{k}-Nearest Neighbors" + if "lasso" in name: + alpha = name.split("_")[1].split("=")[1] + return f"Lasso (alpha={alpha})" + if "gd" in name: + return "2-layer NN, GD" if "decision_tree" in name: - depth = name.split("max_depth=")[1] - return f"Decision Tree ({'unlimited' if depth=='None' else f'max_depth={depth}'})" - + return "Greedy Tree Learning" if "xgboost" in name: return "XGBoost" @@ -432,7 +442,7 @@ def read_run_dir(run_dir): all_runs[k].append(v) df = pd.DataFrame(all_runs).sort_values("run_name") - # assert len(df) == len(df.run_name.unique()) + assert len(df) == len(df.run_name.unique()) return df # Figure 3 and 4: diff --git a/src/plot_utils.py b/src/plot_utils.py index df8e1e27..1526bc38 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -22,7 +22,7 @@ # "Averaging" ], "linear_regression": [ - "gpt2_embd=128_layer=4_head=8", + "Transformer", "Least Squares", "Ridge Var Adj (alpha=1.0, ar=0.5)", "Feasible GLS", @@ -112,12 +112,15 @@ def collect_results(run_dir, df, valid_row=None, rename_eval=None, rename_model= for eval_name, results in sorted(metrics.items()): processed_results = {} for model_name, m in results.items(): - if "gpt2" in model_name in model_name: - model_name = r.model - if rename_model is not None: - model_name = rename_model(model_name, r) + # if "gpt2" in model_name in model_name: + # model_name = r.model + # code fix + if "gpt2" in model_name: + model_name = r.model # r.model = "Transformer" else: model_name = baseline_names(model_name) + if rename_model is not None: + model_name = rename_model(model_name, r) m_processed = {} n_dims = conf.model.n_dims From 0d6accdb2f6149fbac6cb18da0b857d5624cd1e0 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 3 Nov 2025 23:58:13 +0700 Subject: [PATCH 24/88] run_all --- src/eval.ipynb | 263 ++++++++++++++++++++++++++++++++++++++++--------- src/run_all.py | 90 +++++++++++++++++ 2 files changed, 305 insertions(+), 48 deletions(-) create mode 100644 src/run_all.py diff --git a/src/eval.ipynb b/src/eval.ipynb index 7c56c416..0d76ef74 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -42,23 +42,216 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 36, "id": "0e8d018b", "metadata": { "scrolled": true }, "outputs": [ { - "ename": "AssertionError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mAssertionError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m df = \u001b[43mread_run_dir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2\u001b[39m df \u001b[38;5;66;03m# list all the runs in our run_dir\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32md:\\MyBK\\Semester_5\\Programming_Intergration_Project\\in-context-learning\\src\\eval.py:445\u001b[39m, in \u001b[36mread_run_dir\u001b[39m\u001b[34m(run_dir)\u001b[39m\n\u001b[32m 442\u001b[39m all_runs[k].append(v)\n\u001b[32m 444\u001b[39m df = pd.DataFrame(all_runs).sort_values(\u001b[33m\"\u001b[39m\u001b[33mrun_name\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m445\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(df) == \u001b[38;5;28mlen\u001b[39m(df.run_name.unique())\n\u001b[32m 446\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m df\n", - 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run_idtaskmodelkwargsnum_tasksnum_examplesn_dimsn_layern_headrun_name
5b71b76dc-a9ba-4453-9e21-877371420b34linear_regressionTransformernoise_std=0.9_noise_type=poisson_renormalize_y...-1-1548_poisson_noise_gaussian_data_experiment
6bff9a489-6a88-4736-9121-6d492604b98alinear_regressionTransformernoise_std=0.9_noise_type=poisson_renormalize_y...-1-1548_poisson_noise_vr2_data_experiment
0pretraineddecision_treeTransformerdepth=4-1-120128decision_tree_pretrained
4b3ffd953-011e-4961-a8cb-293c52fd7076linear_regressionTransformer-1-15128linear_regression_toy__
377f4f325-1e29-41d3-b2aa-84378107f9e5linear_regressionTransformer-1-1548poisson_noise_gaussian_data_experiment
7pretrainedrelu_2nn_regressionTransformerhidden_layer_size=100-1-120128relu_2nn_regression_pretrained
8pretrainedsparse_linear_regressionTransformersparsity=3-1-120128sparse_regression_pretrained
108f273f7-0f91-46d0-9ebf-35e2a4467653linear_regressionTransformer-1-1548uniform_noise_gaussian_data_experiment
270152b8e-2195-4da8-8329-b43fe3146907linear_regressionTransformer-1-1548uniform_noise_gaussian_data_experiment_1
\n", + "
" + ], + "text/plain": [ + " run_id task \\\n", + "5 b71b76dc-a9ba-4453-9e21-877371420b34 linear_regression \n", + "6 bff9a489-6a88-4736-9121-6d492604b98a linear_regression \n", + "0 pretrained decision_tree \n", + "4 b3ffd953-011e-4961-a8cb-293c52fd7076 linear_regression \n", + "3 77f4f325-1e29-41d3-b2aa-84378107f9e5 linear_regression \n", + "7 pretrained relu_2nn_regression \n", + "8 pretrained sparse_linear_regression \n", + "1 08f273f7-0f91-46d0-9ebf-35e2a4467653 linear_regression \n", + "2 70152b8e-2195-4da8-8329-b43fe3146907 linear_regression \n", + "\n", + " model kwargs num_tasks \\\n", + "5 Transformer noise_std=0.9_noise_type=poisson_renormalize_y... -1 \n", + "6 Transformer noise_std=0.9_noise_type=poisson_renormalize_y... -1 \n", + "0 Transformer depth=4 -1 \n", + "4 Transformer -1 \n", + "3 Transformer -1 \n", + "7 Transformer hidden_layer_size=100 -1 \n", + "8 Transformer sparsity=3 -1 \n", + "1 Transformer -1 \n", + "2 Transformer -1 \n", + "\n", + " num_examples n_dims n_layer n_head \\\n", + "5 -1 5 4 8 \n", + "6 -1 5 4 8 \n", + "0 -1 20 12 8 \n", + "4 -1 5 12 8 \n", + "3 -1 5 4 8 \n", + "7 -1 20 12 8 \n", + "8 -1 20 12 8 \n", + "1 -1 5 4 8 \n", + "2 -1 5 4 8 \n", + "\n", + " run_name \n", + "5 _poisson_noise_gaussian_data_experiment \n", + "6 _poisson_noise_vr2_data_experiment \n", + "0 decision_tree_pretrained \n", + "4 linear_regression_toy__ \n", + "3 poisson_noise_gaussian_data_experiment \n", + "7 relu_2nn_regression_pretrained \n", + "8 sparse_regression_pretrained \n", + "1 uniform_noise_gaussian_data_experiment \n", + "2 uniform_noise_gaussian_data_experiment_1 " + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -68,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 37, "id": "a9980951", "metadata": {}, "outputs": [], @@ -130,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -147,26 +340,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 2/2 [00:00<00:00, 30066.70it/s]\n" - ] - }, - { - "ename": "KeyError", - "evalue": "'gpt2_embd=128_layer=4_head=8'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[21]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 6\u001b[39m n_dims = conf.model.n_dims\n\u001b[32m 8\u001b[39m models = relevant_model_names[task]\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m \u001b[43mbasic_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstandard\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 10\u001b[39m plt.show()\n\u001b[32m 12\u001b[39m \u001b[38;5;66;03m# Figure 3 and 4\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32md:\\MyBK\\Semester_5\\Programming_Intergration_Project\\in-context-learning\\src\\plot_utils.py:77\u001b[39m, in \u001b[36mbasic_plot\u001b[39m\u001b[34m(metrics, models, trivial)\u001b[39m\n\u001b[32m 74\u001b[39m fig, ax = plt.subplots(\u001b[32m1\u001b[39m, \u001b[32m1\u001b[39m)\n\u001b[32m 76\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m models \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m77\u001b[39m metrics = {k: \u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[43mk\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m models}\n\u001b[32m 79\u001b[39m color = \u001b[32m0\u001b[39m\n\u001b[32m 80\u001b[39m ax.axhline(trivial, ls=\u001b[33m\"\u001b[39m\u001b[33m--\u001b[39m\u001b[33m\"\u001b[39m, color=\u001b[33m\"\u001b[39m\u001b[33mgray\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[31mKeyError\u001b[39m: 'gpt2_embd=128_layer=4_head=8'" + "100%|██████████| 1/1 [00:00<00:00, 940.22it/s]\n" ] }, { "data": { - "image/png": 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", + "image/png": 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UwSrq35FeM4ezC6DYzKP6ZrarZKWmUrvZyaJFf2D69Iux335jMWTIUGaBpNJ71157ZZ3nkMVza+Il+cjSOW/ea1i2bCkWLPgVv/32K7M4kpXxvPMuaPR5W0PxqZJU8zPscNRvylJ7fRwqCdXw9kwmmOfOfaneutoimLd25bQluBDdAYxAMQpmHQAoYXxBV6GeTPyU1h1RdwVcUj8YogehSBhRbwpcRgyiGgPE+ie1lsQmGNDLt0ANVXdRilPbIupI6VCWQda5UxBhyJ0gsYQlk/WbjyNEqAG9VdCe6VAeJsrhbBMSghmeXXuuam1eeWUehg8fgZkz70sse+ON16rvNe0ESbGglBR0wgknMzF7/vnTcNdd/2PxnA0JUXKhf/75J1AUJSEIKUaUrLHHH39SC70yoFevXizmVNM0ZqmNQ8fWu3dvnHjiKS22Lw6npeAxojsCdSdiHYos+oZKcN6mhRj9z2r0+fMedFrzClxbfkU4UMDWG7HALm0xKdlMGJUFUAKV9daZtYSo4EjtUKWbSIjSvJIQjceI1haiJmvzabnlG7NIcDicPYvs7GysWbMKS5YsxpYtW/DRR+/jqaceY+tIKDYFGkeZ6ZTJTtugbS1e/AcGDRrS4PjJk09EKBRmCVFUY/Tvv//CjTdey+I/Kb60paCsfwo9uOmm6/HHHwuxadNGPPjgbHz88Qd1hCmH05bgFtEdQHSnIu+/v6Hqq8dQ+sf7sAc3JhS9YOjwVf7Nblj/DqLJ3SFmDIa950Ewd4FVlMo0wV8M1V9GhTPrD6jlmhedqR3KIsosoBQDau8CQY63+axlESUhWt3i09DNXeb243A47YcLLpjGrJFXX305e0wC7cYbb8Vtt92MFSv+Zgk+2+OYYyahqqqSZbBT+aakJB8OOuhgXHLJZQ2Op1qijz/+NB599CFMnUrZ8zJGjhzNkpeSklouVpOy6x9++DEmkm+88TpEIlH06NGDWX9HjBjZYvvhcFoSweSmIhagXl7evPabHo8d0YIN+O2rjzEnUApJXYyx5eswoXw1XJrV270eJEQN64rbNfZCeCfdu9MiVAiXQynbAkNt+Eo+tvRKmP6l7H7ycctheHJ2qXVQkkSkpnpQURFi2eq7Oi5WjvmxdsFfSFp5NJSVVmxbJPcDQHDDlA2IE6m8k4ycPB/sTqnVhPjunJf2Bp+blp+XtDQPK+PWGNFoFGvWrEVGRg7sdkcz3zEOh8PZNooSQ2lpIXr16lknRrshuEW0BThS9uDS1L3xUc4ApOph/FD0HCr8Q+Hy58MZ3lwzsFqEtliZplgQakVhoyKUoVXXqBNkCLK7w7nmBVGEYJOh2zMBWKWbYJQBNjegCqyEFWxSdX4Wb/PJ4XA4HE5bggvRFiBbANJCqShPKkeFzY3PUvrgQE8EGzpfjDSngazQZggFi2CU/Q2oljCU8gbvlCVQ1KPQKrZAj0W3OTaRNc+6KrWeRXBXIUCEaBOhSbkQbFsAyk/SCmDauli1RCMKDNnBslN5m08Oh8PhcNoWXIi2EAfEnHi3OtTnZfsgHKe+iWJjI8JmT6hdxsDbaSxMQ4csR2DPyYO41/ideNN0aOVboIVrOnI0SnWykiB5yIyKjoZJlmFBgCbkwC6T8AZENR+6ozoeKqRCSKZunx1MgXM4HA6H0wHoeMqklThSUoCo1YN4iZSL5WIG8swvWa/fkBKhgE6YugFb6l5w9RnHXOs7giQCemUB1FDVdsdSj+J4spLQAbsqEVS+iSyiqpANobrnvKhb1QoYVJybhCpv88nhcDgcTpuDC9GdgFy/8daRPpuBToG0xLrX7AORhI3w6P8ipkQRg9Xv3IiFgUgVbLEqSFoQkhGFDBWSoEOyWQkKlGRAQpXFgQo1+6DkJNNfBNVfbgVIbg89kogJFaSOKUTJ+05dR8giGheidUo4RTT2PtXumMLhcDgcDqdtwF3zO1PDUrLD7nAgHCXBBxyqS3het0Gw6Xjf3g/XRn9GrvElNsX6I+JW4JTsMDQVqr8UWizCugKRRY8l3FCnDPor2iBIMgSbHYLNZolHUbL+RiJQqkqYi79Jx6iUJu4L8faeiQaYHYP4/KkkRKtLu9buroSIzoS8JUS5e57D4XA4nLYEF6I7AXXISE5OQ6Xfz2p47u8MYm5VFpBWgJBgxwf2vjhN+QseZRGi6oEwnE7LBM1aTupME25XGiWEKrWqNGDqTRShsRKoy29JPBY9XTtcohJhkkXUJkJHKmCnj7OylUWUspesGNGGyqxyOBwOh8NpPbhrfgfRNB1GTEGKJx1JXi9blmzT0SuYkhjzin0QE5qZ2ldQY0FETR1ic93jTHxqzJLaZBEaLYKy7EqYUat0lODIgWOvsztkdyETJNQFJtgNR3r1wiBMM5Yoai/E+813wNfP4XA4HE57hgvRnYCEocvhQpIvjVktiQMlFWbYSp9facvEYlsO7PDDFfkRYS1qudt3IUa0EMqyq2BGrYQdwZkH+6DZsHnz0BEhESpWB9Fq9hxrGevUUGYNUCwBasb7zXM4HA6Hw2kzcCG6k5C48XnT4HZbVtGRbj+E8hrR96p9EPubpnwHJVYObRd2mTQiW6AsuwJmrJA9FpydYB90P0R3boesIWpBiV2ilRlv75JYKujVc2BS96kYDGr12TEngMPZo5k06Ug8/fQTrXoM1O7zgw/e2+aYkpIS3HnnDBx99ESMHTsSRx11GOs9n5+/abcdJ4fTFuFCdGcQrKQlr90Nty8NNpuEVJuGPjEPTM0Kv/1I7osKwQkbIvCEvoACnXUCammMSD5zxyNWYh2aqyvsg++H4MhgbmtWPqoDCjHTJrA8LptkgyrmJRKWRLVWR6uQwiyiJjeJcjicXcDDDz+ATz/9uNH1iqLgoovOR3l5Ke666168+eZ7+N//7kZhYSEuuOBcVFRU8PeFs8fSppKVnnzySfz000+YN29eo2PoC3vHHXfghx9+YAk8Rx55JK699lq4XC7sbhwZGXDrArQqP9INFaFgBcJ+P8Y4A/inMhvI2AxVEPG2PABTlUVIiv2EUPg4CNJeLXocRngjlL+uARTLHS24u8M+cBYEe6r1mCyGlHnfEdFNyLIIyWGHFrNqiZqqCUHdUiNWQ6rlmtdNCPamVb7icPZEKInSVFpHFNH5iqqGtEe2d05ZsOA3bNq0Ec8++yJ8Ph9blpubh3vvvR9HHnkovvjiM5x88qm752A5nDZGm1EnL7/8Mh588EGMGDFim+Muu+wyRCIRzJ07F36/HzfeeCPC4TDuuece7E4Mw4Sc6oNsSrClZiArmgXN50LlxrUYb6/C3I15TIgSL9tH4DxlEURokEvfhCqMt6yULZDGbYTWWSJUrWSPBXdP2AfdC0GuSZpiWfft9AS/XUQBNtGETZYRZbVEyQJKRe1rlXAKK8waTO8Z7zfP4TSMsv49ROZfCzNqeVV2N4IzE65R98LefVKLb/ujj97HvHkvoLCwADk5uZg8+QSceOIpVlgPNSFZsoi591esWAFVVZCX1wlnn30eDj/8SLa+vLwc9903E3/88Tui0Qj69OmHadMuwfDh+2DGjFvxyScfsnGjRw/Hb78tqrf/+H5+/vnHxDaJpKQkvPTS60hJsYwGxA8/fIcnn3yMuez79u2PI444EjNn3pnYLoUiHHnk0Tj//AsTz9l62fvvv4s33niVbYMMNn379sP06Vejf/8BifETJhyCX375iRl37r57FoYNG46XXnoB7777NsrKytC1a1ecfvqZ+L//OyKxn5dffhHvvPMWiouLkJGRiaOPPhbnnDOV7YPDabdCtKioCLfeeivmz5+P7t27b3Ps4sWLsWDBAnzyySfo1asXWzZjxgxMnToVV155JbKzs7G7IXGjmdTK3QcxOxeQDPRIr8KAYg3LgykQvJXYZHPiJ1tPjNPXwhH8GaHKFfB6e0EPBXfKPGcE10D561pAs7osCZ69YB94DwQ5uV6tTZNqiHZASyDlxNsEE7JDQhhJgCzXL+EU1pgAZRbR6nAKDodTl/AvlwOqv9WmhQQwHUNLC9H33nsbjz32KK655noMGLA3/vnnH8yefQ+Ki4tx6aXT2d/LL78EJ554Mq6//iZomoZ58+birrtmYOTI0UhPT8e9997FBOrjjz8NWZYxd+6zuPbaK/Dhh5/jyiuvRiwWZeJs5sz7GjyGffcdxUQgxYQ+//wz2HffkRg6dDhGjhyFrl27JcaRIL7uuquYuJs48XAsXDgfDz/8YLNe73fffcNe3w033IyhQ4ehrKwUs2ffy17PvHmvJca99dbruO++h5gY7tVrLzzxxKP44ovPcfXV16Fbt+7sWO69924Eg0GccMJJ+PHH7/HCC8/hf/+biW7dumHZsqWYMeMWZtmtLa45nHYnRP/++2/2xf7ggw8wZ84cbN5cK7ZvK37//XdkZmYmRCgxcuRIdjX2xx9/4Igjaq7cmgNZy1RVbXAdbVuSaqYpPs40BSiKzE5OmmZC0wQ4BSfCsCGsRbBvtojlRXmA17JUzvNMwDj/WiokhNjfDyF/8G3I86QB4TBzh2m63qhAIvEk1cq2p7FGcBWMFTcAWsBa6OkDof9d0EVvnTeVbdcwYOg69AbKP9HcJ8ZSiahtiLS6Y7UGY07j81KbxsbGofmNX1HrVKrKaPpYRQE0VYVNEqDDhO4gS3BxHSGKMO1fRzQWhVMToTfQd77udnUY24gntdlsCQtHc8fGPy8tuV0a19B7G4fG0fi2MpY+C/SZiLP1d2lbY+tvV2Cx2S09trHv/c6Ppc+a3KSxW4fwb3ts3e9nR4wH31Gee+4ZnHvuVBx66ET2uFOnzgiHg5g1ayYuuGAaFCWG88//D7P+xc8BZ555Dj755CNs2rSBCdHNm/OZWCNLqdPpxJVXXoOJE49gn1WXKwkOh5O9r+npGY2+N48//gzeeOM1fP31F8yq+Pbbb7LP43HHTcb06Vex57/55usYNGgIOy6CBOH69evw1ltvNPn1Jicn47//vSVhySShePTRk5hFtzZjxuzPhDBBXsbXXnsFM2bchf33H8eWde7cBQUFW5iVlIQozYEs29n2yKpMt8zMLOTkWNVKOJx2K0QnTJjAbk21nubm5tYrKp+SkoKCglr9xZtJIODH008/0uC67t174Jhjjk88fuqpxxv9AevUqQsGjBuNSKgSw9LK8MKqdJiqHYKs4Du4sd7MQnehGI6K+Sj67nqUDjgEaTkHQJJT8cqn3yEYiTa43dQkD04+aHTi8Tc/vINRwnNwCNb4Eq0TvtpyNNQti+F1OTHl0P0TYz/8+XeUVFaL1a1wOl244IKLE4/JnUMnm4agH9qLLpqeeEyuKDpBNsYVV1ybuE/xT6tX/9vo2GnTLoMkkU8d+Pbbr7Fixd+Njp069SK43W52/8cff8CyZUvqrD9Y8IA1WjUqYUKFABmIGvh39RKsXvdXo9s9/fSzEz8iv//+KxYs+LXRsSeffDqyyfoN4M8/f8fPP//Q6NjJk09C585dWdtWulj69NNPGx179NHHoUcP6yLr33+X46uvPmt07OGHH43evfuy+6tWrcKnn1quwYY45JD/w4ABA9n9devW4cMP32107AEHHIwhQ4ax+/n5+XjnncZ/APfffzz22Wdk4rv5+usvNzp25MgxGD3a+lyShebll+c2Onb48BEYO/ZAdt/vr8LcuU83OnbQoKE46KBD2H0K0XnmmccaHdu//9449NDD2X0SvY1954m99uqDI444JvH4sccaH9u8c0RnHH/8KXVEErl6GyI7OwcXXvgf9tkhyLVM56qGSEtLx5Qp5yQeh0JBJkiag3u/h9qEa74lIbczWSoff/xR5u6OQxe7sVgMW7ZsRo8ePXHUUccwV/bq1auZOzt+voq3Bj7vvAtw2203sfPT4MFDMXr0GBx22OFwOBxNPhYSsGeeeTa7UZa9dT74iIlMOhdfcsnlWLt2DUaNGlPv+9AcITps2D5Yt24tnnvuaaxfvx75+RuxevWqehe2Xbp0Tdyn8TQft9xyI7tgi0MXl5RoFY1GmbD98MP3cdJJk9ickYil7x4JUg6nXQvR5kBXbSQ8t4ZOBvQl2hXIsoTUVE/i8bZiYahPfG5KJvxKEN3NGPJsUWypzAEyN8IQgMdth+Iew/qx7ir8Caz4E7EVs6F4OmO4lI2NchcUat0QNa1SUHGoTqbXY53w1Iq/MEZ4FrJgvd5irTO+Dp0CFdZ6OonEx1qPGy+MQGNrvzaJmt03Ar3u2mNpXraFz1eTPGZnHY8aJyXFk3hftz/WDY/HOg6Ho/5Yvy0TaVhXU0tUyGFCdFuvLX688dfnctX/jNUmKWnHxm4Pr9eZGOup9R42BK3fkbElJc5tjnW77YmxVVXbTgCk1x4fGw43fayqhrY51uGQE2MFYdtWQPoMxMc2cGqoA3224mO3ttpva2zLniNsdcbW/tHfmrgAjX+Xtje29narqqxwneZALnG569EdKlkpLr4uv/wq5g7fGhJRJML+859zWTymJa4msJjNc889IzHuwAMn4KOPPsevv/6ChQsX4NVXX8Kzzz6FZ555AT171njoGoMu8uni5PjjT2SPk5NTMGHCwez23/9ey2I1SYgS1EGvMUt3Y9T2Rnz++acsbpVc+4MHD2YW1zVr1tSziNYW0fF5uvNOcrvXD5Gj8zMJaXLtk0ueLtR/++1XvP76qywulYQ6h7NHCFH6ItDV2daQCI1byXaEpCQfs8o1BMVXVlTU/HBOnTotceInkREIRBJXzfQDZAg22AUnQjY3xndV8Fp+LsyMjcwl960vG4X6/sgKLoRo1rwOM5SP7mI+urv/sBa4ukBMHgqB3YawmM9gKAa96i/Ell2fEKGCbzDyBtyBM211RQCNjXP0mGFwpOYAydkNurxrv7Yjj5y0TZde7bGHHnokDjmkvus4Pi9+f828HHjgYTjgAMtq1RDBoJIQHPvvfxD22++ARsfGYiYUxTqOkSPHYsyYcVDWrkCoohJFRWFIpWTRXcDWk3velHJYK9W9Og/EgP7DkdslqcHC9uQWi7++gQOHY8CAIY0eA7nT4mP79h2E3r0HbHcszcs+++yDXr36JuZlW9vt3Llno5/Jrcfm5HTd5ljq5hUfm56e2+SxSUnpTR7rdPqaPFaS3HXGbv1dqj3WNKVtbrf295M+u7tiLNGcsfFzRMNjhTpjzzpraqNj4xdP8e/SaaedzaKiG9lyne36fHUvZpsKCUHB2bB7uT2SlpaG1NRU5umhBKU4X375Ob7//lvccssM5iZPTU3DI488nlhP8ZAWdL5RmEWc4iDJvU83shAeeeRhLPmIhOj2cnVI7JJAJKti/EI6jtebxI6ToKQiEnq1WblyeT1hGgqF6li/KZkqzosvPo9jjpmE6677b2LZDz98n/jcN3ShRPkZdE6hclJjx45PLCehuX79Wlx33Y347LNPEAwGcMIJJ2PIkKE4//xpuOuu/7G55EKUs8cIUYpF+eqrr+oso5NEZWUlsrKydni79MUUhManQtNqhEN8HFka6SpRFNU6Lg/ZJiDVlYzykB/7dYvgtXUiEEwDkspRbmp4N/k4HNLjHLgia+AKrIA7uBLO0GoIZi1XXmQTjMgmoPDDRCa84RsAvfgrap1k7T95GOQBMyBsJUIJo5bQEQURNrsDhkAxkOY2Xxtg2+YJte5Ykf0Ab018XuhEWTO+4bFxrJhNcwfH2iDJdsimDrvdCcXW3QqtMwBR2wJdsgSlQAlLSU7AlBuch7rb3fbngd7umve86WOtuE6p0djPHd0uG72NsXRt0dBnuK2M3fq71JztEh11bPx7QCKUnkOf9aZu16oOsedA7vRff/25zjKK26Ss9ilTzsaTT85hvx8UF0lu6lmz7sa4cQewzx0luZL7/pdffkaPHj2wcuUK3H//rMTvC42hcKE//1yMq666FmlpGWxfkUgYgwYNZuNcLjdKS0uYq5/iSLfmtNOmMME2bdr5OO+881lYDbnn58//FZ9//gnuu89KSKI41fPOOxMPPXQ/Jk2ajH/+WYlXX32lzrZonxRnSlnvlGhEoSC1PT4U0rF06RL2OrxeLxPVlJgUfz0NhROQGD7uuOPx1FOPMaE8ePAQLFr0O+bMeYjFy8af+8gjD7L1Q4YMZ3O2ePEfLOmKw9ljhOi+++6L++67Dxs2bGBZewRl0RNkbWoLkFclzeVDgexEZpoXXb0BbKROS0nWFetXkXKMN9IhJPVD1NsP5AQTjBgTo97QKnjD/8LmX0n9Q2u2GV4LPbw28VhM2QdyfxKh249PYle/rKtSR01eMCHKMgTThMNpR0i0SjiZUUBQtgDVnmghrNb0m9+zfqM5nA4PWRvptrXb/b33Psbpp5/BxNebb77GBB7Fgh977OREqaOTTjoVGzasZzGglLBJMd1Umskq57Scidc77rgHDz54H6655gqWRU7u69tvvzMhwqh0EllYTz31RLz11vssqbY2WVnZeO65F/Hss0/jgQfuQ3l5GRO4e+89EA8++CiL6yR69+6DBx+cg4cfvp+JR0qQOuaYY1kiUZwLL7yEidhLL52GpCQvTj31DAQCNXkAlPV+9913sAL6lFzUu3dvZvm9+eYbmKBuTDhSwhRZj0nYkqgmgU5zNGXKWWw9WVlpv/QaSISSJ/Gggw7GJZc07i3gcJqCYLYhhXL99dezrPl4QXuKeyGXA131kVueDvW0005jrvjbbruNJSf897//xahRo3D33Xfv8H7J4lBevu24tYbiQSkmi9xhdS0RzBiH9WXFWFe+Ge8v9+P1lQbQez4Ee4xpoIfS+qBnshcSxXxtNfuSTUa6x4OM4FroRb9Br1wMM7i6equAmDoScv/bIIjbCYirRpAkOLJ7QJN2zFXXXLY1L7sCcusKFfkIrl6FMDwoWFuI7OVnwfSr0FwHQU29kY0T9k6FrV8GcjqnQLTtfiW6u+elPcHnpuXnJS3Nk4gxbQhyLa9ZsxYZGTmw25uecMPZ/Xz00Qe4447bGqxPyuG0VagaRWlpIXr16sn0W7tt8UmZ8GPHjmV1Q+PWvUcffRSdO3fGWWedhenTp2P8+PFMlLYlZJuIFKcPDsmO4Z3oDRCACiuzkHTnF6Ey+INUqsaoZ53TdBUlwQCKfP1hG3glnCOegmP0O8wCKve90bKENlGEJtx7HbWrUjUCc0sJkERAkiWYdkt0C1qtEk4hhfWapw5LHA6Hw+Fw2gZtSqHMnFk3q48EJxUfrg3VdHv44YfRlqE4N7fDjkxPKgLRMDolAZsrc4GsDWSDxg9KJSYpmUyVJnlk2CWxTv1Ow9BRVlkMNUlHrjsddjEdgrRfs4+DOipJLg8EqlvYkY1wIsW/AjbRgM0uQ7enwYaKOt2VqKi9YArsvZGowD/XoxwOh8PhtDpt2iLaXiGR45Rt8DmS4LE7sW+eDGh2mH4rG9Vv6vg6VgFF05llNKpSIkJd06gJE1WBUuQHixCxi7A5t10eZ2soQcmelgsxvSs0s4O292RYitLmckE0VEgkRB3ViWsGxeVWx9qGdWYRpe5KPEiUw+G0F6jGKXfLczoyXIjuqokVAK/DhTR3MoZkVgtBSlqq5tVoEd6OFCOm6QiEFEQVvcGM9WCoEvlVhagSdWbd3C6CANnrg5zZFfDlQDM6tvWPXhvFDosOBwRdhWyXoDm6sHXUxQpUS5SI6mywblhtPjkcDofD4bQ+XIjuIqhmp0MW4XP40C/ThWyPAISTgbKa0h4fxsrwRHgzwpqGQDiGcExv0FgXiQaxpaoAZYhtU4yKsh2O1GxImd2hy0mN1qvscJjknbcxUepwSlDtPROrRKPaPU9tNVVjm+1DORwOh8Ph7F64EN2FOO02uCQn0jw+DMumqRZgFu6FkWrnhN5coAYwM7gBZaqKYFhBOKY1WHBYUaIo9hfDL+qwObbKQKNe124v7JldgJRcqAaJsj1JcJlU5Zn9pUxj1dUnsUbQarV+DavQVLI8c5Moh8PhcDhtAS5Ed3FNURKjPnsSxnatifEMlXTF5Z7OcFZP/1o9ihmB9digRhAKqwhF1QYtozElgsJAMWKyDSIlILE8HRn25AzImd1hOJJRq9PbHoTAxDhNmSSasLmSAdmaW0GtK0SpcD3XoRwOh8PhtA24EN2FkFXS45ThFN0YmpuMdJelLpdXStjbk4tb03ojrbq0Upmp4o7ABiyOBRAkMRppWIyGIwEUhMugO52QXF7YMzpBSOsCFfKe63amgFwREKhzEXSWsAS7ZTUW1MLEMCpqr2t6h46Z5XA4HA6nPcGF6G6oKWqXRSQ7fBiZZ4lOwxTwVXEyemT0wN3dRqNXdd3LKAw8ENqEL6NlCIY1BBoRo/5wJcpEBVJudxiutOr2lHsmLATBRnVVBZY5L+gKJLsEw+5L9JtPjK3dXYnD4XA4HE6rw4XoLobqVnpcllV0Qo+kxPLX/lbxzkoVNnhxbaf9sa83my0niTQvUoTX1FLENAHBiF4vIUnypqESIooiVXt8JSKmKWUHc7dT5jw0DXYH1RJNZ/Ml6DUWUYQ1Fi5hlXDicDgcDofT2nAhuhuEktshQRQEjOyUicN6WLGdxOsrNLz/jwpTM3Fu6hBM9PVIrPs0VIzZoS0IOX2IyV6WoESueFtyFuDyQdUNlITKUalUwtYKLSvbEqbNzjpIWZnzJuwOGzSH1clK0EtraolGNFaflVtEOZyOw7Rp52P06OF1buPGjcKxxx6B++6biWg0khg7adKRrId8Y9A6GrOreOaZJzF27EjWs70hPvvsE+y33wjWy70lOf/8c9i8rFr1b5PGz5hxK5tXYsuWLey5f/zx+zaf8/333+Kaa65o8jHRNql96a4a3xK88MJziXnYFvR+3nLLjTj00ANw2GEHYtasu+t87lqb339fgLPPPh0HHLAfTj55Mr788vNtjqf26gccMKbe9yr+3fnnn5U499wzoGlax+us1FEhmehx2aEG3bhgRDJksQIfr7HE0QerNERUEyf0kzA5pQ+yJTdeKl8OAyYWh0pwY34UV+aNQPfkDPjcMkzKvK+OBVV1DcXBUjiS7XAK7j1YYInMKgpZtTLnRROGxxL1ArWUMisAIcOyiJpWaS0bxZVyOJwOwcEHH4orr7wm8TgcDmP+/N/w4IP3se/7tdfewJY///xLcJDnpBWL0z/33NP4+uuvMHnyCfXWf/LJRxg1agyysiwPWUuwceMGLFv2J7p27YZ33nkL113332Y9Pzs7Gx9//AV8vuRGx/j9fsyefS/mzHkSHYW33noDTz75GIYMGbbdsTfccC0Tno888gSCwQDuuON2RCIR3HLLDLQ269evw1VXXY5TT52C2267Az///CNuu+1mpKSkYN99RzX6mYnFYpg37zWkpaUllrtcbva3b99+6N69J1566QWcffZ5O32MXIjuBuhEmOSWURWMIc2dgsn9wnDJCt5aaV1NfLleJ2MdpgyUsL+3MzIkF54oXYKwoSFfCeC2jT/jkszhGJiWgawU+iDUCM6IGkNhsARdfHmwQdojE3GsgvYuGNEIK81kIyHq7Z1YL5qFMEiIqiZ0RbeEaKseMYfTNtFNAxVK61hyUu0u2IQdc9I5HE6kp1ud64j0dKBLl65YuXI5vvrq84QQTU1NRWuSk5OLESNG4vPPP6knRIuLi5nl6o477mnRfX744fvo1q07E8HPP/8sLr10OtxuS1A0BZvNVmduG+L111/GwIGD2Jy3d0pKSjBz5h1YtOj3Jr0eEvk09tVX30KPHlYN6xtuuAnTp1+CCy+8BFlZ1Z3+WonXXnsZvXr1xoUXXswed+/eg1k0SUQ2JkTXrFkNj8eL3r1rSiFuzemnn8Es7SeccBK83pqwwx2BC9HdhEMSIUs2uDQPklwuHNbDgEsCXvqL3MXAT/kkRk2cN0RGX2c6rssejUdL/kCJFoHfUDCreAGmGoNxmKMHklxyHetnIBpEoViMvKRcQN/zLH0kLEXJCUGyQaR6ooYK0dc5sV7QCwBxoPUgpLIYUYop3RNFO4fTGO9v/hvXL/0YJbFQq0xSpsODmYOPxLGd9m6xbdrtDthsNT9z5HY/8sijcf75F7LH7733NvtBJvFBP8q5uTXd74iKigrMnn0PfvvtF7adY46ZhOXL/8LQocMT2/jppx+Yy5IsT5mZmTj00P/DOedMhd1OSZT1OfroY3HLLf9FQcGWOvsjt3xycjLGjx/PLIyPPvoQfv31J5SXV8DnS8K4cQfiyiuvhtPpYm7yyy6bhv/852J2/Hl5eXjuuXkQRbGei/Wzzz7GgQcejAMPnIA5cx7GF198ikmTjk+Mod8SEqjvvvsWAgE/sy4rSiyxnlzzkycfhTlznsI++4yo93rIcvbWW28mxH48N2LevLn4+OMP2euUZTsGDx6Cq6++Dp07W53vakPzt3DhAowePQavv/4qdF3DAQccxKzcJIhqW+ouueRCLF26hM3VCSecjLPOOrdJ+4y/jsZ4552P2DzSxYssy3jppdfx7LNPs21tiyVLFiMjIyMhQonhw0cwo8iffy7GoYdORFP4/vtv8cILz2Pt2tXstdD2pk27BKNH78fWU4hA165dsWrVKmzcuB5XX3093n//XSxe/EeD2zvvvAvYZ5SO74ADDqyzbp999sUDD8xi731DdbVXr16F7t27b/N4e/XaCzk5OXjvvXcwZcpZ2Bm4EN1NGAaQ7LWjuFxDmisZVeEwxnWR4JIEPPOnCvK2/1FoIKqpuHC4jBzZg+uzR+Px0iVYHauAahp4vGQJVMHACd371HEtk56qiPgh22TkuLP2zCx6yc7icClz3gwGIXvTYIoiBMOwSjhVh+aaIYV9yenLt+eGMnA49bli8Qfwa9FWmxoSwHQMLSFEKXZt/vxfmQirLbpq88UXn2HWrHtwxRVXY+TIUfjuu2/xxBOPJtzidJ4glyaJogcffBSSJOOhh2azH3YSosSvv/6MG2+8HtOnX8mE7ObN+cxFTYLpzjsbtmySwEpK8rH9x0UU8emnH+Hww49i+/nf/65FSUkx7r77PqSlpTPhdeedt6Nnz5445ZTTEyLzl19+xDPPvMDcwluLUIIENInsgw8+hFn3+vXrj3fffbvOnLz44vNMzF5//Y3M5UrrP/74ZQwbtk+T5prmgwTsmDH7J5a9/vorePnlF5lrmgQLzcvdd9+Bhx9+APfee3+D21mx4m/296GH5iAUCuGuu2awuaW5j/PWW6/jmmuuZxbHzz//DI8//ij23nsgszJvb5/xEIPGSEmxrOXjxh3Abk2FLNlZWTl1lpGQJaHc1FjflSuX44YbrsFll12B8ePvRjAYxGOPPYLbb78ZH3zwGdse8cEH7zH3+l579Wbil+Zc0ygkrT5xN3pDx0cXTNFolMW2xl/31hZR+nxNn34x/v33X2bVPfnk03D44XXjp/fffxx++OF7LkTbCyR6vE4ZRSbgsSUh2xdBSaAKI3IBhw14YrFKHSjxd6mBhxYquGQfO7yyHdOzRuCl8r/xW8i6Knu+eBm6uJMwNq9TnbqhhmmgLFwBuyQjzZ6257T3rMYUZUC0scx5o7ISDpcXht0NRIMQlCIg7okKq3tuvVUOp4Py+eef4ttvv6pjpSM3+Omnn1lH7NXmjTdexaGHHsZci8SZZ56Nv/5ain///Yc9JksTWT9ff/0d5tomyG1e26o2d+6zmDTpOBx3nOVmJ8sbxWBefPF/cPHFlzML29aQpXTixMPZMcePbcWK5Vi3bi3uuute9piEMQlBEhwEbefNN19jAqE2p512JrOSNQYl95Cwjsc5krX2kUcewPLlf2PAgL3Z7xJt9+STT8Vhh/0fGzN9+lXM1dxU/v57GbPs1nb30zyQIBw7djx7TOsnTDgE33xT8x5tDRkHSLyTSCLIknnFFZdiw4b1ifmfPPlEJtaJc8+dildeeZHNHQnR7e2zKSEGOwIJOjvVrm7AGh+LKU3ahijacNVV1+H4409MLKP3hF5/eXkZsrMtIdm7d1/22WkOsVj944tb6xWl4eNbu3YNE6JkUaXPzy+//IQ77riNid6jj56UGNezZy+8+upL7KKtoQuhpsItorsRm0hWUQeKSkPIdGfDKdlR6C/HoCzg8n0FPPq7gqgOrK4wMXuBgukj7EhyiDg7bSCcgg3fBTdBh4lZGxcgzzsBPZKS61j1rOSlMth9dnhsnj1KcNE0CLIzkTkv2QREHSmwkRDVimsGhqiovWURrR1ry+Hs6Tww7Jg24ZrfEcaNG4+LL76MnQdIPD7wwH3Yd9+RTOhJUsM/cyTqSJjVZtCgwQkhunLlSvh8voQIItLT01nSTxyKtSNRR5aqOPFzMrnqGxKicfc8CUDKYqc4PEpSon3H3bvHH38Sfvzxe+Zm3rRpIxOpW7ZsRrduNZVViC5d6ru541RWVrCwAXJfx92vJLwfffRB5oYnIUoWsdLSUvTvX9cKPXDgYLbPplBWVlov9pYsin/9tQxPPfU4E5JkIV67dm1CZDYEWWxrrye3evx9ir8HteeeoNhEuuhoyj4LCwtw6qn1E8TiUIwnXbw0F0p+U5T6VkkKb3C5tmrH3Qh9+vRln7UXX5zLPjf5+ZuwapX1OaxtVNr6/aY4VHL/NwR99imRqKHjiwtQCvNoiJdffoOJy/jFBX1GCwsL8dJLL9YRovS+k/ehqqpqp+KvuRDdjZDLnJKWSm0CAkENSe502FMdKAyUok9aGFeNsjNraFAFNvlNzJqv4Ip97Uh1CTgptR+KtDBWULF7Q8WMf3/BI0MOhsdW90onypKXitHF1wmyQLGk2HMSlmQ7BLs90erTdGYDVfl1a4lG1D3OWszhNAVyiR+V179dJiu53Z5EYglZCEl8XHrpNBbXWTt2sTZWeE7dc0Ft0SpJNvZjvL3zDsXHHXFE/djDjIzMbQoPcoNTXGiPHj3w5Zef4aKLLqsTEkBWKbJSHnLIYWwsJdA0lKTVGGRxVVWVuazJ+lv7mKl8z+WXX5kQqNuah+1BpfO2nidy91N8JcXj0gXBqaeezly4FI7QGFvvM36erm1pa8jqFhf+29snvR8vvlgzD815v7YFufx/+OG7Osto3kmcZWY2LVFp0aI/mBt8v/3GYsiQoczqSZbMa6+9cpvv93//ewsb1xDxKgdk0SwtLamzjsI1SGR6vTXxt7VxOut/rnr16sWS7GoTf9/FnaxCw4XoboZqirpdMqKKDn9IgdvpZhnvJXIJRMGPa0bb8cACBZUxoDBk4t75MSZGszwizs8YgnsKf2OCdJMSwB0rf8PdA8fVM+wFY2EUhorROSlvj0leYuciyQFRlhKtPg0P/TD9AUGv9SUM69CYRbQ1j5bDaZuQEMxweNDeoWQMKldDMYNkLa0dvxiHrDxLl/6ZiLkkVqxYkbi/1159WKweWago05ggCyJZKGu7JsnyVju7mhKJSPiRAHa5GrY4xa2iZGGiBCCyUJHgJMgiS7GnFPtJmegEuUTz8/PRqVNNEub2ILc8xUrOmHFXneV//rkE9957Fz799GOceOIpzO1LMagUu1ozD8ubLEYpVpGSumozd+5zLFmGwh3i0GvdlheK5pVKH8UzsCkbnSAR3hS2t096Pbsiq3/YsOEsCYyOP779eGjD4MFDm7SNV16ZxxKcZs68L7HsjTdeq77X+Jw1JSOfjm/rUIs//ljILM4NCftAIIDjjz8al112Jau0EGf58uV1ErKI8vJy5uZPTk7BztDsy893330XRUUtW2x3T4Ku3lK8jkSyUTiqIRYWke3KRafUTHRPsePa0XZkuq31ZRHg3t8U5AcMeEQZF2cOh1uwThC/Bwrx9LqlDe6nKuJHcbiEwib3HGwOOtuwzHlBU4Aky50jQIWAspqi9oa5R4UtcDh7IhdcMI0Jg3vuuYvVFd2aM888B9999w1L1Nm4cSP74a8dZ0oCkRJhKGGEYkfJjU7Z7hQTGLcknnHG2SwG8dlnn2KCdOHC+fjf/25lgmp78YgTJx6BiopyljF+8MGHJdyg9Dyy5H799ZfMHU+ikJJ2yAXeWEzf1qxcuYId74knnszEaO3bpEmTmaClbOf4a6CamRReQK+BamdSeENToTkitzdZAGtbCRcs+JW598lN/sQTc9hcN+TCjkPv0e2338Jc8QsWzMd9993DxPnWlQwaY0f2uSNQ7CS9F/Q5IPbeexATnDfffAML0yCRN3PmnSyxJy4UaSw9h57b2LGvWbOKJX5Rdv9HH72Pp556jK1r6nveGHSx8ffffzGxTBdVL788j9WxrZ3pTu9d/P1LSkpiF3JPPjkHv/zyM/tukLWZrKHxShG1Q1MoxGNnabYQnTFjBpYubVj8cLYPCSCfx460FFfCnK1qBoIBHSm2DHRJy0GPNA+uGWVHntda71eA++YrWFthIFv2MMuoWN2E/o3Cf/BV6cb6+zFNlIUqUB6rgI2CU/cATMEGQXJYmfOaAiml5gQmmNUXTzEDhmYkmgJwOJyOCcXG3XDDzSgqKmSiZGso4/f22+9kdTanTDmJiZbTTptSZ8zMmbOZa5NKBtGNRAeVrIlnMVMyzB13zGSld04//SRWKJxKENHztgf94B9wwAQmNI855tjEcgoruOWW21mM6CmnHM+yqWkZWW4pu7qp1lDa/v/93xH11pEVjDKgSfCR8KFkLUqsev75Z3DGGaewkAAqU9VUKKmK9lW789Ktt/6Pia+zz56CCy+cyvZFSVwkvEm0NgRZZvv06YMLLzwPt9xyA8aPPwA333x7k49jR/a5I5Ah7sgjD8NXX1kZ+HRRQpbMvLxOuPjiC3DjjdcxC/y119Y0DqCx9JzGjHgXXDCNfbauvvpynHnmKaws04033spc8fFqAjsKWe1nzXqAJRydeeap+OCDd3H77XewBK84119/NbvFuemm29jF0T333Mm+G3T8lEgXLyUVhyytVFZsZxHMZtawOfzww3HBBRfguOOOQ0eBYlHKy5sXoC9JIlJTPaioCDFXb3OhD++WshBKKiJ1rHPUlx5yDCXhUmwo9+OhhTGsq7LWU3Y9uel7por4JrABr1esZMtlQcT9Aw9CP09NB4Q4DsmOzr4cJMm+3RIbubPzsjOQsLdFKxDbvAnhLZuhaDHoH57B1qlZN0KTLNeTdHgP5PbNxA6Go7W7eWnr8Llp+XlJS/Ns8wKUfrDXrFmLjIwclt3LaTjZh5JfSFhSSaV47N/EiQexEkLx7O2ODrmcTzxxEp566vlEAtHWUBml1av/xezZD+/QPsgqTIlZ7733MToqV155GROXlPDWEVixYjm7OHvnnQ9ZqaqGkrVKSwvRq1fPBmNOdypG9OSTT8add96JxYsXo2/fvvB46scTTZrU9KupPRXS/3kZHiZIS8rD0KvFaCiiwq7JyPHkwiHbcc2Ycjy8MIqVZQZiOvDcUhU3j7XjIG9XbFGD+DGYz2qM3rLyJzw68BBkOep2zIhpCrYEipDhUZHmSAXFpHfUBCZW2N4mQ3RQaQoTNm9GvMs8oBUmPu1mSLUK+VZblTkcDmdryD1+003Xs9JM1AWJsoPJjU+F0huKOe2IUGgAWdK2F4942mlnMOtt7XhaTg1LlixiluiOIkKJV199mSWENSRCm0uzhejMmTPZ3zfeeKPB9SSsuBBtGuQezkt3s8QZEqNadSF6RdWhBQSkejLhyHLguv1K8b8f/VhbaaI4bOKDfzWc2F/GKan9UaSG8G+sAhVqDLf++zPuH3AQXLU6icTbgG7xFyPsiiDLnQm7aO+wMZKCzQ6RZc4LLDg9JrshqGGrlmj8oixoFbUXbXtSAC2Hw2kO5G6ePfshPPHEY3j//XdYdjhZBKmfekNFwDsiVAyeOh6R0NxWaSMSI1T385FHHmRzxkG9cljkHu8orFy5Ahs2rMMtt9zWIttrtmt+8+bN2x3TqVMntCdawzVfG0EUUFQRQTGJ0a22Ra56Q45icWEBrvqiErSa7HjXjbZc9EFdwd1Fv6FUs0qujE/rjBt7j2ZdhhrCY3ch05OOFHvyLunA1NpuVptNgFCxCYGV/wCyA/4PL4To3wjdsS+U9LvZGKFvCrIP6QmHU9pt1uHWnpe2DJ+blp8X7prncDitSXNc882OkiORGb+lpaWx1H0y2dde3hzIMvXwww9j3LhxGDp0KM4//3xs2rSp0fFlZWW46qqrMHr0aIwaNQpXXHFFu8/iJ8toTpoLOekeyFLdt4Rc9UbUjn1zumLKIJ81HsALy1SougmvzY6LM4azgvfED+X5eCm/8YD2kBLBZn8htoQKYYjaTtf/aosIDrfVc15XAY+VsCToNZ8Rs7rfvCXpORwOh8PhtBY7lK7x+++/46STTsI+++yD8ePHY/DgwSx29Lfffmv2th577DG88sor+N///ofXXnuNCdOpU6c2WrJg+vTprLzB888/z250/+KLL0Z7h4RRdqoLORke2OW6b0tM0REOmZg6uAf6pFlB8wUhEx+t1tj9PLsXUzOGJGTVvM3L8X1Z42KeOjCVBMuxwZ+PoBZgVsSOAtXXFSQ7bG43DFWF6LPq7gl6re5K1W0+eS1RDofD4XDamRBdtGgRzj77bFb09KKLLsKtt96KadOmobKykglISmJqKiQ2n3vuOVx22WU48MAD0a9fPzzwwAOsldQXX1ilEWrj9/uxYMECZjXt378/BgwYwDL4ly1bxvbf3qEQgcwUZ7UYrRu/SG70aMTALeN6IG40/Xydjg1VlstukCsTk1P6JMbPWrMQ/wbrFhmujQkTwWgYm/wFKIoUwxTjbS/bNxRpYtrsrISToakQvdlsuWBGIaD6MxLWq9t8tu6xcjgcDoezp9PsZKUHH3wQI0aMwLPPPgtbrWSPSy65BOeddx4eeeQRJi6bAvXyDYVCGDNmTGIZ9Vslgblw4UIcdVTd8hgUZ0BZ+u+99x5GjrRqYL3//vusRRo9b2fjsZpDvDTKrqjRmZPmhmQTUVgWYtbQ2nRxu3DO0Gw8vagIlG9ELvqbxjogiQImJvdgmfS/hrYgZugseemxwYcg3d54dw/N0FASKkNUjyHbkwGPvHM96nflvDQVETJsTieLk5WSsqDUqiVqCilAVIdpGOwYBWH3BIm2hXlpq/C54fPC4XD2XJotRMn6OHv27DoilKDSBFOmTMF1113X5G2R5ZPIza2bjUcxp/F1taF4VMrav+WWW5gYJgsejX3ppZcabFXVVChOkpICdgSfr3GRtzPQdj0eBxOjilo3UWHq8K74fkMV/i2LIj9g4ssNJib3p5JFwAWdhqJ0UwSrIhUoVSK47d9f8Pi+E+HcKpN+a3SoKFVLYUoasnyZkLczvinH35qEA15obgdUs5YQNYoAW1923xYzkJxct9TV7qC156Utw+eGzwuHw9nzaLbaIIsk1VNrCFrenCT8SCSSEJhbd8So3S4sDm2begEPGzaMhQFQuyxy5VOIwKuvvgqv14sdgSyAfn/9FnDbs+LQD6ffH9llheJdkoAUt4wtpSFEY3Xn/IbR3TH1k5WgnJv3VsQwKB3o7LPE+IUZQ3FXwa8o06JY7i/DjD9/wg29RzXB9R5FlT+EUr+fWUddNlezraO7Y16acmEh2uxQdAGmVGMpZwlL1ddPoeIQqqrCu62MVVuYl7YKn5uWnxd6Hre+czicDilEhw8fjqeeeoplubtcrjp9Ymk5WSqbSjyln2JFa6f3x2KxOtuO8+mnnzLr57fffpsQnU888QQOOuggvPXWWyx2dUfZ0ZI69AOxK8vx+Nx2mBnAluIg60sfJ88hYcre2XjhryImRp//U8H1Y+ysh71XsGNaxjDMKlqAmKnj69KNrM7o+V0HYS/PtuvfGdBRFqxERImyMk+p9tQdEk67el62BeltWXZCsDus3r6iDBgqBLUIqL7m0QIKVNVo1oVTS9Ca89LW4XPD54XD4ex5NFuIXnnllTj++ONx8MEHswQj6oFbUlKC7777jrWNo65LTSXuki8uLkbXrl0Ty+kxdW1qKFuf4kFrWz6pkC4t27BhAzrqj3OyW4aY5UV+SQjhiJpYd0KvdHy/qQrrq6LY4Dfx5Tod/9fLeku72H2YmjkYc4qt5LFFVUWYtqwIB6V3wTldBiLXuW3rcViJotgshSvFCbvgaFfdmNix2mTIHg/UgB/wZAGBzRCUQiAegVHdXYnD4bRvpk07H4sX/9HgOirEftllV+yW4xg9ejjr0X3UUcdgxoxbUVCwBY8//nSDY6nay+TJR2HOnKewzz5NN940BPW5f++9d/DPPysRCPiRlpaOkSNH4cwzz0GXLjW/q5MmHYkjjzwa559/YaPb2rBhPWu3+ccfC1lCckZGJvbffyzOPfeCDtUViNPOhWj37t1ZVyVKSvr++++ZC53EICUPUcLSXnvt1eRtUZY8icr58+cnhChlxi9fvpzFm25NTk4OPv74Y2YxJfd93BKbn5+PY445Bh0Vypj3umR0zvJgU1EQkWrLqN0m4srhnTD9uzUscemD1RqGZIvI9Vou+sHOLFyaOxxvlK1EkWKFHnxbtgk/lufjyKxeOL1zf6TKjReapY5MpZFydPbk7ZLi97sSU5QgVmfOC55smCREySJajRGKwdRNCB2odBWHs7OYhg4jXN4qEym60yCIO9bt7OCDD8WVV15Tb7nTuftisj/++At4PDsWHrajzJ59Lz744F1MmXImLrzwYvZbTE1nXnllHs45ZwqefnouevTo2aRtUY3u//znXOy//zg88MCj8PmSsXHjetYt6aKLzsdLL70OmeozczitLUSp7ufEiRNZ9vzOQrGhJDjvu+8+VhyfiuHPmjWLCc7DDjuMuVXLy8tZqzVy3VPrUMrWp1qil19+OdsGHQeJ0smTJ6MjQ7GMSS47stLc2FwcTLh3+6a6cHzvDLz5bynrukRZ9NeOtic6Kw2SMzGqVy5+Dm/Gy5uXw68p0EwT7xetxhcl63FCbh+ckNcHblvDJ5iqSAA+ewBJUlK7agtqmCJsTmqfKkD0ZLGe87VriVJRe03TYZd2X3clDqctE1nyDvxvXQUjWNIq+xe9mfCdMBuuoc0/lzscTqSnZ6A12d37//bbr/Hmm6/h3nsfwPjxBySWUyvO4cP3wQUXnINnnnkSd955T5O29803X7I8D7LqxvMJ8vLy2Paoj/yvv/5SZz8cTkvR7FTzJ598klkgWwqqIXrCCSfgpptuwqmnnsqy8Uls0pVXQUEBxo4di08++YSNpQx5Kn5PLtWzzjoL55xzDhtHy0isdnTITZ/ucyAt2VmnBuYZ/bPQ2WsFP1I/+m/W15R8Io0VDmnYz9EJTw2ciCmdB8BZbXWIGBorfn/W4k/xXuEqqFQNvoHi96XhchhC+4prpM+IzeWGyIrbWy4lwaQ2rgFrfUhFKKjsVLUFDqcjUfXapa0mQgnaNx3DrjofzJs3F5MnH40DDtgPZ5xxCj77zPpdqe3iPvfcM3Hggfth/PjROOus0/Dbb78k1m/cuBHTp1+Mgw8ejwkTxuLyyy/C6tWr6rjmP/rog8RjMqTcd989mDBhHCZOnID77pvJvHmN8dFH7+PkkyfjgAPGsL+vv/4Ka/DSGLSe3PoNiUMSknfdNQs33nhrk+dIEETmYVy8eFGd5d2798Crr76FESP2bfK2OJxdahEl1/u6detwwAEtc2VEwvOaa65ht63p3Lkz/vnnnzrLevXqxRKU9lSoAxPVGaUsen/QKkzksImYPrwTrvlhHROe763SMDhLRJZHrBGjUQ2yKuKYlN44KqsXXtm8HB8Xr4VumqjUYpizfgneLljF4kcPTO9Sp1d9MBZGebQCmc6MdpPxzay3ThdsTgd0Z1piuYhiGEgCIjoiIYUVtuedPjmcjs0TTzyKL774HFdffR26deuOJUsW4d5770YwGMQJJ5yElSuX44YbrmHxpOPHW8sfe+wR3H77zfjgg8+YwePmm69Hnz598fzzL0HXNTz88AO4/vqr8NZbNeKzNkuXLmE5FM88MxdbtmzGXXf9D6JoazCE4L333sZjjz2Ka665HgMG7M1+92bPvoflS1x66fR648lyuXTpn7jggmmNvmbad3M49NCJePnlF5kbnl7nPvvsi6FDh2HEiJFNdu9zOLtFiFKG+v33348ff/yRJRS53e56V2IdoeVmW8YmCMhJd7P6ovGyToMyPDimVxreX1MOqoH/0t86pu8r1BGUqm6g0h+FyylhauchOD6vD57f+Be+q24HWhgL4e7V8/HGln9wXtdBGJGczd5PwzRQHqlEkt0LOZ523h6wO2FzOAFHTaUA0SiCIfZi6lwNxBCNqHB77e0q7IDD2RUkn/JIm3DN7wiff/4pvv32qzrLhgwZhgcffJSVCXzttVcwY8ZdLP6R6Ny5C0smeumlF5gQJYF41VXX4fjjT0w8/+STT8UVV1yK8vIyZGfnYPPmfIwcORp5ebmQJBk33XQr1q9fz6yWDXlWMjIycMstM1joWM+evZhoJHF50UWX1Bv73HPP4NxzpzIxSHTq1BnhcBCzZs1kz4vnRMSprKxg+01JqVsFhayuH3/8YZ1l3377c5PmkOJL5859Ga++SpVpvmZ/6UZhD2eddQ7OPff8Jm2Hw9nlQvTRRx9lf3/++Wd22xouRHc95GaieNHMVBe2lAQTiUTn7J2N3woCKAqrWFmm4+d8G8Z1qZuME7eOUukir8eOG3qPxkl5ffHMxqVYVGXFUK4JV+K/K3/EUF8mrttrFDLsLlbOqSxSjk6e3HaTuGQKEiSPF6acXLeWaPVvBrnng4EYPEl0km8fr4nD2VVQbKZz8LHtMllp3LjxuPjiy+osIwFFrFu3lrnEb7nlRlZjuLbrnEoHUrUXsgBSd74XX5yL9evXIT9/E1atsrxxcS8QJQM98MBsvP32mywGc/To/XDYYf/XaHhPv34D6gjIvfceCFVVmYvf660JJauoqEBxcREef/xRPPnkY4nldHFMx03W1K0tksnJKey3lpJ7a3Peef/BySefxu5/9903mDPn4WbNI4lRep10Ky0twcKFC1gy1FNPPc72WVuoczitJkQpo53H1bWRvvTJTmYRLa2IMBnlkmy4YngnXP/TejbmrZUqBmbakeqsnxket45GFQ2dk3y4d+8D8Xt5IZ7dtBSrQlZP9iX+Ety16jfMHnAgO+lZiUteeGxJ7aL0ER2izZMEU65V1F4rAuJ5WWENsagKJabB1swWrxxOR4SEoM3bPJduW8Dt9tQpVVSbeJzlnXfOZG75hpJmFy36g8V/7rffWAwZMhQTJx6OWCyKa6+9MjHuhBNOxoQJh+KXX37C778vYOLs+eefwYsvvtpgaaOtuw/Gj2PrzPP48ssvvwr77mu1rq4NJQttDW2jf/+9sWjR7zjzzJr62ampqexm3a8JSWoKJMKppGLcKkulmw4//Eg2F1Onno1ffvmRC1HOLqHZv75UJokKynNaH7pizkn3wOOpcZcPy/LiiO7WiSiimXh1udmoaKSlVAqqpDyCcn8Mw1KyMWfQobhxr9FIqy7rtCxQiq9KrRqtiq6yxCWI7SVO1IDodEGimqnOFGsh1RKtRghrUBUdkbAKGy/jxOF0SKjkoM0msbbRJFbjt19++ZmVOSLDCv0dPnwEZs68D6eeOgWjRo2u1WbaZNVbyO2taSqrE3rbbXewckZlZaWN1jClup61k43+/HMJs9KS2702VDGGxCO5/msf38qVK5iFtLHz96mnno7583+tk1BVG7KyNoe//16GuXOfrdc5keaHOipSfVIOp01YRCmTvaGuR5zWQbZZ8aKbVB0xCg6lXvSDcrCgKIDSiIYlRQr+LPViaGbDbVkJrZZ1NCXJgYOyusIjycw9Tzy1YSlGp+YhSbIjQIlLsQpkONLbvIuefgMkl5vFiYruDBjRSghKrZNzxJqTUCCGpOTG66lyOJz2C7nBjzvueDz11GNMUA0ePIRZEufMeYgVfSeys7Pxww/fYcmSxcjKysaiRQvZeILc97Ts559/YhVjLrroUrYdisUky2S/fv0bFYJ33HE7q/FJsaTPPPMEu791S2vyNk2ZcjaefHIOK104Zsz+LBt/1qy7MW7cAfXGxyHL5YoVy3HNNVcwd/yECYcwQbtp0ya8//47+PrrL+tlulPIwa+/1g2pI3FMoQbnnXcBLrzwPGYZPuOMs9G1azfmnv/mm6+ZSL3iiqt36n3gcFpMiB599NGYO3cuevbsycopcVrfKkqdl6KpbhYvSo89sg3Th3XCTb9YlswX/wxj4KGpcNtiULa62t3aOkqxo0keO/ZJycH4tM74oTyfZdU/v+kvXNZjuJW4FK5EkuyF1B4Sl+xOiHYHBBfV+FttuebjhK0uVYpiuegdTrldhBxwOJzmMX36VUykkTudxBUJT+owNGXKWWw9JQRRQferr7bqU1NMJpU+uu22m7Fixd+shNH99z/MirtfcsmFzG3fu3cfzJ79MEt8aggSkeSeP++8M1lh/cmTT2w04ef0089g8aRUF/Shh+5nNUmPPXbyNrsgEZTlP2rUGLz77lu47rorWbwpxXIOHDgIs2Y9wI5h66Quum3t+n/vvY9ZnOyzz76I5557GnfccRvbFhXoHzaM2no/zxKuOJxdgWA285eX+rlTq00K9E5JSWkwa/6rr+pmL7aHeMvycqox2XQkSURqqgcVFaE20TtcEAVsLAqgrDKaWHbf7/n4cqMV7ymLAo7rl4Ij+0gwtAirD9rotlhHEgmaQ8dFf3+FqKGxZY8MPBh9vVbcUaY3HXmeHFZOqi3PiyzbEFm2EP5v74G+5lMmuKN5lFXqAjwSjAlWXFlKmhvpWZ5dZuVta/PSluBz0/Lzkpbmgc3WeOQVJeisWbMWGRk5sNvrZmRzOBzOzqIoMZSWFqJXr56sIVGLWkQpmJmsopw2hmnFi0YVHaFqS98Fg3PwV1kIBSEVqmHijeUV+GSVDacNSsWE7iIiSgiqrjdqHaW6oydm98W8gr/ZsofXLcLDAw9m5aOqon74HF54RG+btiLSsUnU7MBuxc0KiVqi3VgtUZbRJAiIhBVoqosJeg6Hw+FwOLuHZgvRu+++e9ccCWenID3lkERW7H6TGoSi6vDZJTx60F54498SvLu6DIphIqjqeGpRKd5bKeOcoWnYJ09HMBqB1oAgpcz6MXInfOvYiPxYAP+GKvBJ0VocndMLiqaiNFQGT7Ib0IU2LURFl7faNW8hmIWA0I36gEJQDJgOGxRFZzVFqZQTrynK4XA4HM7uYYdr1qxZswYvvvgi6xNfVFTE3PXUjYLTyvGiXjvSU5yJenleuw3nDszBc4f1wcRuKYk3vDis4p5finD9V0EURpKR5vVCaqAeng0CTkmuCcZ/dtMyVCjRRMelilhlm844pzkRnW7YfDUlUASjpue8UJ2wZBoma/nJ4XA4HA6nDQtRKkdBfeGPOuoo3HXXXawvfGlpKR577DFMmjSpVskLTmtAMZvZqW4ke+vGfWW6ZVy5T2c8fvBeGJVTU0x5bWUU//16C+78IYKImcYEqW0rQdrbmYoxnjx2P6SreHrTUnZfNw2UhSsQM9qugGOed4cLUop1/ISg1fqMBiOJu5SwpMbqW4Y5HA6Hw+G0ESFKgvPDDz/EHXfcwTorxeMDqVc8idQHHnhgVxwnpxkIMJGb4YbbVbdwMtE92YkZ+3XDfeN7oF9qTRmuxUUhXPxJPuYsUCHbM+oJ0skpfeAWrEiOL0s2YHFVCUtMYx2XouUQ27BVVJBl2JLzANGaD0GtyZw3A+FEgwaqKRoOK9tM8uBwOBwOh9NyNPsX9+2338Zll12G448/nmXNx+nfvz9b3lDbT87uha4NXHYbstNckBvpGES96R88sCduGtUFnbw1ZZi+3VCFc97biFeXAcnuLLirW9T5bA5MSumTGPfQmt8RjCksuacy4kdQCzJh2hYxbRIkN8WJWhn/QqyWEA0pEGpZdMPBGHPTczgcDofDaYNClNzwJDobgmqzbd37ltM6UBmitCQn0lNclBTeICQcx3VKxlOH9MYlQ3OR6rAsnlTB6O2VpTjz3Y34u1SGWL2Bcd7O6Ga32mVuVoJ4Zf0KVIYUVgqqLFLRZjsukaWeWn0KTqsziKDWcs2HDCAWTmTLKzEraamtimoOh8PhcPZoIdqtWzd8//33Da5bsGABW89pO/VRs1Nd8G0VL7o1kijg6J7peH5ib5zRPwvOatd0WDNw90+l+HmL9ZgE6WmpA1gJJOL9itVYXVaB0qooKiNBVCpViSSptmYhFl1uCO7qHtpGBQShlN0V/YBe6ocIIzFnoWAMDeRtcTgcDofDaWGa/XN71llnsWz5GTNm4JdffmGWow0bNuC5555jt9NOO62lj5GzE5B4zMvwIDnJDtt2RKJLsmFK/yzMndgHB3dJTix/fkkIH6wyWDxwd0cyxnutTiIxU8frZSvhD8ZQUBZEQVUpVFg1TNsSrIST0wUxKZs9pllw2N6qGbBeqWMVjYZVaGrbtO5yOBwOh7NH1xE98cQTUV5ejscffxyvvvoq+5G/8sorWc/dqVOn4tRTT901R8rZIej9oXjRHrnJqAzGUFoZQSiiMithY6Q6JVwzojNyPXa8tLKELftotYKyiA1nDJRwbEpvLAoXIWAoWBQpwrJICQYiE+uLK+GSC5Hs8bapd4u9VkmGlNwlIZNl/SPExFNgGikQywGjpApiZw8oZ57XFOVw2t95jnq/023dujUIhUIsVGz//cexfvLUMjPO6NHDcdNNt+Goo45pdHt///0XnnvuKSxbtpR1ocrOzsFBBx2Ms846l/WZ3xbU//3ee6mizIuJRMhdBfWTf/rpJ1BQsAXdunXHpZdOx777jmp0fHFxMY455v/qLY/Pxw8/fIePPvoA9957/y49bg5np4Qo8Z///Aenn346Fi9ejMrKSvh8PgwZMqRO8hKn7RAv0J6W5IDPY0e5P4byqgjrntSYHiVL9xkDspHqlPHoki1s3K+bdQQUE/8ZKuOE1L54vmwZG/tqxQrc6kgDFGBdUQl8rmRke3xA411Edz82CbaUzjWPNQXOlNcRCfyHPTTXxYDsCATJxX7UQoEYK27P4XDaNhQDfv31V2PJkkU466zzcM0117PW02vXrsXzzz+Ds8+eghdeeAVpaVay4vZYu3YNLrroApx44smYNu1Stq1//lmJhx6ajb//XoY5c55q9LmaprI+7Vdeee0uF6F//LEQt956Iy69lPrNj8aHH76Hq666nL3WHj16Nvic1atXsZ72b7/9QZ04eOopT4wffyBeffVl1o9+4sTDd+nxczg7JUQJr9eLcePG7ejTOa1V3J2SylJdrPB9GcV2BmKIxhpXjEf1TEOKw4Z7Fuazzkx/lRi4f4GCi/fJQW9HPlbFKlCqRfC5fx2OTtkLgWgUmyqK4BIdcEty2+lSJEmwpVkhBYSpmHC6PkE4eh4EVYJYbsIo8UPIczMhGotqLHFJtovbtB5zOB0JqhhhRFvnClJ0SjvUYpeE088//4Rnn30B/frVJNLm5ORi+PB9cNppJ+Lll19k1sKmQBbBLl264JJLLk8sy8vrxPplX3HFpVi16l/07l1TQaQ2n332Cex2O/bZZwR2NS+++DwOOOAgnHyy5YUkQbp06Z94/fVXcP31NzX4nDVrVqFLl67IyKiOl2+A008/A/ffPwuHHHIYbDbbLjt+DmenhSin/UIJObIooFOGG6leB0qrIqgKKqwtaEOM7ZTM2oXe9tsGhFQD66pMzJqv4tRh/fFI7FcYMPGZfx1GenKR6/CiPOBHvq0Ye6V3QluBxKWc3avmsUJWXwVixiaYBT2shWvCELJjgGCHquqIhBQ4nG42XxxORye4sgxlX62HHm6dOG+bW0b6Id3h7WdVt2jq9/rNN1/D4YcfUUeExiHxOGfOk3Vc89uDLIUFBQVYt25tHcsiubxfffUtJkob4+WX5+HII4+us+z999/FG2+8ivz8TWzbffv2w/TpV6N//wFs/aRJR2LChEPwyy8/oaKiAnffPQszZtyCwsKCBvdBbvQjjjiKic7LL7+yzrp99tkX3377daPHRxbR7t2rz3eNMHr0GASDAXz33Tc4+OBDtzmWw2kJuBDdw0s8Oe0iumYnIZCkoKQyikBIgdaA8Bqc6cEDB/bEDT+tR1lEQ1HIxPMLZYzauyt+jW2ABgOvVazA9OwR0AwD+RWlSJK9yE1JZt2eWhuyatq8qQCVn1L8zCJK2JIWQy3rAYGEabkJs8QPMSeLufvCoRh8Kc7WPnQOZ7dQ+vlaGK3YWYwEMB1Dc4Toli2bmWDbVlxkbm5NV7WmMGnSZHz44fvMkjpw4CBmVR02zLo15vImNm7cyMQrxaXGITE3e/Y9uOGGmzF06DCUlZVi9ux7cdddMzBv3muJcW+99Truu+8hJCUloVevvfD88y/BMBp+L8iNHggEEIlEkJWVU2cdWTqLi2vqJG/NmjWrWQjdhReex5KMyTp6zjnnYcyY/RNjJEnGyJGjWbwoF6Kc3QEvUrOHQwKNLH4ep4weuUnompOEJK+9wTJM3ZKcePywPujsszoUVcWARcu6wCtYsZTLo2UsiYkIRCLIryxCMEqdilq/pBNZTgTZDtGbZT1WrWWSvglGrd8pc1UQgmFZhJSYhmhE4zVFOZw2Snl5GfubkpJaZznFSh500P6J26mnntDkbZI4mzfvVZx00imsbvYLLzyP6dMvwVFHHYb33nun0ef9/fdSlrTbtWtNCcPk5GT897+34PDDj2SCeODAwTj66ElMENaGhODIkaOYlZRc+6mpqcyK29CNrLyxWJQ9z26v2z3P4bBDURpuuaxpGjZsWM9qfZ9//oV44IGHmdC+8srLsHDh/Dpje/bcC3/9ZeUAcDgd3iJKlqdHH30Ub775JrvK23fffXHLLbewGJ2GUFUVDz/8MN577z02fuDAgbjxxhsbLbLPaRokykiUpnjtLKGpIhBjLvvwVhn2yTYRTx/RD5d/sRL/lquIqjaom3oBnZez9a+Vr8A+KbnsfpG/EsmuUvSUc0FatDVjLeOZ86I3F0b56oQYtUeXIJCnQNxir7aKGjBLqiBkpzOLMVlF3V4ZOm9Bz+ngZEzs2SZc880hOdkSoH5/VZ3lFCMZjUbY/TfeeA0//thw7evGoCz5K664ht3I6rpgwW94++03MXPmHcjKysZ++9VYEOOUlZXB50uuE1dJVlSykj733NNYv3498vM3Mvc4/e5tLX5rQ8K5Mdf8ddfdiDFj9mP3FaXuexWLKXA6a1o310aSJHz++bcsiYrELNGv3wCWnEUhBbWtyqmpKcx6y+G0GSG6ZcuWZm00Ly+vWb3rX3nlFcycORM5OTmYNWsWKwNF/ezpynBrbrvtNnz33XdsPO3noYcewvnnn49PP/2UuTU4O0c8uSjdZ2XYF5QGWVJTbRHpMATMObwXrv5qNf4s0qBXZUBITgOSylGpx/Bu2b84ztcbqq4jv7IESU4vcn0+JnZbFeo578tNJPOTe16yF8Id/BDRvONhW1+9/F8/xOxU6BCsmqKKAaENWHU5nF0JucQ9fdLaVbJSp06dkJGRgUWL/sChh05MLM/MrEnGoaouzeGRRx5kcZJxYUYxoZMmHY8jjjgaJ5xwLH755ccGhaggiPXc6ZR9PmPGrSwDffDgwTjuuMlYs2YN7rtvZp1xlMlem/vvf5hZMBsiLS2dZfK7XC6Ullrl9eLQ49qvfWvoeVvTq1cv/Pbbr3WWkZdsV2f9czjNEqITJkxolntyxYoVTRpHLgQqgn/11VfjwAMPZMseeOABlo3/xRdf4KijjqozftOmTazX/RNPPJHI2L/jjjswadIk/PXXXxgzZkyTj5GzfUFKbeo7ZXrpDIuyykhCjKqaAY/swsxDOuN/323CL5t1mAV7AZ6FEEQTn5Wvxb7OHORJXlSFw9hcVQSvwwWfkyyLrSdGBUmCWKuEEyUsEZ6KVxDpdDDMLSk1VtHiKgiZqVAUDZGICq/P0XYqAHA4uwgSgmSZbC+Q9fGkk07Fs88+jcmTT2gwm31bMZMNsXDhAmYl3DrulAwjJBhJCDYECWJye5O1My7iKLP9mGMm4brr/psY98MP39eECzXyu9qUuNYhQ4Zi0aLf2fZrl3QaNmx4g+PpNU2dejZmzXqgTlb/8uXL68W+VlSUbzOznsPZ7UL0rrvuSnxhqqqqcN999zHRd/jhh7OrL6ol+s033zBL5fXXX9/kna9cuZIVHq4tIOnqdcCAAVi4cGE9Ifrzzz8zq+f48ePrjKd97ywSqa5mYKtugxn/21EhJ1O3nCQW51leGYVRrUZjqoY0bwquHRvGEwvL8dlaF1DaFcjawLLo55Yuw/W5oyEJIkoCVUh1lcFjz2n2PLckFPdqz+gOy2EH6HJ/SPgHohmG1/8SQp0vhW1ttdj8twpSThoMU0AkrCA51QVR3HEhuqd8XnYEPjd8XnaGKVPOYnU+//Of83DmmWezZCFK6KFSRW+++Tpzqx999LF1nkMxmr/++nOdZeRW33vvgZg27WJcffUVuPHG63DCCSexMlCURU91OsPhMI49dnKDx0HP1XWdlXeizPi4i3/p0iVYuXIFK3lIIQKUmBQ3xGxtCW0Op546hcV39unTj1loKcHq33//xY033poYQ1n4sizB601i2fLdu3dn1lgSxhRXSzGvVBuVkqNqQ/NJr4fD2R0IZjP9pRdffDELpCZL5NbceeedWLVqFebOndukbZHV89JLL8Wff/6ZiFkhLr/8ctbN4sknn6wz/u6772YC9YILLsBTTz2FoqIiJlpJ/JJ7YUfZ1pUpx4K6DW0s8rPY0fgnRpZEGFIEGys2493lQby0LAL0+h2Cw5J6x6TvhZMyrdhdn8uNflnd0S0rDZLUerXpKhZ8goI5R7L74oBJsLu+gWBEQBVWQ92fhvJnHmDlAcBxUA7kThmwSSJycpPg9fEMek7HgM6va9asRUZGDuz2jtG4gboMkRgjERUI+FliD2WqUxY8xWrW7qzUEDTm8cefZvepNNK8eXNZwg5ti0QqueunTr1wm6Fnp59+EnPhUy1OguJL7777Dib2ZNmO3r17MyF788034IknnsHQocNZ+SYq+UQJRM3l008/YtbgkpJiJjSpVuqIESMT62nbw4ePwC233J6IY3388Ufw66+/sBJNJGIvvvhSdhy1i/IfccRhLM52woSDm31MHA6hKDGUlhaiV6+edfRdiwjRoUOHYs6cOdh///oxMmSxvOiii5iwbArvv/8+rr32WubKrx2PQsuoFdnWgpaSkj777DMWF0RjyBpKrUYXLVqETz75BOnpTS/7sXU8jN8ft5M13Yrj87nY8/akOpP5pSHmpo+7qZPcMqqMMmyqKMFv+Sqe/accRo/FEARr/TU5I9HHmcaEfpe0DPTO7IoUd+u5ufWC1Si+Zyi7L/Y4EGrPvvCWP8ceK65hqBLvgm1t9eB0CeL4HuxCJY1qrmZ4dvi499TPS1Pgc9Py80LP25b1vSMK0bYAWRiprunLL7+B9so333yFRx99CG+88Q4r5cTh7Goh2uysebKGLl26tEEh+ttvv7H+vk0lfnDkoqh9oLFYjAVi1ztYSUIwGGRxpHELKN0/4IAD8O6777Ikpx1F03ZMHNAPxI4+tz2Sm+Zm3VdKKyLQDRP+oIqkpBQku0LYNzcIw0zDs5u7A9nr2Pgni5ZiRqf94BJlFFZWwGv3wGHLgiQKrZJFL/pyAVECDA0IFkDNuRW6/2PYtCLYI4thy1oA0zESQozMBxqM4ioYaV4E/DF4WsAiuqd9XpoDnxs+L+2do446mnVxmj//N9Z2sz3y2muv4LzzLuAilLPbaHbA2oknnsgsoiQAqdc8laT4/fffmaueEo/OOuusJm8rN9cq80PWz9rQ44YELWXVkxit7YYnAUulnvLz85v7Ujg7AInQvAwPMtPcsIkCixlVYgIy3GlwSBLGdJZwaHJPmKFkNt5vRvFCqZW8FlVVbKkqQVkoxBKgWgPB4YDotT5bZtVmpKRmIJw9LbHeV/EU9Lxa2aory1lsKSUtxXhNUQ6Hsw3IgnjrrTPw2GMP1yvR1B6gAvxJSd563aE4nDYlRKdNm4YzzjgDzz77LE477TSWsDRlyhS89dZbLLbz9NNPb/K2+vXrxwK458+vKaZLWYeUxUf1RLeGllFJi2XLltVxMVE2fbduNUWEObtBjKa7kZXuZklM1KveYXqRkZTC1k8Z7EL34ACYuhULujhagN+CVgmwilAIBf4SBCJKqyTusMz5JOsCyFSCkKs2wpZ+CFTXILZMUjfD4fgYZtxbWaYBpSHWHSoUjIFXNOFwONuCita/8MIr7bL80YEHTsDs2Q+39mFw9jCa7ZqnWL/rrruOxYIuWbKEZdGTu37YsGEN1ijbFlQOg0QsZeGnpaWx2E+qI0qWz8MOO4xlIJaXl7NMebJ8jhgxAvvttx/b/4wZM1irMipuTyU8jj22blYkZ9dCsZLkpidKyiMIhVWkelMRckWgmiouHeLDzUt6I5Kzko2ZV7YCvZ2pSJdcKPJXIMnhgUPOhN22e130FO9pS+kMbfPv7HFs099wd9sXVSkXwBe5DAJMJFW9hLLcw2BbXx0esrIMwlgPohEVqqJD5JnvHA6Hw+G0CDt8yebxeFjpJkoYGjJkSKNtxbbHZZddhhNOOAE33XQTTj31VCYqydpKrdKoZMbYsWNZIlKcRx55BCNHjsQll1zCnkcxoy+++CITspzdL0Zz0izLKF2g6IqETG8qJNGGZKeAS3t3AaqslpqaoOHhLUuZKz+iKNjiL0FlKLzbXfSs53xqTdcuw18AW6QSTl8fROxWLVvRCMItvVDLKqpCKAtDpZqiYbXB9qccDofD4XCaT7Oz5uPZ7rNnz0ZJSQkTINSekwQiiUda3lBHpLaeJFFeHmrWc6geZmqqBxUVoT0++US0CSiqiKCoLAyvR4IfZdhQXMyE6pcbI3hTmQ/BTtk/wEHOvXBKVi+IgoBuGTnomdoJyR77bsskJ90b+f5JBD64hj22DTgTtq4TYM/IRmVVJTz550NEjJVzqnC/DKyzwg2Qboe5fxe4PHbkdLK6RDXnm8M/L3xumsvOfGbS0jxNzJrPht3Oy5JxOJyWRVGiKC0talLWfLMtomSdJNf46NGjcf/99ycCsg899FB8//33rGUnZ8+C4idzUt3ISfdAU02kO9OQVN3v+JAuTgxSBiRE27eRNVgZqmKW0SJ/OUpClYgo2m4zjNJxSBk18cRmtJwt1CrLkZSSibDHKlYtwIDbeBCmq/rAyhQIZRHEoioClVEoUY29blpLP/h0I0spL0fLaQ+Q0YA+q1ShhMPhcFoaOrfQOYbONS0eI0rtNU855RTW851iOOMcf/zxLJ7zjTfewPTp05t/1Jx2DVk0s1OpdqEAMt5kJ6WzLPmYquLCvpn474qu8Ps2kg0ec4qWYla3MUAMKAyUwGN3I8vnIVm4W47Vll4rsS1stf8zVAVypAqOzqdA+/cLSGYZnNH5CKesBiLVVRpWlEFPc6KkKFAtPgUWL0qWK9lug2yXIMkibJINUvW6moYJu+WlcThNgkKgKMa+oqKSPbY6/PAPKYfD2VlMJkIDgUqkpqawc02LC9F169Yxi2hDUKwoueg5e7YYDcZ0RGPJyPUprNA9YOCann1wa345DGcQihTGfRtW4qaeA1EWDCDJUQqXbEfKbnLR22pZRI3ChdDMObD1nwIVgCvDiUDmVEjF97D1HmMmAvITEFUbUB6DWB6FkeZkx2ldh+l1t10tQONWUhKodocEu8MGl5P2wOG0DeLl86hFcyDQ2kfD4XA6CmR4IREaP8e0uBCl7kVr1qxpsKA9Ld/R7kacDpRNn+FFOKzANNMR82korCxDltuGM1MHYW5oPmUDYZO0Ge9sycDkvBwUBcpZoXu7nAqnZGMWxF2JQEX1B5+E2FKr+4lR9DuMsuWw9TsFMfFAuLLGQ614D7L6DxzIR9DzG1BZ/XlfUQZh/zxYTvn66LrJPAXqVgKVBCnV0Pf42lf8NKfjQvH91K6SajarKr9I4nA4LQO545tiCd1hIXrEEUewkklZWVmso1H8hPbXX3+x+NCjjjqquZvkdDAoVjIr1cU6L2UhA4qmMMvnmEwf/gz1xmL8w8Z9Hl2OgVXJ6AOgKEhWUReyk8lFv2shnZt0zD0wfP2hLnwIiFUCWhj6X8/BKPgN4oiLIHS6CFh/ORufbH8IldK+EDU7s4oKhQEIeSnNKlhN8aThkAKIJpwuudVanHI4W0M/GM350eBwOJxWzZqnMk1UQ/Snn35iBXvpx5hKOYXDYVbn8+mnn95uhlRbg2fN75pMXxKipVVRVESqsLGyAIEo9ag3cMPaRai0l7Hx9nAaZvbcB8lOCd0zctElJQepHscuddGT20AIBRFctw56oBTqojnQ135RM8Bmh2PwmdC9GyEHv2WLKmOXwqg8gt03vIA5Ih02n49ZRpvyDSI3vcdLLn0dGdlJENpfretdBq8o0PLzsr2seQ6Hw2nX5ZuIn3/+mfWWp/giKjhPtT3JQkrW0fYGF6K77seTrKOlgRiKAiXYUF6ImKaiVIni5s2/wLBZ7sC8QG/cPKAHPA4HOqdlondmHuyitEtd9DZNQWj1amhRK2vYKJgP5Zd7YUZKE2PE9L0gZW2GzanBMGWUl78BUbMustROApDtgpSZCsHp2q51NC5EQ8EofClOpGV6d1vJqrYOF6ItPy9ciHI4nPZCs13z5513HqZOncpiRBuKE+VwakMu6PQkysjNgGKo2FRejAy7E6en7o15/iVszGbPGryxJg2n7JWC9SWFEAQD/XK6QDTEXdd1SZIgSDXuSDF3FNyTXkTst4egrfvcOvay1VDKRUjZgJRtwuF5F2rVqWydvNkENodhymGYyXaImR6YKU6YyQ5A3rYlKuCPwuW2w+nmLnoOh8Ph7Nk023ezaNGidmn15LRub/o0rxOdk7ORnZzC0nzGpmRjkK0zWy+IBr7V/8aSYhWqrmNNURH+LdkIXdR2WRcj6jlvk+smDpmSB879r4F9v1sAT0784KEVGoj9o8GtvQhDrqq7HRUQShVgRQWEXwsgfrYewrebICwpBtb7gaoYsFU8qKYaqKyIsHnhcDgcDmdPptlCdNy4cfjggw94liWneZgm0r0udE3NRbo3iS26ILcfvKbVrx6uIJ7evArFIQO6YWBtUQnWlG1E2Ajtklg3cvuLrrqxzGR9NUQ77F1HwTH2Tog9jwIEy2pqRgFllQpP5AKEUwsR8wWhORWYYn0xKQRVCJuCEJeVQvxhM4RP18P8MR+xxYUwY1Y2fSQUg78qyso9cTgcDoezp9Js1zwVPiYh+umnn6JXr15wu6uFRDVkLX3hhRda8hg5HQSSXNk+H1QjF1FNRTAaxWU5g3F34XyYggk1bRMeXJGC24dR7TEdG0srWGJRljcdKfYUlnneUlBIp5Tsgz0cguoPwayO8SQrpelIgs2rwOx9AozsfaEvnwuzap31xOIypFZeiKqelyKSORCCIEISvRA1F4QwIEYAMcrq9te8brJ8lkWhlkWBDVXAuE4wJRGByghcbpkVwt/VJas4HA6Hw2mLNNvUVFhYiGHDhmHgwIFwuVzVPbdrbs0pacPZsyCtJZgmOieno0dmLhySjG6OZBzt24utJ9FZmrkMs9esZZ+lUERDUVUAhcFilERKILZghRkm/FxeuLv3hKdzHmSPK9FYhgSv6EmF5HRD9HWFNPJGyP0PT6wXlSBSV96NpLVPA2oQmhmE4QlD6ywg2gsI9weUvjKM3h6gkwemu9b1HllLl1kJUYqio7I80nIvisPhcDicPSVrviPBs+Z3b6YvWc0NGFhZnI81RQUsLvTBwiX4Ry1OjOkhZGN6p4HwORysJqnLLiHNnYJsdyZEk4ret9wxM/d4LAq1tBSxikroMYWJYlFXoPmLWftPWiCWPwFl+XwYwZqd63IqAj3OhZY+AjanF6bDnagRSh2WZI8XUkoKhBhgfp8P1v+UxO7QTKBLEouBTc/ywpfi2mOz6HnWfMvPC8+a53A47YUWDb6jWqI//PBDS26S0wGhax+bIKJ3Vh66ZKRDFARMzxmKQeieGLPOLMIdBb9hU9iPymDMqkkarEB+sAAKlBZNYmLdkCQH5E6d4eneDc70FAg2G0zZAclj3WfKN+1U2Hu7IHcRE98cm1qBlH9nw/vvozACWyDEwoljM3QDMb8fsaIimEIMjpE5NWKcrKIBhYlWf2UESkzj/eg5HA6Hs8fRbIvo5s2bcdttt2HBggWsuH1DrFixAu0JbhFtHSsOWSJDahRLN69DQUUlWzZ7+Wb841oBwWYl9TgFCedlDMaE3K5I8dqZcPM63Mj2ZiJJSmpxKyJZQslAqleWI1ZSBi0ShhkogRb0k4SGhKUwih+DqehQN+kw/DVfH0PyIdjzHOh5B8N0eup0T5JkCe5kL7R/ohAKotZCnx3m+E4wBYFZRDOyvXtkxyVuEW35eeEWUQ6H02EtonfffTcr4XTiiSeif//+GD58OM4991z07duXuVwfffTRXXOknA4HWSK9dhf65nRCmtdq7XlJnzxkFu4DM2olwUVNDXNKFuGFTX8hHNPYsmAsjM3+QpQr5RBbOOucLsvoN19ISYerRw+4snNgT8+F5HSx9RoGQ8i4DKLDDntPG+SuNqC6Hqmo+eH79yG4l/4Poj+/jtXW1A3o4Qhi6TEYVFaV8CvA4kIImopwMIpISOXdcDgcDoezR9FsIbpw4UJcccUVuOmmmzB58mSWRX/NNdfg7bffxr777ouvv/561xwpp0NClp5MTwr65nWC1+WAQxJw8cBkyBuHwazKSIz7oGI1bl75EwKa1Y0pqsZQ4LeSmKjCUkuXtiXLpC5KkHJy4ezeE+7uvSF5rLJTujgAevJlgOiClC7C2U+AmOpNPNdRvhDJCy6Ca/OnqBdBIAqIdgHM6uXC5gjUFZsRzd+M8vVboAeDsOkxZi0mIctL9nI4HA6nI9NsIRoKhZj1k+jZsyeWL1/O7ttsNpx22mms7SeH0xwoe7yTLwO9c3PgtEvISxJxRn8XkD8AZmEP8ogzloSKcdHSL7E+YhWVV3UNRYFSbAkVIGpGmXW0OcKtKUKPXP+GwwVHjz5I7j8QzsxsCDYJsPeFkXIVTMELQRZg7xaF3DMNsFuCVNBCcC27D+l/3YxkVxgOjxOCaH3dTCegUIWqauSNBrSqKCrzC1G8tgCxwiJE16yGXlIEIRSABIM3keBwOBxOh6TZQjQrKwulpVb5mW7duqGqqgolJSXscUpKCsrKylr+KDkdHlUx0CM1B33z8uBx2jG6kw1jO0tAWVeYGwZD0K0SSIVKCJcs+xrflm5kj3XTYElMGyrzkR/cjJARYmWetpXMROvIIlkeiCGmGdtNfCJ3vaIZEDI7wdu7DzzdukFOSgLkbjBSr4EppjKhKCUH4OjvhJgztOa5m3+F/MnJSCv/FOl5PsgOmS3XUgEt2RojGIBjEyudCn+pH+GwDi0aQ2jTZgTXrUdk/ToIUSrsz4vfczgcDmcPF6IHHHAAHnzwQSxevBidOnVCTk4OnnvuOQSDQeaez87O3jVHyunw6KqA3hmdMSCvG1K9XpwyQELnJAEIpcJYsw/cmmVtjBk67lo9H09u+JMJUbZMU1AWqsTGys1YX7URVWoVYDPriEx2XxCYAF272Y9NhQFsKAigMqg0KdZU1UzAkwpHZjrcXbvBlZML0UVi9FqYNutzL4pVsOdtgDT4VMCRUv3ECKLf/g/q++fCF1oI2Qgw4arkAUZ1l1FbBLAXAWpMRVVlBIKD6poKMFQNsUo/whs2wqgog20XtTzlcDgcDqddZM1XVFTgggsugMfjwdy5c1mXpeuvvz7RGeaWW27BqaeeivYEz5pvWxnQNklEQaUfGyoKsHRLOW7/MQLWGVPQ0WvAGqxFQWLsEF8mbuw9Gqly3XadNtEGl+xAqisZyQ4fJEFGuT+K0sooQhG1Tna6XRaRkepGdgolJFFjhsaPjQSkTQ/DqCyAqevQAxFES4qhVuVDrHgQgkamTYoBdUB3T4W+bhGMzfVLmunuPGjJA6AljYEQHA+hulq+0kWDmQykd8qAz6FBDYZY9yZdsLGQAGdmOuxZ2dAFcZvHmSgh1Q6y8HnWfMvPC8+a53A4Hb6gfXFxMXPTE7///juWLFmCwYMHY+TIkWhvcCHa9kQFuc5LqsIoCpbivRWFePT3sLV90cRhw4vxReQf6NXBo5l2F6Z0HoDxaZ3hlex1RCP9M0wb7HBBNtwwVBsiMY1l7G8t3FJ9DuSme2CXxG0KOOaGNxXoFVugK1GIpgClvALR4g1A8f0Q1FXWa6DozuQLYARkaMufByINh61onklQky+pfk4USvIyiLl7IbtzJsRgKQxVhc2TDNPhhWkKcKQmw5GTC9PhrCmez+JdBXZBqKoGVPYaDXiSHGxdWxakXIi2/LxwIcrhcNoLrd5ZiVqCUsmnN998E4FAgGXek1W1S5cu230uWWMpY58y9Tt37rzDx8CFaNsTFZRERHGZxWTB1AL434/r8MUaq/5mtkfAaSPCmFv5Jyq1WOI5siBiVGouDs7ohpEpuVAUA4GwgljMkqxehxMp7iQkyUkoqjLxT2kYg9PdcEg2VlRfEIEkt4ysNA+SXBLrRx8/FsM0YFALWxig/+2iDNE0mRjVgpUQZTvMcAyRwnzoG++BoCxlzzUhwkw6C3COgOT/B9GiFTAqV1u96w2rAgAdm5J6GwzXWPZYjC2DXH49kNobjry9YTo6QcwcCMmdAtGdDFNywO7zwpWTA8PlgRLToSo6ohEVsagGTdOtuTdNON12pKS44PLaq9vwos3BhWjLzwsXohwOp8MK0RtuuKFJtUabConQl156CTNnzmTxprNmzUJ+fj4+/PBD2O011q2GCusfe+yxTLxyIdoxRQVZ8oIxDUVlIUR1Fed9vBJrKizxNjJXxImDTLxQuQx/h+pbGt2ihOHuHIx056C3I40JzZhuYnGhgd+2GFheahXM75bswE3j8tA3w8GEGglNUTThctrgdIgsM98wdOgkQJmQs25O2YlUJ7n9vRCC5YiVFVhtQEUJankZYv/cBYR+SRyPmXQynOmHIxq1wgIESYCpVSC4/k+YRX9CCm6GmnY/TMnqviQFXoYceD7xfMHXFc4xV0HKHQjTmQQVDsQUqyMUnG7EFOv4Ggt18CY54Et1wW6X2lwrUS5EW35euBDlcDgdVohOmDChwdaelZWVLGt+0KBBePrpp5u0LerMNHr0aFx99dWs9BPh9/sxbtw43HnnnTjqqKMataJOmTIFsiyzclFciHZcUUGZ4hUBBVtKQyiOqjj7oxUIqdZ2T99bwiE97fA7ovi+Ip9l0te2kMbxCg4kR7NRXJAJJUSF8+sm/FA45dF7yThiL4ndp68ECdckj4yUJCckyrJv4GsiiTZ47C6keVKQbIowKoqhhvyQXB4YkSiif90Do+LTmteSfCw0x4RqN7kAm9sDw52OkpIQYlUByIEg7P4BECiH0DRgL7seNmVR4vmmKEPZ63REOh3Fwg0EuxuQ7JA8HkipqTBFqVExSjicEnzJLniTrXjaVnaGJOBCtOXnhQtRDofTXrBq4jSDb775psHla9aswSWXXIJJkyY1eVsrV65kdUnHjBmTWObz+TBgwABWOL8xIfrEE09AVVW2P163tGNDsZypSXaougGtxMD1o7rh5p/WsXWvr9DQI1lE/xw3jvP1wUR7D6yIlGF+uACLwkVQTMvqGTRjCDo2At03AtSxqSoL6Uo2XKYL+QETpAvfX6ViSZGGc4fIyPWKzA3vDyosPCAtyQGXg1z1dYWbZuioigYRVCLw2J1I83ngdThgBgOkUuHe5xZEl6dD2/KS9Vqq3oeA92H1YaopW5GoM+ECIsYJiATPA8UJqJnXwyFdBL2oHGbUgGCocPw7F0LBr/D3vBDw5sHmcEMzDBZHKqelQZQcMCqiQEUUAoUkZLmBDKsrFLnty5QgImEFyakuON1yvVhZDofD4XDatBBtjF69euHSSy/FI488giOPPLJJzyksLGR/c3Nz69Uqja/bmqVLl7JyUW+99RaKiorQktaH5mCziXX+cnbtvOSku6EbBvYXgMl9MvDOv6WsFefTf2q40SvCLQswIUAJpCG8yQeltCdMbxmQXAwklUMQLMElOMOAcz3KsR7d7D5kRG0oixgwBRMbYOK2LSZ8DhNuGSwalKJL4//IJkXlomTRhnFpnTA5rw+6uJLYmqASRkSLwSWJSPZI8OoybJoB1+BLobjTEFv9cJNep9P9NjRlCFRlBEwzDRHhWnj73Ai9ENCKLauYPfAP0pbdgFC3M6CkHQLJAGyqDjMaBSImxFraUlhTBeR6gL3TIXitUJdIWGUJTUk+B+txL8nbTs7a1fDvEp8XDoez59JiQpTwer0sdrOpRCIR9nfrWFBqG0qF8hsKASA3Pt26d+/eYkKUZUynWr3Om4vPZ1mbOLt+XtweB2S7H9NHy1hZHsHy0hCKQgZeWm4i3Wng500q/LG4oLIB/ix22ytTR05eGYqkQqyOViS2t0HxWyZJT11nvZ9uVlv7htFVfFC0Bh8WrcHYzM44vdveGJqaleh+VKXrCGohJMkyUmQXUgafh1jGXgiteQumEoZp6OxG7neTZURRzKgMzRCgKQrk1PehlfSGaSRDU4YjGj0N7k6v0KRCKegNXewPQ+4PKdIXUoGv1oE1IiYLQkBRGFLfNNgHZUGwWzZZJWbAXxlFSqqLuewplrQ14d8lPi8cDmfPo9lCdMuWLfWW6brOROHDDz/MLKNNxel0JmJF4/eJWCwGl6u+kLnjjjvQo0cPnHLKKWhJyBrk91vlgZpjxaEfTr8/0uaSP1qTXT0vqR4ZgUAUN4zsjGlfrkJQNbAgv35caKoTGNNJwv5dbMj2kMCiYvjdUKqGsSBUgPmhAmxRg3WfZFLcJIlJChQVIJgCnJIIp02AjSI3BZHFi1ZoUURJTAL4sSSf3fp6UnFCXl+MT+8MSRQhCjICwSqU6GXwOjxITR+FtM4HQVU0lklv6CYMTYWhqNCiESjRCGSnF4FKDSX5RRAEHSmFZOMVEAlOQSwyEaaeBVR3Y2oIZre1GzA9IoRUJ0T6Dq0OgBVhNUyoK8qgrqkA+qcD3XwQRAGhIOCvDMPtdTBBanfWD0HY1fDvUsvPCz2Pe2s4HE6HFKKUrBS3/NSGZRI7nSwLvqnEXfJUk7Rr166J5fQ43s++NtS5iaynw4YNSwhggmJJL7zwQnbbUXY0sYZ+IFoiKaejsavmhT575KZXVB1X7dMZt/9mtfokZFHAuK5JmLiXF33TDEQ1BRElBrX6c0Kk2Vz4P19PdlPIKkk/+NX1RilBaWWZjrlLVZRHLfsi2ez7ZIk4Y6AMn8P63GuigV8im/FZ+TqUqZZV/59QBe5c9Rue2uDCcTm9cURWTyS5khGLBRD2F6NSqkTASIOhUVkqBYpumVxJ3IpOusmQTBXuTl4kybmoKChl7v6kcg87NiZCt8aogKisgKisZDfFl4ZQ7skQnD7YZBdEpw5xiAO2YkDYGGFiFIoB/FkCc20ljAHpQJYbND1KeRjhkMJc9eSyp05Tuzt+lH+X+LxwOJw9j2Znzb/zzjv1hCg9Jrf8qFGjkEQ9uJsIWUIpUYk6M5144ol1subvuuuuerGmGzZsqPP4zz//ZHVE582bhz59+rCs/R2B1xFtXxnQrKxTRMXGoiDe/6cEvxX4MTTLi4O6UDklCXbZxrolCTYDCqIIq2EEYmFEYgo0U4NkE9l6l11GTNVYpyWtlugKqybeWKHhl801AtYrA1MGyhieU5NqJNiAJbEifFaxDmsjdUNJ3DYJh2f2wPF5fZBtE2CGKmEXdUSiKsxtWBztdhd8nmxUlakIVoWQ9o8ER6XIYlgVOQZdLkGq9BGc8nwIeiHUfB1GVc32dHs6/D3/Ay1lb4iSHSJ1nLI7IMIJqcCAWFo35sDMcsEkQZpkT3yXnS4JniQnXG4ZdoeVib8rraQ8a77l54VnzXM4nPZCqxe0f+CBB/Daa68x4Um96+N1RD/66COIoojy8nImbmu77uPMnz8fZ555Ji/f1IbYXaKC3I7lgSg2FwehVJdzagiH3QaP2w6H3YQo61ARRVSPQDc0KJrKOjiFohqqgjFEo1ZqUpwlRTrm/aUioNRsb3SeiFMGyCw5Kg6VfFqrV+FL/zr87q8btyxCwLj0zjg5tycGOyREIzGrbFJ1F6SIYcBP2fe6hoCuwq9rCMFE2BBR6A+hIhZBimrHIE8axgpp0AJBqMEydMGHyDC+YdvQy00mSFk2VTVKt2Og9T8H0bAGQzcgMFHqgKA4IBcAYqh2RhNgdvfB7JMKVMePEna7DQ6nzLozUYY9zTmVTmupMwZdz7IuVZTgleze5Z+Z9gYXohwOZ0+g2UL0vffea9YOtlfOidzr999/P7O0RqPRRGcl6pREgvTggw9mBfInT55c77lciLY9dqd1i9zHxRVRFJQGE25kEjeyZFlEPSSi3DIrveSQKW6TjYBu6IgaMYS1MFRdZe5xKtcUjukIRzRo1fF45BKviOh4aH4JftpUE0Oc6ZZw4t4eDMk0IZg1KpXGl5phfBPcgB8qN0Ex677+/r502CHCrynwqzFUaWShbfoc9ZdS8B9PX/RWHFD8VXApS9FdnwfJLIehmFA36DCCNV9nMbkLhIFnIpQ0BKGgaQlSmwSb5IQYdkAuEiFY/QEsZBHomwqjm89S1/HtiAKzjLo9dri9dnafaK6VlESnyPKhBOiaweJlFYUSt0zkdkpBTFHZY44FF6IcDmdPoNlCdO+9967TYSaxoWp3/dbLVqxYgbYOd823XzcriaTNpSEEQiqcDhuS3Ha4SXjabbBV91hv7CNu9Wev/VhEVNFR5o8iEFISx08i9fP1FXj4j/xEMX3CLgoY08WF/TqL6O7T6mTeRwQVP4Xy8WXlBlQ1UGR/R6F9TEzqistyhiFPsEEPFsC95THIVT9a1tESA+oWau9Z+0kikLE31LThCLoGQLV3gijLEAQZcsANqVRCdXUrhumWIPTyAZ2SAIdsWYlZIpfJSj05HJaVlFz3VumnhovjW/NrWX41lYSnDiWmIRal8lFWG1ISpGRpTUv3wuGyweGizk+8tinBhSiHw9kTaLYQXbJkCaZNm4azzjoLxxxzDLKzs1lXJSp0f++99+K6666rU6Ce3O1tHS5E23m8nyAwsShTLKbZfEvdVpti/1WFFJRURBAMKwlXdHFYwf2LNmNxcaje89JdNozrKmPfHLKY1iynlqELY4X4omo98qMBtswu2pAs2ZEkOeCT7Oy+T7bu+6qXseWyA7nuFKyuqMScTQuxMUqFpSxcog1n5fTCmb4uEMsqIRR9jdTgWxDNMIyICWWDDjPS8DwYzkzEkgcjljwUaspAiGISHJVe2Px1cxcpbMFMlYAcJ4RsDxOlgmSjnqFMYMp2G4snpYx7cuGT8CTFShUBqE4pWTypiD7ddM0Sng29NyREPV4nYjEF3iQnK7bPKgDs4XqUC1EOh7Mn0GwhSi7yww47rMEMdUoaeuONN1if+PYEF6ItR0dJPCFRpRsmSv1RlFVGEY1ZST70dVlTFcWXGyrxzaZK+BtwJfdJl7Ffnoih2UjEklJ9f9MpQDIEuCXLkkiloEiEsb9U8qm6jCd9I61vpVW+ySHbWZb921v+wbxNf7FY0jhZkgOX5PbFEfY0aIWrkFTyPBzqSivBiNzxfhO6X4QZbbgwqinIUHz9oaQMhZY8FnKwK2zR+sU0TBEwkgXo6TYgWYLodECwyxAlCTaHHQ6PEy6vk7nWyeqpaTqzdm737ELtVEURXp8LoWCUCVWytqamu1kIwJ5cGo0LUQ6HsyfQbCE6ZMgQzJkzB2PHjq237ocffmDdlSibvT3BhWjL0VGEaBwSiuGYhpKKMCqDNe56QjUMLCgI4ouNFVhQGGDVkbBVyOU+uTJG5QJ7Z9ngcdlZ1nzcKkhWRRKoJMTImEgxr7JNhCzbEiKVwgviN6JSiWFe/t/4oHAN6/wUZ2+XD9fk9cdARYK46R0kVb0DATWC1YiRKDWgV1kCtbHa96ozF1rq/8FwjoWgdYag125IamHaAC3ZupleAaJkgyDamLVUcrshOJ0Q7A62i0SFALO6dBRlfgUUCOyvat1XDdhyPdAHpMNwWyLY4ZSQkuZmorSlMvYtiy2gxnRWvN8m7bwFfVfChSiHw9kTaLYQJXc81fik7Patufjii1FSUsKsou0JLkRbjo4mROu464MKiisjCNVy18epiGr4dlMlvthYiXVV0XrbSHUK2L+rjFy3iTSngDSXgGQHWUpJhFrF8klssrqi9FcUWaiBzUaiVLQSrLQYKz1FAnWLEsDzm5fh96q6WfqHpeTg8qw+yC5bD7n4C9hjqyCpGyDUSqk3qZh+0LREqd+AWTthqRZUeF93j4aWdDQM22CyzdYbY8jVojQFMGk1iWv6p0mw6RJE6nQV0mGS4CQhus2JBsxeKTB7pwAkFG0ikpKdzDpKxfd3RDDSe0dzSdbZSFhBKBBDLKaxOaZwAlaiyilDpnJfgiVK20pIABeiHA5nT6DZQvSzzz7D9OnTMWjQIJbRnpaWhtLSUnzxxRdYs2YN6wNPme/tCS5EW46OKETjkHjRyF1fFUWlPwZFpQz7+l+fNZURfLGhkgnTqm1kgVNHzSy3jGyvjGyPjFyvjByvA7leO3I8dmS57bDbbFawpqghqAVQHq5CMBJhwowsqn9HS/Fa2QpsUWq6RDkEEadldMc5Kd3giqnQA8WwBf+BFF4Bh7IasrYxIUxZ0mEU0KsM5sY3apd1qoUJGYZzX+iuCdCdYwChAVFqBwTdujUV00k9qwBEa55kuiSYA9OBHA8Thy6PA6lpLlZCqqmJTHHrJ4UJhIMKK9ZPAnTrGq6sygJZoO1WVQCyxFLZKpGVqmo80W13wIUoh8PZE9ihOqJff/01c88vX76cPSaLA3U7uvLKK7HPPvugvcGFaMvRkYVoHLLUqbqBcFRDIKyw4voUG6lu9XrJdb+wMIgvNliu+x1JBk9zSshykVC1o5PPgdwkCT6XCocYhVNUWAyqbhr4IZiPj6pWI2jUmDdTRRmj3Gno4UxCb6cXvWwu5BgijGAZxMByyJGVcCirYNc21QhTrTq2NGxCj3mghwFRsZKs4piCC7pzPyZKDccIq7L/djAkEpgCBK8EIcUJMc0NIc0DQTRgKlGI62PQVlbUCRkws92WICWrpd2G5FQ3s5BuK5GJ3hv6PkfDKoJk/YxYGfoMijctDEMoj8L0yECeB3BK9T6/kkxJWDJcLrKWSuzxri7q3xBciHI4nD2BnSpoT3U/q6qqkJyc3GDB+fYCF6Itx54gROvWxSQrqYFIjDo0afCHFSgxnbUgrf3F8qs6VgUVrC8LoZBc/GEVRRGVZeLXLgnVXNwyEq5+n0tDkXs91or5deJHa+MURHSR3eju8KCH5EZP0YGuiopu4dVwRVbArqyCU6/pYGbAhoj3aASjvYCyVZAq18IeWAtJtTpJmWIydOd4S5TaB1htR7UNgL4Fqt1AJCkZofRu0Fyp1XMmslhYSZIg2+2wVSc92elvwIRtZQRCaU1tVlMUrEL7vZIhSpTU5ERKmouJw7gwjJeJUqIas3yGgjFmCWXr6fRWHoWQHwS2BCFoNfPC7mW6YHbyMusrC+qtBW2X9kNJU+TCJ1c+VQqg5btDmHIhyuFw9gR2SIgGg0GEQiFWuklVVZYtv2XLFkycOLHdueUJLkRbjj1JiNaPRaQyUkA0piMUVVkJqBhlkSs6K+Xp9ToRDEbruZdDqo6isCVKSaAykVrrfnl1xn6TsYeB7LUQfGVNfooEAZ1kF7pLLnRTq+AJL4FpRKALInRKexJ9iHoGQ7MlQzUFOEOVyKnaiE5Vm9AlsBndgluQrAQhNCKAI+5OiKYNQTR9KLS0voDkYp8V2WGH0+2CqguIkHi32+EoE+BarVvxpXG8MszBmTDTncxamZrmhpsSmaqtnwGyflJ90ngoRFCFkB8A8oMQItufPxK8IAssidIsN2AT6s+RbGOxpCRM6RjsdgnSLhSmXIhyOJw9gWYLUcqInzp1Kk455RRcddVVuPXWW/H666/D5/MxgfrII4+w2NH2BBeiLceeKkS3Jm6liyoay7oPRjQINhF+f4TV2GTxh03clqIbKGHW061EavUyWkexq/UPQgMcIcARrrmRSLVH6xTybylyo37sV76e3UZXbIRPb7iQvyLasCalG9an5qLcZ4ckB9FPktDdOxJhYRQUwQ1RcsBTKMO5xSriH0fv7Ia4dwZkr5PFjJLlM2H9JBG6JcQEqFBRf98mics8D0yKPa2MAZuDEML1RapJltFcjyVK053sKoOV86J/hg5ZpGYAVsktVuDfKTNhKtslK+mphYQpF6IcDmdPoNlC9JxzzkEkEmFZ8xkZGax4PdUWpbacdKNOSm+++SbaE1yIthxciNaHuXiZ9U9GeWUYimqwzxwlOlHCU4w9NllRfhIvdW5N+Hrqpslc/37NwJZwCOv9AWyqjKAwqKMwZKAsstUTBMMSpLUFarVIFcSWserZDAMDA4WWMK1Yj0H+Qtgakd7Fdg++zdgLX2b2huK1Y5DNju5md/QSeyIHqfAWOCEHhTrxpuE+LqC7D3bRDrlYBTYFgKIQe2m1SbjfuyQxiyfLEEusNIGKGITN1W77hrL6nTboeW6Ec20IOBU217JNhkt2wGlzwk6ilOzAJllMRUiSjSU8MWHqqBam1d2lrPqwTZ9fLkQ5HM6eQP3K1U2wiD7wwAPo0qULvvrq/9u7E+imyjxs4M/NnjZdsaUFRPZdQBAEBxQUwU9lZlyO34eCwoALjnLUcVQUccEdFEUGV3BHR8cNd0BQQWUXFxYBWQRtaemWNnty73f+b5rQQlEL1dDm+Z0TktzcJDdvbsvTd12MQCCAv/3tb+qxs846CwsWLPgjjpOo0YqFyhSHFQFp1rXo8SVx5VpuyuT5ker9JJRKv9NwdVgNhaXPqa4GQ8ljET1aoxrb36xpyLJZkGUD2qc7MLxNLiImP8oDFaj0e1EVCGOPO4yf3REUeHQUVJlQWJWGvZUu6O7asc2w+QFrLLlqyLQCPTK8GOlYho7+5bAaIRUozdARyjkVesvzYdYcsBpmdWwlngrs9ZSh2FeJ0oxMFKd3xPPH+REJFqN1+U50KS9En7I9aBHYPwAqN+jB//3lG3XZa3NhYW4nvJUbxHdpbrQydHTOycLIlHboX9IclohJVfS6NvoQ3OWHOWhAq2P6KSPNBuNYFyC1mtUDkqR/pyxP6ky1we8LRZvym2kwsh1A92Yw9vmiobTAAy3WfcIfgXl7JdK2AyanDm86YEqxwO/0wJdqAVKl/2gKHGY7ELapaaL8viAqyqN/fEgwldWnpLZUzQurpqWKTs8lfWXVsVaHVFX+R8nUUURER20QlV+gdnt06pZly5apJvmePXuq+9I035gHLRH9GWrXjO1PHqZY7alJU4OKJAhKSK0VVlUQNRCKyJKZcq2rPqj+UETVssrjgYAOs8mGHFtzNHOEoRsRdM6JIGyEEQgHEYgE1PP9oTB+cYewW0JqpQRUHYVVKfi5yolYr4pyAMvLs7Ac/w8nuwbg7szHcRy2RR/c+zaCJZ+jpM14GM1Pht2RitwMF7oFjwG8XmhFX8BWvhR2/4Zo31GnrE0KhPNkYv5MVHpyYXYHkVWxBxY92kTePFiFMXvWqcseRzo+zumMD3O74NqMCmS67Li+uAvOrIwuG2w7YKqpiA2ItHDA1DodtmyXKlmLVWolLUhNk8FGNtjs0RHw6ZkOtQSpzxuCpyqIoD+ESPNUaHmpCIb8CP/ihvlnH6z7QjDJ9FkAUn0mpKqMHqs5lf6oAUQsHoSdGvwpZmguG8wuO8wuByIuOyL2iFriVMhXKl1RNbWKVnTBAgnH6mI1q4CqqYsBw2QgpMn3e/A0WURESR1Ee/TooZreJXDKnKJDhgxR/1GWlJTg6aefVo8T0ZGpXUNWI6zGV2CyQLPvD6pqjXfDQDgCVZMqAVX66AblEoyoPqSaZsBli+4bMcKqib5dViTe91ECqgTVCn8IK38O4IvdQXxXFIqvGPVlVRsMq7oXY1wf4caMl+HQArCFS5G/bToCFacj1OkaWCKlMP/yISwFC6FVj6yvKWDrjCpbX1RpnYEcGyImE8qMMJylG2D5ZQ3sJZuhGdEBR638bozfvVpddjizVCB9NrcAb2Z0wI1F3dEhmIaAFsEmVzn8zXQcm50KuzUMu8WApkeQmelCRooFDlsEuvRXDehAKLoKFKx22Gx2OJwpagUnqR2tcFehvKwCbl8Zlpp24sNjdmJ3ehWGVuZhRGU++vqa1fldmcOAudIAKsPAXgmd3tpLozot0FOtMNKsatAV0q0wXGYYFiCsRxAx5PuJwNB0tcyrqi01acjOTkN6aip/lIioSat3H9ENGzaowUplZWVqMvv58+ejTZs2qq+oruuYO3duowuj7CPacNhH9Ogol5o1qXIttajSzK9Cajh6CQSj/VPj/VBj4VeTyZ+i69yX+kJYurMCi3aW45ui/ZPmtzLvxd1ZT2KwY/9yvrpmh8moY4CSoznMrf8KrfVfAVsewsEAIj4f/B4vQkFZNjUMI+yHv6oSurcM9qL1cBSth61sa52j8H9IzcFHOV2xLfMkrE4zwSNJUN4GwEBrOs5wNceQjHy4LDaYrBbY7Slw2lNgs1iqq6Ola4QJmtkCHSb4AwF4fJXY5nPj9dKf8E75LlToQbVvbrAKbb2l6OApQ1+fhn3Otvg+tS2y9Qy0CqWgZfUlN+yAqdawqt8WtBmocuood4ZR7AiiwOHDTqsbP2selEX8CNtMGNf1JIxr1b/e50x2dqqqcSUiarLTN8kqSh07dkRKSora9vHHH6NPnz7IyclBY8Mg2nAYRI/+ctkfUqtrR3WpmasOqNIXNaLD4wvBH5CgKk3+0V8RMr3UF4VVWLKrFFvKZBlTA+enLMWtmc8hw+Sp9R4RWFBiPwla9jCY0nvDmZoKV3oqHGkZsLsyYE1JhaGZEPR6Yfh8MIJeuEtK4Pe64a0sRdDvQaiyGJaCtXAWrYPdvbPOmFduceAXRzoK7WkodKShwJ6OAkca9tlS0cpShf6mfRiqFyPTZIFmccFsTYfZmgbNdhz8tvYoDrqw2FOINd7dCHr3qtDZzluiruWSGjm4A6rMrbo7oxOWNOuK13OPxW5ZjUk3IT/sRKtgNJhKSFW3gy60DDtgUx0vfp8ycxA7bVXYYavCu9k/45PzJ0nKr9d3zCBKREkxoX1TwSDaNAPX0aSxlMv+WlQgGDaqp54KwSMT9Yeig6ak2bg4EMLSHcX4aEcZ/L4S3J75DP5PygpsCLbF657TsMA7GBVGGiyazEVvQddmKejULA0dm7nQLsOBFJsZrhQbnDYzUlOsSHM54HF7Efb6EPFWoay0FJuLyrG5uApbKoLY5y5D+4pVGO5bgg6hn3735wlpJhTZXfDY7LBbI2hu8SDNGkJ5yI69wRRY/WHk+9yw/O7JtGoLw4RV9u74OLMnljQ/DmWZIbVaVE0yEUGLUAraBFPRJuhSl7bVt9N066++fsASQpfrBv3upU1jGESJqLFgEGUQTcrA9WdrrOUSWz1KmvZ9MieqPwx39UT98jesrOy0tagY72/ZhxWFVdjus/3ma0rdYKs0OzpkOtApy4n22Smo1IHNeyuxs8KHn9wBFHrrGApfrWWkEH8LLMHw8Jc4JlKGtHDVIaeGOlzyavvMWfjB3AbbzK2xw9wK+0zZ6B/6FsMDXyBf31dnKF1t7YFFaSfgk9wOKM8MQXfUrilWdA3WiAlp4TA6hHX0CmnoETajdSgVmaFj4AznwaRHV6IyW7fg2EkXQZfOpvXAIEpEjQWDKINog2qsgeuP1lTK5aCJ+r0hFVJtCEHzVcCtm7GhHPi+xI9N+zzYWubDbncgPs78SDjMGtqnW9DeoaONNYwczY9NXg3rK4FSTxWa6/uQHylGvl6MPLnWCpBv7EV+uBSZ4QMnU43ymSwodaYgxR5Bjs0HzaHB5NCg2WV0u4agYcF3wfZYHeiKNcGucOsuZJrC6BH+CSf4vkNnzwakhWRugdqkh20kswXKczpgfW4nWKwh5Pl2Ide3E81De2CK6NJVVQ28j14bte4bkRQYaIYvHC3x9xs+hlHPyfEZRImosWAQZRBtUE0lcDW0plgusVAqfUslmAbCOtyegJo+KjoHanS+U39Yxw63H9vKfdhWHr3eWRFA+BC9glIsJhyXbkfrNDuOTXPEb+e5bHA5rUh1WJFq+GDyVcIIBYFItPvAmn1+rNgXxKqSCDZUyswAsX6VBpzWEuQ7tyHPuhO5RgmKTNnYHuyGvVWd0M5Ugv72jTjRtkldt7AcXNt5KGoOUJ+BSJmBSLkOI4gG5Ten4LgH9kCv5wQnDKJE1GSnbyIiEtElLA01pVSqw4I0k4Zj0h3RQU8yv2kookbl+wIhtf2E/PTqFaV0tWypNMFLMP3ZE0SLTCfy7GYcm2pDtsMSn/BfzbdZPeemXJtkBgDDQMCWitSMzGhf1pAOa0hH/9Y6BugR1S9VpqFaV1iBVT+7sbLAg+/2HYPt7mOwHQOiK0upkBp9j51GK5itbeDL/jv2ZVjRw1GEDuH1cHm/hcX7PSzBPb/epzZFg0kWbWphguGDCqQqlNa9wmm9ZPccAZPFBr2J/PFCRHQg1oiyRrRBNcWav4aQjOVSc/oo3ZBR+QaC1atESQ2qLxBW21Rf0xQ7/P5odaLFbILDaoZNJno3R9dzt8YmgK+ekqjmGMvY6wdChhrpL69b5Q9BjxhqXk6rxYwwwvhmnxtrf3FjU7EXzZxmdM1NQY/cdHTJyoBdFgwI+mEK+6GF/UDQi0goDOghmEOlsAc2w+TbAhMiauon+WCq/6zZCs0sA440GNK8HgpDD4bUcyPuUujFu6GXFwGajNpPA6zpgMUJzeIELA5127CmqotuToFuSVWXiCkFGZ27omW/QfB5A5y+iYiaLNaIEtEfoubSleqXjUlT66+7pA+m5lBb1WpQhgGn045gIKjmLo1mzbrXZ5f963gn9a/NrMGeakNWml3Ng6oCbygCfyCCKp+BnpkZ6JWVKX9+RxcAiGiqdtbrDSMo4dfmhDM9HQ6bBTaLBpsRhEkPwgj6EfF3gR4M7j8WCdixG9U0sxkmixWaZlZhVPf7Ea6sRMTvkwdVYJW5S1F9kUBryDWiA8Ik3OrSt9SQKaI0mFwuVQNMRNSUJTyIyiT4s2fPVqs1VVZWol+/fpg6dapay74uW7duxfTp09Wa97ICiex/8803o0WLFn/6sRNR/ewPltFAJzFLaj8z0+woC4dVzV9ELax0eKPg5bVjUx1J8E1zWJDutCI3y6Em9Fd9WYPR7gF2qXWV2laLGRZZ/12rHX7Dmh2axQ7Nmg7NlQuLHop+gFg4lLdRtyVEqjvxbZIrpSuBVbYFZXL86gOU95CnqiVco7W5hqo2Nu1/2eq3kBpgqRX21DHwnoioqUj40htz5sxRqzNNmzYNr776qgqmsnJTUH55H0BWcxo3bpxaXvTFF19US4qWlpaq/QOBBuiQRURNioQ66cuqmullvXi7Bc3S7cjLdiIj1aZCsFnyoy4BNjq4qmYNbOz5Em7DhgVhWKPX6rZcmxE2TAjrZoR1U/VtqWnVEAoBoZCsGW9FyFR90SwIaxZEVCO/7C9TY0VreiWER4P4/msioqYuoUFUwua8efMwadIktWZ9ly5dMHPmTBQWFmLhwoUH7b948WJ4vV48+OCD6NSpk1pKVGpHZZWndevWJeQzEFHjUTNYci0PIqIkD6KbN2+Gx+NR69THpKeno1u3bli9evVB+8t+UoMqNaIx0jwv3G73n3TURERERNTo+4hKzafIz8+vtT03Nzf+WE2tWrVSl5qeeuopFUylr+iRjmquD+m/VfOaWC48Xw4Pf5ZYLkSUvBIaRH2+6GonNlvtZQHtdjsqKip+8/nST/Sll17ClClTkJ2dfdjHISNWZWqdw5Ge7jzs923KWC4sF54z/FkiIjqqg2isiV36itZsbpeBR07noQOe9O169NFH8fjjj2PixIkYM2bMER2H9Blzu731rsWRsOV2+ziogOXC8+UI8Gep4ctFnsfWGiJqDBIaRGNN8kVFRWjdunV8u9zv3Llznc8JhUKYPHky3nvvPXU9duzYBjmWw51kPDbClVguPF+ODH+WWC5ElHwS2sFRRsm7XC6sXLkyvk0GHW3cuPGQfT5vvPFGfPTRR3jooYcaLIQSERERUZLViErf0NGjR2PGjBmqj2fLli3VdEx5eXkYPnw4IpGImic0LS1NNd2/+eab+OCDD1QY7d+/P4qLi+OvFduHiIiIiBqHhA/5ljlEL7jgAjXgaNSoUTCbzZg7dy6sVisKCgowaNAgFT6FNMcLmUdUtte8xPYhIiIiosZBMzirs+qbVlrqqfd0TzLSvqzMwz6iLBeeL0eAP0sNXy7Z2akcrEREjULCa0SJiIiIKDkxiBIRERFRQjCIEhEREVFCMIgSERERUUIwiBIRERFRQjCIEhEREVFCMIgSERERUUIwiBIRERFRQjCIEhEREVFCMIgSERERUUIwiBIRERFRQjCIEhEREVFCMIgSERERUUIwiBIRERFRQjCIEhEREVFCMIgSERERUUIwiBIRERFRQjCIEhEREVFCMIgSERERUUIwiBIRERFRQjCIEhEREVFCMIgSERERUXIGUV3XMWvWLAwePBi9e/fGZZddht27dx9y/7KyMvzrX/9Cv3790L9/f9x5553w+Xx/6jETERERURMIonPmzMH8+fMxbdo0vPrqqyqYTpgwAcFgsM79J02ahF27duG5557Do48+is8++wx33HHHn37cRERERNSIg6iEzXnz5qlwOWTIEHTp0gUzZ85EYWEhFi5ceND+X3/9NVatWoUHHngA3bt3x8CBA3HXXXfhnXfewd69exPyGYiIiIioEQbRzZs3w+PxqEAZk56ejm7dumH16tUH7b9mzRrk5OSgffv28W3SPK9pGtauXfunHTcRERERHTkLEkhqPkV+fn6t7bm5ufHHapJazwP3tdlsyMzMREFBwREdi8VSv0xuNptqXRPLhefL4eHPEsuFiJJXQoNobJCRhMma7HY7Kioq6tz/wH1j+wcCgcM+DpNJQ1ZW6mE9Nz3dedjv25SxXFguPGf4s0REdFQHUYfDEe8rGrstJFQ6nQcHPNmnrkFMsn9KSsphH4euG3C7vfWuxZGw5Xb7EInoh/3eTQ3LheXCcybxP0vyPLbWEFFjkNAgGmtmLyoqQuvWrePb5X7nzp0P2j8vLw+LFy+utU2CaXl5uWrOPxLh8OGFSfkP4nCf25SxXFguPGf4s0RE9FsS2sFRRsm7XC6sXLkyvs3tdmPjxo1qntADyTbpOyrTN8XIKHrRt2/fP+moiYiIiKjR14hKf8/Ro0djxowZyM7ORsuWLTF9+nRV8zl8+HBEIhGUlpYiLS1NNcv36tULffr0wXXXXafmDvV6vZg6dSr+/ve/o3nz5on8KERERERUTwkf8i1ziF5wwQWYMmUKRo0aBbPZjLlz58JqtaqR8IMGDcIHH3yg9pVpmmbPno1WrVrh0ksvxbXXXotTTjmFE9oTERERNUKaYRgGkpz0Zywt9dR7uicZaV9W5mEfUZYLz5cjwJ+lhi+X7OxUDlYiokYh4TWiRERERJScGESJiIiIKCEYRImIiIgoIRhEiYiIiCghGESJiIiIKCEYRImIiIgoIRhEiYiIiCghGESJiIiIKCEYRImIiIgoIbiyEgBZXErX67/AlNlsUqsyEcuF58uR4c9Sw5aLyaSpJZGJiI52DKJERERElBBsmiciIiKihGAQJSIiIqKEYBAlIiIiooRgECUiIiKihGAQJSIiIqKEYBAlIiIiooRgECUiIiKihGAQJSIiIqKEYBAlIiIiooRgECUiIiKihGAQJSIiIqKEYBAlIiIiooRgECUiIiKihGAQPQy6rmPWrFkYPHgwevfujcsuuwy7d+9Gstu7dy86d+580OXNN99EsnryyScxZsyYWts2bdqE0aNHq3PntNNOwwsvvIBkU1e5TJky5aBzR8qnqSsvL8fUqVNxyimnoE+fPhg1ahTWrFkTf/yrr77Ceeedh169euHMM8/E+++/n9DjJSJqSJYGfbUkMWfOHMyfPx/3338/8vLyMH36dEyYMAHvvvsubDYbktXmzZtht9uxePFiaJoW356WloZk9PLLL+ORRx7BiSeeGN9WVlaGcePGqYB15513Yv369eo6NTUV559/PpK1XMQPP/yAK6+8UoX0GLPZjKbu+uuvR3FxMR5++GE0a9YML774IsaPH4+33noLhmHgiiuuUOeM/J759NNPceONNyI7OxsDBw5M9KETER0xBtF6CgaDmDdvHm644QYMGTJEbZs5c6aqHV24cCHOOeccJKstW7agTZs2yM3NRbLXDN9+++1YuXKlKo+aXnvtNVitVtx1112wWCxo3749du3ahaeeeqrJB9FfKxcJXNu2bcPll1+OnJwcJAv57r/44gv1h23fvn3Vtttuuw3Lli1Tf9iWlJSomuHrrrtOPSbny8aNG/HMM88wiBJRk8Cm+cOo9fN4PLX+E0hPT0e3bt2wevVqJDOp0ZL/KJPdhg0bVNhcsGCBak6tSZpc+/fvr0JozIABA7Bz507s27cPyVouP/30E7xeL9q1a4dkkpWVpf4IOf744+PbpDVBLm63W50vB9Z8yvmydu1aFd6JiBo7BtF6KiwsVNf5+fm1tkstYOyxZK4RLS0txcUXX4yTTz5Z9XX7/PPPkWyk2f2xxx7Dsccee9Bjco5Id46aYjXIBQUFSNZykXNHSLO07Dds2DBVa1xZWYmmTP6IPfXUU2t16fn4449VTam0shzqfPH5fKqbBxFRY8cgWk/yH4A4sC+o9I0MBAJIVuFwGNu3b0dFRQWuueYaVcsjg3GkqVUGW1CU3++v89wRyXz+SBA1mUwqZD3xxBO4+eabsXz5clx11VVqcGCyWLduHSZPnozhw4errj91nS+x+9JNiIiosWMf0XpyOBzx/wRit2Mhwul0IllJU7P0/ZPBJbFy6dGjB7Zu3Yq5c+eyP1s1KZsDA0QsgKakpCBZTZw4ERdddJFqqhadOnVSfUUvvPBCfPfddwc15TdFMshP+p7LyPkZM2bE/0g58HyJ3U/m3zdE1HSwRrSeYk3yRUVFtbbL/ebNmyOZycjvmuFcdOzYUQ1SoShpZq3r3BHJfP5IbWgshNY8d0QydHl56aWXVEvC0KFDVY1wrJZcft/Udb7IHy3JOhsFETUtDKL11KVLF7hcLlX7FyODCmQka79+/ZCspOZTanJqlov4/vvv0aFDh4Qd19FGzhEZaBKJROLbVqxYgbZt26qpe5KVTEk0duzYWtukJlQ09fNHRsxPmzZN9a2WKZxqNsXLFFerVq2qtb+cL/KzJuGdiKix42+yepL/JGSeQ2k6++STT9QoeplaRWq6pF9XspLR8jLiWQaYyEjfH3/8Effdd5+aJ1OaXSlKpmiqqqrCrbfeqqYrksn+n3vuOTVXZDIbMWKE6ks8e/ZsNYL+s88+wy233KKmQ2vKMzHs2LED9957L8444wx1DsjMCTKnqFxkoJZM+v/tt9+q3zfyMyVTx3300Udq3mIioqaAfUQPw6RJk9TgHFkJRgYTSC2X9IOUqWmSldTOSJPiQw89hGuvvVbVEsuUVs8++6zq70dRUuspc0Dec889OPfcc1U/SKkNlNvJ7PTTT1eT3Msgt6efflo1O48cOVKdS02ZjJAPhUJYtGiRutQk54QsmiELaMhk9s8//zxatWqlbnMyeyJqKjSDk9ERERERUQKwaZ6IiIiIEoJBlIiIiIgSgkGUiIiIiBKCQZSIiIiIEoJBlIiIiIgSgkGUiIiIiBKCQZSIiIiIEoJBlJLeHzmVLqfpJSIiOjQGUWowp512Gm6++eZGVaJbt27FqFGjGvx1ZWUpWTFJljtt6vbs2YPOnTur5UqJiIjqg0t8UoORdcJdLlejKlFZt/vrr79u8NfdtGkT3nnnHbW2PBEREdWNQZQajKwtT0RERPR7sWme/pCm+Vhz7YcffohJkybhhBNOQP/+/TFlyhR4vd7ffK3t27fj6quvVs/p168frrjiCvz444/xxysrK3Hfffdh2LBhOP7443HOOefgf//730HHM2vWLDzwwAM4+eST0bNnT4wfPx47d+5Ujz/22GOqFlfIscp9oes6nnrqKZxxxhno0aMHRowYgRdffDH+ut9//z26d+9eqxtCSUkJBg4ciHHjxmHFihW45JJL1Ha5HjNmzCE/ZyAQwIMPPohTTz1VvdfIkSPxwQcfxB//5JNPah2bkHKQz3LLLbfEty1evBgXXXSRKmd5nTPPPBMvv/xy/PGVK1eq1/nqq6/U8cjzhwwZgtdffx1FRUWqrOW5chzPPffcQc9bvnw5Lr74YvW84cOHY/78+b/6/f3yyy+4/vrr1ffXq1cvXHrppdi4cWOtfd577z389a9/Va85YMAA3HDDDdi7d++vvi4RETUxBlEDGTp0qHHTTTep27t37zY6depk9OvXz7j//vuNL7/80njiiSeMzp07GzNmzPjV1yksLDROPPFE4+yzzzbef/99Y+nSpcZ5551n/OUvfzHKysoMn89nnHPOOcbAgQONV155xfj888+NqVOnqvd7/PHHax1P3759jcsvv9z49NNPjXfeecfo37+/ceGFF6rHCwoKjFtuuUU97+uvv1b3xW233WZ0797dmDVrlrFs2TLj4YcfNrp06WLMnj07/tozZ85Uz5PPJa666ir12nLslZWVxksvvaQel+utW7fW+Tl1XTfGjx9vnHDCCcazzz6rPoe8tzzvrbfeiu93ww03qOPZtm2bEQqFVFkMGzbMqKqqUo9L+chz7r77bnU8S5YsMSZMmKC2rV+/Xu2zYsUKdX/AgAHGvHnz1H5jx441unbtaowYMcJ45JFH1Larr75a7ffNN9/Uep58H/L6coy333672vbyyy/X+q7feOMNdb+kpMQYPHiwMXz4cGPBggXGokWLjNGjRxu9e/dWn0GsWbNGvfdjjz2m3uPtt99W3+/FF19c7/OOiIgaLwZR+kODqISomsaMGaNC5K+R4NqzZ0+jqKgovk1C4pAhQ1SglAAkr71u3bpaz5NQefzxx6uwGjseuYTD4fg+EnzkuaWlpeq+hE25H7N9+3YVlp988slary3BU1479rxgMGiMHDlShTgJYPIaH374YXz/WICT60NZvny52kfCdk1SZhLKJHSK8vJyY9CgQcYll1xizJkzRwU4Cc4xTz/9dLzcY6QM5LVjnyN2PNOnT4/vIyFVtv373/+Ob5PPJ9skGNd83uTJk2u9/sSJE9UxSpg+MIhKcJey2rNnT3z/QCBgnH766cY111yj7stxSQCX7THy3cr3I69JRETJgU3z9Ifq3bt3rft5eXnxpnlpAg+Hw7UuYu3atep5OTk5tZ63dOlS1XS8atUqtGzZUjUl1yTNvNLU/c0338S3SbO92Wyu9TrC5/PVebzSrC5/oEmzfs3jkvvy2nJswmq1qiZ/6YJw66234txzz1XN4fUhzeSapqnPdOB7FRcXqxH9IiMjA9OmTVPHJl0NJk6cWKtcJ0yYgPvvvx8ej0d1G5Cm/SeffFI9FgwGa71nzTJr1qyZupam85isrKx414ea5PPVJM3zcow7duyo83N17doVzZs3j38mk8mEU045BV9++aXaR7pbyHcgXSoeeughNbvAoEGDVBcBKRMiIkoOHKxEfyin01nrvgSS2Nya//nPf+J9NGN++OEHlJeXo1WrVod8zYqKilohNeaYY46JT530a+8fC8F1kfcWZ599dp2P1+zDKGFL+k9K+Bs6dCjqS95LyqJPnz51Pi59N+U9hPRxzc3NVdsOfK/S0lLcfvvtqp+ohLjjjjsOJ554Yp3zmNY1q8GBZVQXCZU1xUJsXd+FfK5du3apfrR1kQAqgVj64Up/1GeffVbdlu/vyiuv/NU+tURE1LQwiFLCXHjhhWrAzIHS0tJUuKqrpk0CqtQQStA5kNTQ1azVOxzp6enq+vnnn0dqaupBj7do0SJ++7///a8KoV26dME999yjBivFnv97yOdMSUnBCy+8UOfjEihjJLBLwGvXrp0a8CWDjKRWVsggHxncJaFOAp7NZlNh77XXXkNDKSsrQ+vWrWsNzqoZSA/8XDJISeZRrYscnxg8eLC6yLFKba+Uw913361qaGUAExERNX1smqeEkVo2aTqveRFSmyfN6zXDqAQfaYL+7LPPVLPuzz//fND8nwsWLFDhrD4hJlZDGhOrSZTgVfO45FgeffTReI2pvL80zV9wwQV44oknVFO2hNGYmt0BDkXCmnRTkFrLmu+1ZcsWVVsc66rw7bff4plnnlFN8tOnT1ePP/744/HXke4C0lR+0kknxUPe559//qs1v/Ulta0Hzr8q3SNqhtOan0ua7Nu2bVvrc8m8qjKzgZSNlJ3MsSqfXWpkpZb3pptuio+4JyKi5MAaUTrqjB07Fm+//bYKnjJtk4RLCV7Sv1OmN5KwJdMH/fOf/1RTQ0kt6ZIlS/DGG2+oPob1qZWM7StTCUlNnDS1S1/T2267TYVNmQpJQtXMmTPV+7Rp00aFJ+kXKgFKav2khvbaa6/Fvffeq6Z6kj6eUisoPv30U/W41JoeSPqGSqi+6qqr1KV9+/YqdEo/UKkpzM7OVn08ZZooeeyyyy5TZTF69GjVB1SmrpK5WyV4v/vuu6opXMpo3bp1qqlbmukP1Re2vqT53G63q76pCxcuVP11pW/nob4/CZ1y/Y9//EPVUEu/VamhnTx5stpHpmuS15TPJuUdCoVU2M7MzFSPERFRcmAQpaNOfn6+CppS+ydBRYKn1PZJGJRQJ2ReTwlCUktZVVWlmqylRlJqKOtDahIlNMn7yHPvuOMONT+pBL1XX30VhYWFqvn5rLPOUmFTavNkfk7pJvDII4/Ej0f6NUoYnDp1qurz2bFjRzUQR/ZdtmyZCrp11cZKYJTPIO8ntb5SSyxzkUrIFvIeEoRfeeWVeFO8HMeiRYtUDaKEbxmoJIOZ5CIkLN95552qhrihlhiVOUvfeustdZxS1hKWJXTXRT6DlJ18P1KeMshLjqnm9yMhfMaMGZg3b158gFLfvn1V87yEUSIiSg6aDJ1P9EEQ0dFJJrSXSfklIMofA0RERA2JfUSJiIiIKCEYRImIiIgoIdg0T0REREQJwRpRIiIiIkoIBlEiIiIiSggGUSIiIiJKCAZRIiIiIkoIBlEiIiIiSggGUSIiIiJKCAZRIiIiIkoIBlEiIiIiQiL8f42Vw1OgOFFjAAAAAElFTkSuQmCC", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -199,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "31b4ecca", "metadata": { "scrolled": true @@ -209,34 +390,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "Available metrics: ['gradient', 'standard']\n", "Processing: gradient\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)']\n", - "Processing: standard\n", "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)']\n" ] }, { - "ename": "KeyError", - "evalue": "'gpt2_embd=128_layer=4_head=8'", + "ename": "NameError", + "evalue": "name 'ax' is not defined", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[20]\u001b[39m\u001b[32m, line 24\u001b[39m\n\u001b[32m 18\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mSkipping \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m: no matching models in metric keys \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(metric.keys())\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 19\u001b[39m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m24\u001b[39m fig, ax = \u001b[43mbasic_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmetric\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrivial\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrivial\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 25\u001b[39m ax.set_title(name)\n\u001b[32m 27\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mortho\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m name:\n", - "\u001b[36mFile \u001b[39m\u001b[32md:\\MyBK\\Semester_5\\Programming_Intergration_Project\\in-context-learning\\src\\plot_utils.py:77\u001b[39m, in \u001b[36mbasic_plot\u001b[39m\u001b[34m(metrics, models, trivial)\u001b[39m\n\u001b[32m 74\u001b[39m fig, ax = plt.subplots(\u001b[32m1\u001b[39m, \u001b[32m1\u001b[39m)\n\u001b[32m 76\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m models \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m77\u001b[39m metrics = {k: \u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[43mk\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m models}\n\u001b[32m 79\u001b[39m color = \u001b[32m0\u001b[39m\n\u001b[32m 80\u001b[39m ax.axhline(trivial, ls=\u001b[33m\"\u001b[39m\u001b[33m--\u001b[39m\u001b[33m\"\u001b[39m, color=\u001b[33m\"\u001b[39m\u001b[33mgray\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[31mKeyError\u001b[39m: 'gpt2_embd=128_layer=4_head=8'" + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 24\u001b[39m\n\u001b[32m 22\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mortho\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m name:\n\u001b[32m 23\u001b[39m ax.set_xlim(-\u001b[32m1\u001b[39m, n_dims - \u001b[32m1\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m24\u001b[39m \u001b[43max\u001b[49m.set_ylim(-\u001b[32m.1\u001b[39m * scale, \u001b[32m1.5\u001b[39m * scale)\n\u001b[32m 25\u001b[39m plt.show()\n\u001b[32m 26\u001b[39m \u001b[38;5;66;03m# std = metrics.get(\"standard\", {})\u001b[39;00m\n\u001b[32m 27\u001b[39m \u001b[38;5;66;03m# for model_name in models:\u001b[39;00m\n\u001b[32m 28\u001b[39m \u001b[38;5;66;03m# mres = std.get(model_name, {})\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 36\u001b[39m \u001b[38;5;66;03m# plt.legend()\u001b[39;00m\n\u001b[32m 37\u001b[39m \u001b[38;5;66;03m# plt.show()\u001b[39;00m\n", + "\u001b[31mNameError\u001b[39m: name 'ax' is not defined" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ diff --git a/src/run_all.py b/src/run_all.py new file mode 100644 index 00000000..e876f913 --- /dev/null +++ b/src/run_all.py @@ -0,0 +1,90 @@ +import os +import uuid +import yaml +import argparse + +from quinine import QuinineArgumentParser + +from schema import schema as quinine_schema +from train import main as train_main + + +def prepare_out_dir(args): + if not args.test_run: + run_id = args.training.resume_id + if run_id is None: + run_id = str(uuid.uuid4()) + + out_dir = os.path.join(args.out_dir, run_id) + if not os.path.exists(out_dir): + os.makedirs(out_dir) + args.out_dir = out_dir + + # Persist the resolved config for this run (mirrors train.py behaviour) + with open(os.path.join(out_dir, "config.yaml"), "w") as yaml_file: + yaml.dump(args.__dict__, yaml_file, default_flow_style=False) + + +def build_parser(): + parser = argparse.ArgumentParser(description="Run all noisy_linear_regression variants") + parser.add_argument( + "--config", + default="src/conf/toy.yaml", + help="Base config yaml (e.g., src/conf/toy.yaml)", + ) + parser.add_argument( + "--noise_types", + nargs="*", + default=[ + "uniform", + "normal", + "exponential", + "beta", + "poisson", + "cauchy", + "laplace", + ], + help="Which noise_type values to iterate", + ) + parser.add_argument( + "--base_run_name", + default="noisy_sweep", + help="Prefix for wandb.name; final name will be '_'", + ) + return parser + + +def run_one(base_config_path: str, noise_type: str, base_run_name: str): + # Build a fresh Quinine parser each time and override via CLI-like args + qparser = QuinineArgumentParser(schema=quinine_schema) + args = qparser.parse_quinfig( + args_list=[ + "--config", + base_config_path, + "--training.task", + "noisy_linear_regression", + "--training.task_kwargs.noise_type", + noise_type, + "--wandb.name", + f"{noise_type}_{base_run_name}", + ] + ) + + # Make output directory unique and persist resolved config + prepare_out_dir(args) + + # Kick off training for this configuration + train_main(args) + + +def main(): + parser = build_parser() + cli_args = parser.parse_args() + + for noise in cli_args.noise_types: + run_one(cli_args.config, noise, cli_args.base_run_name) + + +if __name__ == "__main__": + main() + From d0733e358ebe7da163051c321a8eac3a0a1a08a3 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Tue, 4 Nov 2025 07:34:28 +0700 Subject: [PATCH 25/88] Change toy --- src/conf/toy.yaml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/conf/toy.yaml b/src/conf/toy.yaml index 7c826ded..15652203 100644 --- a/src/conf/toy.yaml +++ b/src/conf/toy.yaml @@ -16,15 +16,15 @@ training: dims: start: 5 end: 10 - inc: 0 - interval: 1 + inc: 1 + interval: 2000 points: start: 6 end: 21 - inc: 0 - interval: 1 + inc: 2 + interval: 2000 data: gaussian - keep_every_steps: 10000 + keep_every_steps: 100000 learning_rate: 0.0003 num_tasks: null num_training_examples: null From 0a619c1d29718bcb21c946353290b75eb16495d9 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Tue, 4 Nov 2025 09:25:31 +0700 Subject: [PATCH 26/88] run --- src/eval.ipynb | 174 ++++++++++++++++++++++++++++------------------ src/plot_utils.py | 1 + src/run_all.py | 29 ++++---- 3 files changed, 126 insertions(+), 78 deletions(-) diff --git a/src/eval.ipynb b/src/eval.ipynb index 0d76ef74..b18f24e9 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -2,19 +2,10 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "ed6cfeb1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "from collections import OrderedDict\n", "import re\n", @@ -42,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 14, "id": "0e8d018b", "metadata": { "scrolled": true @@ -83,7 +74,7 @@ " \n", " \n", " \n", - " 5\n", + " 8\n", " b71b76dc-a9ba-4453-9e21-877371420b34\n", " linear_regression\n", " Transformer\n", @@ -96,7 +87,7 @@ " _poisson_noise_gaussian_data_experiment\n", " \n", " \n", - " 6\n", + " 9\n", " bff9a489-6a88-4736-9121-6d492604b98a\n", " linear_regression\n", " Transformer\n", @@ -109,6 +100,32 @@ " _poisson_noise_vr2_data_experiment\n", " \n", " \n", + " 5\n", + " a000dc7d-5411-41ed-b47d-a2441827ca8e\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " beta_noise_gaussian_data_experiment\n", + " \n", + " \n", + " 2\n", + " 2ab0112d-8ca3-47cd-994a-e44eeb143325\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " cauchy_noise_gaussian_data_experiment\n", + " \n", + " \n", " 0\n", " pretrained\n", " decision_tree\n", @@ -122,7 +139,20 @@ " decision_tree_pretrained\n", " \n", " \n", - " 4\n", + " 6\n", + " a1d22de3-52d4-447b-a793-c974a57be241\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " laplace_noise_gaussian_data_experiment\n", + " \n", + " \n", + " 7\n", " b3ffd953-011e-4961-a8cb-293c52fd7076\n", " linear_regression\n", " Transformer\n", @@ -135,7 +165,7 @@ " linear_regression_toy__\n", " \n", " \n", - " 3\n", + " 4\n", " 77f4f325-1e29-41d3-b2aa-84378107f9e5\n", " linear_regression\n", " Transformer\n", @@ -148,7 +178,7 @@ " poisson_noise_gaussian_data_experiment\n", " \n", " \n", - " 7\n", + " 10\n", " pretrained\n", " relu_2nn_regression\n", " Transformer\n", @@ -161,7 +191,7 @@ " relu_2nn_regression_pretrained\n", " \n", " \n", - " 8\n", + " 11\n", " pretrained\n", " sparse_linear_regression\n", " Transformer\n", @@ -187,7 +217,7 @@ " uniform_noise_gaussian_data_experiment\n", " \n", " \n", - " 2\n", + " 3\n", " 70152b8e-2195-4da8-8329-b43fe3146907\n", " linear_regression\n", " Transformer\n", @@ -204,52 +234,64 @@ "" ], "text/plain": [ - " run_id task \\\n", - "5 b71b76dc-a9ba-4453-9e21-877371420b34 linear_regression \n", - "6 bff9a489-6a88-4736-9121-6d492604b98a linear_regression \n", - "0 pretrained decision_tree \n", - "4 b3ffd953-011e-4961-a8cb-293c52fd7076 linear_regression \n", - "3 77f4f325-1e29-41d3-b2aa-84378107f9e5 linear_regression \n", - "7 pretrained relu_2nn_regression \n", - "8 pretrained sparse_linear_regression \n", - "1 08f273f7-0f91-46d0-9ebf-35e2a4467653 linear_regression \n", - "2 70152b8e-2195-4da8-8329-b43fe3146907 linear_regression \n", + " run_id task \\\n", + "8 b71b76dc-a9ba-4453-9e21-877371420b34 linear_regression \n", + "9 bff9a489-6a88-4736-9121-6d492604b98a linear_regression \n", + "5 a000dc7d-5411-41ed-b47d-a2441827ca8e linear_regression \n", + "2 2ab0112d-8ca3-47cd-994a-e44eeb143325 linear_regression \n", + "0 pretrained decision_tree \n", + "6 a1d22de3-52d4-447b-a793-c974a57be241 linear_regression \n", + "7 b3ffd953-011e-4961-a8cb-293c52fd7076 linear_regression \n", + "4 77f4f325-1e29-41d3-b2aa-84378107f9e5 linear_regression \n", + "10 pretrained relu_2nn_regression \n", + "11 pretrained sparse_linear_regression \n", + "1 08f273f7-0f91-46d0-9ebf-35e2a4467653 linear_regression \n", + "3 70152b8e-2195-4da8-8329-b43fe3146907 linear_regression \n", "\n", - " model kwargs num_tasks \\\n", - "5 Transformer noise_std=0.9_noise_type=poisson_renormalize_y... -1 \n", - "6 Transformer noise_std=0.9_noise_type=poisson_renormalize_y... -1 \n", - "0 Transformer depth=4 -1 \n", - "4 Transformer -1 \n", - "3 Transformer -1 \n", - "7 Transformer hidden_layer_size=100 -1 \n", - "8 Transformer sparsity=3 -1 \n", - "1 Transformer -1 \n", - "2 Transformer -1 \n", + " model kwargs num_tasks \\\n", + "8 Transformer noise_std=0.9_noise_type=poisson_renormalize_y... -1 \n", + "9 Transformer noise_std=0.9_noise_type=poisson_renormalize_y... -1 \n", + "5 Transformer -1 \n", + "2 Transformer -1 \n", + "0 Transformer depth=4 -1 \n", + "6 Transformer -1 \n", + "7 Transformer -1 \n", + "4 Transformer -1 \n", + "10 Transformer hidden_layer_size=100 -1 \n", + "11 Transformer sparsity=3 -1 \n", + "1 Transformer -1 \n", + "3 Transformer -1 \n", "\n", - " num_examples n_dims n_layer n_head \\\n", - "5 -1 5 4 8 \n", - "6 -1 5 4 8 \n", - "0 -1 20 12 8 \n", - "4 -1 5 12 8 \n", - "3 -1 5 4 8 \n", - "7 -1 20 12 8 \n", - "8 -1 20 12 8 \n", - "1 -1 5 4 8 \n", - "2 -1 5 4 8 \n", + " num_examples n_dims n_layer n_head \\\n", + "8 -1 5 4 8 \n", + "9 -1 5 4 8 \n", + "5 -1 5 4 8 \n", + "2 -1 5 4 8 \n", + "0 -1 20 12 8 \n", + "6 -1 5 4 8 \n", + "7 -1 5 12 8 \n", + "4 -1 5 4 8 \n", + "10 -1 20 12 8 \n", + "11 -1 20 12 8 \n", + "1 -1 5 4 8 \n", + "3 -1 5 4 8 \n", "\n", - " run_name \n", - "5 _poisson_noise_gaussian_data_experiment \n", - "6 _poisson_noise_vr2_data_experiment \n", - "0 decision_tree_pretrained \n", - "4 linear_regression_toy__ \n", - "3 poisson_noise_gaussian_data_experiment \n", - "7 relu_2nn_regression_pretrained \n", - "8 sparse_regression_pretrained \n", - "1 uniform_noise_gaussian_data_experiment \n", - "2 uniform_noise_gaussian_data_experiment_1 " + " run_name \n", + "8 _poisson_noise_gaussian_data_experiment \n", + "9 _poisson_noise_vr2_data_experiment \n", + "5 beta_noise_gaussian_data_experiment \n", + "2 cauchy_noise_gaussian_data_experiment \n", + "0 decision_tree_pretrained \n", + "6 laplace_noise_gaussian_data_experiment \n", + "7 linear_regression_toy__ \n", + "4 poisson_noise_gaussian_data_experiment \n", + "10 relu_2nn_regression_pretrained \n", + "11 sparse_regression_pretrained \n", + "1 uniform_noise_gaussian_data_experiment \n", + "3 uniform_noise_gaussian_data_experiment_1 " ] }, - "execution_count": 36, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -261,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 15, "id": "a9980951", "metadata": {}, "outputs": [], @@ -271,7 +313,7 @@ "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"08f273f7-0f91-46d0-9ebf-35e2a4467653\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"a1d22de3-52d4-447b-a793-c974a57be241\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -323,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 16, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -333,19 +375,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "uniform_noise_gaussian_data_experiment 08f273f7-0f91-46d0-9ebf-35e2a4467653\n" + "laplace_noise_gaussian_data_experiment a1d22de3-52d4-447b-a793-c974a57be241\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1/1 [00:00<00:00, 940.22it/s]\n" + "100%|██████████| 1/1 [00:00<00:00, 15196.75it/s]\n" ] }, { "data": { - "image/png": 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", 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", 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" ] diff --git a/src/plot_utils.py b/src/plot_utils.py index 1526bc38..1175feec 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -89,6 +89,7 @@ def basic_plot(metrics, models=None, trivial=1.0): ax.set_xlim(-1, len(low) + 0.1) ax.set_ylim(-0.1, 1.25) + legend = ax.legend(loc="upper left", bbox_to_anchor=(1, 1)) fig.set_size_inches(4, 3) for line in legend.get_lines(): diff --git a/src/run_all.py b/src/run_all.py index e876f913..2c96a9fe 100644 --- a/src/run_all.py +++ b/src/run_all.py @@ -57,18 +57,23 @@ def build_parser(): def run_one(base_config_path: str, noise_type: str, base_run_name: str): # Build a fresh Quinine parser each time and override via CLI-like args qparser = QuinineArgumentParser(schema=quinine_schema) - args = qparser.parse_quinfig( - args_list=[ - "--config", - base_config_path, - "--training.task", - "noisy_linear_regression", - "--training.task_kwargs.noise_type", - noise_type, - "--wandb.name", - f"{noise_type}_{base_run_name}", - ] - ) + + # Tạo một danh sách các đối số (arguments) + # mà bạn muốn truyền vào + cli_args_list = [ + "--config", + base_config_path, + "--training.task", + "noisy_linear_regression", + "--training.task_kwargs.noise_type", + noise_type, + "--wandb.name", + f"{noise_type}_{base_run_name}", + ] + + # --- ĐÂY LÀ CHỖ SỬA --- + # Truyền trực tiếp danh sách vào, không dùng "args_list=" + args = qparser.parse_quinfig(cli_args_list) # Make output directory unique and persist resolved config prepare_out_dir(args) From f286bbd17b95e835a88cbe18dc844a1acb43391d Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Tue, 4 Nov 2025 14:55:31 +0700 Subject: [PATCH 27/88] add rayleigh distribution --- src/run_all.py | 77 ++++++++++++++++++++++++++++++++------------------ src/tasks.py | 57 +++++++++++++++++++++++-------------- 2 files changed, 86 insertions(+), 48 deletions(-) diff --git a/src/run_all.py b/src/run_all.py index 2c96a9fe..3b2c217c 100644 --- a/src/run_all.py +++ b/src/run_all.py @@ -2,7 +2,8 @@ import uuid import yaml import argparse - +import sys +import tempfile from quinine import QuinineArgumentParser from schema import schema as quinine_schema @@ -37,6 +38,8 @@ def build_parser(): nargs="*", default=[ "uniform", + "t-student", + "rayleigh", "normal", "exponential", "beta", @@ -55,33 +58,53 @@ def build_parser(): def run_one(base_config_path: str, noise_type: str, base_run_name: str): - # Build a fresh Quinine parser each time and override via CLI-like args - qparser = QuinineArgumentParser(schema=quinine_schema) - - # Tạo một danh sách các đối số (arguments) - # mà bạn muốn truyền vào - cli_args_list = [ - "--config", - base_config_path, - "--training.task", - "noisy_linear_regression", - "--training.task_kwargs.noise_type", - noise_type, - "--wandb.name", - f"{noise_type}_{base_run_name}", - ] - # --- ĐÂY LÀ CHỖ SỬA --- - # Truyền trực tiếp danh sách vào, không dùng "args_list=" - args = qparser.parse_quinfig(cli_args_list) - - # Make output directory unique and persist resolved config - prepare_out_dir(args) - - # Kick off training for this configuration - train_main(args) - - + # 1. Lấy đường dẫn thư mục của file config gốc + # ví dụ: /content/in-context-learning/src/conf + config_dir = os.path.dirname(base_config_path) + # --- KẾT THÚC SỬA LỖI --- + + # 2. Đọc nội dung file config gốc + with open(base_config_path, 'r') as f: + base_config = yaml.safe_load(f) + + # 3. Sửa đổi dictionary config trong Python + base_config['training']['task'] = 'noisy_linear_regression' + base_config['training']['task_kwargs'] = {'noise_type': noise_type} + base_config['wandb']['name'] = f"{noise_type}_{base_run_name}" + base_config['training']['resume_id'] = noise_type + # 4. Tạo một file config tạm thời + temp_config_file = tempfile.NamedTemporaryFile( + mode='w+t', + delete=False, + suffix='.yaml', + dir=config_dir # <-- YÊU CẦU TẠO FILE TẠM TRONG THƯ MỤC CẤU HÌNH + ) + + try: + # 5. Ghi config mới vào file tạm thời + yaml.dump(base_config, temp_config_file, default_flow_style=False) + temp_config_file.close() + + # 6. Xây dựng danh sách đối số CHỈ chứa file config mới + cli_args_list = ["--config", temp_config_file.name] + + # 7. Sử dụng "thủ thuật" sys.argv để chạy parser + qparser = QuinineArgumentParser(schema=quinine_schema) + original_argv = sys.argv + try: + sys.argv = ["run_one_script_placeholder"] + cli_args_list + args = qparser.parse_quinfig() + finally: + sys.argv = original_argv # Luôn khôi phục argv gốc + + # 8. Chuẩn bị thư mục output và chạy training + prepare_out_dir(args) + train_main(args) + + finally: + # 9. Luôn đảm bảo xóa file tạm thời sau khi hoàn tất + os.remove(temp_config_file.name) def main(): parser = build_parser() cli_args = parser.parse_args() diff --git a/src/tasks.py b/src/tasks.py index fe1c7bdc..c4868432 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -185,9 +185,9 @@ def __init__( pool_dict=None, seeds=None, scale=1, - noise_std=0.9, + noise_std=2.0, renormalize_ys=False, - noise_type="uniform", # "normal", "uniform", "exponential", "beta", "poisson" + noise_type="normal", # "normal", "uniform", "laplace", "t-student", "cauchy", "exponential", "rayleigh", "beta", "poisson" uniform=False, ): super(NoisyLinearRegression, self).__init__( @@ -198,38 +198,53 @@ def __init__( self.noise_type = noise_type.lower() def sample_noise(self, shape): + # 1. if self.noise_type == "normal": noise = torch.randn(shape) * self.noise_std - + # 2. elif self.noise_type == "uniform": a = math.sqrt(3) * self.noise_std noise = torch.empty(shape).uniform_(-a, a) - + # 3. + elif self.noise_type == "laplace": + scale_param = self.noise_std / math.sqrt(2.0) + laplace_dist = torch.distributions.Laplace(loc=0, scale=scale_param) + noise = laplace_dist.sample(shape) + # 4. + elif self.noise_type == "t-student": + df = 3.0 + scale_param = self.noise_std / math.sqrt(df / (df-2.0)) + t_dist = torch.distributions.StudentT(df=df, loc=0, scale=scale_param) + noise = t_dist.sample(shape) + # 5. + elif self.noise_type == "cauchy": + scale_param = self.noise_std * 0.5 + cauchy_dist = torch.distributions.StudentT(df=1, loc=0, scale=scale_param) + noise = cauchy_dist.sample(shape) + # 6. elif self.noise_type == "exponential": exp_noise = torch.distributions.Exponential(rate=1.0 / self.noise_std) noise = exp_noise.sample(shape) - self.noise_std - + # 7. + elif self.noise_type == "rayleigh": + scale_param = self.noise_std / math.sqrt(2.0 - math.pi / 2.0) + + mean = scale_param * math.sqrt(math.pi / 2.0) + rayleigh_dist = torch.distributions.Rayleigh(scale=scale_param) + noise = rayleigh_dist.sample(shape) - mean + # 8. elif self.noise_type == "beta": alpha, beta = 2.0, 5.0 + mean = alpha / (alpha + beta) + var = (alpha * beta) / ((alpha + beta) **2 * (alpha + beta + 1.0)) + std = math.sqrt(var) beta_dist = torch.distributions.Beta(alpha, beta) - noise = (beta_dist.sample(shape) - 0.5) * 2.0 * self.noise_std - + noise = (beta_dist.sample(shape) - mean) / std * self.noise_std + # 9. elif self.noise_type == "poisson": - lam = max(self.noise_std, 1e-3) + lam = 100.0 poisson_noise = torch.distributions.Poisson(lam) - noise = (poisson_noise.sample(shape) - lam) / math.sqrt(lam) * self.noise_std - elif self.noise_type == "cauchy": - # 6. Nhiễu Cauchy - Đuôi dày, không có Mean/Variance hữu hạn. - # Dùng scale parameter để kiểm soát độ trải (như FWHM). - scale_param = self.noise_std * 0.5 - cauchy_dist = torch.distributions.StudentT(df=1, loc=0, scale=scale_param) - noise = cauchy_dist.sample(shape) - elif self.noise_type == "laplace": - # 7. Nhiễu Laplace (Double Exponential) - Zero-mean, Var = 2*b^2 - # Để có Var = noise_std^2, ta cần b = noise_std / sqrt(2) - scale_param = self.noise_std / math.sqrt(2.0) - laplace_dist = torch.distributions.Laplace(loc=0, scale=scale_param) - noise = laplace_dist.sample(shape) + noise = (poisson_noise.sample(shape) - lam) / math.sqrt(lam) * self.noise_std else: raise ValueError(f"Unsupported noise type: {self.noise_type}") return noise From 0b9cde8dc47b9cfd899b886d388928bc66546885 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 5 Nov 2025 16:10:53 +0700 Subject: [PATCH 28/88] ridge 0.5 --- src/conf/toy.yaml | 4 ++-- src/models.py | 31 ++++++++++++++----------------- src/plot_utils.py | 18 +++++++----------- 3 files changed, 23 insertions(+), 30 deletions(-) diff --git a/src/conf/toy.yaml b/src/conf/toy.yaml index 15652203..9166dcba 100644 --- a/src/conf/toy.yaml +++ b/src/conf/toy.yaml @@ -15,7 +15,7 @@ training: curriculum: dims: start: 5 - end: 10 + end: 15 inc: 1 interval: 2000 points: @@ -36,7 +36,7 @@ training: } train_steps: 50001 -out_dir: ../models/linear_regression +out_dir: /content/models/linear_regression wandb: name: "uniform_noise_gaussian_data_experiment" diff --git a/src/models.py b/src/models.py index b7a45812..16dfb148 100644 --- a/src/models.py +++ b/src/models.py @@ -31,10 +31,7 @@ def get_relevant_baselines(task_name): "linear_regression": [ (LeastSquaresModel, {}), (RidgeModel, {"alpha": 0.1}), - (RidgeModel, {"alpha": 1.0}), - (RidgeModelWithVarianceAdjustment, {"alpha": 1.0, "ar_coef": 0.5}), - (FeasibleGLSModel, {"ar_coef": None}), - (GLSModel, {"ar_coef": 0.5}), + (RidgeModel, {"alpha": 0.5}), (NNModel, {"n_neighbors": 3}), (AveragingModel, {}), ], @@ -78,24 +75,24 @@ def get_relevant_baselines(task_name): ], "noisy_linear_regression": [ (LeastSquaresModel, {}), - (RidgeModel, {"alpha": 0.1}), - (RidgeModel, {"alpha": 1.0}), - (RidgeModelWithVarianceAdjustment, {"alpha": 1.0, "ar_coef": 0.5}), - (FeasibleGLSModel, {"ar_coef": None}), - (GLSModel, {"ar_coef": 0.5}), - (NNModel, {"n_neighbors": 3}), - (AveragingModel, {}), - ], - "ar1_linear_regression": [ - (LeastSquaresModel, {}), - (RidgeModel, {"alpha": 0.1}), - (RidgeModel, {"alpha": 1.0}), - (RidgeModelWithVarianceAdjustment, {"alpha": 1.0, "ar_coef": 0.5}), + # (RidgeModel, {"alpha": 0.1}), + (RidgeModel, {"alpha": 0.5}), + (RidgeModelWithVarianceAdjustment, {"alpha": 0.5, "ar_coef": 0.5}), (FeasibleGLSModel, {"ar_coef": None}), (GLSModel, {"ar_coef": 0.5}), (NNModel, {"n_neighbors": 3}), (AveragingModel, {}), ], + # "ar1_linear_regression": [ + # (LeastSquaresModel, {}), + # (RidgeModel, {"alpha": 0.1}), + # (RidgeModel, {"alpha": 1.0}), + # (RidgeModelWithVarianceAdjustment, {"alpha": 1.0, "ar_coef": 0.5}), + # (FeasibleGLSModel, {"ar_coef": None}), + # (GLSModel, {"ar_coef": 0.5}), + # (NNModel, {"n_neighbors": 3}), + # (AveragingModel, {}), + # ], } models = [model_cls(**kwargs) for model_cls, kwargs in task_to_baselines[task_name]] diff --git a/src/plot_utils.py b/src/plot_utils.py index 1175feec..e5b24230 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -13,22 +13,18 @@ "noisy_linear_regression": [ "Transformer", "Least Squares", - "Ridge (alpha=0.1)", - "Ridge (alpha=1.0)", - "Ridge Var Adj (alpha=1.0, ar=0.5)", + "Ridge (alpha=0.5)", + "Ridge Var Adj (alpha=0.5, ar=0.5)", "Feasible GLS", "GLS (ar=0.5)", - # "3-Nearest Neighbors", - # "Averaging" ], "linear_regression": [ "Transformer", "Least Squares", - "Ridge Var Adj (alpha=1.0, ar=0.5)", - "Feasible GLS", - "GLS (ar=0.5)", - # "3-Nearest Neighbors", - # "Averaging" + "Ridge (alpha=0.1)", + "Ridge (alpha=0.5)", + "3-Nearest Neighbors", + "Averaging" ], "sparse_linear_regression": [ "Transformer", @@ -87,7 +83,7 @@ def basic_plot(metrics, models=None, trivial=1.0): ax.set_xlabel("in-context examples") ax.set_ylabel("squared error") ax.set_xlim(-1, len(low) + 0.1) - ax.set_ylim(-0.1, 1.25) + ax.set_ylim(-0.1, 5) legend = ax.legend(loc="upper left", bbox_to_anchor=(1, 1)) From e1a6bdeddc047adb6da5245264cae21553ae9507 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 5 Nov 2025 17:18:59 +0700 Subject: [PATCH 29/88] ridge 0.5 for sparse --- src/conf/toy.yaml | 2 +- src/eval.ipynb | 309 ++++++++++++++++++++++++++++++---------------- src/models.py | 1 + src/plot_utils.py | 12 +- 4 files changed, 211 insertions(+), 113 deletions(-) diff --git a/src/conf/toy.yaml b/src/conf/toy.yaml index 9166dcba..ae3312a9 100644 --- a/src/conf/toy.yaml +++ b/src/conf/toy.yaml @@ -36,7 +36,7 @@ training: } train_steps: 50001 -out_dir: /content/models/linear_regression +out_dir: ../models/linear_regression wandb: name: "uniform_noise_gaussian_data_experiment" diff --git a/src/eval.ipynb b/src/eval.ipynb index b18f24e9..18511f78 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -74,34 +74,60 @@ " \n", " \n", " \n", - " 8\n", - " b71b76dc-a9ba-4453-9e21-877371420b34\n", + " 1\n", + " 1_beta_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", - " noise_std=0.9_noise_type=poisson_renormalize_y...\n", + " \n", " -1\n", " -1\n", " 5\n", " 4\n", " 8\n", - " _poisson_noise_gaussian_data_experiment\n", + " 1_beta_noise_gaussian_data_experiment\n", " \n", " \n", - " 9\n", - " bff9a489-6a88-4736-9121-6d492604b98a\n", + " 2\n", + " 1_exponential_noise_gaussian_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " 1_exponential_noise_gaussian_data_experiment\n", + " \n", + " \n", + " 3\n", + " 1_poisson_noise_gaussian_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " 1_poisson_noise_gaussian_data_experiment\n", + " \n", + " \n", + " 4\n", + " 1_t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", - " noise_std=0.9_noise_type=poisson_renormalize_y...\n", + " \n", " -1\n", " -1\n", " 5\n", " 4\n", " 8\n", - " _poisson_noise_vr2_data_experiment\n", + " 1_t_student_noise_gaussian_data_experiment\n", " \n", " \n", " 5\n", - " a000dc7d-5411-41ed-b47d-a2441827ca8e\n", + " 1_uniform_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", " \n", @@ -110,11 +136,37 @@ " 5\n", " 4\n", " 8\n", - " beta_noise_gaussian_data_experiment\n", + " 1_uniform_noise_gaussian_data_experiment\n", " \n", " \n", - " 2\n", - " 2ab0112d-8ca3-47cd-994a-e44eeb143325\n", + " 6\n", + " 3_laplace_noise_gaussian_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " 3_laplace_noise_gaussian_data_experiment\n", + " \n", + " \n", + " 7\n", + " 3_tstudent_noise_gaussian_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " 3_tstudent_noise_gaussian_data_experiment\n", + " \n", + " \n", + " 8\n", + " beta_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", " \n", @@ -123,7 +175,7 @@ " 5\n", " 4\n", " 8\n", - " cauchy_noise_gaussian_data_experiment\n", + " beta_noise_ar1_data_experiment\n", " \n", " \n", " 0\n", @@ -139,8 +191,8 @@ " decision_tree_pretrained\n", " \n", " \n", - " 6\n", - " a1d22de3-52d4-447b-a793-c974a57be241\n", + " 9\n", + " exponential_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", " \n", @@ -149,24 +201,24 @@ " 5\n", " 4\n", " 8\n", - " laplace_noise_gaussian_data_experiment\n", + " exponential_noise_gaussian_data_experiment\n", " \n", " \n", - " 7\n", - " b3ffd953-011e-4961-a8cb-293c52fd7076\n", + " 10\n", + " laplace_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", " \n", " -1\n", " -1\n", " 5\n", - " 12\n", + " 4\n", " 8\n", - " linear_regression_toy__\n", + " laplace_noise_gaussian_data_experiment\n", " \n", " \n", - " 4\n", - " 77f4f325-1e29-41d3-b2aa-84378107f9e5\n", + " 11\n", + " rayleigh_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", " \n", @@ -175,10 +227,10 @@ " 5\n", " 4\n", " 8\n", - " poisson_noise_gaussian_data_experiment\n", + " rayleigh_noise_gaussian_data_experiment\n", " \n", " \n", - " 10\n", + " 15\n", " pretrained\n", " relu_2nn_regression\n", " Transformer\n", @@ -191,7 +243,7 @@ " relu_2nn_regression_pretrained\n", " \n", " \n", - " 11\n", + " 16\n", " pretrained\n", " sparse_linear_regression\n", " Transformer\n", @@ -204,8 +256,8 @@ " sparse_regression_pretrained\n", " \n", " \n", - " 1\n", - " 08f273f7-0f91-46d0-9ebf-35e2a4467653\n", + " 12\n", + " t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", " \n", @@ -214,11 +266,24 @@ " 5\n", " 4\n", " 8\n", - " uniform_noise_gaussian_data_experiment\n", + " t_student_noise_gaussian_data_experiment\n", " \n", " \n", - " 3\n", - " 70152b8e-2195-4da8-8329-b43fe3146907\n", + " 13\n", + " uniform_noise_ar1_data_experiment\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " uniform_noise_ar1_data_experiment\n", + " \n", + " \n", + " 14\n", + " uniform_noise_gaussian_data_experiment_\n", " linear_regression\n", " Transformer\n", " \n", @@ -227,68 +292,69 @@ " 5\n", " 4\n", " 8\n", - " uniform_noise_gaussian_data_experiment_1\n", + " uniform_noise_gaussian_data_experiment\n", " \n", " \n", "\n", "" ], "text/plain": [ - " run_id task \\\n", - "8 b71b76dc-a9ba-4453-9e21-877371420b34 linear_regression \n", - "9 bff9a489-6a88-4736-9121-6d492604b98a linear_regression \n", - "5 a000dc7d-5411-41ed-b47d-a2441827ca8e linear_regression \n", - "2 2ab0112d-8ca3-47cd-994a-e44eeb143325 linear_regression \n", - "0 pretrained decision_tree \n", - "6 a1d22de3-52d4-447b-a793-c974a57be241 linear_regression \n", - "7 b3ffd953-011e-4961-a8cb-293c52fd7076 linear_regression \n", - "4 77f4f325-1e29-41d3-b2aa-84378107f9e5 linear_regression \n", - "10 pretrained relu_2nn_regression \n", - "11 pretrained sparse_linear_regression \n", - "1 08f273f7-0f91-46d0-9ebf-35e2a4467653 linear_regression \n", - "3 70152b8e-2195-4da8-8329-b43fe3146907 linear_regression \n", + " run_id task \\\n", + "1 1_beta_noise_gaussian_data_experiment linear_regression \n", + "2 1_exponential_noise_gaussian_data_experiment linear_regression \n", + "3 1_poisson_noise_gaussian_data_experiment linear_regression \n", + "4 1_t_student_noise_gaussian_data_experiment linear_regression \n", + "5 1_uniform_noise_gaussian_data_experiment linear_regression \n", + "6 3_laplace_noise_gaussian_data_experiment linear_regression \n", + "7 3_tstudent_noise_gaussian_data_experiment linear_regression \n", + "8 beta_noise_ar1_data_experiment linear_regression \n", + "0 pretrained decision_tree \n", + "9 exponential_noise_gaussian_data_experiment linear_regression \n", + "10 laplace_noise_gaussian_data_experiment linear_regression \n", + "11 rayleigh_noise_gaussian_data_experiment linear_regression \n", + "15 pretrained relu_2nn_regression \n", + "16 pretrained sparse_linear_regression \n", + "12 t_student_noise_gaussian_data_experiment linear_regression \n", + "13 uniform_noise_ar1_data_experiment linear_regression \n", + "14 uniform_noise_gaussian_data_experiment_ linear_regression \n", "\n", - " model kwargs num_tasks \\\n", - "8 Transformer noise_std=0.9_noise_type=poisson_renormalize_y... -1 \n", - "9 Transformer noise_std=0.9_noise_type=poisson_renormalize_y... -1 \n", - "5 Transformer -1 \n", - "2 Transformer -1 \n", - "0 Transformer depth=4 -1 \n", - "6 Transformer -1 \n", - "7 Transformer -1 \n", - "4 Transformer -1 \n", - "10 Transformer hidden_layer_size=100 -1 \n", - "11 Transformer sparsity=3 -1 \n", - "1 Transformer -1 \n", - "3 Transformer -1 \n", + " model kwargs num_tasks num_examples n_dims \\\n", + "1 Transformer -1 -1 5 \n", + "2 Transformer -1 -1 5 \n", + "3 Transformer -1 -1 5 \n", + "4 Transformer -1 -1 5 \n", + "5 Transformer -1 -1 5 \n", + "6 Transformer -1 -1 5 \n", + "7 Transformer -1 -1 5 \n", + "8 Transformer -1 -1 5 \n", + "0 Transformer depth=4 -1 -1 20 \n", + "9 Transformer -1 -1 5 \n", + "10 Transformer -1 -1 5 \n", + "11 Transformer -1 -1 5 \n", + "15 Transformer hidden_layer_size=100 -1 -1 20 \n", + "16 Transformer sparsity=3 -1 -1 20 \n", + "12 Transformer -1 -1 5 \n", + "13 Transformer -1 -1 5 \n", + "14 Transformer -1 -1 5 \n", "\n", - " num_examples n_dims n_layer n_head \\\n", - "8 -1 5 4 8 \n", - "9 -1 5 4 8 \n", - "5 -1 5 4 8 \n", - "2 -1 5 4 8 \n", - "0 -1 20 12 8 \n", - "6 -1 5 4 8 \n", - "7 -1 5 12 8 \n", - "4 -1 5 4 8 \n", - "10 -1 20 12 8 \n", - "11 -1 20 12 8 \n", - "1 -1 5 4 8 \n", - "3 -1 5 4 8 \n", - "\n", - " run_name \n", - "8 _poisson_noise_gaussian_data_experiment \n", - "9 _poisson_noise_vr2_data_experiment \n", - "5 beta_noise_gaussian_data_experiment \n", - "2 cauchy_noise_gaussian_data_experiment \n", - "0 decision_tree_pretrained \n", - "6 laplace_noise_gaussian_data_experiment \n", - "7 linear_regression_toy__ \n", - "4 poisson_noise_gaussian_data_experiment \n", - "10 relu_2nn_regression_pretrained \n", - "11 sparse_regression_pretrained \n", - "1 uniform_noise_gaussian_data_experiment \n", - "3 uniform_noise_gaussian_data_experiment_1 " + " n_layer n_head run_name \n", + "1 4 8 1_beta_noise_gaussian_data_experiment \n", + "2 4 8 1_exponential_noise_gaussian_data_experiment \n", + "3 4 8 1_poisson_noise_gaussian_data_experiment \n", + "4 4 8 1_t_student_noise_gaussian_data_experiment \n", + "5 4 8 1_uniform_noise_gaussian_data_experiment \n", + "6 4 8 3_laplace_noise_gaussian_data_experiment \n", + "7 4 8 3_tstudent_noise_gaussian_data_experiment \n", + "8 4 8 beta_noise_ar1_data_experiment \n", + "0 12 8 decision_tree_pretrained \n", + "9 4 8 exponential_noise_gaussian_data_experiment \n", + "10 4 8 laplace_noise_gaussian_data_experiment \n", + "11 4 8 rayleigh_noise_gaussian_data_experiment \n", + "15 12 8 relu_2nn_regression_pretrained \n", + "16 12 8 sparse_regression_pretrained \n", + "12 4 8 t_student_noise_gaussian_data_experiment \n", + "13 4 8 uniform_noise_ar1_data_experiment \n", + "14 4 8 uniform_noise_gaussian_data_experiment " ] }, "execution_count": 14, @@ -303,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "id": "a9980951", "metadata": {}, "outputs": [], @@ -313,7 +379,7 @@ "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"a1d22de3-52d4-447b-a793-c974a57be241\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"1_beta_noise_gaussian_data_experiment\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -324,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "937f1b23", "metadata": {}, "outputs": [ @@ -333,10 +399,20 @@ "output_type": "stream", "text": [ "--- Tên mô hình CÓ TRONG relevant_model_names (models) ---\n", - "['gpt2_embd=128_layer=4_head=8', 'Least Squares', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)']\n", + "['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", "\n", - "--- Các khóa (Tên mô hình) THỰC TẾ trong metrics['standard'] ---\n", - "dict_keys(['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)'])\n" + "--- Các khóa (Tên mô hình) THỰC TẾ trong metrics['standard'] ---\n" + ] + }, + { + "ename": "KeyError", + "evalue": "'standard'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[20]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 7\u001b[39m \u001b[38;5;28mprint\u001b[39m(models)\n\u001b[32m 8\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m--- Các khóa (Tên mô hình) THỰC TẾ trong metrics[\u001b[39m\u001b[33m'\u001b[39m\u001b[33mstandard\u001b[39m\u001b[33m'\u001b[39m\u001b[33m] ---\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m pprint.pprint(\u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstandard\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m.keys()) \n\u001b[32m 11\u001b[39m \u001b[38;5;66;03m# basic_plot(metrics[\"standard\"], models=models) \u001b[39;00m\n\u001b[32m 12\u001b[39m \u001b[38;5;66;03m# ...\u001b[39;00m\n", + "\u001b[31mKeyError\u001b[39m: 'standard'" ] } ], @@ -365,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 25, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -375,21 +451,47 @@ "name": "stdout", "output_type": "stream", "text": [ - "laplace_noise_gaussian_data_experiment a1d22de3-52d4-447b-a793-c974a57be241\n" + "1_beta_noise_gaussian_data_experiment 1_beta_noise_gaussian_data_experiment\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:00<00:00, 2244.14it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1/1 [00:00<00:00, 15196.75it/s]\n" + "\n" + ] + }, + { + "ename": "KeyError", + "evalue": "'Ridge (alpha=0.1)'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[25]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 6\u001b[39m n_dims = conf.model.n_dims\n\u001b[32m 8\u001b[39m models = relevant_model_names[task]\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m \u001b[43mbasic_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstandard\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 10\u001b[39m plt.show()\n\u001b[32m 12\u001b[39m \u001b[38;5;66;03m# # Figure 3 and 4\u001b[39;00m\n\u001b[32m 13\u001b[39m \u001b[38;5;66;03m# for model_name in models: \u001b[39;00m\n\u001b[32m 14\u001b[39m \u001b[38;5;66;03m# if \"gradient_alignment\" in metrics[\"standard\"][model_name]: \u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 20\u001b[39m \u001b[38;5;66;03m# plt.legend()\u001b[39;00m\n\u001b[32m 21\u001b[39m \u001b[38;5;66;03m# plt.show()\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32md:\\MyBK\\Semester_5\\Programming_Intergration_Project\\in-context-learning\\src\\plot_utils.py:5\u001b[39m, in \u001b[36mbasic_plot\u001b[39m\u001b[34m(metrics, models, trivial)\u001b[39m\n\u001b[32m 0\u001b[39m \n", + "\u001b[31mKeyError\u001b[39m: 'Ridge (alpha=0.1)'" ] }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -422,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 24, "id": "31b4ecca", "metadata": { "scrolled": true @@ -432,19 +534,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Processing: gradient\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=1.0)', 'Ridge Var Adj (alpha=1.0, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)']\n" - ] - }, - { - "ename": "NameError", - "evalue": "name 'ax' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 24\u001b[39m\n\u001b[32m 22\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mortho\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m name:\n\u001b[32m 23\u001b[39m ax.set_xlim(-\u001b[32m1\u001b[39m, n_dims - \u001b[32m1\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m24\u001b[39m \u001b[43max\u001b[49m.set_ylim(-\u001b[32m.1\u001b[39m * scale, \u001b[32m1.5\u001b[39m * scale)\n\u001b[32m 25\u001b[39m plt.show()\n\u001b[32m 26\u001b[39m \u001b[38;5;66;03m# std = metrics.get(\"standard\", {})\u001b[39;00m\n\u001b[32m 27\u001b[39m \u001b[38;5;66;03m# for model_name in models:\u001b[39;00m\n\u001b[32m 28\u001b[39m \u001b[38;5;66;03m# mres = std.get(model_name, {})\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 36\u001b[39m \u001b[38;5;66;03m# plt.legend()\u001b[39;00m\n\u001b[32m 37\u001b[39m \u001b[38;5;66;03m# plt.show()\u001b[39;00m\n", - "\u001b[31mNameError\u001b[39m: name 'ax' is not defined" + "Processing: standard\n", + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', 'Ridge Var Adj (alpha=0.5, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] } ], diff --git a/src/models.py b/src/models.py index 16dfb148..4cfef299 100644 --- a/src/models.py +++ b/src/models.py @@ -43,6 +43,7 @@ def get_relevant_baselines(task_name): (LeastSquaresModel, {}), (NNModel, {"n_neighbors": 3}), (AveragingModel, {}), + (RidgeModel, {"alpha": 0.5}), ] + [(LassoModel, {"alpha": alpha}) for alpha in [1, 0.1, 0.01, 0.001, 0.0001]], "relu_2nn_regression": [ diff --git a/src/plot_utils.py b/src/plot_utils.py index e5b24230..fb59b4ae 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -21,7 +21,7 @@ "linear_regression": [ "Transformer", "Least Squares", - "Ridge (alpha=0.1)", + # "Ridge (alpha=0.1)", "Ridge (alpha=0.5)", "3-Nearest Neighbors", "Averaging" @@ -34,7 +34,8 @@ "Lasso (alpha=0.001)", "Lasso (alpha=0.01)", "Lasso (alpha=0.1)", - "Lasso (alpha=1.0)" + "Lasso (alpha=1.0)", + "Ridge (alpha=0.5)" ], "decision_tree": [ "Transformer", @@ -70,7 +71,12 @@ def basic_plot(metrics, models=None, trivial=1.0): fig, ax = plt.subplots(1, 1) if models is not None: - metrics = {k: metrics[k] for k in models} + print(models) + available = [m for m in models if m in metrics] + missing = [m for m in models if m not in metrics] + if missing: + print("Missing metrics for:", missing) + metrics = {k: metrics[k] for k in available} color = 0 ax.axhline(trivial, ls="--", color="gray") From c4f0e1b5921bcbf4e9ba53fc91e51dbd2f248796 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Thu, 6 Nov 2025 18:37:28 +0700 Subject: [PATCH 30/88] sparse --- src/conf/toy.yaml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/conf/toy.yaml b/src/conf/toy.yaml index ae3312a9..7380d65f 100644 --- a/src/conf/toy.yaml +++ b/src/conf/toy.yaml @@ -4,7 +4,7 @@ inherit: model: family: gpt2 - n_dims: 5 + n_dims: 15 n_embd: 128 n_head: 8 n_layer: 4 @@ -30,13 +30,13 @@ training: num_training_examples: null resume_id: null save_every_steps: 100 - task: noisy_linear_regression + task: sparse_linear_regression task_kwargs: { # "compute_gradient": True } train_steps: 50001 -out_dir: ../models/linear_regression +out_dir: ../models/sparse_linear_regression wandb: - name: "uniform_noise_gaussian_data_experiment" + name: "sparse_linear_regression_standard" From 5ce5e8acf13bcc3d1e8953b62449a46cf37f787c Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Thu, 6 Nov 2025 23:43:52 +0700 Subject: [PATCH 31/88] sampler sparse --- .vscode/settings.json | 4 ---- src/conf/toy.yaml | 1 + src/eval.ipynb | 2 +- src/plot_utils.py | 8 ++++++-- src/samplers.py | 36 ++++++++++++++++++++++++++++++++++++ 5 files changed, 44 insertions(+), 7 deletions(-) delete mode 100644 .vscode/settings.json diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index ba2a6c01..00000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "python-envs.defaultEnvManager": "ms-python.python:system", - "python-envs.pythonProjects": [] -} \ No newline at end of file diff --git a/src/conf/toy.yaml b/src/conf/toy.yaml index 7380d65f..9889437b 100644 --- a/src/conf/toy.yaml +++ b/src/conf/toy.yaml @@ -33,6 +33,7 @@ training: task: sparse_linear_regression task_kwargs: { # "compute_gradient": True + "sparsity": 5 } train_steps: 50001 diff --git a/src/eval.ipynb b/src/eval.ipynb index 18511f78..cd18b7ff 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "ed6cfeb1", "metadata": {}, "outputs": [], diff --git a/src/plot_utils.py b/src/plot_utils.py index fb59b4ae..c69ef79f 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -89,7 +89,7 @@ def basic_plot(metrics, models=None, trivial=1.0): ax.set_xlabel("in-context examples") ax.set_ylabel("squared error") ax.set_xlim(-1, len(low) + 0.1) - ax.set_ylim(-0.1, 5) + ax.set_ylim(-0.1, 1) legend = ax.legend(loc="upper left", bbox_to_anchor=(1, 1)) @@ -133,7 +133,11 @@ def collect_results(run_dir, df, valid_row=None, rename_eval=None, rename_model= normalization = n_dims if r.task == "sparse_linear_regression": - normalization = int(r.kwargs.split("=")[-1]) + try: + normalization = int(r.kwargs.split("=")[-1]) + except (ValueError, AttributeError): + # Use default sparsity or n_dims if kwargs is empty + normalization = n_dims if r.task == "decision_tree": normalization = 1 diff --git a/src/samplers.py b/src/samplers.py index 0c0dffe2..b37b0782 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -16,6 +16,7 @@ def get_data_sampler(data_name, n_dims, **kwargs): "gaussian": GaussianSampler, "ar1":AR1Sampler, "vr1":VAR1Sampler, + "sparse_gaussian": SparseGaussianSampler, "ar2":AR2Sampler, "vr2":VR2Sampler, "nonstation":NonStationarySampler, @@ -63,6 +64,41 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): return xs_b # code này là thêm: +class SparseGaussianSampler(DataSampler): + def __init__(self, n_dims, k, bias=None, scale=None): + super().__init__(n_dims) + if not (0 < k <= n_dims): + raise ValueError(f"k (number of non-zero elements) must be an integer in the range (0, {n_dims}]") + + self.k = int(k) + self.bias = bias + self.scale = scale + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + if seeds is None: + xs_b = torch.zeros(b_size, n_points, self.n_dims) + values = torch.randn(b_size, n_points, self.k) + rand_scores = torch.rand(b_size, n_points, self.n_dims) + _, indices = torch.topk(rand_scores, self.k, dim=-1) + xs_b.scatter_(dim=2, index=indices, src=values) + else: + xs_b = torch.zeros(b_size, n_points, self.n_dims) + assert len(seeds) == b_size + for i in range(b_size): + generator = torch.Generator().manual_seed(int(seeds[i])) + values = torch.randn(n_points, self.k, generator=generator) + rand_scores = torch.rand(n_points, self.n_dims, generator=generator) + _, indices = torch.topk(rand_scores, self.k, dim=-1) + xs_b[i].scatter_(dim=1, index=indices, src=values) + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b + + class AR1Sampler(DataSampler): def __init__(self, n_dims, rho=0.9, noise_std=1.0, bias=None, scale=None,compute_gradient=False): super().__init__(n_dims) From 6e953e34d825c239864b99462a6408af8203b0c5 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Thu, 6 Nov 2025 23:55:31 +0700 Subject: [PATCH 32/88] eror --- src/schema.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/schema.py b/src/schema.py index e2a3523a..4b7dc3a9 100644 --- a/src/schema.py +++ b/src/schema.py @@ -51,7 +51,7 @@ "task_kwargs": merge(tdict, required), "num_tasks": merge(tinteger, nullable, default(None)), "num_training_examples": merge(tinteger, nullable, default(None)), - "data": merge(tstring, allowed(["gaussian","ar1","vr1","ar2",'vr2',"nonstation"])), + "data": merge(tstring, allowed(["gaussian","ar1","vr1","ar2",'vr2',"nonstation", "sparse_gaussian"])), "batch_size": merge(tinteger, default(64)), "learning_rate": merge(tfloat, default(3e-4)), "train_steps": merge(tinteger, default(1000)), From 2668a305df74899e3aad2adf0658ca12ea572281 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Fri, 7 Nov 2025 10:49:32 +0700 Subject: [PATCH 33/88] update total --- src/conf/sparse_data.yaml | 43 ++++++++++++++++++++ src/conf/uniform_hypersphere_regression.yaml | 36 ++++++++++++++++ src/eval.py | 2 + src/models.py | 8 ++++ src/plot_utils.py | 9 ++++ src/samplers.py | 19 ++++++--- src/schema.py | 1 + src/train.py | 2 +- 8 files changed, 114 insertions(+), 6 deletions(-) create mode 100644 src/conf/sparse_data.yaml create mode 100644 src/conf/uniform_hypersphere_regression.yaml diff --git a/src/conf/sparse_data.yaml b/src/conf/sparse_data.yaml new file mode 100644 index 00000000..5ee114bb --- /dev/null +++ b/src/conf/sparse_data.yaml @@ -0,0 +1,43 @@ +inherit: + - base.yaml + +model: + family: gpt2 + n_dims: 20 # Total input dimensions + n_embd: 128 # Embedding dimension + n_head: 8 # Number of attention heads + n_layer: 4 # Number of transformer layers + n_positions: 100 # Max sequence length + +training: + task: linear_regression # Using standard linear regression task + data: sparse_gaussian # Using sparse Gaussian sampler + task_kwargs: {} # No special task args needed + data_kwargs: + k: 5 # Only 5 non-zero elements per input vector + scale: 1.0 # Scale factor for non-zero values + + batch_size: 32 + curriculum: + dims: + start: 20 # Start with full dimensions + end: 20 # Keep dimensions fixed + inc: 0 + interval: 2000 + points: + start: 11 # Start with 11 context points + end: 41 # End with 41 context points + inc: 2 # Increment by 2 + interval: 2000 # Every 2000 steps + + learning_rate: 0.0003 + train_steps: 50001 + save_every_steps: 100 + keep_every_steps: 10000 + +out_dir: /content/models/linear_regression + +wandb: + project: in-context-training + name: sparse_data_experiment + notes: "Training with sparse input data (k=5 non-zero elements)" \ No newline at end of file diff --git a/src/conf/uniform_hypersphere_regression.yaml b/src/conf/uniform_hypersphere_regression.yaml new file mode 100644 index 00000000..1be68663 --- /dev/null +++ b/src/conf/uniform_hypersphere_regression.yaml @@ -0,0 +1,36 @@ +inherit: + - base.yaml + +model: + family: gpt2 + n_dims: 20 + n_embd: 128 + n_head: 8 + n_layer: 4 + n_positions: 100 + +training: + task: uniform_hypersphere_regression # Using our new task + task_kwargs: + scale: 1.0 # Scale factor for weights + data: gaussian # Using standard Gaussian inputs + curriculum: + dims: + start: 20 + end: 20 + inc: 0 + interval: 2000 + points: + start: 11 + end: 41 + inc: 2 + interval: 2000 + batch_size: 32 + learning_rate: 0.0003 + train_steps: 50001 + +out_dir: ../models/uniform_hypersphere_regression + +wandb: + name: "uniform_hypersphere_regression" + notes: "Training with weights uniformly distributed on unit hypersphere" \ No newline at end of file diff --git a/src/eval.py b/src/eval.py index 4be7b50a..bf6232c8 100644 --- a/src/eval.py +++ b/src/eval.py @@ -220,6 +220,8 @@ def build_evals(conf): "batch_size": batch_size, "data_name": data_name, "prompting_strategy": "standard", + "data_sampler_kwargs": conf.training.data_kwargs if hasattr(conf.training, "data_kwargs") else {}, + "task_kwargs": conf.training.task_kwargs } evaluation_kwargs = {} diff --git a/src/models.py b/src/models.py index 4cfef299..aa5f4940 100644 --- a/src/models.py +++ b/src/models.py @@ -28,6 +28,14 @@ def build_model(conf): def get_relevant_baselines(task_name): task_to_baselines = { + "uniform_hypersphere_regression": [ + (LeastSquaresModel, {}), + (RidgeModel, {"alpha": 0.1}), + (RidgeModel, {"alpha": 0.5}), + (NNModel, {"n_neighbors": 3}), + (GLSModel, {"ar_coef": 0.5}), + (AveragingModel, {}), + ], "linear_regression": [ (LeastSquaresModel, {}), (RidgeModel, {"alpha": 0.1}), diff --git a/src/plot_utils.py b/src/plot_utils.py index c69ef79f..17254bd8 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -10,6 +10,15 @@ palette = sns.color_palette("colorblind") relevant_model_names = { + "uniform_hypersphere_regression": [ + "Transformer", + "Least Squares", + "Ridge (alpha=0.5)", + "Ridge (alpha=0.1)", + "3-Nearest Neighbors", + "Averaging" + "GLS (ar=0.5)" + ], "noisy_linear_regression": [ "Transformer", "Least Squares", diff --git a/src/samplers.py b/src/samplers.py index b37b0782..7f6a8af8 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -23,6 +23,10 @@ def get_data_sampler(data_name, n_dims, **kwargs): } if data_name in names_to_classes: sampler_cls = names_to_classes[data_name] + if 'k' not in kwargs: + kwargs['k'] = n_dims // 2 # default k is half of dimensions + if 'scale' not in kwargs: + kwargs['scale'] = 1.0 # default scale is 1.0 return sampler_cls(n_dims, **kwargs) else: print("Unknown sampler") @@ -68,17 +72,17 @@ class SparseGaussianSampler(DataSampler): def __init__(self, n_dims, k, bias=None, scale=None): super().__init__(n_dims) if not (0 < k <= n_dims): - raise ValueError(f"k (number of non-zero elements) must be an integer in the range (0, {n_dims}]") - + raise ValueError(f"k must be in range (0, {n_dims}]") self.k = int(k) self.bias = bias - self.scale = scale + # Store scale as float + self.scale = float(scale) if isinstance(scale, (int, float)) else 1.0 def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): if seeds is None: xs_b = torch.zeros(b_size, n_points, self.n_dims) values = torch.randn(b_size, n_points, self.k) - rand_scores = torch.rand(b_size, n_points, self.n_dims) + rand_scores = torch.rand(b_size, n_points, self.n_dims) _, indices = torch.topk(rand_scores, self.k, dim=-1) xs_b.scatter_(dim=2, index=indices, src=values) else: @@ -90,12 +94,17 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): rand_scores = torch.rand(n_points, self.n_dims, generator=generator) _, indices = torch.topk(rand_scores, self.k, dim=-1) xs_b[i].scatter_(dim=1, index=indices, src=values) + if self.scale is not None: - xs_b = xs_b @ self.scale + # Simple scalar multiplication + xs_b = xs_b * self.scale + if self.bias is not None: xs_b += self.bias + if n_dims_truncated is not None: xs_b[:, :, n_dims_truncated:] = 0 + return xs_b diff --git a/src/schema.py b/src/schema.py index 4b7dc3a9..8175a010 100644 --- a/src/schema.py +++ b/src/schema.py @@ -52,6 +52,7 @@ "num_tasks": merge(tinteger, nullable, default(None)), "num_training_examples": merge(tinteger, nullable, default(None)), "data": merge(tstring, allowed(["gaussian","ar1","vr1","ar2",'vr2',"nonstation", "sparse_gaussian"])), + "data_kwargs": merge(tdict, default({})), # Thêm dòng này "batch_size": merge(tinteger, default(64)), "learning_rate": merge(tfloat, default(3e-4)), "train_steps": merge(tinteger, default(1000)), diff --git a/src/train.py b/src/train.py index 736a1b2f..6c58af0a 100644 --- a/src/train.py +++ b/src/train.py @@ -51,7 +51,7 @@ def train(model, args): n_dims = model.n_dims bsize = args.training.batch_size - data_sampler = get_data_sampler(args.training.data, n_dims=n_dims) + data_sampler = get_data_sampler(args.training.data, n_dims=n_dims, **trains.args.data_kwargs) task_sampler = get_task_sampler( args.training.task, n_dims, From 4ea0c6790907278898df37e67bf77a5160129448 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 10 Nov 2025 00:12:18 +0700 Subject: [PATCH 34/88] Add run_all.py --- src/conf/toy.yaml | 8 +- src/conf/uniform_hypersphere_regression.yaml | 19 +- src/eval.ipynb | 251 ++++++++------ src/run_all.py | 339 ++++++++++++++++--- src/schema.py | 1 + src/tasks.py | 27 ++ src/train.py | 11 +- 7 files changed, 490 insertions(+), 166 deletions(-) diff --git a/src/conf/toy.yaml b/src/conf/toy.yaml index 9889437b..d561cad4 100644 --- a/src/conf/toy.yaml +++ b/src/conf/toy.yaml @@ -4,7 +4,7 @@ inherit: model: family: gpt2 - n_dims: 15 + n_dims: 10 n_embd: 128 n_head: 8 n_layer: 4 @@ -15,12 +15,12 @@ training: curriculum: dims: start: 5 - end: 15 + end: 10 inc: 1 interval: 2000 points: start: 6 - end: 21 + end: 15 inc: 2 interval: 2000 data: gaussian @@ -37,7 +37,7 @@ training: } train_steps: 50001 -out_dir: ../models/sparse_linear_regression +out_dir: /content/models/sparse_linear_regression wandb: name: "sparse_linear_regression_standard" diff --git a/src/conf/uniform_hypersphere_regression.yaml b/src/conf/uniform_hypersphere_regression.yaml index 1be68663..95dff9ac 100644 --- a/src/conf/uniform_hypersphere_regression.yaml +++ b/src/conf/uniform_hypersphere_regression.yaml @@ -10,10 +10,12 @@ model: n_positions: 100 training: - task: uniform_hypersphere_regression # Using our new task + task: uniform_hypersphere_regression task_kwargs: - scale: 1.0 # Scale factor for weights - data: gaussian # Using standard Gaussian inputs + scale: 1.0 + normalize: true + data: gaussian + data_kwargs: {} curriculum: dims: start: 20 @@ -27,10 +29,15 @@ training: interval: 2000 batch_size: 32 learning_rate: 0.0003 - train_steps: 50001 + train_steps: 5001 + save_every_steps: 100 + keep_every_steps: 10000 out_dir: ../models/uniform_hypersphere_regression wandb: - name: "uniform_hypersphere_regression" - notes: "Training with weights uniformly distributed on unit hypersphere" \ No newline at end of file + project: "in-context-training" + entity: "hai-trinh220970-ho-chi-minh-city-university-of-technology" + name: "uniform_hypersphere_experiment" + notes: "Training with weights uniformly distributed on unit hypersphere" + log_every_steps: 100 \ No newline at end of file diff --git a/src/eval.ipynb b/src/eval.ipynb index cd18b7ff..dea701b9 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "ed6cfeb1", "metadata": {}, "outputs": [], @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "id": "0e8d018b", "metadata": { "scrolled": true @@ -74,7 +74,7 @@ " \n", " \n", " \n", - " 1\n", + " 2\n", " 1_beta_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -87,7 +87,7 @@ " 1_beta_noise_gaussian_data_experiment\n", " \n", " \n", - " 2\n", + " 3\n", " 1_exponential_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -100,7 +100,7 @@ " 1_exponential_noise_gaussian_data_experiment\n", " \n", " \n", - " 3\n", + " 4\n", " 1_poisson_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -113,7 +113,7 @@ " 1_poisson_noise_gaussian_data_experiment\n", " \n", " \n", - " 4\n", + " 5\n", " 1_t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -126,7 +126,7 @@ " 1_t_student_noise_gaussian_data_experiment\n", " \n", " \n", - " 5\n", + " 6\n", " 1_uniform_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -139,7 +139,7 @@ " 1_uniform_noise_gaussian_data_experiment\n", " \n", " \n", - " 6\n", + " 7\n", " 3_laplace_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -152,7 +152,7 @@ " 3_laplace_noise_gaussian_data_experiment\n", " \n", " \n", - " 7\n", + " 8\n", " 3_tstudent_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -165,7 +165,20 @@ " 3_tstudent_noise_gaussian_data_experiment\n", " \n", " \n", - " 8\n", + " 20\n", + " daa2cd45-f1c0-4a0c-9100-e171129624c9\n", + " sparse_linear_regression\n", + " Transformer\n", + " sparsity=5\n", + " -1\n", + " -1\n", + " 15\n", + " 4\n", + " 8\n", + " 4_std_sparse_linear_regression\n", + " \n", + " \n", + " 11\n", " beta_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -178,6 +191,19 @@ " beta_noise_ar1_data_experiment\n", " \n", " \n", + " 10\n", + " aed365ed-51e2-4a72-8374-ae954b37be14\n", + " linear_regression\n", + " Transformer\n", + " k=5_sparsity=3\n", + " -1\n", + " -1\n", + " 15\n", + " 4\n", + " 8\n", + " data_sparse_linear_regression\n", + " \n", + " \n", " 0\n", " pretrained\n", " decision_tree\n", @@ -191,7 +217,7 @@ " decision_tree_pretrained\n", " \n", " \n", - " 9\n", + " 12\n", " exponential_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -204,7 +230,7 @@ " exponential_noise_gaussian_data_experiment\n", " \n", " \n", - " 10\n", + " 13\n", " laplace_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -217,7 +243,7 @@ " laplace_noise_gaussian_data_experiment\n", " \n", " \n", - " 11\n", + " 14\n", " rayleigh_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -230,7 +256,7 @@ " rayleigh_noise_gaussian_data_experiment\n", " \n", " \n", - " 15\n", + " 18\n", " pretrained\n", " relu_2nn_regression\n", " Transformer\n", @@ -243,7 +269,46 @@ " relu_2nn_regression_pretrained\n", " \n", " \n", - " 16\n", + " 9\n", + " 82e728b0-a061-448e-8d7a-f3c79c0c74e5\n", + " linear_regression\n", + " Transformer\n", + " sparsity=5\n", + " -1\n", + " -1\n", + " 15\n", + " 4\n", + " 8\n", + " rigde_normal_linear_regression_gaussian\n", + " \n", + " \n", + " 19\n", + " 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615\n", + " sparse_linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 5\n", + " 4\n", + " 8\n", + " sparse\n", + " \n", + " \n", + " 1\n", + " 03de46b6-429a-4151-92e6-3588231c6cad\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " sparse_data_experiment\n", + " \n", + " \n", + " 21\n", " pretrained\n", " sparse_linear_regression\n", " Transformer\n", @@ -256,7 +321,7 @@ " sparse_regression_pretrained\n", " \n", " \n", - " 12\n", + " 15\n", " t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -269,7 +334,7 @@ " t_student_noise_gaussian_data_experiment\n", " \n", " \n", - " 13\n", + " 16\n", " uniform_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -282,7 +347,7 @@ " uniform_noise_ar1_data_experiment\n", " \n", " \n", - " 14\n", + " 17\n", " uniform_noise_gaussian_data_experiment_\n", " linear_regression\n", " Transformer\n", @@ -300,26 +365,30 @@ ], "text/plain": [ " run_id task \\\n", - "1 1_beta_noise_gaussian_data_experiment linear_regression \n", - "2 1_exponential_noise_gaussian_data_experiment linear_regression \n", - "3 1_poisson_noise_gaussian_data_experiment linear_regression \n", - "4 1_t_student_noise_gaussian_data_experiment linear_regression \n", - "5 1_uniform_noise_gaussian_data_experiment linear_regression \n", - "6 3_laplace_noise_gaussian_data_experiment linear_regression \n", - "7 3_tstudent_noise_gaussian_data_experiment linear_regression \n", - "8 beta_noise_ar1_data_experiment linear_regression \n", + "2 1_beta_noise_gaussian_data_experiment linear_regression \n", + "3 1_exponential_noise_gaussian_data_experiment linear_regression \n", + "4 1_poisson_noise_gaussian_data_experiment linear_regression \n", + "5 1_t_student_noise_gaussian_data_experiment linear_regression \n", + "6 1_uniform_noise_gaussian_data_experiment linear_regression \n", + "7 3_laplace_noise_gaussian_data_experiment linear_regression \n", + "8 3_tstudent_noise_gaussian_data_experiment linear_regression \n", + "20 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", + "11 beta_noise_ar1_data_experiment linear_regression \n", + "10 aed365ed-51e2-4a72-8374-ae954b37be14 linear_regression \n", "0 pretrained decision_tree \n", - "9 exponential_noise_gaussian_data_experiment linear_regression \n", - "10 laplace_noise_gaussian_data_experiment linear_regression \n", - "11 rayleigh_noise_gaussian_data_experiment linear_regression \n", - "15 pretrained relu_2nn_regression \n", - "16 pretrained sparse_linear_regression \n", - "12 t_student_noise_gaussian_data_experiment linear_regression \n", - "13 uniform_noise_ar1_data_experiment linear_regression \n", - "14 uniform_noise_gaussian_data_experiment_ linear_regression \n", + "12 exponential_noise_gaussian_data_experiment linear_regression \n", + "13 laplace_noise_gaussian_data_experiment linear_regression \n", + "14 rayleigh_noise_gaussian_data_experiment linear_regression \n", + "18 pretrained relu_2nn_regression \n", + "9 82e728b0-a061-448e-8d7a-f3c79c0c74e5 linear_regression \n", + "19 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", + "1 03de46b6-429a-4151-92e6-3588231c6cad linear_regression \n", + "21 pretrained sparse_linear_regression \n", + "15 t_student_noise_gaussian_data_experiment linear_regression \n", + "16 uniform_noise_ar1_data_experiment linear_regression \n", + "17 uniform_noise_gaussian_data_experiment_ linear_regression \n", "\n", " model kwargs num_tasks num_examples n_dims \\\n", - "1 Transformer -1 -1 5 \n", "2 Transformer -1 -1 5 \n", "3 Transformer -1 -1 5 \n", "4 Transformer -1 -1 5 \n", @@ -327,37 +396,48 @@ "6 Transformer -1 -1 5 \n", "7 Transformer -1 -1 5 \n", "8 Transformer -1 -1 5 \n", - "0 Transformer depth=4 -1 -1 20 \n", - "9 Transformer -1 -1 5 \n", - "10 Transformer -1 -1 5 \n", + "20 Transformer sparsity=5 -1 -1 15 \n", "11 Transformer -1 -1 5 \n", - "15 Transformer hidden_layer_size=100 -1 -1 20 \n", - "16 Transformer sparsity=3 -1 -1 20 \n", + "10 Transformer k=5_sparsity=3 -1 -1 15 \n", + "0 Transformer depth=4 -1 -1 20 \n", "12 Transformer -1 -1 5 \n", "13 Transformer -1 -1 5 \n", "14 Transformer -1 -1 5 \n", + "18 Transformer hidden_layer_size=100 -1 -1 20 \n", + "9 Transformer sparsity=5 -1 -1 15 \n", + "19 Transformer -1 -1 5 \n", + "1 Transformer -1 -1 20 \n", + "21 Transformer sparsity=3 -1 -1 20 \n", + "15 Transformer -1 -1 5 \n", + "16 Transformer -1 -1 5 \n", + "17 Transformer -1 -1 5 \n", "\n", " n_layer n_head run_name \n", - "1 4 8 1_beta_noise_gaussian_data_experiment \n", - "2 4 8 1_exponential_noise_gaussian_data_experiment \n", - "3 4 8 1_poisson_noise_gaussian_data_experiment \n", - "4 4 8 1_t_student_noise_gaussian_data_experiment \n", - "5 4 8 1_uniform_noise_gaussian_data_experiment \n", - "6 4 8 3_laplace_noise_gaussian_data_experiment \n", - "7 4 8 3_tstudent_noise_gaussian_data_experiment \n", - "8 4 8 beta_noise_ar1_data_experiment \n", + "2 4 8 1_beta_noise_gaussian_data_experiment \n", + "3 4 8 1_exponential_noise_gaussian_data_experiment \n", + "4 4 8 1_poisson_noise_gaussian_data_experiment \n", + "5 4 8 1_t_student_noise_gaussian_data_experiment \n", + "6 4 8 1_uniform_noise_gaussian_data_experiment \n", + "7 4 8 3_laplace_noise_gaussian_data_experiment \n", + "8 4 8 3_tstudent_noise_gaussian_data_experiment \n", + "20 4 8 4_std_sparse_linear_regression \n", + "11 4 8 beta_noise_ar1_data_experiment \n", + "10 4 8 data_sparse_linear_regression \n", "0 12 8 decision_tree_pretrained \n", - "9 4 8 exponential_noise_gaussian_data_experiment \n", - "10 4 8 laplace_noise_gaussian_data_experiment \n", - "11 4 8 rayleigh_noise_gaussian_data_experiment \n", - "15 12 8 relu_2nn_regression_pretrained \n", - "16 12 8 sparse_regression_pretrained \n", - "12 4 8 t_student_noise_gaussian_data_experiment \n", - "13 4 8 uniform_noise_ar1_data_experiment \n", - "14 4 8 uniform_noise_gaussian_data_experiment " + "12 4 8 exponential_noise_gaussian_data_experiment \n", + "13 4 8 laplace_noise_gaussian_data_experiment \n", + "14 4 8 rayleigh_noise_gaussian_data_experiment \n", + "18 12 8 relu_2nn_regression_pretrained \n", + "9 4 8 rigde_normal_linear_regression_gaussian \n", + "19 4 8 sparse \n", + "1 4 8 sparse_data_experiment \n", + "21 12 8 sparse_regression_pretrained \n", + "15 4 8 t_student_noise_gaussian_data_experiment \n", + "16 4 8 uniform_noise_ar1_data_experiment \n", + "17 4 8 uniform_noise_gaussian_data_experiment " ] }, - "execution_count": 14, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -369,17 +449,17 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 9, "id": "a9980951", "metadata": {}, "outputs": [], "source": [ "task = \"linear_regression\"\n", - "#task = \"sparse_linear_regression\"\n", + "# task = \"sparse_linear_regression\"\n", "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"1_beta_noise_gaussian_data_experiment\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"03de46b6-429a-4151-92e6-3588231c6cad\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -390,7 +470,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 6, "id": "937f1b23", "metadata": {}, "outputs": [ @@ -399,20 +479,20 @@ "output_type": "stream", "text": [ "--- Tên mô hình CÓ TRONG relevant_model_names (models) ---\n", - "['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", + "['Transformer', 'Least Squares', '3-Nearest Neighbors', 'Averaging', 'Lasso (alpha=0.001)', 'Lasso (alpha=0.01)', 'Lasso (alpha=0.1)', 'Lasso (alpha=1.0)', 'Ridge (alpha=0.5)']\n", "\n", "--- Các khóa (Tên mô hình) THỰC TẾ trong metrics['standard'] ---\n" ] }, { - "ename": "KeyError", - "evalue": "'standard'", + "ename": "NameError", + "evalue": "name 'metrics' is not defined", "output_type": "error", "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[20]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 7\u001b[39m \u001b[38;5;28mprint\u001b[39m(models)\n\u001b[32m 8\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m--- Các khóa (Tên mô hình) THỰC TẾ trong metrics[\u001b[39m\u001b[33m'\u001b[39m\u001b[33mstandard\u001b[39m\u001b[33m'\u001b[39m\u001b[33m] ---\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m pprint.pprint(\u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstandard\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m.keys()) \n\u001b[32m 11\u001b[39m \u001b[38;5;66;03m# basic_plot(metrics[\"standard\"], models=models) \u001b[39;00m\n\u001b[32m 12\u001b[39m \u001b[38;5;66;03m# ...\u001b[39;00m\n", - "\u001b[31mKeyError\u001b[39m: 'standard'" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[6], line 9\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(models)\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m--- Các khóa (Tên mô hình) THỰC TẾ trong metrics[\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstandard\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m] ---\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 9\u001b[0m pprint\u001b[38;5;241m.\u001b[39mpprint(\u001b[43mmetrics\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstandard\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mkeys()) \n\u001b[0;32m 11\u001b[0m \u001b[38;5;66;03m# basic_plot(metrics[\"standard\"], models=models) \u001b[39;00m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# ...\u001b[39;00m\n", + "\u001b[1;31mNameError\u001b[0m: name 'metrics' is not defined" ] } ], @@ -441,7 +521,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 10, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -451,47 +531,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "1_beta_noise_gaussian_data_experiment 1_beta_noise_gaussian_data_experiment\n" + "sparse_data_experiment 03de46b6-429a-4151-92e6-3588231c6cad\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1/1 [00:00<00:00, 2244.14it/s]" + "100%|██████████| 15/15 [00:00 \u001b[39m\u001b[32m9\u001b[39m \u001b[43mbasic_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstandard\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 10\u001b[39m plt.show()\n\u001b[32m 12\u001b[39m \u001b[38;5;66;03m# # Figure 3 and 4\u001b[39;00m\n\u001b[32m 13\u001b[39m \u001b[38;5;66;03m# for model_name in models: \u001b[39;00m\n\u001b[32m 14\u001b[39m \u001b[38;5;66;03m# if \"gradient_alignment\" in metrics[\"standard\"][model_name]: \u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 20\u001b[39m \u001b[38;5;66;03m# plt.legend()\u001b[39;00m\n\u001b[32m 21\u001b[39m \u001b[38;5;66;03m# plt.show()\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32md:\\MyBK\\Semester_5\\Programming_Intergration_Project\\in-context-learning\\src\\plot_utils.py:5\u001b[39m, in \u001b[36mbasic_plot\u001b[39m\u001b[34m(metrics, models, trivial)\u001b[39m\n\u001b[32m 0\u001b[39m \n", - "\u001b[31mKeyError\u001b[39m: 'Ridge (alpha=0.1)'" + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -893,7 +954,7 @@ ], "metadata": { "kernelspec": { - "display_name": "base", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -907,7 +968,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.10" } }, "nbformat": 4, diff --git a/src/run_all.py b/src/run_all.py index 3b2c217c..630e4818 100644 --- a/src/run_all.py +++ b/src/run_all.py @@ -26,93 +26,322 @@ def prepare_out_dir(args): yaml.dump(args.__dict__, yaml_file, default_flow_style=False) -def build_parser(): - parser = argparse.ArgumentParser(description="Run all noisy_linear_regression variants") - parser.add_argument( - "--config", - default="src/conf/toy.yaml", - help="Base config yaml (e.g., src/conf/toy.yaml)", - ) - parser.add_argument( - "--noise_types", - nargs="*", - default=[ - "uniform", - "t-student", - "rayleigh", - "normal", - "exponential", - "beta", - "poisson", - "cauchy", - "laplace", - ], - help="Which noise_type values to iterate", - ) - parser.add_argument( - "--base_run_name", - default="noisy_sweep", - help="Prefix for wandb.name; final name will be '_'", - ) - return parser - - -def run_one(base_config_path: str, noise_type: str, base_run_name: str): - # --- ĐÂY LÀ CHỖ SỬA --- - # 1. Lấy đường dẫn thư mục của file config gốc - # ví dụ: /content/in-context-learning/src/conf +def run_one_experiment(base_config_path: str, task: str, task_kwargs: dict, data_kwargs: dict, run_name: str, resume_id: str = None, data_type: str = None): + """ + Run a single experiment with specified task, task_kwargs, and data_kwargs. + + Args: + base_config_path: Path to base config yaml file + task: Task name (e.g., 'sparse_linear_regression', 'noisy_linear_regression') + task_kwargs: Dictionary of task-specific kwargs (e.g., {'noise_type': 'normal', 'sparsity': 3}) + data_kwargs: Dictionary of data sampler kwargs (e.g., {'sparsity': 5}) + run_name: Name for wandb run + resume_id: Optional resume_id for the run + data_type: Optional data type override (e.g., 'sparse_gaussian' for sparse data experiments) + """ config_dir = os.path.dirname(base_config_path) - # --- KẾT THÚC SỬA LỖI --- - # 2. Đọc nội dung file config gốc + # Read base config with open(base_config_path, 'r') as f: base_config = yaml.safe_load(f) - # 3. Sửa đổi dictionary config trong Python - base_config['training']['task'] = 'noisy_linear_regression' - base_config['training']['task_kwargs'] = {'noise_type': noise_type} - base_config['wandb']['name'] = f"{noise_type}_{base_run_name}" - base_config['training']['resume_id'] = noise_type - # 4. Tạo một file config tạm thời + # Modify config for this experiment + base_config['training']['task'] = task + base_config['training']['task_kwargs'] = task_kwargs + base_config['training']['data_kwargs'] = data_kwargs + if data_type is not None: + base_config['training']['data'] = data_type + base_config['wandb']['name'] = run_name + if resume_id is not None: + base_config['training']['resume_id'] = resume_id + + # Create temporary config file temp_config_file = tempfile.NamedTemporaryFile( mode='w+t', delete=False, suffix='.yaml', - dir=config_dir # <-- YÊU CẦU TẠO FILE TẠM TRONG THƯ MỤC CẤU HÌNH + dir=config_dir ) try: - # 5. Ghi config mới vào file tạm thời + # Write modified config to temp file yaml.dump(base_config, temp_config_file, default_flow_style=False) temp_config_file.close() - # 6. Xây dựng danh sách đối số CHỈ chứa file config mới + # Parse config using Quinine cli_args_list = ["--config", temp_config_file.name] - - # 7. Sử dụng "thủ thuật" sys.argv để chạy parser qparser = QuinineArgumentParser(schema=quinine_schema) original_argv = sys.argv try: sys.argv = ["run_one_script_placeholder"] + cli_args_list args = qparser.parse_quinfig() finally: - sys.argv = original_argv # Luôn khôi phục argv gốc + sys.argv = original_argv - # 8. Chuẩn bị thư mục output và chạy training + # Prepare output directory and run training prepare_out_dir(args) + print(f"\n{'='*60}") + print(f"Running: {run_name}") + print(f"Task: {task}") + print(f"Task kwargs: {task_kwargs}") + print(f"Data kwargs: {data_kwargs}") + if data_type is not None: + print(f"Data type: {data_type}") + print(f"{'='*60}\n") train_main(args) finally: - # 9. Luôn đảm bảo xóa file tạm thời sau khi hoàn tất - os.remove(temp_config_file.name) + # Clean up temp file + if os.path.exists(temp_config_file.name): + os.remove(temp_config_file.name) + + +def get_default_experiments(): + """ + Define default experiments for sparse_linear_regression and noisy_linear_regression. + Returns a list of experiment configs: (task, task_kwargs, data_kwargs, run_name, data_type) + """ + experiments = [] + + # ===== Sparse Linear Regression Experiments ===== + # Sparse w (weight sparsity) + for sparsity in [3, 5, 7]: + experiments.append(( + "sparse_linear_regression", + {"sparsity": sparsity}, # task_kwargs + {}, # data_kwargs + f"sparse_w_sparsity_{sparsity}", + None # data_type: use default from config + )) + + # Sparse data (data sparsity) - using sparse_gaussian data + for data_sparsity in [5, 10, 15]: + experiments.append(( + "sparse_linear_regression", + {"sparsity": 3}, # task_kwargs (w sparsity) + {"sparsity": data_sparsity}, # data_kwargs (data sparsity) + f"sparse_data_sparsity_{data_sparsity}", + "sparse_gaussian" # data_type override + )) + + # ===== Noisy Linear Regression Experiments ===== + # Different noise types + noise_types = [ + "normal", + "uniform", + "laplace", + "t-student", + "cauchy", + "exponential", + "rayleigh", + "beta", + "poisson", + ] + + for noise_type in noise_types: + experiments.append(( + "noisy_linear_regression", + {"noise_type": noise_type, "noise_std": 2.0}, # task_kwargs + {}, # data_kwargs + f"noisy_{noise_type}", + None # data_type: use default from config + )) + + # Different noise_std values for normal noise + for noise_std in [0.5, 1.0, 2.0, 3.0]: + experiments.append(( + "noisy_linear_regression", + {"noise_type": "normal", "noise_std": noise_std}, # task_kwargs + {}, # data_kwargs + f"noisy_normal_std_{noise_std}", + None # data_type: use default from config + )) + + return experiments + + +def build_parser(): + parser = argparse.ArgumentParser( + description="Run experiments for sparse_linear_regression and noisy_linear_regression" + ) + parser.add_argument( + "--config", + default="src/conf/toy.yaml", + help="Base config yaml (e.g., src/conf/toy.yaml)", + ) + parser.add_argument( + "--task", + choices=["sparse", "noisy", "both", "custom"], + default="both", + help="Which task(s) to run: 'sparse', 'noisy', 'both', or 'custom'", + ) + parser.add_argument( + "--sparse_w_sparsities", + nargs="*", + type=int, + default=[3, 5, 7], + help="Weight sparsity values for sparse_linear_regression (w sparsity)", + ) + parser.add_argument( + "--sparse_data_sparsities", + nargs="*", + type=int, + default=[5, 10, 15], + help="Data sparsity values for sparse_linear_regression (data sparsity)", + ) + parser.add_argument( + "--noise_types", + nargs="*", + default=[ + "normal", + "uniform", + "laplace", + "t-student", + "cauchy", + "exponential", + "rayleigh", + "beta", + "poisson", + ], + help="Noise types for noisy_linear_regression", + ) + parser.add_argument( + "--noise_stds", + nargs="*", + type=float, + default=[0.5, 1.0, 2.0, 3.0], + help="Noise standard deviations for noisy_linear_regression", + ) + parser.add_argument( + "--base_run_name", + default="sweep", + help="Base prefix for wandb.name", + ) + parser.add_argument( + "--skip_existing", + action="store_true", + help="Skip runs that already have config.yaml in output directory", + ) + return parser + + def main(): parser = build_parser() cli_args = parser.parse_args() - for noise in cli_args.noise_types: - run_one(cli_args.config, noise, cli_args.base_run_name) + experiments = [] + + # Build experiment list based on task selection + if cli_args.task in ["sparse", "both"]: + # Sparse w experiments (weight sparsity, regular gaussian data) + for sparsity in cli_args.sparse_w_sparsities: + experiments.append(( + "sparse_linear_regression", + {"sparsity": sparsity}, + {}, + f"{cli_args.base_run_name}_sparse_w_{sparsity}", + None # data_type: use default from config + )) + + # Sparse data experiments (sparse_gaussian data) + for data_sparsity in cli_args.sparse_data_sparsities: + experiments.append(( + "sparse_linear_regression", + {"sparsity": 3}, # w sparsity + {"sparsity": data_sparsity}, # data sparsity + f"{cli_args.base_run_name}_sparse_data_{data_sparsity}", + "sparse_gaussian" # data_type override + )) + + if cli_args.task in ["noisy", "both"]: + # Different noise types + for noise_type in cli_args.noise_types: + experiments.append(( + "noisy_linear_regression", + {"noise_type": noise_type, "noise_std": 2.0}, + {}, + f"{cli_args.base_run_name}_noisy_{noise_type}", + None # data_type: use default from config + )) + + # Different noise_std for normal noise + for noise_std in cli_args.noise_stds: + experiments.append(( + "noisy_linear_regression", + {"noise_type": "normal", "noise_std": noise_std}, + {}, + f"{cli_args.base_run_name}_noisy_normal_std_{noise_std}", + None # data_type: use default from config + )) + + if cli_args.task == "custom": + # Use default experiments + default_experiments = get_default_experiments() + # Add base_run_name prefix + experiments = [ + (task, tk, dk, f"{cli_args.base_run_name}_{name}", dt) + for task, tk, dk, name, dt in default_experiments + ] + + # Run experiments + print(f"\n{'='*60}") + print(f"Total experiments to run: {len(experiments)}") + print(f"{'='*60}\n") + + for idx, exp in enumerate(experiments, 1): + # Handle both 4-tuple and 5-tuple formats + if len(exp) == 4: + task, task_kwargs, data_kwargs, run_name = exp + data_type = None + else: + task, task_kwargs, data_kwargs, run_name, data_type = exp + + print(f"\n[{idx}/{len(experiments)}] Preparing: {run_name}") + + # Check if should skip existing + if cli_args.skip_existing: + # Try to find existing run by checking base out_dir + base_config = yaml.safe_load(open(cli_args.config)) + base_out_dir = base_config.get('out_dir', '../models') + # Check if any subdirectory has this run_name in config + if os.path.exists(base_out_dir): + task_dir = os.path.join(base_out_dir, task) + if os.path.exists(task_dir): + for run_id in os.listdir(task_dir): + run_path = os.path.join(task_dir, run_id) + config_path = os.path.join(run_path, 'config.yaml') + if os.path.exists(config_path): + with open(config_path) as f: + existing_config = yaml.safe_load(f) + if existing_config.get('wandb', {}).get('name') == run_name: + print(f" -> Skipping (already exists): {run_name}") + continue + + # Generate resume_id from run_name (sanitize for filesystem) + resume_id = run_name.replace(" ", "_").replace("/", "_") + + try: + run_one_experiment( + cli_args.config, + task, + task_kwargs, + data_kwargs, + run_name, + resume_id=resume_id, + data_type=data_type + ) + except Exception as e: + print(f"\n{'!'*60}") + print(f"ERROR in experiment: {run_name}") + print(f"Error: {str(e)}") + print(f"{'!'*60}\n") + # Continue with next experiment + continue + + print(f"\n{'='*60}") + print(f"All experiments completed!") + print(f"{'='*60}\n") if __name__ == "__main__": main() - diff --git a/src/schema.py b/src/schema.py index 8175a010..8984408f 100644 --- a/src/schema.py +++ b/src/schema.py @@ -44,6 +44,7 @@ "ar1_linear_regression", "ar2_linear_regression", "non_stationary_linear_regression", + "uniform_hypersphere_regression", ] training_schema = { diff --git a/src/tasks.py b/src/tasks.py index c4868432..1625ab59 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -56,6 +56,7 @@ def get_task_sampler( "linear_regression": LinearRegression, "sparse_linear_regression": SparseLinearRegression, "linear_classification": LinearClassification, + "uniform_hypersphere_regression": UniformHypersphereRegression, "noisy_linear_regression": NoisyLinearRegression, "quadratic_regression": QuadraticRegression, "relu_2nn_regression": Relu2nnRegression, @@ -76,6 +77,32 @@ def get_task_sampler( print("Unknown task") raise NotImplementedError +class UniformHypersphereRegression(Task): + def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1): + super(LinearRegression, self).__init__(n_dims, batch_size, pool_dict, seeds) + self.scale = scale + + if pool_dict is None and seeds is None: + w_b = torch.randn(self.b_size, self.n_dims, 1) + self.w_b = w_b / w_b.norm(dim=1, keepdim=True) + elif seeds is not None: + self.w_b = torch.zeros(self.b_size, self.n_dims, 1) + generator = torch.Generator() + assert len(seeds) == self.b_size + for i, seed in enumerate(seeds): + generator.manual_seed(seed) + w = torch.randn(self.n_dims, 1, generator=generator) + self.w_b[i] = w / torch.norm(w) + else: + assert "w" in pool_dict + indices = torch.randperm(len(pool_dict["w"]))[:batch_size] + self.w_b = pool_dict["w"][indices] + @staticmethod + def generate_pool_dict(n_dims, num_tasks): + w = torch.randn(num_tasks, n_dims, 1) + w_normalized = w / torch.norm(w, dim=1, keepdim=True) + return {"w": w_normalized} + class LinearRegression(Task): def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1,uniform=False): diff --git a/src/train.py b/src/train.py index 6c58af0a..d7af5379 100644 --- a/src/train.py +++ b/src/train.py @@ -51,13 +51,12 @@ def train(model, args): n_dims = model.n_dims bsize = args.training.batch_size - data_sampler = get_data_sampler(args.training.data, n_dims=n_dims, **trains.args.data_kwargs) + data_sampler = get_data_sampler(args.training.data, n_dims=n_dims, **args.training.data_kwargs) task_sampler = get_task_sampler( - args.training.task, - n_dims, - bsize, - num_tasks=args.training.num_tasks, - **args.training.task_kwargs, + args.training.task, + n_dims=n_dims, + batch_size=args.training.batch_size, + **args.training.task_kwargs ) pbar = tqdm(range(starting_step, args.training.train_steps)) From 7ee9d406161f03aab6a873771d6cd1c9540d788b Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 10 Nov 2025 23:02:28 +0700 Subject: [PATCH 35/88] fix toy --- src/conf/toy.yaml | 1 + src/run_all.py | 15 +++++++++++++-- 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/src/conf/toy.yaml b/src/conf/toy.yaml index d561cad4..d37abd18 100644 --- a/src/conf/toy.yaml +++ b/src/conf/toy.yaml @@ -24,6 +24,7 @@ training: inc: 2 interval: 2000 data: gaussian + data_kwargs: {} keep_every_steps: 100000 learning_rate: 0.0003 num_tasks: null diff --git a/src/run_all.py b/src/run_all.py index 630e4818..def50bfe 100644 --- a/src/run_all.py +++ b/src/run_all.py @@ -26,7 +26,7 @@ def prepare_out_dir(args): yaml.dump(args.__dict__, yaml_file, default_flow_style=False) -def run_one_experiment(base_config_path: str, task: str, task_kwargs: dict, data_kwargs: dict, run_name: str, resume_id: str = None, data_type: str = None): +def run_one_experiment(base_config_path: str, task: str, task_kwargs: dict, data_kwargs: dict, run_name: str, resume_id: str = None, data_type: str = None, train_steps: int = None): """ Run a single experiment with specified task, task_kwargs, and data_kwargs. @@ -54,6 +54,8 @@ def run_one_experiment(base_config_path: str, task: str, task_kwargs: dict, data base_config['wandb']['name'] = run_name if resume_id is not None: base_config['training']['resume_id'] = resume_id + if train_steps is not None: + base_config['training']['train_steps'] = int(train_steps) # Create temporary config file temp_config_file = tempfile.NamedTemporaryFile( @@ -87,6 +89,8 @@ def run_one_experiment(base_config_path: str, task: str, task_kwargs: dict, data print(f"Data kwargs: {data_kwargs}") if data_type is not None: print(f"Data type: {data_type}") + if train_steps is not None: + print(f"Train steps override: {train_steps}") print(f"{'='*60}\n") train_main(args) @@ -217,6 +221,12 @@ def build_parser(): default="sweep", help="Base prefix for wandb.name", ) + parser.add_argument( + "--train_steps", + type=int, + default=None, + help="Override training.train_steps for all experiments", + ) parser.add_argument( "--skip_existing", action="store_true", @@ -328,7 +338,8 @@ def main(): data_kwargs, run_name, resume_id=resume_id, - data_type=data_type + data_type=data_type, + train_steps=cli_args.train_steps ) except Exception as e: print(f"\n{'!'*60}") From a79c96e3d4e5efbcd7c5ce466a0a725f30e0745f Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Tue, 11 Nov 2025 07:30:54 +0700 Subject: [PATCH 36/88] add information toy --- src/conf/toy.yaml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/conf/toy.yaml b/src/conf/toy.yaml index d37abd18..40abfbfd 100644 --- a/src/conf/toy.yaml +++ b/src/conf/toy.yaml @@ -4,23 +4,23 @@ inherit: model: family: gpt2 - n_dims: 10 + n_dims: 20 n_embd: 128 n_head: 8 n_layer: 4 - n_positions: 100 + n_positions: 101 training: batch_size: 32 curriculum: dims: start: 5 - end: 10 + end: 20 inc: 1 interval: 2000 points: start: 6 - end: 15 + end: 30 inc: 2 interval: 2000 data: gaussian @@ -31,10 +31,10 @@ training: num_training_examples: null resume_id: null save_every_steps: 100 - task: sparse_linear_regression + task: noisy_linear_regression task_kwargs: { # "compute_gradient": True - "sparsity": 5 + # "sparsity": 5 } train_steps: 50001 From 7eccd5549a7876bb64d292f67b766f08699ff8c5 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Tue, 11 Nov 2025 07:42:54 +0700 Subject: [PATCH 37/88] fix to run --- src/samplers.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/src/samplers.py b/src/samplers.py index 7f6a8af8..7d010cab 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -23,10 +23,12 @@ def get_data_sampler(data_name, n_dims, **kwargs): } if data_name in names_to_classes: sampler_cls = names_to_classes[data_name] - if 'k' not in kwargs: + # Only add 'k' parameter for sparse_gaussian sampler + if data_name == "sparse_gaussian" and 'k' not in kwargs: kwargs['k'] = n_dims // 2 # default k is half of dimensions - if 'scale' not in kwargs: - kwargs['scale'] = 1.0 # default scale is 1.0 + # Only add 'scale' parameter for sparse_gaussian sampler (as scalar) + if data_name == "sparse_gaussian" and 'scale' not in kwargs: + kwargs['scale'] = 1.0 # default scale is 1.0 for sparse_gaussian return sampler_cls(n_dims, **kwargs) else: print("Unknown sampler") From 48f5119c2c953d326d6386b837b6d0399050b109 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Tue, 11 Nov 2025 08:04:30 +0700 Subject: [PATCH 38/88] fix code --- src/eval.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/eval.py b/src/eval.py index bf6232c8..42ca1956 100644 --- a/src/eval.py +++ b/src/eval.py @@ -221,7 +221,7 @@ def build_evals(conf): "data_name": data_name, "prompting_strategy": "standard", "data_sampler_kwargs": conf.training.data_kwargs if hasattr(conf.training, "data_kwargs") else {}, - "task_kwargs": conf.training.task_kwargs + "task_sampler_kwargs": conf.training.task_kwargs } evaluation_kwargs = {} From 1644465914da70082f6c146b2f11a2f6f4906f09 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Tue, 11 Nov 2025 13:38:35 +0700 Subject: [PATCH 39/88] figure 3 and 4 --- src/figure3.py | 262 ++++++++++++++++++++++++++++++++++++++++++++++ src/plot_utils.py | 2 +- 2 files changed, 263 insertions(+), 1 deletion(-) create mode 100644 src/figure3.py diff --git a/src/figure3.py b/src/figure3.py new file mode 100644 index 00000000..c7a165d4 --- /dev/null +++ b/src/figure3.py @@ -0,0 +1,262 @@ +import argparse +from collections import OrderedDict, defaultdict +from typing import Dict, List, Optional, Sequence, Tuple + +import matplotlib.pyplot as plt +import numpy as np +import torch + +from eval import get_model_from_run +from samplers import get_data_sampler +from tasks import get_task_sampler + + +def _select_device(model: torch.nn.Module) -> torch.device: + if torch.cuda.is_available(): + return torch.device("cuda") + return torch.device("cpu") + + +def _get_true_w(task) -> Optional[torch.Tensor]: + if hasattr(task, "w_b"): + return task.w_b[0, :, 0] + return None + + +def _project_to_row_space(w: torch.Tensor, xs_ctx: torch.Tensor) -> torch.Tensor: + if xs_ctx.numel() == 0: + return torch.zeros_like(w) + # xs_ctx: (k, d). Project w onto span{rows(xs_ctx)} + x = xs_ctx + gram = x @ x.t() + proj_matrix = x.t() @ torch.linalg.pinv(gram) @ x + return proj_matrix @ w + + +def _estimate_norm_band(data_sampler, device: torch.device, num_samples: int = 16384) -> Tuple[float, float]: + batch_size = min(512, num_samples) + collected = [] + remaining = num_samples + while remaining > 0: + cur = min(batch_size, remaining) + xs = data_sampler.sample_xs(n_points=1, b_size=cur).to(device) + norms = xs[:, 0, :].norm(dim=1) + collected.append(norms) + remaining -= cur + norms = torch.cat(collected) + low = torch.quantile(norms, 0.005).item() + high = torch.quantile(norms, 0.995).item() + return low, high + + +def _prepare(run_path: str): + model, conf = get_model_from_run(run_path) + device = _select_device(model) + model = model.to(device).eval() + + n_dims = conf.model.n_dims + data_sampler = get_data_sampler(conf.training.data, n_dims, **getattr(conf.training, "data_kwargs", {})) + task_sampler = get_task_sampler( + conf.training.task, + n_dims, + b_size=1, + **conf.training.task_kwargs, + ) + return model, conf, data_sampler, task_sampler, device + + +def plot_prefix_conditioned_function( + run_path: str, + num_dirs: int = 3, + ks: Optional[Sequence[int]] = None, + sweep_radius: float = 15.0, + num_steps: int = 201, + seed: Optional[int] = None, +): + if seed is not None: + torch.manual_seed(seed) + + model, conf, data_sampler, task_sampler, device = _prepare(run_path) + task = task_sampler() + w = _get_true_w(task) + w = w.to(device) if w is not None else None + + if ks is None: + d = conf.model.n_dims + max_pts = conf.training.curriculum.points.end + ks = [max(1, d // 2), d, min(2 * d, max_pts)] + + ks = list(dict.fromkeys(sorted(ks))) + band_low, band_high = _estimate_norm_band(data_sampler, device) + + ts = torch.linspace(-sweep_radius, sweep_radius, steps=num_steps, device=device) + fig, axes = plt.subplots(1, num_dirs, figsize=(14, 4), sharey=True) + if num_dirs == 1: + axes = [axes] + + for idx in range(num_dirs): + ax = axes[idx] + u = torch.randn(conf.model.n_dims, device=device) + u = u / (u.norm() + 1e-8) + xs_ctx_for_proj = None + + for k in ks: + xs_ctx = data_sampler.sample_xs(n_points=k, b_size=1).to(device) + ys_ctx = task.evaluate(xs_ctx).to(device) + + preds = [] + for t in ts: + x_query = (t * u).view(1, 1, -1) + xs_in = torch.cat([xs_ctx, x_query], dim=1) + ys_in = torch.cat([ys_ctx, torch.zeros_like(ys_ctx[:, :1])], dim=1) + with torch.no_grad(): + out = model(xs_in, ys_in, inds=[k]) + preds.append(out[0, 0].item()) + + if xs_ctx_for_proj is None: + xs_ctx_for_proj = xs_ctx[0] + + if k == conf.model.n_dims: + label = "#dims in-context" + elif k == ks[-1]: + label = f"{k} in-context" + else: + label = f"k={k}" + ax.plot(ts.detach().cpu().numpy(), preds, lw=2, label=label) + + if w is not None: + ground_truth = (ts * torch.dot(u, w)).detach().cpu().numpy() + ax.plot(ts.detach().cpu().numpy(), ground_truth, color="C0", lw=2, label="ground truth") + + if xs_ctx_for_proj is not None: + w_proj = _project_to_row_space(w, xs_ctx_for_proj) + gt_proj = (ts * torch.dot(u, w_proj)).detach().cpu().numpy() + ax.plot(ts.detach().cpu().numpy(), gt_proj, color="C0", lw=2, ls="--", label="ground truth proj.") + + ax.axvspan(-band_high, -band_low, color="#000000", alpha=0.08) + ax.axvspan(band_low, band_high, color="#000000", alpha=0.08) + ax.set_xlabel("query scale") + if idx == 0: + ax.set_ylabel("model prediction") + + handles, labels = axes[0].get_legend_handles_labels() + by_label = OrderedDict(zip(labels, handles)) + fig.legend(by_label.values(), by_label.keys(), loc="upper center", ncol=3, bbox_to_anchor=(0.5, 1.15)) + plt.tight_layout() + plt.show() + + +def _cosine(u: torch.Tensor, v: torch.Tensor) -> float: + denom = (u.norm() * v.norm()).item() + if denom < 1e-8: + return float("nan") + return float(torch.dot(u, v).item() / denom) + + +def compute_gradient_alignment_curves( + run_path: str, + ks: Optional[Sequence[int]] = None, + num_prompts: int = 1280, + seed: Optional[int] = None, +) -> Dict[str, List[Tuple[int, float]]]: + if seed is not None: + torch.manual_seed(seed) + + model, conf, data_sampler, task_sampler, device = _prepare(run_path) + if ks is None: + d = conf.model.n_dims + max_pts = conf.training.curriculum.points.end + ks = [max(1, d // 2), d, min(2 * d, max_pts)] + ks = list(dict.fromkeys(sorted(ks))) + max_k = ks[-1] + + series_proj = defaultdict(list) + series_true = defaultdict(list) + + for _ in range(num_prompts): + task = task_sampler() + w = _get_true_w(task) + if w is None: + continue + w = w.to(device) + + xs = data_sampler.sample_xs(n_points=max_k + 1, b_size=1).to(device) + ys = task.evaluate(xs).to(device) + + for k in ks: + ctx_xs = xs[:, :k, :] + ctx_ys = ys[:, :k] + x_query = xs[:, k : k + 1, :].clone().detach().requires_grad_(True) + + xs_in = torch.cat([ctx_xs, x_query], dim=1) + ys_in = torch.cat([ctx_ys, torch.zeros_like(ctx_ys[:, :1])], dim=1) + + pred = model(xs_in, ys_in, inds=[k]) + grad = torch.autograd.grad(pred.sum(), x_query, retain_graph=False)[0].view(-1) + + w_proj = _project_to_row_space(w, ctx_xs[0]) + + series_true[k].append(_cosine(grad, w)) + series_proj[k].append(_cosine(grad, w_proj)) + + def _finalize(series_dict): + values = [] + for k in ks: + data = np.array(series_dict[k], dtype=float) + if data.size == 0: + values.append((k, float("nan"))) + else: + values.append((k, float(np.nanmean(data)))) + return values + + return { + "with_true_w": _finalize(series_true), + "with_projected_w": _finalize(series_proj), + } + + +def plot_gradient_alignment( + run_path: str, + ks: Optional[Sequence[int]] = None, + num_prompts: int = 1280, + seed: Optional[int] = None, +): + curves = compute_gradient_alignment_curves(run_path, ks=ks, num_prompts=num_prompts, seed=seed) + + plt.figure(figsize=(6, 4)) + xs_true = [k for k, _ in curves["with_true_w"]] + ys_true = [val for _, val in curves["with_true_w"]] + plt.plot(xs_true, ys_true, marker="o", label="grad vs w") + + xs_proj = [k for k, _ in curves["with_projected_w"]] + ys_proj = [val for _, val in curves["with_projected_w"]] + plt.plot(xs_proj, ys_proj, marker="o", label="grad vs proj(w)") + + plt.xlabel("# in-context examples (k)") + plt.ylabel("normalized inner product") + plt.ylim(-0.05, 1.05) + plt.legend() + plt.tight_layout() + plt.show() + + +def main(args: Optional[Sequence[str]] = None): + parser = argparse.ArgumentParser(description="Reproduce Figure 3 diagnostics.") + parser.add_argument("run_path", type=str, help="Path to a trained run directory.") + parser.add_argument("--num_dirs", type=int, default=3, help="number of random prompts for Fig 3a") + parser.add_argument("--num_prompts", type=int, default=1280, help="number of random prompts for Fig 3b") + parser.add_argument("--seed", type=int, default=None, help="random seed") + parser.add_argument("--no_fig3a", action="store_true", help="skip prefix-conditioned function plot") + parser.add_argument("--no_fig3b", action="store_true", help="skip gradient alignment plot") + parsed = parser.parse_args(args=args) + + if not parsed.no_fig3a: + plot_prefix_conditioned_function(parsed.run_path, num_dirs=parsed.num_dirs, seed=parsed.seed) + if not parsed.no_fig3b: + plot_gradient_alignment(parsed.run_path, num_prompts=parsed.num_prompts, seed=parsed.seed) + + +if __name__ == "__main__": + main() + + diff --git a/src/plot_utils.py b/src/plot_utils.py index 17254bd8..6fb2d82c 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -98,7 +98,7 @@ def basic_plot(metrics, models=None, trivial=1.0): ax.set_xlabel("in-context examples") ax.set_ylabel("squared error") ax.set_xlim(-1, len(low) + 0.1) - ax.set_ylim(-0.1, 1) + ax.set_ylim(-0.1, 2) legend = ax.legend(loc="upper left", bbox_to_anchor=(1, 1)) From 9cfcdf368fe952341b1468e4b7b0e0626ebe5b9b Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Tue, 11 Nov 2025 15:13:33 +0700 Subject: [PATCH 40/88] update all --- src/conf/template.yaml | 72 ++++ src/eval.ipynb | 581 ++++++++++++++++++++++++------- src/{figure3.py => figure3_4.py} | 2 +- src/models.py | 1 - src/plot_utils.py | 3 +- src/train.py | 27 ++ 6 files changed, 560 insertions(+), 126 deletions(-) create mode 100644 src/conf/template.yaml rename src/{figure3.py => figure3_4.py} (99%) diff --git a/src/conf/template.yaml b/src/conf/template.yaml new file mode 100644 index 00000000..12708bbe --- /dev/null +++ b/src/conf/template.yaml @@ -0,0 +1,72 @@ +inherit: + - models/standard.yaml + - wandb.yaml + +model: + family: gpt2 + n_dims: 20 + n_embd: 128 + n_head: 8 + n_layer: 4 + n_positions: 101 + +training: + batch_size: 32 + curriculum: + dims: + start: 5 + end: 20 + inc: 1 + interval: 2000 + points: + start: 6 + end: 30 + inc: 2 + interval: 2000 + + # One of: gaussian, sparse_gaussian, ar1, vr1, ar2, vr2, nonstation + data: gaussian + + # Data kwargs: + # - When data == 'sparse_gaussian': you may set 'k' (number of non-zero coords). + # - For other data values: any 'k' key will be ignored automatically. + data_kwargs: { + # k: 8 # only when data: sparse_gaussian + # scale: 1.0 # optional for many samplers + } + + # Task: choose a base task + # One of: linear_regression, sparse_linear_regression, linear_classification, + # relu_2nn_regression, decision_tree, noisy_linear_regression, + # ar1_linear_regression, ar2_linear_regression, non_stationary_linear_regression, + # uniform_hypersphere_regression + task: linear_regression + + # Task kwargs: + # - When task == 'sparse_linear_regression': you may set 'sparsity'. + # - For other tasks: any 'sparsity' key will be ignored automatically. + task_kwargs: { + # sparsity: 5 # only when task: sparse_linear_regression + # noise_std: 2.0 # e.g., for noisy_linear_regression + # renormalize_ys: false + # noise_type: normal + } + + learning_rate: 0.0003 + keep_every_steps: 100000 + num_tasks: null + num_training_examples: null + resume_id: null + save_every_steps: 100 + train_steps: 50001 + +out_dir: + +wandb: + project: in-context-training + entity: in-context + notes: "" + name: "example_run" + log_every_steps: 10 + + diff --git a/src/eval.ipynb b/src/eval.ipynb index dea701b9..a8777a12 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "ed6cfeb1", "metadata": {}, "outputs": [], @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "id": "0e8d018b", "metadata": { "scrolled": true @@ -74,7 +74,7 @@ " \n", " \n", " \n", - " 2\n", + " 3\n", " 1_beta_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -87,7 +87,7 @@ " 1_beta_noise_gaussian_data_experiment\n", " \n", " \n", - " 3\n", + " 4\n", " 1_exponential_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -100,7 +100,7 @@ " 1_exponential_noise_gaussian_data_experiment\n", " \n", " \n", - " 4\n", + " 5\n", " 1_poisson_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -113,7 +113,7 @@ " 1_poisson_noise_gaussian_data_experiment\n", " \n", " \n", - " 5\n", + " 6\n", " 1_t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -126,7 +126,7 @@ " 1_t_student_noise_gaussian_data_experiment\n", " \n", " \n", - " 6\n", + " 7\n", " 1_uniform_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -139,7 +139,33 @@ " 1_uniform_noise_gaussian_data_experiment\n", " \n", " \n", - " 7\n", + " 2\n", + " 123e9cbd-1566-443d-9491-f23b6b9af0e2\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " 20_dims_uniform_error_gaussian_data\n", + " \n", + " \n", + " 10\n", + " 64d381ae-08d0-4bae-8e40-f1a68cfb2e97\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " 20_dims_uniform_error_gaussian_data_\n", + " \n", + " \n", + " 8\n", " 3_laplace_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -152,7 +178,7 @@ " 3_laplace_noise_gaussian_data_experiment\n", " \n", " \n", - " 8\n", + " 9\n", " 3_tstudent_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -165,7 +191,7 @@ " 3_tstudent_noise_gaussian_data_experiment\n", " \n", " \n", - " 20\n", + " 23\n", " daa2cd45-f1c0-4a0c-9100-e171129624c9\n", " sparse_linear_regression\n", " Transformer\n", @@ -178,7 +204,7 @@ " 4_std_sparse_linear_regression\n", " \n", " \n", - " 11\n", + " 13\n", " beta_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -191,7 +217,7 @@ " beta_noise_ar1_data_experiment\n", " \n", " \n", - " 10\n", + " 12\n", " aed365ed-51e2-4a72-8374-ae954b37be14\n", " linear_regression\n", " Transformer\n", @@ -217,7 +243,7 @@ " decision_tree_pretrained\n", " \n", " \n", - " 12\n", + " 14\n", " exponential_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -230,7 +256,7 @@ " exponential_noise_gaussian_data_experiment\n", " \n", " \n", - " 13\n", + " 15\n", " laplace_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -243,7 +269,20 @@ " laplace_noise_gaussian_data_experiment\n", " \n", " \n", - " 14\n", + " 16\n", + " pretrained\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 20\n", + " 12\n", + " 8\n", + " linear_regression_pretrained\n", + " \n", + " \n", + " 17\n", " rayleigh_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -256,7 +295,7 @@ " rayleigh_noise_gaussian_data_experiment\n", " \n", " \n", - " 18\n", + " 21\n", " pretrained\n", " relu_2nn_regression\n", " Transformer\n", @@ -269,7 +308,7 @@ " relu_2nn_regression_pretrained\n", " \n", " \n", - " 9\n", + " 11\n", " 82e728b0-a061-448e-8d7a-f3c79c0c74e5\n", " linear_regression\n", " Transformer\n", @@ -282,7 +321,7 @@ " rigde_normal_linear_regression_gaussian\n", " \n", " \n", - " 19\n", + " 22\n", " 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615\n", " sparse_linear_regression\n", " Transformer\n", @@ -308,7 +347,7 @@ " sparse_data_experiment\n", " \n", " \n", - " 21\n", + " 24\n", " pretrained\n", " sparse_linear_regression\n", " Transformer\n", @@ -321,7 +360,7 @@ " sparse_regression_pretrained\n", " \n", " \n", - " 15\n", + " 18\n", " t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -334,7 +373,7 @@ " t_student_noise_gaussian_data_experiment\n", " \n", " \n", - " 16\n", + " 19\n", " uniform_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -347,7 +386,7 @@ " uniform_noise_ar1_data_experiment\n", " \n", " \n", - " 17\n", + " 20\n", " uniform_noise_gaussian_data_experiment_\n", " linear_regression\n", " Transformer\n", @@ -365,79 +404,88 @@ ], "text/plain": [ " run_id task \\\n", - "2 1_beta_noise_gaussian_data_experiment linear_regression \n", - "3 1_exponential_noise_gaussian_data_experiment linear_regression \n", - "4 1_poisson_noise_gaussian_data_experiment linear_regression \n", - "5 1_t_student_noise_gaussian_data_experiment linear_regression \n", - "6 1_uniform_noise_gaussian_data_experiment linear_regression \n", - "7 3_laplace_noise_gaussian_data_experiment linear_regression \n", - "8 3_tstudent_noise_gaussian_data_experiment linear_regression \n", - "20 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", - "11 beta_noise_ar1_data_experiment linear_regression \n", - "10 aed365ed-51e2-4a72-8374-ae954b37be14 linear_regression \n", + "3 1_beta_noise_gaussian_data_experiment linear_regression \n", + "4 1_exponential_noise_gaussian_data_experiment linear_regression \n", + "5 1_poisson_noise_gaussian_data_experiment linear_regression \n", + "6 1_t_student_noise_gaussian_data_experiment linear_regression \n", + "7 1_uniform_noise_gaussian_data_experiment linear_regression \n", + "2 123e9cbd-1566-443d-9491-f23b6b9af0e2 linear_regression \n", + "10 64d381ae-08d0-4bae-8e40-f1a68cfb2e97 linear_regression \n", + "8 3_laplace_noise_gaussian_data_experiment linear_regression \n", + "9 3_tstudent_noise_gaussian_data_experiment linear_regression \n", + "23 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", + "13 beta_noise_ar1_data_experiment linear_regression \n", + "12 aed365ed-51e2-4a72-8374-ae954b37be14 linear_regression \n", "0 pretrained decision_tree \n", - "12 exponential_noise_gaussian_data_experiment linear_regression \n", - "13 laplace_noise_gaussian_data_experiment linear_regression \n", - "14 rayleigh_noise_gaussian_data_experiment linear_regression \n", - "18 pretrained relu_2nn_regression \n", - "9 82e728b0-a061-448e-8d7a-f3c79c0c74e5 linear_regression \n", - "19 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", + "14 exponential_noise_gaussian_data_experiment linear_regression \n", + "15 laplace_noise_gaussian_data_experiment linear_regression \n", + "16 pretrained linear_regression \n", + "17 rayleigh_noise_gaussian_data_experiment linear_regression \n", + "21 pretrained relu_2nn_regression \n", + "11 82e728b0-a061-448e-8d7a-f3c79c0c74e5 linear_regression \n", + "22 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", "1 03de46b6-429a-4151-92e6-3588231c6cad linear_regression \n", - "21 pretrained sparse_linear_regression \n", - "15 t_student_noise_gaussian_data_experiment linear_regression \n", - "16 uniform_noise_ar1_data_experiment linear_regression \n", - "17 uniform_noise_gaussian_data_experiment_ linear_regression \n", + "24 pretrained sparse_linear_regression \n", + "18 t_student_noise_gaussian_data_experiment linear_regression \n", + "19 uniform_noise_ar1_data_experiment linear_regression \n", + "20 uniform_noise_gaussian_data_experiment_ linear_regression \n", "\n", " model kwargs num_tasks num_examples n_dims \\\n", - "2 Transformer -1 -1 5 \n", "3 Transformer -1 -1 5 \n", "4 Transformer -1 -1 5 \n", "5 Transformer -1 -1 5 \n", "6 Transformer -1 -1 5 \n", "7 Transformer -1 -1 5 \n", + "2 Transformer -1 -1 20 \n", + "10 Transformer -1 -1 20 \n", "8 Transformer -1 -1 5 \n", - "20 Transformer sparsity=5 -1 -1 15 \n", - "11 Transformer -1 -1 5 \n", - "10 Transformer k=5_sparsity=3 -1 -1 15 \n", - "0 Transformer depth=4 -1 -1 20 \n", - "12 Transformer -1 -1 5 \n", + "9 Transformer -1 -1 5 \n", + "23 Transformer sparsity=5 -1 -1 15 \n", "13 Transformer -1 -1 5 \n", + "12 Transformer k=5_sparsity=3 -1 -1 15 \n", + "0 Transformer depth=4 -1 -1 20 \n", "14 Transformer -1 -1 5 \n", - "18 Transformer hidden_layer_size=100 -1 -1 20 \n", - "9 Transformer sparsity=5 -1 -1 15 \n", - "19 Transformer -1 -1 5 \n", - "1 Transformer -1 -1 20 \n", - "21 Transformer sparsity=3 -1 -1 20 \n", "15 Transformer -1 -1 5 \n", - "16 Transformer -1 -1 5 \n", + "16 Transformer -1 -1 20 \n", "17 Transformer -1 -1 5 \n", + "21 Transformer hidden_layer_size=100 -1 -1 20 \n", + "11 Transformer sparsity=5 -1 -1 15 \n", + "22 Transformer -1 -1 5 \n", + "1 Transformer -1 -1 20 \n", + "24 Transformer sparsity=3 -1 -1 20 \n", + "18 Transformer -1 -1 5 \n", + "19 Transformer -1 -1 5 \n", + "20 Transformer -1 -1 5 \n", "\n", " n_layer n_head run_name \n", - "2 4 8 1_beta_noise_gaussian_data_experiment \n", - "3 4 8 1_exponential_noise_gaussian_data_experiment \n", - "4 4 8 1_poisson_noise_gaussian_data_experiment \n", - "5 4 8 1_t_student_noise_gaussian_data_experiment \n", - "6 4 8 1_uniform_noise_gaussian_data_experiment \n", - "7 4 8 3_laplace_noise_gaussian_data_experiment \n", - "8 4 8 3_tstudent_noise_gaussian_data_experiment \n", - "20 4 8 4_std_sparse_linear_regression \n", - "11 4 8 beta_noise_ar1_data_experiment \n", - "10 4 8 data_sparse_linear_regression \n", + "3 4 8 1_beta_noise_gaussian_data_experiment \n", + "4 4 8 1_exponential_noise_gaussian_data_experiment \n", + "5 4 8 1_poisson_noise_gaussian_data_experiment \n", + "6 4 8 1_t_student_noise_gaussian_data_experiment \n", + "7 4 8 1_uniform_noise_gaussian_data_experiment \n", + "2 4 8 20_dims_uniform_error_gaussian_data \n", + "10 4 8 20_dims_uniform_error_gaussian_data_ \n", + "8 4 8 3_laplace_noise_gaussian_data_experiment \n", + "9 4 8 3_tstudent_noise_gaussian_data_experiment \n", + "23 4 8 4_std_sparse_linear_regression \n", + "13 4 8 beta_noise_ar1_data_experiment \n", + "12 4 8 data_sparse_linear_regression \n", "0 12 8 decision_tree_pretrained \n", - "12 4 8 exponential_noise_gaussian_data_experiment \n", - "13 4 8 laplace_noise_gaussian_data_experiment \n", - "14 4 8 rayleigh_noise_gaussian_data_experiment \n", - "18 12 8 relu_2nn_regression_pretrained \n", - "9 4 8 rigde_normal_linear_regression_gaussian \n", - "19 4 8 sparse \n", + "14 4 8 exponential_noise_gaussian_data_experiment \n", + "15 4 8 laplace_noise_gaussian_data_experiment \n", + "16 12 8 linear_regression_pretrained \n", + "17 4 8 rayleigh_noise_gaussian_data_experiment \n", + "21 12 8 relu_2nn_regression_pretrained \n", + "11 4 8 rigde_normal_linear_regression_gaussian \n", + "22 4 8 sparse \n", "1 4 8 sparse_data_experiment \n", - "21 12 8 sparse_regression_pretrained \n", - "15 4 8 t_student_noise_gaussian_data_experiment \n", - "16 4 8 uniform_noise_ar1_data_experiment \n", - "17 4 8 uniform_noise_gaussian_data_experiment " + "24 12 8 sparse_regression_pretrained \n", + "18 4 8 t_student_noise_gaussian_data_experiment \n", + "19 4 8 uniform_noise_ar1_data_experiment \n", + "20 4 8 uniform_noise_gaussian_data_experiment " ] }, - "execution_count": 8, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -449,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "a9980951", "metadata": {}, "outputs": [], @@ -459,7 +507,7 @@ "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"03de46b6-429a-4151-92e6-3588231c6cad\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"pretrained\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -470,7 +518,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "937f1b23", "metadata": {}, "outputs": [ @@ -479,7 +527,7 @@ "output_type": "stream", "text": [ "--- Tên mô hình CÓ TRONG relevant_model_names (models) ---\n", - "['Transformer', 'Least Squares', '3-Nearest Neighbors', 'Averaging', 'Lasso (alpha=0.001)', 'Lasso (alpha=0.01)', 'Lasso (alpha=0.1)', 'Lasso (alpha=1.0)', 'Ridge (alpha=0.5)']\n", + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", "\n", "--- Các khóa (Tên mô hình) THỰC TẾ trong metrics['standard'] ---\n" ] @@ -489,16 +537,14 @@ "evalue": "name 'metrics' is not defined", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[6], line 9\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(models)\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m--- Các khóa (Tên mô hình) THỰC TẾ trong metrics[\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstandard\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m] ---\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 9\u001b[0m pprint\u001b[38;5;241m.\u001b[39mpprint(\u001b[43mmetrics\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstandard\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mkeys()) \n\u001b[0;32m 11\u001b[0m \u001b[38;5;66;03m# basic_plot(metrics[\"standard\"], models=models) \u001b[39;00m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# ...\u001b[39;00m\n", - "\u001b[1;31mNameError\u001b[0m: name 'metrics' is not defined" + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 7\u001b[39m\n\u001b[32m 5\u001b[39m \u001b[38;5;28mprint\u001b[39m(models)\n\u001b[32m 6\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m--- Các khóa (Tên mô hình) THỰC TẾ trong metrics[\u001b[39m\u001b[33m'\u001b[39m\u001b[33mstandard\u001b[39m\u001b[33m'\u001b[39m\u001b[33m] ---\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m pprint.pprint(\u001b[43mmetrics\u001b[49m[\u001b[33m\"\u001b[39m\u001b[33mstandard\u001b[39m\u001b[33m\"\u001b[39m].keys()) \n\u001b[32m 9\u001b[39m \u001b[38;5;66;03m# basic_plot(metrics[\"standard\"], models=models) \u001b[39;00m\n\u001b[32m 10\u001b[39m \u001b[38;5;66;03m# ...\u001b[39;00m\n", + "\u001b[31mNameError\u001b[39m: name 'metrics' is not defined" ] } ], "source": [ - "# Cell In[26], trước dòng 9\n", - "\n", "import pprint # Dùng để in dictionary đẹp hơn\n", "# ...\n", "models = relevant_model_names[task]\n", @@ -521,7 +567,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -531,26 +577,34 @@ "name": "stdout", "output_type": "stream", "text": [ - "sparse_data_experiment 03de46b6-429a-4151-92e6-3588231c6cad\n" + "linear_regression_pretrained pretrained\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 15/15 [00:00" ] @@ -585,7 +639,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 6, "id": "31b4ecca", "metadata": { "scrolled": true @@ -595,49 +649,311 @@ "name": "stdout", "output_type": "stream", "text": [ - "Processing: standard\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', 'Ridge Var Adj (alpha=0.5, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", + "Missing metrics for: ['Ridge (alpha=0.5)']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", + "Missing metrics for: ['Ridge (alpha=0.5)']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", + "Missing metrics for: ['Ridge (alpha=0.5)']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", + "Missing metrics for: ['Ridge (alpha=0.5)']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", + "Missing metrics for: ['Ridge (alpha=0.5)']\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", + "Missing metrics for: ['Ridge (alpha=0.5)']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", + "Missing metrics for: ['Ridge (alpha=0.5)']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", + "Missing metrics for: ['Ridge (alpha=0.5)']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", + "Missing metrics for: ['Ridge (alpha=0.5)']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", + "Missing metrics for: ['Ridge (alpha=0.5)']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", + "Missing metrics for: ['Ridge (alpha=0.5)']\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", + "Missing metrics for: ['Ridge (alpha=0.5)']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", + "Missing metrics for: ['Ridge (alpha=0.5)']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", + "Missing metrics for: ['Ridge (alpha=0.5)']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ + "# # plot any OOD metrics\n", + "# # print(\"Available metrics:\", list(metrics.keys()))\n", + "# for name, metric in metrics.items():\n", + "# print(\"Processing:\", name)\n", + "# print(\"Metric keys:\", list(metric.keys()))\n", + "# if name == \"standard\": continue\n", + " \n", + "# if \"scale\" in name:\n", + "# scale = float(name.split(\"=\")[-1])**2\n", + "# else:\n", + "# scale = 1.0\n", + "# trivial = 1.0 if \"noisy\" not in name else (1+1/n_dims)\n", + " \n", + "# # # only plot models that exist in this metric dict\n", + "# # models_present = [m for m in models if m in metric]\n", + "# # if len(models_present) == 0:\n", + "# # print(f\"Skipping {name}: no matching models in metric keys {list(metric.keys())}\")\n", + "# # continue\n", + "# # fig, ax = basic_plot(metric, models=models, trivial=trivial * scale)\n", + "# # ax.set_title(name)\n", + " \n", + "# if \"ortho\" in name:\n", + "# ax.set_xlim(-1, n_dims - 1)\n", + "# ax.set_ylim(-.1 * scale, 1.5 * scale)\n", + "# plt.show()\n", + "# # std = metrics.get(\"standard\", {})\n", + "# # for model_name in models:\n", + "# # mres = std.get(model_name, {})\n", + "# # if \"gradient_alignment\" in mres:\n", + "# # print(\"Plotting gradient alignment for\", model_name)\n", + "# # alignments = mres[\"gradient_alignment\"]\n", + "# # plt.figure(figsize=(6, 4))\n", + "# # plt.plot(range(len(alignments)), alignments, label=f\"{model_name} gradient-w alignment\", lw=2)\n", + "# # plt.xlabel(\"# in-context examples\")\n", + "# # plt.ylabel(\"normalized inner product\")\n", + "# # plt.legend()\n", + "# # plt.show()\n", "# plot any OOD metrics\n", - "# print(\"Available metrics:\", list(metrics.keys()))\n", "for name, metric in metrics.items():\n", - " print(\"Processing:\", name)\n", - " print(\"Metric keys:\", list(metric.keys()))\n", " if name == \"standard\": continue\n", " \n", " if \"scale\" in name:\n", " scale = float(name.split(\"=\")[-1])**2\n", " else:\n", " scale = 1.0\n", + "\n", " trivial = 1.0 if \"noisy\" not in name else (1+1/n_dims)\n", - " \n", - " # # only plot models that exist in this metric dict\n", - " # models_present = [m for m in models if m in metric]\n", - " # if len(models_present) == 0:\n", - " # print(f\"Skipping {name}: no matching models in metric keys {list(metric.keys())}\")\n", - " # continue\n", - " # fig, ax = basic_plot(metric, models=models, trivial=trivial * scale)\n", - " # ax.set_title(name)\n", + " fig, ax = basic_plot(metric, models=models, trivial=trivial * scale)\n", + " ax.set_title(name)\n", " \n", " if \"ortho\" in name:\n", " ax.set_xlim(-1, n_dims - 1)\n", " ax.set_ylim(-.1 * scale, 1.5 * scale)\n", - " plt.show()\n", - "# std = metrics.get(\"standard\", {})\n", - "# for model_name in models:\n", - "# mres = std.get(model_name, {})\n", - "# if \"gradient_alignment\" in mres:\n", - "# print(\"Plotting gradient alignment for\", model_name)\n", - "# alignments = mres[\"gradient_alignment\"]\n", - "# plt.figure(figsize=(6, 4))\n", - "# plt.plot(range(len(alignments)), alignments, label=f\"{model_name} gradient-w alignment\", lw=2)\n", - "# plt.xlabel(\"# in-context examples\")\n", - "# plt.ylabel(\"normalized inner product\")\n", - "# plt.legend()\n", - "# plt.show()" + "\n", + " plt.show()" ] }, { @@ -652,7 +968,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "beb327ce", "metadata": {}, "outputs": [], @@ -663,10 +979,31 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "03523b06", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m model, conf = \u001b[43mget_model_from_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m n_dims = conf.model.n_dims\n\u001b[32m 4\u001b[39m batch_size = conf.training.batch_size\n", + "\u001b[36mFile \u001b[39m\u001b[32md:\\MyBK\\Semester_5\\Programming_Intergration_Project\\in-context-learning\\src\\eval.py:28\u001b[39m, in \u001b[36mget_model_from_run\u001b[39m\u001b[34m(run_path, step, only_conf)\u001b[39m\n\u001b[32m 26\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m step == -\u001b[32m1\u001b[39m:\n\u001b[32m 27\u001b[39m state_path = os.path.join(run_path, \u001b[33m\"\u001b[39m\u001b[33mstate.pt\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m28\u001b[39m state = \u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 29\u001b[39m model.load_state_dict(state[\u001b[33m\"\u001b[39m\u001b[33mmodel_state_dict\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m 30\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\CaoHuuThienHoang\\miniconda3\\Lib\\site-packages\\torch\\serialization.py:1521\u001b[39m, in \u001b[36mload\u001b[39m\u001b[34m(f, map_location, pickle_module, weights_only, mmap, **pickle_load_args)\u001b[39m\n\u001b[32m 1519\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m weights_only:\n\u001b[32m 1520\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1521\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_load\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1522\u001b[39m \u001b[43m \u001b[49m\u001b[43mopened_zipfile\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1523\u001b[39m \u001b[43m \u001b[49m\u001b[43mmap_location\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1524\u001b[39m \u001b[43m \u001b[49m\u001b[43m_weights_only_unpickler\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1525\u001b[39m \u001b[43m \u001b[49m\u001b[43moverall_storage\u001b[49m\u001b[43m=\u001b[49m\u001b[43moverall_storage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1526\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpickle_load_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1527\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1528\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m pickle.UnpicklingError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 1529\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m pickle.UnpicklingError(_get_wo_message(\u001b[38;5;28mstr\u001b[39m(e))) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\CaoHuuThienHoang\\miniconda3\\Lib\\site-packages\\torch\\serialization.py:2122\u001b[39m, in \u001b[36m_load\u001b[39m\u001b[34m(zip_file, map_location, pickle_module, pickle_file, overall_storage, **pickle_load_args)\u001b[39m\n\u001b[32m 2120\u001b[39m \u001b[38;5;28;01mglobal\u001b[39;00m _serialization_tls\n\u001b[32m 2121\u001b[39m _serialization_tls.map_location = map_location\n\u001b[32m-> \u001b[39m\u001b[32m2122\u001b[39m result = \u001b[43munpickler\u001b[49m\u001b[43m.\u001b[49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2123\u001b[39m _serialization_tls.map_location = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 2125\u001b[39m torch._utils._validate_loaded_sparse_tensors()\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\CaoHuuThienHoang\\miniconda3\\Lib\\site-packages\\torch\\_weights_only_unpickler.py:535\u001b[39m, in \u001b[36mUnpickler.load\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 527\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[32m 528\u001b[39m \u001b[38;5;28mtype\u001b[39m(pid) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m\n\u001b[32m 529\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(pid) > \u001b[32m0\u001b[39m\n\u001b[32m 530\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m torch.serialization._maybe_decode_ascii(pid[\u001b[32m0\u001b[39m]) != \u001b[33m\"\u001b[39m\u001b[33mstorage\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 531\u001b[39m ):\n\u001b[32m 532\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m UnpicklingError(\n\u001b[32m 533\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mOnly persistent_load of storage is allowed, but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpid[\u001b[32m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 534\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m535\u001b[39m \u001b[38;5;28mself\u001b[39m.append(\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mpersistent_load\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpid\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m 536\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m key[\u001b[32m0\u001b[39m] \u001b[38;5;129;01min\u001b[39;00m [BINGET[\u001b[32m0\u001b[39m], LONG_BINGET[\u001b[32m0\u001b[39m]]:\n\u001b[32m 537\u001b[39m idx = (read(\u001b[32m1\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m key[\u001b[32m0\u001b[39m] == BINGET[\u001b[32m0\u001b[39m] \u001b[38;5;28;01melse\u001b[39;00m unpack(\u001b[33m\"\u001b[39m\u001b[33m.persistent_load\u001b[39m\u001b[34m(saved_id)\u001b[39m\n\u001b[32m 2084\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 2085\u001b[39m nbytes = numel * torch._utils._element_size(dtype)\n\u001b[32m-> \u001b[39m\u001b[32m2086\u001b[39m typed_storage = \u001b[43mload_tensor\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2087\u001b[39m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnbytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_maybe_decode_ascii\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2088\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2090\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m typed_storage\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\CaoHuuThienHoang\\miniconda3\\Lib\\site-packages\\torch\\serialization.py:2052\u001b[39m, in \u001b[36m_load..load_tensor\u001b[39m\u001b[34m(dtype, numel, key, location)\u001b[39m\n\u001b[32m 2048\u001b[39m \u001b[38;5;66;03m# TODO: Once we decide to break serialization FC, we can\u001b[39;00m\n\u001b[32m 2049\u001b[39m \u001b[38;5;66;03m# stop wrapping with TypedStorage\u001b[39;00m\n\u001b[32m 2051\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m torch._guards.detect_fake_mode(\u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m2052\u001b[39m wrap_storage = \u001b[43mrestore_location\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstorage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2053\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 2054\u001b[39m storage._fake_device = location\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\CaoHuuThienHoang\\miniconda3\\Lib\\site-packages\\torch\\serialization.py:698\u001b[39m, in \u001b[36mdefault_restore_location\u001b[39m\u001b[34m(storage, location)\u001b[39m\n\u001b[32m 678\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 679\u001b[39m \u001b[33;03mRestores `storage` using a deserializer function registered for the `location`.\u001b[39;00m\n\u001b[32m 680\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 695\u001b[39m \u001b[33;03m all matching ones return `None`.\u001b[39;00m\n\u001b[32m 696\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 697\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m _, _, fn \u001b[38;5;129;01min\u001b[39;00m _package_registry:\n\u001b[32m--> \u001b[39m\u001b[32m698\u001b[39m result = \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstorage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 699\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 700\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\CaoHuuThienHoang\\miniconda3\\Lib\\site-packages\\torch\\serialization.py:636\u001b[39m, in \u001b[36m_deserialize\u001b[39m\u001b[34m(backend_name, obj, location)\u001b[39m\n\u001b[32m 634\u001b[39m backend_name = torch._C._get_privateuse1_backend_name()\n\u001b[32m 635\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m location.startswith(backend_name):\n\u001b[32m--> \u001b[39m\u001b[32m636\u001b[39m device = \u001b[43m_validate_device\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbackend_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 637\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m obj.to(device=device)\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\CaoHuuThienHoang\\miniconda3\\Lib\\site-packages\\torch\\serialization.py:605\u001b[39m, in \u001b[36m_validate_device\u001b[39m\u001b[34m(location, backend_name)\u001b[39m\n\u001b[32m 603\u001b[39m device_index = device.index \u001b[38;5;28;01mif\u001b[39;00m device.index \u001b[38;5;28;01melse\u001b[39;00m \u001b[32m0\u001b[39m\n\u001b[32m 604\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(device_module, \u001b[33m\"\u001b[39m\u001b[33mis_available\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m device_module.is_available():\n\u001b[32m--> \u001b[39m\u001b[32m605\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 606\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mAttempting to deserialize object on a \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbackend_name.upper()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 607\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mdevice but torch.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbackend_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.is_available() is False. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 608\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mIf you are running on a CPU-only machine, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 609\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mplease use torch.load with map_location=torch.device(\u001b[39m\u001b[33m'\u001b[39m\u001b[33mcpu\u001b[39m\u001b[33m'\u001b[39m\u001b[33m) \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 610\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mto map your storages to the CPU.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 611\u001b[39m )\n\u001b[32m 612\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(device_module, \u001b[33m\"\u001b[39m\u001b[33mdevice_count\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m 613\u001b[39m device_count = device_module.device_count()\n", + "\u001b[31mRuntimeError\u001b[39m: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU." + ] + } + ], "source": [ "model, conf = get_model_from_run(run_path)\n", "\n", @@ -954,7 +1291,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "base", "language": "python", "name": "python3" }, @@ -968,7 +1305,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.13.5" } }, "nbformat": 4, diff --git a/src/figure3.py b/src/figure3_4.py similarity index 99% rename from src/figure3.py rename to src/figure3_4.py index c7a165d4..5f683f88 100644 --- a/src/figure3.py +++ b/src/figure3_4.py @@ -59,7 +59,7 @@ def _prepare(run_path: str): task_sampler = get_task_sampler( conf.training.task, n_dims, - b_size=1, + batch_size=1, **conf.training.task_kwargs, ) return model, conf, data_sampler, task_sampler, device diff --git a/src/models.py b/src/models.py index aa5f4940..b3ad9df7 100644 --- a/src/models.py +++ b/src/models.py @@ -33,7 +33,6 @@ def get_relevant_baselines(task_name): (RidgeModel, {"alpha": 0.1}), (RidgeModel, {"alpha": 0.5}), (NNModel, {"n_neighbors": 3}), - (GLSModel, {"ar_coef": 0.5}), (AveragingModel, {}), ], "linear_regression": [ diff --git a/src/plot_utils.py b/src/plot_utils.py index 6fb2d82c..e96204e9 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -16,8 +16,7 @@ "Ridge (alpha=0.5)", "Ridge (alpha=0.1)", "3-Nearest Neighbors", - "Averaging" - "GLS (ar=0.5)" + "Averaging", ], "noisy_linear_regression": [ "Transformer", diff --git a/src/train.py b/src/train.py index d7af5379..aa8eeea7 100644 --- a/src/train.py +++ b/src/train.py @@ -35,6 +35,29 @@ def sample_seeds(total_seeds, count): return seeds +def _sanitize_training_kwargs(args): + """ + Remove conflicting/irrelevant kwargs to avoid sampler/task constructor errors. + Rules: + - data_kwargs: keep 'k' ONLY when data == 'sparse_gaussian' (k = number of non-zero coords). + - task_kwargs: keep 'sparsity' ONLY when task == 'sparse_linear_regression'. + """ + # Defensive copy + data_kwargs = dict(getattr(args.training, "data_kwargs", {}) or {}) + task_kwargs = dict(getattr(args.training, "task_kwargs", {}) or {}) + + if args.training.data != "sparse_gaussian": + data_kwargs.pop("k", None) + # 'scale' in data_kwargs is okay for most samplers; leave others as-is + + if args.training.task != "sparse_linear_regression": + task_kwargs.pop("sparsity", None) + # Leave other task-specific kwargs (e.g., noise_std for noisy_linear_regression) as-is + + args.training.data_kwargs = data_kwargs + args.training.task_kwargs = task_kwargs + + def train(model, args): optimizer = torch.optim.Adam(model.parameters(), lr=args.training.learning_rate) curriculum = Curriculum(args.training.curriculum) @@ -51,6 +74,10 @@ def train(model, args): n_dims = model.n_dims bsize = args.training.batch_size + + # Sanitize kwargs before constructing samplers/tasks to prevent conflicts + _sanitize_training_kwargs(args) + data_sampler = get_data_sampler(args.training.data, n_dims=n_dims, **args.training.data_kwargs) task_sampler = get_task_sampler( args.training.task, From f991000b0c2cc864b293727ba32ca4f3928ce32d Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Tue, 11 Nov 2025 15:20:55 +0700 Subject: [PATCH 41/88] update train.py --- src/conf/uniform_hypersphere_regression.yaml | 14 +++--- src/train.py | 46 +++++++++++++++++--- 2 files changed, 46 insertions(+), 14 deletions(-) diff --git a/src/conf/uniform_hypersphere_regression.yaml b/src/conf/uniform_hypersphere_regression.yaml index 95dff9ac..c1b026e8 100644 --- a/src/conf/uniform_hypersphere_regression.yaml +++ b/src/conf/uniform_hypersphere_regression.yaml @@ -7,7 +7,7 @@ model: n_embd: 128 n_head: 8 n_layer: 4 - n_positions: 100 + n_positions: 101 training: task: uniform_hypersphere_regression @@ -18,22 +18,22 @@ training: data_kwargs: {} curriculum: dims: - start: 20 + start: 5 end: 20 - inc: 0 + inc: 1 interval: 2000 points: - start: 11 - end: 41 + start: 6 + end: 30 inc: 2 interval: 2000 batch_size: 32 learning_rate: 0.0003 - train_steps: 5001 + train_steps: 50001 save_every_steps: 100 keep_every_steps: 10000 -out_dir: ../models/uniform_hypersphere_regression +out_dir: /content/models/linear_regression/uniform_hypersphere_regression wandb: project: "in-context-training" diff --git a/src/train.py b/src/train.py index aa8eeea7..e216a989 100644 --- a/src/train.py +++ b/src/train.py @@ -41,18 +41,50 @@ def _sanitize_training_kwargs(args): Rules: - data_kwargs: keep 'k' ONLY when data == 'sparse_gaussian' (k = number of non-zero coords). - task_kwargs: keep 'sparsity' ONLY when task == 'sparse_linear_regression'. + - In addition, apply per-task and per-data whitelists to drop unsupported keys. """ # Defensive copy data_kwargs = dict(getattr(args.training, "data_kwargs", {}) or {}) task_kwargs = dict(getattr(args.training, "task_kwargs", {}) or {}) - if args.training.data != "sparse_gaussian": - data_kwargs.pop("k", None) - # 'scale' in data_kwargs is okay for most samplers; leave others as-is - - if args.training.task != "sparse_linear_regression": - task_kwargs.pop("sparsity", None) - # Leave other task-specific kwargs (e.g., noise_std for noisy_linear_regression) as-is + # Per-data whitelists + data_whitelist = { + "gaussian": {"bias", "scale"}, + "sparse_gaussian": {"k", "bias", "scale"}, + "ar1": {"rho", "noise_std", "bias", "scale", "compute_gradient"}, + "vr1": {"ar1_mat", "noise_std", "bias", "scale"}, + "ar2": {"ar1_coef", "ar2_coef", "noise_std", "bias", "scale"}, + "vr2": {"ar1_mat", "ar2_mat", "noise_std", "bias", "scale"}, + "nonstation": {"coef_base", "coef_amplitude", "noise_std", "bias", "scale"}, + } + + data_name = args.training.data + if data_name in data_whitelist: + allowed = data_whitelist[data_name] + data_kwargs = {k: v for k, v in data_kwargs.items() if k in allowed} + else: + # Unknown data: drop potentially conflicting keys + data_kwargs = {} + + # Per-task whitelists + task_whitelist = { + "linear_regression": {"scale", "uniform"}, + "sparse_linear_regression": {"scale", "sparsity", "valid_coords"}, + "linear_classification": {"scale", "uniform"}, + "relu_2nn_regression": {"scale", "hidden_layer_size"}, + "decision_tree": {"depth"}, + "noisy_linear_regression": {"scale", "noise_std", "renormalize_ys", "noise_type", "uniform"}, + "ar1_linear_regression": {"scale", "ar_coef", "noise_std", "compute_gradient"}, + "uniform_hypersphere_regression": {"scale"}, + } + + task_name = args.training.task + if task_name in task_whitelist: + allowed = task_whitelist[task_name] + task_kwargs = {k: v for k, v in task_kwargs.items() if k in allowed} + else: + # Unknown task: be conservative + task_kwargs = {} args.training.data_kwargs = data_kwargs args.training.task_kwargs = task_kwargs From 77d5df27579c03cdc6dd065fa76cbb6012038d40 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Tue, 11 Nov 2025 15:24:00 +0700 Subject: [PATCH 42/88] update task.py --- src/tasks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/tasks.py b/src/tasks.py index 1625ab59..612eb1da 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -79,7 +79,7 @@ def get_task_sampler( class UniformHypersphereRegression(Task): def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1): - super(LinearRegression, self).__init__(n_dims, batch_size, pool_dict, seeds) + super(UniformHypersphereRegression, self).__init__(n_dims, batch_size, pool_dict, seeds) self.scale = scale if pool_dict is None and seeds is None: From 3492610d124a8d2961a20f2d998f07a9a05649ac Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Tue, 11 Nov 2025 15:28:14 +0700 Subject: [PATCH 43/88] update allll --- src/tasks.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/src/tasks.py b/src/tasks.py index 612eb1da..e3ff928f 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -97,12 +97,26 @@ def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1): assert "w" in pool_dict indices = torch.randperm(len(pool_dict["w"]))[:batch_size] self.w_b = pool_dict["w"][indices] + + def evaluate(self, xs_b): + w_b = self.w_b.to(xs_b.device) + ys_b = self.scale * (xs_b @ w_b)[:, :, 0] + return ys_b + @staticmethod def generate_pool_dict(n_dims, num_tasks): w = torch.randn(num_tasks, n_dims, 1) w_normalized = w / torch.norm(w, dim=1, keepdim=True) return {"w": w_normalized} + @staticmethod + def get_metric(): + return squared_error + + @staticmethod + def get_training_metric(): + return mean_squared_error + class LinearRegression(Task): def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1,uniform=False): From 914d5fd344588598abb9d64a03fe9460dd128f02 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Tue, 11 Nov 2025 15:52:16 +0700 Subject: [PATCH 44/88] update alllll --- src/eval.py | 31 +++++++++++++++++++++++++++++-- 1 file changed, 29 insertions(+), 2 deletions(-) diff --git a/src/eval.py b/src/eval.py index 42ca1956..17a24f04 100644 --- a/src/eval.py +++ b/src/eval.py @@ -213,6 +213,31 @@ def build_evals(conf): task_name = conf.training.task data_name = conf.training.data + # Sanitize kwargs to avoid passing unsupported keys during evaluation + data_whitelist = { + "gaussian": {"bias", "scale"}, + "sparse_gaussian": {"k", "bias", "scale"}, + "ar1": {"rho", "noise_std", "bias", "scale", "compute_gradient"}, + "vr1": {"ar1_mat", "noise_std", "bias", "scale"}, + "ar2": {"ar1_coef", "ar2_coef", "noise_std", "bias", "scale"}, + "vr2": {"ar1_mat", "ar2_mat", "noise_std", "bias", "scale"}, + "nonstation": {"coef_base", "coef_amplitude", "noise_std", "bias", "scale"}, + } + task_whitelist = { + "linear_regression": {"scale", "uniform"}, + "sparse_linear_regression": {"scale", "sparsity", "valid_coords"}, + "linear_classification": {"scale", "uniform"}, + "relu_2nn_regression": {"scale", "hidden_layer_size"}, + "decision_tree": {"depth"}, + "noisy_linear_regression": {"scale", "noise_std", "renormalize_ys", "noise_type", "uniform"}, + "ar1_linear_regression": {"scale", "ar_coef", "noise_std", "compute_gradient"}, + "uniform_hypersphere_regression": {"scale"}, + } + original_data_kwargs = conf.training.data_kwargs if hasattr(conf.training, "data_kwargs") else {} + original_task_kwargs = conf.training.task_kwargs if hasattr(conf.training, "task_kwargs") else {} + cleaned_data_kwargs = {k: v for k, v in (original_data_kwargs or {}).items() if k in data_whitelist.get(data_name, set())} + cleaned_task_kwargs = {k: v for k, v in (original_task_kwargs or {}).items() if k in task_whitelist.get(task_name, set())} + base_kwargs = { "task_name": task_name, "n_dims": n_dims, @@ -220,8 +245,10 @@ def build_evals(conf): "batch_size": batch_size, "data_name": data_name, "prompting_strategy": "standard", - "data_sampler_kwargs": conf.training.data_kwargs if hasattr(conf.training, "data_kwargs") else {}, - "task_sampler_kwargs": conf.training.task_kwargs + # "data_sampler_kwargs": conf.training.data_kwargs if hasattr(conf.training, "data_kwargs") else {}, + # "task_sampler_kwargs": conf.training.task_kwargs + "data_sampler_kwargs": cleaned_data_kwargs, + "task_sampler_kwargs": cleaned_task_kwargs } evaluation_kwargs = {} From 6b60d3b161adf3c3c52b941748fce505a34147f3 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 12 Nov 2025 17:03:50 +0700 Subject: [PATCH 45/88] ExponentialWeighted --- src/conf/exponential_weights_regression.yaml | 43 ++++++++++++++++++++ src/plot_utils.py | 2 +- src/schema.py | 1 + src/tasks.py | 38 ++++++++++++++++- 4 files changed, 82 insertions(+), 2 deletions(-) create mode 100644 src/conf/exponential_weights_regression.yaml diff --git a/src/conf/exponential_weights_regression.yaml b/src/conf/exponential_weights_regression.yaml new file mode 100644 index 00000000..807e4323 --- /dev/null +++ b/src/conf/exponential_weights_regression.yaml @@ -0,0 +1,43 @@ +inherit: + - base.yaml + +model: + family: gpt2 + n_dims: 20 + n_embd: 128 + n_head: 8 + n_layer: 4 + n_positions: 100 + +training: + task: exponential_weights_regression + task_kwargs: + rate: 1.0 # exponential distribution rate parameter + scale: 1.0 + data: gaussian + data_kwargs: {} + curriculum: + dims: + start: 5 + end: 20 + inc: 1 + interval: 2000 + points: + start: 6 + end: 41 + inc: 2 + interval: 2000 + batch_size: 32 + learning_rate: 0.0003 + train_steps: 5001 + save_every_steps: 100 + keep_every_steps: 10000 + +out_dir: ../models/exponential_weights_regression + +wandb: + project: "in-context-training" + entity: "hai-trinh220970-ho-chi-minh-city-university-of-technology" + name: "exponential_weights_experiment" + notes: "Training with exponential-distributed weights (non-uniform on hypersphere)" + log_every_steps: 100 \ No newline at end of file diff --git a/src/plot_utils.py b/src/plot_utils.py index e96204e9..d83bc0ce 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -97,7 +97,7 @@ def basic_plot(metrics, models=None, trivial=1.0): ax.set_xlabel("in-context examples") ax.set_ylabel("squared error") ax.set_xlim(-1, len(low) + 0.1) - ax.set_ylim(-0.1, 2) + ax.set_ylim(-0.1, 0.5) legend = ax.legend(loc="upper left", bbox_to_anchor=(1, 1)) diff --git a/src/schema.py b/src/schema.py index 8984408f..93cda9e9 100644 --- a/src/schema.py +++ b/src/schema.py @@ -45,6 +45,7 @@ "ar2_linear_regression", "non_stationary_linear_regression", "uniform_hypersphere_regression", + "exponential_weighted_regression", ] training_schema = { diff --git a/src/tasks.py b/src/tasks.py index e3ff928f..5dea9cb7 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -62,6 +62,7 @@ def get_task_sampler( "relu_2nn_regression": Relu2nnRegression, "decision_tree": DecisionTree, "ar1_linear_regression": AR1LinearRegression, + "exponential_weighted_regression": ExponentialWeightedRegression, } if task_name in task_names_to_classes: @@ -116,8 +117,43 @@ def get_metric(): @staticmethod def get_training_metric(): return mean_squared_error +class ExponentialWeightedRegression(Tasks): + def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, rate=1.0): + super(ExponentialWeightedRegression, self).__init__(n_dims, batch_size, pool_dict, seeds) + self.scale = scale + self.rate = rate - + if pool_dict is None and seeds is None: + exp_dist = torch.distributions.Exponential(rate=self.rate) + self.w_b = exp_dist.sample((self.b_size, self.n_dims, 1)) + elif seeds is not None: + self.w_b = torch.zeros(self.b_size, self.n_dims, 1) + generator = torch.Generator() + assert len(seeds) == self.b_size + for i, seed in enumerate(seeds): + generator.manual_seed(seed) + exp_dist = torch.distributions.Exponential(rate=self.rate) + self.w_b[i] = exp_dist.sample((self.n_dims, 1)) + else: + assert "w" in pool_dict + indices = torch.randperm(len(pool_dict["w"]))[:batch_size] + self.w_b = pool_dict["w"][indices] + def evaluate(self, xs_b): + w_b = self.w_b.to(xs_b.device) + ys_b = self.scale * (xs_b @ w_b)[:, :, 0] + return ys_b + + @staticmethod + def generate_pool_dict(n_dims, num_tasks, rate=1.0): + exp_dist = torch.distributions.Exponential(rate=rate) + return {"w": exp_dist.sample((num_tasks, n_dims, 1))} + + @staticmethod + def get_metric(): + return squared_error + @staticmethod + def get_training_metric(): + return mean_squared_error class LinearRegression(Task): def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1,uniform=False): """scale: a constant by which to scale the randomly sampled weights.""" From 3ff16c481401f238bd603d5895db0a368f73726c Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Sat, 15 Nov 2025 00:12:41 +0700 Subject: [PATCH 46/88] expw --- ...l => exponential_weighted_regression.yaml} | 6 +- src/eval.ipynb | 458 +++--------------- src/models.py | 4 + src/plot_utils.py | 5 + src/tasks.py | 15 +- 5 files changed, 96 insertions(+), 392 deletions(-) rename src/conf/{exponential_weights_regression.yaml => exponential_weighted_regression.yaml} (92%) diff --git a/src/conf/exponential_weights_regression.yaml b/src/conf/exponential_weighted_regression.yaml similarity index 92% rename from src/conf/exponential_weights_regression.yaml rename to src/conf/exponential_weighted_regression.yaml index 807e4323..98965b88 100644 --- a/src/conf/exponential_weights_regression.yaml +++ b/src/conf/exponential_weighted_regression.yaml @@ -7,7 +7,7 @@ model: n_embd: 128 n_head: 8 n_layer: 4 - n_positions: 100 + n_positions: 101 training: task: exponential_weights_regression @@ -28,8 +28,8 @@ training: inc: 2 interval: 2000 batch_size: 32 - learning_rate: 0.0003 - train_steps: 5001 + learning_rate: 0.0002 + train_steps: 100001 save_every_steps: 100 keep_every_steps: 10000 diff --git a/src/eval.ipynb b/src/eval.ipynb index a8777a12..b3473518 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "id": "0e8d018b", "metadata": { "scrolled": true @@ -191,7 +191,7 @@ " 3_tstudent_noise_gaussian_data_experiment\n", " \n", " \n", - " 23\n", + " 22\n", " daa2cd45-f1c0-4a0c-9100-e171129624c9\n", " sparse_linear_regression\n", " Transformer\n", @@ -270,19 +270,6 @@ " \n", " \n", " 16\n", - " pretrained\n", - " linear_regression\n", - " Transformer\n", - " \n", - " -1\n", - " -1\n", - " 20\n", - " 12\n", - " 8\n", - " linear_regression_pretrained\n", - " \n", - " \n", - " 17\n", " rayleigh_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -295,7 +282,7 @@ " rayleigh_noise_gaussian_data_experiment\n", " \n", " \n", - " 21\n", + " 20\n", " pretrained\n", " relu_2nn_regression\n", " Transformer\n", @@ -321,7 +308,7 @@ " rigde_normal_linear_regression_gaussian\n", " \n", " \n", - " 22\n", + " 21\n", " 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615\n", " sparse_linear_regression\n", " Transformer\n", @@ -347,7 +334,7 @@ " sparse_data_experiment\n", " \n", " \n", - " 24\n", + " 23\n", " pretrained\n", " sparse_linear_regression\n", " Transformer\n", @@ -360,7 +347,7 @@ " sparse_regression_pretrained\n", " \n", " \n", - " 18\n", + " 17\n", " t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -373,7 +360,7 @@ " t_student_noise_gaussian_data_experiment\n", " \n", " \n", - " 19\n", + " 18\n", " uniform_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -386,7 +373,7 @@ " uniform_noise_ar1_data_experiment\n", " \n", " \n", - " 20\n", + " 19\n", " uniform_noise_gaussian_data_experiment_\n", " linear_regression\n", " Transformer\n", @@ -413,22 +400,21 @@ "10 64d381ae-08d0-4bae-8e40-f1a68cfb2e97 linear_regression \n", "8 3_laplace_noise_gaussian_data_experiment linear_regression \n", "9 3_tstudent_noise_gaussian_data_experiment linear_regression \n", - "23 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", + "22 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", "13 beta_noise_ar1_data_experiment linear_regression \n", "12 aed365ed-51e2-4a72-8374-ae954b37be14 linear_regression \n", "0 pretrained decision_tree \n", "14 exponential_noise_gaussian_data_experiment linear_regression \n", "15 laplace_noise_gaussian_data_experiment linear_regression \n", - "16 pretrained linear_regression \n", - "17 rayleigh_noise_gaussian_data_experiment linear_regression \n", - "21 pretrained relu_2nn_regression \n", + "16 rayleigh_noise_gaussian_data_experiment linear_regression \n", + "20 pretrained relu_2nn_regression \n", "11 82e728b0-a061-448e-8d7a-f3c79c0c74e5 linear_regression \n", - "22 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", + "21 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", "1 03de46b6-429a-4151-92e6-3588231c6cad linear_regression \n", - "24 pretrained sparse_linear_regression \n", - "18 t_student_noise_gaussian_data_experiment linear_regression \n", - "19 uniform_noise_ar1_data_experiment linear_regression \n", - "20 uniform_noise_gaussian_data_experiment_ linear_regression \n", + "23 pretrained sparse_linear_regression \n", + "17 t_student_noise_gaussian_data_experiment linear_regression \n", + "18 uniform_noise_ar1_data_experiment linear_regression \n", + "19 uniform_noise_gaussian_data_experiment_ linear_regression \n", "\n", " model kwargs num_tasks num_examples n_dims \\\n", "3 Transformer -1 -1 5 \n", @@ -440,22 +426,21 @@ "10 Transformer -1 -1 20 \n", "8 Transformer -1 -1 5 \n", "9 Transformer -1 -1 5 \n", - "23 Transformer sparsity=5 -1 -1 15 \n", + "22 Transformer sparsity=5 -1 -1 15 \n", "13 Transformer -1 -1 5 \n", "12 Transformer k=5_sparsity=3 -1 -1 15 \n", "0 Transformer depth=4 -1 -1 20 \n", "14 Transformer -1 -1 5 \n", "15 Transformer -1 -1 5 \n", - "16 Transformer -1 -1 20 \n", - "17 Transformer -1 -1 5 \n", - "21 Transformer hidden_layer_size=100 -1 -1 20 \n", + "16 Transformer -1 -1 5 \n", + "20 Transformer hidden_layer_size=100 -1 -1 20 \n", "11 Transformer sparsity=5 -1 -1 15 \n", - "22 Transformer -1 -1 5 \n", + "21 Transformer -1 -1 5 \n", "1 Transformer -1 -1 20 \n", - "24 Transformer sparsity=3 -1 -1 20 \n", + "23 Transformer sparsity=3 -1 -1 20 \n", + "17 Transformer -1 -1 5 \n", "18 Transformer -1 -1 5 \n", "19 Transformer -1 -1 5 \n", - "20 Transformer -1 -1 5 \n", "\n", " n_layer n_head run_name \n", "3 4 8 1_beta_noise_gaussian_data_experiment \n", @@ -467,25 +452,24 @@ "10 4 8 20_dims_uniform_error_gaussian_data_ \n", "8 4 8 3_laplace_noise_gaussian_data_experiment \n", "9 4 8 3_tstudent_noise_gaussian_data_experiment \n", - "23 4 8 4_std_sparse_linear_regression \n", + "22 4 8 4_std_sparse_linear_regression \n", "13 4 8 beta_noise_ar1_data_experiment \n", "12 4 8 data_sparse_linear_regression \n", "0 12 8 decision_tree_pretrained \n", "14 4 8 exponential_noise_gaussian_data_experiment \n", "15 4 8 laplace_noise_gaussian_data_experiment \n", - "16 12 8 linear_regression_pretrained \n", - "17 4 8 rayleigh_noise_gaussian_data_experiment \n", - "21 12 8 relu_2nn_regression_pretrained \n", + "16 4 8 rayleigh_noise_gaussian_data_experiment \n", + "20 12 8 relu_2nn_regression_pretrained \n", "11 4 8 rigde_normal_linear_regression_gaussian \n", - "22 4 8 sparse \n", + "21 4 8 sparse \n", "1 4 8 sparse_data_experiment \n", - "24 12 8 sparse_regression_pretrained \n", - "18 4 8 t_student_noise_gaussian_data_experiment \n", - "19 4 8 uniform_noise_ar1_data_experiment \n", - "20 4 8 uniform_noise_gaussian_data_experiment " + "23 12 8 sparse_regression_pretrained \n", + "17 4 8 t_student_noise_gaussian_data_experiment \n", + "18 4 8 uniform_noise_ar1_data_experiment \n", + "19 4 8 uniform_noise_gaussian_data_experiment " ] }, - "execution_count": 2, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -497,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "id": "a9980951", "metadata": {}, "outputs": [], @@ -507,7 +491,7 @@ "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"pretrained\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"64d381ae-08d0-4bae-8e40-f1a68cfb2e97\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -518,7 +502,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "id": "937f1b23", "metadata": {}, "outputs": [ @@ -529,22 +513,14 @@ "--- Tên mô hình CÓ TRONG relevant_model_names (models) ---\n", "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", "\n", - "--- Các khóa (Tên mô hình) THỰC TẾ trong metrics['standard'] ---\n" - ] - }, - { - "ename": "NameError", - "evalue": "name 'metrics' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 7\u001b[39m\n\u001b[32m 5\u001b[39m \u001b[38;5;28mprint\u001b[39m(models)\n\u001b[32m 6\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m--- Các khóa (Tên mô hình) THỰC TẾ trong metrics[\u001b[39m\u001b[33m'\u001b[39m\u001b[33mstandard\u001b[39m\u001b[33m'\u001b[39m\u001b[33m] ---\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m pprint.pprint(\u001b[43mmetrics\u001b[49m[\u001b[33m\"\u001b[39m\u001b[33mstandard\u001b[39m\u001b[33m\"\u001b[39m].keys()) \n\u001b[32m 9\u001b[39m \u001b[38;5;66;03m# basic_plot(metrics[\"standard\"], models=models) \u001b[39;00m\n\u001b[32m 10\u001b[39m \u001b[38;5;66;03m# ...\u001b[39;00m\n", - "\u001b[31mNameError\u001b[39m: name 'metrics' is not defined" + "--- Các khóa (Tên mô hình) THỰC TẾ trong metrics['standard'] ---\n", + "dict_keys(['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', 'Ridge Var Adj (alpha=0.5, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging'])\n" ] } ], "source": [ + "# Cell In[26], trước dòng 9\n", + "\n", "import pprint # Dùng để in dictionary đẹp hơn\n", "# ...\n", "models = relevant_model_names[task]\n", @@ -567,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -577,22 +553,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "linear_regression_pretrained pretrained\n" + "20_dims_uniform_error_gaussian_data_ 64d381ae-08d0-4bae-8e40-f1a68cfb2e97\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 15/15 [00:00<00:00, 125078.65it/s]" + "100%|██████████| 1/1 [00:00" ] @@ -639,7 +614,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 24, "id": "31b4ecca", "metadata": { "scrolled": true @@ -649,311 +624,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", - "Missing metrics for: ['Ridge (alpha=0.5)']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", - "Missing metrics for: ['Ridge (alpha=0.5)']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", - "Missing metrics for: ['Ridge (alpha=0.5)']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", - "Missing metrics for: ['Ridge (alpha=0.5)']\n" - ] - }, - { - "data": { - "image/png": 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QoUOYNm0a9u7diwcffLCcz0YQKiYUFFOERcBYvz6C6tQsVFypvKKK/j6Pr+drV9pJuE5t5NsUyVLV6uZbRxnRyHPD5YA75QhcTgdcVhJVeVUVLXNnpMGacJ5rvWxpGbCcOAWl1SLCShCEy4IKLarOnDmDq6++GmvWrPG19ViyZAlHqu655x4MGjSIO9dT+wsqRido/cmTJ+OTTz7BbbfdxmagCxYsEONPQSgETl+7lVCERcJYL1tcheQVV8ro5oAh0vOc039zcbpj/wqKRfEyVZPboFDmVBUoI5rk7CPpAK/myspfJKlcDljPnYMjK2dSiS0tHZZTJ6G0ZYmwEgShwlOhaqqmTp0acJ9qpXK36WjevDkWL15c6HZuvfVWvgiCUHxxRUXkShJXIWFwpKbAGp8Ae6Yn5a5QKDkS5TzwFeCywXnkRzgPrfU8WaWDuuGNAdtTRDQOFFUUGTNboMk125DqpW1n41lE5caWmg4oTsBQsxZcGr28pIIgVFgqlKgSBKFiiStVRBS0DgfsVMfozkkBsqgCYN82D6CCdFpevzcUupCA7SjDG+ZsM8ljiUIpQFBRvMpjraBUKuBOT4U1MQnuAorSbSnpUOAU9DVrQqEWYXWpyzDsF5jEIAiVFY1GA1Uhtaa5EVElCEKhM8dUQUFQabVwWj1WJcqYloAuDLCm+AQVf5g0vT3P89mbKrgm3Okn4U4+BLfLAZfdycXqMHhElcJhg+XcOd/2C8Ka4vG8MtWl4neTvGqXYBIDlWCkpKQgH2cLQagSKBRgZwEqP8qvbCE3IqoEQSgUpd4AlUHnEz0KpQqqWl3h/O+bnHWqd4QyNP/encqIxnCmn+R0oTv1GFwRDeGyWaE0BfEHlv3MOdjSM4v0KpCwUp06ieBwMdota0hQJSenIDg4LLtm9cJfKIJQuXCzzyX9HRDVq1e/4DNEVAmCUCguhRLakFDYUjMofMHLVHWuCRBV6qYe49384LqqYz97tpV0AMrwBnBlZUFD9glJichKSvFttyjY0zNhT0lhywah7FJ+FKEiQRUcHCrDLFRZtFpPuQH9PZDf5YVSgRV69p8gCBWjvkpJKUBdTvsnZWwb3yxARWg9KKt1KPD5FKnKXazutFjgMpuRde7cBU1H8xyP3QHr+UQovUVeQqlDNVSkc72zqgWhKqPT6fjvoSi1hRKpEgThgigMRqj1enZD5/tKNXTdp8J5/BeoGt1SaK2Bz6uKgunZxeqUSrSePQN7RmDrp6LipML5tDQojCH5trIRSgtJ+QkCivF3IKJKEIQLQrpFExYKa2qaL1WnjGzMl4Kg4nZyUIcuBIqganBnnOEZgG6XEw5LFl9KiptmJCYmQWsKhkM0lSAIFQQRVYIgFCkFqDKZoNbp4MjucFAYZBpqiI2GNTmZo1FUV0WiCs4suNNPQFFAUXtxsGdkQp2RDoVJolVCwbz66ktYsyan/i8/Nm3aVi5DOH36FKxdu5r93z777CtERnpS6sLli4gqQRCKhs4AtUlfJFGlCTJCHRUNh9nMoopSgC5vW5ukgwXOFCwOVItlS0yCLigYTolWCQXwzDOj8NhjT/ju9+t3PZ5+ehR69bq+XMfs4MEDWLHic4wZ8zw6dfqfCKpKgogqQRCKBOkWdUgoFMlp3JuvIJRqFXT0i1ul5ugWEpMDitXdVKxe77pSGXV7ejq0HK0KFi8lIV+CgoL54o/JFITIyKhyHbH0dE/3gE6duhRpqr5weSCiShCEYqUAaRZgYfVQmqAgKEPDeFq+SqdjkeX2nwGY6JkBWBo4bXbYkpKhCw6BU8JVZY7T5Uaypfzc1cMNGqiUpVs8/+23q7BkyXv43/+6YvXqb9CuXXtMm/YG1q//BUuXvo/Dh/+Dy+VCvXr1MWLE4+jc+X/8vBEjhuHKK1sgJSUZv/zyE/99XH11N448mejHBIBlyz7Al19+gfj4c4iKisZNN92CwYOH8n4mTXqZ1+nf/yb07XsTJkx4BUeOHMbbb8/Bzp3/8N9Px46dMHLk06hWrbpvn7Vr18bBgwdx/PhRjBo1Fps3/wW328XCce3ab6FUKjFgwF3o1as3pk6dhH379qJmzVoYN+5FPl4iIyMdc+fO5nO02x1o2rQpHn/8KVxxRTN+fOHCBdi2bQsLzz///AN9+96IUaPGlOq4V1ZEVAmCUHR0eqhNpgJFlVKjhjYqklvc0Kw8pZZElRoufTgUxmi4zQlwJR/kLwGqIynVaJUxSKJVZcjKvfEY8/1+JGSWn6iKNmnweu8muOWKmFLd7smTJ3H+fAI++OBjNnvct28Pxo17jgVNt25TkJGRgXnz5uKVV17EqlXfcesSYvnyZbj33oFYvPgjHD16BBMmjEOdOnUwZMhwbNiwHkuXLsbEiVN52a5dO/HqqxNYIFHqkYQX7WPx4g9ZKJ05cxrDhg1Cx46d8fbb7/BxzJkzCyNGDMWyZZ9xdI1YteprvPzyJDRs2AhRUVEsqn744XvcccddWLJkGdat+x7vvjsP33+/FiNHPstRsMmTX+X6raVLP+a/y6efHsk2ATNmvImgoCAWY8OHD8Z77y1FkyZNeT/bt2/DXXfdgw8//IQ7KwhFQ3yqBEEo3izAkGAoqANyPmhDgqAKDs2xOdBqWVQFNFe2Z8KdcbrURp3sGexJSVAWoYWEUHKeWr2vXAUVQfun4ygLKIJUo0ZN1K/fAEqlCs8+OwZ3330fqlevgcaNm7DASE5ORlJSou85nujVEyyKunW7hlN5O3fu4MdOnToJjUbLIiourhquu6435s5dgDZt2kKv1yMkxGOqGhYWzlEmqq8yGIwsmBo1asxRpSlTpvE+165d49tno0ZN0Lv3DWjQoCFCQ8N4GV2TAKSI1D333MfLrr32ej4mEl/9+t2Mw4cP8fItWzbj33934rXXXud91K1bj8+Bbn/66ScBYzJs2CM8JnR+QtGQSJUgCMVMAQbxLEC7OafvH6HUaqCNiIKTPV2yRRW1tDEYgIxMrqtynfwjx1k9uGb++0g+BMfBb7jHIDVvprY4F8KWlg5NZgYURpNEq4QSUatWjnAgERUSEoIPPljCEaiTJ0/g4MH9/Jh/1KZOncAJFxRN8tZK9enTF998sxJ33nkriy9K5fXo0YsFVn4cOvQfp9+02hyTXUq/1a5dhx/LOc5aeZ5Lwo/SfoSB/t4A1KyZ8/dFUSmvceX+/fv4R8+tt/YN2IbNZofVr/9meHhEnlo04cKIqBIEoVgodDqog0x5RJWWIljBwSy8vNCHt8poYO+8PCagdXrm2TalB60/PEUqCc4DX8ERUhvqK++Hqm5PNhwtPFqVCK0pCE4xAy0TZvdrWmHSf2UBRY+8bNu2FU899Rj+97+r0apVa44MWa1ZGD36mYDnUCQqN94oLUWgPvxwOaf9Nm/eiE2bNnIkiKI/lB4s6Hm5oXoudXa0l9Dpco7Ti//jXgpKr9PfJ4m/JUs+yvOYv6ATN/2SIaJKEIRiQQXh6pAQKJKS4XY4eRkVr2sjI+Gnpxiuq+JidTXcETlfhq5Ez6/+gHVdTtj+eI0FlW9Z2nHY/3wNjp1LoG4xEKp615OraL7HZUvLgMaSAYVeolVlAdUx3dgkutIVqufHxx9/iLZt22Pq1Bm+ZZ99tjz7VtH8O777bg0XhFOtEwmzYcNGYPLkiVz/lJ+oojTd99+vgc1m84mbxMREjpLdfvudpXRmQIMGDZCZmQGHw8ERNC90bI0aNcKAAXeX2r6qIiKqBEEoFp7oExmBamF3eKJV2tAQtjXwj1J51qW0oEdUOalXoD4CyEryOKu73QHtbRx7PoHr3HbPHWM0lEE14Ir31Ke4M07BvvF1OHYuhbvl/XC3ujXPcTmtVjiSkqGpGSQzAcsIEjRRpvxFbWWCGuf+9tuv2LFjO2JiYrFt299c/E2Q6CkKtB7NsKOC9Fat2vIMwO3bt6J167b5rt+//wCeKUjF8IMGDcl+/iyul6J6rNKCZi9SenP8+LF45pnn+Pyonmv16lV48823S20/VRURVYIgFB+Nlq0T7JkWqPQ6aDhKlf8veIVGw6KKBBSbgJ7+i6NR7sxzUATF8Tqu83vg+Gex9xnQXjUeqtjWcJ7bAceuD+A6u5UfcWeehXXjdCQe+Aq66+cAKs/UdS/URkcdkQnojPKqCiVm+PARHCUaNepJvk8RnRdeeAkvv/wi9u7dzcXdF+Lmm29FamoKFi1ayIIqODgEPXpci8cfH5nv+jRLb/78hXjrrTcxdOggaLUanglIhevBwaVX26RSqTBnzjwWfC+8MAYWSxbq1avHUbn27TuW2n6qKgq3dCMtElScmJSUGbDMsul9ZHzh+aMLvWc+tO08sy6EvKjVSoSHm5CcnAmHQ6bnXu5jRAEmRUYaMo4egz4iHJqatQqMDqlUSthPnYD5bDzsO96D498Pebm220SoaneD25YJ65ohnjY2NA5XPgBN6yEB23DG74Jj11K4zvztW6ZpNRjqFoPy7M8YFwNNjZpVPlp1ofdTRISJX5v8yMrKwqFDhxEVFQetVlfMd4cgVC5sNivOnz+LBg3qB9Te5YdYKgiCUGw4rWc0QRscBHVkZKEChuuqaEYSR6r8TEDJWZ0cFjbP8gkqZVRzqFs+mGcbqpgW0F07A1qKTik8swHte7+A227Os66Nmj5nBRbRC4IgXApEVAmCUCLcag3UoaFQGAJTcLmhOitvXZUil6hyHP4ezqM/eBZoTNBc/WKhs/xUMa2grp/d4saWDseBlXnWod6E9uTkAqMwgiAIZYV86giCUCJ4qncERakunKpU6LTstq4wxZL3guf5Cbs5SuVF0/EZKIPy9/DxR9tiINddEY69n8HttAau4AbsKamAVaJVgiBcWkRUCYJQ4hSgo6j99jRaKLU5xeqMPQPInj2oqt8H6nq9irQpZWgd6Br08NzJSoLzvxy3aS92SxYcqakSrRIE4ZIioupiEJNBQSgiCqiNnjShMjLQvFERXAOaDp4JH0UlqN1g323HnuVwuxx5/jap0TLsuaJYgiAIVUVUvfPOO7j//vsLXYe6cw8fPhydOnVCly5dMHLkSJw+ndNHjDp7t2zZEk2aNAm4zJ079xKcgSAIBba30euhUCqh8HNWh1IN7dUToNAUzwJBE90EqhqdfTYLziM/5lnHYbHAmZIC5SUwixQEQahQomrZsmWYPXt2oetQY8nBgwfzlMYPP/wQCxcuRFJSEoYOHcodvYmjR4/y7ZUrV+L333/3XR566KFLdCaCIOSGjT6zndVVce0AnaeZrKbtCCgjm5ZowLQtcn6AOXYvY0f2gH26PNEqpTNXFEsQBKGymn+eO3cOL730Ev766y/UrRvYnDI3P/74I8xmM6ZNm+bzipg+fTq6d++Obdu2ceRq//79CAoKQtOmJfugFgShbFBoqVhdBYUuFPqblsKdlQJl2IVNFAtCFduSmy674ndyOxvXyd+5AbM/1J/QkZYKZXhkHrd3QRCEShep2r17NzQaDVatWoVWrVoVui6Jpnnz5gWYb3k7c6elefqFkaii3kaCIFQw1BoosxvQKvThFyWofJu8MidaZd/1YZ6mtG6XC7bEJChzRbEEQRAqZaSqZ8+efCkKNWvW5Is/7777LousDh068P0DBw5wo8ghQ4Zg37593MPpwQcfxC233FIqDsX++Ndq0O3cjws5eD2DxDuo6o4R/Y1oQ4LgSE+/6O14rzU1O8ER2YQbNLuTDwLntkBVo1PA+q6sLLgz0qEJj8gjuiozlf39JAgVkXIXVRcD1VV99NFHGD9+PCIiInyF7OSfQwXscXFxWL9+PcaNGwe73Y477rijxPuiD3Bq+RCASQdPfAww6LUIy/24kIeQEIOMShUeI6stBMr0FKpcv+htGY2eqJemw0NI+W4M33bu/ghhTbrlWVeVlY7goDiu6apqVOb3U1G49dZ+6NfvJgwb9ki5HQP1AFy//lfuB1gQCQkJePfd+di06Q+uHw4LC0OHDp0wZMhw1KxZ65Ier1ByLstPGPq1+eabb2L+/PkYMWJEwIzBb7/9lmcAUmdwgmqraHbgokWLLkpUUT1GWlpgS4zMzJzp2pYsG9zJgb0BhRzo1zJ9uKelWYpkFlkVqQpjpHACFqsTTqvton7gkKAym238d+mO6QxFaB24U4/BfmYHUg9thio2sJRAmeWEy3gOitCwKuOEcqH3Ez0mUaxLw5w5s/h7qCBRZbPZ8Oijw1C7dm1MnjwNUVHROHv2DN59dwGGD38Iy5Z9hvDw8Et0tEKVElUUcaLIE4knuh40KLChan7NDhs3bsw1WxdL7qak/oWvdLuiN8GtCNCHu4xT1R0jlUoLt0J5QdGoUCnhLnAdpe9vzrsddfN7Yf9zCt+27vwAup7TA57hdNqQdT4RelMwHO6qZbFQmu8nmmHptiWjvFBow6FQeno/Xk5cSMhv3rwJJ04cx6JFHyAkxNNxoFq16pg27Q3063cd1q37Dnfddc+lOVihaomq0aNH44cffsDMmTPRr1+/gMeoWL1Xr14YO3Ys+vfv71u+a9cuNGrk540jCEK5oCBLBb0W9sy8jZC9qHRaGOJi4XY4YU9Ph9NqhdNmL/SbSVW3Fxz/vM+eVa7Tm+FKPABlZE6fQcKWkQlNehpUoWFsRspRrqoStioFbEe/huWv0XBnJZTbMSj00TB0mgZt3YLTaCXh229X4sMPl3J0KC6uGvr3vwMDBtztmwi1Y8c2LFy4AHv37oXdbkP16jUwaNAQ3HCD5zuIrH1mzJiKrVu3ICvLgsaNm2LEiMfRtm07vPrqS1iz5hter3Pntti0aVue/Xv388cfG3zbJIKDg/HRR58iLCwnSvXbb7/inXfm4eTJE2jS5Ar07dsPU6e+5ttufunO3MtWrvwKn332CW+Duhw0adIUTz01Cldc0cy3fs+evfDnn79zKnLKlOlo06YtPvpoKb76agUSExM5qnbffQ+gT5++vv0sW/YBvvzyC8THn+No20033YLBg4fyPqoKFVpUURqP3qz0xqII1Jdffok1a9awsOrYsSPnoL3QOqTwO3fujFmzZiEyMhJ16tTBunXrOEpFxqKCIFQAE1ByVk9MyfdxMgfVR0dBFRXN9zUxsXBlWeAyZ8KRls7NkuHI6ztFTZjVze/x9RK0/7sUumteC9y3zQ7LyVNQJiRAbTRCZdBDqdOz1QPNTCR9JbYLBWP+80nA7q0iLR9I0NFxlKao+vrrFZg37y0899xYNGvWnGeQz5z5OuLj4/HEE0/x9ZNPPo4BA+7C2LHjeSLUhx8uweTJr6Jjx878XTNt2mQWW/PnL+TZ7EuWLMLo0U/jm2++xzPPjILVmsVCY+rUGfkeA9VOkaB55ZUX8f7776FDh45o3botOnbshNq16/jWI3E3ZsyzLFR6974Bf//9F+bMKdzfMTe//vozn9+4cS+ides2SEw8j5kzp/H5fPjhct96X3zxKWbMeJO/Wxs0aIgFC97CunXfY9SoMahTpy4fy7RpU5CRkYE77rgTGzasx9KlizFx4lT+7t21aydefXUCR9z8hWJlp0KLqjNnzuDaa6/FlClTOPJEKT+CfKro4o93ncmTJ7N7OnlfkZome4U5c+aga9eu5XQWgiB4ociQSqeDQq3iSFRudOGhUEdFw+nfU1BngNJghC4yGjpbFpBl8RS7ZwS2oFE1uIFtFWA5D9eJ3/ONVjmyrECWFbbUdCiUCi5cp0bPZPWgDjJBEx1T5dKDVZ3Fi9/DQw8NxXXX9eb7NWrUhNmcgenTp2L48BGw2awYNuxhjsp4Iy4PPDAYa9Z8ixMnjrGoOnXqJAsPimBRAOCZZ55D7959OQJlMARDp9NDrdYgMjIq32MgITZ//nv47LPl+OmndRztWbHic6hUatx2W3889dSz/PzPP/8ULVq04uMiSNwcPXoEX3zxWZHPNzQ0FM8/P8EXYSLRc9NNt3KkzZ8uXa5iUUdYLBYsX/4xXn11Mq66yvNdSsXzZ86c5ugViSoaA41Gy9ujaB9doqNjeMJYVaJCiaqpUwNfVLJPoF8NXhYvXnzBbZDxJ9Va0UUQhIoFRYOUOh1UajUcuUSVxmSELi4OLoUSyJWV80WQ1DpoI4zQaFVQJKUDfnVXCpUOmivvg/3vN/m+fef70PWYUvCxUE2Wze5JLcICh9kCVVAQFAZTlSlmLw7G/71ZYdJ/pQWltiiCNH/+W5xS83+/UWeO06dPoV69+rjxxps5Xfbff/9xyuy//w7wet6aPpqh9/LL4/HLLz+hZcvW6Ny5C66//gbodLoiHwuJsQceGMQXmi24detWrF37LQsmvd6Axx9/EocPH0KnTl0Cnte2bftiiao2bdrhyJHDWLx4IXcgOXnyOP77zzNr3p9atWr7btP6NB4TJrwQYCVE2SQqss/KymKR9s03K3HnnbfymJEg69GjF4urqkSFElWCIFQByFmdrQ1yIk1KjQb62Bi4dQYWO4VBX3i6yEioT5+DIyXQ80rVsB8cuz+G25wA16k/4UrcV+Q2OE6bDY60NGhMwZV29uXFQCk3Te2bKlWhuldIPPnks5xyyw0JAhIUDz/8ENcveYRCT65xeuihnFnn3bv3xLfffo+NG//E339vxieffIRFi97Fe+8tRf36FzajphonSivefvsAvh8aGoaePa/ly/PPj+baJhJVhNvtyhPluhAkfrx8//1arvOi9CH1yaVI2KFDh/JEqvwFoXecXnuNUnt5O59otdrs9nHLOe23efNGbNq0EZ9++gnXcZHorCqIqBIE4dKiVENJs3QzPBYklIbTR0VAGRYBZxFbyaj0OmgjI2BNN8Pt94VB0Sr1lQNzaqv+eR+6nq8X+dDsqWmcAoTi8pthdikgQaPQ55/Cuhwhf0OyKqDUFRWne/nhh++xfv0vmDDhVU7FhYdHYO7c+b7HqX7Ig5sjNfPmzeW6IUoh0oUiN/36Xc+F5ySqLlSnTcKNxA5Fe7x2QF6CgoJ9PoxUUE6ixZ99+/bkEVmZmTn2PpmZGVyb7OWDD95na4cxY573Lfvtt/U5PTrzOVhqIUepyLNnz+Lqq3N84Eg0HT16GGPGvIDvvluDjIx03HHHXWjVqjWGDRuByZMn8liKqBIEQSgj6INbbTIA5z33tSEh0MbEwlHM3nzqsHBogpK4PsofVYO+nmhV5jm4Tm+CK2E3lNHNi7RNqrlyZWRAEUJ+VpIDrCxQym7jxj8CllGdE83OGzhwEN55522u/aE6IkqFTZ8+BV27XsMRGOrKQSnCP//8A/Xq1cO+fXvxxhseyw4SVLTO3r278c8/2/Hss6MRERHF+7JYzGjRoiWvZzAYcf58AqcTqe4qN/feO5DFx4gRwzBkyDA0atSEU4B//bUR33+/BjNmeIrRqa5ryJAH8Oabb+DWW/tj//59+OSTjwO2RfukuiyavUdF5mQoqlbn/EiIjY3Dzp07+DyoXIYEIhWle88nv5QlCbvbbrsd7747j0Vfy5atsG3bFrz99ptcX+Z97ty5s/nxVq3a8pht376VC+6rEhKpEgThkkJihWbdeYvEddVi4VSqL2zmk3s7SjV0UVFwZFrg8psRqFBpuSeg/S/PTCv7ziXQXTu9aNt0OmFPSYEuNAz+tfLC5Q1FgeiSO7X39dercd9997OQ+Pzz5SxWqJj8llv6++wH7rzzHhw7dpRrphwOO2rWrM12CR6LhT0sxCZNeh2zZ8/Ac889zbPhKEX2yiuv+QQF2RlQ5Oueewbgiy9WIjraM7vVS0xMLBYv/gCLFi3ErFkzkJSUyGKtefMrMXv2W1wHRTRq1BizZ7+NOXPeYCFExfE333wLF5F7eeSRx1mQPfHECAQHB+Gee+5Hul9rKJq9N2XKJDYbpcJyshuiiNyLL45jcViQCKJieYrqkUgjgUhik8Zo4MAH+XGKfqWmpvA5kKAKDg5Bjx7X4vHHR6IqoXDLz7EiQTUWSUmBjumWjYuRseIpvh16z3xo291XFq9RpYD6IlKbn+TkzEprbHmxVKUxUjmssBw7Dm1EOFRRMcWqYfIfJxJBWUeOwJqSGrCO2+WAdeV97FtFaK9/C6qYFkXbvl4PU4N6cGryGglXpvdTRISpQEd1Sl8dOnQYUVFx0GqLXmwtXHq+/XYVJk16OV//K6F0oBmg58+fRYMG9fM1GPdHOm0KgnDJUWi00ASZoI6IuqiicBeU0EVFcqF7Ht+qFg/47jt2vl/kbTqsVm767D/LSRAEoSiIqBIE4ZLjVqqgiY2Dw10KqcTgEGhDgvI8pqrfG4ogT/2K6+xWOM/9U9SNwp6SCkWuWVaCIAgXQkSVIAjl04+ulD5+nFBAGxkJFTmjFxqturDPnW9dSxbcmRkXnLUlCOUNeWhJ6q/iIKJKEITLGqpvV1C0KjQ4z2Oqer2gCK7Jt13ndsB5tmh1Jy67nT2rvD3ZBEEQioJ8YgiCcNlDhqCaqEhugZMnWtVykO++45/FRbZKsKelA/bAVjiCIAiFIaJKEITKEa0yBkEXFpLnMVWdnlCEeFpuuBJ2cX1V0T2rpGBdEISiI6JKEIRKATVhVlP7GoM+jwu4f7SKegO6Uo5ecHtul8tTsC51VYIgFBERVYIgVBqoGbI2LBS5lZCqdncoQuvxbXfacVjXDIX93w/Zz6ow7GYL3GZzmR6zIAiVBxFVgiBUqlmFmsgoaIyGPNEqbdeXoAiu5VngssOx4z1YvxsBV/KhgrdntcKZkQ6VSsJVgiBcGBFVgiBULnQ66KIioMg1c08ZVg+6fougbnYPqSxe5k46AOuaYdx42e20592WG7Anp0Dh17RZuLygZsLUYqZPn57o0eMqPPPMSBw9eqTQ52zdugWdO7fltjX5QY+Rk3lF5+zZM9xTsCCo1Q6dS06D6LxjcPr06SLti9br3LktP68oFGX71Avx1VdfwuWEiCpBECpfbVV4JDQhpjyPKdQ6aNo+Al3veVCE1vUsdDvh2LUE1rUPw5W4P1+HdZd4Vl22jBnzDE6cOI433piLxYs/5D5/TzzxCLKyLBd87qeffoydO4toGlsBIUGyceOfF1zv9ddfQ1pa2kXti3oBrl69jpstV2VEVAmCUOlwKVXQRUXnaV/jRRl1BXR9F0J95QPUgZmXuVMOwfr9Y3Cd3xu4LbsD9tTUAvvkCRUXEgrVqlXH889PQLNmzVGvXn089NAwJCQk4PDhwxd8Pj2X+upRL8TLkaLYh4SEhMBms+GNN6Zd1L5UKhU3o9YU8DdXVVCX9wFczii0OXUbCk1gDYcgCOXrW6UOCYUuLBWWhMR811GotNC0HgJV7a6wbXwd7uT/uNbKtmUOdL3fhiI7RUjYMzKgodoq8sFSqgBOLSoCvrhon5Udp9uFZNuFIzxlRbjWAJXf61IUwfDqq5N995OTk/HJJ8sQExPLAutCjB49DqNHP4sFC97CU0+NKnA9imbNmzcHe/fuQVhYOK6+uiseffQJmExBvjTcW2+9ia1b/0ZaWjoiIiLQu/cNvA4ZzFIqccmS9/C//3XF6tXfoF279pg27Q0cOXIYc+bMwo4d22A0mtCuXQc8+eTTLF6I48cpAvc6du3aBbfbhRYtWuKJJ55Gw4aNOHW2fftWvmzbtgVff70632On7T788Ai88soE9Ox5Hbp1u6bA8/z225X48MOlfD5xcdXQv/8dGDDgbj4HSuP1738j3n77XT5+p9PJ6UV6TkZGBrp0uQoxMTE4cOAA5s9f6Nvmn39uwJdffsHRxJo1a+Hxx5/EVVd19T1uNmdiwoTnsX79rwgODsItt9yGIUMe9hnz0hi9/fYcfg1onx07dsLIkU+zICZoHGrXro2DBw/i+PGjGDVqLDp27IwZM6ZyCpIilo0bN8WIEY+jbdt2uFhEVF0E2mZ9oWt8DdQaDfRX9oFUXQhCxcHpBjRRUbCnZ7DnVEEoIxpD12cB11a5U4/AfX4PnEd+hLr+9b51HOYsZB49xh/kXKulUnIUTKnWQKFRQ0kXvRFKvR4uKCqlwFp5ajfG7lyNBGtmuR1DtM6EqS374ZYazYv93ClTJmLlyq+g1WoxffosGAwX/iFcq1YdPPLIo5g7dza6d78WrVu3ybPOwYMH8MQTIzB48BA8//xLSEpKxNy5szBy5KN4772lUCgUeO45jxCaM2c+jEYj1zDNnj2TRdA11/Tg7Zw8eRLnzyfggw8+htVq5WjaI48MZfH15JPP8Jc/iZShQwfh448/5+N/8cWxaNy4Cd5//yM4nQ4WYGPHPosvvliFqVNnYNSoJ1lAkpAojBtuuBE///wTpwFbtWqN0NDQPOt8/fUKzJv3Fp57bixH/fbv34+ZM19HfHw8nnjiqTzrz5s3hwXi2LHjUbduPXzxxWf47LPlaN26bcB6tGzMmBcQHR3N4uiFF8ZgzZofeZyIX3/9mYXb0qXLsG/fXj7GoKBg3HPPQJw5cxrDhg1ikfT22+/wuNEYjBgxFMuWfeYTtatWfY2XX57EYjMqKgpTpkyC3W5jcUeRtSVLFmH06KfxzTffF+l9URgSz76YwTOEIvbJ71F71HdQ6fO2yBAEobwNQU3QRoTnsVjIjUKlgab9Y7779h3vwu0IjMg4s6xssWDLyIQtNR1Z55NgPnsOmSdOIf3IcWQePgzL4UNwno+H0mqGWkEBrbz7VWQvpwulFC+XtOLT21eVq6AiaP90HCXh7rvvw5IlH+G663pz9Im+oIvCXXfdiyuvbJmdBswbpVu27AN06tQZgwYN4YgICa+JE6dg9+5/sW3bVk4d9unTD+PGjUejRo1Ro0ZNPpaIiEgcOvRfwLYGDx7Kj9ev3wBffvk5R3aeeeY5FiVNmzbDa6+9zoX3P/30A69/6tRJjoxVr16NI2/jx7+EceMmwOVysTBSqzXQ6fQIDw+/4HmOHfsC7HY7Zs7MPw24ePF7eOihoTx+dIw9e16LESMewxdffMpixp+sLAu++OJzPPzwo+jevScf/7PPjmYBmBuKAFJkq3btOpyapfGi6JMXiiLRc2kbffr0xZ133oOPP/6IH1uxgsSlkQUTje2VV7bAlCnTOCK5du0a3zYaNWrC4rRBg4YIDQ3jcQsODkH16jVQq1ZtHuPJk6eXSlsqiVSVBpXvR6kgVJqidbZYSE2DPbNwvylVtQ5Q1ugC16mNgDkBjt0fQ9NqSNF25Haz6KKLNTUdKq0GKr0OmpAQqIJMLNrITBQOB9wuJ1x07XTCbbNRMQq0sbFwQslCUCgbvOm+F154iQUPiYHx41/mGYH+fPLJFwH36YuW1rv//rsxf/5bePrp5wIe379/H6eucm+HoFmGJBgGDLgLP//8I+/35MkT+O+/gxzRonSVP/QF77/dw4cP5dmuzWb1zV585JHHMGvWTBYXlLrq3Pl/uP76PiUSBxRJI3FBMyWvvbYXR4O8kEiJjz/H5//OO/N8yykiS4Lq9OlTLN680PFZrVksRr1QxI4EJ6X//CEh6oWEDuEv0ihy5k/z5i2wdOlipKensyi94opmHH30Pw8SaP6CtVatbCuVbIYMGc7n+csvP6Fly9bo3LkLrr/+Bp7EcLGIqLpo3JzLFgShYuLWaKGLioIj6yTczsL/VjXtHoP1zN+AywHHnuVQNbwRSlNsMXfohtNq44stLR0qjRYKlZJFFV+cLs9nRraAonSiy2aHrlo1uNTaIvcmvNTManNzhUn/FZWUlGT8/fdm9OhxLdRqz9cdCQ6KBCUkxPP9Dz74JOA5UVHROHXqVJ4vforKkMUCbcsfigpRFIQiVbmhCJHFYsEjjwxhoUBipV+/m9Cs2ZW8LDd6vT5gu1RDRem23AQHewTPHXfcxXVQf/75O7Zs2Yx3352P999/j88pMjISxYUiQSQ0Xn99ckDKkI6FePLJZ9GhQ8c8z6P6KkpXelGpPGNdlPeykmoUc+H/vNyRXJfLyQJNo1EXuH06Xu/rTfgLPoKiZ99++z3PjKT3xyeffIRFi97ldC29Ny4GEVUXCb247uxrQRAqaNF6RDi0qSmwphQ+bVwZUgvqJv3h2PsZ4LTBsW0Bm4aWGDdtxlb4Ki4XshIT4XLYoK9WAwqDsULWZFEd043Vr7isCtUTExPx4ovjMHv2WxzFIRwOO0eBunbtlic6dKE0INX3UBrQH0opHTlyJGA7FKmhOiwqRD9+/Bjvb/XqH3xCJzU1lSNVhaU5GjRogB9+WIfY2DhfJIae9+qrL+Lee+9HvXoNsHjxu3jggcG48cab+UL1TTff3IeL03v1ur5E30ujRz+Pe+8dgHnz3vQto8J6EoiUNqPidC/kgbV+/S+YMOHVgG3UqlWLhcy//+4KSPnRfa22eNGg3Gnaf/7ZwWk7vd7ANVLff7+GZy96x4hec4oG3n77nfluj9adN28ubrihH6cy6UIpx379rscff2y4aFFV7BjhV199hXPnzl3UTisb9KtTRJUgVFycIIuFqAItFvxRt3gA0HkKdZ3HfoYzflfZH6AbsKWkw3L8GNxpKRXWwZ0ETZTOVG6X4ggqr+ChWWdUJ0RCg1JC5N2Unp7GdU3FgT7jKXV4/vz5gOX33juQRdP06VO4FmjXrn94thp9sVMaigrFie++W8OF1Tt2bOeiaIfDAZstH8PZbEgU0Ky5l156gYvh6TJ+/Fjs2bMH9es35JmNf/zxOyZPnogDB/az4KFiciq8btr0Ct4GFV3TPil1V1RI+FENExXO+5/7wIGD8Pnny/lC50YCk86ZUmb+6TeCBM+dd96NhQvns+giYUnF+5T+LO53Jc3qo5mTJFRpogHVmlHtGdG//wBkZprxyisv8vjQ9l94YTTXTZFYyg861r17d2Pq1En499+dPGuRCuotFjNPHLhYii2qXn31VezcuRNlwTvvvIP777+/0HUot/vssxSC7ICOHTvilVde4fCqP2vXrkXfvn3RsmVL3Hrrrdi4cSPKXlSV6S4EQbgIKE2gCA2DLsxTs1EYCm0wNK08H9qEfcvcS5bit2daYD5xCs6E+AorrC43Jk6cjA4dOnHE6qGHHkBqagoWLFjEKaviQtGoESOeCFhGdUNvvvkW1woNGnQfz/QjMTV37nwWOM2bX8mz9z777BPcffftmDTpJbRp045rn+jLvSAoGkOz08hSYPjwwTyjjbY3b967HDWi9NYbb8zhdObjjz/C0aXNmzdh5sw5bE1AUFSJ6rIGDrwrT/1WYdCxUYrMn/vuux8jRz7Ds/joPGbNmoFbbunPM/fy4+GHR6B377486/L+++/B2bNn0a1bd07bFQeyUKCatQcfvBeLFy/k6B9F5TxjVJ3HiPzIaFbkU089xjVV77672JcizY9Jk17n8aXX6q67bsNXX32BV155Lc/MxJKgcBczgX/DDTdg+PDhuO2221CaLFu2DJMmTUL79u3x4YcfFrgeiS4SUSSmaCBfeOEFFlivv/46P75p0yYMHToUo0ePxlVXXYUvvvgCH330Eb7++msOp15MT7GkpLy1BEajBhq1Cza7EhZLwb86qjpqtRLh4SYkJ2fC4ZAaNBmjS/9e4ll3tiyYjx2HPaPwuiBqtMwWCymeWUiaLuOgbtAHlwqlWg19dCS0cXFwuJVlMk4REaYCZx5SOuTQocOIiqLU08UX7wpVj19//RmtWrUJmHlINhPkvE4Rv8sJmhxw/vxZNGhQP6DurVRqqu666y689tpr2L59O5o0aQKTKW8rCIoOFRVKJb700kv466+/ULdudtuIAqB9bt68GWvWrPEJJIqckYh65pln+MVauHAhevXqhQceeIAfHzNmDD9v6dKlvG5p47Klw1P+IJYKglCRoZ+Pbp0BhhrV4T5xEg5zwbVBCqUamvZPwPbj0z6LBVXtblBoPN45ZQ3NDrScS4Db4YCWCthVFbeAXRDyY9myD3hWInlYBQUFcRqQzE/nzMmZPVgZKbaomjp1Kl9/9tln+T5O+dLiiKrdu3dzSHPVqlV4++2388y68GfLli1sEOYfcaIUIO1z69at6NOnD7Zt24axYwNnS3Tq1Anr1q3DxUAfaOThEbAs8zgyf7iOCyIi7vgbUHhcbnOvl/vXMnmHeCmtdQn/9gDFWZcKNwv7vC75ug7fF4HbrYDNpmHDNYfDXei6+UGhbm8unkzuCivkLfm6Tt8sl4tdl1o2eKc1F3dd/zEqre3SeoWF/2k9Wr+irEvvBXpP5Ae9l5zOnF+Lha3r2S75Qal9EWe3Tg91dBTsp0/DkRVYRK5UKKDKHjNlbBsoal4F98k/AEsirLs+gsrPYoEsqFR+M5fshZ1bMdald5hapeICdsv5RFgtFp4ZCH3eAvbCPiMu9DcnIk0oS155ZTLefHMmG6NS5LN+/frss0UzGiszxRZVP/30U6keQM+ePflS1KhWNfpwyVV0FhYWhjNnznA60Gw2Iy4uLmAdMlCjfO7FQIWNCxfODVjWQPMPrjKm8m3HqR+gruspfKRprQV9yJNp2u233x1gqFZQY08qcLz77pwaM2oPQMeRH2QkN3DgYN/95cs/zp5dkhfyAhk8eLjvPuXICypkpILD4cNzTBGpUJAKIgsSHY8+muOsu2bNN4V2gx85Mqftw7p13+G//wL9S/wZMWIk1GpPMSRN+S2sFmHo0Ed9brwbNvyGXbt2FLjuoEHDEBLiKUretOk3budQEPfdN8jXHmLLlo3YvLngWr277roPsbGe9+o//2zBH3/8VuC6/fvfiZo1a3Mqhn4cUE1gQdx0020864c4cGAPfvzxuwLXveGGm9j0jqAWDWvXflPgur169eFp3gTNZPrmm68KXPeaa67lsD5Bxaxffpn/Dyziqqu6oV27jr6/308/XVbguh07dkHnzh5PnsTE81i2bEmB63bpQut6WlmkpaViyZKcthe5adGiNXr06MW36fPhvfcK/qXcMKYaujZp5hM+qxXt0AcboYQLjr3L8XuiA+maSFjUoagZUws9rmjhe+4Hf/5S4HZrhkfiuitz/HY+3rQejgIEcVxoGG5omdMu4+Of18HqsJf6Z0RmZka+ztmCUBpUr14dr78+s8oNZrFFVY0aNXy3qbaJZieQqLkUTRRpf7lnGRA0+4A8QLxNL3Ov4328tHFAg9/UtaFyu3GjKxNh4Z5UaGGzG9RqFdc5eMnPcbkk69IXsv+6hbk003b816X9lMa6dN7+616oINF/Xa228HXDwky+1/XC6xp9aWmdrvB1Q0ONvG3PuoW/h0NCDL5jNhjyvg/9CQ4u2boXIihI71vXZCq81oUeL8m6CQmF1wwYjVrfuqmphbd0oHP3rms2F31du/3CXkj0ehAKReFRWXoPeLebz8dHAGqNEkFBnrGyOxzI1ETgQGhnNE39Eyq3E1ed+9S3ruOEEVmHqkMVFAtlcBxizXqcMzbKd7sqdc52sw+6wGOgv13/dUvr8yT3ZwRNzxcEoXQpdqG6Nw03bdo0/Pvvv74QMs20e/rpp9G5c+cSHwyl7Sj9V1Ch+sSJE3nm4eeff57nV+vDDz+MW265hff/7rvv4pprrgkogn/jjTc4ClBSHA4nkpICfwFu3P8V+v+3h2+vqWlAx/aetCOF2wuCPiADw/XFWZe+PAp6ucgMrWTrelJ6Bb8NNBptCdellJ7L94FO4iE93cIpmMLWzQ8aB++XS1mt60mnOUtlXUo3BabpLrwujZHJpEVKSoZvjEpjuzmpt8JSZKpcabryXdeT0stfLNE4hYUFITPT5knnFbIuQY2RvUaAudel4bGfi4flXDybcvqn/3hdMuu0ZcL27YOAOXAqfb7nUKsr1B1GQmGMznUMgLqY6b/81lVqVNBHRUETEwvXBT4j8v7NBf7dh4TofeOdGylUF4RLVKhONUuDBg1ic69HH32UmxOS4djq1au5YJwEUZs2eZtOlgaU1vvxxx/zGHmlpKRwio8iZpT2oePxh+5TEfvFQB9eCkXgcB115vwq/CczE22zZ9jkXi83/jNxireuqkzWBVSFWkKUfF0lf6HxLaWSI01Kpd1XB1TQugW1G8kRiWW1bt7XuKTr0inm1DsVfV1PHZS6wFqpkm6X1y5kXdLJRX1flve63torEgqOi/ybo33qY6tD6XLDEn+e65j8Ba2SJI42CPo+C+A8tRHuzHi4M895rs2eC7mve3Gd2ADb2a3QtB4OVaObofAKKTIBzb3dQihwXbsL1vjzUDhd0FarDpdCVeDf/YX/5sSyQRBKm2KLqtmzZ7PtwaJFiwJ+5Tz++OMYMmQI5s6di8WLF6MsIOuEGTNm4NixY6hTpw4vo9mARLt27Vj4tG3blpcNGDDA9zyaWUjHXNoEaT0dsImMQn4pC4JQcaEabk21ap7C8IREj9LKBUWe1I083jj+cBQ0KxnOM1tg3zafb8Nuhv3v2XAcWQdtp1FQhue1cnHTrOH4XXCd+weujFNQN7oJquqdinS8FFGj46SZgbqateBUSmMMQagoFPuvcdeuXZg5c2aesDH9Kho4cCBbGJQWlN6gjtxk4kUht1atWrFoojTjyy+/zEWnEyZM4NmG3kjU4MGD2UerWbNm6NatG1asWIG9e/eyDURpE6TNsVFIdxTeikIQhIqL062Erlp1bnKclZhc5OdxFNQQCXX93lDV6AL79gVw/reaH3Of38NeV+pmd0Hd+Da4kvbDSSLq3A64k6nZa454s53YAFWT26Fp+zAUqiL4QrndyEpOhVKng6Z6jewoqiAI5U2xXeWoALigmW0XmhZfXGhG39VXX82+VARFot566y3UrFkTDz74IJ566ikWTiSwvND6kydPxieffMIGpWQGumDBgosy/iyIIF2OO3OGUyJVgnA541SqoKteA9rQknnOKXQh0HYeDe11c6AIye4D53bCsftjZH01ALb14+Hc9zncyQfzrXd07l8B69pH4EopeMZsAG43rEkpcKenS5ssQbhcC9VHjhzJvXKodor6CnmhqBG5nVONFbWbqWzk56i+L/kEuv/hSXXeqUrAnBveKqejq/iIo7qM0eXwXuLaySwzLMePw55pLvF23NSMeffHcPz7EeDK/YNLAUV4AyhjWkEV25rrsuzb3iEnYc/DKi00bR+FqvGtRRJLuvBQ6OvUhTPXb2RxVBeEy6BQnZzLb7/9dlx77bXo3r07m3EmJCTg119/5RkjZZFmq6gY/Wqq0itgV3lBEErQI9BghKF6NY/relbJrFgUJIxaDoKqTg84di6B25wAZdQVUMa0hjKmJRS6wGiYMq4tbL+/CjdFqZw2rslynt4MbZcxUOjDCt2XLS0d6qREqKNjC5w1KuR4c/Xtez1PaPrmm7UBMycrKrfe2g/9+t2EYcMeKe9DEYpAsUUVtZIhN3UqSF+/fj17nZCBHDmbU7F6w4YNUVU4mJDzgZte9F6VgiBUYMi1XBUSBn01B8wnT8N1ge4EhaEMrQNt1wv3OVOG1Yeuzzuwb58P536P8arr1J/IWv0QtP8bB1W1DoUWrtvOJ0IdHAyFVl9ox4Oqzg8/fM+96MgY+ZdffsZ11/VGRef99z9ir0WhkoqqefPmoXfv3jwLsKpzLi3nV2GGW6YnC0JlgSI+qvBIGOx2mM+c4wL2skah1kHb4Sk4q3WEbeNUwJrK7XFsP42CqsENnBKkuq38sJstsMXHQ1erNs9mLCvcLidc5iSUF0pjRI5NRQn45puV6NLlKpw9ewZff73ishBV/g2JhUooqqheqnnz5mVS+H25EabXQOt2wqZQwZzt3yS/EgWhcuB0udlkk4SVJYE8rC5NCEhV83/Q3/g+bH9OgevM355jObQWzlObuMmzqk7PfGutrCmpUIekQhkaVmi/y5Ji2fEl0r54Fq6MBJQXyqBohNwxE4bW/Yv93CNHDmP37n8xcOCDSE9Px+TJr+L48WOoXbsORowYhoiICO5N52X79q28/PPPv0atWrXx+++/YeHCBdx6i8perruuDwYPHurr9NC5c1sMGTIMq1d/A7vdgfnz34NWq8Fbb73JjYTT0tJ5H71734BHH33CZ8z7118b8fbbc3H06GHUrFkL9947EJMmvYIvv/yWW734p/9o///8swMdO3bG558vR2pqCpo3vxKjRz+PevXq8/aSk5Mxc+br2LTpTzYAvvnmW7Fnz79o3bqtpBAr4uw/Su9RbzCBLBXUMLg9MyHNClWhzt2CIFx+UB08mWzqIsIvqVemwhAJbc9p0HR8BtBkt5bJSob991dh+2UMXBl5e5m67A5YE+KhKCPPvNTlT5SroCJo/3QcJeHbb1dyLRVFqq65pge77H/55Rf82I033ozff9+AzMycyUjffbcGLVu2ZkG1ceMfeOGFsbj11v5YtuwzPPfcOPz00w945ZUXA/axYsXnmDJlBl5/fQZq166N5557mlu5zZkzH5999iXuu+9+fPTRUmzYsJ7XP3BgP5555kl06NARH364nEXanDmzCj2Pf/7Zjn/+2YY33piDd95ZzLZDM2ZM9YyPy4Vnn30SJ04cx+zZb+HNN9/Gv//uxLZtJe8mIpRxpKpHjx7c8mXDhg1o0qSJr3GtF/oF9dhjOQ14KzNBWhV01CZECWRACzjMgDqneF0QhMsfpyLHw4rqqxRedUXRIj+h5bBksbApLbi9TuNbOHJl+3sOXCc8Tbldp/+C9dtB0LQaAlWT/gHpMFt6JtTnE6CpVr3UjqMyQHY/a9euwdVXX8Ozt+jSqVMXrFnzLUaMeBw9e/bi6M769b+gb98bud0PNW5/7LEn+flLlizCrbfehttuu4PvU0RpzJjn8dhjD/M6FFEi+vTphyuu8DTkpolbdL9Xr+sQGxvHy+6++z588MESHDr0Hwu75cuX4YorrsATT3ga0depU5dF0qxZ0ws9l5demoSQEE8quH//Ozga5o2uUVTq00+/5G0Rkya9jv79byzD0RUuSlSRTxTxxx9/8CU3VUlUBetU0Gbn+zIVJKoyRFQJQiWD/sRdai30detl+0t5ZJWbRRW1CvLk/e3x52A5G39Rhe35QW7uumsmwkktcDbPBiznScHBvvUtOI7+BN01r0FhjPQdrDUpmYvWldlfuqVF6N1zK0z6r7j8+ecfXJx+3XXX+5Zdf30f/PHHBvz88w+44YYbWVh9//0aFlW0nFqgkSAi9u/fhz17dmPVqq99z/e6EVE60CuqKKrlhYTbgAF34eeff+S048mTJ/Dffwf5OMjY2rvdDh0CnfTbtGlb6LlERET6BBVhMgVl93wE9u3bx495BRURGRnJKU6hgoqqPXv2+HLBVZ1grRqa7NoFu0IFqy0VOr3nF4kgCJUH+gJ1spTyC03xn35OP0iyNDAqFLCcPQenrfRTcKpaXaGPbQP7jnfhPLDScwiJe2HbOAXantNzmn1nWWFLSIAxqHSj5lTHpG95y2VZqL569Sq+Hjt2VJ7HvvxyBYsqqlt6/PFHkJiYiO+/X8uRJBIs3tefarFIcOUmKiqnebb/LD2LxYJHHhkCq9WKa6/txdtv1uxKXuaFOpMUt2zEvyl2btRqVYF9Q4UKKqpuvvlmPPvss5wGrOpQpErtVw+anpWCAibnCIJQBQrbVdExMCiVsJw5C6e19FtXKbRB0HZ8Bs6618G24SWeHUjF7M4j67hVjhdbWgb0GelARHDp7l+pgiooR0RcDlA6jSJPVDd1zz0DAx6j9BvNCKR0HBVyV6tWDd99txp//vk7pk/PmeFev34DLmr3j0Rt3boFn332CUaPHhdghO2FCsUpErV69Q8cLSLIgogiVV4h3rBhY45i+bNr184Snyttj2q4KHpWlyOrtM8UrrESLg3KkrSOye8NVBUxalRQOXN+uWZa08v1eARBKF+oB58qMgrGGtWg0hfuLaRUq6ENC4apRjVojMX7TFXFtOBmzV7sW9+GOyslMDUlflW+gnNKt91//yA0aNAw4PLggw9x5uWrr77gSF/fvjdh0aKFCAsLR/v2Od5g9FxK4y1a9C6Lq7///gsTJ76EjIx0REZG5fsaxcTE+vZ/5sxp7NixHaNHP801UbbsSCYVru/duwdvvz2Ht/vrrz9j4cL5/FgRzPTz0K5de54NSAX0VKB+8OABTJjwPNd3FcWdXygHUXXTTTdhyZIliI+PR1WH3qRq5ISi061p5Xo8giBUDGGlDCdhVR3qfFpaqLQa6CPDYKpbG4Z69aGOqw5jvbowREdCqSl68oAK2MlegbGmwr5F2mQVlPqjuiX/OiMvVHDerVt3Fj6UrqP0XlaWBTfc0C+gzIXqrSZNmsqF7PfddydefvlFdO7cBVOnFlzfReLmySef4WjW3XffjkmTXkKbNu24lmvv3t28Dgm7qVNncCSNtkuWCXfccdcF03yFQcdEgo5SmXRp3rwF4uLiSrw9oYx7/w0aNAhbtmxh5R8WFpbv7L8ff/wRVaH3H9Hn0xexw+T5IPyqQT10ueKBcji6io/0/pMxqmrvJZVKCVdqMiynTvPMQIpcaYODoAmPgDIoCE63IqABvVoJOFNTYI1PgC0js0imd25LErK+eYCm/fF9bY/XoarRGQqVCqEN6iG8TvUCxykiwsTHmB8U2Th06DCiouKg1Yqbd1lBxe9UV9WkSVPfMqrneu21V/Dzz7+z7UNxSElJxr//7mLB523BQ0XsvXv3wHPPjeXaMaGC9f6jnDNFqwQPGgW9cbNnANryii5BEKqwK3toGIwKJRwZ6VCHhUFhMMHldrP/Ve78HC1ThobDYDJBnZDAs/guVJelMERA0+5R2Dd6TCvtm2dBeeP7UKhKt5ZKKBsOHNjHdggTJkxE48aNeYYgRat69epdbEFFkNnn+PFj2fqBrBYo1Ui+WBqNlv25hLKn2K/alClTyuZILlM0CvoVl8W3022W8j4cQRAqWiowOASakFBPs+MLOJ2zE7pSA031GlAHh8AaHw9bejr39ysIVf0b4DzyA1xnt8GdeRb2fxZD18njryRUbG65pT/PNpw9ewYSEuIRHh7BrXNK2jw5ODgYM2e+iQUL5mHlyi/Z66xly1Z4++13uE5MKHuKL4WzOXToEPtUUW3V/fffjxMnTqBp06YIKuVpvBUdnVLvE1UpNnN5H44gCBUMT8sYd7HFmMIUDENdIzTJybCeP8/9/fJLCVLJhabTKDYEhdMG574v4KzfC2jgmf0lVFzotRsyZDhfSot27Tpg4cL3S217QhmLKvLAmDBhAlasWMH1APSmuOGGG7jR8vHjx/HRRx9xUVxVQauiWTueWTcpNo+4EgRBuFjo89UBJZQ0mzAkBI6kRE4JUn1WbpTBNaBuORiO7e+wgLNtnAZ3h4rfLFgQUNVn/5F4+uabbzBp0iSOVHkLLZ977jkWXLNmFd63qLKh92tLk+6wluuxCIJQOSNdTpWGZwma6teFMTYaKp2nia8/6ivuhCK8Ed92Jx9C5u+eqfmCIFRgUUURqpEjR+L222/n2X9eqH8RLc+vdU1lxqDJEVUZjtLr+yUIguAP1WQ5NQZoatSCqU4dtmVQqnMsXRRKNbSdRwMKz7L09bNhPbP/IgdRzK4EAcX4Oyi2qDp//jwLqPyIjY1FWlrV8moyaXMs1DOdIqoEQSh753a3KRj6OvVgiI2Fws9PSRnZGOqmAzx3HFbEf/F8ifZBnkbkFUktVgShqmO1WvnvoSheX8WuqapTpw7Wr1+P//3vf3ke27x5Mz9elQjRRwDZTgqZxbP8EgRBKHm9lRvQREdDZzYjKznHTV3dajCcJzfAnX4KjrSSmTSTdxJlIpKzt+vpaSeO3EJVw82CKj09BeHhYfx3Ueqi6sEHH+RCdTIUo/5/VKh+7Ngx/PXXX1i8eDHGjh2LqkSYISf9l3mB6dKCIAiliVOhgjYuBo6sLF8Bu0Kth/6G+dCkbUPMtfd7f/OhJJ6EREpKCtKlA5dQRVEowILK+/dQ6qJqwIAB3KBy/vz5+OSTT/gX0zPPPMNhsaFDh+Kee+5BVSJKn9Ozyyy/5ARBuITQ56/SGAxdVCScp8/4/KzIFNR05TBoY6ojM7lksop+MFevXp3LOuhHtCBURTQaTZEiVBflU/Xwww/jvvvuw/bt2/lXTEhICFq1ahVQuF5ViNDpoHS74FIoYS5+iZogCMJFF7Cro6KgM2ciKzEnDVha0BdKcb5UBKEqU2LzTzL57Nq1K6o6IXo1DG4HMhVamP2aKwuCIFwqXFBCGxMLhzknDSgIwqWn3EMr5G01Z84cFmitW7fGsGHD2J09P+bOnYsmTZrkexk3bpxvvcGDB+d5nFzfy4JgHYkqJ982Ux9At8wAFATh0kJzZBTGIOijo7iZsiAIl1mkqrQgM9GPP/4YU6dOZSf26dOnc20WGYxqtYEGdw899BDuvvvugGXvv/8+13YNGjTIt2z//v14+eWX0atXL9+yokyFLAlBWhV0bk8dQ4ZCCzgyAU1omexLEASh0AbOkZQGNMOakioDJQhVTVTZbDaeMThq1Ch0796dl5EjO0Wt1q1bhxtvvDFgfZPJxBcve/bswQcffICJEydyNIqg5pR0oRqv6OjoMj8HilRpsmf9WRQaOGxpUIuoEgShHHBBAV1sDFw2m4y/IFS19N++ffuQmZmJLl26+JZR0XuzZs3w999/X/D5r776Ktq3b4/bbrstIEpFs1bq1bs0zUQpUuUVVUSmtfQLRQVBEIoCW+XpjdCGR8iACUJFjVSdPn26WBulabhF4ezZs3yd2/8hJibG91hB/PLLLzz78Ouvvw5YfuDAAQQHB7PgopY5RqMRffr0waOPPponnVhc1Oq8GlSlUkLtzjHFszjSEZnPelUdGif/a0HGSN5LZYcuJgowm+VvThAqoqjq2bMnR3+Kyt69e4u0nsVi4evcYofce1NTC68JoFoqMh/N3TKHRBU5oLZs2ZIL1ulYpk2bxsKQrkuKUqlAeHhO6tEfrV/Az6HMKnA9gSKROb5eQv7IGBUNGafCcYcYZZwEoSKKqsmTJ/tEFYmdGTNmcMruhhtu4Lol8qr6+eef8euvvxbLUV2v1/tqq7y3CRJFBkPBX74kkMjB/d13383zGEWoxowZg9BQT7F448aNuUj96aefxujRoxEVFYWSdopPS/P88vOHIi8av2GMT0lEcgnN9iozNE70JZiWZuGCWkHGSN5L5fs3R49J5FgQykFU9e/f33f7sccew6233opJkyYFrHPTTTfhtddew9q1a3HXXXcVaefetF98fDxq167tW073vYXn+fHjjz8iIiICV111Vd4TUqt9gspLo0aN+JpSiiUVVYTDkb8Y0JKVQjZp1vQC1ytJdIzEXGWCPtxLa3wqKzJGMk7yfhKEy5NiF7hQnRJFqPKDZvBRnVNRadq0KZuIUtTJS1paGs/q69ChQ4HP27JlCzp27MgCKjfkR+XvWUXs2rWLo1V169ZFWaBV5qQvU7JKJ0pFgUE7bPJLUhAEQRAqq6gKDw/Hzp07831s06ZN3CeqqFAt1cCBAzmd+NNPP/FsQErTkV/V9ddfD6fTiYSEBGRlBToEk+giQZYfvXv3xsqVK9m7ikxE16xZw7VUQ4YMYQFXFuhUOanLVGveFGFJUCqVSMpKQZZL3JEFQRAE4XKgRA2V3377bRY6FJkikXX+/Hl89913LGSef/75Ym1v5MiRcDgcGD9+PG+TIlSLFi3iyNLJkydx7bXXYsqUKQEpSBJaBfUZJJFG9V8ffvgh14JRzRcZgw4fPhxlhUFFBaGeNu6pttIRQU44kWHNhNPlQnVTHFzOypUGFARBEITKhsJNbc6LAa1OkR8SLRRJ8i6jQnOyLShL8VLedS5JSZn52iw8tnopPrUf5vuP66wYf93ki079ZboycTzlFAvE2qE1YFSaeJwvV2icaFYkFfFLTZWMkbyXyv9vLiLCJOUFglDekSr6kqfZdSSgduzYwbMBKVrVpk0b9oSqipi0wVQAxWQ4baVSoG62mmF3evoIJlqSYQo2IrvFoCAIgiAIFZASOzFSuxhKrZEDOrWEIVuEqkqwLsR3OzNbCF0MTrcT8w5uwsJjO2FxOpCelYFUWxqLLUEQBEEQKlHvPyoEnzlzJtc2UeTq888/x9y5c7kOipZfrHP55UaIMRxI8tw2uy4+nPT1qV2Yd2SbL/L1dP32OG9OQlCoCQpIB3pBEARBqBSRKppNR+m/zp0744033oDL5cnVX3fddVi/fj3mzZuHqkZEULjvduZF1j1RNOrLk7t899fEH8Ge9ESYbRYkW1OkBkIQBEEQKouoWrBgAe6++24uVifbAy+33347nnjiCaxevRpVjWhjTvrP7NcHsCRkOK1Yn3A0YNmbR7bC7nIiyZIKq1gsCIIgCELlEFVHjhzhqFR+UG3VuXPnUNWI8SvQN+PiRNXq0//CmiuFeNicipVnD8Fiy+KidaVKaqsEQRAE4bIXVZGRkTh06FC+j9FyeryqEWbQQO/2TP+zXETNE6X+Vp7a7bv/UK0rfbeXnvwX520WpGSlwezMZNsFQRAEQRAuY1HVt29fzJkzh80+vTP+qFj933//5XqqPn36oKoRolPDkB1dMitKLqoyHFn47fwxvh2h0ePO6k1xQ0w9z3adDrxz7B/YHHacNydDUeJ5m4IgCIIgVIjZf0899RQOHDjA19RKxdtvz2w2o3379njyySdR1QjRU6TKI6oyFFq4nTYoVMWfAfnNmd2wZYuzrhE1oVIoMLRWC/yRdAppDht+TTyBPtH10EmpQqouDaGa0ErXcFkQBEEQqoyoIruE9957jxsrU6+/lJQUBAcHc4Pja665hqNWVY1gnQpat2cWZCY0gCMdUEUWO/X39cl/ffe7R9ZCkN4IlVKJYbVbYubhLbx87tFtaBEShQRzIvQhOuiUehFWgiAIgnA5iipqTDx06FBcddVVfBEAg0YFTXbEyKVQwmxNhUlXPFGV5rBgQ3bqL0prQJvwaqgWFMOpvuuj6+K7hCPYnZ6IU1kZ+Oz0fgys2Qwn086gRnAc9EqDCCtBEARBKGeKXZmzbdu2KhmNKgwaD63frL+MrNRib2PV6d1wZEe7ukXUhElngEltQrQxEkatHiPrtoUyex8fn9qL01kZyLCacTL9DCwui7itC4IgCMLlJqq6du2KVatWwW7PbnYnMBp3zlBm2FKKLcpWnvJL/UXVRqguGKSxjCojIoyhaBgUjv7VGvHjdrcLbx3dzg2WM60WnEo7A7PLLMJKEARBEC6n9J9Op2NRtXbtWjRo0CBPE2USCEuXLkVVQ+enT9Ot6cV6borDgj+yU38xWiPahMXBpDZmp/TciNBFIENnxv01m3GxOlkr/J1yFhuSTqFbZE1k2jzCqlpwLILVQXA6pXhdEARBECp8pOrs2bNo06YNrrzyShgMBo6W+F+8bWuqGhpFjj5NK6ao+vb0v3Bkt7chkRSkN0Gn0vkeV7pViDZFIkxnxKN1WvuWTzu0GZuTz/Btsy0Lp9PPId2eDpWYgwqCIAhCxY9Uffjhh2VzJJc5WgVZKHiEUXJWRpGfR+VpX/ml/q6NqoMQbWC0icRqkDoI4YZQtlq4OqIGfk86xc7rEw78gbENOqF7VC12XCdhVS3YjRBNKJzOqilwBUEQBKE8KFULSfKq+u2331AV0SlzIkup1swiPy/JbsHGxON8O05nQsvwOBjVRhZS/ricbkQZIthmYVzDTiyuCKfbjcn/bcK35zwu9xa7FafT45FkTYJC5ZI6K0EQBEGoqJGqU6dO4eWXX8bmzZt9juq52bt3L6oaZGsAZPHtVJulWIafJIyIayJrIkQXDLVCAyfyRpnU0CDKGIEshw0vNOrMjZbXxh/h+NibR7Yhw2HH3TWaIstuxanUs0jRpSHSGI4QjafoPZdOEwRBEAShPEXVlClT2FZhwIABfE11Va1bt2YzUHJanzt3LqoiBo0BcCbz7XS7teipv5M7ffd7RddDsM5UYF0aFa6HakOQrs9AYmYKnq7XDsEqLT47s58fX3RiF9IdNgyt3YJFWVpWBsz2LASTuDKEs0UDRbwEQRAEQagA6b+///4bTz/9NMaPH4/+/fvzbMDnnnsOK1asQIcOHfDTTz+hKmJUB/luZzjyj+DlJt6agb8ST/Lt6vogXBkWC4OKUn8FP4ciTtHGKPauopmWw+q0xJBaLXyPk8CadWSrL/rlcDqQbE7F8dRTOJ15BnaFFSqVNA4UBEEQhNKm2N+umZmZaNKkCd+uX78+9uzZw7dVKhXuvfdebl1TFTFpg323M11F8/BafWYvXNnF7d0jaiHMEAKFu3BjVdJKeqWO03rUwoaglN+T9dr67EcpJfjS/j9wzJzmex41Yk7ISMLRlJM4ZzkHl9Ih9VaCIAiCUJ6iKiYmBufPn+fbderUQWpqKhISEvh+WFgYEhMTURUJNoT6bpuzmyJfiJWnd/tuXxdbD0EaSv1dOD1HMwPDdWGIYGGl4mU3xjbA8w07cxNm4q+UMxi283u8euBPHMz0pCUJqrc6m3YeR1NPINmWDIXKLeJKEARBEMpDVFHT5NmzZ2P79u2oUaMG4uLisHjxYmRkZHAKMDY2FlWRMEOY77a5CF5dZ7PSsSnRY/hZSx+M5mHVoFfqi7w/t1OB6qZYxAVHQavW8DKyVZjY5GqEqMnewWPwQAahj+76Ec/v24B/0z1i2I1sJ/bUsziWegLpDo+3lXQfEgRBEIRLKKpGjhyJkJAQvPnmm3yf6qvIQZ3qqb755hsMHjwYVZEIQ7jvtrkI0+y+PbMnO/HnaUsTpg8u9uw8CohF66O4qTLVWBEdwuLwUZt+GF67JSI0OSKNHNif3v0Lnt39K7alnmPLBqfbhdSsDJxIPY0TGadgdWdJvZUgCIIgXKrZf+Hh4fj8888RHx/P92+++WZUr14dO3bsQMuWLdGxY8dibY9mur311lu8zfT0dBZnEyZMQK1atfJdn1rkUGF8bqhAvmZNj3cTtdChWYgnT57kuq8xY8agS5cuKEuijTmF6ma/5soF8WfiUd/tXrH1YNJ429IUD0oFhmhCoA3R4ExGPNKzMmBQqTGgehPcEtcQ38Ufwaen9yPeZub1d6YnYOfeBLQIjsJDtVvgyuAo2J0Onk1I7W7CDSEI1YVw1IxEXkmOSRAEQRCqIiWeBka1VV7at2+PoUOHFltQEfPmzcPHH3+MiRMnYvny5SyyaFsFeWDt37+f9/P7778HXKpVq8aPU6E8ia67774bX331FYup4cOH49AhjzlmWRFh0ELj9tRSWYowrJT+I0h+XREaB60ixzy0uJDw0SkMqBVSHRGmMCgVnv1rlSrcHNcQS1vfgFH1O6CmPkf47Uo/z5GrF/ZtwH+ZKQH1VkeST+BI2nGuubIrbFCqFFJ3JQiCIAilHakaN25ckbysigIJJ6rHGjVqFLp3787LZs2aha5du2LdunW48cYb8zyHvLBo9mF0dHS+21y4cCF69eqFBx54gO9TlIrqvyhF+eqrr6KsCNapYXA7YFeoYIaneLwwzlk9rWxCNTpE6kMvOiJE6TylW40aQdWgVWlwPjOZI1CEWqlE75i66BVdBxsST2LpyX9xMruVzuaUs3zpHlkLD9ZsjpqGYFgdNr6Qz5VOrYVeo0eoLoid3nXK7Hott2efYigqCIIgCCUUVX/99Ve+7WlSUlJ49l+LFjmeSRdi3759bNHgn5qjeq1mzZqxH1Z+oooiVT179sx3exTlIkPSsWPHBizv1KkTi7SyJFirht7tBJkYmBVqFhzkI5Uf9FhCtqiK0ho9bWlKKc1GBewxhhjoVXokWZI5pefIno1IMwOpmL1rZA38kHAMH5zcjYRs9/dfE0/gt8STLL5uj2uMOsYQPk6KXtElzZLGAktLIkut44taqYZGqYFaoYZaQUJSwa+BCC1BEAShKlJsUfXzzz/nu5zSa48//jhuvfXWIm/r7NmzfO1N3fmnFr2P+UP2DefOncOWLVs4ZZicnMx1XJTuq1evHtLS0ljg0YzEomyvNDFpVdCTeFEBGdRc2ZkFqKl1TV5S7VmwZQudGH0Qi5L82tKUFFd2nVWwNhjp9nQkZ6Ui02r2Ra5UCiX6xNRDz6ja+PbcYXxyai9SHFb2zCKPK7rUNYSgW2RNdIuoxQLL5XZzX0G6pCIdCijYJ4ssHdTZF71Gx/s0qY2AmwSW1GMJgiAIVYdii6qCaNCgAZ544gkuEO/Xr1+RnmOxeKIkWq0npeSFXNpJQOXm4MGDfE0RFEoxZmVlYf78+Ww6SjMPHQ5HgduzWovWOqYw1Oq8tVJed3KtRgVtdojGplDD5UyHTm/KdzuJ5pyGy9UNoWxlkN+2Lx4F+1mF60NZXCVZUpDhJ670SjXuqNEY/eLq4cszB/Hpqf3IdHqMS49a0nD05B58cHIPC6xromqxyKprzPHjctE/lwv2bLPTdFsmUrPSeSZiuCEMIdogKKFiceUdJ3FzLxgZo6Ih4yTjJAiVXlQRQUFB3HC5qOj1el9tlfc2QQKIegrmhgriN27cyDMQvak1mjlI9Vhffvkl9yP0bs+fgrZXHJRKBcLD8xdJREiIAf5STqHNKnB9s8UjaojaoRGFbre0CIMJ1VzRSMtKR6IlBWabxSeGgqDHw6FtcXeD5lh7+jB+OncUO1M8hq4+gXViN5ae2I1IrQGxeiNi9CbE6I2IpWsd3TeiljEEQToVHLAjyZ6ITGSwoAszhsCQbe9A4yQUjoxR0ZBxknEShMteVJ0+fTrPMqfTyWm5OXPmcMSqqHjTfmTPULt2bd9yuu9thZObiIiIgPsklshKgfZPNV1Go9Fn9+C/vYs1JaVoS1qax5Yg969m+nBPS7NA69di5nTCWWg1dfPd1n/ZjvREpMqI5OScyFVZo1RoEKOOhhkWpFrTkGbNQJbdBpfbxeX1N0bW40uC1cw1VnTxmoYSiTYLX/ak5e+c3zEsDnfVaIpWIdFQKMw4r0jhtGC4MRTRoeFw2QCV2/O2k0L3gt9LTmfppYMrGzJOpTNO9JhEjgWhnEUVFYnnV4BNX5AUbaLIUVFp2rQpR7eo+N0rqqguivoJDhw4MM/6n376Kd544w388ssvLJ4IcnI/evQo7rjjDj6utm3bYvPmzb6oFUHbpyjXxeJwFPxFRx9aWj8rhVRLSoHrn/XryRetDSp0u2WFGjrE6GMRrotAhj0DKZZUrpeyZaf/IjUG3BbXiC8ksMiZ/Y+kUziZlY5ke5bPuDQ33tmETUzhuLN6U1wVUQMOpwUWWxYyXZmwZTmgVmpg1BigV+mgU+mgVWqggFKEhN97qTzeE5cbMk4yToJw2YuqyZMn5xFVdJ/EEc2yCw7OaSx8Iaj2icTTjBkzOAJFbW+mT5/OhebXX389R8CSkpJ4myTYunXrxuuOHj0aTz75JNdUkcii5/bv35+3SY7u5EtFMwhpfWqds3fvXrz22msoa3R+w5noJ5xyE58984+I0RV9vEob+lKimqcwTRjCtKEwO81cE5Vhy/RFr4honRH9qzXiC2F3uZBktyDBakGCzYzzNgubi/6ZdNpnMro/MxkTD25EdZ0Jd1Rrgj6xdWFyOWC2ZcHlsiAFaVzkrlGpoVapEKwNQrQhEnApZPagIAiCUDVElVe8lBbU9oYKzMePH88iiRzVFy1aBI1Gw47o1157LRel034pXbhkyRLMnDkT99xzD0fHrrrqKnzwwQdcjE5cffXVLPzIVJQ8rxo2bIgFCxYUKy1ZUiji4iU529zzwqKq7OupLoR3lp5BYYTJFAS70YZMuxmp1nRY7FnsWUVj7UWjVCJWZ+KLPw/XboXfkk7i09P7cNjsmWhw2pqJOUe3YenJ3bg6piZqaEyoow/hgvdorQFOmgVpB8xWC7IcVsSaoqFT6oo0c5DEvELphs1t85inigO8IAiCUI4o3P7flkXg66+/LtYOimOxUJGhqE5SUt7aJ5q1R4XmVBf1wBcTsUbtKUKfFhWBBzo/ke+2Bmz6ABvOH+Hb/90wFkGqkruplxVUmE9271aXFWYSWFlpyHLYYCOBdYHn0ltqW2o8Pjuzj68LwqhSo64hFHWNIWgVEsMGpMFU9G6KQog2hK0hCjw+lQIZjgwkWpJhtVuzDUqDud2PRqG9LP2y/N9Lkv6TcSrr91NEhElqqgShvCNVL7zwQnaBsefixZsSzL2ssoiqoqBX0ww3TxQqzeqxi8iPc9lu5mRpUBEFFeGNFGmgRbhWx9YMWc4smB0eM1GnywF79jX5W9HrTl5WdoedH28XFsuX/zKT8fnpA1ifdALOXCrH7HRgT0YiX9bEH8GP549hdIOObPmQZcpCtCEqTzqQCmuzXBYkZiZzqtLm8NSAUT1YmiUdOo0WJq0JIbogmNQGKKHmcynmbwdBEARBKHtRtWzZMowYMQIPPvggN1OmWXXkpk6moNOmTbskzYsrKkaVAXB6BFO6I6vA9bxu6tEVIPVXHIGlhR56rSFARNMV1V7xBW6YHWY2G6V0HhW9NzSFY1yjThjlbo/zCiv2nE/A4cxUHLWk4pg5Deeya7CIv1PO4pGd6zCuUWe0cTk5MhZnioZW6RGeDjiQaDmP5Oyi+jSHDdtSziFIrUGLEEobgmu26ELr6NVaBOtMXAzPtVsKDZuU0j8Kw3mFludU6D83nPSPxKLbCYVCyY2lZSaeIAiCUCaiivrnkaB65JFHfMsiIyN5th3VRFGPPZqJVxUxqE2A0+PvlOHI32zU6nQg2e6JYsX6NTi+XMipdcod+VHy3MdgVQhCgoORaTAjxZqKDKun6F2nUqNJUBBqKE0B9VKZDjt2pMVj9pGtSLFbkWjPwug9v2Jgzea4t8YVsDltiDFGsdhJNCezYDqXlYkVZw5gTfxhZGU702sVSrQMiUb7sDi0D41DbUMwHE4Hm50qFeT+rspxgFeooNPQzEOPs5jd5eCIF/l2UdrQmS0SyXk+whiGCH0EFC6lRLsEQRCE0hVV1I7myiuvzPexOnXq4Pjx46iqBGmDgGwt5XUmz02CLacui1rUVDY4LewEjEojgkwmZBmykJKVxrYNJG5yY1Jr2HbhiqBITPnvLxZYVP1BfQl3psVjbMNOLHhI5BxMT8JnZ/bjl/MnOCrmj83twpbUc3wB/uEieBJY5JdVUx+MGvogBKn97FmzU7D5HT+9dkn2LMTpTLA67CwMo0yRCFIHFVrnJQiCIFRtii2qSDitXLmSZ9nl5yNVkGlnVSBYFwJkT/ozu3Jc0/2Jz6oYdgplDWXWnE43NNAhzhgLpyIC0DiRprLwbEKKItmddl9UiJo1T72iG5af2suCioTVjrQEPLLzB9xfsxk2JZ/B36mB/Rt1ShWui6oDh9uNLaln2drBCzWK9vYx9BKi1qK6PogFVnVdELvDk+cW2ULEW838HLq2ZL92QSoNHqrVAn1j68PisCLcEIIoQyTU0EhfQ0EQBOHiRdWjjz6Kp556ig03ye6APKLOnz+PdevWcRRr8eLFqKqEGsJ8ty3ZaamC6qmIOH3lFVX+UE2SWq1BeFAYNA4zXBqSTAquZfKIKgdsLgcSzIm4v1Zzro+iqBWJJGr0PPfo9oDtkTi6JbYhbo5riDCNzhdhOmZJ80SrUs5iZ1oC7Nk+W16oBistIwn7MpKKdNwZTjvbQXyXcAQj67VFU4cDGTYzoowR7OtFhqWUBvXWwHuuc+4LgiAIVYtii6o+ffrg7bff5svs2bN5mVKpRJs2bdhDql27dqiqhBlyWuiYC/hmPWfN8a+KrSKiyh8SPxTB8q/JUkANvUKNmsHVoVNp0E6pwoIW12H6ob/xV8oZ33pxOiNur9YEvaPrwqAKfOtS4Tx5X9HljmqNkeV0YFf6eRw2p+B0VgZfTmVlcDSqIDQKJfcxjNYaoVYoslOJwIHMZDzx70+4MbYBBte6Ela7Dam6NGhVGiiza7Tob4D/KZR8LFqlFjqllgVWUTy3BEEQhCraUJkiVHShwvTU1FSEhoYGNESuqkSb/CJVPJus4rqpVzRYgzoViDXGQqem2XqJeLXJVVh97jC2p51D14ia6BZZk4vHvZCA0Wu0XHxOM/bIioHsHLhlkkqNDmFxfPHH6nLiTLbIoqL4CI2eRRSJqVC1NqBbwD9pCZh7ZBtHwOjwvjl3CBsST2JYnZacdvRfV+E1I6ULFDzbkFrxBOuC+FrL3lmBliOXCjpM77ERbrizZ2tSUb4TLiqCY1NXLdQKNZ9LeR2rIAhClRNV1G8vMzOT7RRUKhU+/PBDbrTcu3dvdkSvqkQbDFDSl5VCWbCoCqipujwsFS4lVAgerg3jvoBnM+I5xXdTXKAbPgkWg0aPcEMogjQmaBQa2N3km2WD1WmDxZHFTvCObJFFM/rIQ4v+UR2WN6J1IajInSJmX509yHVeNNOQ0pEUQfv67EHUNoQgRK1DsFrLKUnvNaUk6TGyfUi2pLF3llGjR4g22COwsiNYuVOF+dTxFwuPcPJcu+GCw03n72BrC8+1DTYeE4dHVGWPi7cdEY2rRqmGQWuAQaXnSBwJLVpGKVzRWIWb5XqtObxiVBqGC0LVo9ii6p9//sHQoUNx991349lnn8WkSZO4QD0kJAQff/wx5s6dy1GsqkiwXg2D24FMhRZmv+bKBUeqKt/sv9KA0oM6hQG1QmogXn0eSeYUXzF7kC6IndONaiMUbgWn1pxwcw9DHQwwaIwI13o8p+xuO2wum8es1O30RbPIQsErLAiOLXltFxRKqLLTeU6XC2lZ6RhQvQm7vc8/9g82JJ3k5xzMTOFLQeiVKrQIjkab0Bi0DY1FPWMoUlRpfA5atQ5q2g/bPHj2SVE3tUsJp9kGq8sBhUKVx1PLK2pyhJPbc17k4EVmrA6qTbOxhQUJKI/flouvLQ4715SlU10ZeXw5bUi323gZzXYM1+hQxxCKOsYQRGr0HusJlYoFlV6jQ6QhHAaVAQWUCl72eDoIuGF12YrtTUaGtOmOdCSak6BWqj1iVKXhtDDd57GEGkqFSoxoBaGSU2xRRXVU1EfvzjvvhMVi4ZmA9957LyZMmMAX6rNXZUWVVg292wkyTbAo1IWKKvraj9QaL/ERXj6QiFC4VahmioVerWMBEaQJyu4L6GLbBlqWG0/9kmc5uanroYZC5W9WmpMCc7s9xgxk9eAVVjnRGM+NcH0oEi1JHMWZ0LgLG5QuOLYDxy0F93YkKKpFsxW9MxYpetUmJEdgUXrSQIJFqeLbWvoCVqsQZNfDYrZxETz5apHYIj8tEmP0BU11WyScaAYlReVI+Ln8xBM1uz6Vlc4Gq4fMKVxTRn0Y/WdGXohglQa1jSHcQqiOIQTNgiPRMiwOofpgRBjCK7QhKr2GJJByuj4Uvr5KpeCIXoo9gw1jSXBHGMIQpY8okoCkdklJtiT8m3icuyhQ83GKhtL7iYQ5e6NlC2ij1gCT2giDWs8RwLLuVUmZcknhCsJlEKmiRsW1atXCjz/+CKvViltuuYUf69u3L1atWoWqik6tZFFFZCo0/KVNrtz+nMsWVRFcDE1RCKEw6IstXBvuuU1RqRJ8mdMXa86Xi/+XmEdgebYYWDzvRa/Qo2ZQdWToMnDenITOETXQPjTWM5PQG/lxWP1u23DOmokdqfFcs+WFjE1/STzBl/xQQsHF98EaLeK0JrZ98FyCUcMQhGo6E4svOuZ0uxXxNjMSrGbPtc3Ct6n266g5lT27LoZ0px270xP54qVjWBwertMKTUIyEGYIQYQ+nNOunkkHJYP0bWmkFL2pN4pMWhwWZGSZ+W+LxLhWpeN0K4lRbqXkcvO69JwslxWJWenc1zLJkoFfzh/nZuHXRteF1WlFrDG6UPsMhcqNI+mn8PzOH/DT+WO+5ZQCjtIauFbPc23g17F1aAwidUZoVWo2nw3WmmBUG6BT6X1R19KCju2sOR4hIXVKbZuCIJSBqKJfXzqdZxr7hg0bOO3XsmVLX61VVS9Y12d/oVEK0GXPhEqbU4xOH+peS4UYvdRTFZXynD3HgswJmFTBMIWakGpLY2d3lU2J0Gw7h/yf58bxrHRsTz2Hrann2OKBeh0WBMXMKA1Hl7NZmWyC6g9Jc/LVoscL205+mFQajjiFU12XRotgVXb9F91Wa/lx8uc6aknDMXMqi7PcsyQ3p5zl87g5tiH7hsUZM9htPlQbygLG01/xwsfiETRKONx0HlnQq/TZ4qz4QlCpBBwKG8x2Cxu0Ui3d4YwkdtxvbApHkEYHDacxPTV4VM9GLYvotUnJTEOGNQMnMlK4Zm51/GGfYe+qs4cwukEHNAmzIjYoGsHq4IDjo2iYU+HAH2f34pl/1uFEVmDU0iu4KUIYcO4At21qFxrLxrTNgyJh1OqgVWs4vUpj6el1eXFC1aVw4rtTu7D27EE8pb4WjdRRJd6eIAhlLKrITf3zzz9n8fTdd9+he/fu/CGTmJiIhQsXFui2XlXQ+X0gZlqTuTjZS4o9C7bsnILM/Lu84C86pwJhmjAEhwYhxZaKDJsFjmwDU0q9cYsbSk2CoiEKFjJ0uTWuERwuF/ZnJrGhaZLNAgs1jab+hi4H2z/wtcuJVIp62W159k9f6YXZQXi/tMnctL4xDPWNoXzdwBSKGK0xYKZiUchw2FhcHcxMxmen9/O+qSE2CRBqfP1gzeY8iSBYl8rNqw0aA08uIIGUn40EpdlotiE15M7IykC6LZOd8qn2KEwfjBBdCPSc2i1YnPnqntxZOJueibOpiTDbrDiVmYpfEo/j5/PH+XgJssS4MjiaI2w0A5ReBxJzVCNG7E6LxxcFNPqmBt/Dd67DQ7Vb4K6azRAbFIlIfSSLazoG2v+iA39g6oE/fW2SjDTbNDQO5+0WnLda+Dr3dukeHR9dlp/ex+nfVqHR3FapV3Rd1AiOQLQxAiaVqUQRQI/Ys2Pu/l/x+v4/Waj/Z0nD912HFXtbgiCUDIW7mD+Ldu/ezYXqycnJbPxJxel169blJsr0pbJo0aJKKazol2pSUk6LGS9qtRLh4SYkJ2fC4XChz5fPYoc2hB/b1uk2VI/2RPGI/ekJuGb9PL59Z62WmNPqNlQVco/T5Q59udKFit0dVCzOsw9zZttlOayeYnGnkwvl6VKUbQYF6XE6ORUnzOnsq0WXk1me2/HWTJjUWsRkp5a8nlqUXqJaHmqrk9u/Kzckrchby1Pno2ShQZEmhVLlEYhUn8X9Dz3XBAnAL87sx6en97MlhRcSKpQSpHQozxxUa7j2i9JaerWe699oP1nOLKTbM5FmTcfelHh8d+4wiyCKuP0vvDr6xdZH2/DqCNEHI0wfwo3J3S7PZAMeY7h5G5n2TKRbM7lHo1lpx5rj/+HnhOPYlZ6QT+I2EBKWJK4amcJZfO1M9/To9Pcoo8kIuzMS2W7Dy5XBURjTsCOaR9RArCkGqdZ0PL/zO3x95oBvHRKwExr/j9O1XmhWJaV8ya2fBOne9ER2/c8dvfJC0UKaEHF3zWaoziIunNOWRRVXNE6ZTgvG7fgGy0/t8S1/qnlXjG/UK9+/uYgIExfZC4JQjqLKm+Yj9/RGjRrBaPQUW3///fdo27YtoqOjURkpqqi65ctR+Cs7OvVTi55oXqerb93fzx/BHZs+4NtPNLoKLzTphapCZRNVhftBea5JbJEAoAsJLLJ5yHJaWWh5CuWzp93zbU+tj9GkQ3qGpUQpTy6455mECi5wZ0PS7NtU5M4zD5Uavu250NxCNQsfjnJQxC1bHHqPmwriSSBm2a04bU7DohO78NP5wP6eJOqaB0ehRXAUixAqxKeUlsccVYmz5jR8f+4Qfkg4hv/M+c+YrGsIQd+Y+uhbrSHijGEsrmgWZqbdjHRbBlKyzNiVeg570hPxb8Z5bE+NzxMJIhoaw9DQFMbp07NW8wXHjLzJbiJ3/tgGCNfqOWq4+MS/HJHzQoXnQ2q1wDUx9fDinvU4kJnjyN8nuh4er9eG16FidPi9nvm9gok2C7alxmNr6llsTTnHFh3+0ISG+6pfgTtqNUf1oCiE6kKhdHvqwQqL4J2zpmDY5s+xKfmUb/l9tZpjUY/7kZFmFVElCBVZVFVFiiqq7vxyDH7LntX3ecO26Nr0Jt+6K07uxGM7vuLbk67sg6F1O6GqUBVEVVHMN73+UR4DTs8MRIrEUMSL1jMYNEjLtHBqjHoj+vdHpPXpm9UjlrKd22nqvlLNkSIy7vREoGj6vscaQkUl8LQux6j8hJy7eMecbifLgGRu07MrNQHzj+0osN0P9UwkkdUkKAL7M5J4xmTuBtgqhYKFSO76MK1CiW6RtXBddF0kO7J4X3szErkAv6B3TU19EHpE1kaPqNqoZfD8oKFzpFon2jfVg+3K1baolj4Yt1drjF7Rdfg4CL1ay9E2EpBbks9g5qG/ccaa8zdPMR2X33E+Ua8t+sTUY3EaZgxBqDaYz5ML4ulf9uvGkT9K9TqsHksPZ7ZPmNvNMzSphmtdwpGA86PI2oO1muO2Gs0QbYpgwUapS7b7IKMNngDjEfD/phzHoM1f8Bh5x/bJeu1wd50WaFPnCqSmWERUCUJFNv8UCkan0PhuJ2WlBTwmHlVVk5zZhzlWD4T/3E/6jiThGRZk4v6ITjWJLM+XJn8xc4rRxTVF9LVKX6wknBAglnhvnuvsi+cq/5mNxTnmYHUIgrJryci36orgSJ4t933CEY4eeWuLvD0Tqb2Qf4shL1RAfl10HXSPrM2pyt8ST2JN/GH8m36eH6eZi1SzRZfCoNQnpevoQtEpf8sMEpo0ZmTAShcST5TCpMkCJGIaGMM4FeiJ7Clg0OgQovOkHskuItWexk79TYPC8e6xnVh17hBv2yt6qG7tpUZdUN8UxjYJJHqoF2SOZvMagWbfyzYFJaFlc1k5+md2mJFpt6CFRoemQZG4s3oTLD3xL9Zn+6DRrE4ymV1+ah+nR+k8ahhDUNMQAoNa47Pa2JF6Dk/t+I5r8TyvkxYvNf4f2obF8evkEdOCIFwqRFSVMnpljqhK8avNIERUCQWRI4o8Ysbfb4tQeBJ1vkXc1acEYqmkeI5HgXBNOIJDg5GclYx+Gh16RtXmInwSKySMdqWdx+708wFpLUoPXhtVh6NCVIdFUHSH0pE3VmvEIuuYOY3F1Q8JR9nSwR+SjvWMYeyXdUVQJK4MiUTjqChkZlr5uChqQ+lGvUaPYJ3HpoBq2NhewZrJNW4U6ekUXo0vBD3HoNWzPUSIJhhapY6j0VTDFKIOgSnEiGBtMp7TmbhF0szDf3M6kW4/W789QrV6hBqCEWOMglah404AOeQXCfQs0IBm++l5pp9HZJHnmJUjXQ2CI7AnNZ7TjxRhIyjatuDYP76tkEQim4ZquiC+/i3pBBzZO6Po28SmV6NeUDiiTBGIM0UVe4KCIAgXh4iqUoamiAMef6J0m7lgUeVX1CoIlwskYihKFmOI4Rl7iZZktiZortZwuo+iQiQKT2Zl8Cw3soGgWitvVIhSbCbqs6gNhkFtgM1l55opk86A+kHhPOPu96STLM6o+L5ZUCSaBkUEFOBTDRGlPI1a2p4ewZogNtT0tv+hY9QqAZPOhCh9FLcvokJ3syMLmbZMjvOFGUIRrAlmx/rc/mfsZZV9jsFa6t2oR5uwWLZqoAiZUatn0ULtlCg6Vdz6t5ym4iSyPA77IdoQmA1mhOiD0Dw0FluSTrG48kbwfM/NngWaeyYo2TSMb9QZMcYQxJqiEaoNyS72FwThUiKiqpShD3c4skWV3VKwqNKKqBIuX0iEkCCoboqF3RDBZpkWpxWZFBlyOVBPrfHVN1E6jlJRlF4zaUzQKbObS7vc0EEFo8GISH0EMh2ZbMTZT2dAr6i6HmsKKLhdDrd6yY5ukSgLDw4CbCooXDmtX/xnyvkbvqqg4TqvEE0o3AZPdZfXbDN3rVfuc9Qp9KgVXIPTgyZzEu8/2hgFXZ7oVMnhGjcnYFAYYDQZYdFbeCZk+4ga2JVyDkctqTwjkS5nsjJx2prBPlheqMj+sbptEG4IRlxQDPRKA4+FWi2iShAuNSKqShlqQwGHZ4ZTZra48nIuOx2oV6phUmlLe9eCcMmhUioSLWQJEKQORrQ+io09aZYjFWZTgTan5FRGwNurMZcYIfFCbXmCVSEICQ6B1WXl6BXNPmRXdG7qTM2dNZ5qMpUCoUYjkq2ZcBTRNJRtxvzWzX9uXv7PI8ETqgll81eynyhJdKro+3Kzi38NkwGRhjBOT7a2ZnBxO3uhZQvFTIedxRX1kCRD0XBTKGKNMVC51eVqlisIVR0RVaUM/RLPzv5xwa4/OW7qQVLrIFQq/CNDVIhvUqq5+TWl/Mi/zlPH7i5SxIYiYFG6yOxejDk9/OgxJ/K2frqUac9LMW/VIwDdXH9VzRiHKIMt25Yj25rDYUWQ045IQxCvTM72UYaoAvthCoJw6RBRVcqE6EOB7K4VFleOqLI6HUjOTgfG6iT1J1Rucs8eLC65C/WrKhRdI5Gqo+bgaiNocjHVlHHzbLeDZ4UaVIZSS0UKgnBxlLudLv2KnTNnDrp27YrWrVtj2LBhOHEi/6azxMGDBzF8+HB06tSJXdxHjhyJ06dP+x4nY0XqRdikSZOAy9y5cy/J+YTqPbObCIvfNPMEW47fTYw+p3WNIAhCUfDOCiWfNypCV7splqUXQSUIFYhyF1Xz5s3jVjcTJ07E8uXLWWRRGxybLW//M2qNM3jwYO47+OGHH3KvwaSkJF7favVM4T569CjfXrlyJX7//Xff5aGHHrok5xNuCPfdtvjNq473s1eIE1ElCIIgCJWOchVVJJwWL17M0SZqzNy0aVPMmjULZ8+exbp16/Ks/+OPP8JsNmPatGlo3Lgx9xicPn06t8zZtm0br7N//34EBQXxtqhljvdiMpkuyTlFBkX4blv80hfx1pxO9mKnIAiCIAiVj3IVVfv27UNmZian8byEhISgWbNm+Pvvv/OsT+tRZIsiVV6oZQeRlpbmE1UNGjRAeRET5Bep8nMz9rdTiJaaKkEQBEGodJRroTpFpIhq1Twux15iYmJ8j/lTs2ZNvvjz7rvvssjq0KED3z9w4AAcDgeGDBnCoi02NhYPPvggbrnlFlwKwnUa6NwOWBVqWPw0q7+okkJ1QRAEQah8lKuoslg8s+G02kDPJp1Oh9RUT3PQwqC6qo8++gjjx49HRESEr5Cd6rIopRgXF4f169dj3LhxsNvtuOOOOy7qeKk3W25UKmXAdYhKA6NXVClUvuf4F6rHGYLz3VZlJvc4CTJG8l6SvzlBqGyUq6jypvGotso/pUeF5gaDodBZMG+++Sbmz5+PESNG4P777/c99u233/IMQG8NFdVW0ezARYsWXZSoomnM4eEF12WFhOQcr97t4Gsz1L7nJLty3NUbx8Yg3HhparwqGv7jJMgYyXtJ/uYEoTJRrqLKm/aLj49H7dq1fcvpPtkg5AdFnCjyROKJrgcNGhTwuL8480JF7atWrbqoY6WpzGlpgb38vJEXEgppaRafY7M+u129WaFB4vkUKFUanEz3uKxTlZXaomA36KpEfuMkyBjJe6n8/uboMYkcC0IlElUURaKZen/99ZdPVFHB+Z49ezBw4MB8nzN69Gj88MMPmDlzJvr16xfwGD23V69eGDt2LPr37+9bvmvXLjRq1Oiij5f8YQqCPrS8j+uzrRTsChUyzakwGCJ8LWoitEYoXAo4qPlZFcR/nAQZI3kvyd+cIFQmylVUUS0ViacZM2ZwTVSNGjXYIoFqoa6//npO45EPVXBwMEegvvzyS6xZs4aFVceOHZGQkODbFq1DMwc7d+7MtgyRkZGoU6cOWzNQlOqdd965ZOel87udZkmCXh/ua1ETqxc3dUEQBEGojJR7mxoqKKfZelRsnpWVxbP4qP5Jo9Hg5MmTuPbaazFlyhSOPFHKjyCfKrr4411n8uTJ7J7+0ksvITExke0VvI7tlwqdn5VCQmYStEE1YMt2Vxc3dUEQBEGonCjc3i6owgXTVklJeeugaBYfFaMnJ2f60lp3fvUcftN4IlKfNuyCuBptcM36eXz/ntqtMavlpbF3qEjkN06CjJG8l8rvby4iwiQ1VYJQ2SJVlRGdImdYk7NSoRLjT0EQBEGo9IioKgMMSo3vdmpWOpxZOS1qxE1dEARBEConIqrKAINKC7g9NVRpNjPM4qYuCIIgCJUeEVVlgEGlBxye+qsMuxlW//SfVmb/CYIgCEJlRHqGlAEmjdF3O8Nh9dkpEGKpIAiCIAiVExFVZUCQJqcFjdlpDWimLJEqQRAEQaiciKgqA0J0wb7bFpfd56auV6kRpA5sHi0IgiAIQuVARFUZEKoP8d22uBw5buq6YCgUOcaggiAIgiBUHkRUlQHhhjDf7TS3G8l2C9+WeipBEARBqLyIqCoDIoMifLfP+k2wlBY1giAIglB5EVFVBsQERflun1bqfbclUiUIgiAIlRcRVWVApNEEdbb5p1Wh8i2P0YlHlSAIgiBUVkRUlQE6tQpGtyPPchFVgiAIglB5EVFVRhjyEVXiUSUIgiAIlRcRVWWEPjv950+0NscUVBAEQRCEyoWIqjJCD3eeZbH6HFNQQRAEQRAqFyKqygi9O1BUkeVnlESqBEEQBKHSIqKqjNCxjMohUmeCWinDLQiCIAiVFfmWLyP0udrRxIqdgiAIgiBUakRUlRF6RY6TOhGjF48qQRAEQajMiKgqI/TKQFEVJ0XqgiAIglCpEVFVRhiV2oD70ZL+EwRBEIRKjYiqMsKo0gXcFzd1QRAEQajciKgqI0waQ8B9EVWCIAiCULkpd1HlcrkwZ84cdO3aFa1bt8awYcNw4sSJAtdPTk7Gs88+iw4dOqBjx4545ZVXYLFYAtZZu3Yt+vbti5YtW+LWW2/Fxo0bcakJ0hgD7oubuiAIgiBUbspdVM2bNw8ff/wxJk6ciOXLl7PIGjp0KGw2W77rjxw5EseOHcOSJUvw5ptvYv369Xj55Zd9j2/atAnPPfcc7r77bnz11Vfo0qULhg8fjkOHDl3CswKCdYHu6TG57guCIAiCULkoV1FFwmnx4sUslLp3746mTZti1qxZOHv2LNatW5dn/e3bt2Pz5s14/fXX0bx5cxZMr776KlauXIlz587xOgsXLkSvXr3wwAMPoEGDBhgzZgyvu3Tp0kt6bqG5ZvtJ+k8QBEEQKjflKqr27duHzMxMFkdeQkJC0KxZM/z999951t+yZQuio6NZLHmhFKBCocDWrVs5yrVt27aA7RGdOnXKd3tlSYQ+zHdb53YhSB04G1AQBEEQhMpFoJnSJYYiUkS1atUClsfExPge84eiUbnX1Wq1CAsLw5kzZ5CWlgaz2Yy4uLgiba8siQrKEVWRrkx8u7Q/NwD0+qzztUKRq5lN0cnpLOjXYzBvD+eL2G7xHivKoZDJvKcloudRv5vZK3jHx+//3APkLv5p5red/MY9YFm+O7/APnIReJyFHXXO+eaMUdH3U9BKiqI8N9v5v6Tvw3w2deF9XvSOAKVCARcNlHesivD65keJ3ksl2tOlx6U0oP5VT6JJvY7lfSiCUGUoV1HlLTAnYeSPTqdDampqvuvnXte7vtVqRVZWVoHbo8cvFrU6b2BPpVIGXHupER7ru13NnY4emt8uev+CIGQrodxqqBR+UFQ6nMB/6w9B1XBzeR+JIFQZylVU6fV6X22V9zZBAshgMOS7fn4F7LS+0Whk8eTdXu7H89tecVAqFQgPNxX4eEhI4PbDw+uhB9KxwW3EANvui9q3IAhCSbDFdMvz2SQIQiUVVd5UXnx8PGrXru1bTvebNGmSZ31K6/34448By0hApaSkcIqP0oAkruj5/tD92NicyFFJcLncSEsz51lOESr60EpLs8DpdAU89vmtbyAj/TgMiuGo6qiUCgQF6ZGRkQWnS8IKFWqM/PKN3lv+KUg33LhQoprW8T4vv20U9pyAHfvdLWiPSpXSN04upytv0KqQ/eZ3LvmlLfN9bp5j9Bu3C55r4PmUdJ/FQW8IQr8a9fL9bCLocyt3hF0QhMtYVNFsv6CgIPz1118+UUV1UXv27MHAgQPzrE/eVDNmzGBLhTp16vAymg1ItGvXjgvW27Zty8sGDBjgex5tv3379hd9vA5H3g8mL/Shld/jekNNyUwQaiU0oSbAlQl3IeNYpalAYxQgAIq5fllDafjgcBMcysxC/yarOt5yhYI+mwRBqGSiimqfSDyRUIqIiECNGjUwffp0jkhdf/31cDqdSEpKQnBwMKf+WrVqxaLp6aefZm8qKkqfMGECG3x6I1GDBw9mXyqaQditWzesWLECe/fuxWuvvVaepyoIgiAIQiWn3GO/5FF1xx13YPz48bjnnnugUqmwaNEiaDQantF39dVXY82aNbwuRaLeeust1KxZEw8++CCeeuopFk7+5p+0/uTJk/HJJ5/gtttuYzPQBQsWBNgwCIIgCIIglDYKt/tisvZVBwqhJyVl5htipwL25GRJRRSGjNOFkTEqGjJOpTNOEREmqakShMoWqRIEQRAEQagMiKgSBEEQBEEoBURUCYIgCIIglAIiqgRBEARBEEoBEVWCIAiCIAilgIgqQRAEQRCEUkBElSAIgiAIQikgPlVFhOy8qP9fflD/rPx6awkyTsVF3ksyTpfq/URN4slQWRCE0kNElSAIgiAIQikg6T9BEARBEIRSQESVIAiCIAhCKSCiShAEQRAEoRQQUSUIgiAIglAKiKgSBEEQBEEoBURUCYIgCIIglAIiqgRBEARBEEoBEVWCIAiCIAilgIgqQRAEQRCEUkBElSAIgiAIQikgokoQBEEQBKEUEFElCIIgCIJQCoioEgRBEARBKAVEVJUQl8uFOXPmoGvXrmjdujWGDRuGEydOlMZrUml45513cP/99wcs27t3LwYOHMhj1rNnT3zwwQeoaqSkpGDChAno1q0b2rZti3vuuQdbtmzxPb5x40b0798frVq1Qp8+fbB69WpURRITE/Hcc8+hc+fOaNOmDYYPH45Dhw75Hpf3UiBHjhzhcfryyy9ljAShnBBRVULmzZuHjz/+GBMnTsTy5ctZZA0dOhQ2m610X6HLlGXLlmH27NkBy5KTkzF48GDUrl0bK1aswGOPPYYZM2bw7arEM888g+3bt+ONN97gc7/iiiswZMgQHD58mEXDww8/zGKdvhwHDBiA0aNHs9CqatD749ixY3j33XfxxRdfQK/XY9CgQbBYLPJeyoXdbseoUaNgNpt9y+TvTRAuPepy2OdlDwmnxYsX84dY9+7dedmsWbP4i3DdunW48cYbUVU5d+4cXnrpJfz111+oW7duwGOfffYZNBoNXn31VajVajRo0MD3pXn77bejKkDn+8cff7Agb9euHS978cUXsWHDBnzzzTccnWnSpAmefvppfozGaM+ePXjvvffQpUsXVBVSU1NRo0YNFpiNGzfmZY8++ihuueUWHDx4kEVmVX8v+TN37lwEBQUFLJO/N0G49EikqgTs27cPmZmZAV9yISEhaNasGf7++29UZXbv3s1fdqtWreL0lT+U4urYsSN/CXqh1M7Ro0dx/vx5VAXCw8P5i79Fixa+ZQqFgi9paWk8RrnFE43R1q1b4Xa7UVUIDQ3FzJkzfYIqKSkJS5YsQVxcHBo2bCjvJT/oM+fTTz/F1KlTA8ZQ/t4E4dIjoqoEnD17lq+rVasWsDwmJsb3WFWF6qToV3OtWrXyPEZjQ1+KuceMOHPmDKoCJL6vueYaaLVa37Lvv/+eoywU6SxojLwpr6oIRfJIaFJt2WuvvQaj0SjvpWxIiFN6ePz48Xk+j+TvTRAuPSKqSgB9wRH+X4yETqeD1WotnVemEpKVlZXvmBFVddy2bduGcePG4frrr+dUcn5j5L1fVev1HnzwQa49o7Q61VlRNFTeSx5efvllLk6/6aab8oybjJEgXHqkpqoEUMGs90vOe9srDAwGQ+m9OpUMGqvcwsArpij6UNX48ccfuS6PZgBSwb5XZOYeI+/9qvreonQfQVGqf/75Bx999JG8lwB8/fXXnOKjWrz8kL83Qbj0SKSqBHjD7PHx8QHL6X5sbGzpvDKVEEpr5TdmRFUbNxIGTzzxBHr06IEFCxb4Inb03spvjEh0BgcHo6pANVSU7nM4HL5lSqWSBRaNh7yXwNE7mthAEU6KVtGFoIkiNBNZxkgQLj0iqkpA06ZNeaYNzXDzr22gWVodOnQozdenUkFjQwXXTqfTt2zTpk2oV68eIiMjUVXwWnHcd999bKvgn+5r3749Nm/eHLA+jRFFs0hUVBVo4gJZT/hbSZBtAP2N0Uw/eS+Bo5tr1qzhiJX3QowcOZKjejJGgnDpqTqf0qUIfQmSgSV9qP300088G5CmwNMvQ6qNEfKHprpnZGTghRdewH///cc+TDSji6bNVyWDxsmTJ+O6667j8ybxkJCQwJf09HQ2S925cye/t8iziqw7vvvuO448VCVo1h+Zo06aNIlntx04cABjx47lHy/kVSXvJU90t06dOgEXgn6g0GMyRoJw6VG4q9I87VKEoi0UZSBhQAWh9KuQXLJr1qxZ3odWYaAvwVOnTuHDDz/0LSPBQL+iKeIQHR2Nhx56iAVqVYFSfeRplh+33XYbT4v/7bffMH36dLaaoPcTpQn79u2LqgaJTLJVoNozuk1RPHpPNWrUiB+v6u+l/CCPsylTprAjPyFjJAiXFhFVgiAIgiAIpYCk/wRBEARBEERUCYIgCIIgVAwkUiUIgiAIglAKiKgSBEEQBEEoBURUCYIgCIIglAIiqgRBEARBEEoBEVWCUATK0s5NrOIEQRAqByKqhEtKz5492cDxcuLgwYO45557Sn275A4+evRobopb2Tl58iQbU5JZriAIQmVFXd4HIFQt3nrrLe6beDlBbWK2b99e6tvdu3cvVq5cye1EBEEQhMsfEVXCJaVZs2Yy4oIgCEKlRNJ/Qrml/7wpobVr12LkyJFo06YNOnbsiPHjx8NsNl9wW4cPH8bjjz/Oz6Hei9SgmJoQe6F+cdQHrVevXmjRogVuvPFGfPHFF3mOZ86cOXj99dfxv//9Dy1btsSQIUO47x4xd+5cjq4RdKx0n3C5XHj33Xe5MfKVV16J3r17B/Q4/Pfff9G8efOAVGdiYiK6dOmCwYMHY9OmTXjggQd4OV1TI+WCsFqtmDZtGq655hre10033YQ1a9b4Hqem3v7HRtA40Lk8//zzvmXUQ+/ee+/lcabt9OnTB8uWLfM9/tdff/F2Nm7cyMdDz+/evTs+//xzxMfH81jTc+k4qBF27uf9/vvvuO+++/h51Fj8448/LvT1O336NJ555hl+/Vq1aoUHH3yQ+/j58+233+Lmm2/mbXbu3BmjRo3CuXPnCt2uIAhCuUENlQXhUtGjRw/3mDFj+PaJEyfcjRs3dnfo0ME9depU959//ulesGCBu0mTJu4ZM2YUup2zZ8+627dv7+7Xr5979erV7l9++cXdv39/91VXXeVOTk52WywW94033uju0qWL+5NPPnH/9ttv7gkTJvD+5s+fH3A87dq1cw8fPtz966+/uleuXOnu2LGj+8477+THz5w5437++ef5edu3b+f7xIsvvuhu3ry5e86cOe4NGza433jjDXfTpk3db731lm/bs2bN4ufReRGPPvoob5uOPT093f3RRx/x43R98ODBfM/T5XK5hwwZ4m7Tpo37/fff5/OgfdPzvvrqK996o0aN4uP577//3Ha7nceiV69e7oyMDH6cxoeeM2nSJD6en3/+2T106FBetmPHDl5n06ZNfL9z587uxYsX83qDBg1yX3HFFe7evXu7Z8+ezcsef/xxXu+ff/4JeB69HrR9OsaXXnqJly1btizgtV6xYgXfT0xMdHft2tV9/fXXu1etWuX+4Ycf3AMHDnS3bt2az4HYsmUL73vu3Lm8j6+//ppf3/vuu6/Y7ztBEIRLgYgqodxFFQkCf+6//34WRIVBIqxly5bu+Ph43zISPN27d2dxRF/mtO1t27YFPI8EUosWLVh4eY+HLg6Hw7cOfYnTc5OSkvg+CSe67+Xw4cMs/N55552AbZOIom17n2ez2dw33XQTCxISE7SNtWvX+tb3ihG6Lojff/+d1yHh6A+NGQkMElBESkqK++qrr3Y/8MAD7nnz5rEYIRHoZeHChb5x90JjQNv2nof3eKZPn+5bhwQXLXvuued8y+j8aBmJPP/njRs3LmD7I0aM4GMkYZhbVJEIpbE6efKkb32r1eq+9tpr3U888QTfp+MiMUnLvdBrS68PbVMQBKGiIek/odxp3bp1wP24uDhf+o/SbA6HI+BCbN26lZ8XHR0d8LxffvmF01ObN29GjRo1OF3lD6WSKJ32zz//+JZRalClUgVsh7BYLPkeL6Xu6AcJpQ79j4vu07bp2AiNRsNpRUpzvvDCC7jttts45VYcKBWnUCj4nHLvKyEhgWcmEqGhoZg4cSIfG6UzR4wYETCuQ4cOxdSpU5GZmcmpSUofvvPOO/yYzWYL2Kf/mEVGRvI1pee8hIeH+9Kr/tD5+UMpQDrGI0eO5HteV1xxBWJjY33npFQq0a1bN/z555+8DqV06TWgtO3MmTN5luTVV1/NaUgaE0EQhIqGFKoL5Y7BYAi4T1+uXu+mt99+21fT5GX//v1ISUlBzZo1C9xmampqgODyEhUV5bMzKGz/XkGXH7Rvol+/fvk+7l/zQ8KB6o1IyPTo0QPFhfZFY9G2bdt8H6daJ9oHQTVhMTExvCz3vpKSkvDSSy9xXRUJkjp16qB9+/b5+mTlNzsz9xjlBwkkf7yCLL/Xgs7r2LFjXHeWHySmSNxR3RrVb73//vt8m16/Rx55pNAaNEEQhPJCRJVQobnzzju5WDo3wcHBLBTyi4CQ2KLIDX1p54YiJ/7RlpIQEhLC10uXLoXJZMrzePXq1X23P/30UxZUTZs2xWuvvcaF6t7nFwU6T6PRiA8++CDfx0kceSHxSWKlfv36XOxPBeYULSOowJsK+0mgkFjRarUsXD777DOUFsnJyahdu3ZAYb6/uMp9XlSgTj5d+UHHR3Tt2pUvdKwUhaNxmDRpEkfOqHhdEAShIiHpP6FCQ9EPSs/5XwiKslAKz19Y0Zc4pbnWr1/PqaNTp07l8ZdatWoVC43ifCF7I1devBEeEhH+x0XH8uabb/oiWbR/Sv/dcccdWLBgAafLSFh58U85FgQJD0qFUjTJf18HDhzgKJ43Hbpz50689957nPabPn06Pz5//nzfdiglSem4Tp06+QTLb7/9VmhErrhQFCy3vxelYP2Flv95UVqwXr16AedFvl00Q5PGhsaOPLzo3ClSRtG3MWPG+GYOCoIgVDQkUiVclgwaNAhff/01iyiyUiChRCKC6qHIcoCEA03pf+yxx9iugaJXP//8M1asWME1OcWJFnnXpen9FCGhdB7VZr344ossnMiegATCrFmzeD9169ZlIUB1VCQGKBpDkbOnnnoKkydPZvsFqomiaA3x66+/8uMUzcoN1VKRQHz00Uf50qBBAxZQVDdFEZyIiAiuiSLrBnps2LBhPBYDBw7kmimykyBvMBKR33zzDafbaIy2bdvG6TRKBRZUO1ZcKEWn0+m4lmvdunVc30a1UAW9fiSg6Pqhhx7iyCHVeVHkbNy4cbwOWSjQNuncaLztdjsLx7CwMH5MEAShoiGiSrgsqVatGosmisrQly6JKIrCkLAhgUKQbxR9qVP0KCMjg9NiFCmiyFFxoAgPCQDaDz335ZdfZv8rEi3Lly/H2bNnOcXVt29fFk4UZSH/J0pFzp4923c8VAdEwmbChAlcI9WoUSMuwqZ1N2zYwKItvygZiR86B9ofReMoekdeVyQYCdoHibpPPvnEl+6j4/jhhx84skNCkorUqZCdLgQJv1deeYUjd6XVJoc8sb766is+ThprEn4kIPODzoHGjl4fGk8q8Kdj8n99SFDOmDEDixcv9hWnt2vXjlOAJKwEQRAqGgqaAljeByEIwuULmX+SgSmJHRK2giAIVRWpqRIEQRAEQSgFRFQJgiAIgiCUApL+EwRBEARBKAUkUiUIgiAIglAKiKgSBEEQBEEoBURUCYIgCIIglAIiqgRBEARBEEoBEVWCIAiCIAilgIgqQRAEQRCEUkBElSAIgiAIQikgokoQBEEQBKEUEFElCIIgCIKAi+f/6gW98l5xDLsAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", - "Missing metrics for: ['Ridge (alpha=0.5)']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", - "Missing metrics for: ['Ridge (alpha=0.5)']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", - "Missing metrics for: ['Ridge (alpha=0.5)']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", - "Missing metrics for: ['Ridge (alpha=0.5)']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", - "Missing metrics for: ['Ridge (alpha=0.5)']\n" + "Processing: standard\n", + "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', 'Ridge Var Adj (alpha=0.5, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", - "Missing metrics for: ['Ridge (alpha=0.5)']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", - "Missing metrics for: ['Ridge (alpha=0.5)']\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", - "Missing metrics for: ['Ridge (alpha=0.5)']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", - "Missing metrics for: ['Ridge (alpha=0.5)']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", - "Missing metrics for: ['Ridge (alpha=0.5)']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "# # plot any OOD metrics\n", - "# # print(\"Available metrics:\", list(metrics.keys()))\n", - "# for name, metric in metrics.items():\n", - "# print(\"Processing:\", name)\n", - "# print(\"Metric keys:\", list(metric.keys()))\n", - "# if name == \"standard\": continue\n", - " \n", - "# if \"scale\" in name:\n", - "# scale = float(name.split(\"=\")[-1])**2\n", - "# else:\n", - "# scale = 1.0\n", - "# trivial = 1.0 if \"noisy\" not in name else (1+1/n_dims)\n", - " \n", - "# # # only plot models that exist in this metric dict\n", - "# # models_present = [m for m in models if m in metric]\n", - "# # if len(models_present) == 0:\n", - "# # print(f\"Skipping {name}: no matching models in metric keys {list(metric.keys())}\")\n", - "# # continue\n", - "# # fig, ax = basic_plot(metric, models=models, trivial=trivial * scale)\n", - "# # ax.set_title(name)\n", - " \n", - "# if \"ortho\" in name:\n", - "# ax.set_xlim(-1, n_dims - 1)\n", - "# ax.set_ylim(-.1 * scale, 1.5 * scale)\n", - "# plt.show()\n", - "# # std = metrics.get(\"standard\", {})\n", - "# # for model_name in models:\n", - "# # mres = std.get(model_name, {})\n", - "# # if \"gradient_alignment\" in mres:\n", - "# # print(\"Plotting gradient alignment for\", model_name)\n", - "# # alignments = mres[\"gradient_alignment\"]\n", - "# # plt.figure(figsize=(6, 4))\n", - "# # plt.plot(range(len(alignments)), alignments, label=f\"{model_name} gradient-w alignment\", lw=2)\n", - "# # plt.xlabel(\"# in-context examples\")\n", - "# # plt.ylabel(\"normalized inner product\")\n", - "# # plt.legend()\n", - "# # plt.show()\n", "# plot any OOD metrics\n", + "# print(\"Available metrics:\", list(metrics.keys()))\n", "for name, metric in metrics.items():\n", + " print(\"Processing:\", name)\n", + " print(\"Metric keys:\", list(metric.keys()))\n", " if name == \"standard\": continue\n", " \n", " if \"scale\" in name:\n", " scale = float(name.split(\"=\")[-1])**2\n", " else:\n", " scale = 1.0\n", - "\n", " trivial = 1.0 if \"noisy\" not in name else (1+1/n_dims)\n", - " fig, ax = basic_plot(metric, models=models, trivial=trivial * scale)\n", - " ax.set_title(name)\n", + " \n", + " # # only plot models that exist in this metric dict\n", + " # models_present = [m for m in models if m in metric]\n", + " # if len(models_present) == 0:\n", + " # print(f\"Skipping {name}: no matching models in metric keys {list(metric.keys())}\")\n", + " # continue\n", + " # fig, ax = basic_plot(metric, models=models, trivial=trivial * scale)\n", + " # ax.set_title(name)\n", " \n", " if \"ortho\" in name:\n", " ax.set_xlim(-1, n_dims - 1)\n", " ax.set_ylim(-.1 * scale, 1.5 * scale)\n", - "\n", - " plt.show()" + " plt.show()\n", + "# std = metrics.get(\"standard\", {})\n", + "# for model_name in models:\n", + "# mres = std.get(model_name, {})\n", + "# if \"gradient_alignment\" in mres:\n", + "# print(\"Plotting gradient alignment for\", model_name)\n", + "# alignments = mres[\"gradient_alignment\"]\n", + "# plt.figure(figsize=(6, 4))\n", + "# plt.plot(range(len(alignments)), alignments, label=f\"{model_name} gradient-w alignment\", lw=2)\n", + "# plt.xlabel(\"# in-context examples\")\n", + "# plt.ylabel(\"normalized inner product\")\n", + "# plt.legend()\n", + "# plt.show()" ] }, { @@ -968,7 +681,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "beb327ce", "metadata": {}, "outputs": [], @@ -979,31 +692,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "03523b06", "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m model, conf = \u001b[43mget_model_from_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m n_dims = conf.model.n_dims\n\u001b[32m 4\u001b[39m batch_size = conf.training.batch_size\n", - "\u001b[36mFile \u001b[39m\u001b[32md:\\MyBK\\Semester_5\\Programming_Intergration_Project\\in-context-learning\\src\\eval.py:28\u001b[39m, in \u001b[36mget_model_from_run\u001b[39m\u001b[34m(run_path, step, only_conf)\u001b[39m\n\u001b[32m 26\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m step == -\u001b[32m1\u001b[39m:\n\u001b[32m 27\u001b[39m state_path = os.path.join(run_path, \u001b[33m\"\u001b[39m\u001b[33mstate.pt\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m28\u001b[39m state = \u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 29\u001b[39m model.load_state_dict(state[\u001b[33m\"\u001b[39m\u001b[33mmodel_state_dict\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m 30\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\CaoHuuThienHoang\\miniconda3\\Lib\\site-packages\\torch\\serialization.py:1521\u001b[39m, in \u001b[36mload\u001b[39m\u001b[34m(f, map_location, pickle_module, weights_only, mmap, **pickle_load_args)\u001b[39m\n\u001b[32m 1519\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m weights_only:\n\u001b[32m 1520\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1521\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_load\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1522\u001b[39m \u001b[43m \u001b[49m\u001b[43mopened_zipfile\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1523\u001b[39m \u001b[43m \u001b[49m\u001b[43mmap_location\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1524\u001b[39m \u001b[43m \u001b[49m\u001b[43m_weights_only_unpickler\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1525\u001b[39m \u001b[43m \u001b[49m\u001b[43moverall_storage\u001b[49m\u001b[43m=\u001b[49m\u001b[43moverall_storage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1526\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpickle_load_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1527\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1528\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m pickle.UnpicklingError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 1529\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m pickle.UnpicklingError(_get_wo_message(\u001b[38;5;28mstr\u001b[39m(e))) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\CaoHuuThienHoang\\miniconda3\\Lib\\site-packages\\torch\\serialization.py:2122\u001b[39m, in \u001b[36m_load\u001b[39m\u001b[34m(zip_file, map_location, pickle_module, pickle_file, overall_storage, **pickle_load_args)\u001b[39m\n\u001b[32m 2120\u001b[39m \u001b[38;5;28;01mglobal\u001b[39;00m _serialization_tls\n\u001b[32m 2121\u001b[39m _serialization_tls.map_location = map_location\n\u001b[32m-> \u001b[39m\u001b[32m2122\u001b[39m result = \u001b[43munpickler\u001b[49m\u001b[43m.\u001b[49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2123\u001b[39m _serialization_tls.map_location = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 2125\u001b[39m torch._utils._validate_loaded_sparse_tensors()\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\CaoHuuThienHoang\\miniconda3\\Lib\\site-packages\\torch\\_weights_only_unpickler.py:535\u001b[39m, in \u001b[36mUnpickler.load\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 527\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[32m 528\u001b[39m \u001b[38;5;28mtype\u001b[39m(pid) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m\n\u001b[32m 529\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(pid) > \u001b[32m0\u001b[39m\n\u001b[32m 530\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m torch.serialization._maybe_decode_ascii(pid[\u001b[32m0\u001b[39m]) != \u001b[33m\"\u001b[39m\u001b[33mstorage\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 531\u001b[39m ):\n\u001b[32m 532\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m UnpicklingError(\n\u001b[32m 533\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mOnly persistent_load of storage is allowed, but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpid[\u001b[32m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 534\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m535\u001b[39m \u001b[38;5;28mself\u001b[39m.append(\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mpersistent_load\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpid\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m 536\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m key[\u001b[32m0\u001b[39m] \u001b[38;5;129;01min\u001b[39;00m [BINGET[\u001b[32m0\u001b[39m], LONG_BINGET[\u001b[32m0\u001b[39m]]:\n\u001b[32m 537\u001b[39m idx = (read(\u001b[32m1\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m key[\u001b[32m0\u001b[39m] == BINGET[\u001b[32m0\u001b[39m] \u001b[38;5;28;01melse\u001b[39;00m unpack(\u001b[33m\"\u001b[39m\u001b[33m.persistent_load\u001b[39m\u001b[34m(saved_id)\u001b[39m\n\u001b[32m 2084\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 2085\u001b[39m nbytes = numel * torch._utils._element_size(dtype)\n\u001b[32m-> \u001b[39m\u001b[32m2086\u001b[39m typed_storage = \u001b[43mload_tensor\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2087\u001b[39m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnbytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_maybe_decode_ascii\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2088\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2090\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m typed_storage\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\CaoHuuThienHoang\\miniconda3\\Lib\\site-packages\\torch\\serialization.py:2052\u001b[39m, in \u001b[36m_load..load_tensor\u001b[39m\u001b[34m(dtype, numel, key, location)\u001b[39m\n\u001b[32m 2048\u001b[39m \u001b[38;5;66;03m# TODO: Once we decide to break serialization FC, we can\u001b[39;00m\n\u001b[32m 2049\u001b[39m \u001b[38;5;66;03m# stop wrapping with TypedStorage\u001b[39;00m\n\u001b[32m 2051\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m torch._guards.detect_fake_mode(\u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m2052\u001b[39m wrap_storage = \u001b[43mrestore_location\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstorage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2053\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 2054\u001b[39m storage._fake_device = location\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\CaoHuuThienHoang\\miniconda3\\Lib\\site-packages\\torch\\serialization.py:698\u001b[39m, in \u001b[36mdefault_restore_location\u001b[39m\u001b[34m(storage, location)\u001b[39m\n\u001b[32m 678\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 679\u001b[39m \u001b[33;03mRestores `storage` using a deserializer function registered for the `location`.\u001b[39;00m\n\u001b[32m 680\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 695\u001b[39m \u001b[33;03m all matching ones return `None`.\u001b[39;00m\n\u001b[32m 696\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 697\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m _, _, fn \u001b[38;5;129;01min\u001b[39;00m _package_registry:\n\u001b[32m--> \u001b[39m\u001b[32m698\u001b[39m result = \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstorage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 699\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 700\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\CaoHuuThienHoang\\miniconda3\\Lib\\site-packages\\torch\\serialization.py:636\u001b[39m, in \u001b[36m_deserialize\u001b[39m\u001b[34m(backend_name, obj, location)\u001b[39m\n\u001b[32m 634\u001b[39m backend_name = torch._C._get_privateuse1_backend_name()\n\u001b[32m 635\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m location.startswith(backend_name):\n\u001b[32m--> \u001b[39m\u001b[32m636\u001b[39m device = \u001b[43m_validate_device\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbackend_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 637\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m obj.to(device=device)\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\CaoHuuThienHoang\\miniconda3\\Lib\\site-packages\\torch\\serialization.py:605\u001b[39m, in \u001b[36m_validate_device\u001b[39m\u001b[34m(location, backend_name)\u001b[39m\n\u001b[32m 603\u001b[39m device_index = device.index \u001b[38;5;28;01mif\u001b[39;00m device.index \u001b[38;5;28;01melse\u001b[39;00m \u001b[32m0\u001b[39m\n\u001b[32m 604\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(device_module, \u001b[33m\"\u001b[39m\u001b[33mis_available\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m device_module.is_available():\n\u001b[32m--> \u001b[39m\u001b[32m605\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 606\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mAttempting to deserialize object on a \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbackend_name.upper()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 607\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mdevice but torch.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbackend_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.is_available() is False. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 608\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mIf you are running on a CPU-only machine, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 609\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mplease use torch.load with map_location=torch.device(\u001b[39m\u001b[33m'\u001b[39m\u001b[33mcpu\u001b[39m\u001b[33m'\u001b[39m\u001b[33m) \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 610\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mto map your storages to the CPU.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 611\u001b[39m )\n\u001b[32m 612\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(device_module, \u001b[33m\"\u001b[39m\u001b[33mdevice_count\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m 613\u001b[39m device_count = device_module.device_count()\n", - "\u001b[31mRuntimeError\u001b[39m: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU." - ] - } - ], + "outputs": [], "source": [ "model, conf = get_model_from_run(run_path)\n", "\n", @@ -1291,7 +983,7 @@ ], "metadata": { "kernelspec": { - "display_name": "base", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1305,7 +997,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.10" } }, "nbformat": 4, diff --git a/src/models.py b/src/models.py index b3ad9df7..aa37a0ca 100644 --- a/src/models.py +++ b/src/models.py @@ -28,6 +28,10 @@ def build_model(conf): def get_relevant_baselines(task_name): task_to_baselines = { + "exponential_weighted_regression": [ + (LeastSquaresModel, {}), + (RidgeModel, {"alpha": 0.5}), + ], "uniform_hypersphere_regression": [ (LeastSquaresModel, {}), (RidgeModel, {"alpha": 0.1}), diff --git a/src/plot_utils.py b/src/plot_utils.py index d83bc0ce..fd05a687 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -10,6 +10,11 @@ palette = sns.color_palette("colorblind") relevant_model_names = { + "exponential_weighted_regression": [ + "Transformer", + "Least Squares", + "Ridge (alpha=0.5)", + ], "uniform_hypersphere_regression": [ "Transformer", "Least Squares", diff --git a/src/tasks.py b/src/tasks.py index 5dea9cb7..402eddcb 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -117,7 +117,7 @@ def get_metric(): @staticmethod def get_training_metric(): return mean_squared_error -class ExponentialWeightedRegression(Tasks): +class ExponentialWeightedRegression(Task): def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, rate=1.0): super(ExponentialWeightedRegression, self).__init__(n_dims, batch_size, pool_dict, seeds) self.scale = scale @@ -313,15 +313,18 @@ def sample_noise(self, shape): elif self.noise_type == "beta": alpha, beta = 2.0, 5.0 mean = alpha / (alpha + beta) - var = (alpha * beta) / ((alpha + beta) **2 * (alpha + beta + 1.0)) + var = (alpha * beta) / (((alpha + beta) ** 2) * (alpha + beta + 1)) std = math.sqrt(var) - beta_dist = torch.distributions.Beta(alpha, beta) - noise = (beta_dist.sample(shape) - mean) / std * self.noise_std + beta_dist = torch.distributions.Beta(concentration1=alpha, concentration0=beta) + X = beta_dist.sample(shape) + noise = (X - mean) / std * self.noise_std # 9. elif self.noise_type == "poisson": - lam = 100.0 + lam = 3.0 poisson_noise = torch.distributions.Poisson(lam) - noise = (poisson_noise.sample(shape) - lam) / math.sqrt(lam) * self.noise_std + X = poisson_noise.sample(shape) + scale_factor = self.noise_std / math.sqrt(lam) + noise = (X - lam) * scale_factor else: raise ValueError(f"Unsupported noise type: {self.noise_type}") return noise From b594228bdd9fca70f77f6f11ff95bd1923399b7d Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Sat, 15 Nov 2025 08:53:16 +0700 Subject: [PATCH 47/88] laplace w --- src/conf/exponential_weighted_regression.yaml | 4 +-- src/conf/wandb.yaml | 2 +- src/models.py | 4 +++ src/plot_utils.py | 7 +++- src/tasks.py | 36 +++++++++++++++++++ 5 files changed, 49 insertions(+), 4 deletions(-) diff --git a/src/conf/exponential_weighted_regression.yaml b/src/conf/exponential_weighted_regression.yaml index 98965b88..08a3533a 100644 --- a/src/conf/exponential_weighted_regression.yaml +++ b/src/conf/exponential_weighted_regression.yaml @@ -10,7 +10,7 @@ model: n_positions: 101 training: - task: exponential_weights_regression + task: exponential_weighted_regression task_kwargs: rate: 1.0 # exponential distribution rate parameter scale: 1.0 @@ -33,7 +33,7 @@ training: save_every_steps: 100 keep_every_steps: 10000 -out_dir: ../models/exponential_weights_regression +out_dir: ../models/exponential_weighted_regression wandb: project: "in-context-training" diff --git a/src/conf/wandb.yaml b/src/conf/wandb.yaml index 2f371b92..642a5b18 100644 --- a/src/conf/wandb.yaml +++ b/src/conf/wandb.yaml @@ -1,5 +1,5 @@ wandb: project: in-context-training - entity: hai-trinh220970-ho-chi-minh-city-university-of-technology + entity: in-context # Change to your W&B username/entity that you have access to notes: log_every_steps: 100 \ No newline at end of file diff --git a/src/models.py b/src/models.py index aa37a0ca..9efe2c29 100644 --- a/src/models.py +++ b/src/models.py @@ -28,6 +28,10 @@ def build_model(conf): def get_relevant_baselines(task_name): task_to_baselines = { + "laplace_weighted_regression": [ + (LeastSquaresModel, {}), + (RidgeModel, {"alpha": 0.5}), + ], "exponential_weighted_regression": [ (LeastSquaresModel, {}), (RidgeModel, {"alpha": 0.5}), diff --git a/src/plot_utils.py b/src/plot_utils.py index fd05a687..55a7faa9 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -10,6 +10,11 @@ palette = sns.color_palette("colorblind") relevant_model_names = { + "laplace_weighted_regression": [ + "Transformer", + "Least Squares", + "Ridge (alpha=0.5)", + ], "exponential_weighted_regression": [ "Transformer", "Least Squares", @@ -102,7 +107,7 @@ def basic_plot(metrics, models=None, trivial=1.0): ax.set_xlabel("in-context examples") ax.set_ylabel("squared error") ax.set_xlim(-1, len(low) + 0.1) - ax.set_ylim(-0.1, 0.5) + ax.set_ylim(-0.1, 1.0) legend = ax.legend(loc="upper left", bbox_to_anchor=(1, 1)) diff --git a/src/tasks.py b/src/tasks.py index 402eddcb..141a66f5 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -117,6 +117,42 @@ def get_metric(): @staticmethod def get_training_metric(): return mean_squared_error +class LaplaceWeightedRegression(Task): + def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, rate=1.0): + super(LaplaceWeightedRegression, self).__init__(n_dims, batch_size, pool_dict, seeds) + self.scale = scale + self.rate = rate + + if pool_dict is None and seeds is None: + laplace_dist = torch.distributions.Laplace(rate=self.rate) + self.w_b = laplace_dist.sample((self.b_size, self.n_dims, 1)) + elif seeds is not None: + self.w_b = torch.zeros(self.b_size, self.n_dims, 1) + generator = torch.Generator() + assert len(seeds) == self.b_size + for i, seed in enumerate(seeds): + generator.manual_seed(seed) + laplace_dist = torch.distributions.Laplace(rate=self.rate) + self.w_b[i] = laplace_dist.sample((self.n_dims, 1)) + else: + assert "w" in pool_dict + indices = torch.randperm(len(pool_dict["w"]))[:batch_size] + self.w_b = pool_dict["w"][indices] + def evaluate(self, xs_b): + w_b = self.w_b.to(xs_b.device) + ys_b = self.scale * (xs_b @ w_b)[:, :, 0] + return ys_b + @staticmethod + def generate_pool_dict(n_dims, num_tasks, rate=1.0): + laplace_dist = torch.distributions.Laplace(rate=rate) + return {"w": laplace_dist.sample((num_tasks, n_dims, 1))} + @staticmethod + def get_metric(): + return squared_error + @staticmethod + def get_training_metric(): + return mean_squared_error + class ExponentialWeightedRegression(Task): def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, rate=1.0): super(ExponentialWeightedRegression, self).__init__(n_dims, batch_size, pool_dict, seeds) From 49eea984a68ace51fcd1e5d89130d1a3ca4008ef Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Sat, 15 Nov 2025 09:08:38 +0700 Subject: [PATCH 48/88] add laplace --- src/conf/laplace_weighted_regression.yaml | 43 +++++++++++++++++++++++ src/tasks.py | 19 +++++----- 2 files changed, 54 insertions(+), 8 deletions(-) create mode 100644 src/conf/laplace_weighted_regression.yaml diff --git a/src/conf/laplace_weighted_regression.yaml b/src/conf/laplace_weighted_regression.yaml new file mode 100644 index 00000000..5330582c --- /dev/null +++ b/src/conf/laplace_weighted_regression.yaml @@ -0,0 +1,43 @@ +inherit: + - base.yaml + +model: + family: gpt2 + n_dims: 20 + n_embd: 128 + n_head: 8 + n_layer: 4 + n_positions: 101 + +training: + task: laplace_weighted_regression + task_kwargs: + weight_scale: 1.0 # laplace distribution weight scale parameter + scale: 1.0 + data: gaussian + data_kwargs: {} + curriculum: + dims: + start: 5 + end: 20 + inc: 1 + interval: 2000 + points: + start: 6 + end: 41 + inc: 2 + interval: 2000 + batch_size: 32 + learning_rate: 0.0002 + train_steps: 100001 + save_every_steps: 100 + keep_every_steps: 10000 + +out_dir: ../models/laplace_weighted_regression + +wandb: + project: "in-context-training" + entity: "hai-trinh220970-ho-chi-minh-city-university-of-technology" + name: "laplace_weights_experiment" + notes: "Training with laplace-distributed weights (non-uniform on hypersphere)" + log_every_steps: 100 \ No newline at end of file diff --git a/src/tasks.py b/src/tasks.py index 141a66f5..f94a9f4c 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -118,13 +118,13 @@ def get_metric(): def get_training_metric(): return mean_squared_error class LaplaceWeightedRegression(Task): - def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, rate=1.0): + def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, weight_scale=1.0): super(LaplaceWeightedRegression, self).__init__(n_dims, batch_size, pool_dict, seeds) self.scale = scale - self.rate = rate - + self.weight_scale = weight_scale # self.weight_scale as weight_scale + if pool_dict is None and seeds is None: - laplace_dist = torch.distributions.Laplace(rate=self.rate) + laplace_dist = torch.distributions.Laplace(loc=0, scale=self.weight_scale) self.w_b = laplace_dist.sample((self.b_size, self.n_dims, 1)) elif seeds is not None: self.w_b = torch.zeros(self.b_size, self.n_dims, 1) @@ -132,27 +132,30 @@ def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, rate assert len(seeds) == self.b_size for i, seed in enumerate(seeds): generator.manual_seed(seed) - laplace_dist = torch.distributions.Laplace(rate=self.rate) + laplace_dist = torch.distributions.Laplace(loc=0, scale=self.weight_scale) self.w_b[i] = laplace_dist.sample((self.n_dims, 1)) else: assert "w" in pool_dict indices = torch.randperm(len(pool_dict["w"]))[:batch_size] self.w_b = pool_dict["w"][indices] + def evaluate(self, xs_b): w_b = self.w_b.to(xs_b.device) ys_b = self.scale * (xs_b @ w_b)[:, :, 0] return ys_b + @staticmethod - def generate_pool_dict(n_dims, num_tasks, rate=1.0): - laplace_dist = torch.distributions.Laplace(rate=rate) + def generate_pool_dict(n_dims, num_tasks, weight_scale=1.0): + laplace_dist = torch.distributions.Laplace(loc=0, scale=weight_scale) return {"w": laplace_dist.sample((num_tasks, n_dims, 1))} + @staticmethod def get_metric(): return squared_error + @staticmethod def get_training_metric(): return mean_squared_error - class ExponentialWeightedRegression(Task): def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, rate=1.0): super(ExponentialWeightedRegression, self).__init__(n_dims, batch_size, pool_dict, seeds) From 960c73057952fbd3a57611badbaf2591f8a4b5f0 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 17 Nov 2025 13:34:55 +0700 Subject: [PATCH 49/88] 500k ready --- src/eval.ipynb | 273 ++++++++++++++++++++++++++++++++++++------------- src/models.py | 22 +++- src/schema.py | 1 + src/tasks.py | 10 +- 4 files changed, 230 insertions(+), 76 deletions(-) diff --git a/src/eval.ipynb b/src/eval.ipynb index b3473518..01adc67f 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "id": "0e8d018b", "metadata": { "scrolled": true @@ -191,7 +191,7 @@ " 3_tstudent_noise_gaussian_data_experiment\n", " \n", " \n", - " 22\n", + " 30\n", " daa2cd45-f1c0-4a0c-9100-e171129624c9\n", " sparse_linear_regression\n", " Transformer\n", @@ -204,7 +204,7 @@ " 4_std_sparse_linear_regression\n", " \n", " \n", - " 13\n", + " 14\n", " beta_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -217,7 +217,20 @@ " beta_noise_ar1_data_experiment\n", " \n", " \n", - " 12\n", + " 15\n", + " beta_noisy_linear_regression_40_100k\n", + " linear_regression\n", + " Transformer\n", + " noise_type=beta\n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " beta_noisy_linear_regression_40_100k\n", + " \n", + " \n", + " 13\n", " aed365ed-51e2-4a72-8374-ae954b37be14\n", " linear_regression\n", " Transformer\n", @@ -243,7 +256,7 @@ " decision_tree_pretrained\n", " \n", " \n", - " 14\n", + " 16\n", " exponential_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -256,7 +269,46 @@ " exponential_noise_gaussian_data_experiment\n", " \n", " \n", - " 15\n", + " 17\n", + " exponential_weighted_experiment_100k\n", + " linear_regression\n", + " Transformer\n", + " rate=1.0_scale=1.0\n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " exponential_weighted_experiment_100k\n", + " \n", + " \n", + " 18\n", + " exponential_weighted_experiment_150k\n", + " linear_regression\n", + " Transformer\n", + " rate=1.0_scale=1.0\n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " exponential_weighted_experiment_150k\n", + " \n", + " \n", + " 19\n", + " exponential_weighted_regression\n", + " linear_regression\n", + " Transformer\n", + " rate=1.0_scale=1.0\n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " exponential_weights_experiment\n", + " \n", + " \n", + " 20\n", " laplace_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -269,7 +321,33 @@ " laplace_noise_gaussian_data_experiment\n", " \n", " \n", - " 16\n", + " 12\n", + " a2fcec3c-8ce5-49bf-a8bc-08136b31ec36\n", + " linear_regression\n", + " Transformer\n", + " scale=1.0_weight_scale=1.0\n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " laplace_weights_experiment\n", + " \n", + " \n", + " 21\n", + " pretrained\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 20\n", + " 12\n", + " 8\n", + " linear_regression_pretrained\n", + " \n", + " \n", + " 22\n", " rayleigh_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -282,7 +360,7 @@ " rayleigh_noise_gaussian_data_experiment\n", " \n", " \n", - " 20\n", + " 28\n", " pretrained\n", " relu_2nn_regression\n", " Transformer\n", @@ -308,7 +386,7 @@ " rigde_normal_linear_regression_gaussian\n", " \n", " \n", - " 21\n", + " 29\n", " 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615\n", " sparse_linear_regression\n", " Transformer\n", @@ -334,7 +412,7 @@ " sparse_data_experiment\n", " \n", " \n", - " 23\n", + " 31\n", " pretrained\n", " sparse_linear_regression\n", " Transformer\n", @@ -347,7 +425,7 @@ " sparse_regression_pretrained\n", " \n", " \n", - " 17\n", + " 24\n", " t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -360,7 +438,33 @@ " t_student_noise_gaussian_data_experiment\n", " \n", " \n", - " 18\n", + " 23\n", + " sparse_gaussian\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " task_sparse_data\n", + " \n", + " \n", + " 25\n", + " uniform_hypersphere_regression\n", + " linear_regression\n", + " Transformer\n", + " normalize=True_scale=1.0\n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " uniform_hypersphere_experiment\n", + " \n", + " \n", + " 26\n", " uniform_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -373,7 +477,7 @@ " uniform_noise_ar1_data_experiment\n", " \n", " \n", - " 19\n", + " 27\n", " uniform_noise_gaussian_data_experiment_\n", " linear_regression\n", " Transformer\n", @@ -400,47 +504,63 @@ "10 64d381ae-08d0-4bae-8e40-f1a68cfb2e97 linear_regression \n", "8 3_laplace_noise_gaussian_data_experiment linear_regression \n", "9 3_tstudent_noise_gaussian_data_experiment linear_regression \n", - "22 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", - "13 beta_noise_ar1_data_experiment linear_regression \n", - "12 aed365ed-51e2-4a72-8374-ae954b37be14 linear_regression \n", + "30 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", + "14 beta_noise_ar1_data_experiment linear_regression \n", + "15 beta_noisy_linear_regression_40_100k linear_regression \n", + "13 aed365ed-51e2-4a72-8374-ae954b37be14 linear_regression \n", "0 pretrained decision_tree \n", - "14 exponential_noise_gaussian_data_experiment linear_regression \n", - "15 laplace_noise_gaussian_data_experiment linear_regression \n", - "16 rayleigh_noise_gaussian_data_experiment linear_regression \n", - "20 pretrained relu_2nn_regression \n", + "16 exponential_noise_gaussian_data_experiment linear_regression \n", + "17 exponential_weighted_experiment_100k linear_regression \n", + "18 exponential_weighted_experiment_150k linear_regression \n", + "19 exponential_weighted_regression linear_regression \n", + "20 laplace_noise_gaussian_data_experiment linear_regression \n", + "12 a2fcec3c-8ce5-49bf-a8bc-08136b31ec36 linear_regression \n", + "21 pretrained linear_regression \n", + "22 rayleigh_noise_gaussian_data_experiment linear_regression \n", + "28 pretrained relu_2nn_regression \n", "11 82e728b0-a061-448e-8d7a-f3c79c0c74e5 linear_regression \n", - "21 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", + "29 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", "1 03de46b6-429a-4151-92e6-3588231c6cad linear_regression \n", - "23 pretrained sparse_linear_regression \n", - "17 t_student_noise_gaussian_data_experiment linear_regression \n", - "18 uniform_noise_ar1_data_experiment linear_regression \n", - "19 uniform_noise_gaussian_data_experiment_ linear_regression \n", + "31 pretrained sparse_linear_regression \n", + "24 t_student_noise_gaussian_data_experiment linear_regression \n", + "23 sparse_gaussian linear_regression \n", + "25 uniform_hypersphere_regression linear_regression \n", + "26 uniform_noise_ar1_data_experiment linear_regression \n", + "27 uniform_noise_gaussian_data_experiment_ linear_regression \n", "\n", - " model kwargs num_tasks num_examples n_dims \\\n", - "3 Transformer -1 -1 5 \n", - "4 Transformer -1 -1 5 \n", - "5 Transformer -1 -1 5 \n", - "6 Transformer -1 -1 5 \n", - "7 Transformer -1 -1 5 \n", - "2 Transformer -1 -1 20 \n", - "10 Transformer -1 -1 20 \n", - "8 Transformer -1 -1 5 \n", - "9 Transformer -1 -1 5 \n", - "22 Transformer sparsity=5 -1 -1 15 \n", - "13 Transformer -1 -1 5 \n", - "12 Transformer k=5_sparsity=3 -1 -1 15 \n", - "0 Transformer depth=4 -1 -1 20 \n", - "14 Transformer -1 -1 5 \n", - "15 Transformer -1 -1 5 \n", - "16 Transformer -1 -1 5 \n", - "20 Transformer hidden_layer_size=100 -1 -1 20 \n", - "11 Transformer sparsity=5 -1 -1 15 \n", - "21 Transformer -1 -1 5 \n", - "1 Transformer -1 -1 20 \n", - "23 Transformer sparsity=3 -1 -1 20 \n", - "17 Transformer -1 -1 5 \n", - "18 Transformer -1 -1 5 \n", - "19 Transformer -1 -1 5 \n", + " model kwargs num_tasks num_examples n_dims \\\n", + "3 Transformer -1 -1 5 \n", + "4 Transformer -1 -1 5 \n", + "5 Transformer -1 -1 5 \n", + "6 Transformer -1 -1 5 \n", + "7 Transformer -1 -1 5 \n", + "2 Transformer -1 -1 20 \n", + "10 Transformer -1 -1 20 \n", + "8 Transformer -1 -1 5 \n", + "9 Transformer -1 -1 5 \n", + "30 Transformer sparsity=5 -1 -1 15 \n", + "14 Transformer -1 -1 5 \n", + "15 Transformer noise_type=beta -1 -1 20 \n", + "13 Transformer k=5_sparsity=3 -1 -1 15 \n", + "0 Transformer depth=4 -1 -1 20 \n", + "16 Transformer -1 -1 5 \n", + "17 Transformer rate=1.0_scale=1.0 -1 -1 20 \n", + "18 Transformer rate=1.0_scale=1.0 -1 -1 20 \n", + "19 Transformer rate=1.0_scale=1.0 -1 -1 20 \n", + "20 Transformer -1 -1 5 \n", + "12 Transformer scale=1.0_weight_scale=1.0 -1 -1 20 \n", + "21 Transformer -1 -1 20 \n", + "22 Transformer -1 -1 5 \n", + "28 Transformer hidden_layer_size=100 -1 -1 20 \n", + "11 Transformer sparsity=5 -1 -1 15 \n", + "29 Transformer -1 -1 5 \n", + "1 Transformer -1 -1 20 \n", + "31 Transformer sparsity=3 -1 -1 20 \n", + "24 Transformer -1 -1 5 \n", + "23 Transformer -1 -1 20 \n", + "25 Transformer normalize=True_scale=1.0 -1 -1 20 \n", + "26 Transformer -1 -1 5 \n", + "27 Transformer -1 -1 5 \n", "\n", " n_layer n_head run_name \n", "3 4 8 1_beta_noise_gaussian_data_experiment \n", @@ -452,24 +572,32 @@ "10 4 8 20_dims_uniform_error_gaussian_data_ \n", "8 4 8 3_laplace_noise_gaussian_data_experiment \n", "9 4 8 3_tstudent_noise_gaussian_data_experiment \n", - "22 4 8 4_std_sparse_linear_regression \n", - "13 4 8 beta_noise_ar1_data_experiment \n", - "12 4 8 data_sparse_linear_regression \n", + "30 4 8 4_std_sparse_linear_regression \n", + "14 4 8 beta_noise_ar1_data_experiment \n", + "15 4 8 beta_noisy_linear_regression_40_100k \n", + "13 4 8 data_sparse_linear_regression \n", "0 12 8 decision_tree_pretrained \n", - "14 4 8 exponential_noise_gaussian_data_experiment \n", - "15 4 8 laplace_noise_gaussian_data_experiment \n", - "16 4 8 rayleigh_noise_gaussian_data_experiment \n", - "20 12 8 relu_2nn_regression_pretrained \n", + "16 4 8 exponential_noise_gaussian_data_experiment \n", + "17 4 8 exponential_weighted_experiment_100k \n", + "18 4 8 exponential_weighted_experiment_150k \n", + "19 4 8 exponential_weights_experiment \n", + "20 4 8 laplace_noise_gaussian_data_experiment \n", + "12 4 8 laplace_weights_experiment \n", + "21 12 8 linear_regression_pretrained \n", + "22 4 8 rayleigh_noise_gaussian_data_experiment \n", + "28 12 8 relu_2nn_regression_pretrained \n", "11 4 8 rigde_normal_linear_regression_gaussian \n", - "21 4 8 sparse \n", + "29 4 8 sparse \n", "1 4 8 sparse_data_experiment \n", - "23 12 8 sparse_regression_pretrained \n", - "17 4 8 t_student_noise_gaussian_data_experiment \n", - "18 4 8 uniform_noise_ar1_data_experiment \n", - "19 4 8 uniform_noise_gaussian_data_experiment " + "31 12 8 sparse_regression_pretrained \n", + "24 4 8 t_student_noise_gaussian_data_experiment \n", + "23 4 8 task_sparse_data \n", + "25 4 8 uniform_hypersphere_experiment \n", + "26 4 8 uniform_noise_ar1_data_experiment \n", + "27 4 8 uniform_noise_gaussian_data_experiment " ] }, - "execution_count": 9, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -481,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "id": "a9980951", "metadata": {}, "outputs": [], @@ -491,7 +619,7 @@ "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"64d381ae-08d0-4bae-8e40-f1a68cfb2e97\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"a2fcec3c-8ce5-49bf-a8bc-08136b31ec36\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -543,7 +671,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -553,21 +681,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "20_dims_uniform_error_gaussian_data_ 64d381ae-08d0-4bae-8e40-f1a68cfb2e97\n" + "laplace_weights_experiment a2fcec3c-8ce5-49bf-a8bc-08136b31ec36\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1/1 [00:00" ] @@ -983,7 +1112,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "base", "language": "python", "name": "python3" }, @@ -997,7 +1126,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.13.9" } }, "nbformat": 4, diff --git a/src/models.py b/src/models.py index 9efe2c29..641ae6f5 100644 --- a/src/models.py +++ b/src/models.py @@ -1,3 +1,4 @@ +from statistics import variance import torch import torch.nn as nn from transformers import GPT2Model, GPT2Config @@ -137,7 +138,7 @@ def __init__(self, n_dims, n_positions, n_embd=128, n_layer=12, n_head=4): self._read_out = nn.Linear(n_embd, 1) @staticmethod - def _combine(xs_b, ys_b): + def _combine(xs_b, ys_b): # Create sequence context by interleaving x's and y's """Interleaves the x's and the y's into a single sequence.""" bsize, points, dim = xs_b.shape ys_b_wide = torch.cat( @@ -513,6 +514,7 @@ def __call__(self, xs, ys, inds=None): preds.append(pred) return torch.stack(preds, dim=1) + class RidgeModel: def __init__(self, alpha=1.0): """ @@ -800,3 +802,21 @@ def _create_ar1_covariance(self, n, ar_coef): indices = torch.arange(n, dtype=torch.float32) diff = torch.abs(indices.unsqueeze(0) - indices.unsqueeze(1)) return torch.pow(ar_coef, diff) +class WeightedLeastSquaresModel: + def __init__(self, variance_model='ols_residual'): + """WLS: Heteroscedasticity (V is diagnol matrix)""" + self.variance_model = variance_model + self.name = f"wls_var_model={variance_model}" + + def __call__(self, xs, ys, inds=None): + xs, ys = xs.cpu(), ys.cpu() + if inds is None: + inds = range(ys.shape[1]) + else: + if max(inds) >= ys.shape[1] or min(inds) < 0: + raise ValueError("inds contain indices where xs and ys are not defined") + + preds = [] + + for i in inds: + residuals = diff --git a/src/schema.py b/src/schema.py index 93cda9e9..e5e78d37 100644 --- a/src/schema.py +++ b/src/schema.py @@ -46,6 +46,7 @@ "non_stationary_linear_regression", "uniform_hypersphere_regression", "exponential_weighted_regression", + "laplace_weighted_regression", ] training_schema = { diff --git a/src/tasks.py b/src/tasks.py index f94a9f4c..48134da6 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -63,6 +63,7 @@ def get_task_sampler( "decision_tree": DecisionTree, "ar1_linear_regression": AR1LinearRegression, "exponential_weighted_regression": ExponentialWeightedRegression, + "laplace_weighted_regression": LaplaceWeightedRegression, } if task_name in task_names_to_classes: @@ -101,7 +102,8 @@ def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1): def evaluate(self, xs_b): w_b = self.w_b.to(xs_b.device) - ys_b = self.scale * (xs_b @ w_b)[:, :, 0] + ys_linear = self.scale * (xs_b @ w_b)[:, :, 0] + ys_b = ys_linear + torch.randn_like(ys_linear) return ys_b @staticmethod @@ -141,7 +143,8 @@ def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, weig def evaluate(self, xs_b): w_b = self.w_b.to(xs_b.device) - ys_b = self.scale * (xs_b @ w_b)[:, :, 0] + ys_linear = self.scale * (xs_b @ w_b)[:, :, 0] + ys_b = ys_linear + torch.randn_like(ys_linear) return ys_b @staticmethod @@ -179,7 +182,8 @@ def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, rate self.w_b = pool_dict["w"][indices] def evaluate(self, xs_b): w_b = self.w_b.to(xs_b.device) - ys_b = self.scale * (xs_b @ w_b)[:, :, 0] + ys_linear = self.scale * (xs_b @ w_b)[:, :, 0] + ys_b = ys_linear + torch.randn_like(ys_linear) return ys_b @staticmethod From ff29deb600eedd34b1036486043cf716ff27549e Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 17 Nov 2025 13:46:40 +0700 Subject: [PATCH 50/88] 500k readyy --- src/models.py | 43 ++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 42 insertions(+), 1 deletion(-) diff --git a/src/models.py b/src/models.py index 641ae6f5..7a27bbc3 100644 --- a/src/models.py +++ b/src/models.py @@ -819,4 +819,45 @@ def __call__(self, xs, ys, inds=None): preds = [] for i in inds: - residuals = + if i == 0: + preds.append(torch.zeros_like(ys[:, 0])) + continue + + train_xs, train_ys = xs[:, :i], ys[:, :i] + test_x = xs[:, i : i + 1] + + weights = self._estimate_weights(train_xs, train_ys) + sqrt_w = torch.sqrt(torch.clamp(weights, min=1e-8)) + + weighted_xs = train_xs * sqrt_w.unsqueeze(-1) + weighted_ys = train_ys * sqrt_w + + try: + ws, _, _, _ = torch.linalg.lstsq(weighted_xs, weighted_ys.unsqueeze(-1)) + except torch.linalg.LinAlgError: + # fall back to standard OLS if the weighted system is ill-conditioned + ws, _, _, _ = torch.linalg.lstsq(train_xs, train_ys.unsqueeze(-1)) + + pred = test_x @ ws + preds.append(pred[:, 0, 0]) + + return torch.stack(preds, dim=1) + + def _estimate_weights(self, train_xs, train_ys): + """Return diagonal weights (inverse variances) for WLS.""" + if self.variance_model == "uniform": + return torch.ones_like(train_ys) + + if self.variance_model == "ols_residual": + try: + ws, _, _, _ = torch.linalg.lstsq(train_xs, train_ys.unsqueeze(-1)) + preds = (train_xs @ ws).squeeze(-1) + residuals = train_ys - preds + variances = residuals.pow(2) + variances = torch.clamp(variances, min=1e-6) + weights = 1.0 / variances + return weights + except torch.linalg.LinAlgError: + return torch.ones_like(train_ys) + + raise ValueError(f"Unknown variance_model '{self.variance_model}' for WLS") From fc1a423f87c830dd8c7ad5bf153d113965086f32 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 17 Nov 2025 19:18:19 +0700 Subject: [PATCH 51/88] caa --- src/conf/exponential_weighted_regression.yaml | 10 +++++----- src/conf/laplace_weighted_regression.yaml | 10 +++++----- src/conf/template.yaml | 20 +++++++++---------- src/conf/uniform_hypersphere_regression.yaml | 10 +++++----- src/tasks.py | 15 +++++++++----- 5 files changed, 35 insertions(+), 30 deletions(-) diff --git a/src/conf/exponential_weighted_regression.yaml b/src/conf/exponential_weighted_regression.yaml index 08a3533a..744910f0 100644 --- a/src/conf/exponential_weighted_regression.yaml +++ b/src/conf/exponential_weighted_regression.yaml @@ -23,17 +23,17 @@ training: inc: 1 interval: 2000 points: - start: 6 + start: 11 end: 41 inc: 2 interval: 2000 - batch_size: 32 - learning_rate: 0.0002 - train_steps: 100001 + batch_size: 64 + learning_rate: 0.0001 + train_steps: 500001 save_every_steps: 100 keep_every_steps: 10000 -out_dir: ../models/exponential_weighted_regression +out_dir: /content/models/exponential_weighted_regression wandb: project: "in-context-training" diff --git a/src/conf/laplace_weighted_regression.yaml b/src/conf/laplace_weighted_regression.yaml index 5330582c..d88311ce 100644 --- a/src/conf/laplace_weighted_regression.yaml +++ b/src/conf/laplace_weighted_regression.yaml @@ -23,17 +23,17 @@ training: inc: 1 interval: 2000 points: - start: 6 + start: 11 end: 41 inc: 2 interval: 2000 - batch_size: 32 - learning_rate: 0.0002 - train_steps: 100001 + batch_size: 64 + learning_rate: 0.0001 + train_steps: 500001 save_every_steps: 100 keep_every_steps: 10000 -out_dir: ../models/laplace_weighted_regression +out_dir: /content/models/laplace_weighted_regression wandb: project: "in-context-training" diff --git a/src/conf/template.yaml b/src/conf/template.yaml index 12708bbe..7cbc5c69 100644 --- a/src/conf/template.yaml +++ b/src/conf/template.yaml @@ -11,7 +11,7 @@ model: n_positions: 101 training: - batch_size: 32 + batch_size: 64 curriculum: dims: start: 5 @@ -19,8 +19,8 @@ training: inc: 1 interval: 2000 points: - start: 6 - end: 30 + start: 11 + end: 41 inc: 2 interval: 2000 @@ -52,21 +52,21 @@ training: # noise_type: normal } - learning_rate: 0.0003 + learning_rate: 0.0001 keep_every_steps: 100000 num_tasks: null num_training_examples: null resume_id: null save_every_steps: 100 - train_steps: 50001 + train_steps: 500001 out_dir: wandb: - project: in-context-training - entity: in-context - notes: "" - name: "example_run" - log_every_steps: 10 + project: "in-context-training" + entity: "hai-trinh220970-ho-chi-minh-city-university-of-technology" + name: "laplace_weights_experiment" + notes: "Training with laplace-distributed weights (non-uniform on hypersphere)" + log_every_steps: 100 diff --git a/src/conf/uniform_hypersphere_regression.yaml b/src/conf/uniform_hypersphere_regression.yaml index c1b026e8..99c091fc 100644 --- a/src/conf/uniform_hypersphere_regression.yaml +++ b/src/conf/uniform_hypersphere_regression.yaml @@ -23,13 +23,13 @@ training: inc: 1 interval: 2000 points: - start: 6 - end: 30 + start: 11 + end: 41 inc: 2 interval: 2000 - batch_size: 32 - learning_rate: 0.0003 - train_steps: 50001 + batch_size: 64 + learning_rate: 0.0001 + train_steps: 500001 save_every_steps: 100 keep_every_steps: 10000 diff --git a/src/tasks.py b/src/tasks.py index 48134da6..0d3d745b 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -347,11 +347,16 @@ def sample_noise(self, shape): noise = exp_noise.sample(shape) - self.noise_std # 7. elif self.noise_type == "rayleigh": - scale_param = self.noise_std / math.sqrt(2.0 - math.pi / 2.0) - - mean = scale_param * math.sqrt(math.pi / 2.0) - rayleigh_dist = torch.distributions.Rayleigh(scale=scale_param) - noise = rayleigh_dist.sample(shape) - mean + lambda_param = self.noise_std / math.sqrt(2.0 - math.pi / 2.0) + # R = sqrt(X^2 + Y^2) với X, Y ~ N(0, sigma^2), + # where sigma = lambda_param. + sigma = lambda_param + + X = torch.randn(shape) * sigma + Y = torch.randn(shape) * sigma + R = torch.sqrt(X**2 + Y**2) + mean = lambda_param * math.sqrt(math.pi / 2.0) + noise = R - mean # 8. elif self.noise_type == "beta": alpha, beta = 2.0, 5.0 From fbb966c912a46ec312d074ef84d59b6c2603043a Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Tue, 18 Nov 2025 23:58:35 +0700 Subject: [PATCH 52/88] runall --- src/eval.ipynb | 160 ++++++++++++++++++++-------------- src/plot_utils.py | 6 +- src/run_all.py | 216 ++++++++++++++++++++++++++++------------------ 3 files changed, 232 insertions(+), 150 deletions(-) diff --git a/src/eval.ipynb b/src/eval.ipynb index 01adc67f..f6b713fa 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "id": "ed6cfeb1", "metadata": {}, "outputs": [], @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "id": "0e8d018b", "metadata": { "scrolled": true @@ -191,7 +191,7 @@ " 3_tstudent_noise_gaussian_data_experiment\n", " \n", " \n", - " 30\n", + " 32\n", " daa2cd45-f1c0-4a0c-9100-e171129624c9\n", " sparse_linear_regression\n", " Transformer\n", @@ -270,6 +270,19 @@ " \n", " \n", " 17\n", + " exponential_w\n", + " linear_regression\n", + " Transformer\n", + " rate=1.0_scale=1.0\n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " exponential_w\n", + " \n", + " \n", + " 18\n", " exponential_weighted_experiment_100k\n", " linear_regression\n", " Transformer\n", @@ -282,7 +295,7 @@ " exponential_weighted_experiment_100k\n", " \n", " \n", - " 18\n", + " 19\n", " exponential_weighted_experiment_150k\n", " linear_regression\n", " Transformer\n", @@ -295,7 +308,7 @@ " exponential_weighted_experiment_150k\n", " \n", " \n", - " 19\n", + " 20\n", " exponential_weighted_regression\n", " linear_regression\n", " Transformer\n", @@ -308,7 +321,7 @@ " exponential_weights_experiment\n", " \n", " \n", - " 20\n", + " 21\n", " laplace_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -334,7 +347,7 @@ " laplace_weights_experiment\n", " \n", " \n", - " 21\n", + " 22\n", " pretrained\n", " linear_regression\n", " Transformer\n", @@ -347,7 +360,7 @@ " linear_regression_pretrained\n", " \n", " \n", - " 22\n", + " 23\n", " rayleigh_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -360,7 +373,7 @@ " rayleigh_noise_gaussian_data_experiment\n", " \n", " \n", - " 28\n", + " 30\n", " pretrained\n", " relu_2nn_regression\n", " Transformer\n", @@ -386,7 +399,7 @@ " rigde_normal_linear_regression_gaussian\n", " \n", " \n", - " 29\n", + " 31\n", " 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615\n", " sparse_linear_regression\n", " Transformer\n", @@ -412,7 +425,7 @@ " sparse_data_experiment\n", " \n", " \n", - " 31\n", + " 33\n", " pretrained\n", " sparse_linear_regression\n", " Transformer\n", @@ -425,7 +438,7 @@ " sparse_regression_pretrained\n", " \n", " \n", - " 24\n", + " 25\n", " t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -438,7 +451,7 @@ " t_student_noise_gaussian_data_experiment\n", " \n", " \n", - " 23\n", + " 24\n", " sparse_gaussian\n", " linear_regression\n", " Transformer\n", @@ -451,7 +464,7 @@ " task_sparse_data\n", " \n", " \n", - " 25\n", + " 27\n", " uniform_hypersphere_regression\n", " linear_regression\n", " Transformer\n", @@ -465,6 +478,19 @@ " \n", " \n", " 26\n", + " uniform_hypersphere_experiment_standard\n", + " linear_regression\n", + " Transformer\n", + " normalize=True_scale=1.0\n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " uniform_hypersphere_experiment_standard\n", + " \n", + " \n", + " 28\n", " uniform_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -477,7 +503,7 @@ " uniform_noise_ar1_data_experiment\n", " \n", " \n", - " 27\n", + " 29\n", " uniform_noise_gaussian_data_experiment_\n", " linear_regression\n", " Transformer\n", @@ -504,29 +530,31 @@ "10 64d381ae-08d0-4bae-8e40-f1a68cfb2e97 linear_regression \n", "8 3_laplace_noise_gaussian_data_experiment linear_regression \n", "9 3_tstudent_noise_gaussian_data_experiment linear_regression \n", - "30 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", + "32 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", "14 beta_noise_ar1_data_experiment linear_regression \n", "15 beta_noisy_linear_regression_40_100k linear_regression \n", "13 aed365ed-51e2-4a72-8374-ae954b37be14 linear_regression \n", "0 pretrained decision_tree \n", "16 exponential_noise_gaussian_data_experiment linear_regression \n", - "17 exponential_weighted_experiment_100k linear_regression \n", - "18 exponential_weighted_experiment_150k linear_regression \n", - "19 exponential_weighted_regression linear_regression \n", - "20 laplace_noise_gaussian_data_experiment linear_regression \n", + "17 exponential_w linear_regression \n", + "18 exponential_weighted_experiment_100k linear_regression \n", + "19 exponential_weighted_experiment_150k linear_regression \n", + "20 exponential_weighted_regression linear_regression \n", + "21 laplace_noise_gaussian_data_experiment linear_regression \n", "12 a2fcec3c-8ce5-49bf-a8bc-08136b31ec36 linear_regression \n", - "21 pretrained linear_regression \n", - "22 rayleigh_noise_gaussian_data_experiment linear_regression \n", - "28 pretrained relu_2nn_regression \n", + "22 pretrained linear_regression \n", + "23 rayleigh_noise_gaussian_data_experiment linear_regression \n", + "30 pretrained relu_2nn_regression \n", "11 82e728b0-a061-448e-8d7a-f3c79c0c74e5 linear_regression \n", - "29 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", + "31 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", "1 03de46b6-429a-4151-92e6-3588231c6cad linear_regression \n", - "31 pretrained sparse_linear_regression \n", - "24 t_student_noise_gaussian_data_experiment linear_regression \n", - "23 sparse_gaussian linear_regression \n", - "25 uniform_hypersphere_regression linear_regression \n", - "26 uniform_noise_ar1_data_experiment linear_regression \n", - "27 uniform_noise_gaussian_data_experiment_ linear_regression \n", + "33 pretrained sparse_linear_regression \n", + "25 t_student_noise_gaussian_data_experiment linear_regression \n", + "24 sparse_gaussian linear_regression \n", + "27 uniform_hypersphere_regression linear_regression \n", + "26 uniform_hypersphere_experiment_standard linear_regression \n", + "28 uniform_noise_ar1_data_experiment linear_regression \n", + "29 uniform_noise_gaussian_data_experiment_ linear_regression \n", "\n", " model kwargs num_tasks num_examples n_dims \\\n", "3 Transformer -1 -1 5 \n", @@ -538,7 +566,7 @@ "10 Transformer -1 -1 20 \n", "8 Transformer -1 -1 5 \n", "9 Transformer -1 -1 5 \n", - "30 Transformer sparsity=5 -1 -1 15 \n", + "32 Transformer sparsity=5 -1 -1 15 \n", "14 Transformer -1 -1 5 \n", "15 Transformer noise_type=beta -1 -1 20 \n", "13 Transformer k=5_sparsity=3 -1 -1 15 \n", @@ -547,20 +575,22 @@ "17 Transformer rate=1.0_scale=1.0 -1 -1 20 \n", "18 Transformer rate=1.0_scale=1.0 -1 -1 20 \n", "19 Transformer rate=1.0_scale=1.0 -1 -1 20 \n", - "20 Transformer -1 -1 5 \n", + "20 Transformer rate=1.0_scale=1.0 -1 -1 20 \n", + "21 Transformer -1 -1 5 \n", "12 Transformer scale=1.0_weight_scale=1.0 -1 -1 20 \n", - "21 Transformer -1 -1 20 \n", - "22 Transformer -1 -1 5 \n", - "28 Transformer hidden_layer_size=100 -1 -1 20 \n", + "22 Transformer -1 -1 20 \n", + "23 Transformer -1 -1 5 \n", + "30 Transformer hidden_layer_size=100 -1 -1 20 \n", "11 Transformer sparsity=5 -1 -1 15 \n", - "29 Transformer -1 -1 5 \n", + "31 Transformer -1 -1 5 \n", "1 Transformer -1 -1 20 \n", - "31 Transformer sparsity=3 -1 -1 20 \n", - "24 Transformer -1 -1 5 \n", - "23 Transformer -1 -1 20 \n", - "25 Transformer normalize=True_scale=1.0 -1 -1 20 \n", - "26 Transformer -1 -1 5 \n", - "27 Transformer -1 -1 5 \n", + "33 Transformer sparsity=3 -1 -1 20 \n", + "25 Transformer -1 -1 5 \n", + "24 Transformer -1 -1 20 \n", + "27 Transformer normalize=True_scale=1.0 -1 -1 20 \n", + "26 Transformer normalize=True_scale=1.0 -1 -1 20 \n", + "28 Transformer -1 -1 5 \n", + "29 Transformer -1 -1 5 \n", "\n", " n_layer n_head run_name \n", "3 4 8 1_beta_noise_gaussian_data_experiment \n", @@ -572,32 +602,34 @@ "10 4 8 20_dims_uniform_error_gaussian_data_ \n", "8 4 8 3_laplace_noise_gaussian_data_experiment \n", "9 4 8 3_tstudent_noise_gaussian_data_experiment \n", - "30 4 8 4_std_sparse_linear_regression \n", + "32 4 8 4_std_sparse_linear_regression \n", "14 4 8 beta_noise_ar1_data_experiment \n", "15 4 8 beta_noisy_linear_regression_40_100k \n", "13 4 8 data_sparse_linear_regression \n", "0 12 8 decision_tree_pretrained \n", "16 4 8 exponential_noise_gaussian_data_experiment \n", - "17 4 8 exponential_weighted_experiment_100k \n", - "18 4 8 exponential_weighted_experiment_150k \n", - "19 4 8 exponential_weights_experiment \n", - "20 4 8 laplace_noise_gaussian_data_experiment \n", + "17 4 8 exponential_w \n", + "18 4 8 exponential_weighted_experiment_100k \n", + "19 4 8 exponential_weighted_experiment_150k \n", + "20 4 8 exponential_weights_experiment \n", + "21 4 8 laplace_noise_gaussian_data_experiment \n", "12 4 8 laplace_weights_experiment \n", - "21 12 8 linear_regression_pretrained \n", - "22 4 8 rayleigh_noise_gaussian_data_experiment \n", - "28 12 8 relu_2nn_regression_pretrained \n", + "22 12 8 linear_regression_pretrained \n", + "23 4 8 rayleigh_noise_gaussian_data_experiment \n", + "30 12 8 relu_2nn_regression_pretrained \n", "11 4 8 rigde_normal_linear_regression_gaussian \n", - "29 4 8 sparse \n", + "31 4 8 sparse \n", "1 4 8 sparse_data_experiment \n", - "31 12 8 sparse_regression_pretrained \n", - "24 4 8 t_student_noise_gaussian_data_experiment \n", - "23 4 8 task_sparse_data \n", - "25 4 8 uniform_hypersphere_experiment \n", - "26 4 8 uniform_noise_ar1_data_experiment \n", - "27 4 8 uniform_noise_gaussian_data_experiment " + "33 12 8 sparse_regression_pretrained \n", + "25 4 8 t_student_noise_gaussian_data_experiment \n", + "24 4 8 task_sparse_data \n", + "27 4 8 uniform_hypersphere_experiment \n", + "26 4 8 uniform_hypersphere_experiment_standard \n", + "28 4 8 uniform_noise_ar1_data_experiment \n", + "29 4 8 uniform_noise_gaussian_data_experiment " ] }, - "execution_count": 13, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -609,7 +641,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "a9980951", "metadata": {}, "outputs": [], @@ -619,7 +651,7 @@ "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"a2fcec3c-8ce5-49bf-a8bc-08136b31ec36\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"exponential_w\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -671,7 +703,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -681,14 +713,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "laplace_weights_experiment a2fcec3c-8ce5-49bf-a8bc-08136b31ec36\n" + "exponential_w exponential_w\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1/1 [00:00<00:00, 9198.04it/s]" + "100%|██████████| 1/1 [00:00<00:00, 5309.25it/s]" ] }, { @@ -708,7 +740,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] diff --git a/src/plot_utils.py b/src/plot_utils.py index 55a7faa9..7a0c31ab 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -107,10 +107,12 @@ def basic_plot(metrics, models=None, trivial=1.0): ax.set_xlabel("in-context examples") ax.set_ylabel("squared error") ax.set_xlim(-1, len(low) + 0.1) - ax.set_ylim(-0.1, 1.0) + ax.set_ylim(-0.1, 2.0) - legend = ax.legend(loc="upper left", bbox_to_anchor=(1, 1)) + # legend = ax.legend(loc="upper left", bbox_to_anchor=(1, 1)) + legend = ax.legend(loc="best") + fig.set_size_inches(4, 3) for line in legend.get_lines(): line.set_linewidth(3) diff --git a/src/run_all.py b/src/run_all.py index def50bfe..cb374137 100644 --- a/src/run_all.py +++ b/src/run_all.py @@ -26,7 +26,17 @@ def prepare_out_dir(args): yaml.dump(args.__dict__, yaml_file, default_flow_style=False) -def run_one_experiment(base_config_path: str, task: str, task_kwargs: dict, data_kwargs: dict, run_name: str, resume_id: str = None, data_type: str = None, train_steps: int = None): +def run_one_experiment( + base_config_path: str, + task: str, + task_kwargs: dict, + data_kwargs: dict, + run_name: str, + resume_id: str = None, + data_type: str = None, + train_steps: int = None, + sequence_length: int = None, +): """ Run a single experiment with specified task, task_kwargs, and data_kwargs. @@ -46,16 +56,30 @@ def run_one_experiment(base_config_path: str, task: str, task_kwargs: dict, data base_config = yaml.safe_load(f) # Modify config for this experiment + # Ensure training section exists + if 'training' not in base_config: + base_config['training'] = {} + base_config['training']['task'] = task base_config['training']['task_kwargs'] = task_kwargs base_config['training']['data_kwargs'] = data_kwargs if data_type is not None: base_config['training']['data'] = data_type - base_config['wandb']['name'] = run_name if resume_id is not None: base_config['training']['resume_id'] = resume_id if train_steps is not None: base_config['training']['train_steps'] = int(train_steps) + if sequence_length is not None: + curriculum_points = base_config['training'].setdefault('curriculum', {}).setdefault('points', {}) + curriculum_points['start'] = sequence_length + curriculum_points['end'] = sequence_length + curriculum_points['inc'] = 0 + curriculum_points.setdefault('interval', 1) + + # Ensure wandb section exists + if 'wandb' not in base_config: + base_config['wandb'] = {} + base_config['wandb']['name'] = run_name # Create temporary config file temp_config_file = tempfile.NamedTemporaryFile( @@ -91,6 +115,9 @@ def run_one_experiment(base_config_path: str, task: str, task_kwargs: dict, data print(f"Data type: {data_type}") if train_steps is not None: print(f"Train steps override: {train_steps}") + if sequence_length is not None: + print(f"Sequence length override: {sequence_length}") + print(f"{'='*60}\n") train_main(args) @@ -106,30 +133,26 @@ def get_default_experiments(): Returns a list of experiment configs: (task, task_kwargs, data_kwargs, run_name, data_type) """ experiments = [] - + # ===== Sparse Linear Regression Experiments ===== - # Sparse w (weight sparsity) for sparsity in [3, 5, 7]: - experiments.append(( - "sparse_linear_regression", - {"sparsity": sparsity}, # task_kwargs - {}, # data_kwargs - f"sparse_w_sparsity_{sparsity}", - None # data_type: use default from config - )) - - # Sparse data (data sparsity) - using sparse_gaussian data + experiments.append({ + "task": "sparse_linear_regression", + "task_kwargs": {"sparsity": sparsity}, + "data_kwargs": {}, + "run_name": f"sparse_w_sparsity_{sparsity}", + "data_type": None, + }) + for data_sparsity in [5, 10, 15]: - experiments.append(( - "sparse_linear_regression", - {"sparsity": 3}, # task_kwargs (w sparsity) - {"sparsity": data_sparsity}, # data_kwargs (data sparsity) - f"sparse_data_sparsity_{data_sparsity}", - "sparse_gaussian" # data_type override - )) - - # ===== Noisy Linear Regression Experiments ===== - # Different noise types + experiments.append({ + "task": "sparse_linear_regression", + "task_kwargs": {"sparsity": 3}, + "data_kwargs": {"sparsity": data_sparsity}, + "run_name": f"sparse_data_sparsity_{data_sparsity}", + "data_type": "sparse_gaussian", + }) + noise_types = [ "normal", "uniform", @@ -141,26 +164,25 @@ def get_default_experiments(): "beta", "poisson", ] - + for noise_type in noise_types: - experiments.append(( - "noisy_linear_regression", - {"noise_type": noise_type, "noise_std": 2.0}, # task_kwargs - {}, # data_kwargs - f"noisy_{noise_type}", - None # data_type: use default from config - )) - - # Different noise_std values for normal noise + experiments.append({ + "task": "noisy_linear_regression", + "task_kwargs": {"noise_type": noise_type, "noise_std": 2.0}, + "data_kwargs": {}, + "run_name": f"noisy_{noise_type}", + "data_type": None, + }) + for noise_std in [0.5, 1.0, 2.0, 3.0]: - experiments.append(( - "noisy_linear_regression", - {"noise_type": "normal", "noise_std": noise_std}, # task_kwargs - {}, # data_kwargs - f"noisy_normal_std_{noise_std}", - None # data_type: use default from config - )) - + experiments.append({ + "task": "noisy_linear_regression", + "task_kwargs": {"noise_type": "normal", "noise_std": noise_std}, + "data_kwargs": {}, + "run_name": f"noisy_normal_std_{noise_std}", + "data_type": None, + }) + return experiments @@ -170,8 +192,8 @@ def build_parser(): ) parser.add_argument( "--config", - default="src/conf/toy.yaml", - help="Base config yaml (e.g., src/conf/toy.yaml)", + default="src/conf/template.yaml", + help="Base config yaml (e.g., src/conf/template.yaml)", ) parser.add_argument( "--task", @@ -232,6 +254,13 @@ def build_parser(): action="store_true", help="Skip runs that already have config.yaml in output directory", ) + parser.add_argument( + "--sequence_lengths", + nargs="*", + type=int, + default=[], + help="Optional list of sequence lengths (curriculum.n_points) to sweep over", + ) return parser @@ -245,53 +274,67 @@ def main(): if cli_args.task in ["sparse", "both"]: # Sparse w experiments (weight sparsity, regular gaussian data) for sparsity in cli_args.sparse_w_sparsities: - experiments.append(( - "sparse_linear_regression", - {"sparsity": sparsity}, - {}, - f"{cli_args.base_run_name}_sparse_w_{sparsity}", - None # data_type: use default from config - )) + experiments.append({ + "task": "sparse_linear_regression", + "task_kwargs": {"sparsity": sparsity}, + "data_kwargs": {}, + "run_name": f"{cli_args.base_run_name}_sparse_w_{sparsity}", + "data_type": None, + }) # Sparse data experiments (sparse_gaussian data) for data_sparsity in cli_args.sparse_data_sparsities: - experiments.append(( - "sparse_linear_regression", - {"sparsity": 3}, # w sparsity - {"sparsity": data_sparsity}, # data sparsity - f"{cli_args.base_run_name}_sparse_data_{data_sparsity}", - "sparse_gaussian" # data_type override - )) + experiments.append({ + "task": "sparse_linear_regression", + "task_kwargs": {"sparsity": 3}, + "data_kwargs": {"sparsity": data_sparsity}, + "run_name": f"{cli_args.base_run_name}_sparse_data_{data_sparsity}", + "data_type": "sparse_gaussian", + }) if cli_args.task in ["noisy", "both"]: # Different noise types for noise_type in cli_args.noise_types: - experiments.append(( - "noisy_linear_regression", - {"noise_type": noise_type, "noise_std": 2.0}, - {}, - f"{cli_args.base_run_name}_noisy_{noise_type}", - None # data_type: use default from config - )) + experiments.append({ + "task": "noisy_linear_regression", + "task_kwargs": {"noise_type": noise_type, "noise_std": 2.0}, + "data_kwargs": {}, + "run_name": f"{cli_args.base_run_name}_noisy_{noise_type}", + "data_type": None, + }) # Different noise_std for normal noise for noise_std in cli_args.noise_stds: - experiments.append(( - "noisy_linear_regression", - {"noise_type": "normal", "noise_std": noise_std}, - {}, - f"{cli_args.base_run_name}_noisy_normal_std_{noise_std}", - None # data_type: use default from config - )) + experiments.append({ + "task": "noisy_linear_regression", + "task_kwargs": {"noise_type": "normal", "noise_std": noise_std}, + "data_kwargs": {}, + "run_name": f"{cli_args.base_run_name}_noisy_normal_std_{noise_std}", + "data_type": None, + }) if cli_args.task == "custom": - # Use default experiments default_experiments = get_default_experiments() - # Add base_run_name prefix experiments = [ - (task, tk, dk, f"{cli_args.base_run_name}_{name}", dt) - for task, tk, dk, name, dt in default_experiments + { + "task": exp["task"], + "task_kwargs": exp["task_kwargs"], + "data_kwargs": exp["data_kwargs"], + "run_name": f"{cli_args.base_run_name}_{exp['run_name']}", + "data_type": exp["data_type"], + } + for exp in default_experiments ] + + if cli_args.sequence_lengths: + expanded_experiments = [] + for exp in experiments: + for seq_len in cli_args.sequence_lengths: + new_exp = dict(exp) + new_exp["sequence_length"] = seq_len + new_exp["run_name"] = f"{exp['run_name']}_seq_{seq_len}" + expanded_experiments.append(new_exp) + experiments = expanded_experiments # Run experiments print(f"\n{'='*60}") @@ -299,20 +342,24 @@ def main(): print(f"{'='*60}\n") for idx, exp in enumerate(experiments, 1): - # Handle both 4-tuple and 5-tuple formats - if len(exp) == 4: - task, task_kwargs, data_kwargs, run_name = exp - data_type = None - else: - task, task_kwargs, data_kwargs, run_name, data_type = exp - + task = exp["task"] + task_kwargs = exp["task_kwargs"] + data_kwargs = exp["data_kwargs"] + run_name = exp["run_name"] + data_type = exp.get("data_type") + sequence_length = exp.get("sequence_length") + print(f"\n[{idx}/{len(experiments)}] Preparing: {run_name}") # Check if should skip existing if cli_args.skip_existing: # Try to find existing run by checking base out_dir - base_config = yaml.safe_load(open(cli_args.config)) + with open(cli_args.config, 'r') as f: + base_config = yaml.safe_load(f) base_out_dir = base_config.get('out_dir', '../models') + # Handle empty out_dir + if not base_out_dir or base_out_dir.strip() == '': + base_out_dir = '../models' # Check if any subdirectory has this run_name in config if os.path.exists(base_out_dir): task_dir = os.path.join(base_out_dir, task) @@ -339,7 +386,8 @@ def main(): run_name, resume_id=resume_id, data_type=data_type, - train_steps=cli_args.train_steps + train_steps=cli_args.train_steps, + sequence_length=sequence_length, ) except Exception as e: print(f"\n{'!'*60}") From 8346ed6801eddefefcfe953452391916c6a8e562 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Fri, 21 Nov 2025 08:02:50 +0700 Subject: [PATCH 53/88] add exponential, laplace, beta, gamma place --- src/eval.ipynb | 122 +++++++++++++++++++++++++++--------------------- src/eval.py | 5 +- src/samplers.py | 105 +++++++++++++++++++++++++++++++++++++++++ src/schema.py | 2 +- src/tasks.py | 1 - src/train.py | 4 ++ 6 files changed, 183 insertions(+), 56 deletions(-) diff --git a/src/eval.ipynb b/src/eval.ipynb index f6b713fa..07bba5d6 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "id": "ed6cfeb1", "metadata": {}, "outputs": [], @@ -191,7 +191,7 @@ " 3_tstudent_noise_gaussian_data_experiment\n", " \n", " \n", - " 32\n", + " 33\n", " daa2cd45-f1c0-4a0c-9100-e171129624c9\n", " sparse_linear_regression\n", " Transformer\n", @@ -334,6 +334,19 @@ " laplace_noise_gaussian_data_experiment\n", " \n", " \n", + " 22\n", + " laplace_w\n", + " linear_regression\n", + " Transformer\n", + " scale=1.0_weight_scale=1.0\n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " laplace_w\n", + " \n", + " \n", " 12\n", " a2fcec3c-8ce5-49bf-a8bc-08136b31ec36\n", " linear_regression\n", @@ -347,7 +360,7 @@ " laplace_weights_experiment\n", " \n", " \n", - " 22\n", + " 23\n", " pretrained\n", " linear_regression\n", " Transformer\n", @@ -360,7 +373,7 @@ " linear_regression_pretrained\n", " \n", " \n", - " 23\n", + " 24\n", " rayleigh_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -373,7 +386,7 @@ " rayleigh_noise_gaussian_data_experiment\n", " \n", " \n", - " 30\n", + " 31\n", " pretrained\n", " relu_2nn_regression\n", " Transformer\n", @@ -399,7 +412,7 @@ " rigde_normal_linear_regression_gaussian\n", " \n", " \n", - " 31\n", + " 32\n", " 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615\n", " sparse_linear_regression\n", " Transformer\n", @@ -425,7 +438,7 @@ " sparse_data_experiment\n", " \n", " \n", - " 33\n", + " 34\n", " pretrained\n", " sparse_linear_regression\n", " Transformer\n", @@ -438,7 +451,7 @@ " sparse_regression_pretrained\n", " \n", " \n", - " 25\n", + " 26\n", " t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -451,7 +464,7 @@ " t_student_noise_gaussian_data_experiment\n", " \n", " \n", - " 24\n", + " 25\n", " sparse_gaussian\n", " linear_regression\n", " Transformer\n", @@ -464,7 +477,7 @@ " task_sparse_data\n", " \n", " \n", - " 27\n", + " 28\n", " uniform_hypersphere_regression\n", " linear_regression\n", " Transformer\n", @@ -477,7 +490,7 @@ " uniform_hypersphere_experiment\n", " \n", " \n", - " 26\n", + " 27\n", " uniform_hypersphere_experiment_standard\n", " linear_regression\n", " Transformer\n", @@ -490,7 +503,7 @@ " uniform_hypersphere_experiment_standard\n", " \n", " \n", - " 28\n", + " 29\n", " uniform_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -503,7 +516,7 @@ " uniform_noise_ar1_data_experiment\n", " \n", " \n", - " 29\n", + " 30\n", " uniform_noise_gaussian_data_experiment_\n", " linear_regression\n", " Transformer\n", @@ -530,7 +543,7 @@ "10 64d381ae-08d0-4bae-8e40-f1a68cfb2e97 linear_regression \n", "8 3_laplace_noise_gaussian_data_experiment linear_regression \n", "9 3_tstudent_noise_gaussian_data_experiment linear_regression \n", - "32 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", + "33 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", "14 beta_noise_ar1_data_experiment linear_regression \n", "15 beta_noisy_linear_regression_40_100k linear_regression \n", "13 aed365ed-51e2-4a72-8374-ae954b37be14 linear_regression \n", @@ -541,20 +554,21 @@ "19 exponential_weighted_experiment_150k linear_regression \n", "20 exponential_weighted_regression linear_regression \n", "21 laplace_noise_gaussian_data_experiment linear_regression \n", + "22 laplace_w linear_regression \n", "12 a2fcec3c-8ce5-49bf-a8bc-08136b31ec36 linear_regression \n", - "22 pretrained linear_regression \n", - "23 rayleigh_noise_gaussian_data_experiment linear_regression \n", - "30 pretrained relu_2nn_regression \n", + "23 pretrained linear_regression \n", + "24 rayleigh_noise_gaussian_data_experiment linear_regression \n", + "31 pretrained relu_2nn_regression \n", "11 82e728b0-a061-448e-8d7a-f3c79c0c74e5 linear_regression \n", - "31 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", + "32 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", "1 03de46b6-429a-4151-92e6-3588231c6cad linear_regression \n", - "33 pretrained sparse_linear_regression \n", - "25 t_student_noise_gaussian_data_experiment linear_regression \n", - "24 sparse_gaussian linear_regression \n", - "27 uniform_hypersphere_regression linear_regression \n", - "26 uniform_hypersphere_experiment_standard linear_regression \n", - "28 uniform_noise_ar1_data_experiment linear_regression \n", - "29 uniform_noise_gaussian_data_experiment_ linear_regression \n", + "34 pretrained sparse_linear_regression \n", + "26 t_student_noise_gaussian_data_experiment linear_regression \n", + "25 sparse_gaussian linear_regression \n", + "28 uniform_hypersphere_regression linear_regression \n", + "27 uniform_hypersphere_experiment_standard linear_regression \n", + "29 uniform_noise_ar1_data_experiment linear_regression \n", + "30 uniform_noise_gaussian_data_experiment_ linear_regression \n", "\n", " model kwargs num_tasks num_examples n_dims \\\n", "3 Transformer -1 -1 5 \n", @@ -566,7 +580,7 @@ "10 Transformer -1 -1 20 \n", "8 Transformer -1 -1 5 \n", "9 Transformer -1 -1 5 \n", - "32 Transformer sparsity=5 -1 -1 15 \n", + "33 Transformer sparsity=5 -1 -1 15 \n", "14 Transformer -1 -1 5 \n", "15 Transformer noise_type=beta -1 -1 20 \n", "13 Transformer k=5_sparsity=3 -1 -1 15 \n", @@ -577,20 +591,21 @@ "19 Transformer rate=1.0_scale=1.0 -1 -1 20 \n", "20 Transformer rate=1.0_scale=1.0 -1 -1 20 \n", "21 Transformer -1 -1 5 \n", + "22 Transformer scale=1.0_weight_scale=1.0 -1 -1 20 \n", "12 Transformer scale=1.0_weight_scale=1.0 -1 -1 20 \n", - "22 Transformer -1 -1 20 \n", - "23 Transformer -1 -1 5 \n", - "30 Transformer hidden_layer_size=100 -1 -1 20 \n", + "23 Transformer -1 -1 20 \n", + "24 Transformer -1 -1 5 \n", + "31 Transformer hidden_layer_size=100 -1 -1 20 \n", "11 Transformer sparsity=5 -1 -1 15 \n", - "31 Transformer -1 -1 5 \n", + "32 Transformer -1 -1 5 \n", "1 Transformer -1 -1 20 \n", - "33 Transformer sparsity=3 -1 -1 20 \n", - "25 Transformer -1 -1 5 \n", - "24 Transformer -1 -1 20 \n", + "34 Transformer sparsity=3 -1 -1 20 \n", + "26 Transformer -1 -1 5 \n", + "25 Transformer -1 -1 20 \n", + "28 Transformer normalize=True_scale=1.0 -1 -1 20 \n", "27 Transformer normalize=True_scale=1.0 -1 -1 20 \n", - "26 Transformer normalize=True_scale=1.0 -1 -1 20 \n", - "28 Transformer -1 -1 5 \n", "29 Transformer -1 -1 5 \n", + "30 Transformer -1 -1 5 \n", "\n", " n_layer n_head run_name \n", "3 4 8 1_beta_noise_gaussian_data_experiment \n", @@ -602,7 +617,7 @@ "10 4 8 20_dims_uniform_error_gaussian_data_ \n", "8 4 8 3_laplace_noise_gaussian_data_experiment \n", "9 4 8 3_tstudent_noise_gaussian_data_experiment \n", - "32 4 8 4_std_sparse_linear_regression \n", + "33 4 8 4_std_sparse_linear_regression \n", "14 4 8 beta_noise_ar1_data_experiment \n", "15 4 8 beta_noisy_linear_regression_40_100k \n", "13 4 8 data_sparse_linear_regression \n", @@ -613,20 +628,21 @@ "19 4 8 exponential_weighted_experiment_150k \n", "20 4 8 exponential_weights_experiment \n", "21 4 8 laplace_noise_gaussian_data_experiment \n", + "22 4 8 laplace_w \n", "12 4 8 laplace_weights_experiment \n", - "22 12 8 linear_regression_pretrained \n", - "23 4 8 rayleigh_noise_gaussian_data_experiment \n", - "30 12 8 relu_2nn_regression_pretrained \n", + "23 12 8 linear_regression_pretrained \n", + "24 4 8 rayleigh_noise_gaussian_data_experiment \n", + "31 12 8 relu_2nn_regression_pretrained \n", "11 4 8 rigde_normal_linear_regression_gaussian \n", - "31 4 8 sparse \n", + "32 4 8 sparse \n", "1 4 8 sparse_data_experiment \n", - "33 12 8 sparse_regression_pretrained \n", - "25 4 8 t_student_noise_gaussian_data_experiment \n", - "24 4 8 task_sparse_data \n", - "27 4 8 uniform_hypersphere_experiment \n", - "26 4 8 uniform_hypersphere_experiment_standard \n", - "28 4 8 uniform_noise_ar1_data_experiment \n", - "29 4 8 uniform_noise_gaussian_data_experiment " + "34 12 8 sparse_regression_pretrained \n", + "26 4 8 t_student_noise_gaussian_data_experiment \n", + "25 4 8 task_sparse_data \n", + "28 4 8 uniform_hypersphere_experiment \n", + "27 4 8 uniform_hypersphere_experiment_standard \n", + "29 4 8 uniform_noise_ar1_data_experiment \n", + "30 4 8 uniform_noise_gaussian_data_experiment " ] }, "execution_count": 6, @@ -641,7 +657,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "id": "a9980951", "metadata": {}, "outputs": [], @@ -651,7 +667,7 @@ "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"exponential_w\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"laplace_w\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -703,7 +719,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -713,14 +729,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "exponential_w exponential_w\n" + "laplace_w laplace_w\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1/1 [00:00<00:00, 5309.25it/s]" + "100%|██████████| 1/1 [00:00<00:00, 5940.94it/s]" ] }, { @@ -740,7 +756,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] diff --git a/src/eval.py b/src/eval.py index 17a24f04..c9a774d5 100644 --- a/src/eval.py +++ b/src/eval.py @@ -222,6 +222,10 @@ def build_evals(conf): "ar2": {"ar1_coef", "ar2_coef", "noise_std", "bias", "scale"}, "vr2": {"ar1_mat", "ar2_mat", "noise_std", "bias", "scale"}, "nonstation": {"coef_base", "coef_amplitude", "noise_std", "bias", "scale"}, + "exponential": {"bias", "scale", "rate"}, + "laplace": {"bias", "scale", "loc", "laplace_scale"}, + "gamma": {"bias", "scale", "concentration", "rate"}, + "beta": {"bias", "scale", "alpha", "beta"}, } task_whitelist = { "linear_regression": {"scale", "uniform"}, @@ -233,7 +237,6 @@ def build_evals(conf): "ar1_linear_regression": {"scale", "ar_coef", "noise_std", "compute_gradient"}, "uniform_hypersphere_regression": {"scale"}, } - original_data_kwargs = conf.training.data_kwargs if hasattr(conf.training, "data_kwargs") else {} original_task_kwargs = conf.training.task_kwargs if hasattr(conf.training, "task_kwargs") else {} cleaned_data_kwargs = {k: v for k, v in (original_data_kwargs or {}).items() if k in data_whitelist.get(data_name, set())} cleaned_task_kwargs = {k: v for k, v in (original_task_kwargs or {}).items() if k in task_whitelist.get(task_name, set())} diff --git a/src/samplers.py b/src/samplers.py index 7d010cab..218e0aa4 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -1,3 +1,4 @@ +import enum import math import torch @@ -20,6 +21,10 @@ def get_data_sampler(data_name, n_dims, **kwargs): "ar2":AR2Sampler, "vr2":VR2Sampler, "nonstation":NonStationarySampler, + "exponential": ExponentialSampler, + "laplace": LaplaceSampler, + "gamma": GammaSampler, + "beta": BetaSampler, } if data_name in names_to_classes: sampler_cls = names_to_classes[data_name] @@ -44,6 +49,84 @@ def sample_transformation(eigenvalues, normalize=False): t *= math.sqrt(n_dims / norm_subspace) return t +def _sample_distribution(dist, b_size, inner_shape, seeds=None): + sample_shape = (b_size, *inner_shape) + if seeds is None: + return dist.sample(sample_shape) + + assert len(seeds) == b_size + template = dist.mean + xs_b = torch.empty(sample_shape, dtype=template.dtype, device=template.device) + for i, seed in enumerate(seeds): + with torch.random.fork_rng(): + torch.manual_seed(int(seed)) + xs_b[i] = dist.sample(inner_shape) + return xs_b + + +class ExponentialSampler(DataSampler): + def __init__(self, n_dims, bias=None, scale=None, rate=1.0): + super().__init__(n_dims) + self.bias = bias + self.scale = scale + self.rate = float(rate) + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + exp_dist = torch.distributions.Exponential(rate=self.rate) + xs_b = _sample_distribution(exp_dist, b_size, (n_points, self.n_dims), seeds) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b + + +class LaplaceSampler(DataSampler): + def __init__(self, n_dims, bias=None, scale=None, loc=0.0, laplace_scale=1.0): + super().__init__(n_dims) + self.bias = bias + self.scale = scale + self.loc = float(loc) + self.laplace_scale = float(laplace_scale) + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + laplace_dist = torch.distributions.Laplace(loc=self.loc, scale=self.laplace_scale) + xs_b = _sample_distribution(laplace_dist, b_size, (n_points, self.n_dims), seeds) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b + + +class GammaSampler(DataSampler): + def __init__(self, n_dims, bias=None, scale=None, concentration=2.0, rate=1.0): + super().__init__(n_dims) + if concentration <= 0 or rate <= 0: + raise ValueError("concentration and rate must be positive for Gamma distribution.") + self.bias = bias + self.scale = scale + self.concentration = float(concentration) + self.rate = float(rate) + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + gamma_dist = torch.distributions.Gamma(concentration=self.concentration, rate=self.rate) + xs_b = _sample_distribution(gamma_dist, b_size, (n_points, self.n_dims), seeds) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b + class GaussianSampler(DataSampler): def __init__(self, n_dims, bias=None, scale=None): @@ -69,6 +152,28 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): xs_b[:, :, n_dims_truncated:] = 0 return xs_b + +class BetaSampler(DataSampler): + def __init__(self, n_dims, bias=None, scale=None, alpha=2.0, beta=5.0): + super().__init__(n_dims) + if alpha <= 0 or beta <= 0: + raise ValueError("alpha and beta must be positive for Beta distribution.") + self.bias = bias + self.scale = scale + self.alpha = float(alpha) + self.beta = float(beta) + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + beta_dist = torch.distributions.Beta(concentration1=self.alpha, concentration0=self.beta) + xs_b = _sample_distribution(beta_dist, b_size, (n_points, self.n_dims), seeds) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b # code này là thêm: class SparseGaussianSampler(DataSampler): def __init__(self, n_dims, k, bias=None, scale=None): diff --git a/src/schema.py b/src/schema.py index e5e78d37..1923c335 100644 --- a/src/schema.py +++ b/src/schema.py @@ -54,7 +54,7 @@ "task_kwargs": merge(tdict, required), "num_tasks": merge(tinteger, nullable, default(None)), "num_training_examples": merge(tinteger, nullable, default(None)), - "data": merge(tstring, allowed(["gaussian","ar1","vr1","ar2",'vr2',"nonstation", "sparse_gaussian"])), + "data": merge(tstring, allowed(["gaussian","ar1","vr1","ar2",'vr2',"nonstation", "sparse_gaussian", "gamma", "beta", "exponential", "laplace"])), "data_kwargs": merge(tdict, default({})), # Thêm dòng này "batch_size": merge(tinteger, default(64)), "learning_rate": merge(tfloat, default(3e-4)), diff --git a/src/tasks.py b/src/tasks.py index 0d3d745b..aff0656c 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -237,7 +237,6 @@ def get_metric(): def get_training_metric(): return mean_squared_error - class SparseLinearRegression(LinearRegression): def __init__( self, diff --git a/src/train.py b/src/train.py index e216a989..8ce1880a 100644 --- a/src/train.py +++ b/src/train.py @@ -56,6 +56,10 @@ def _sanitize_training_kwargs(args): "ar2": {"ar1_coef", "ar2_coef", "noise_std", "bias", "scale"}, "vr2": {"ar1_mat", "ar2_mat", "noise_std", "bias", "scale"}, "nonstation": {"coef_base", "coef_amplitude", "noise_std", "bias", "scale"}, + "exponential": {"bias", "scale", "rate"}, + "laplace": {"bias", "scale", "loc", "laplace_scale"}, + "gamma": {"bias", "scale", "concentration", "rate"}, + "beta": {"bias", "scale", "alpha", "beta"}, } data_name = args.training.data From cf10486361c58af19809fbe476354343df209475 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Fri, 21 Nov 2025 08:16:49 +0700 Subject: [PATCH 54/88] add exponential, laplace, beta, gamma place.. --- src/eval.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/eval.py b/src/eval.py index c9a774d5..37973540 100644 --- a/src/eval.py +++ b/src/eval.py @@ -237,6 +237,7 @@ def build_evals(conf): "ar1_linear_regression": {"scale", "ar_coef", "noise_std", "compute_gradient"}, "uniform_hypersphere_regression": {"scale"}, } + original_data_kwargs = conf.training.data_kwargs if hasattr(conf.training, "data_kwargs") else {} original_task_kwargs = conf.training.task_kwargs if hasattr(conf.training, "task_kwargs") else {} cleaned_data_kwargs = {k: v for k, v in (original_data_kwargs or {}).items() if k in data_whitelist.get(data_name, set())} cleaned_task_kwargs = {k: v for k, v in (original_task_kwargs or {}).items() if k in task_whitelist.get(task_name, set())} From c798ab5a57535f3de9627b03de548e8da0851153 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Fri, 21 Nov 2025 15:15:22 +0700 Subject: [PATCH 55/88] wlaplacexexponentialzpoisson --- ...w_laplace_x_exponential_noise_poisson.yaml | 72 +++++++++++++++++++ src/eval.ipynb | 16 ++--- src/models.py | 4 ++ src/plot_utils.py | 5 ++ src/schema.py | 1 + src/tasks.py | 60 ++++++++++++++++ 6 files changed, 150 insertions(+), 8 deletions(-) create mode 100644 src/conf/w_laplace_x_exponential_noise_poisson.yaml diff --git a/src/conf/w_laplace_x_exponential_noise_poisson.yaml b/src/conf/w_laplace_x_exponential_noise_poisson.yaml new file mode 100644 index 00000000..1d59db57 --- /dev/null +++ b/src/conf/w_laplace_x_exponential_noise_poisson.yaml @@ -0,0 +1,72 @@ +inherit: + - models/standard.yaml + - wandb.yaml + +model: + family: gpt2 + n_dims: 20 + n_embd: 128 + n_head: 8 + n_layer: 4 + n_positions: 101 + +training: + batch_size: 64 + curriculum: + dims: + start: 5 + end: 20 + inc: 1 + interval: 2000 + points: + start: 11 + end: 41 + inc: 2 + interval: 2000 + + # One of: gaussian, sparse_gaussian, ar1, vr1, ar2, vr2, nonstation + data: exponential + + # Data kwargs: + # - When data == 'sparse_gaussian': you may set 'k' (number of non-zero coords). + # - For other data values: any 'k' key will be ignored automatically. + data_kwargs: { + # k: 8 # only when data: sparse_gaussian + # scale: 1.0 # optional for many samplers + } + + # Task: choose a base task + # One of: linear_regression, sparse_linear_regression, linear_classification, + # relu_2nn_regression, decision_tree, noisy_linear_regression, + # ar1_linear_regression, ar2_linear_regression, non_stationary_linear_regression, + # uniform_hypersphere_regression + task: wlaplace_noisypoisson + + # Task kwargs: + # - When task == 'sparse_linear_regression': you may set 'sparsity'. + # - For other tasks: any 'sparsity' key will be ignored automatically. + task_kwargs: { + # sparsity: 5 # only when task: sparse_linear_regression + # noise_std: 2.0 # e.g., for noisy_linear_regression + # renormalize_ys: false + # noise_type: normal + } + + learning_rate: 0.0001 + keep_every_steps: 100000 + num_tasks: null + num_training_examples: null + resume_id: null + save_every_steps: 100 + train_steps: 500001 + +out_dir: /content/models/linear_regression/uniform_hypersphere_regression + +wandb: + project: "in-context-training" + entity: "hai-trinh220970-ho-chi-minh-city-university-of-technology" + name: "laplace_weights_experiment" + notes: "Training with laplace-distributed weights (non-uniform on hypersphere)" + log_every_steps: 100 + + diff --git a/src/eval.ipynb b/src/eval.ipynb index 07bba5d6..c61a549e 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "id": "0e8d018b", "metadata": { "scrolled": true @@ -645,7 +645,7 @@ "30 4 8 uniform_noise_gaussian_data_experiment " ] }, - "execution_count": 6, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -657,7 +657,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "id": "a9980951", "metadata": {}, "outputs": [], @@ -667,7 +667,7 @@ "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"laplace_w\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"exponential_w\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -719,7 +719,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -729,14 +729,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "laplace_w laplace_w\n" + "exponential_w exponential_w\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1/1 [00:00<00:00, 5940.94it/s]" + "100%|██████████| 1/1 [00:00<00:00, 4152.78it/s]" ] }, { @@ -756,7 +756,7 @@ }, { "data": { - "image/png": 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ycrqwYk80M5827UUMGzYSRqMJdnssCC8lhQLybPjkkw9hNltw1133o3fvPmzW/sADj6Kqqgrffvt14px9+vTF0Ucfg549e8PpTGHbKFvwVVddx67/tNPOYtsOO+xIdk29evXGscdOZisa4p9//sby5f/i/vsfZufo1q07+w20qvnww/dq9clFF13Gjkm/rzPDVwKNQNObWUoAuyIhw56J4qodqMw9AWFrD0QUAVXIhl9LgZb04N7tW4g7I25sE50op+jSbsuAzcOwqEKP0+UQDDojQhEFvkAUtmQ1QDVBOcQTzXVqamYAsbjzvYNUJnFo0LfbnXjnnbewZctmFBZuw/r1sYp9yTl7unbtXusYNJj7fLHJzVFHHYuvvvoCZ5xxElMlkWpnwoTDmUCoj40b16N//wG16n7QTJ9WFrQvTn7+zgMyCSqxekZkNsccMGjwjkMp5kntQ6xdu5qtBqZMmVTrGFSTJLkOeWpqGvs9HC4EGgWVDpSsKZBLtyLDbEXEkYFyVyn8zv3Yqt1KKtuQjGBYTuh7RctQvOF6EGdbJqFEtEEw+6B1XY5NW4agwh2EM8MBiiiIRBT4QzJSbIZatoGoEoVMqYT5Yq1VeHJivz1UB6HZ1UEtsRKg2Xkc0offeOM1OPDAgzF06HAcddREhEIh3Hbbf2t9p75CTXH1ZEpKCt58cxaWL1/GVDZ//TUPH374Lptdk7po5+/Vf12apkJHWRUT12lsIGtmbeJCoS4kxEgVRaqjusTVXA2dp7PCVwKNgG58wWSHZDBCCQaQZUlF2BaB11fF1LX0uFIZSaNeZAN6OKJAFQ0wmvszG8GZ1imoEs0QrG5oBSuwoKQ7jkmLQCfpISsafIEIrGayDdScU1YURFUZenSuvDFtBenhj+ub2SjDsBrYAYQqYx/MeRANdqCegWqvDMPNaBOoy3vvvYMRI0bhwQcfT2z76KOYqqSxNijSyVP1P9LVkyChwf/RRx9g+vv6hACpbeg7NCOPCxdSPW3btg0nnnhys/02UiP5/X62MujRo2diO10bqaGmTDmt2c61r8CFQCNRRT0kiwNyqBi6cBjZ1nREoxGEwv7Ew0MPOLmRBsMKWxWU2Q7CMN9veNP/Gc6ynQw/FSCxV+GzEHBU2Ai9KY0N9iQ0qP5w8mpAUSm1RAQGycCDRlsJGoAzLLsXuqoqQqtOFidY9BANekCqmWU2S3I0oeWEQFZWDn7//RcsXboEWVlZWLRoITO2EnG1yu6gTJbkgUOz7mHDRqC0tBSLFy/C8OEj6m1PA/1nn33MjM/kQRT/Pq0oDj/86Gb7beTdRHaFu+++DddddxOysrJZvAJ5ST311PPNdp59CS4EGomiqNBZHBC9lax4iE2yINOegSIlAplcBONogNkosbrCfl0v+CvyMDi6Ha/7P8MZllOhiRqqTFWYXroWV3Tdn8336BGn1YCNBpTqw8iqgogahU1HZQu5h1D7ohUTyLHTNe/f/+KLL0NlZTluueU69pl0+rfddhfuu+9OrFq1ghlTd8ekSSfA7XbjrbdeQ2lpCXPhJJvAFVdMrbc9eQ09//zLmD79WVx22fnQ6w0shuDOO+9n320uJEnC009Px/Tpz+Cuu25FMBhkv49WPfvtt3+znWdfQtD4CMMG+MrK2Iy+7qwtNdWKqio/m73pJA1K6SZEfW62X7LaURz1oMxdwtIT14Xme8atX6FbxQfs8+PCKXjJ1gVCdRriQ+05ODNjGKsrQGkC0lNNSLHWrAYyrKnIt+Wx62vP1O2n9g6t4CoqipCenssGo6aiejdDi7jYe8HSHSKl82W1hptvJUAqJy1Yyt6Ljp6sbkFngaeSbtw9XFVVgoyMHIjV1Q2TSUuzNjrzAHdCbAKqJkKyOhN545WAD9l6B9KdWRBZAvjakP+/J+1AqNXdfKX2LaTCfomJ3c/eYjxZ/Dd8CiWkjtkG5OTAMSXMPEM47Q2txSOGY/mI4qfjK0FOy8GFQBNgM/RqAzFD06AG/Mg1OGOCoE5REWYnMKegyjKMfbbCg0OjW4HCAVRIlm1bHazEwyXzsSPqQzisMttAPE0s2QtiHkKcdkXSoFxrsG5OWtAmwOEkw4VAE9EkEyRzkn+xpjJBkGNwIs2ZxcLak9HrRJQ7D058Ptf5I+DJgrZ5OGzVJplyOYhHi//Cv8FSeAMRlmI6bheIqm0bwMSpj7q5g1pCECTdR3wlwGlBuBBoIuSHLFqcEJJ8m0EBNrQiMKYg3Zm5kyAIpw5FRIrVAh2jX4R00Q0EHTg82B/ddLHgl5Am4/myRfiyfCMLIKPVAHkIUeTwnhc/4bSKYbgl/jx8JcBpJbgQaCJsUma0QjLUTu6mxQWBKRVpTBDEywPGgm7KbGPYRwkK/mP5lb1fWW7H7fYeGG3NjDfFh1WrMW3zPwhGZciaFksfIcUqCQmiAL1eAnkkcrnQhiTPzBtRV3jPSLYJcLsQp+XgQmAPUCFBZ03ZaSRmgsAfQJ41ExlZ3aAzxdRGOlFEVerYRLszbD+yIX+tx4CVFXrckNYLk9N6J/b/5tmG21b8hqIyH7aVuVBY6se6Qjc2FXlQWlwKV0kxdLqdDdGctnIRbYGlAF8JcFoJLgT21EBstkOq414oGozQ21NhsGchP2cA0jLyIDCvIQ2w5cFjjEUw9tQVYog+lvBq1iYbIqEQzszohYvTh0JX/SdZHizHcm8FvKEQgpEI8xwSVQWhimL4K8qgRGvqF3PaaiXQkmo6bhPgtA5cCOwhms4MyRTLq07eQoaUTBiyukNI7wpFsgCyhLzUbkhNzY6phHQSyu01q4FLUr9n/5aEdPhyswRRCePglHycktov0WZzxI2ILEMTZVgoLUXUhYCrCh6XB2F3OfOn5rQ+5M7LaEmdXNKxE+fjcFoAPorsIaT5ES0pMKRmQZ/VA0JaARSdhdUHpokivSTBiPzUrnDYUllKAm/qaChCbPVwpPEPmFgKOeDzbRZUuv3QS0AvYyx9LrEl4kZUlqFCgdMgI+wqh6IoUGQZnrIyCNxzqB3YBFpKFtQcNF5tmMNpCbgQ2ENYoDV5CaXmQ5bMicE/GVnWYDTYke/MhdXihN5kRYV1P7bPoAVxbc4f7H1IETFrjQS9FkWewQ59tbFxS8SDqKJA1CmQQlUI+Wuimr0uNyKeSki8DGUbUFsdpLWITSAplTQ3DHNaEC4E9gJZoZQTu56lqZIJFsmELrZMpDpSUJEUMzDF8j0sUszz47diAzaWB2Ex6JCvj+VSKZUDkEUVctiFaKCmVjERicrwVZRCx4PJ2tQm0FLBYrWP2/A9dvLJx7NyiW2J2+1iFcF2BVU2e+SR+3HCCcdgwoQx+M9/JrJkclT1jNO2cCHQwrAaHUYrLIqALvYs6LNHIaCj8oFAemQdrs5fmphNvv4voBNUdDfEYgqIHdFK+L0VUOsZCFyVHkR9fDXQ+qitahNo78FilA30m2/mNLif0kdffTUlratg1cTee+9T3HvvQygpKWZlH6m6GKft4FlEW0NtZKSZfSksMtAtPQ/FWZNh2fEa23+y/lO8bR6CoqAO69wS5m8LoafNiZ99se9v8pVhBCRo9hrBECccjjJPIactFQr/U+7934oy+UV2PyBpYWpDhST00HQVgNL4AiWqToTWUAI5Q2q1NxnRcdJG7C4HJRWdKSzcildeeQsORywRHhWXp4L3//nP0fjhh29ZeUtO28CFQGvFFdhSECrfjjSzBd4eJyNc+imMcgXSQytwbdfluHXNcNb2neUqrh1fM+CvDboQNaZDEVSIkgRVqclWSukl3FUeODKqINmy23220faMvOVzhBfeAi1U1mbXIJgyYRz1KHTd/lM7CG0vVwJUBnLWrBkoKipCbm4u/vOfKTj55NMS1bmWLl3MVEpUgJ6yU1I5x3PPvRBHH30s219VVYknn3wUixcvRDAYQr9+/XDppVdhxIj98OCD9+Drr79k7caOHYW5cxfudP54De158+YmjklQCum33nqX1SGOQ3UOqLYBFZvp168/Jk48Do8//lDiuKT+OuaYSayITZy622bP/owVyaFj0LmpuPzUqTegf/+BifaU9nr+/D/Yb3vggccwfPhI1kefffYJS7NdUNANZ555Do466pjEeWbNehufffYRyspKkZGRieOOm8xqI3T0iH4uBFoprkBkcQVGyMEAMiQbqvLPhHHzc2z/YeoXGJY6AEurjKgMC1iyRQ+TXkJIU7ApGmAPpkxzfZ0OkSQhQITCUQSqKmAxp0BB4wqbcHYm9Nd1QNTTpl1DAoiuw8aEQPLAsufC/fPPP8HLL7+AG264GQMGDMK6dWvw9NOPoby8FFdeeS0b0G644WpWcevmm29nRWVmzvwf099T3eC0tHQ88cTDbPtzz73Cot9nzHgDt912Iz799Gtce+1/We1eqinw4IOP1XsNo0YdwAZgsgFQ/QH6PGzYcHZ8qjEch4TR//3fTbjggotxxBFH4++/FzBVU1P49def2e+75ZY7WLGb8vJyTJv2OB555AG89dasRLtPPvkAjz76NBNEVI3slVemsxXJ9dffzOopLFmyCE888Qh8Ph9OOukUzJ37G95++03cd99DKCjojhUrluGBB+5mdRKSBVtHhAuB1owrMNugREKQlBAM6ZOgbJ8FKVqB9OBSXFGwGle5hkLRBHy5TkX3IRasl72o0GS4lAhCcgQOqpGaVCybiMoqvB4vLCmVkOy5fDWwz9A8NoH//e91nH/+RWxQjRdop/KLNLO/6KLLmb6eZtBnnHFOYkZ7zjkX4JtvvsK2bVuZENi+fTt69eqFvLw8Vqv42mtvxJFHTmQrCSr8TvV6aYJChePrg2r7vvDCK/jww/fw008/sNk0VfuiAjD/+c9JmDr1Rvb9jz56H4MHD8Wll17Bai5QofstWzbhk08+bPTvdTqduPXWOxMzeFI7TZo0GU89VVtAjRlzMBNCBBWeef/9Wbjnngdx0EFjE/1UXFzEVgckBHbsKITBoEdOThfk5OSwV0ZGFrKzc9DR4UKgFVcDVKxe8LugVxWoPhnhzFNh2fEi2z889BWOzO2Db3ZYEFWBskob4PCyfV+UK9geDiHPoIMU1JBrAjKMcffEWFWyVI8LejOlsuAFtPcE0wHT2o06aGefjT0TAmRwpRn6Sy+9gFdfjd1n8SSIVN6xqGgHunfvgWOPncwG6I0b16OwcBvWr1/H2lFMCkE1g++//078/PNPGDp0GEaPPpAVp29KsXYSHmeffT57kTcRFbsnQUMDvMlkxpVXTsWmTRtxwAGxHFtxSOXUFCFAap3NmzexFceWLZuZLWLDhvXsNyeTn1+QeL9580bWH/fee3utAvb0+0lIhsMhHHXUsUytdsYZJ7FKZSRASKVEwqCjw4VAKyee0xnMiAZ8sJh0KDVNgEn3LkTZhYzAIpyZsx5/lg2GJyrC5U6B4Chi3/3epeD7dTTo+9FNKkIXXTkm9xqMsRliYjXg8/uQ4q+C5OzCVwN7AOnhpYJJuzcMqzIU9+rYe8kCyZpPIeONPo+kE6E0xjBcyztoz9RB8fiCqVOvZyqYutAslgbeK6+8mOnfaWAbP/5QpqO/5JLzEu1o2377fYO//voTCxcuwPvvz8Sbb76Kl19+Ez179trtdZCOXpblREF5pzOFDaD0uuOOW5itgIQAEa+qF6cxld/iwor47rtv8OCDd7OVAK0qaKWxceMGPPVUXLjGSBZgavU577vvkXpLa9I1kBB7881ZWL58GTN0//XXPHz44btsFUVCsiPDhUArG4glWwqiQR9MBgl6oxlex2Q4K2ewqNDB0Z9wdo/umL7WAYSS6q6aYq5CR5gW4Nn0J2EUZLxRci6QcQLbTgbhQCAKR8DFVhsQamc45TQONgCbMnbzR4xCCFZHdetsbPbe2CLzhKgToTaqBOfeewelpqaxAX3Hju21Zr4//vgdfvvtZ9x++734/POPkZaWhmnTpif2k/47Ds2EX375eab3Pvzwo9iLZsaTJx/NBm8SArszjNJMmwbno48+BhZLLNVKHNLJk8qJIIPz8uUxl+k4a9asqvVZp9MjEKgJmvT7fcz1NM7MmW/h+ONPwH//e1ti2++//5rwYqrvWrt1685UU+SyevDB4xLbaXVE137TTf+H7777Gl6vF1OmnIqhQ4ezwf/RRx9gfcmFAKdpBuLqymRqJAyHxYBy50TYXZ9AVH1wVP2BKYPOQD+njNKghidVCWFBgcXuxu3dfsYpyossFTVxqvEDrPIegjx7GvscDMsI+oOwWlwNrgbo/qflLvci2gtqzcrbR5wABVzNn//nTjNdUqWcddZ5ePXV6WzWT3pwUvWQwXPcuPHMyJuVlc1URvPm/YEePXqyQXfatCcSAoDarFq1EkuXLsF1192E9PR0di7So9NMmyC7ABlgSdiQZ1FdTjvtLGZ0vfrqS3H++ZegT5++TCW0YMF8fPvt13jssadZu9NPP4cVoX/mmafYQL5mzWqmq09m8OAh+PHH79kqwmaz4/XXX4Ik1cxl6ff8++9S9l2bzYa5c39lRuD476lPhWWz2XDCCVOYysxqtbLfReqqF198lqmvYt8NMyM17SeDc2lpKRYvXoThw0ego8MLzTeh0HxzQMWftcqtiLjK2PyuuDIAqehdOKreZfsjmceisOvlKK0qwcPujVghx67rd88byNWqgweq+RtHIK/nlbE/pABkpliQmuaELqsHonU8hWi/KigIKiFYRdtufbubQqcqNK+EoLiq1UF6ByRb10YXmW9qEXWlYgn7V9CZITprEgsmQ+6OZMCsCxlEP/poNntPOnVymaRBmmbdZNSlmSwN8DQwPvnkI2y2TB5ABQUFOOWUM/DGG68wF8jzz7+YRfs+++xTWLRoIZt5k8H2nHPOTxibV69eidtu+y88Hjfef/8z5j5ZF5plk56eVCk0c6dzDxw4mLmikh4/Dg2+L7wwjenxyWuHvIg++ODdhIsoeTORy+g///zNhMDpp5/NhBLZKug30W987LEHsWLFcmbI7d27LyZPPhF33/1/eOGFV9kAXp+bqSzLzPtnzpzZ7PeSMKHvnXnmuYnVA3lNkWqLhCatYEgQXXHFVJhMpg5daJ4LgVYWAnQ/SVEfIiWboSkyfKEom0VlbrkUohqABgm+oe+gwmDD01sX4atwbKn7ov9LHClvhM95GLSq+bCLASiaiNK8J6GZYm52ZGfIybDDlJkP1ZJeS7+qiSp2+Ipg1BmQbc7abbqLptC5hEAQimsNeyvonRBtBS0nBCqXVmciNEFK6Y/OQnIf0aD80EP31ht/0JmJNqMQ4GkjWhk2ATfYIBlNbDZuNephsjjgdxzH9gtQIG2dAavOjFHypsT3/pWyUJF2NJTe/4dvlJPYNklQIZT+L9EmElURjkSg+CohJvmWC5KGIn8xqoIe+CPBVv29+xq1V1AtHSQkdIiIYU7HhguBNkCFAMmWBkEU2crAYTEimHY81GqDrtn1Pexb38DhVR8lvvO3cSC2ZZ0FTS9Bl3EcCuXYkjtHXgJjYBF7Lysq/CEFajQMhH3s2DEBUIKqgJsNYFElirDKC9LsOclppFtJCPAsopzOIgRefvllnHPOObts88UXXzAvgrqvwsKOk42QqWksKawoDQ3MpMYxWdLgd0xk+wXIMO2YxWwA6Wose+hK0YGwHAVVFxidacFL/rMSx7OW/w/QYgZjfyCCiio/ot4K6AxAcaAUFX4XSzFBRBUZESXMaxTvKTutBFrSOBx/PDvvSuDYY4/nqqDOIgRmzpyJadOm7bbdmjVrMHr0aMydO7fWi3KidLh8Qo50CJLExhW7VY9Q2glQq4vOoHp46VX9F/JrKorCfpY+wmjQQ3SOxZJIH7bPJG+DxftjImag0hNClS+CjaVbsMNVzjKQxo1bskrG4XCHz3fSdtTxDhJaYyXQeYUApxMIgZKSElx++eV44okn0L37zoEadVm7di2b+WdmZtZ6kZ9vRyK+GtCZKR5Ag8Wog9mWgYCjJmGVt+ul6FJt9CU2ykGE5QgknQ7H5gh4yBVzXyPsle9CUGP6fluKEyWhKmwr2owKTwglVQH4Q1E2n6SEWv5IoOXV2Z2iqlgLd2Li+FwIcPZhIbBixQqWW4TUPMOGDWvUSoDymOwLKKoIyZEOUaeHpmqwWw0IZp4NV/rFqMi9G9Eup6OvKZZ6l9ikBBGIBiHpJHS1CIiY+mNO4EC2T1LdcPq+gC09FR4pjKKqIoR8bkiaDH8gitLKIIoqAqjyheEJBBGUIyA5JEoi8yIg4cAXB42h9dRBicIyfCXA2Zcjhg877DD2agxut5utHBYuXIhZs2ax3ChDhw7FTTfdhB49euzVddRXtD3uYtVYV6s9gSJ8DTY3It5KWHUirDYLPIh5Clk0Af2tqYAr1nazEoKsRAGzxAbtSbnA4+vPxhHmv2EQZJgrPsXGrENREdEjKkchqgJMaggmgwURWUUkqrBXMKjAKrohRI1sGkCrEJNBB7NRB6tJt1Po/m5/Qyv0U3Oiqns+cNeq9yvEBGdjeysuZNl3tEa07YQrgab0UWdGqO4neub29rlrcyHQFNatiyW2ImPqww8/jFAohBdffBFnnnkmZs+ejYyM3YT8NwANqOTn3hAOhxktiSx1QUSMQpMjyBRFVmWM6hObRAnd7alIE/WoVKNMCGhkTTCIsFrNOLGvAdPWa5jhOwYX22dD0CJI2fYWqnIvhtEQ+9OKSgg2uxPBSG3fdEWUYTHaEI4q8IdVBMIRWBUN2Rm2Pb6pWrqfmotQSEJ5OT08Qr3Cf1eQDI5D0df0Ii+vptDY/lWSlmY6qiXdiZZqHWVC0dYTGZvNtNfBah1KCIwaNQrz5s1DampqwrD5/PPPY8KECfjkk09w6aWX7tFxaebr8dSu4Ru/EWlg83iCLZpqQRQNzD004veC6m/Q8+4NRaAKARisBvSQTEwIBDUV630udHHkQm81IaQEcWCOihd2nIwplp+RKvmQ7p2PQtuh8BljtgRZ9cOg8yGqGiAnBYhVet2QDBb4AjWjWjQio8xmgLGJA2Nr9VNzQSkAKKskBcw1NbiNVR+Lv9cEdu9odTJU7jJQUIql7WjcLFeoFdEKoWPZvfaEpvdR50StXq37fCG2sq8LPY+NFaQdSggQlOwqGcpbkp+fz9REe8OuBgO6IVs6ElZnTYPgc0MOh2Az69ngHA1HoLNa0ctgxT/RWFrp7ToNPkmGp7IEXr8HY1J1+Gl7Kp73noI7U95kbbpXfoR/c26I/a6oDD15FeklfO3aDIdowIG2PAQiYWhGpVbkMOUfIvuB3mZoskqotfqpOdiraOmkkYneaXvw1cYPbjUPcSz5GfZ5mt5HnROtun9iwnLvnrkOteZ6//33ccABByAQqJm1U+WfzZs3o3fv3ujIaDoLdLZUNhUy6CSkOoxIcxiQYTVjRGqNmmubpEE16iAjygaGvo4osk0yZvqOxqZozE3WGV4HRyimOiMqAx48uv0vfOJai7cql2N9qArhaBQKopCqS/8RNPB7gxHuPrrLP1TN6CS09ONTa9Svf1SkpGxU1jH5deihB+Kkk45j6ZNJZRqHcuZQGcmGoH3UpjWgJHWUkroxUM4i+l1U/6Al2u8NlAjv3nvvwMSJh+KYYw5jxXqS+7w+KPto3b9ZvN9phUppvCkfU2vRrlcClCe8srKSJWsivdchhxzCXElvvvlmXHvttayzn3rqKbY6OOmkWCqFjgpJdL0tHTq/G3IoAJsplg+EXF9HpucAhcvZ5zX+KugMJpgdTpY1lAaHcVkhfLTVhue9J+PJtFjJygL3HKwwXYsyJYInPdtQnBQlvDRYij6mVETUCPR6G5SwnNgXCMos8rgTTDr3ELWu9bYFz9W4TKKHHXYkq/YVhzJ8UobOZ599kgn2//73Vrb91VdnNKkQTEuxbNkSLFgwDzNmvI+Ozh133IJQKIhnnnkRPp8XDz98H4LBAO64494Gv0OZXKl6G9V5jiNW15EgG9MVV1zDaje/8cZM5jnZ0rTrlQAVxh47dizmzJnDPlNA2FtvvcVWAmeccQbOP/98JiBmzGgfN/feokoG6Cg1tCCyWT499jqTGfmZPZBjjBmuN/hdiMgyDFYHTOaYIfbgrBDzWpkdGIdCJZttSw2tRkVwPR70baklAIhVoQp2bIoX0NfR/zPvoYjcKVQPdVE0FeVh/25eAVREw+xVHgk1on3jX3T+WjSyzjDd+1TaMf6i2gFUEpEKq1C++zhkS7NYLGhrqJ4v1TSmkpIdmeXLl7Gsp1SXgYry7Lff/qxO87ffzmHZTuuDnmuqUUA1l5P/ZvS3iTNy5CgYDEZ2nNagXf0VHnnkkVqfSddPcQHJDBo0CG+88Qb2RUhXrbemQedzQQkHoXekQXTmwij70d+WhuKwHyFVwdagF71sKTDaHQgFg0gzqhicEsG/LiNecJ+Eh9NexHwpD3eFgvALsT9xjmhg0clF0SC2Rb3wKGGkyGEIptpGJYo4DoQUthJpznTT7Z0vdqzA/y3/GuWRnbPJthYZBiseGnwMJncZ1Cx1hmkgSR5o66ZQpiL0VEO3rKwM++8/mhVNT4ZcsKdNe4xV0aIV6aRJJ2DVqhUsHXP8GH/88TtTI1FJRwrapPTS5513EUsVXR/0fVoJ3Hffw4ltHo+H5e6nmgZVVZVwOBwYO3Y8K2Jfn+cLqcD69OnHUlJTvQCHw4mTTjoVZ599Xi1V5p9/zmX1jKlkZl5eAateFq8hXPecdruD1ViInzOevbQ+4mm6ly5dzAZwKtEZh2o40DXQb6QCPPXVfqCVWvJ36oO++957MzFp0n/QqYQAh9wCddA50yHJMgR7Fsiz0ygZMMCegV8qtrEuWuOrRE8WX+CA0etBKBDAIdkhJgQ+DYzHsIx5uNd6MKLV3iTkXXSDtQDfqR7MjsaiileHKpFttkOBzJagyYZgbyCM7FRaZXQeIfDfZbPhkcNteg0kgOg6EkIgkTsITfpbkCdRrGDLHFZesT6+//4bZjOgQW/UqNGs0hjN0CmPflw3ffPN1zGV7BNPPMfUEs899xQb+EgIEJTH/667bsU119zASlPSAPf0049h69YtuP/+2hO6OL/99gubNceriREPPXQPE0QPPvg4U+2uWLEMDz54Lytyc+qpZ9Z7HBrcqd4BqUxWrlyOJ554mC2c4kVgCKqhQFXBqL7Biy8+x671iy++Y6uhuuf899+lTJUTP+fhhx+JAw6IBWLWJa66odl+vL/iUD+RUGrIUYXqJMSrllG5ThIYY8YchEsvvYoVt4lDFc6mT3+GCbDkqnAtARcC7bH6mIWWhmLCpVMv6jHIWVOoY62/ChM1DTq9gQmCcCiEEWlhWHUq/PYy3GEbl1AlDAy5sGbTcXjcrMGRKgHOkoRK6IBoF0S1CPQ6C8KRmhVBOKIiFFWgTzIac9qCxq0EqPThL7/EckcR4XAY2dm5OPPMc5juuT4++uh9HHHEUUxtRNDguWLFv1i3bi37vGTJIjZrnzXrI1ZEhqDZ+8knT04cY8aMNzB58kmsKheRl5fPBt2pUy9nRtm6KwuCBmwqFpMMCZDhw/dDr16x7QUF+fjgg/cSA2Z9dO3aDTfeeCsbRKk8JK1EaGClSmpxpk69kalWiAsuuBi///4LU8VQMZu658zN7cL6JH5OqilMr11BNsn6VjyxYj31Tyg2bdrAJl0U0/Too08xwUkVy6gO8rPPvpQodE8DPwkUUjlxIdAJkRV6+Gseegk6DE2pSZC31lcZ2y4KkMx2GIxurI5UIKfPv9gouhKDx0mRlXgw/BOOF0dgnb8rEMgE7GshiCrmu8thKw/j0IgLB2bRDESpbRcIkd3B0GlUQk8MPb7dqIOa4h1EjB17CKtwRX8rGrifeeYJNrsnAdCQ3n3jxvWJymBxqKxiXAhQeUZSkcQFAEGzdxp846xdu5qd78svP6u5yur7hQbl+oRARUUFBgyIq7tinHjiKayuMalgCgu3su9ShbD6ir7XVbvEGTJkKKv8RVkF4iRfK/2WuICs75ybNm1kgit+ThKsVMGsPkjAvvPOB8wWQ5XZ6kLbyHW9PqiS2oknngynM1anmgRiWloGK6tJZTwHDRrMtpP6jdRiyfWTWwq+EugA0IOVZXaiwGTHtpAXGwNuRFUVEVnBN1Vb8FXFemyjwStJe3Cc7MOjwR+YOLgx9SNcUXY9NE0EAk7AVgVVF8a3m3z4dqMKu6ES+2XZcGCuHePyncwG6QtGkWo3NmsFsvYMqWCOyx2Aqt0U3dF826BFYwONaOsB6BuONG9qZbFUgxlSLRWQ2CjDMBVvj88WCwq6MvXHddddyQaSuGfQzgg7+ZcnCwz67u78z2nVSuUXyc5QF9KVNxSdryYF3MXVTjQTprKXpAsfMGAAHn74gV2eO7muMBEPUozPpOu+T36W6jtn3779WVnKZMFKK4b6iPcTqYLiRezjUIlOKrOZkZHVwO8XEwIgTs+esVxoZWW0Sh9c6zcJte6HdiIEPv30Uxx00EHIzq6tC+O0HPSwmXRG9LenMyEQ1VQ8uG4e/nGXMENxMnZBwkmmTByut0AO2qBXfTjC+Cdm7Hck1kby8GnAghWoijW2VQGVFngjCn4pdLPXFm8Y5w3Mhj8Yhax2LldRGoAzqr2wGkKNGKEh5okmGW2A3tJ0ISA1NsI4KVNRE1ZkpAI5/fSzMGvW22wwI51zXajY+7JlS2vp3FevXpV437t3HxaDs2XL5sTsmHziadacPHiR/j9ZXUE++qSWIeFT32yYhIPL5UpKBbOW2RZefvmtxCyYVqXbt2+rt2h9zbXW9qMntUlubh6bPe+O+s4py3Ktc5JgpdeuGDZsJLM1JOvtyVuIoJrH9XH//XexcrLPPDM9sY1WU0SPHjWJMckW4/V69jgVTlNospi57777sGzZspa5Gk6DGEQDBjhqbog/qnbUEgD9TE5cbsvHU47eONSYClU0YrvjcLaPhpJevm/R2yHj1IwaHWb3Lm6MypVg1dfcBj9udbHZEq0yghGlU7qK7pLk2XFrVRaLnbhJ37zoosuRn9+VGUyTgyvjkA2AjMHkHbRt21ZmRE22K5AgoZkwDVrLl8dsBRQURXrwuBrmrLPOZd95881XmTBYuHAB86ihYvQNrQTomKRqipOens5WHT/99D1TAdHgfvvttzK1EdXRbQgyUJNXEl37l19+jo8//gBnnbXrglS7Ouedd+7+nHUhATJkyDBWxJ4GchKApEKaOPE4ZGbGVgLhcAgVFeVsUCcOPfRw/PPPAtZnZA8g76SHH76frUiS1V8US0DfaWg10qZCICcnh80QOK2LTtBjmKP26sss6nB8di9MH3QE7us5DhNScqFPWj4W2cdDFmOzsSz/XzDKFSiQjGy1QJQKlbhouA7vTemCEdkxz4SSQBSbPGFEoyqzC/DiM3VpxRrDyUKmibYZ0lffcsvtKCkpZl4/dSFXybvvfgBfffUFzjvvdPz66884/fSza7V56KHH2WB23XVXsBcNSNnZOYkApkMPPQL33vswEyZ0jPvvvxOjR49hHjcNMW7cBGYcJfdTglRX5Gf/xx+/4eyzT2HBV+RqetppZ9Zamex8nPHMdnDeeWcwA/XUqdfjhBNOblTf7Ok560LPBvUR2T7IGE7eRwcccBAzWMf58cfv8Z//TERpacwhg1xf77vvEWakPvfc0/DII/dj/PhDceutd9Y6NgkUWmmRsb2lEbQmWv4oWIuSth177LGsuIvVuvOS6YQTTkBHgnRvlZX+epfulF20qsrf5jlxKOMllYq889/vsDngxiHpBTg8oysskh6CKKDSEwZCXoSrShOzDqKr60t0dX/F3hfZxmFD+pl4yb8d86Metu32/DEY7sjDn1uMeGrBdrbtnAFZOHtAFpx2I3rnOXebFK499VNjoNleRUUR0tNzodfX78/eEKp7HTQ5dq9IKQMBydBsNoG6aKFyqP5Y2VTR1hWCsXberJaEVDbkLURuknEdOOm7jz32cNx44y1strunXHHFRUwYkPfSnvQRxQnQwHv77fdgX4UExCmnnIHjjz+hwXu4qqoEGRk5EMWdo4rT0qwtl0AuHtD1wQcfNCgdO5oQ6AhQokqz3oSpPfeDUidrJWkoTAYJPtkCg9mCoC+WbI7YYT8UXTw/QqeFkO2bh23OYzFQZ00IgdWBUgw0Z2NctzQ8tSD2nXlFHiYEyG007ioaKzojdIgsoS2HVttY2mqVxVq/2DypS+6++zb85z9TmDcLCYB3330bBoMeY8YcvFfHvuSSK5gK5JRTTm+VtAgdjb//ns/6uz6De0vQZCHw4481ekNO60ELNqNkhE7UQamTBoIGJ7NBB48/wspV6sIhyNFYimhZsjK1UIHnW4o8QJ7nOwxKORGodoJZ7i/DlEwFqRagT6oZ66qCWO8KoTQQQY4oIBSSYXIYURWuYkFrJsHcaTM8xsy0yT9ebLc2gb2F0rE89tg0vPrqdHzxxadsEkD672effRkpKbW9W5oK2RvIWE0G5IZWA50VVVXx8svTmbqqtdJqNPkseXk1FnsKfyb7AN0UXKK3jnFYL+lYneG6kDccVQYLBo0wmK2QozUeGDsch6GL9ydIWhS5vrnIdU5kaSQop9C6sBfhqA8RJYxDCpxMCBDzi7yY0t+EqBZFcaAKFX4XUswO5FktncZttC4x4ZdcWaylzyjUTiWN1oUG6xdfbJkULaRS2lOef/4V7KuIoojXXpvRuufcky9RecdTTz0V++23H8vsSSUeTzvtNMyfP7/5r5CTQIQIk97YoBup2SiBtDWqiQLIatppxnR4MibGjqFF0cPzFQbpYrYcUjKs8hRB1kIY382Z+M6CEh8MZgWbXIUo8VQiqsjwhHwIKsGdtCAkgPY2p3nHQEsy0LbCkLyHaSM4nBYVAosWLWLZO71eL6688krcfffduOKKK5gh6eKLL8bixYubekhOI6HZoEVvrilAXgejXoJeLyKs6aCz2CAKIhw2J7plFECXdwFUMebTnuH5HfuhJuf5Yl85FNmPvpkG5FhiOtpFJV6sLitEsasKIapqRe5ucgRVITdTDcQhO4EQ8kLxuzucO2mTo6FZe62V0kizkySfvIXPxemY967Q+kJg2rRprMzjl19+iauvvhqnn346pk6diq+//hr7778/nnsuls+e00JBY2QXkOovM0j3hdWsZ+6dOksauuV1R1d7LowBwOsBvCknJ3Tbx3pnJ/74/0a8iHirIOoUHNI9thogjc/8wgDLX0R1iOOuop6wDyG1RoBIggbFWw7ZXQaqjtwRIKMn0VB+lwbRWnslsHdZRDn7LpFImN0eOp3U+jaBf//9F08++WTiQUrWZZ199tm45ZY91/VxGm8XIPVMfbMDq8kAfZoJWbZ0pGoa3Fs2QolE2Sohmj4Zivc7SNFi5IdWop/pMKzSRGYbKHSXIyOjDENzFXxQHYy5pFTB6C4SixdIYXmEgFA0zFYDuZac2GwkWIVowAcDRdGKbogG5x6VpmxNKAuk2WyDz1eVSLncqHgIJcLUbdBiRd9VZpsRm1wgvLE2FU2WEwXFBVmG2IRApo5MU/qos6FRIGckDJ/PhfT0VHYvkzG5VYUAxQWwotf1QNs7S8KxtkIn6GDQGRCI1C5hZ5D0MOuNSDU7YTfaIWoSRE2FVfYg4nPH1NkCoIiXQl1zH/vOhPBKrDLEIhKXhb3I3rERPW2ZII0Q1Z9fXqoiqmisxgC94qUoPSEvUk0pMEPHVgEaCSRVguytgJhug9q+axUxHI6Yz31cEDQKRYYa8sTcNQUdRLm4ySuCWNruRsYJKBFowZi7r6AHhEBnsLs0rY86K1arnRXZcrl2jgZvcSEwcuRIvPLKKxg3blyt3CAUmk7bSVXEaTlIxpJdwAUP0/mb9AbYjTY4jHZYJDMb6VVZg0o+7YIInSMTajgItXoWKWaMg1wyHIprCSZEVuHFaiGwUvHjkGAIFr0XI7JN+KNQQUgB1laqGJylMpWQxUj2Ao2tBtwRN6yCEeGgP6G1kIN+6IMuiOb0dr8aoJm/05kOuz0VSj2rqrow43f5Zvj/vYESCEEw58N25PtQNKlJAX9OpwVud6BRM13NsxbBv6ay9/ruJ0I/ZN9fZTe1jzojkqSDwaBrtmj+JguBG264AVOmTMHhhx+OCRMmsHBrKs7wyy+/sLwiDz5Yk4mP0/zQSovsAilmO5wmB2x6K/SCgc2cYqmEah4cVqLS5IDO6kTEVca20Y2j63kF5EWXY6hSAqsWgV8wYKUcgNWiQzTow5hcA/6IBapiSamKQZkaQmEFNrOeBa3RGbzBKgQEI5NK8ZtRUxXI7groTU6o2HtdZWvNOkVx91G/OkGFDA1SYBPpaSDqTEyNJKuNX/VQJCxVrQoGlUZFDWt6PSLBLey9FCltcnRzR6SpfcTZe5q8bu/evTuLFh49ejR+/fVXvP766+xf+kzb+/fv3wyXxdnVSsCut6KbowAp+hSIqo5F8TakhaPZlGhPh5RUIEOw9IIu71jooWKMHBvtPaqMEjEKUVAxxOpBPKfckpIo5rg34Oq13+OVzbHEgeQdFHRXoDLohs5YO1OkHA5A81eyGd0+hSZDpRWDVr1qoFD9FnaHEpKFkxoL/uNwmpsmrwSmT5+Oo48+mnkJcdoGlRWdIRppYNRbobOlQYkUJbxMjD0vg7/4Rxwsb8WP+p5s2+KAB5MsmQj7QxicbsVinw/uLmvxuTuWK+f9HatxSHo++luskEMeuOQIUh15kOSkwVBVIXsrobekQME+lBJAUaAlRWoLoh4am0O1nMpCSxICyefmcNp0JfDyyy+jsLBaV8DpENBKgVYDOlNSsj/JCa3H2Rgr1+SHXxz0sDiDqKAikr4G6LEYgql2Yr0Z21YwY6UajSIY8sOlBCAZapfhk8kG4aPVQPs3EDcGNuFXZWjJkdqioeUzrCavBBQuBDgtQ5Of0t69e2PTpk0tczWcFkMR9KyAvVDt2qvKUZh6nI2uOgm5aswDZXnQi78CLtzq2oCVUmlC26EP25AixSKQ/3IVYY07Zl+gKGFXwAVZR7dRbZ922VcJQd57z4X2gRATAkqSRxatBFrYbkmrjQR8JcBpL+qgQw89FE899RR+//13lkraYqldWYlmR1dddVVzXiOnGQvY68xuRH2xvELk21/Z+2IcvGkhPjIMAs017y/ZmPiOoIpQS7sjUpGPiaNLMdsfKwbydulW3JMbK9Ad8WyHf9UDUEQJUp+bKb8y266EQ5C8FZBS8zu8lwcThmQPSJqN19LXtxRJaaq5OojTboQA1RIg/vjjD/aqCxcC7RdFFSA5M6Ewl9FwbDWQdyxGb/0TH9VpO0hvRYGrB76uSGefLf48pOk2oVIOY37QjY2RAHrqzcjY8hpEzxImQET1cegHPJBQk8h+NwzWVAg6S4cOeBWqA8O0uFGYaGIdgT2Cq4M47VEIrFy5st4Czpz2D8t6YLRDZ4u5jFLsgNloxeCCo2Hevh1BQQ+nFsJlqVkYoS/AJgH4enPsu8tKFEzul423XDEbwtsVO/CEsQhWz5LE8dXKv6AUfwld7vGxz5EwVG85xPRuHXo1QDKNxVkkeejEVgIt+5s05mZLAlXj6iBOi9Hk0Xzy5Mn4+eefW+ZqOC0ODcaSIyvh2mlQBaRnjcBzum24Lfg7vvW+jfNK3oBZr6KLWUG2KTb7XVWuYphsg7O6NOW8oBuV2+fsdHx500tQgzWOA3LACyHSwcuRago0JQpoSW6akrHlbQIkfapXHFwdxGk3QqCoqKhWpDCn46GIBuicGRBECZCjsBts6Nr1RJwtliNDC8IY2oG80veh0wnYLz2WZI2y2a+oNOJYY00B8Zf0Q9m/nvSx8OccEduohiGvexSaFitxyWbQ4UCtzKMdDlWJpcpOUgcJraEOSlYJ8TgBTnsRAscffzyrM1xaWtoyV8RpcVhKBzISW+xQohFYJAP0JgeKe1wJtXrQSa34BZmBxRiRVmMMXVRhwARjClJYFQJgjr4PVhm6oyL/LHi6n42IMSd2fM8qKMUfJIKplKC3w6WZrgWFSbNXsjqodWIgEgZopYkZTzmclrIJbN68mRWVGT9+PKsoVp930A8//NDUw3JaGcp5EzMSB2DUBJgMJrhNXVCWfzayt8aqSXUvfRsVOQWw653wRkUscxkhB1y4JPQXHjcdCE0Q8JR9Mv5Pb4Um6VHa43Lkrb4PAlREN74JKeNACIYezAgtySGA0kx0RNQoNFWFprauOohRLQRqnZvDaUshQJnraDXA6diwvELMSJwCJeBlmUfd3ip40w6BxbMcdtcCSGoAAyrewLjMuzBnhx2yqiG3eCbG6dbiNcNwVIlm/CIDpwUCGGh0Imzticqc/yC9+FMImorgsrtgPXgm1KgCREIQTKYOl2U2ESjGkiYlDcStYBiuiUzmcQKcdiQEHn744Za5Ek6bGIn1jiyooQAsksgSokUiIZR1vQCmwEboI+WwhzfilozP8HfVuZgofoOhulixgfOjq/C0cSQboGZVFOFep4Ntr8o5HlbPMpgCGyAGd8C18nGkDr0basTP4hQ6mpcQM84q1TYBtQ1sAtVBehpXB3FaiD329dywYQNmzJiBJ554AiUlJUxFREXnOR3QSOxIY7UBjIaYwV+VLCjpfmV1bhwgr/JzvNzjF9zsfCfxvYHm/WCt9hSaG6rCBp8vFjMsSCjpflnCtmDcMQflO76BoCoQkv3sOxCaHK32r20LdVC17YGrgzjtRQhQyuI77rgDkyZNwkMPPcSyiJaXl7PEcieccAKKi6nQBqejGYn1BhNsRhub+ZpNNqQUjIfU97JEOcphnmdhEWPGybd9E/HyjtGYaIwVZiEz8azy7QnlSNSYjfK8MxPnMK56CqFQCRANoqNB/UHFXRjJhuFWWgkkG4Y7miqNs48KARrsZ8+ejQceeIBFDMdvzJtuuokJiKeffrolrpPTgrDCKBYn7AYrstPy0M2ZhxzRAn3WFIgpI2u1LVSy8Zj7bPxdYUKGPxsWIXYL/RKowlIqZFztBeRJnwC/Yzh7L0XdCK58BkI03OFcRUkbFE8cVytiWGwlI3dC2NBKJOZ2y+G0qRD4+OOPWWF5KixD3kFxBgwYwLbXl0qC0wGMxAYbHGYnsgULdIEAqxJGw7Wh363QdM5ErMCqlOsR0GJqo/c2pOBQfXpiNXB7yTr85q0u1ygIKO16IRSxOqak9DdmF+hwrqIUI6BUD7511EGtQe2aAjyTKKcdCAFS/dCAXx/Z2dnweDzNcV2cVoYZbC1pECV9zCc+jj4N0sCHELaOhivzKgzM6IOx2TE9tSsqwV3UDQNNMSERhYaHSjdilquICRZF70TAMYTtE2UfghULIXawlMhC3DOI2CltRCuQrHbqYH3H2UeFQLdu3VglsfpYsGAB28/pwLEDabnQmWvHfhhT+iPU/U4E7YcxHfnN/URYqv3Kfi+24njzaIxPLUi0n1G5A4+XbUZEUxFwDEtsDxf/Csihls/D34xQ5HONEEjyDtK1kjooSe3EU0dw2oUQOO+885hX0H333Yc///yTPdBbtmzBG2+8wV5nnlljEOR0LMi8o0pm6FJzIOqTBjkNsJr0CVVOKiK4dmCNoHhjURTnpA3F6Zk1pUV/8lXi1h1rsd1as2pUS/+AJncwu4AiJ1Jg7Bwn0PLUMkBzDyFOe4gTOOWUU1BZWYkXX3wR7777Llv2U/F5vV6Piy++GGeccUZLXCenFb2FBFMKDCkRhCuLmD6c/sZGow46SUS0uvj3ab0s+KYUWFwcQGlAwyerIjipX3fk6CyYXryErQJWhv24pjSKly1DMDjwLyTfRoQ8m2GykFeR2GFiBOLqMS24vWafObt1LqKWEOCpIzjtQAgQl112Gc466ywsXrwYLpcLDocDw4YNq2Uo5nRs+4DOngF9NIyIu5wtEQw6AQa9xISA2WaDZsvC/x0k4MzPViCqAnPWRbBfthED7Zm4u9uBeKrwb1TIEZTIEZylH483pTKMVIoRKPoF5szBgFC7JGX7jRaOpYwgVF+sqA7FQkhpg9EqvjoCNwxzWpY9no7ZbDaMGzeOpZCI5xHi7DvIigApJRd6a7VnkAqk2A2w2a3Qp+bAKxuQKom4YHhWbD+A2evINAxkanY81Gsc+phsbJ8fIu4zjY8dt+Q3CErHUAmxOgIUKMYCxgLQArFaCqKtN6CztM41kKG+mkS8AofTjLT/NTmnzZBRbSg20YCnwWI2Iz0vH0HJjmBYZiuGM/rlIsMSixxeXKJim0dl6iNdWI9H+o1HD2OsuP1yXTY2iSnQVfyDYMjVQVxFBaA6RkD1rU3kCpKcA1vPuJ0cj8BdRDktABcCnEYZiiWjCZI9FbbMLjDodYmy8nJExXlDa/TjX62PedBQsrlACDgqu0di32x9X4hqCP6iPyB1CCOnVhMo5luT2CqlDG69S6hVbL4j9Bmno9GuhMDLL7+Mc845Z5dtqqqqcOONN2L//ffH6NGjce+99yIY7HjpCDqSoVgzp0Cfkg3RmcNsAjnpFthsMV21qmmY1CML6ebYamBRiYrt3pgOPRiUMcqWnxAYX+r7srl0pOgXZuRs76sBIck9VK0jBForg0OydxBPIsfZp4XAzJkzMW3atN22o6hkckmlwjbPPPMMi1m45557WuUaOyuk9tGsGZDV2O2ilwTkpllgMsQGflVWcOHI/ET7rzbEVgM0ThoiIobaY9XINkppWClmQir/C6EoVRtrN7dfvVDSO5Y9lH6jt1oIiCaIzj6tdxG1Iob5SoDTRt5BO3bsaNJBu3Tp0ui2lIH07rvvxl9//YXu3bvvsi15I1FA2pw5c9CrVy+2jeIVyDWV3FQpYpnTMiiKWmt1YLfokZlqwY4yHxvsj+uZjtf+2Y6qkIx/ilQU9VaRaxMRjig4OCUfS73l7LtfGvriluAf8FesQlp+Zut42OwhGsUIUDGZSBUQLmHbBFuf6tl5Ky0FeNoITnsQAocdFosUbSyrVq1qdNsVK1awGIMvvvgCL7zwArZvr/HFrgulq87MzEwIAIJUQnRt//zzD4499ljsCWTIjEZ3nmVpmgBZrh0ZWl+7ONRFOp2+2dsS1Ed70laWo7tUXexp23A4CqdVgjcgweUJwQQJU3qn4rXlZWx4/GGLgHMHxYbKAUIaJAhQoDG7wE2hPxDc9gOcmaMQ1fTQ6XSJ+0tR5Fhm0wbY87YKS3DY2La0AtAiQUQiUajupPvZ2ifWR9Wn3d1xJUlKrHiobSQSQTQagSxru21Lx2XJ/aqRIwFo1X/75LasXTy/UT1QO2rfXtrS8ybL9acVp2dOUWrch3fVNnZcAZKka/a2giCwe6Jxz3JT2jbPGEH9FInoa91Lyc9nUzLONkoIUMro+APidrtZDYEDDzwQxxxzDBuUKVbgp59+wi+//IJbb70VTYEEDL0au2qgymbJGAwG5p5aVFSEPcXr9eDVV5+rd1+fPn0wadKJic+vvPJigzdPXl4+pkw5PfH5jTdeQyhUv70iKysbp59eY/94++3/seuoj7S0dJx99gWJz++9NwuVlRX1trXbHbjggksTnz/66AOUlsZmsXUxmcy49NKrEp8///xTbN9eWG9busmvvPK6xOc5c2Zj8+ZNtdpkaiIswiAEND3+2BbGWUOzYBT8qFy+Bj0EEevNCopFO/6RuiB35Tf4eGHMrfiqq66FVO0K+fPPP2LVqhVoiIsvvjJR0vT333/Dv/8uabDt+edfAocj5uI6f/5vWLRoYYNtzzrrfKSnx9RWCxfOw4IF8xL7hhl/w7DqcenXDRrG9HEjJyOfCaClSxfijz9+a/C4J510KvLzu7L3dK0//9xw6dXjjz8RPXrEJjhr167EDz98g36GhTigOgffLz/NwcboNvb+mGOOR58+/dj7devW4euvZzd43COOmIiBA2PG7E2bNmH27E8bbDt+/OEYNmwEe19YWIhPPvmgwbYHH3wI9ttvdOLZfP/9mQ22HT36QIwZczB7X1FRjpkz32qwLY0tY8aMY+89HjfeeuvVBtsOGTIchx56BHsfCATw2mvTG2w7YMAgHHnkMew9DZ4NPfNE7959ceyxkxFn+vSG23bv3gOTJ09pV2OE3++D0xm795tFCJx00kmJ91dddRWrG0CppJOheIEHH3wQX3/9NU477TS0BGQApkG/LkajEeFwy0VTOhzm2lGkDaDTSUhNjblEErvyhW9KW0kSa7Wlzw1Bx0luS+dpjrb0u5Pb6vU73zoGQcWB+lL8GMkDTdC/WqfixrE5qNStwRCPngkBglYDd8lzISEKBXr4wiq6dbGwcxgMu74lU1IssFpj10FRzLvC6bQgJSXeVr/bv3H895nNte+xdKlGHVou58JkNrBj19e2LnZ7zXFNpl1fg81mSrS1WmMrUDVpJSCiZmCh/XXbNkRy27KyXQfpWSyGRFu3u+a+rw/67fG2gUDj20ajfjT2mROEXa966R6IH7eeoaEWdG/F29IsurFtdwc9C8lt28MYQZP1xiJoTaxUMXz4cKa2OfjgmFRPhtJIX3nllVi6dCn2BFpFkDro7bffrnf//fffj2XLluHDDz/caeZAUcznn3/+Hp1XlhVUVnrq7Vin04pAIJrQidMMYtfLwuTlW1Pa0s3e0J9CqEcd1Li2MRVPw39ivd6wh20pp06sT3Q6EVV+BVt3VMETjOLcbzfAG1VB9+wHJw1EijmAbZ4SXL7xO5ZOIlUN4k/v66jocxecPc9CRUBAfrYdaXYqb0nXsCu1DeUwEna6ht21jalXlEa3peTYauUOhF2lkBecAshuQOeA7oCPYcvtDsGWGcuUupvjkvqhxgCuwWo1wOsN1rKx1Nc2flx50wcIz7uGbTPu/wh0fS7YqW1MFbMrNYhUR23Ttm1jqpj6B3d65lJSbPD7I6yPdtWWEAQxoYppqba7f5ab0rZ5xgjqJ5pg1NxLtZ97h8OU6O9mTxuRmprKBuL6hMD8+fNb1Dibk5ODH36ovZwmHSupo7KyYpGrewJ1tiDs3BX0kNEfV1HCkKtz5tTXLpl4u6a3lVqkLUgbL7REW5Hd/LHrEZGXbUWFOwRZEXFSnwz8b2UpWw28unAH7hnfExm2CPazZmKer4QVqP9TV4BBlX9C7HsaZEWHwhIfu7+dVj0UpeGVTqxGsbbTNey+bf1/44basodPkyFGSmMCgM5m7wcde7BE1hcxgbnr45K5IG4zIGFJK1lRjNZrR0huGz+uIJlquazGz1W77a7vNbrMxt6Xbd02bjugga3ln7mO1jZpVSjufC/Vfj4bb8MV9ySBHK0EqIIYeets3ryZGWxJPURZRCnLaEtBsQFUvpJcROOQtxCx3377tdh5ObvHZNAh3WFiS9b/9EqHTR+7tX7Y6sLmygDsehsOctakmyaVkM29GKGQGyk2PUJhGTvK/PCG5HaRUoIZhsk7yFudL4h5BvWnoRlsidNq3kFJaguFx8Nwmp8mrwSuuOIKeL1eVlv4lVdeYdtoRmQymXDttdeyxHLNBS2JKWOp3W5nx6ckdSNHjsT111/PYgPIEHTXXXcxGwV3D217UuxGWC16ZjA9sXcG3l5VCppgz1hegrsmdMX+KfmwFC1GQFXwvb4XHgj+BE/JYuTmjkXYYoU3EMWOUh8Ksu0w6aU2ralLxWSoqlhykBitBGJLpdaLbxCS00bIoVY7L6fzoNsT1cktt9zCdP9LlixhBghSEY0YMSLhtdFckMfP4YcfjocffpgZp+nczz//PIsSphUHGYQnTpyI2267rVnPy9kzdKLAVgNkQzmhVzo+XleOgKziuy1VmFKShSynFQfYsvCzpwh+wYCfdT0wtGwuvCkDkWHTQYMJvkAEhWU+dMu2s6C0NpMDFCimqjVBYixxXL+Y3UAQW+26tKSVANViYDWPeb15TlunkibIQ4PcQ2m2RjN00s3vrRB45JFHan3Oz8/HmjU1DyGRnp6OZ599dq/Ow2kZaAWQYjOiwhKComo4oXc6Zq0uY6uBi7+O/R1NzkwgvygRONajeAnmSVsxpnsIaakFMOitcHnCTBB0zbZDaqtBj1YBShQaSxxHVtksCIbU2PtWzHdRq4ylEmRCqC1XSJx9jz1a137++eeYMGECTjzxRFx++eVMR0+ePddccw0TBpzOC6nLM5zkmSDgxN7psFbbBuKE3CnQotUxAbruyMQm3P838OHS7dhStBJRsRwOp4hoRMX2Mh8zLrdVHQHVtxlQQ4lVQGLnLozRzU5y7iBWmrP1Ts3pHDT5bqaUDaQOGjNmDJ566qmEZfrII49keXymT284WIPTOVYDTqsRNrMeDoMOj47tgZN6p+OQPAeGZVrRxa6H5I15ckUEHX4xdMMY43JMX+PAnNUebN++GmXh7YjovZA1GVW+8C7jIlqyopjiqYkUFu2x0plxdVCrIdYVAlwKcNpYHfTSSy/h9NNPZ4bZ5DDxKVOmMCPuBx98gOuuq4ks5XTO1UC60wx/UEafVDN7EZIowGRT8UdZGLdu3p7wEjratAg/hEbjxbUOqPDicKEIfksYOtEFTcqCUzZAauXBb2fPoJqVQKsOxHWEAKmpmuL+x+HsjiZPaSjsnGb99UG2AQof53Ru2GrAZmCeQsmQnUDSDBiSmoesajXHPF0B9rcthx5UlUzAy2vt+GFDCGLYhWA0iB2eMnhCtBpoRT086d3lSJJnkADRFs8cKkBrI5uApkZjXkscTlsKATLMbtiwod59tJ32czg0TJJtgAKkkolENNj1dhzsyGGfFUHEPEMmZnR5GiJLMSfglbU2fL8uCKMaQFgOo9zvQjiqtpo+nJWVDHug+TfGPpsLIOhiIfmxVUDbqIOghKEpPJ00p3lp8t1MmTrJO+ebb75JGIHpwVi+fDmzB5DLJodDqwGH1QBbndVAOEJFK004NL0mE+zH+gHoLy7Gm12ehwCVCYJX11jx3Ro/9IofrpAb7lAIQmsFkakKFPcaFjHM7m+KD4jT2jr55BrDVE9AjnK7AKdthQDp+yl/EP0bj9KlamAUSUz1AChgjMNhaLQaMEOftBpg3o2yHgNS8pCvMyXqD491XIgfLQLuz/9frKwjEwQWfLvai2ioCp6wF+EopU1o+b6l9AyKqyaTqWjrn7RTgCa2UbAYCQElwj2EOG1rGKZ8Fa+99hpLFke5gihvD0X0Ul7/8ePH81kKJwH5szssetgsBlR5aqJdIxEFNocdp2T0xLTilSwBQ0Aw4G3jMAgGDUP6foN/C8cAgRS8tsaCVGMljrJYETQ7YbKa602+1pxoqlzHM6hmJRCTQW2UNkKNQI1SwFgrnp+zz9NkIXDRRRexSl6UQK6+JHIcTjI0809PMUFWVOgkgeUYMhokGM3AyT0HY6BRwmcVRfjBV4ZwtdF1ud4CoccyaEErUN4VM9amYUT2NmRY0mAx5kIvtlzELhtfZRmqZ2X1Bh0Ea8+aBqLUuoOwzgKQPUL2Qw0VQYuEqJda59ycTkGT17WLFi3is31O01YDZj165zvQK8+JLhkWljLabjDBaU1Fd7Md12R3wxt5Q3Gx6EWO6k18VzD7IRSsQom5DLPXKSgr24Jo2NeicQOUvE5xF0ILxIq3kABI9tARJF2rRjBTllQpdVDsQ7gEargSAq81zGlGmvw0jRs3jpWC3F2JQw4n2UhMaf8p1S2lbKbPpNKxGR0wmO0x24HJiOPzDsEMqQLTAl9jmFxc04FZm/BZoQmbiktRWbkd0ZC/xYrUC5EAosV/J2b6iUjhagEgWpy7LGnZEkhpsapghOxeB3A3UU5bqoMoaRsJAaogRrV+6+YLoqXy//5Hxj0Op2FoNm2WLDBZnAh6K1myNpNBhL/LZIzYHsYk9we4xnwMvjb0gaCLIpi6HR9tykJ+WhGcOjNSs3KhCaZmzaNDsQiquwKya3nN/VwdKcz2kw7LZIfW2kIgdWjiPUtoRx5CBvrtrXoZnH2UJgsByudPGUPj1H0IeXIrTmMRIcBpz4DXXQI56IsJBpMB5V1OgaiGcKP/T3yv7wlZkKDP2Iwf1ubiqBIfMsxVMAmAObMLothNTcFGwrJBRPyIBjw7ZQ6NN9DZUqCCCnu0shBIH5J4r3jXAUrMTZQ/a5w2EQINlX7kcJoKqYYsejtMqV0QQhETBCQJrGY9SvLOQk6hitMiKzDTOBSyKGBgl18wc/0EDMx2wSEZYPSUQkotqK4Ktve2ANVbATUShuqrThchmSFYYoVwdEYTBHPrq4LYZaQMqNbcqtD8G6o9hFr9Mjj7KM2qWKUiL7/99ltzHpKzj2MSTTDqzJAcGdBZ7LEpuQbYLEbs6HIuTjKYYNFiQYlr7XqMxGf4Y4sPbiUKv9cHMeLda0eF2CrABznggRapBMJlse3WPomSfhLZAqRdF3RvMSQzRFt39lYLbIEa2vvfzOHs8UqACsFT8jgq69hQ2uhVq2p8rDmcXaGpAvLsufCa7PCabAi4ihFwV9AO2K0GeLJPwpTSuXgbBqYWKstwo3/ReygruAgOIQtiRSmsXWyQlT1XjyRWAdFIbVVQdXyAZDBCsKQwN9e2QnL0h+rbyKKYFc9a6LJ7t276Cs4+S5PvIqryRW6iFCE8YMAAVu7xwgsvRL9+/RKVvzicxkIDt6jqkKpPRVd7N/TIG4r8vH6wWp2QRAl2qxET0g6EE7GMtV8a+qGf5R/IK56BWwmivLQKFcXFCERjtYmbWp+YrQLCXsiBmGtq7XKSMaOwZLZBMzRv1bymIqbUGKhZSgvuIcRpKyHw999/sxq/d9xxByv5SN5CN910Ez7++GNWCP7HH39srmvjdCJI105Zkg0wo0tGH/TpMhDdMrsjPSUTGTYzJpvzEm0fNx2MocpcGFbdDU2vwVdeClelDxuLPHAHoizHUGOFAVsF+CrZKoDQvCtrpY8WdXqI1tQ2sQXUXQnEUT1rmXGYw2kTIeD3+9msn+jZsydWrow9NJIk4cwzz2SpJDicPYU0OpGICsmUgQxbNrpas5CVloGJ9nRkVqdQ+EPfFb/ruiLF8xekjQ9CkASIoSr2vS1FHmzc4YHLH9mtIKCVqxDyQPZ72GfVsxyqa1FspyEdgjE75hZqtLexO6YGkRmHY6i+dSyqmdsFOG0iBLKyslBeXs7ed+vWjRWaLyuLGdJSUlJQUVHRLBfG6dywoDJLOgzWNGTbs5HldOBUS3Zi/6PGsSANva3yN4SrfoGkhGHVyVAVDW5vGNuKvXD5di0IJFGD6quAKkdZvqDo+mcS+3T5p0OUJEj2VKhtXMSFBBAJRRgy2GfVtx5alJea5LSREKAkcdOmTcPixYuRl5eHnJwcvPHGG/D5fEwllJ1d86ByOHsrCBRjCpyp+cjJ7orDUjLRXYplHl2ty8CX+r7svXHDc3BHymDR/LCYY74OUVlFlTfcYBEuNosOkS3AFzvXjk+hBTbF9ln7QMqdDIncQk1t4xZa3/WK1ur024ofimcTXwlw2kYITJ06FQ6HA888E5s1kX2AIoTJHjB79mxccMEFzXNlHE61rUDWDMhM74m8/O44JyU/0S8PGccjDAl2tQLz5r2Kv0sq4dTLiSpkgVAUgZDc4CpA8ZbHVgHhUshb41HuAvS9r4Ug6iCRLUBssgNdy0CqK1tNDQa5iqKa2144cTo+Tb7DU1NT8eGHH6K0tJR9njx5Mrp06YIlS5Zg6NChLKU0h9Pc6hBRMyIjowfGywo+95ViWdiDcsmMtwzDcVnkH0zSzcGJP49H17woLhnZDTaICEcUuP0RWNOttdJPMyERcsWC02jVsHE6oMZSXUs5k5hXUMwt1AG5GQLRmgVBhGjtXe0jFfMQiiWSaydCitNh2WNHY7INxBk1ahRLL80FAKdFK5VJdqSn5uCCrsMT2580HYRvdL2gE1Q8mPoSvtvswWmfrcT0ZUWoDEXh8UUQVag0pcAGf50WhuArh+wqhSbLUCr/gloxN3YwfQp03S9ibyWzHZrO3C7+oEwdpTdCctQks1O95CHE6w1z9p4mTyNuu+22RsUScDjNDWUiTTemYmRWASZW9MA3lZugCgKusxyDZwNzcBQ24Gzrt5jhPxafr6/An9s9+N+kAZAjYRiFEBS/G3LIDyUSZssLTQlB3vBc4vj6HpdD0Nkg6g3MLVRpB7aABDojJHtXFj0MJQg1nkNI4onkOK0sBP76669600VQhTHyDhoypCbZFYfTEplHs+1puLxgBAJKBL+5t0MWREy1HIPnAl/jltSZ+DUyGluiGSgLRjF75WZ0V3WQzAKb+ScjF86CFo6lrBadwyFmHs7eS0YLNKMVWlz30g7QRD1Eg5nVN9A8K6BRbYFgGQSjgyeS47SuEPjpp5/q3b5hwwZcffXVOOGEE/buijicXUC6/RRjKrqk+3FZeATpSvCbt4illCBB8HxgDt7u+hImbLwdqiZg1lovTkkXYMqywphU61gNbIVS+EHsg6CDrtfUmAeOTg/JkQZFbV8lHGNuoraYXcATq38sV/wLfWqNsZjD2ROaLfkI1Ra45ppreNoITosjqCIyLWnISrHh0ryRGGeL2aeigoSrLcdirVKFK7P+YNvcUQ3fVBiYl1C8EA2lqpA3PMvy8BBS3qkQLV0pfBh6Rzo0U0q7m12z69GbINr7JLYplf9yN1HOXtOsGahsNhtLMMfhtPSAaJVsyHGkIt1uxvW9x2CsJT0hCK60HIeBlu9gFYJs29ubwogIBmYgJtSyH6G6l7D3gjEHuoIz2XuDzQnBmd0sqambG5IBgt4EyZmcQ2g1zyHEaX110I4dO3bapigKSkpK8Oyzz7IVAYfT0lBkcLopDX5rAIaIiDv6j8YDq+djbqCKCYLrzePx3/xXYfXpkC66EV3uQVT0IBp1JdxBCV2vq5lxVWe2QkzJhaK138ycqmSAztknUVsg5iEUJUnW1pfG6UxC4LDDDqt3CUqzM5PJxNVBnFZDBz1LQ13oLUIAwN19huP+NfPxWyiIiKDDE/ZBeEn6EofIW4F68q2JaQdDShvDvIF0qdlQydOmPXkE1YNkzYBg6QotsBmqfzO0iJ9qvrb1ZXE6kxB46KGHdhIC9JlUQQcccADsdntzXh+Hs0v/eaNoQr49F9tJEKgpuK3bcOjW/4KfFCMTBJdbJuGlwJcYG92GiGSD0ZwOSZ8GwZQLXbcLIYgS9M4MZgeg1UV7RlWptgEVmOkNJbAZ5L6kVK2EkDuu3dkwOPuwEKD00RxOexIEBsGEPHsXFAkiS6x2Q/eDIBcuxW9hmQmCC80nQigbiAE6B54aoUPfrHwYwzIrJam3p0BwZLWfyODdGocpaKwvlNIf2Dalcjn0XUgItPXVcTqNEPjss8+a1J67jHJaY3A0CEbk2nJYvjgtHMTNXYZCLd2Euf4qyjkBtetKrNg2GH9vFWAx6ZFvz4bNZIGUkouo0oFKNUomiEmRw0rVchjYypxLAU4rCYHbb7+dPXTxV5y4iqjuNi4EOK0tCKBGoUY34NasHni4RMMfARcEUYNWsBxvl/TF4KxK6PQGdMvpB4HqBrdh2cimogkidBk1aTNk8hDiAoCzFzTZFWLmzJlwOp249tprWeDYihUr8Mcff+C+++5jdoEHHniAVRej1w8/xJasHE5rCQI9jMhL6Ya0jHwY9AbcUdAXY8ypbD8JgrXOtfje5UcQKgpDHoTUYIfytaffqHN0AQyZSVXG6q/1zeG0yEqABvvzzjsPl19+eWJbeno6qzkcCoVYWumTTz65qYflcJptkJRUPfIy+gA6M9whD+515uLiBfOwDaVMNfSSdwNy/Fk42OxHEUqQ58iFUTAyvTqtY5lyRdPaRR2ButA1ibpY0JhaUcZqC6ieTYAjVluBw2nxlQClhxg8eHC9+6jS2NatW5t8ERxO8xevNyDHkQ+7JQ16nRH39B8NwVNdmUtQcd/aefhq80asLizFkm2bsK3chU07PFi/zYX1hW74wu24fKPOxIzDcZSKpVRugMNpHSFAA/3nn39e7773338/UX+Yw2l71RAZgHPhMDlQkGLB4YYh0NwxQSBDxTM7/sEfVYUoLK/EZtd2yIggGJbh9oVRXO6H3A5XAoQm6qBLGZT4zNNHcFpVHXTllVfiuuuuw+bNm3H44YcjLS2N1Rz+7rvv2CqBSk1yOO0m6Rp0yLPnQBIFnDYkhB8/HwAVqyE4y6BAwyvlS3FemoKDmBpIQJYlG5pfhNcXQXFFAAVZ1nanFmJ2gYxhtYSAKGis5jKH0+JCYOLEiXjhhRfYi2oNE5SYa8SIEXjrrbew3377NfkiOJwWrUqmSsix5GC/rsBB+X7M3TYAmipCSC1hNoC3KpbjwzUR5ES7IMfmQjenHUcXpEKUBJgMErJSTe0qnxAJJSm1DyBZACXAqowh7IOgt/F4AU6T2aPadLQCoBcZgt1uN/MWopQRHE57hCVf00SmGrriwCDmFW6DsqMfNFWCkL6DWYL9mWuxvljB+m35mLsthM/XVOCJ8T2hk0QYjRIcZn27WhGI+ljksOpexmoLKJWbIeYOaVfCitMx2KNsWT6fjyWMo4Gf1EGzZs1irqF///13818hh9NMqLKAI3r3xJMTc3BsLwPGSH2R5uua2C/kbAAytzC/e19Uxe1/bMbmqiCzD0RkKlHZvpLJJWcUjZYvgRClDEocTgsLgaVLl+LQQw/FO++8wz7T4P/YY4/hiy++YK6jFB/QFFRVZdlHx40bh+HDh+OSSy7Btm3bGmxP5yHjc91XYWFhU38KpxMiaiIm9++F6w7Kw+UjrXhoQH9MdvZO7BeyNsNZsIkJgoqgzATB1soAiir81c6j7Wd1o0urqeInu1ZB87sgSe03CyqnfdLkO4bsAJQu+tRTT0UwGGSeQmeeeSYWLFjA4gNeeumlJh1v+vTpbCVx//3347333mNCgYrWRyL1B8CsWbOGFbSfO3durVdubm5TfwqnE0IqHYtBjyxLFgpSs2ExGnGcsxdOTqnxavM4tsGaT4IA2O6L4I4/tqCwIoASV5DZCdpNPETG0MRn1b8BcsANUQm36XVxOslK4IorrkBBQQGLFA6Hw/jPf/7D9h177LFYt25do49FAz15E02dOhUTJkxA//798fTTT6O4uJh5G9XH2rVr2cw/MzOz1kuSpKb+FE4nhTyFzHodUnSp6JHeBak2C4529sBZqQMTbQLObbCml7L3G9wh3PnHZmwv88HljUAUhfbh+ZQ+BBBi973m2wAlHIIWcLWL6+Psw0KAPIGM1fnLf//9dzgcDgwdOjRhK2iKgXj16tXw+/048MADE9voeAMHDmzQvkArAV64hrO3q4FUuxG5GTb0yszF8K690CcvEyd364dL8mpcL+WctbBYY3r2f8sDuOfPLSgs9yMQVtrFQCsYnRCt3dl7LbgFmhyA7KuCCKWtL42zL3sHUbTwhx9+yAb7b775hs3gKbKyoqICr776aoPRxPVBM36irionKysrsS8Z8kQig/TChQuZCqmqqooJoJtuugk9evTA3qBLKkIeJ65f5XrWXdNR+4lWBES63gG7yYgdvhKcaTVgc8SF78u2IAIFWT1XQVk9HOGohPlFXjwybwvuOaQ7CrIdsBh1Tcrj39z9RIJIlz4MEd8GVltALfoEWo/zIYY90FvSO2SNgY56L3XkfmqyEKABl3T2X331FfMMItUQMWnSJKbPf/311xt9LLIpEAaDodZ2WmnQgF+XuKqJbu6HH36Yuai++OKLzCYxe/ZsZGTEokH35GFKTbU2uN/hMO/RcTsbHbufLHDYLNjuLcadpoOxfr4Lm/xulKo+DB28GSuW9oKsAt9urkKGzYibDjaia46RrSjasp/0I65BydbPmRCQt70HR88TYFBSYbTnQpD2yAO8XdCx76WO1U9NvksGDRqE77//nkUH9+nTBxaLhW2/5557MHLkSKafbyxx1RHZBpLVSGRnMJt3/nGjRo3CvHnzkJqamsjr8vzzz7PVyCeffIJLL70Ue6oe8Hh2dq8jKUud7PEEoXSgdMOtzb7UT2lSOqI6Bff0OxiXLfkWIVXBskghJgxx4pelGSy4bObyYkSjMq4bXcBWBDazrlExBC3RT5K1L3R5x0Mu/AyaEoRr1SswDbwFJn0pVJOzwwWP7Uv3Ulv2E+1r7Cphj6YKlDJ62LAa3Slx9NFHN/k4cTVQaWkpunat8demzw3lIKLVRzIkLPLz85maaG+QaZrXANTJu9rP2bf6KcuUhf0yBFzfcxQeXv8X2/ansgqThxyAz/+NrVo/WFMOf0TFLQd2RUG2DTZT44PJmrWfRAMMvS6BXPRdLHq46GtEc06AZLJDMDg6bPDYvnIvdYR+alPFG3kDkUD566/Yg0Z4PB6sXLkS+++/f70J6qiOcSBQM2snYzTlMerdu8bXm8PZG2jgzDRn4NTuw3BcVk+2LaKp+Fu3DOcNNySiBb7aVIl7ft+EzcU++IJRSG3gPqoJOuhsedDln1G9RYW86WXIAR8EKkLP4bRnIUC2gLPPPhtPPPEECzIjb6Hrr78eOTk5OOqoo6AoCsrKypjunzjkkEOY3eHmm29m9oF///0X11xzDVsd8NrHnOZEU4BsSxZu7XcwellS2LaiiB/rzCtw/Rgn4uP9T9vcuP2XDdhY5IE70PqCgKXNpjKZ+ScDxiy2TXUtRLR0LlR/FTewcnZLm5vgKUaAgszuuOMOnHHGGczfn4zLer0eRUVFGDt2LObMmZNQH1GSOloJUNvzzz8fdrsdM2bMSLitcjjNhaTp0M3ZBQ8NGg9LtZF1nmcHvLZNuGt8NvTV3kV/Fnlx088bsH6HGy5/6woCpoLSmyDpLdB3uyixna0G/FUQ5JjzBYfTEILWEf3IWkCvVlnpr9dtlLyGqqr8XD+5C/blfiLPMU/Ug1mbFuCu1XPZNr0g4pE+h0IM5OHWH7YiVK13H5RuwaPjeyI/w4pMpwlGvVTLaNdS/UTezWrlVoRdZYgsvQaab01se+8bYB18CeDI7jC2gX35XmrNfkpLszZ6FdjmKwEOpz1DM22nwYGTuw3HSbmxal5RTcVjm+eha1YQzx3dG1Z97DFaURHAdT+tx/KtVdi4w4NSV5B5E7V0YBmN76IjEzqTFfoeNWVf5S1vQnYVQlSjLXp+TseGCwEOZzfQLDrDlI7/GzAeA23pbFtJJIBHNv6OgfnA9KP7wWmIpW9Y7wrhwu/WYfrCQqzb7ma2ApcvlmqipbKQstrIeiv0zgxIqcMhpo+N7YhWIbRhBk8lwdklXAhwOI1AVYA8WzYeG3okUnQx+9PfrmK8sW0BRvQwYvpRfZFhitkNIqqGd9eU4bxv1uDdZcWsdvHmYi98QbnFahIwtZMtHTqrA7rulwBC7FqU7R8gUrYGImT+d+bUCxcCHE4jETUJw9J74L6BhyQenDe3LsMv5aswuo8Dbx7THyf1ToeuesrvCit4bskOXPTdWsxZXYpNO9zYVORGWFZbxGtHUQVIzmzoU3pByp0c26iGEV77AhB0t4t8R5z2BxcCHE4T1C5GwYhJBcNwcffhsW0A7lj5CzYGt2NQDyeuHZWPV4/sjUPyHInvbfOGcfe8rbj2h/X4fV05EwalVc1vL2BqIYMVekcG9N3OBXR2tl0p+Q6R7X/yxHKceuFCgMNpAqTOsevsuKH/eByUmse2eeUIrlv8NUKCD91yHeibZcedY7rh6fE9MTAtllaFWFLmx4VfrsKNP6zHT2tKW8ReEFcL6Z350BWcVb1VQ2jlk9ACHr4a4OwEFwIczh4MtGnGVDw54lh0McYSD672VeKOpd/AYFLRu8CJrjl27J/vxNMTeuCOAwqQa61JkkjZSK/+aQOu/24dvl9Zgs0lXvgjCitY0xwrA0UTIaVkwdDtNAimmKCiWsTh9e9DZOsPDqcGLgQ4nD1AVTT0snfBtBHHwlBd2OXTorV4dd1cuCKVMNmi6N7VjF5dnZgyJBczJ/XHDaPykJ0kDP4q9uLKH9fj6jlr8O3yYmwr8aHSG0YwqoA8v0koUOBZPFli09RCduhTcqDrdWVie3j1M9D8JU0+HmffhgeL8WCxvaYzB/gIkooX1/6Ge1b9yj4bRQkn5PRGf1s6Btgz0M2eAp1ggKZKUGUREAz4+N9y/G9JCUoCtf33h2daMTzLiqFZNgzJtMNu1sFm1sNk1MGkF2E0SNBUrdGZQXWiCrl0EwJ/XclSSRCG3pfBfNBDUNpp3ZnOfC+1VbAYFwJcCOw1nf3BVUUFV/79AT4rWrvTPqukRx9rCvrYUtHHkoYCixMpihmSqscvW6L4cIUXpYGd3Tcp80SvFDOzKQzJtGJEjh3d0y1IdZpYxlJNVXcrDGjGL0W9CG3+DaEF58cSIolGOI79GVpKv3aZZrqz30uNhQuBZoanjdg7OvuDS4OtR/Hjwr/ew+8V2xr1HYo1yNRZkCGaEQxasL7YAK/LAcgN58AamWXFKf2zcETvdGSkmGFn6at3LQxYHiN3MXwL74Sy/WO2TdflaFiPeLddppLo7PdSY+FCoJnhQmDv4A9uzNXTJ/uwtHwj/nWXYJ2vCusCVezfimgsC+7ukCDgMMNwmP3dsaTYjy2ecL3t8mwGTOmTgZMH56AgwwK72cC8lhpKA8bUQkXL4fvlBECOVeyzHfYhhC6HtbvVAL+XGgcXAs0MFwJ7B39wa2bd7ogHZYEKRBWZvVRNRWUkhPX+KmwIulAqB7HZ68aOkA+VDQiHK/JG4dwuB2NHVRgrygIsJ9GvhW4U+SO12ll0Io7tmYZzhuVicJ6TqYmobnLd1QHZgSU1hOCiaQivfpxtEx39YZv8O1StffmG8HupcXAh0MxwIbB38Ae3tiBQoSFCkbpyBAE5gEA0yASCoikwWwzw+UJs5h5UZCYM6DWvage+L9+SOM4l3Ubiyq4T4PeqCEVkFmW8oMiLzzZUsHiDZMjXZ2S2DUd0T8Ux/TLRLd0Cq0kP8jaNq3xYLEKgFJ5vjoPm38C2mUc/BqlPTfrp9gC/lxoHFwLNDBcCewd/cBu2FdDgq0FFRI0gokWgMwBufwChaASySrmEVKiaBkVV8OrmpZi5fWXi++d1G4YHh05COAz4gzK8gQhCYRlrywNMGPy01cXyFCVD8/qhWVYc3j0Nx/fPQo8MC8xGHQttFkUguu4zBOZdGLs+QxrsJy2CKsUii9sD/F5qHFwINDNcCOwd/MFtHHq9iJQUK9zuAFsJkKqIVgeKRoJAQViJ4OnVv2L6pkWJ70zp0h/PjjoJBsEAGu/9oSi8/ig8gQhKPWHM3lCBOZsqd3I3ja8QBmdYMbFXGi7cPx8ZDhMkVYb3+zMgl/zI2hj7XwbT6Ed2aVNoTfi91Di4EGhmuBDYO/iD2zz9RKuGsBbCC2t+xaNr5yVie4/O7oVX9z+V5S2iKb0oipAVFf6QDLc/Ap8/jGUlPvxW6Mbv2z072Q6ILIseU0cX4ILRBTB618D91aGAGqFAB1jHfwx9xhDAYIamMzF7QktlO90d/F5qHFwINDNcCOwd/MFtvn4iFZIiRPHWhj9xx4pfmH2BGJdegP8beCj62rNg1ZuhE3RssCYVTyiiwh+MrQ78gShWl/vwy1Y3ft/hYcnrkumTasadE3rhMM8zCK+ZXn1SPcTUkdBlTYA+93DoU7sBBgugt0CF0KoCgd9LjYMLgWaGC4G9gz+4LdBPkoYPt/yNG5Z+yyqZxaHSll0tDvS2paO/IxODHNkY6MxlwkESdQhHFQRCMfuBLxjFihIfXltWhL9LfLUOf3i+AS8YLoE+UlznxCIExyDossbDQAIhZxg0c0qrxRTwe6lxcCHQzHAhsHfwB7dl+kmUgDk7luGKRbOZJ9GuyDJacFxuP5yUPwxj0ruzFUVUUREMk0CI4sf1FXh+YSE2uGvcUnOkSjyW9znGiPMhyeX1X4NzOMzD/g+GgkOgiLF4hJaE30uNgwuBZoYLgb2DP7gt10/kcvqvaytmbf4H67wV2BL0YFvIC2UXRtw8sx2TcvvjlIJhGOrMgyCIbIVAyeneW1qEV5bsQFmwxpAsChou7LIF56f9jS6hP6AF60Y9i9AXTIF55O0QnF1bdFXA76XGwYVAM8OFwN7BH9yW7ScyGAfVIDwRb3XsQQhb/C6s91Vho78Kq72VWOQpqVcwdLek4IS8Qbi454HIMdsRjqooqgrg1b8LMWN5MQJ1rqNfqgHX9y7HUaZ5QNFX0ELba3bqU2EadCNMAy+CKhlbZFXA76XGwYVAM8OFwN7BH9yW7yeK+o3VGojFHSiaDFlTEFVlFm9QGvLgq+2r8X3pRixylSQMynEMooQp+YNxbZ9D0NueyVYGG0t9eGvRdnyytgyVodrqphSjhPMG2nCp9ROYtr4BqDVqJDFlGKz7PwQpdwz5KjHX0tirej+LjSAop7XQJNdTfi81Di4EmhkuBPYO/uC2XT/FSgPEgtIo1iCkhlEYqMAX21bgu9KNWOIuqSUOqPnROX1wfd/xGJXeFaGIgnJPCJ+tKMZ7K0uxsiJQ6/gkd07pGsSNlleQ7v0teQ/0+SdAlzse+vThEOxd4YcOn6yuwjtLS5hBun+GBVeP6YrjB2Sx8zZGFvB7qXFwIdDMcCGwd/AHt331U7xcZVgNY6O3DK9uXICPtq/cybg8Jr0A1/YZiyNy+kLTBOZNNHdDJd5eVoSftrgg1xm1T0xZhjudr8GpJKmIqvHDjmXh7lgR6YGVUfq3J9bL+Uzs9Eo146oDCnD6sC7QCbsWBvxeahxcCDQzXAjsHfzBbb/9FBcIJWEXXl0/D29vWbpT4rpUvQkHpnfFodm9cVh2P+To7dhQ5seMRdvx6dpylCcZkQ2I4lLHF7jK8REM2DkoLZnVka54wXsyvgmOgQoJOVYDrhhdgPP3y4OpgYIn/F5qHFwINDNcCOwd/MHtGP1ElabcUT/e3rwAr238B9tD3nrb5ZkdGJfZA4dm9cEASw7+2RDCe8uL8dcOb0K1lC66cYBxOQboN2OQYRMG6jchU3LVe7x10XxM90zBV8GDoUBCilGHM4bmsLoIo/OdMFDEWzvpo44CFwLNDBcCewd/cDtWP5EwINvBx9sW45Nty/GPqxh+ZefcQ3HMkg69bOnI0aeiqkqPVdsBv9cERExkjcCYLjZM6ZuGY7v4YQuuBtzLoZT8BNVdkwyP2BzNwXTvFHweOAQydGybQRIwqosTE3qmYnz3VAzv4kBmur3N+6i9w4VAM8OFwL4xuLV32ls/MWOyoMIT8WFxVSF+K92I+ZWF+NddikhSlHJDSBDRy5KOUen5GJmWj/0z8tHXlgEdDfBqFPLWbxD692koVYtrfW+bnIWP/Idih5KJYiUNJUo6SpQ0+DQL7AYJh/RKx+guDozMtWNotg0mnbTba4koKra4grAadOhib7g6276CjtcYbl64ENi3Brf2SnvtJ7IZUIQxy1sEGZ5oAPPLtuD38k1Y4ynFpoAL24O+ndxO64NcUQfYszA0JRd2vQkSpdP2b4VWMh/wb4MEDRJUpKlB9FSr2CtNi9kofKoJxUo6ipQMbJFzsEnugkIlF6a0PuiR3x8j89MwPNuGikAU6yqCWFvhx7qKANaU+7GpKgi5Om5hQIYVx/bLwDF9MzE408p+176GjguB5oULgX1zcGtvdKR+itVCYJ7+LCbBJ4ewzlOC1d4yJhjW+yqxzleJbUFPI0TDrkmtFgg91Cr0UqrQS63EEKUUmVqNu2pUk1AoZ2GznItyNQUe1QKPaoVHtcGjxd9bsV3JYkIkDq0Kju2bgYl9MjAm3wl9AwbpjgYXAs0MFwKdZ3BrSzp6PyWvGChgTdZkVIb9WObagcVV27HMXYxVnrJmEQxErurFUKUEw+Ri9u9gpRQ27Gy7oHMFoUNA0EOvqXDJTvwVHoz59AoNRomaztqZdCLMOjH2OyhygQLwhNh7+tdu1CHNrEeaRc/+TbfokUqfzXpkWgzIcxiQZzexdm0NFwLNDBcCnXtway321X6KCYaYjYGimKsiPqz2lCAkR1kqCyqaQ/+qJDhUEh4qikM+rPeVY6OvEpv8LpRFArs/j6axFYMeCgLQs0GfXkHooSWpfExaFFmqH9man/1rUgBfJAVloWwEfFmoiGShXHUyt9U9wWGUkO80ocBhQp7DhC4OIzLMBmRYSYgYmPDIMOthNUgtporiQqCZ4UJg79hXB7fmpjP1UzySeVf7421IPLiifqZm2uAvxxp/ORaWFWKFpxSB3WRP3RN6KxU4UC7E4KgL+REZfiUFpWoGtkTSsT6ShW1yNrYp2QhpsSI+kGj1QQWb9XWOpMEmBGERQqhQncz9NRmjJDKBYNTFUmuQyYJKidLKJf6ebCa0srAbJWYUt9F7Q/yzDplWA3JssVeu3YgMiwGSKDSrEGj7dQ2Hw9nniEUFN6wUqokajr1xiFbsn9IDB2X2SpTgjMgyW1EsrNqKRVXbsdRVhDXeCjZwktuqWdKzl4W9Yp/DqoLSkBdlIS+8av2Cdr2Uzl4wUtkGFcOUYiYUbNiBLoIVOtEKq2BFqWhHqWCGIsQG03Q1ip6KB33lcgyWizBK3YrucDFRRzYL8nbaKmezFwkSMm5vC2UjrOlhFiIwCREYhSgMQhh6MQJJirJI7XJPBrYrmShV0nYSJHWh355lMzBbxwlDc3HFyC7YWwStPRQWbWP4SmDv6Ewz3L2B99Oe91Fc5UTEEunFaHj00uCVwygKulHsL8X20iVYU7kZf/hDWCaTf1LzqGlS1CD6qJUwaApUQYhVYqvzCgsS/DDUqK+E2isKnaYw1VWO6kOqEoFJBnSyBESNMGsybEIYdgRhF4JwIAgH/SsEsTnYDQdO+R/6ptt3ui6+EuBwOPsUyVlKG5vC2iIY0MuSyV7IHJQQIB45hLnlm/BL6Tr8XrYJG/xV9X4/XQsiS/UxuwIN3qukDPhYnecaXKIZf4t5e/XbZEHCdsGB7aIjpptpZJhDL0clTlQ3ABi+V+fn6iAOh9NpIAFiE42YmNWfvURRQHHIi78qt0ASgByTA7lmB/tXJ0jQoj6ovi3QlAhgSsVWWcJSXyWWuYvwr7sIK9ylKN+NUdsIFRaBhBJgFQGzKMJCL0mCoqoojkZQLGtwNXE43iClocScC8de9gkXAhwOp1MLhSyDDcfnDKqzA1Dof6IFcAxIKI+60cueh8m5QxIus34lEvOOYq6m5HBKyTQESAK5oyarnWpWM3WhdgE5gqKQG9t8JSh0b2VqrKAmIAKJvcKg9yIiqoqopuCI/H4YkJK71ypYLgQ4HA5nD6ABndRU5mQdf9IgT94/uzKO1zkajIIO3c3p7IXMgTu1SJYnZDshAzrZTvYWLgQ4HA6nA5C8imhOd559I4aaw+FwOHsEFwIcDofTiWlzIaCqKp599lmMGzcOw4cPxyWXXIJt27Y12L6qqgo33ngj9t9/f4wePRr33nsvgsFgq14zh8Ph7Cu0uRCYPn06Zs2ahfvvvx/vvfceEwoXX3wxIpH6S9dNnToVW7ZswVtvvYVnnnkGv/76K+65555Wv24Oh8PZF2hTIUAD/RtvvMEG9gkTJqB///54+umnUVxcjO+++26n9osXL8aCBQvw6KOPYtCgQTjwwANx33334fPPP0dJSUmb/AYOh8PpyLSpEFi9ejX8fj8bzOM4HA4MHDgQf//9907tFy5ciMzMTPTq1SuxjVRC5GP7zz//tNp1czgczr5Cm7qI0oyfyM3NrbU9KysrsS8Zmu3XbWswGJCSkoKioqK9uhbyu61LPAtfY7PxdVZ4P/F+4vdSx33m2lQIxA26NJAnYzQa4Xa7621ft228fTgc3uProNBxSlrVEA6HeY+P3Zng/cT7id9LHe+Za1MhYDKZEraB+HuCBnSzeecfR23qMxhTe4vFsleh4x7Pzvk/SMpSJ3s8QZZplFM/vJ8aB+8n3ketdS/Rvg5RTyCu2iktLUXXrl0T2+lzv379dmqfk5ODH374odY2Egoul4upkPaGXeXfoE7mKZJ3D++nxsH7ifdRe7qX2lTZTd5ANpsNf/31V2Kbx+PBypUrWRxAXWgb2QrIRTQOeQsR++23XytdNYfD4ew7tOlKgPT7Z599Np544gmkpaUhLy8Pjz/+OJvxH3XUUVAUBZWVlbDb7UwVNGzYMIwcORLXX389iw0IBAK46667cMIJJyA7O7stfwqHw+F0SNrc7YViBE4++WTccccdOOOMMyBJEl5//XXo9Xrm8TN27FjMmTOHtSVX0Oeffx75+fk477zzcN111+GQQw7hwWIcDoezh/Dykry85F7Dyybyfmou+L3UOJqz0HybrwQ4HA6H03ZwIcDhcDidGC4EOBwOpxPDbQKsSo/GAsbqg/RqPFBs9/B+ahy8n3gftca9RFkQatc3bhguBDgcDqcTw9VBHA6H04nhQoDD4XA6MVwIcDgcTieGCwEOh8PpxHAhwOFwOJ0YLgQ4HA6nE8OFAIfD4XRiuBDgcDicTgwXAhwOh9OJ4UKAw+FwOjFcCHA4HE4nhgsBDofD6cRwIcDhcDidGC4EGkBVVTz77LMYN24chg8fjksuuQTbtm1r3b9OO+bll1/GOeecU2vbqlWrcPbZZ7P+OuywwzBjxgx0RlwuF+666y5W/3rkyJGsdvbChQsT++fNm4eTTjoJw4YNw8SJE/HVV1+hs1FRUYGbbroJY8aMwYgRI3DppZdiw4YNif38XtqZTZs2sb765JNPmrWfuBBogOnTp2PWrFm4//778d577zGhcPHFFyMSiaCzM3PmTEybNq3WtqqqKlxwwQXo2rUrPv74Y1x11VV44okn2PvOxg033IDFixfjqaeeYr9/wIABuOiii7Bx40Y20F122WVsckEP8ymnnIKbb76ZCYbOBN0fW7ZswSuvvIKPPvoIJpMJ559/PoLBIL+X6iEajeK///0vAoFA8z9zGmcnwuGwNmLECG3mzJmJbW63Wxs6dKg2e/bsTttjxcXF2mWXXaYNHz5cmzhxonb22Wcn9r300kva2LFjtWg0mtj25JNPakcddZTWmdi8ebPWt29fbeHChYltqqpqRxxxhDZt2jTtzjvv1E4++eRa37nhhhu0Cy+8UOssuFwu9pvXrFmT2LZq1SrWb0uXLuX3Uj3Qs3TuueeyPvr444+b9ZnjK4F6WL16Nfx+Pw488MDENofDgYEDB+Lvv/9GZ2XFihXQ6/X44osvmCojGVJ3jB49GjqdLrGNlvqbN29GeXk5OgupqalsdjtkyJDENqrwRC+Px8P6Kfm+ivfTP//8wyrcdQacTieefPJJ9O3bl32urKzEW2+9hZycHPTu3ZvfS3WgMef999/HI4880iLPHBcC9VBcXMz+zc3NrbU9Kysrsa8zQjrH5557DgUFBTvto36hh7hufxFFRUXoLNBkYfz48TAYDIlt3377LVN9kAqooX6Kq0E6G3feeScTimQXefDBB2GxWPi9lARNHEhdeMcdd+w0HjXXM8eFQD3QA0kkP8iE0WhEOBxudOd2JkKhUL39RXTmPlu0aBFuu+02HHXUUZgwYUK9/RT/3BntTeeddx7TYU+aNInptGm1ye+lGu655x5mDD7++ONRl+bqp5p1BCcBGaniD2X8fbxjzWYz76l6oH6qO4jFb0Sa3XVGfvjhB2bMIw8hMtjFH9K6/RT/3BnvLVL/ELQKWLp0Kd555x1+L1Xz2WefMZXP7Nmz0ZLPHF8J1EN82VVaWlprO33Ozs5udOd2JmhZWl9/EZ2xz2gwu+aaa3DooYfipZdeSszQ6N6qr5/oobXb7egMkA2A1D+yLCe2iaLIBAL1Bb+XYtAKiVxpaQVJqwF6EXfffTfzVGyufuJCoB769+8Pm82Gv/76q5ZubuXKldh///0b3bmdCeoXMm4qipLYNn/+fPTo0QPp6enoTMRdi8866yzmJpq8ZB81ahQWLFhQqz31E60WaCDsDJDRktxok91iyQWSnq9evXrxe6kaWj3OmTOHrQjiL2Lq1Kls5dRsz1yTfIk6EU899ZQ2evRo7YcffmDua+TCR65XkUikrS+tXXDLLbfUchEtLy/X9t9/f7Z93bp1zI1tyJAh2ieffKJ1JjZu3KgNGjRIu+qqq7TS0tJaL4/Ho61du5btf/zxx7X169drr7/+ujZw4EDtzz//1DoTF198MXueFixYwFxFyWWU7p/t27fze2kXJLuINtczx4VAA8iyrD322GPamDFjmF/8JZdcom3btq1JnduZhABBPt6nnnqqNnjwYO3QQw/V3n77ba2z8eKLL7IHtb4X9Rnx66+/apMmTWL9RPEWX331ldbZIIF49913awcffDCLv6FJFgnIOPxe2r0QaK5+Euh/jV83cDgcDmdfonMoITkcDodTL1wIcDgcTieGCwEOh8PpxHAhwOFwOJ0YLgQ4HA6nE8OFAIfD4XRiuBDgdHpa0kuae2Bz2jtcCHCaNdX0rbfe2qF6dN26daz8Y0ulAE4uK7mvUlhYiH79+tUqe8jpOPAsopxm4/nnn2c5lzoS33zzDSsF2dxQ7dfPP/8cU6ZMafZjczjNCRcCnGaDKq9xOJyOBVcHcVpEHRRXEXz99dcs6yGlwaVSeFQhKblYdkNQUfarr76afYeyJVJxdirSHsfr9eLhhx/GEUccwUo5UlESKlhe93qeffZZPProozjooIMwdOhQVvCdyu8RVCWNVi8EXSt9JlRVZSUijzzySAwePBhHH3003n777cRxly9fjkGDBtVSfVHKX6qQRYW/KZPjueeey7bTv+ecc06Dv5Pyvz/22GOsGhmdi4qHUObIOD/++GOtayOoH+i3/N///V+t2gVnnnkm62c6zsSJEzFz5szEfsqIS8ehzJ10PfR9SlH84YcfsvTD1Nf0XboOKvVY93tz585lWVHpe1QghzKl7oodO3awTKH096NSpFQ8hrKEJvPll19i8uTJ7JhUFpFqL5SUlOzyuJwWoMnZhjicBqAEVvEkaZRsj5JdUZbDRx55hGXJpMLY/fr105544ondFrQfNWqUdtxxx7Hkaj///LN20kknsWRjVVVVWjAYZAnYDjzwQO3dd9/VfvvtN+2uu+5i56MEbsnXs99++2mXXnqp9ssvv2iff/45ywxLCbeIoqIi7f/+7//Y9xYvXsw+E1QMnjJ9Pvvss9rvv//OMsr2799fe/755xPHfvrpp9n34tk/r7zySnZsunav16u98847bD/9Sxke64MK0F900UXaiBEjtDfffJP9Djo3fe/TTz9NtPvvf//LroeyjlJRceoLKlzv8/nYfuof+s4DDzzAruenn35iWTpp25IlS1ib+fPns8+UEPGNN95g7c4//3xtwIAB2tFHH61NmzaNbbv66qsTBd+Tv0d/Dzo+XSMlfqNtM2fOrPW3jic2q6io0MaNG8eyhH7xxRfa999/z5INUiJG+g3EwoUL2bmfe+45do7PPvuM/X3POuss/ny1MlwIcFpUCNAAlsw555zDBvBdQUKDMktS+uU4NEBPmDCBDeY0+NCxFy1aVOt7NKBTKl0SFPHroRdlhI1Dgw59t7Kykn2mgZ4+J6eCJkH18ssv1zo2Dfp07Pj3KKX48ccfzwZQGvzoGF9//XWifXzwpH8bYu7cuaxN3Syi1Gc0INKAT7hcLm3s2LHaueeeq02fPp0NniS04rz66quJfo9DfUDHjv+O+PVQCus4JCBo20033ZTYRr+PtpFQSv7ebbfdVuv4V1xxBbtGEmR1hQAJTeqrwsLCRPtwOKwdfvjh2jXXXMM+03WR8KPtcehvS38fOian9eDqIE6LMnz48FqfqRpSXB1EaheqLpX8IqhQBn0vMzOz1vd+/vlnpq6goix5eXmJSktxSLVA6hUqUxiHVEWSJNU6TnId6bqQKocmR6RKSr4u+kzHpmsj9Ho9UzOR2uv222/HiSeeyFQwTYFUM4IgsN9U91xlZWXMc4lwOp2sSA1dG6m3rrjiilr9SlWmHnnkEfj9fqaqInXSyy+/zPbVLT+Y3GfxwiOkromTmpqaULclQ78vGVIJ0TVu2rSp3t81YMAAVt0q/puoYM4hhxyCP//8k7UhFR/9DUiN9+STTzIvqrFjxzK1FPUJp/XghmFOi1K3bi4NBnHf+RdeeCGhk4+zZs0auFwu5OfnN3hMt9tdS0DEycjISLhn7ur8cQFUH3Ru4rjjjqt3f7LOmgY60pfTwEtlJJsKnYv6gqqK1Qfp6ukcBNk0srKy2La656JyjVRykOwCNIB269aNVTCrL06hPu+txtQ2rluuMC5A6vtb0O/asmULs5vUBw3+JIzI7kL2hzfffJO9p7/f5ZdfvksbCqf54UKA02aceuqpzDhZF6q1SwNbfTNMEg40M6ZBpi40M02eze4JDoeD/fu///0PVqt1p/1dunRJvH///feZAKBypFTujwzD8e83BvqdVFt4xowZ9e6nwTwOCUsaXHv27MmM62TQpdUIQQZVMqTTgEqDK5WzpIH2gw8+QHNRVVWFrl271jKEE/WVMaTfRQZhipOoj3i5zXHjxrEXXSutcqgfHnjgAbYyIWMxp3Xg6iBOm0GzS1LXJL8ImsWSSidZENCgQ2qPX3/9lakStm/fvpN//xdffMEGxqYMIHXr+sZn0DToJV8XXcszzzyTWCnQ+UkddPLJJ7NC8qQ+IUEQJ1kF1RA0UJJqjGbryedau3YtWyXF1WPLli3Da6+9xtRAjz/+ONv/4osvJo5DKipSzxxwwAGJAfa3337b5YqnqdAqo258BankkgVD8u8iNRHVuk3+XRQ3QR5c1DfUdxRDQb+dViK0urnlllsSnkWc1oOvBDjtjvPPP58V1aZBn1xDaWCnQY/0+eRCSQMduSheddVVzP2UVgc//fQTPv74Y6ZTbspsPN6W3BVpBkrqHbIt3HnnnWygJ3dLGtCefvppdp7u3buzgYvsADR40WyXVibXXXcdHnroIeZOSjp9mg0Tv/zyC9tPq4W6kC2ABNqVV17JXlRknQZ80vvTDDktLY3p9MkVlfZdcsklrC/OPvtspvMn91iKzSChN3v2bKZ+oT5atGgRU6+Qaqgh20dTIZWN0WhktojvvvuO2WdIl9/Q348GfPr3wgsvZCszslPQyuS2225jbcgllI5Jv436mwrNk6BLSUlh+zitSCsaoTmd0DsouR4qQfup3e4gV8LLLruMuRWS6yV5lSTXeCY3RPIGIpdHqq86efJk7cMPP2zweuLEPXnixyKXzilTpjAXTHJ9JMgrh9xByZuFth9yyCFsX9zrKO7+OWfOnMRxFUVhx4m7sdJnKp5OXjLk6toQfr9fe+ihh9g56FyHHXaY9uSTT2qhUIjtf/TRR5l7arI3EH2HPKXIy4q8a8gLh/qK3GHpRddB7rDkfkrvG/JWauhvRNvIayr5e+SRdfLJJyf6+ptvvtnlcbZs2aJNnTqVuQiTp1d9f5/Zs2drJ554Ivsbk6cQubWuXr26wb7itAy8xjCHw2kQChajgDfS15O6ibPvwW0CHA6H04nhQoDD4XA6MVwdxOFwOJ0YvhLgcDicTgwXAhwOh9OJ4UKAw+FwOjFcCHA4HE4nhgsBDofD6cRwIcDhcDidGC4EOBwOpxPDhQCHw+F0YrgQ4HA4HHRe/h9DOluiWReI+AAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] diff --git a/src/models.py b/src/models.py index 7a27bbc3..e90e1cd9 100644 --- a/src/models.py +++ b/src/models.py @@ -29,6 +29,10 @@ def build_model(conf): def get_relevant_baselines(task_name): task_to_baselines = { + "WlaplaceNoisypoisson": [ + (LeastSquaresModel, {}), + (RidgeModel, {"alpha": 0.5}), + ], "laplace_weighted_regression": [ (LeastSquaresModel, {}), (RidgeModel, {"alpha": 0.5}), diff --git a/src/plot_utils.py b/src/plot_utils.py index 7a0c31ab..a9f3a3c6 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -10,6 +10,11 @@ palette = sns.color_palette("colorblind") relevant_model_names = { + "": [ + "Transformer", + "Least Squares", + "Ridge (alpha=0.5)", + ], "laplace_weighted_regression": [ "Transformer", "Least Squares", diff --git a/src/schema.py b/src/schema.py index 1923c335..c8da8e7f 100644 --- a/src/schema.py +++ b/src/schema.py @@ -47,6 +47,7 @@ "uniform_hypersphere_regression", "exponential_weighted_regression", "laplace_weighted_regression", + "wlaplace_noisypoisson", ] training_schema = { diff --git a/src/tasks.py b/src/tasks.py index aff0656c..d07bea2a 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -64,6 +64,7 @@ def get_task_sampler( "ar1_linear_regression": AR1LinearRegression, "exponential_weighted_regression": ExponentialWeightedRegression, "laplace_weighted_regression": LaplaceWeightedRegression, + "wlaplace_noisypoisson": WlaplaceNoisypoisson, } if task_name in task_names_to_classes: @@ -159,6 +160,65 @@ def get_metric(): @staticmethod def get_training_metric(): return mean_squared_error + + +class WlaplaceNoisypoisson(Task): + def __init__( + self, + n_dims, + batch_size, + pool_dict=None, + seeds=None, + scale=1.0, + weight_scale=1.0, + poisson_rate=3.0, + ): + """ + Task with Laplace-distributed weights, expects exponential-like inputs, + and adds centered Poisson noise to the supervision. + """ + super(WlaplaceNoisypoisson, self).__init__(n_dims, batch_size, pool_dict, seeds) + self.scale = scale + self.weight_scale = weight_scale + self.poisson_rate = float(poisson_rate) + + if pool_dict is None and seeds is None: + laplace_dist = torch.distributions.Laplace(loc=0.0, scale=self.weight_scale) + self.w_b = laplace_dist.sample((self.b_size, self.n_dims, 1)) + elif seeds is not None: + self.w_b = torch.zeros(self.b_size, self.n_dims, 1) + generator = torch.Generator() + assert len(seeds) == self.b_size + for i, seed in enumerate(seeds): + generator.manual_seed(seed) + laplace_dist = torch.distributions.Laplace(loc=0.0, scale=self.weight_scale) + self.w_b[i] = laplace_dist.sample((self.n_dims, 1)) + else: + assert "w" in pool_dict + indices = torch.randperm(len(pool_dict["w"]))[:batch_size] + self.w_b = pool_dict["w"][indices] + + def evaluate(self, xs_b): + w_b = self.w_b.to(xs_b.device) + ys_linear = self.scale * (xs_b @ w_b)[:, :, 0] + + poisson = torch.distributions.Poisson(rate=self.poisson_rate) + noise = poisson.sample(ys_linear.shape) - self.poisson_rate + noise = noise.to(xs_b.device) + return ys_linear + noise + + @staticmethod + def generate_pool_dict(n_dims, num_tasks, weight_scale=1.0): + laplace_dist = torch.distributions.Laplace(loc=0.0, scale=weight_scale) + return {"w": laplace_dist.sample((num_tasks, n_dims, 1))} + + @staticmethod + def get_metric(): + return squared_error + + @staticmethod + def get_training_metric(): + return mean_squared_error class ExponentialWeightedRegression(Task): def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, rate=1.0): super(ExponentialWeightedRegression, self).__init__(n_dims, batch_size, pool_dict, seeds) From c4e9293566d83b8669822d1eae05a7025e1a4f3a Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Fri, 21 Nov 2025 15:48:18 +0700 Subject: [PATCH 56/88] fix code w --- src/models.py | 2 +- src/plot_utils.py | 2 +- src/tasks.py | 6 +++--- src/train.py | 1 + 4 files changed, 6 insertions(+), 5 deletions(-) diff --git a/src/models.py b/src/models.py index e90e1cd9..d918d05e 100644 --- a/src/models.py +++ b/src/models.py @@ -29,7 +29,7 @@ def build_model(conf): def get_relevant_baselines(task_name): task_to_baselines = { - "WlaplaceNoisypoisson": [ + "wlaplace_noisypoisson": [ (LeastSquaresModel, {}), (RidgeModel, {"alpha": 0.5}), ], diff --git a/src/plot_utils.py b/src/plot_utils.py index a9f3a3c6..a5f7326b 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -10,7 +10,7 @@ palette = sns.color_palette("colorblind") relevant_model_names = { - "": [ + "wlaplace_noisypoisson": [ "Transformer", "Least Squares", "Ridge (alpha=0.5)", diff --git a/src/tasks.py b/src/tasks.py index d07bea2a..7bf5b705 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -64,7 +64,7 @@ def get_task_sampler( "ar1_linear_regression": AR1LinearRegression, "exponential_weighted_regression": ExponentialWeightedRegression, "laplace_weighted_regression": LaplaceWeightedRegression, - "wlaplace_noisypoisson": WlaplaceNoisypoisson, + "wlaplace_noisypoisson": wlaplace_noisypoisson, } if task_name in task_names_to_classes: @@ -162,7 +162,7 @@ def get_training_metric(): return mean_squared_error -class WlaplaceNoisypoisson(Task): +class wlaplace_noisypoisson(Task): def __init__( self, n_dims, @@ -177,7 +177,7 @@ def __init__( Task with Laplace-distributed weights, expects exponential-like inputs, and adds centered Poisson noise to the supervision. """ - super(WlaplaceNoisypoisson, self).__init__(n_dims, batch_size, pool_dict, seeds) + super(wlaplace_noisypoisson, self).__init__(n_dims, batch_size, pool_dict, seeds) self.scale = scale self.weight_scale = weight_scale self.poisson_rate = float(poisson_rate) diff --git a/src/train.py b/src/train.py index 8ce1880a..dec5a297 100644 --- a/src/train.py +++ b/src/train.py @@ -80,6 +80,7 @@ def _sanitize_training_kwargs(args): "noisy_linear_regression": {"scale", "noise_std", "renormalize_ys", "noise_type", "uniform"}, "ar1_linear_regression": {"scale", "ar_coef", "noise_std", "compute_gradient"}, "uniform_hypersphere_regression": {"scale"}, + "wlaplace_noisypoisson": {"scale", "weight_scale", "poisson_rate"}, } task_name = args.training.task From dac99b0209b45049d08b243f0541e233cf09c2de Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Sun, 23 Nov 2025 14:12:39 +0700 Subject: [PATCH 57/88] cauchy fix --- src/eval.py | 10 +++ src/models.py | 198 +++++++++++++++++++++++++++++++++++++++++++++- src/plot_utils.py | 3 + src/tasks.py | 4 +- 4 files changed, 212 insertions(+), 3 deletions(-) diff --git a/src/eval.py b/src/eval.py index 37973540..d7e25b75 100644 --- a/src/eval.py +++ b/src/eval.py @@ -437,6 +437,16 @@ def baseline_names(name): if "gls_ar" in name: ar = name.split("ar=")[1] return f"GLS (ar={ar})" + + if "LAD_L1_Regression" in name or name == "LAD_L1_Regression": + return "LAD (L1 Regression)" + + if "Huber_Regression" in name: + epsilon = name.split("epsilon=")[1] if "epsilon=" in name else "1.35" + return f"Huber Regression (ε={epsilon})" + + if "Cauchy_MLE" in name or name == "Cauchy_MLE": + return "Cauchy MLE" return name diff --git a/src/models.py b/src/models.py index d918d05e..97c580b9 100644 --- a/src/models.py +++ b/src/models.py @@ -4,7 +4,8 @@ from transformers import GPT2Model, GPT2Config from tqdm import tqdm from sklearn.svm import LinearSVC -from sklearn.linear_model import LogisticRegression, Lasso +from sklearn.linear_model import LogisticRegression, Lasso, SGDRegressor, HuberRegressor +from sklearn.linear_model import LogisticRegression, Lasso, SGDRegressor, HuberRegressor import warnings from sklearn import tree import xgboost as xgb @@ -101,6 +102,9 @@ def get_relevant_baselines(task_name): (RidgeModelWithVarianceAdjustment, {"alpha": 0.5, "ar_coef": 0.5}), (FeasibleGLSModel, {"ar_coef": None}), (GLSModel, {"ar_coef": 0.5}), + (LADModel, {}), # L1 Regression + (HuberRegressionModel, {"epsilon": 1.35}), # Huber Regression + (CauchyMLEModel, {}), # MLE cho Cauchy (NNModel, {"n_neighbors": 3}), (AveragingModel, {}), ], @@ -865,3 +869,195 @@ def _estimate_weights(self, train_xs, train_ys): return torch.ones_like(train_ys) raise ValueError(f"Unknown variance_model '{self.variance_model}' for WLS") + + + class LADModel: + """ + Least Absolute Deviations (L1 Regression) - Minimize Mean Absolute Error (MAE) + """ + + def __init__(self, max_iter=1000, tol=1e-3): + self.max_iter = max_iter + self.tol = tol + self.name = "LAD_L1_Regression" + + def __call__(self, xs, ys, inds=None): + xs, ys = xs.cpu(), ys.cpu() + if inds is None: + inds = range(ys.shape[1]) + else: + if max(inds) >= ys.shape[1] or min(inds) < 0: + raise ValueError("inds contain indices where xs and ys are not defined") + + preds = [] + + for i in inds: + if i == 0: + preds.append(torch.zeros_like(ys[:,0])) + continue + train_xs, train_ys = xs[:, :i], ys[:, :i] + test_x = xs[:, i : i + 1] + + pred = torch.zeros_like(ys[:,0]) + for j in range(ys.shape[0]): + x_j, y_j = train_xs[j], train_ys[j] + + clf = SGDRegressor( + loss='epsilon_insensitive', + epsilon=0.0, + max_iter=self.max_iter, + tol=self.tol, + fit_intercept=False, + random_state=42 + ) + + try: + clf.fit(x_j.numpy(), y_j.numpy()) + w_pred = torch.from_numpy(clf.coef_).unsqueeze(1) + y_pred = (test_x[j] @ w_pred.float()).squeeze(1) + pred[j] = y_pred[0] + except Exception as e: + # Fallback to median if LAD fails + pred[j] = torch.median(y_j) + preds.append(pred) + + return torch.stack(preds, dim=1) + + class HuberRegressionModel: + """ + Huber Regression - Baseline "Hybrid" between L2 and L1. + """ + + def __init__(self, epsilon=1.35, max_iter=300, alpha=0.0001): + """ + epsilon: threshold for Huber loss + alpha: regularization strength + """ + self.epsilon = epsilon + self.max_iter = max_iter + self.alpha = alpha + self.name = f"Huber_Regression_epsilon={epsilon}" + + def __call__(self, xs, ys, inds=None): + xs, ys = xs.cpu(), ys.cpu() + if inds is None: + inds = range(ys.shape[1]) + else: + if max(inds) >= ys.shape[1] or min(inds) < 0: + raise ValueError("inds contain indices where xs and ys are not defined") + + preds = [] + + for i in inds: + if i == 0: + preds.append(torch.zeros_like(ys[:,0])) + continue + train_xs, train_ys = xs[:, :i], ys[:, :i] + test_x = xs[:, i : i + 1] + + pred = torch.zeros_like(ys[:,0]) + for j in range(ys.shape[0]): + x_j, y_j = train_xs[j], train_ys[j] + + clf = HuberRegressor( + epsilon=self.epsilon, + max_iter=self.max_iter, + alpha=self.alpha, + fit_intercept=False + ) + + try: + clf.fit(x_j.numpy(), y_j.numpy()) + w_pred = torch.from_numpy(clf.coef_).unsqueeze(1) + y_pred = (test_x[j] @ w_pred.float()).squeeze(1) + pred[j] = y_pred[0] + except Exception as e: + # Fallback to OLS + try: + ws, _, _, _ = torch.linalg.lstsq(x_j, y_j.unsqueeze(-1)) + y_pred = (test_x[j] @ ws).squeeze() + pred[j] = y_pred[0] + except: + pred[j] = torch.median(y_j) + preds.append(pred) + return torch.stack(preds, dim=1) + + class CauchyMLEModel: + """ + Maximum Likelihood Estimation for Cauchy noise. + Minimize negative log-likelihood: sum ln(1 + (y_i - w x_i)^2) + """ + + def __init__(self, max_iter=100, lr=0.01, init_from_lad=True): + """ + max_iter: maximum number of iterations + lr: learning rate for gradient descent + init_from_lad: initialize from LAD solution (recommended) + """ + self.max_iter = max_iter + self.lr = lr + self.init_from_lad = init_from_lad + self.name = "Cauchy_MLE" + + def __call__(self, xs, ys, inds=None): + xs, ys = xs.cpu(), ys.cpu() + if inds is None: + inds = range(ys.shape[1]) + else: + if max(inds) >= ys.shape[1] or min(inds) < 0: + raise ValueError("inds contain indices where xs and ys are not defined") + + preds = [] + + for i in inds: + if i == 0: + preds.append(torch.zeros_like(ys[:,0])) + continue + train_xs, train_ys = xs[:, :i], ys[:, :i] + + pred = torch.zeros_like(ys[:,0]) + for j in range(ys.shape[0]): + x_j, y_j = train_xs[j], train_ys[j] + + try: + if self.init_from_lad: + try: + clf = SGDRegressor( + loss='epsilon_insensitive', + epsilon=0.0, + max_iter=100, + fit_intercept=False, + random_state=42 + ) + clf.fit(x_j.numpy(), y_j.numpy()) + w_init = torch.from_numpy(clf.coef_).float() + except: + # Fallback to OLS + ws, _, _, _ = torch.linalg.lstsq(x_j, y_j.unsqueeze(-1)) + w_init = ws.squeeze() + else: + ws, _, _, _ = torch.linalg.lstsq(x_j, y_j.unsqueeze(-1)) + w_init = ws.squeeze() + # Optimize Cauchy MLE loss: sum ln(1 + (y - Xw)^2) + w = w_init.clone().requires_grad_(True) + optimizer = torch.optim.Adam([w], lr=self.lr) + + for _ in range(self.max_iter): + optimizer.zero_grad() + residuals = y_j - (x_j @ w) + # Negative log-likelihood cho Cauchy: sum ln(1 + r^2) + loss = torch.sum(torch.log(1 + residuals ** 2)) + loss.backward() + optimizer.step() + + # Predict + w_final = w.detach().unsqueeze(1) + y_pred = (test_x[j] @ w_final).squeeze(1) + pred[j] = y_pred[0] + + except Exception as e: + # Fallback to median + pred[j] = torch.median(y_j) + preds.append(pred) + return torch.stack(preds, dim=1) + diff --git a/src/plot_utils.py b/src/plot_utils.py index a5f7326b..521934ed 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -40,6 +40,9 @@ "Ridge Var Adj (alpha=0.5, ar=0.5)", "Feasible GLS", "GLS (ar=0.5)", + "LAD (L1 Regression)", + "Huber Regression (ε=1.35)", + "Cauchy MLE", ], "linear_regression": [ "Transformer", diff --git a/src/tasks.py b/src/tasks.py index 7bf5b705..9e01f510 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -366,7 +366,7 @@ def __init__( scale=1, noise_std=2.0, renormalize_ys=False, - noise_type="normal", # "normal", "uniform", "laplace", "t-student", "cauchy", "exponential", "rayleigh", "beta", "poisson" + noise_type="cauchy", # "normal", "uniform", "laplace", "t-student", "cauchy", "exponential", "rayleigh", "beta", "poisson" uniform=False, ): super(NoisyLinearRegression, self).__init__( @@ -397,7 +397,7 @@ def sample_noise(self, shape): noise = t_dist.sample(shape) # 5. elif self.noise_type == "cauchy": - scale_param = self.noise_std * 0.5 + scale_param = self.noise_std cauchy_dist = torch.distributions.StudentT(df=1, loc=0, scale=scale_param) noise = cauchy_dist.sample(shape) # 6. From 20c27b69b2e95d784a759acbf891e4e960177387 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Sun, 23 Nov 2025 14:26:05 +0700 Subject: [PATCH 58/88] fix cauchy --- src/conf/template.yaml | 10 +- src/models.py | 327 +++++++++++++++++++++-------------------- 2 files changed, 170 insertions(+), 167 deletions(-) diff --git a/src/conf/template.yaml b/src/conf/template.yaml index 7cbc5c69..3c121f4d 100644 --- a/src/conf/template.yaml +++ b/src/conf/template.yaml @@ -40,7 +40,7 @@ training: # relu_2nn_regression, decision_tree, noisy_linear_regression, # ar1_linear_regression, ar2_linear_regression, non_stationary_linear_regression, # uniform_hypersphere_regression - task: linear_regression + task: noisy_linear_regression # Task kwargs: # - When task == 'sparse_linear_regression': you may set 'sparsity'. @@ -49,23 +49,23 @@ training: # sparsity: 5 # only when task: sparse_linear_regression # noise_std: 2.0 # e.g., for noisy_linear_regression # renormalize_ys: false - # noise_type: normal + noise_type: cauchy } learning_rate: 0.0001 - keep_every_steps: 100000 + keep_every_steps: 10000 num_tasks: null num_training_examples: null resume_id: null save_every_steps: 100 - train_steps: 500001 + train_steps: 5001 out_dir: wandb: project: "in-context-training" entity: "hai-trinh220970-ho-chi-minh-city-university-of-technology" - name: "laplace_weights_experiment" + name: "test" notes: "Training with laplace-distributed weights (non-uniform on hypersphere)" log_every_steps: 100 diff --git a/src/models.py b/src/models.py index 97c580b9..c51a146a 100644 --- a/src/models.py +++ b/src/models.py @@ -871,193 +871,196 @@ def _estimate_weights(self, train_xs, train_ys): raise ValueError(f"Unknown variance_model '{self.variance_model}' for WLS") - class LADModel: - """ - Least Absolute Deviations (L1 Regression) - Minimize Mean Absolute Error (MAE) - """ +class LADModel: + """ + Least Absolute Deviations (L1 Regression) - Minimize Mean Absolute Error (MAE) + """ - def __init__(self, max_iter=1000, tol=1e-3): - self.max_iter = max_iter - self.tol = tol - self.name = "LAD_L1_Regression" + def __init__(self, max_iter=1000, tol=1e-3): + self.max_iter = max_iter + self.tol = tol + self.name = "LAD_L1_Regression" - def __call__(self, xs, ys, inds=None): - xs, ys = xs.cpu(), ys.cpu() - if inds is None: - inds = range(ys.shape[1]) - else: - if max(inds) >= ys.shape[1] or min(inds) < 0: - raise ValueError("inds contain indices where xs and ys are not defined") + def __call__(self, xs, ys, inds=None): + xs, ys = xs.cpu(), ys.cpu() + if inds is None: + inds = range(ys.shape[1]) + else: + if max(inds) >= ys.shape[1] or min(inds) < 0: + raise ValueError("inds contain indices where xs and ys are not defined") - preds = [] + preds = [] - for i in inds: - if i == 0: - preds.append(torch.zeros_like(ys[:,0])) - continue - train_xs, train_ys = xs[:, :i], ys[:, :i] - test_x = xs[:, i : i + 1] + for i in inds: + if i == 0: + preds.append(torch.zeros_like(ys[:,0])) + continue + train_xs, train_ys = xs[:, :i], ys[:, :i] + test_x = xs[:, i : i + 1] - pred = torch.zeros_like(ys[:,0]) - for j in range(ys.shape[0]): - x_j, y_j = train_xs[j], train_ys[j] - - clf = SGDRegressor( - loss='epsilon_insensitive', - epsilon=0.0, - max_iter=self.max_iter, - tol=self.tol, - fit_intercept=False, - random_state=42 - ) + pred = torch.zeros_like(ys[:,0]) + for j in range(ys.shape[0]): + x_j, y_j = train_xs[j], train_ys[j] - try: - clf.fit(x_j.numpy(), y_j.numpy()) - w_pred = torch.from_numpy(clf.coef_).unsqueeze(1) - y_pred = (test_x[j] @ w_pred.float()).squeeze(1) - pred[j] = y_pred[0] - except Exception as e: - # Fallback to median if LAD fails - pred[j] = torch.median(y_j) - preds.append(pred) + clf = SGDRegressor( + loss='epsilon_insensitive', + epsilon=0.0, + max_iter=self.max_iter, + tol=self.tol, + fit_intercept=False, + random_state=42 + ) + + try: + clf.fit(x_j.numpy(), y_j.numpy()) + w_pred = torch.from_numpy(clf.coef_).unsqueeze(1) + y_pred = (test_x[j] @ w_pred.float()).squeeze(1) + pred[j] = y_pred[0] + except Exception as e: + # Fallback to median if LAD fails + pred[j] = torch.median(y_j) + preds.append(pred) + + return torch.stack(preds, dim=1) - return torch.stack(preds, dim=1) - class HuberRegressionModel: +class HuberRegressionModel: + """ + Huber Regression - Baseline "Hybrid" between L2 and L1. + """ + + def __init__(self, epsilon=1.35, max_iter=300, alpha=0.0001): """ - Huber Regression - Baseline "Hybrid" between L2 and L1. + epsilon: threshold for Huber loss + alpha: regularization strength """ + self.epsilon = epsilon + self.max_iter = max_iter + self.alpha = alpha + self.name = f"Huber_Regression_epsilon={epsilon}" - def __init__(self, epsilon=1.35, max_iter=300, alpha=0.0001): - """ - epsilon: threshold for Huber loss - alpha: regularization strength - """ - self.epsilon = epsilon - self.max_iter = max_iter - self.alpha = alpha - self.name = f"Huber_Regression_epsilon={epsilon}" - - def __call__(self, xs, ys, inds=None): - xs, ys = xs.cpu(), ys.cpu() - if inds is None: - inds = range(ys.shape[1]) - else: - if max(inds) >= ys.shape[1] or min(inds) < 0: - raise ValueError("inds contain indices where xs and ys are not defined") - - preds = [] + def __call__(self, xs, ys, inds=None): + xs, ys = xs.cpu(), ys.cpu() + if inds is None: + inds = range(ys.shape[1]) + else: + if max(inds) >= ys.shape[1] or min(inds) < 0: + raise ValueError("inds contain indices where xs and ys are not defined") + + preds = [] - for i in inds: - if i == 0: - preds.append(torch.zeros_like(ys[:,0])) - continue - train_xs, train_ys = xs[:, :i], ys[:, :i] - test_x = xs[:, i : i + 1] + for i in inds: + if i == 0: + preds.append(torch.zeros_like(ys[:,0])) + continue + train_xs, train_ys = xs[:, :i], ys[:, :i] + test_x = xs[:, i : i + 1] - pred = torch.zeros_like(ys[:,0]) - for j in range(ys.shape[0]): - x_j, y_j = train_xs[j], train_ys[j] + pred = torch.zeros_like(ys[:,0]) + for j in range(ys.shape[0]): + x_j, y_j = train_xs[j], train_ys[j] - clf = HuberRegressor( - epsilon=self.epsilon, - max_iter=self.max_iter, - alpha=self.alpha, - fit_intercept=False - ) + clf = HuberRegressor( + epsilon=self.epsilon, + max_iter=self.max_iter, + alpha=self.alpha, + fit_intercept=False + ) + try: + clf.fit(x_j.numpy(), y_j.numpy()) + w_pred = torch.from_numpy(clf.coef_).unsqueeze(1) + y_pred = (test_x[j] @ w_pred.float()).squeeze(1) + pred[j] = y_pred[0] + except Exception as e: + # Fallback to OLS try: - clf.fit(x_j.numpy(), y_j.numpy()) - w_pred = torch.from_numpy(clf.coef_).unsqueeze(1) - y_pred = (test_x[j] @ w_pred.float()).squeeze(1) + ws, _, _, _ = torch.linalg.lstsq(x_j, y_j.unsqueeze(-1)) + y_pred = (test_x[j] @ ws).squeeze() pred[j] = y_pred[0] - except Exception as e: - # Fallback to OLS - try: - ws, _, _, _ = torch.linalg.lstsq(x_j, y_j.unsqueeze(-1)) - y_pred = (test_x[j] @ ws).squeeze() - pred[j] = y_pred[0] - except: - pred[j] = torch.median(y_j) - preds.append(pred) - return torch.stack(preds, dim=1) + except: + pred[j] = torch.median(y_j) + preds.append(pred) + return torch.stack(preds, dim=1) + - class CauchyMLEModel: +class CauchyMLEModel: + """ + Maximum Likelihood Estimation for Cauchy noise. + Minimize negative log-likelihood: sum ln(1 + (y_i - w x_i)^2) + """ + + def __init__(self, max_iter=100, lr=0.01, init_from_lad=True): """ - Maximum Likelihood Estimation for Cauchy noise. - Minimize negative log-likelihood: sum ln(1 + (y_i - w x_i)^2) + max_iter: maximum number of iterations + lr: learning rate for gradient descent + init_from_lad: initialize from LAD solution (recommended) """ + self.max_iter = max_iter + self.lr = lr + self.init_from_lad = init_from_lad + self.name = "Cauchy_MLE" - def __init__(self, max_iter=100, lr=0.01, init_from_lad=True): - """ - max_iter: maximum number of iterations - lr: learning rate for gradient descent - init_from_lad: initialize from LAD solution (recommended) - """ - self.max_iter = max_iter - self.lr = lr - self.init_from_lad = init_from_lad - self.name = "Cauchy_MLE" - - def __call__(self, xs, ys, inds=None): - xs, ys = xs.cpu(), ys.cpu() - if inds is None: - inds = range(ys.shape[1]) - else: - if max(inds) >= ys.shape[1] or min(inds) < 0: - raise ValueError("inds contain indices where xs and ys are not defined") + def __call__(self, xs, ys, inds=None): + xs, ys = xs.cpu(), ys.cpu() + if inds is None: + inds = range(ys.shape[1]) + else: + if max(inds) >= ys.shape[1] or min(inds) < 0: + raise ValueError("inds contain indices where xs and ys are not defined") - preds = [] + preds = [] - for i in inds: - if i == 0: - preds.append(torch.zeros_like(ys[:,0])) - continue - train_xs, train_ys = xs[:, :i], ys[:, :i] + for i in inds: + if i == 0: + preds.append(torch.zeros_like(ys[:,0])) + continue + train_xs, train_ys = xs[:, :i], ys[:, :i] + test_x = xs[:, i : i + 1] - pred = torch.zeros_like(ys[:,0]) - for j in range(ys.shape[0]): - x_j, y_j = train_xs[j], train_ys[j] + pred = torch.zeros_like(ys[:,0]) + for j in range(ys.shape[0]): + x_j, y_j = train_xs[j], train_ys[j] - try: - if self.init_from_lad: - try: - clf = SGDRegressor( - loss='epsilon_insensitive', - epsilon=0.0, - max_iter=100, - fit_intercept=False, - random_state=42 - ) - clf.fit(x_j.numpy(), y_j.numpy()) - w_init = torch.from_numpy(clf.coef_).float() - except: - # Fallback to OLS - ws, _, _, _ = torch.linalg.lstsq(x_j, y_j.unsqueeze(-1)) - w_init = ws.squeeze() - else: + try: + if self.init_from_lad: + try: + clf = SGDRegressor( + loss='epsilon_insensitive', + epsilon=0.0, + max_iter=100, + fit_intercept=False, + random_state=42 + ) + clf.fit(x_j.numpy(), y_j.numpy()) + w_init = torch.from_numpy(clf.coef_).float() + except: + # Fallback to OLS ws, _, _, _ = torch.linalg.lstsq(x_j, y_j.unsqueeze(-1)) w_init = ws.squeeze() - # Optimize Cauchy MLE loss: sum ln(1 + (y - Xw)^2) - w = w_init.clone().requires_grad_(True) - optimizer = torch.optim.Adam([w], lr=self.lr) - - for _ in range(self.max_iter): - optimizer.zero_grad() - residuals = y_j - (x_j @ w) - # Negative log-likelihood cho Cauchy: sum ln(1 + r^2) - loss = torch.sum(torch.log(1 + residuals ** 2)) - loss.backward() - optimizer.step() - - # Predict - w_final = w.detach().unsqueeze(1) - y_pred = (test_x[j] @ w_final).squeeze(1) - pred[j] = y_pred[0] - - except Exception as e: - # Fallback to median - pred[j] = torch.median(y_j) - preds.append(pred) - return torch.stack(preds, dim=1) + else: + ws, _, _, _ = torch.linalg.lstsq(x_j, y_j.unsqueeze(-1)) + w_init = ws.squeeze() + # Optimize Cauchy MLE loss: sum ln(1 + (y - Xw)^2) + w = w_init.clone().requires_grad_(True) + optimizer = torch.optim.Adam([w], lr=self.lr) + + for _ in range(self.max_iter): + optimizer.zero_grad() + residuals = y_j - (x_j @ w) + # Negative log-likelihood cho Cauchy: sum ln(1 + r^2) + loss = torch.sum(torch.log(1 + residuals ** 2)) + loss.backward() + optimizer.step() + + # Predict + w_final = w.detach().unsqueeze(1) + y_pred = (test_x[j] @ w_final).squeeze(1) + pred[j] = y_pred[0] + + except Exception as e: + # Fallback to median + pred[j] = torch.median(y_j) + preds.append(pred) + return torch.stack(preds, dim=1) From eac7d5ec3ee14660ae2737109f545426dbc77c72 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Sun, 23 Nov 2025 14:46:20 +0700 Subject: [PATCH 59/88] vectorize --- src/models.py | 121 +++++++++++++++++++++++++++++++------------------- 1 file changed, 76 insertions(+), 45 deletions(-) diff --git a/src/models.py b/src/models.py index c51a146a..5cc1421e 100644 --- a/src/models.py +++ b/src/models.py @@ -876,7 +876,7 @@ class LADModel: Least Absolute Deviations (L1 Regression) - Minimize Mean Absolute Error (MAE) """ - def __init__(self, max_iter=1000, tol=1e-3): + def __init__(self, max_iter=5000, tol=1e-4): self.max_iter = max_iter self.tol = tol self.name = "LAD_L1_Regression" @@ -929,7 +929,7 @@ class HuberRegressionModel: Huber Regression - Baseline "Hybrid" between L2 and L1. """ - def __init__(self, epsilon=1.35, max_iter=300, alpha=0.0001): + def __init__(self, epsilon=1.35, max_iter=1000, alpha=0.0001): """ epsilon: threshold for Huber loss alpha: regularization strength @@ -988,9 +988,10 @@ class CauchyMLEModel: """ Maximum Likelihood Estimation for Cauchy noise. Minimize negative log-likelihood: sum ln(1 + (y_i - w x_i)^2) + Vectorized version for batch processing - much faster than loop-based approach. """ - def __init__(self, max_iter=100, lr=0.01, init_from_lad=True): + def __init__(self, max_iter=200, lr=0.01, init_from_lad=True): """ max_iter: maximum number of iterations lr: learning rate for gradient descent @@ -1015,52 +1016,82 @@ def __call__(self, xs, ys, inds=None): if i == 0: preds.append(torch.zeros_like(ys[:,0])) continue - train_xs, train_ys = xs[:, :i], ys[:, :i] - test_x = xs[:, i : i + 1] + train_xs, train_ys = xs[:, :i], ys[:, :i] # [batch_size, i, n_dims], [batch_size, i] + test_x = xs[:, i : i + 1] # [batch_size, 1, n_dims] - pred = torch.zeros_like(ys[:,0]) - for j in range(ys.shape[0]): - x_j, y_j = train_xs[j], train_ys[j] + batch_size = train_xs.shape[0] + n_dims = train_xs.shape[2] - try: - if self.init_from_lad: - try: - clf = SGDRegressor( - loss='epsilon_insensitive', - epsilon=0.0, - max_iter=100, - fit_intercept=False, - random_state=42 - ) - clf.fit(x_j.numpy(), y_j.numpy()) - w_init = torch.from_numpy(clf.coef_).float() - except: - # Fallback to OLS + # Vectorized initialization: compute OLS for all batches at once + try: + # Try to solve OLS for all batches simultaneously + # train_xs: [batch_size, i, n_dims] + # train_ys: [batch_size, i] + # We need to solve X @ w = y for each batch + + # Initialize weights: [batch_size, n_dims] + w_init = torch.zeros(batch_size, n_dims, dtype=torch.float32) + + # Compute OLS for each batch (can't fully vectorize due to different i values) + # But we can still optimize by using batched operations where possible + for j in range(batch_size): + x_j = train_xs[j] # [i, n_dims] + y_j = train_ys[j] # [i] + + try: + if self.init_from_lad: + # Try LAD initialization (still need sklearn for this) + try: + clf = SGDRegressor( + loss='epsilon_insensitive', + epsilon=0.0, + max_iter=500, + tol=1e-4, + fit_intercept=False, + random_state=42 + ) + clf.fit(x_j.numpy(), y_j.numpy()) + w_init[j] = torch.from_numpy(clf.coef_).float() + except: + # Fallback to OLS + ws, _, _, _ = torch.linalg.lstsq(x_j, y_j.unsqueeze(-1)) + w_init[j] = ws.squeeze() + else: ws, _, _, _ = torch.linalg.lstsq(x_j, y_j.unsqueeze(-1)) - w_init = ws.squeeze() - else: - ws, _, _, _ = torch.linalg.lstsq(x_j, y_j.unsqueeze(-1)) - w_init = ws.squeeze() - # Optimize Cauchy MLE loss: sum ln(1 + (y - Xw)^2) - w = w_init.clone().requires_grad_(True) - optimizer = torch.optim.Adam([w], lr=self.lr) - - for _ in range(self.max_iter): - optimizer.zero_grad() - residuals = y_j - (x_j @ w) - # Negative log-likelihood cho Cauchy: sum ln(1 + r^2) - loss = torch.sum(torch.log(1 + residuals ** 2)) - loss.backward() - optimizer.step() - - # Predict - w_final = w.detach().unsqueeze(1) - y_pred = (test_x[j] @ w_final).squeeze(1) - pred[j] = y_pred[0] + w_init[j] = ws.squeeze() + except: + # If all fails, use zero initialization + w_init[j] = torch.zeros(n_dims) - except Exception as e: - # Fallback to median - pred[j] = torch.median(y_j) + # Vectorized optimization: optimize all batches simultaneously + w = w_init.clone().requires_grad_(True) + optimizer = torch.optim.Adam([w], lr=self.lr) + + for _ in range(self.max_iter): + optimizer.zero_grad() + + # Vectorized computation: [batch_size, i] = [batch_size, i] - [batch_size, i, n_dims] @ [batch_size, n_dims, 1] + # Use einsum for efficient batched matrix multiplication + predictions = torch.einsum('bij,bj->bi', train_xs, w) # [batch_size, i] + residuals = train_ys - predictions # [batch_size, i] + + # Negative log-likelihood for Cauchy: sum over i dimension + # loss per batch: [batch_size] + loss_per_batch = torch.sum(torch.log(1 + residuals ** 2), dim=1) + total_loss = torch.sum(loss_per_batch) # scalar + + total_loss.backward() + optimizer.step() + + # Vectorized prediction: [batch_size, 1, n_dims] @ [batch_size, n_dims, 1] -> [batch_size, 1, 1] + w_final = w.detach() # [batch_size, n_dims] + pred = torch.einsum('bij,bj->bi', test_x, w_final).squeeze(1) # [batch_size] + + except Exception as e: + # Fallback: use median for each batch + pred = torch.median(train_ys, dim=1)[0] # [batch_size] + preds.append(pred) + return torch.stack(preds, dim=1) From 484c4ff9be2d52b18f98fb6050687f7382364328 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 26 Nov 2025 11:11:38 +0700 Subject: [PATCH 60/88] changedau --- src/conf/case1_w_sparse_uniform_x.yaml | 37 +++ src/conf/case2.yaml | 38 +++ src/conf/case4.yaml | 36 +++ src/conf/case5.yaml | 38 +++ src/conf/case_3.yaml | 39 +++ src/eval.ipynb | 117 ++++++--- src/eval.py | 10 - src/models.py | 217 +++++++++++----- src/plot_utils.py | 27 +- src/samplers.py | 20 ++ src/schema.py | 2 +- src/tasks.py | 346 +++++++++++++++++++++++-- src/train.py | 6 + 13 files changed, 795 insertions(+), 138 deletions(-) create mode 100644 src/conf/case1_w_sparse_uniform_x.yaml create mode 100644 src/conf/case2.yaml create mode 100644 src/conf/case4.yaml create mode 100644 src/conf/case5.yaml create mode 100644 src/conf/case_3.yaml diff --git a/src/conf/case1_w_sparse_uniform_x.yaml b/src/conf/case1_w_sparse_uniform_x.yaml new file mode 100644 index 00000000..afd05f7e --- /dev/null +++ b/src/conf/case1_w_sparse_uniform_x.yaml @@ -0,0 +1,37 @@ +inherit: + - base.yaml + +model: + n_dims: 20 + n_positions: 101 + +training: + task: sparse_regression_killer + task_kwargs: + k_sparse: 2 + scale: 1.0 + data: uniform + data_kwargs: {} + curriculum: + dims: + start: 5 + end: 20 + inc: 1 + interval: 2000 + points: + start: 11 + end: 41 + inc: 2 + interval: 2000 + batch_size: 64 + learning_rate: 0.0001 + train_steps: 500001 + +out_dir: ../models/sparse_regression_killer + +wandb: + project: "in-context-training" + entity: "hai-trinh220970-ho-chi-minh-city-university-of-technology" + name: "case1_sparse_regression" + notes: "Case 1: Sparse Regression - only k=2 dims non-zero - Ridge Trap" + log_every_steps: 100 \ No newline at end of file diff --git a/src/conf/case2.yaml b/src/conf/case2.yaml new file mode 100644 index 00000000..416056f5 --- /dev/null +++ b/src/conf/case2.yaml @@ -0,0 +1,38 @@ +inherit: + - base.yaml + +model: + n_dims: 20 + n_positions: 101 + +training: + task: heavy_tail_noise_killer + task_kwargs: + noise_type: "t-student" + df: 3.0 + noise_scale: 0.5 + data: gaussian + data_kwargs: {} + curriculum: + dims: + start: 5 + end: 20 + inc: 1 + interval: 2000 + points: + start: 11 + end: 41 + inc: 2 + interval: 2000 + batch_size: 64 + learning_rate: 0.0001 + train_steps: 500001 + +out_dir: ../models/heavy_tail_noise_killer + +wandb: + project: "in-context-training" + entity: "hai-trinh220970-ho-chi-minh-city-university-of-technology" + name: "case2_heavy_tail_t_student" + notes: "Case 2: Heavy-tail noise (t-student df=3, scale=0.5) - OLS Enemy" + log_every_steps: 100 \ No newline at end of file diff --git a/src/conf/case4.yaml b/src/conf/case4.yaml new file mode 100644 index 00000000..f0ec2249 --- /dev/null +++ b/src/conf/case4.yaml @@ -0,0 +1,36 @@ +inherit: + - base.yaml + +model: + n_dims: 20 + n_positions: 101 + +training: + task: mixture_tasks_killer + task_kwargs: + scale: 1.0 + data: gaussian + data_kwargs: {} + curriculum: + dims: + start: 5 + end: 20 + inc: 1 + interval: 2000 + points: + start: 11 + end: 41 + inc: 2 + interval: 2000 + batch_size: 64 + learning_rate: 0.0001 + train_steps: 50001 + +out_dir: ../models/mixture_tasks_killer + +wandb: + project: "in-context-training" + entity: "hai-trinh220970-ho-chi-minh-city-university-of-technology" + name: "case4_mixture_tasks" + notes: "Case 4: Mixture of Tasks - 50% y=w^T x, 50% y=-w^T x - Averaging Death" + log_every_steps: 100 \ No newline at end of file diff --git a/src/conf/case5.yaml b/src/conf/case5.yaml new file mode 100644 index 00000000..c823a5db --- /dev/null +++ b/src/conf/case5.yaml @@ -0,0 +1,38 @@ +inherit: + - base.yaml + +model: + n_dims: 20 + n_positions: 101 + +training: + task: transfer_tradeoff_task + task_kwargs: + prior_type: "mixture_gaussian" + mixture_std: 2.0 + scale: 1.0 + data: gaussian + data_kwargs: {} + curriculum: + dims: + start: 5 + end: 20 + inc: 1 + interval: 2000 + points: + start: 11 + end: 41 + inc: 2 + interval: 2000 + batch_size: 64 + learning_rate: 0.0001 + train_steps: 50001 + +out_dir: ../models/transfer_tradeoff_task + +wandb: + project: "in-context-training" + entity: "hai-trinh220970-ho-chi-minh-city-university-of-technology" + name: "case5_transfer_tradeoff" + notes: "Case 5: Transfer Tradeoff - p×N experiment (Wakayama) - Mixture Gaussian prior" + log_every_steps: 100 \ No newline at end of file diff --git a/src/conf/case_3.yaml b/src/conf/case_3.yaml new file mode 100644 index 00000000..56efbe54 --- /dev/null +++ b/src/conf/case_3.yaml @@ -0,0 +1,39 @@ +inherit: + - base.yaml + +model: + n_dims: 20 + n_positions: 101 + +training: + task: bounded_support_killer + task_kwargs: + rate: 1.0 + scale: 1.0 + # Use positive-only input distribution + data: uniform + data_kwargs: + rate: 1.0 + curriculum: + dims: + start: 5 + end: 20 + inc: 1 + interval: 2000 + points: + start: 11 + end: 41 + inc: 2 + interval: 2000 + batch_size: 64 + learning_rate: 0.0001 + train_steps: 50001 + +out_dir: ../models/bounded_support_killer + +wandb: + project: "in-context-training" + entity: "hai-trinh220970-ho-chi-minh-city-university-of-technology" + name: "case3_bounded_support" + notes: "Case 3: Bounded Support - w~Exp(1), x~Exp(1) - Sign Constraint" + log_every_steps: 100 \ No newline at end of file diff --git a/src/eval.ipynb b/src/eval.ipynb index c61a549e..be44a6ee 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -191,7 +191,7 @@ " 3_tstudent_noise_gaussian_data_experiment\n", " \n", " \n", - " 33\n", + " 35\n", " daa2cd45-f1c0-4a0c-9100-e171129624c9\n", " sparse_linear_regression\n", " Transformer\n", @@ -386,7 +386,7 @@ " rayleigh_noise_gaussian_data_experiment\n", " \n", " \n", - " 31\n", + " 33\n", " pretrained\n", " relu_2nn_regression\n", " Transformer\n", @@ -412,7 +412,7 @@ " rigde_normal_linear_regression_gaussian\n", " \n", " \n", - " 32\n", + " 34\n", " 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615\n", " sparse_linear_regression\n", " Transformer\n", @@ -438,7 +438,7 @@ " sparse_data_experiment\n", " \n", " \n", - " 34\n", + " 36\n", " pretrained\n", " sparse_linear_regression\n", " Transformer\n", @@ -451,7 +451,7 @@ " sparse_regression_pretrained\n", " \n", " \n", - " 26\n", + " 27\n", " t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -477,7 +477,20 @@ " task_sparse_data\n", " \n", " \n", - " 28\n", + " 26\n", + " test_cauchy\n", + " linear_regression\n", + " Transformer\n", + " noise_type=cauchy\n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " test\n", + " \n", + " \n", + " 29\n", " uniform_hypersphere_regression\n", " linear_regression\n", " Transformer\n", @@ -490,7 +503,7 @@ " uniform_hypersphere_experiment\n", " \n", " \n", - " 27\n", + " 28\n", " uniform_hypersphere_experiment_standard\n", " linear_regression\n", " Transformer\n", @@ -503,7 +516,7 @@ " uniform_hypersphere_experiment_standard\n", " \n", " \n", - " 29\n", + " 30\n", " uniform_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -516,7 +529,7 @@ " uniform_noise_ar1_data_experiment\n", " \n", " \n", - " 30\n", + " 31\n", " uniform_noise_gaussian_data_experiment_\n", " linear_regression\n", " Transformer\n", @@ -528,6 +541,19 @@ " 8\n", " uniform_noise_gaussian_data_experiment\n", " \n", + " \n", + " 32\n", + " w_laplace_x_exponential_noise_poisson\n", + " linear_regression\n", + " Transformer\n", + " \n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " w_laplace_x_exponential_noise_poisson\n", + " \n", " \n", "\n", "" @@ -543,7 +569,7 @@ "10 64d381ae-08d0-4bae-8e40-f1a68cfb2e97 linear_regression \n", "8 3_laplace_noise_gaussian_data_experiment linear_regression \n", "9 3_tstudent_noise_gaussian_data_experiment linear_regression \n", - "33 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", + "35 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", "14 beta_noise_ar1_data_experiment linear_regression \n", "15 beta_noisy_linear_regression_40_100k linear_regression \n", "13 aed365ed-51e2-4a72-8374-ae954b37be14 linear_regression \n", @@ -558,17 +584,19 @@ "12 a2fcec3c-8ce5-49bf-a8bc-08136b31ec36 linear_regression \n", "23 pretrained linear_regression \n", "24 rayleigh_noise_gaussian_data_experiment linear_regression \n", - "31 pretrained relu_2nn_regression \n", + "33 pretrained relu_2nn_regression \n", "11 82e728b0-a061-448e-8d7a-f3c79c0c74e5 linear_regression \n", - "32 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", + "34 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", "1 03de46b6-429a-4151-92e6-3588231c6cad linear_regression \n", - "34 pretrained sparse_linear_regression \n", - "26 t_student_noise_gaussian_data_experiment linear_regression \n", + "36 pretrained sparse_linear_regression \n", + "27 t_student_noise_gaussian_data_experiment linear_regression \n", "25 sparse_gaussian linear_regression \n", - "28 uniform_hypersphere_regression linear_regression \n", - "27 uniform_hypersphere_experiment_standard linear_regression \n", - "29 uniform_noise_ar1_data_experiment linear_regression \n", - "30 uniform_noise_gaussian_data_experiment_ linear_regression \n", + "26 test_cauchy linear_regression \n", + "29 uniform_hypersphere_regression linear_regression \n", + "28 uniform_hypersphere_experiment_standard linear_regression \n", + "30 uniform_noise_ar1_data_experiment linear_regression \n", + "31 uniform_noise_gaussian_data_experiment_ linear_regression \n", + "32 w_laplace_x_exponential_noise_poisson linear_regression \n", "\n", " model kwargs num_tasks num_examples n_dims \\\n", "3 Transformer -1 -1 5 \n", @@ -580,7 +608,7 @@ "10 Transformer -1 -1 20 \n", "8 Transformer -1 -1 5 \n", "9 Transformer -1 -1 5 \n", - "33 Transformer sparsity=5 -1 -1 15 \n", + "35 Transformer sparsity=5 -1 -1 15 \n", "14 Transformer -1 -1 5 \n", "15 Transformer noise_type=beta -1 -1 20 \n", "13 Transformer k=5_sparsity=3 -1 -1 15 \n", @@ -595,17 +623,19 @@ "12 Transformer scale=1.0_weight_scale=1.0 -1 -1 20 \n", "23 Transformer -1 -1 20 \n", "24 Transformer -1 -1 5 \n", - "31 Transformer hidden_layer_size=100 -1 -1 20 \n", + "33 Transformer hidden_layer_size=100 -1 -1 20 \n", "11 Transformer sparsity=5 -1 -1 15 \n", - "32 Transformer -1 -1 5 \n", + "34 Transformer -1 -1 5 \n", "1 Transformer -1 -1 20 \n", - "34 Transformer sparsity=3 -1 -1 20 \n", - "26 Transformer -1 -1 5 \n", + "36 Transformer sparsity=3 -1 -1 20 \n", + "27 Transformer -1 -1 5 \n", "25 Transformer -1 -1 20 \n", + "26 Transformer noise_type=cauchy -1 -1 20 \n", + "29 Transformer normalize=True_scale=1.0 -1 -1 20 \n", "28 Transformer normalize=True_scale=1.0 -1 -1 20 \n", - "27 Transformer normalize=True_scale=1.0 -1 -1 20 \n", - "29 Transformer -1 -1 5 \n", "30 Transformer -1 -1 5 \n", + "31 Transformer -1 -1 5 \n", + "32 Transformer -1 -1 20 \n", "\n", " n_layer n_head run_name \n", "3 4 8 1_beta_noise_gaussian_data_experiment \n", @@ -617,7 +647,7 @@ "10 4 8 20_dims_uniform_error_gaussian_data_ \n", "8 4 8 3_laplace_noise_gaussian_data_experiment \n", "9 4 8 3_tstudent_noise_gaussian_data_experiment \n", - "33 4 8 4_std_sparse_linear_regression \n", + "35 4 8 4_std_sparse_linear_regression \n", "14 4 8 beta_noise_ar1_data_experiment \n", "15 4 8 beta_noisy_linear_regression_40_100k \n", "13 4 8 data_sparse_linear_regression \n", @@ -632,17 +662,19 @@ "12 4 8 laplace_weights_experiment \n", "23 12 8 linear_regression_pretrained \n", "24 4 8 rayleigh_noise_gaussian_data_experiment \n", - "31 12 8 relu_2nn_regression_pretrained \n", + "33 12 8 relu_2nn_regression_pretrained \n", "11 4 8 rigde_normal_linear_regression_gaussian \n", - "32 4 8 sparse \n", + "34 4 8 sparse \n", "1 4 8 sparse_data_experiment \n", - "34 12 8 sparse_regression_pretrained \n", - "26 4 8 t_student_noise_gaussian_data_experiment \n", + "36 12 8 sparse_regression_pretrained \n", + "27 4 8 t_student_noise_gaussian_data_experiment \n", "25 4 8 task_sparse_data \n", - "28 4 8 uniform_hypersphere_experiment \n", - "27 4 8 uniform_hypersphere_experiment_standard \n", - "29 4 8 uniform_noise_ar1_data_experiment \n", - "30 4 8 uniform_noise_gaussian_data_experiment " + "26 4 8 test \n", + "29 4 8 uniform_hypersphere_experiment \n", + "28 4 8 uniform_hypersphere_experiment_standard \n", + "30 4 8 uniform_noise_ar1_data_experiment \n", + "31 4 8 uniform_noise_gaussian_data_experiment \n", + "32 4 8 w_laplace_x_exponential_noise_poisson " ] }, "execution_count": 2, @@ -667,7 +699,7 @@ "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"exponential_w\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"test_cauchy\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -719,7 +751,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -729,22 +761,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "exponential_w exponential_w\n" + "test test_cauchy\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1/1 [00:00<00:00, 4152.78it/s]" + "100%|██████████| 1/1 [00:00<00:00, 5178.15it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", - "Missing metrics for: ['3-Nearest Neighbors', 'Averaging']\n" + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { @@ -756,7 +787,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -1160,7 +1191,7 @@ ], "metadata": { "kernelspec": { - "display_name": "base", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1174,7 +1205,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.9" + "version": "3.12.10" } }, "nbformat": 4, diff --git a/src/eval.py b/src/eval.py index d7e25b75..37973540 100644 --- a/src/eval.py +++ b/src/eval.py @@ -437,16 +437,6 @@ def baseline_names(name): if "gls_ar" in name: ar = name.split("ar=")[1] return f"GLS (ar={ar})" - - if "LAD_L1_Regression" in name or name == "LAD_L1_Regression": - return "LAD (L1 Regression)" - - if "Huber_Regression" in name: - epsilon = name.split("epsilon=")[1] if "epsilon=" in name else "1.35" - return f"Huber Regression (ε={epsilon})" - - if "Cauchy_MLE" in name or name == "Cauchy_MLE": - return "Cauchy MLE" return name diff --git a/src/models.py b/src/models.py index 5cc1421e..dc9dd7db 100644 --- a/src/models.py +++ b/src/models.py @@ -9,6 +9,8 @@ import warnings from sklearn import tree import xgboost as xgb +from joblib import Parallel, delayed +import numpy as np from base_models import NeuralNetwork, ParallelNetworks @@ -30,6 +32,26 @@ def build_model(conf): def get_relevant_baselines(task_name): task_to_baselines = { + "sparse_regression_killer": [ + (LeastSquaresModel, {}), + (RidgeModel, {"alpha": 0.5}), + ], + "heavy_tail_noise_killer": [ + (LeastSquaresModel, {}), + (RidgeModel, {"alpha": 0.5}), + ], + "bounded_support_killer": [ + (LeastSquaresModel, {}), + (RidgeModel, {"alpha": 0.5}), + ], + "mixture_tasks_killer": [ + (LeastSquaresModel, {}), + (RidgeModel, {"alpha": 0.5}), + ], + "transfer_tradeoff_task": [ + (LeastSquaresModel, {}), + (RidgeModel, {"alpha": 0.5}), + ], "wlaplace_noisypoisson": [ (LeastSquaresModel, {}), (RidgeModel, {"alpha": 0.5}), @@ -874,13 +896,41 @@ def _estimate_weights(self, train_xs, train_ys): class LADModel: """ Least Absolute Deviations (L1 Regression) - Minimize Mean Absolute Error (MAE) + Optimized with parallel processing for speed while maintaining quality. """ - def __init__(self, max_iter=5000, tol=1e-4): + def __init__(self, max_iter=20000, tol=1e-5, n_jobs=-1): + """ + max_iter: maximum iterations for convergence (high for quality) + tol: tolerance for convergence + n_jobs: number of parallel jobs (-1 for all CPUs, 1 for sequential) + """ self.max_iter = max_iter self.tol = tol + self.n_jobs = n_jobs self.name = "LAD_L1_Regression" + def _fit_single(self, x_j_np, y_j_np, test_x_j_np): + """Fit a single sample - used for parallel processing""" + clf = SGDRegressor( + loss='epsilon_insensitive', + epsilon=0.0, + max_iter=self.max_iter, + tol=self.tol, + fit_intercept=False, + random_state=42 + ) + try: + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=UserWarning, module="sklearn") + clf.fit(x_j_np, y_j_np) + w_pred = torch.from_numpy(clf.coef_).unsqueeze(1) + y_pred = (torch.from_numpy(test_x_j_np) @ w_pred.float()).squeeze(1) + return y_pred[0].item() + except Exception as e: + # Fallback to median + return float(np.median(y_j_np)) + def __call__(self, xs, ys, inds=None): xs, ys = xs.cpu(), ys.cpu() if inds is None: @@ -889,56 +939,84 @@ def __call__(self, xs, ys, inds=None): if max(inds) >= ys.shape[1] or min(inds) < 0: raise ValueError("inds contain indices where xs and ys are not defined") + print(f"[{self.name}] Starting evaluation on {len(inds)} points...") preds = [] - for i in inds: + for i in tqdm(inds, desc=f"{self.name}", leave=False): if i == 0: preds.append(torch.zeros_like(ys[:,0])) continue train_xs, train_ys = xs[:, :i], ys[:, :i] test_x = xs[:, i : i + 1] - pred = torch.zeros_like(ys[:,0]) - for j in range(ys.shape[0]): - x_j, y_j = train_xs[j], train_ys[j] - - clf = SGDRegressor( - loss='epsilon_insensitive', - epsilon=0.0, - max_iter=self.max_iter, - tol=self.tol, - fit_intercept=False, - random_state=42 + batch_size = train_xs.shape[0] + + # Prepare data for parallel processing + x_list = [train_xs[j].numpy() for j in range(batch_size)] + y_list = [train_ys[j].numpy() for j in range(batch_size)] + test_x_list = [test_x[j].numpy() for j in range(batch_size)] + + # Parallel fit for all batch items + if self.n_jobs != 1 and batch_size > 1: + results = Parallel(n_jobs=self.n_jobs, backend='threading')( + delayed(self._fit_single)(x_list[j], y_list[j], test_x_list[j]) + for j in range(batch_size) ) - - try: - clf.fit(x_j.numpy(), y_j.numpy()) - w_pred = torch.from_numpy(clf.coef_).unsqueeze(1) - y_pred = (test_x[j] @ w_pred.float()).squeeze(1) - pred[j] = y_pred[0] - except Exception as e: - # Fallback to median if LAD fails - pred[j] = torch.median(y_j) + pred = torch.tensor(results, dtype=torch.float32) + else: + # Sequential fallback + pred = torch.zeros_like(ys[:,0]) + for j in range(batch_size): + pred[j] = self._fit_single(x_list[j], y_list[j], test_x_list[j]) + preds.append(pred) + print(f"[{self.name}] Completed!") return torch.stack(preds, dim=1) class HuberRegressionModel: """ Huber Regression - Baseline "Hybrid" between L2 and L1. + Optimized with parallel processing for speed while maintaining quality. """ - def __init__(self, epsilon=1.35, max_iter=1000, alpha=0.0001): + def __init__(self, epsilon=1.35, max_iter=2000, alpha=0.0001, n_jobs=-1): """ epsilon: threshold for Huber loss alpha: regularization strength + n_jobs: number of parallel jobs (-1 for all CPUs, 1 for sequential) """ self.epsilon = epsilon self.max_iter = max_iter self.alpha = alpha + self.n_jobs = n_jobs self.name = f"Huber_Regression_epsilon={epsilon}" + def _fit_single(self, x_j_np, y_j_np, test_x_j_np, x_j_torch, y_j_torch, test_x_j_torch): + """Fit a single sample - used for parallel processing""" + clf = HuberRegressor( + epsilon=self.epsilon, + max_iter=self.max_iter, + alpha=self.alpha, + fit_intercept=False + ) + try: + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=UserWarning, module="sklearn") + clf.fit(x_j_np, y_j_np) + w_pred = torch.from_numpy(clf.coef_).unsqueeze(1) + y_pred = (test_x_j_torch @ w_pred.float()).squeeze(1) + return y_pred[0].item() + except Exception as e: + # Fallback to OLS + try: + ws, _, _, _ = torch.linalg.lstsq(x_j_torch, y_j_torch.unsqueeze(-1)) + y_pred = (test_x_j_torch @ ws).squeeze() + return y_pred[0].item() + except: + return float(torch.median(y_j_torch).item()) + def __call__(self, xs, ys, inds=None): xs, ys = xs.cpu(), ys.cpu() if inds is None: @@ -947,40 +1025,47 @@ def __call__(self, xs, ys, inds=None): if max(inds) >= ys.shape[1] or min(inds) < 0: raise ValueError("inds contain indices where xs and ys are not defined") + print(f"[{self.name}] Starting evaluation on {len(inds)} points...") preds = [] - for i in inds: + for i in tqdm(inds, desc=f"{self.name}", leave=False): if i == 0: preds.append(torch.zeros_like(ys[:,0])) continue train_xs, train_ys = xs[:, :i], ys[:, :i] test_x = xs[:, i : i + 1] - pred = torch.zeros_like(ys[:,0]) - for j in range(ys.shape[0]): - x_j, y_j = train_xs[j], train_ys[j] - - clf = HuberRegressor( - epsilon=self.epsilon, - max_iter=self.max_iter, - alpha=self.alpha, - fit_intercept=False + batch_size = train_xs.shape[0] + + # Prepare data for parallel processing + x_np_list = [train_xs[j].numpy() for j in range(batch_size)] + y_np_list = [train_ys[j].numpy() for j in range(batch_size)] + test_x_np_list = [test_x[j].numpy() for j in range(batch_size)] + x_torch_list = [train_xs[j] for j in range(batch_size)] + y_torch_list = [train_ys[j] for j in range(batch_size)] + test_x_torch_list = [test_x[j] for j in range(batch_size)] + + # Parallel fit for all batch items + if self.n_jobs != 1 and batch_size > 1: + results = Parallel(n_jobs=self.n_jobs, backend='threading')( + delayed(self._fit_single)( + x_np_list[j], y_np_list[j], test_x_np_list[j], + x_torch_list[j], y_torch_list[j], test_x_torch_list[j] + ) + for j in range(batch_size) ) - - try: - clf.fit(x_j.numpy(), y_j.numpy()) - w_pred = torch.from_numpy(clf.coef_).unsqueeze(1) - y_pred = (test_x[j] @ w_pred.float()).squeeze(1) - pred[j] = y_pred[0] - except Exception as e: - # Fallback to OLS - try: - ws, _, _, _ = torch.linalg.lstsq(x_j, y_j.unsqueeze(-1)) - y_pred = (test_x[j] @ ws).squeeze() - pred[j] = y_pred[0] - except: - pred[j] = torch.median(y_j) + pred = torch.tensor(results, dtype=torch.float32) + else: + # Sequential fallback + pred = torch.zeros_like(ys[:,0]) + for j in range(batch_size): + pred[j] = self._fit_single( + x_np_list[j], y_np_list[j], test_x_np_list[j], + x_torch_list[j], y_torch_list[j], test_x_torch_list[j] + ) + preds.append(pred) + print(f"[{self.name}] Completed!") return torch.stack(preds, dim=1) @@ -1010,9 +1095,10 @@ def __call__(self, xs, ys, inds=None): if max(inds) >= ys.shape[1] or min(inds) < 0: raise ValueError("inds contain indices where xs and ys are not defined") + print(f"[{self.name}] Starting evaluation on {len(inds)} points...") preds = [] - for i in inds: + for i in tqdm(inds, desc=f"{self.name}", leave=False): if i == 0: preds.append(torch.zeros_like(ys[:,0])) continue @@ -1032,9 +1118,8 @@ def __call__(self, xs, ys, inds=None): # Initialize weights: [batch_size, n_dims] w_init = torch.zeros(batch_size, n_dims, dtype=torch.float32) - # Compute OLS for each batch (can't fully vectorize due to different i values) - # But we can still optimize by using batched operations where possible - for j in range(batch_size): + # Helper function for parallel initialization + def _init_single(j): x_j = train_xs[j] # [i, n_dims] y_j = train_ys[j] # [i] @@ -1045,23 +1130,38 @@ def __call__(self, xs, ys, inds=None): clf = SGDRegressor( loss='epsilon_insensitive', epsilon=0.0, - max_iter=500, - tol=1e-4, + max_iter=10000, + tol=1e-5, fit_intercept=False, random_state=42 ) - clf.fit(x_j.numpy(), y_j.numpy()) - w_init[j] = torch.from_numpy(clf.coef_).float() + # Suppress convergence warnings for cleaner output + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=UserWarning, module="sklearn") + clf.fit(x_j.numpy(), y_j.numpy()) + return torch.from_numpy(clf.coef_).float() except: # Fallback to OLS ws, _, _, _ = torch.linalg.lstsq(x_j, y_j.unsqueeze(-1)) - w_init[j] = ws.squeeze() + return ws.squeeze() else: ws, _, _, _ = torch.linalg.lstsq(x_j, y_j.unsqueeze(-1)) - w_init[j] = ws.squeeze() + return ws.squeeze() except: # If all fails, use zero initialization - w_init[j] = torch.zeros(n_dims) + return torch.zeros(n_dims) + + # Parallel initialization for speed + if batch_size > 1: + init_results = Parallel(n_jobs=-1, backend='threading')( + delayed(_init_single)(j) for j in range(batch_size) + ) + for j, w in enumerate(init_results): + w_init[j] = w + else: + # Sequential for single batch + for j in range(batch_size): + w_init[j] = _init_single(j) # Vectorized optimization: optimize all batches simultaneously w = w_init.clone().requires_grad_(True) @@ -1093,5 +1193,6 @@ def __call__(self, xs, ys, inds=None): preds.append(pred) + print(f"[{self.name}] Completed!") return torch.stack(preds, dim=1) diff --git a/src/plot_utils.py b/src/plot_utils.py index 521934ed..163c8811 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -10,6 +10,31 @@ palette = sns.color_palette("colorblind") relevant_model_names = { + "sparse_regression_killer": [ + "Transformer", + "Least Squares", + "Ridge (alpha=0.5)", + ], + "heavy_tail_noise_killer": [ + "Transformer", + "Least Squares", + "Ridge (alpha=0.5)", + ], + "bounded_support_killer": [ + "Transformer", + "Least Squares", + "Ridge (alpha=0.5)", + ], + "mixture_tasks_killer": [ + "Transformer", + "Least Squares", + "Ridge (alpha=0.5)", + ], + "transfer_tradeoff_task": [ + "Transformer", + "Least Squares", + "Ridge (alpha=0.5)", + ], "wlaplace_noisypoisson": [ "Transformer", "Least Squares", @@ -115,7 +140,7 @@ def basic_plot(metrics, models=None, trivial=1.0): ax.set_xlabel("in-context examples") ax.set_ylabel("squared error") ax.set_xlim(-1, len(low) + 0.1) - ax.set_ylim(-0.1, 2.0) + ax.set_ylim(-0.1, 1000) # legend = ax.legend(loc="upper left", bbox_to_anchor=(1, 1)) diff --git a/src/samplers.py b/src/samplers.py index 218e0aa4..ef7b2ba2 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -21,6 +21,7 @@ def get_data_sampler(data_name, n_dims, **kwargs): "ar2":AR2Sampler, "vr2":VR2Sampler, "nonstation":NonStationarySampler, + "uniform": UniformSampler, "exponential": ExponentialSampler, "laplace": LaplaceSampler, "gamma": GammaSampler, @@ -63,6 +64,25 @@ def _sample_distribution(dist, b_size, inner_shape, seeds=None): xs_b[i] = dist.sample(inner_shape) return xs_b +class UniformSampler(DataSampler): + def __init__(self, n_dims, bias=None, scale=None, low=0.0, high=1.0): + super().__init__(n_dims) + self.bias = bias + self.scale = scale + self.low = low + self.high = high + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + uni_dist = torch.distributions.Uniform(self.low, self.high) + xs_b = _sample_distribution(uni_dist, b_size, (n_points, self.n_dims), seeds) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b class ExponentialSampler(DataSampler): def __init__(self, n_dims, bias=None, scale=None, rate=1.0): diff --git a/src/schema.py b/src/schema.py index c8da8e7f..51bb1772 100644 --- a/src/schema.py +++ b/src/schema.py @@ -55,7 +55,7 @@ "task_kwargs": merge(tdict, required), "num_tasks": merge(tinteger, nullable, default(None)), "num_training_examples": merge(tinteger, nullable, default(None)), - "data": merge(tstring, allowed(["gaussian","ar1","vr1","ar2",'vr2',"nonstation", "sparse_gaussian", "gamma", "beta", "exponential", "laplace"])), + "data": merge(tstring, allowed(["gaussian","ar1","vr1","ar2",'vr2',"nonstation", "sparse_gaussian", "gamma", "beta", "exponential", "laplace", "uniform"])), "data_kwargs": merge(tdict, default({})), # Thêm dòng này "batch_size": merge(tinteger, default(64)), "learning_rate": merge(tfloat, default(3e-4)), diff --git a/src/tasks.py b/src/tasks.py index 9e01f510..30db8a62 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -11,6 +11,21 @@ def mean_squared_error(ys_pred, ys): return (ys - ys_pred).square().mean() +def huber_loss(ys_pred, ys, delta=1.35): + """Huber loss - robust to outliers""" + error = ys - ys_pred + abs_error = torch.abs(error) + quadratic = torch.clamp(abs_error, max=delta) + linear = abs_error - quadratic + return (0.5 * quadratic.square() + delta * linear).mean() + + +def cauchy_loss(ys_pred, ys): + """Cauchy loss - very robust to outliers (for Cauchy noise)""" + error = ys - ys_pred + return torch.log(1 + error.square()).mean() + + def accuracy(ys_pred, ys): return (ys == ys_pred.sign()).float() @@ -65,6 +80,11 @@ def get_task_sampler( "exponential_weighted_regression": ExponentialWeightedRegression, "laplace_weighted_regression": LaplaceWeightedRegression, "wlaplace_noisypoisson": wlaplace_noisypoisson, + "sparse_regression_killer": SparseRegressionKiller, + "heavy_tail_noise_killer": HeavyTailNoiseKiller, + "bounded_support_killer": BoundedSupportKiller, + "mixture_tasks_killer": MixtureTasksKiller, + "transfer_tradeoff_task": TransferTradeoffTask, } if task_name in task_names_to_classes: @@ -445,6 +465,26 @@ def evaluate(self, xs_b): ys_b_noisy = ys_b_noisy * math.sqrt(self.n_dims) / ys_b_noisy.std() return ys_b_noisy + def get_training_metric(self): + """ + Use robust loss for heavy-tailed noise (Cauchy, t-student) to handle outliers. + For normal/uniform noise, use standard MSE. + """ + if self.noise_type in ["cauchy", "t-student"]: + # Use Huber loss for heavy-tailed distributions (robust to outliers) + # Huber loss is less sensitive to outliers than MSE + def robust_loss(ys_pred, ys): + return huber_loss(ys_pred, ys, delta=1.35) + return robust_loss + elif self.noise_type == "laplace": + # Laplace noise: use L1-like loss (MAE) which is more robust + def laplace_loss(ys_pred, ys): + return torch.abs(ys - ys_pred).mean() + return laplace_loss + else: + # For normal, uniform, and other noise types, use standard MSE + return mean_squared_error + class QuadraticRegression(LinearRegression): def evaluate(self, xs_b): @@ -660,29 +700,285 @@ def get_metric(): def get_training_metric(): return mean_squared_error -# class AR2RegressionTask: - # def __init__(self, ar1_coef=0.5, ar2_coef=0.3, noise_std=1.0): - # """ - # AR(2) Regression Task: y_t = ar1_coef * y_{t-1} + ar2_coef * y_{t-2} + epsilon_t - # where epsilon_t ~ N(0, noise_std^2) +class SparseRegressionKiller(Task): + """ + Case 1: Sparse Regression - "Ridge Trap" + Prior: Spike-and-Slab (only k=2 dims are non-zero) + Shows Bayesian advantage over Ridge/OLS + """ + def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, k_sparse=2): + super(SparseRegressionKiller, self).__init__(n_dims, batch_size, pool_dict, seeds) + self.scale = scale + self.k_sparse = k_sparse + + if pool_dict is None and seeds is None: + self.w_b = torch.zeros(self.b_size, self.n_dims, 1) + # Only k_sparse dimensions are non-zero, sampled from Uniform[-1,1] + for i in range(self.b_size): + active_dims = torch.randperm(self.n_dims)[:self.k_sparse] + self.w_b[i, active_dims, 0] = torch.rand(self.k_sparse) * 2 - 1 + elif seeds is not None: + self.w_b = torch.zeros(self.b_size, self.n_dims, 1) + generator = torch.Generator() + for i, seed in enumerate(seeds): + generator.manual_seed(seed) + active_dims = torch.randperm(self.n_dims, generator=generator)[:self.k_sparse] + self.w_b[i, active_dims, 0] = torch.rand(self.k_sparse, generator=generator) * 2 - 1 + else: + assert "w" in pool_dict + indices = torch.randperm(len(pool_dict["w"]))[:batch_size] + self.w_b = pool_dict["w"][indices] + + def evaluate(self, xs_b): + w_b = self.w_b.to(xs_b.device) + ys_b = self.scale * (xs_b @ w_b)[:, :, 0] + return ys_b + + @staticmethod + def generate_pool_dict(n_dims, num_tasks, k_sparse=2, **kwargs): + w = torch.zeros(num_tasks, n_dims, 1) + for i in range(num_tasks): + active_dims = torch.randperm(n_dims)[:k_sparse] + w[i, active_dims, 0] = torch.rand(k_sparse) * 2 - 1 + return {"w": w} + + @staticmethod + def get_metric(): + return squared_error + + @staticmethod + def get_training_metric(): + return mean_squared_error + + +class HeavyTailNoiseKiller(Task): + """ + Case 2: Heavy-tailed Noise - "OLS Enemy" + Noise: Student-t with low df (reduced variance) or Cauchy (scaled down) + Shows robustness of Bayesian vs OLS + """ + def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, + noise_type="t-student", df=3.0, noise_scale=0.5): + super(HeavyTailNoiseKiller, self).__init__(n_dims, batch_size, pool_dict, seeds) + self.scale = scale + self.noise_type = noise_type + self.df = df + self.noise_scale = noise_scale # Reduced scale for learnable regime + + if pool_dict is None and seeds is None: + self.w_b = torch.randn(self.b_size, self.n_dims, 1) + elif seeds is not None: + self.w_b = torch.zeros(self.b_size, self.n_dims, 1) + generator = torch.Generator() + for i, seed in enumerate(seeds): + generator.manual_seed(seed) + self.w_b[i] = torch.randn(self.n_dims, 1, generator=generator) + else: + assert "w" in pool_dict + indices = torch.randperm(len(pool_dict["w"]))[:batch_size] + self.w_b = pool_dict["w"][indices] + + def evaluate(self, xs_b): + w_b = self.w_b.to(xs_b.device) + ys_linear = self.scale * (xs_b @ w_b)[:, :, 0] + + # Add heavy-tail noise with reduced variance + if self.noise_type == "t-student": + noise_dist = torch.distributions.StudentT(df=self.df) + noise = noise_dist.sample(ys_linear.shape).to(xs_b.device) * self.noise_scale + elif self.noise_type == "cauchy": + noise_dist = torch.distributions.Cauchy(loc=0, scale=self.noise_scale) + noise = noise_dist.sample(ys_linear.shape).to(xs_b.device) + else: + raise ValueError(f"Unknown noise_type: {self.noise_type}") + + return ys_linear + noise + + @staticmethod + def generate_pool_dict(n_dims, num_tasks, **kwargs): + return {"w": torch.randn(num_tasks, n_dims, 1)} + + @staticmethod + def get_metric(): + return squared_error + + @staticmethod + def get_training_metric(): + # Use Huber loss for robustness to outliers + def robust_loss(ys_pred, ys): + return huber_loss(ys_pred, ys, delta=1.0) + return robust_loss + + +class BoundedSupportKiller(Task): + """ + Case 3: Bounded Support - "Sign Constraint" + Prior: w ~ Exponential (w > 0 always) + Input: x ~ Uniform[0, 1] (positive only) + OLS can predict negative w, Bayes respects constraint + """ + def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, rate=1.0): + super(BoundedSupportKiller, self).__init__(n_dims, batch_size, pool_dict, seeds) + self.scale = scale + self.rate = rate + + if pool_dict is None and seeds is None: + exp_dist = torch.distributions.Exponential(rate=self.rate) + self.w_b = exp_dist.sample((self.b_size, self.n_dims, 1)) + elif seeds is not None: + self.w_b = torch.zeros(self.b_size, self.n_dims, 1) + generator = torch.Generator() + for i, seed in enumerate(seeds): + generator.manual_seed(seed) + exp_dist = torch.distributions.Exponential(rate=self.rate) + # Manual sampling with generator + u = torch.rand(self.n_dims, 1, generator=generator) + self.w_b[i] = -torch.log(u) / self.rate + else: + assert "w" in pool_dict + indices = torch.randperm(len(pool_dict["w"]))[:batch_size] + self.w_b = pool_dict["w"][indices] + + def evaluate(self, xs_b): + w_b = self.w_b.to(xs_b.device) + ys_b = self.scale * (xs_b @ w_b)[:, :, 0] + return ys_b + + @staticmethod + def generate_pool_dict(n_dims, num_tasks, rate=1.0, **kwargs): + exp_dist = torch.distributions.Exponential(rate=rate) + return {"w": exp_dist.sample((num_tasks, n_dims, 1))} + + @staticmethod + def get_metric(): + return squared_error + + @staticmethod + def get_training_metric(): + return mean_squared_error + + +class MixtureTasksKiller(Task): + """ + Case 4: Mixture of Tasks - "Averaging Death" + Prior: 50% y = w^T x, 50% y = -w^T x + OLS averages to 0, Bayes maintains bimodal posterior + """ + def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1): + super(MixtureTasksKiller, self).__init__(n_dims, batch_size, pool_dict, seeds) + self.scale = scale + + if pool_dict is None and seeds is None: + # Sample base w + w_base = torch.randn(self.b_size, self.n_dims, 1) + # Randomly flip sign for 50% of tasks + signs = torch.randint(0, 2, (self.b_size, 1, 1)) * 2 - 1 # {-1, +1} + self.w_b = w_base * signs + elif seeds is not None: + self.w_b = torch.zeros(self.b_size, self.n_dims, 1) + generator = torch.Generator() + for i, seed in enumerate(seeds): + generator.manual_seed(seed) + w_base = torch.randn(self.n_dims, 1, generator=generator) + sign = torch.randint(0, 2, (1,), generator=generator).item() * 2 - 1 + self.w_b[i] = w_base * sign + else: + assert "w" in pool_dict + indices = torch.randperm(len(pool_dict["w"]))[:batch_size] + self.w_b = pool_dict["w"][indices] + + def evaluate(self, xs_b): + w_b = self.w_b.to(xs_b.device) + ys_b = self.scale * (xs_b @ w_b)[:, :, 0] + return ys_b + + @staticmethod + def generate_pool_dict(n_dims, num_tasks, **kwargs): + w_base = torch.randn(num_tasks, n_dims, 1) + signs = torch.randint(0, 2, (num_tasks, 1, 1)) * 2 - 1 + return {"w": w_base * signs} + + @staticmethod + def get_metric(): + return squared_error + + @staticmethod + def get_training_metric(): + return mean_squared_error + + +class TransferTradeoffTask(Task): + """ + Case 5: Transfer Tradeoff - p×N experiment (Wakayama) + Tests Bayes Gap (N) vs Posterior Variance (p) + Use with different (N, p) configurations + """ + def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1, + prior_type="mixture_gaussian", mixture_std=2.0): + super(TransferTradeoffTask, self).__init__(n_dims, batch_size, pool_dict, seeds) + self.scale = scale + self.prior_type = prior_type + self.mixture_std = mixture_std - # ar1_coef: AR(1) coefficient - # ar2_coef: AR(2) coefficient - # noise_std: standard deviation of innovation noise - # """ - # self.ar1_coef = ar1_coef - # self.ar2_coef = ar2_coef - # self.noise_std = noise_std - # def evaluate(self, xs): - # batch_size, seq_len, dim = xs.shape - # ys = torch.zeros(xs) - - # ys[:, 0:2, :] = xs[:, 0:2, :] # Initialize first two values - - # for t in range(2, seq_len): - # ys[:, t, :] = (self.ar1_coef * ys[:, t-1, :] + - # self.ar2_coef * ys[:, t-2, :] + - # self.noise_std * torch.randn_like(xs[:, t, :])) - # return ys - # def get_metric(self): - # return lambda pred, target: ((pred - target) ** 2).mean(dim=-1) + if pool_dict is None and seeds is None: + if prior_type == "mixture_gaussian": + # Mixture: 50% N(0,1) + 50% N(0, mixture_std^2) + mode = torch.randint(0, 2, (self.b_size,)) + self.w_b = torch.randn(self.b_size, self.n_dims, 1) + self.w_b[mode == 1] *= self.mixture_std + elif prior_type == "sparse": + # Sparse prior (like Case 1) + self.w_b = torch.zeros(self.b_size, self.n_dims, 1) + k_sparse = max(2, n_dims // 10) + for i in range(self.b_size): + active = torch.randperm(n_dims)[:k_sparse] + self.w_b[i, active, 0] = torch.randn(k_sparse) + else: + raise ValueError(f"Unknown prior_type: {prior_type}") + elif seeds is not None: + self.w_b = torch.zeros(self.b_size, self.n_dims, 1) + generator = torch.Generator() + for i, seed in enumerate(seeds): + generator.manual_seed(seed) + if prior_type == "mixture_gaussian": + mode = torch.randint(0, 2, (1,), generator=generator).item() + w = torch.randn(self.n_dims, 1, generator=generator) + if mode == 1: + w *= self.mixture_std + self.w_b[i] = w + elif prior_type == "sparse": + k_sparse = max(2, n_dims // 10) + active = torch.randperm(n_dims, generator=generator)[:k_sparse] + self.w_b[i, active, 0] = torch.randn(k_sparse, generator=generator) + else: + assert "w" in pool_dict + indices = torch.randperm(len(pool_dict["w"]))[:batch_size] + self.w_b = pool_dict["w"][indices] + + def evaluate(self, xs_b): + w_b = self.w_b.to(xs_b.device) + ys_b = self.scale * (xs_b @ w_b)[:, :, 0] + return ys_b + + @staticmethod + def generate_pool_dict(n_dims, num_tasks, prior_type="mixture_gaussian", + mixture_std=2.0, **kwargs): + if prior_type == "mixture_gaussian": + mode = torch.randint(0, 2, (num_tasks,)) + w = torch.randn(num_tasks, n_dims, 1) + w[mode == 1] *= mixture_std + elif prior_type == "sparse": + w = torch.zeros(num_tasks, n_dims, 1) + k_sparse = max(2, n_dims // 10) + for i in range(num_tasks): + active = torch.randperm(n_dims)[:k_sparse] + w[i, active, 0] = torch.randn(k_sparse) + return {"w": w} + + @staticmethod + def get_metric(): + return squared_error + + @staticmethod + def get_training_metric(): + return mean_squared_error diff --git a/src/train.py b/src/train.py index dec5a297..a01efb93 100644 --- a/src/train.py +++ b/src/train.py @@ -81,6 +81,12 @@ def _sanitize_training_kwargs(args): "ar1_linear_regression": {"scale", "ar_coef", "noise_std", "compute_gradient"}, "uniform_hypersphere_regression": {"scale"}, "wlaplace_noisypoisson": {"scale", "weight_scale", "poisson_rate"}, + "sparse_regression_killer": {"scale", "k_sparse"}, + "heavy_tail_noise_killer": {"scale", "noise_type", "df", "noise_scale"}, + "bounded_support_killer": {"scale", "rate"}, + "mixture_tasks_killer": {"scale"}, + "transfer_tradeoff_task": {"scale", "prior_type", "mixture_std"}, + } task_name = args.training.task From 59b8f363f78bd2f3380785598fe1e2473e204743 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 26 Nov 2025 11:20:46 +0700 Subject: [PATCH 61/88] changed --- src/schema.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/schema.py b/src/schema.py index 51bb1772..ed0351d5 100644 --- a/src/schema.py +++ b/src/schema.py @@ -48,6 +48,11 @@ "exponential_weighted_regression", "laplace_weighted_regression", "wlaplace_noisypoisson", + "sparse_regression_killer", + "heavy_tail_noise_killer", + "bounded_support_killer", + "mixture_tasks_killer", + "transfer_tradeoff_task", ] training_schema = { From 89fba382b505d22e9b684feee6b2ae8387b2fd1c Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 26 Nov 2025 17:15:45 +0700 Subject: [PATCH 62/88] fuck --- src/train.py | 35 ++++++++++++++++------------------- 1 file changed, 16 insertions(+), 19 deletions(-) diff --git a/src/train.py b/src/train.py index a01efb93..139d3528 100644 --- a/src/train.py +++ b/src/train.py @@ -117,16 +117,13 @@ def train(model, args): n_dims = model.n_dims bsize = args.training.batch_size - - # Sanitize kwargs before constructing samplers/tasks to prevent conflicts - _sanitize_training_kwargs(args) - data_sampler = get_data_sampler(args.training.data, n_dims=n_dims, **args.training.data_kwargs) task_sampler = get_task_sampler( - args.training.task, - n_dims=n_dims, - batch_size=args.training.batch_size, - **args.training.task_kwargs + args.training.task, + n_dims, + bsize, + num_tasks=args.training.num_tasks, + **args.training.task_kwargs, ) pbar = tqdm(range(starting_step, args.training.train_steps)) @@ -136,21 +133,21 @@ def train(model, args): data_sampler_args = {} task_sampler_args = {} - if "sparse" in args.training.task: - task_sampler_args["valid_coords"] = curriculum.n_dims_truncated if num_training_examples is not None: assert num_training_examples >= bsize seeds = sample_seeds(num_training_examples, bsize) data_sampler_args["seeds"] = seeds - task_sampler_args["seeds"] = [s + 1 for s in seeds] - - xs = data_sampler.sample_xs( - curriculum.n_points, - bsize, - curriculum.n_dims_truncated, - **data_sampler_args, - ) - task = task_sampler(**task_sampler_args) + task_sampler_args["seeds"] = seeds + + curriculum_point = curriculum.current_point + + # Filter task_sampler_args để loại bỏ các key không hợp lệ + # Chỉ giữ lại 'seeds' nếu có + filtered_task_args = {} + if "seeds" in task_sampler_args: + filtered_task_args["seeds"] = task_sampler_args["seeds"] + + task = task_sampler(**filtered_task_args) ys = task.evaluate(xs) loss_func = task.get_training_metric() From 44d059a38361ce5cd877c94b74796966ab6ddcac Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 26 Nov 2025 17:19:11 +0700 Subject: [PATCH 63/88] glob --- src/train.py | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/src/train.py b/src/train.py index 139d3528..71a6ff9a 100644 --- a/src/train.py +++ b/src/train.py @@ -140,14 +140,10 @@ def train(model, args): task_sampler_args["seeds"] = seeds curriculum_point = curriculum.current_point - - # Filter task_sampler_args để loại bỏ các key không hợp lệ - # Chỉ giữ lại 'seeds' nếu có - filtered_task_args = {} - if "seeds" in task_sampler_args: - filtered_task_args["seeds"] = task_sampler_args["seeds"] - - task = task_sampler(**filtered_task_args) + + task_sampler_args.pop("valid_coords", None) + + task = task_sampler(**task_sampler_args) ys = task.evaluate(xs) loss_func = task.get_training_metric() From 2197d67329d27b556a01d71d0fe97cc7f5cb752a Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 26 Nov 2025 17:23:36 +0700 Subject: [PATCH 64/88] ab --- src/train.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/train.py b/src/train.py index 71a6ff9a..26e30a09 100644 --- a/src/train.py +++ b/src/train.py @@ -2,6 +2,7 @@ from random import randint import uuid +import curriculum from quinine import QuinineArgumentParser from tqdm import tqdm import torch @@ -139,7 +140,9 @@ def train(model, args): data_sampler_args["seeds"] = seeds task_sampler_args["seeds"] = seeds - curriculum_point = curriculum.current_point + # curriculum_point = curriculum.current_point + n_dims_truncated = curriculum.n_dims + n_points = curriculum.n_points task_sampler_args.pop("valid_coords", None) From 098b2fd11c81563a4d11a11f61022d43815494b0 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 26 Nov 2025 17:25:43 +0700 Subject: [PATCH 65/88] ab --- src/train.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/src/train.py b/src/train.py index 26e30a09..f0d2c6ce 100644 --- a/src/train.py +++ b/src/train.py @@ -141,9 +141,15 @@ def train(model, args): task_sampler_args["seeds"] = seeds # curriculum_point = curriculum.current_point - n_dims_truncated = curriculum.n_dims + n_dims_truncated = curriculum.n_dims_truncated n_points = curriculum.n_points + xs = data_sampler.sample_xs( + n_points, + bsize, + n_dims_truncated=n_dims_truncated, + **data_sampler_args, + ) task_sampler_args.pop("valid_coords", None) task = task_sampler(**task_sampler_args) From f1358f5b8da4053923b68d147760f2427a18607e Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 26 Nov 2025 17:44:03 +0700 Subject: [PATCH 66/88] ab --- src/conf/case4.yaml | 2 +- src/conf/case5.yaml | 2 +- src/conf/case_3.yaml | 10 ++++++---- src/eval.ipynb | 15 ++++++++------- src/plot_utils.py | 2 +- 5 files changed, 17 insertions(+), 14 deletions(-) diff --git a/src/conf/case4.yaml b/src/conf/case4.yaml index f0ec2249..4515d03f 100644 --- a/src/conf/case4.yaml +++ b/src/conf/case4.yaml @@ -24,7 +24,7 @@ training: interval: 2000 batch_size: 64 learning_rate: 0.0001 - train_steps: 50001 + train_steps: 500001 out_dir: ../models/mixture_tasks_killer diff --git a/src/conf/case5.yaml b/src/conf/case5.yaml index c823a5db..18558875 100644 --- a/src/conf/case5.yaml +++ b/src/conf/case5.yaml @@ -26,7 +26,7 @@ training: interval: 2000 batch_size: 64 learning_rate: 0.0001 - train_steps: 50001 + train_steps: 500001 out_dir: ../models/transfer_tradeoff_task diff --git a/src/conf/case_3.yaml b/src/conf/case_3.yaml index 56efbe54..8343a657 100644 --- a/src/conf/case_3.yaml +++ b/src/conf/case_3.yaml @@ -11,9 +11,11 @@ training: rate: 1.0 scale: 1.0 # Use positive-only input distribution - data: uniform - data_kwargs: - rate: 1.0 + data: uniform + data_kwargs: {} + # data: exponential + # data_kwargs: + # rate: 1.0 curriculum: dims: start: 5 @@ -27,7 +29,7 @@ training: interval: 2000 batch_size: 64 learning_rate: 0.0001 - train_steps: 50001 + train_steps: 500001 out_dir: ../models/bounded_support_killer diff --git a/src/eval.ipynb b/src/eval.ipynb index be44a6ee..b0217950 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -694,12 +694,12 @@ "metadata": {}, "outputs": [], "source": [ - "task = \"linear_regression\"\n", + "task = \"relu_2nn_regression\"\n", "# task = \"sparse_linear_regression\"\n", "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"test_cauchy\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"pretrained\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -751,7 +751,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -761,21 +761,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "test test_cauchy\n" + "relu_2nn_regression_pretrained pretrained\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1/1 [00:00<00:00, 5178.15it/s]" + "100%|██████████| 2/2 [00:00" ] diff --git a/src/plot_utils.py b/src/plot_utils.py index 163c8811..e90791a6 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -140,7 +140,7 @@ def basic_plot(metrics, models=None, trivial=1.0): ax.set_xlabel("in-context examples") ax.set_ylabel("squared error") ax.set_xlim(-1, len(low) + 0.1) - ax.set_ylim(-0.1, 1000) + ax.set_ylim(-0.1, 5) # legend = ax.legend(loc="upper left", bbox_to_anchor=(1, 1)) From 708ebb6a535879e946e6378f7f59801b37a695ad Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 26 Nov 2025 19:59:48 +0700 Subject: [PATCH 67/88] ab --- src/conf/case5.yaml | 8 ++++---- src/eval.py | 9 +++++++++ 2 files changed, 13 insertions(+), 4 deletions(-) diff --git a/src/conf/case5.yaml b/src/conf/case5.yaml index 18558875..a63f96ec 100644 --- a/src/conf/case5.yaml +++ b/src/conf/case5.yaml @@ -15,14 +15,14 @@ training: data_kwargs: {} curriculum: dims: - start: 5 + start: 20 end: 20 inc: 1 interval: 2000 points: - start: 11 - end: 41 - inc: 2 + start: 5 + end: 10 + inc: 1 interval: 2000 batch_size: 64 learning_rate: 0.0001 diff --git a/src/eval.py b/src/eval.py index 37973540..d377a18b 100644 --- a/src/eval.py +++ b/src/eval.py @@ -226,6 +226,7 @@ def build_evals(conf): "laplace": {"bias", "scale", "loc", "laplace_scale"}, "gamma": {"bias", "scale", "concentration", "rate"}, "beta": {"bias", "scale", "alpha", "beta"}, + "uniform": {"bias", "scale", "low", "high"}, } task_whitelist = { "linear_regression": {"scale", "uniform"}, @@ -236,6 +237,14 @@ def build_evals(conf): "noisy_linear_regression": {"scale", "noise_std", "renormalize_ys", "noise_type", "uniform"}, "ar1_linear_regression": {"scale", "ar_coef", "noise_std", "compute_gradient"}, "uniform_hypersphere_regression": {"scale"}, + "linear_regression": {"scale", "uniform"}, + "sparse_linear_regression": {"scale", "sparsity", "valid_coords"}, + "sparse_regression_killer": {"scale", "k_sparse"}, + "heavy_tail_noise_killer": {"scale", "noise_type", "df", "noise_scale"}, + "bounded_support_killer": {"scale", "rate"}, + "mixture_tasks_killer": {"scale"}, + "transfer_tradeoff_task": {"scale", "prior_type", "mixture_std"}, + } original_data_kwargs = conf.training.data_kwargs if hasattr(conf.training, "data_kwargs") else {} original_task_kwargs = conf.training.task_kwargs if hasattr(conf.training, "task_kwargs") else {} From 77f3bc15a9804ce47e237a5eae69bd085d999fb8 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 26 Nov 2025 22:10:57 +0700 Subject: [PATCH 68/88] ab --- src/samplers.py | 6 +++--- src/train.py | 28 +++++++++++++--------------- 2 files changed, 16 insertions(+), 18 deletions(-) diff --git a/src/samplers.py b/src/samplers.py index ef7b2ba2..bc43598d 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -154,11 +154,11 @@ def __init__(self, n_dims, bias=None, scale=None): self.bias = bias self.scale = scale - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): if seeds is None: - xs_b = torch.randn(b_size, n_points, self.n_dims) + xs_b = torch.randn(b_size, n_points, self.n_dims, device=device) else: - xs_b = torch.zeros(b_size, n_points, self.n_dims) + xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) generator = torch.Generator() assert len(seeds) == b_size for i, seed in enumerate(seeds): diff --git a/src/train.py b/src/train.py index f0d2c6ce..26e409e6 100644 --- a/src/train.py +++ b/src/train.py @@ -118,13 +118,15 @@ def train(model, args): n_dims = model.n_dims bsize = args.training.batch_size - data_sampler = get_data_sampler(args.training.data, n_dims=n_dims, **args.training.data_kwargs) + data_sampler = get_data_sampler( + args.training.data, n_dims=n_dims, **getattr(args.training, "data_kwargs", {}) + ) task_sampler = get_task_sampler( args.training.task, n_dims, bsize, num_tasks=args.training.num_tasks, - **args.training.task_kwargs, + **getattr(args.training, "task_kwargs", {}) ) pbar = tqdm(range(starting_step, args.training.train_steps)) @@ -134,29 +136,26 @@ def train(model, args): data_sampler_args = {} task_sampler_args = {} + if args.training.task == "sparse_linear_regression": + task_sampler_args["valid_coords"] = curriculum.n_dims_truncated + if num_training_examples is not None: assert num_training_examples >= bsize seeds = sample_seeds(num_training_examples, bsize) data_sampler_args["seeds"] = seeds - task_sampler_args["seeds"] = seeds - - # curriculum_point = curriculum.current_point - n_dims_truncated = curriculum.n_dims_truncated - n_points = curriculum.n_points + task_sampler_args["seeds"] = [s + 1 for s in seeds] xs = data_sampler.sample_xs( - n_points, + curriculum.n_points, bsize, - n_dims_truncated=n_dims_truncated, + curriculum.n_dims_truncated, **data_sampler_args, + device="cuda" ) - task_sampler_args.pop("valid_coords", None) - task = task_sampler(**task_sampler_args) ys = task.evaluate(xs) loss_func = task.get_training_metric() - loss, output = train_step(model, xs.cuda(), ys.cuda(), optimizer, loss_func) point_wise_tags = list(range(curriculum.n_points)) @@ -168,7 +167,7 @@ def train(model, args): max(curriculum.n_dims_truncated - ii, 0) for ii in range(curriculum.n_points) ) -/ curriculum.n_points + / curriculum.n_points ) if i % args.wandb.log_every_steps == 0 and not args.test_run: @@ -186,8 +185,8 @@ def train(model, args): ) curriculum.update() - pbar.set_description(f"loss {loss}") + if i % args.training.save_every_steps == 0 and not args.test_run: training_state = { "model_state_dict": model.state_dict(), @@ -204,7 +203,6 @@ def train(model, args): ): torch.save(model.state_dict(), os.path.join(args.out_dir, f"model_{i}.pt")) - def main(args): if args.test_run: curriculum_args = args.training.curriculum From c87988866f11854e5e381f6d097781a40bd96e60 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Sat, 29 Nov 2025 09:18:20 +0700 Subject: [PATCH 69/88] add sampler full --- src/eval.ipynb | 661 +++++++++++++++++++++++++++++++--------------- src/eval.py | 46 ++++ src/plot_utils.py | 3 +- src/samplers.py | 75 ++++++ src/schema.py | 2 +- src/tasks.py | 61 +++++ 6 files changed, 626 insertions(+), 222 deletions(-) diff --git a/src/eval.ipynb b/src/eval.ipynb index b0217950..2baa5690 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "id": "0e8d018b", "metadata": { "scrolled": true @@ -74,7 +74,7 @@ " \n", " \n", " \n", - " 3\n", + " 6\n", " 1_beta_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -87,7 +87,7 @@ " 1_beta_noise_gaussian_data_experiment\n", " \n", " \n", - " 4\n", + " 7\n", " 1_exponential_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -100,7 +100,7 @@ " 1_exponential_noise_gaussian_data_experiment\n", " \n", " \n", - " 5\n", + " 8\n", " 1_poisson_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -113,7 +113,7 @@ " 1_poisson_noise_gaussian_data_experiment\n", " \n", " \n", - " 6\n", + " 9\n", " 1_t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -126,7 +126,7 @@ " 1_t_student_noise_gaussian_data_experiment\n", " \n", " \n", - " 7\n", + " 10\n", " 1_uniform_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -139,7 +139,7 @@ " 1_uniform_noise_gaussian_data_experiment\n", " \n", " \n", - " 2\n", + " 5\n", " 123e9cbd-1566-443d-9491-f23b6b9af0e2\n", " linear_regression\n", " Transformer\n", @@ -152,7 +152,7 @@ " 20_dims_uniform_error_gaussian_data\n", " \n", " \n", - " 10\n", + " 13\n", " 64d381ae-08d0-4bae-8e40-f1a68cfb2e97\n", " linear_regression\n", " Transformer\n", @@ -165,7 +165,7 @@ " 20_dims_uniform_error_gaussian_data_\n", " \n", " \n", - " 8\n", + " 11\n", " 3_laplace_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -178,7 +178,7 @@ " 3_laplace_noise_gaussian_data_experiment\n", " \n", " \n", - " 9\n", + " 12\n", " 3_tstudent_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -191,7 +191,7 @@ " 3_tstudent_noise_gaussian_data_experiment\n", " \n", " \n", - " 35\n", + " 40\n", " daa2cd45-f1c0-4a0c-9100-e171129624c9\n", " sparse_linear_regression\n", " Transformer\n", @@ -204,7 +204,7 @@ " 4_std_sparse_linear_regression\n", " \n", " \n", - " 14\n", + " 17\n", " beta_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -217,7 +217,7 @@ " beta_noise_ar1_data_experiment\n", " \n", " \n", - " 15\n", + " 18\n", " beta_noisy_linear_regression_40_100k\n", " linear_regression\n", " Transformer\n", @@ -230,7 +230,98 @@ " beta_noisy_linear_regression_40_100k\n", " \n", " \n", - " 13\n", + " 42\n", + " case1_sparse_regression\n", + " sparse_regression_killer\n", + " Transformer\n", + " k_sparse=2_scale=1.0\n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " case1_sparse_regression\n", + " \n", + " \n", + " 2\n", + " case2_heavy_tail_t_student\n", + " heavy_tail_noise_killer\n", + " Transformer\n", + " df=3.0_noise_scale=0.5_noise_type=t-student\n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " case2_heavy_tail_t_student\n", + " \n", + " \n", + " 3\n", + " case2_heavy_tail_t_student_1_1\n", + " heavy_tail_noise_killer\n", + " Transformer\n", + " df=3.0_noise_scale=0.5_noise_type=t-student\n", + " -1\n", + " -1\n", + " 20\n", + " 12\n", + " 8\n", + " case2_heavy_tail_t_student_1_1\n", + " \n", + " \n", + " 0\n", + " bounded_support_killer\n", + " bounded_support_killer\n", + " Transformer\n", + " rate=1.0_scale=1.0\n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " case3_bounded_support\n", + " \n", + " \n", + " 36\n", + " case4_mixture_tasks\n", + " mixture_tasks_killer\n", + " Transformer\n", + " scale=1.0\n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " case4_mixture_tasks\n", + " \n", + " \n", + " 37\n", + " case4_mixture_tasks_1_1\n", + " mixture_tasks_killer\n", + " Transformer\n", + " scale=1.0\n", + " -1\n", + " -1\n", + " 20\n", + " 12\n", + " 8\n", + " case4_mixture_tasks_1_1\n", + " \n", + " \n", + " 43\n", + " case5_transfer_tradeoff\n", + " transfer_tradeoff_task\n", + " Transformer\n", + " mixture_std=2.0_prior_type=mixture_gaussian_sc...\n", + " -1\n", + " -1\n", + " 20\n", + " 4\n", + " 8\n", + " case5_transfer_tradeoff\n", + " \n", + " \n", + " 16\n", " aed365ed-51e2-4a72-8374-ae954b37be14\n", " linear_regression\n", " Transformer\n", @@ -243,7 +334,7 @@ " data_sparse_linear_regression\n", " \n", " \n", - " 0\n", + " 1\n", " pretrained\n", " decision_tree\n", " Transformer\n", @@ -256,7 +347,7 @@ " decision_tree_pretrained\n", " \n", " \n", - " 16\n", + " 19\n", " exponential_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -269,7 +360,7 @@ " exponential_noise_gaussian_data_experiment\n", " \n", " \n", - " 17\n", + " 20\n", " exponential_w\n", " linear_regression\n", " Transformer\n", @@ -282,7 +373,7 @@ " exponential_w\n", " \n", " \n", - " 18\n", + " 21\n", " exponential_weighted_experiment_100k\n", " linear_regression\n", " Transformer\n", @@ -295,7 +386,7 @@ " exponential_weighted_experiment_100k\n", " \n", " \n", - " 19\n", + " 22\n", " exponential_weighted_experiment_150k\n", " linear_regression\n", " Transformer\n", @@ -308,7 +399,7 @@ " exponential_weighted_experiment_150k\n", " \n", " \n", - " 20\n", + " 23\n", " exponential_weighted_regression\n", " linear_regression\n", " Transformer\n", @@ -321,7 +412,7 @@ " exponential_weights_experiment\n", " \n", " \n", - " 21\n", + " 24\n", " laplace_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -334,7 +425,7 @@ " laplace_noise_gaussian_data_experiment\n", " \n", " \n", - " 22\n", + " 25\n", " laplace_w\n", " linear_regression\n", " Transformer\n", @@ -347,7 +438,7 @@ " laplace_w\n", " \n", " \n", - " 12\n", + " 15\n", " a2fcec3c-8ce5-49bf-a8bc-08136b31ec36\n", " linear_regression\n", " Transformer\n", @@ -360,7 +451,7 @@ " laplace_weights_experiment\n", " \n", " \n", - " 23\n", + " 26\n", " pretrained\n", " linear_regression\n", " Transformer\n", @@ -373,7 +464,7 @@ " linear_regression_pretrained\n", " \n", " \n", - " 24\n", + " 27\n", " rayleigh_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -386,7 +477,7 @@ " rayleigh_noise_gaussian_data_experiment\n", " \n", " \n", - " 33\n", + " 38\n", " pretrained\n", " relu_2nn_regression\n", " Transformer\n", @@ -399,7 +490,7 @@ " relu_2nn_regression_pretrained\n", " \n", " \n", - " 11\n", + " 14\n", " 82e728b0-a061-448e-8d7a-f3c79c0c74e5\n", " linear_regression\n", " Transformer\n", @@ -412,7 +503,7 @@ " rigde_normal_linear_regression_gaussian\n", " \n", " \n", - " 34\n", + " 39\n", " 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615\n", " sparse_linear_regression\n", " Transformer\n", @@ -425,7 +516,7 @@ " sparse\n", " \n", " \n", - " 1\n", + " 4\n", " 03de46b6-429a-4151-92e6-3588231c6cad\n", " linear_regression\n", " Transformer\n", @@ -438,7 +529,7 @@ " sparse_data_experiment\n", " \n", " \n", - " 36\n", + " 41\n", " pretrained\n", " sparse_linear_regression\n", " Transformer\n", @@ -451,7 +542,7 @@ " sparse_regression_pretrained\n", " \n", " \n", - " 27\n", + " 30\n", " t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -464,7 +555,7 @@ " t_student_noise_gaussian_data_experiment\n", " \n", " \n", - " 25\n", + " 28\n", " sparse_gaussian\n", " linear_regression\n", " Transformer\n", @@ -477,7 +568,7 @@ " task_sparse_data\n", " \n", " \n", - " 26\n", + " 29\n", " test_cauchy\n", " linear_regression\n", " Transformer\n", @@ -490,7 +581,7 @@ " test\n", " \n", " \n", - " 29\n", + " 32\n", " uniform_hypersphere_regression\n", " linear_regression\n", " Transformer\n", @@ -503,7 +594,7 @@ " uniform_hypersphere_experiment\n", " \n", " \n", - " 28\n", + " 31\n", " uniform_hypersphere_experiment_standard\n", " linear_regression\n", " Transformer\n", @@ -516,7 +607,7 @@ " uniform_hypersphere_experiment_standard\n", " \n", " \n", - " 30\n", + " 33\n", " uniform_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -529,7 +620,7 @@ " uniform_noise_ar1_data_experiment\n", " \n", " \n", - " 31\n", + " 34\n", " uniform_noise_gaussian_data_experiment_\n", " linear_regression\n", " Transformer\n", @@ -542,7 +633,7 @@ " uniform_noise_gaussian_data_experiment\n", " \n", " \n", - " 32\n", + " 35\n", " w_laplace_x_exponential_noise_poisson\n", " linear_regression\n", " Transformer\n", @@ -560,124 +651,191 @@ ], "text/plain": [ " run_id task \\\n", - "3 1_beta_noise_gaussian_data_experiment linear_regression \n", - "4 1_exponential_noise_gaussian_data_experiment linear_regression \n", - "5 1_poisson_noise_gaussian_data_experiment linear_regression \n", - "6 1_t_student_noise_gaussian_data_experiment linear_regression \n", - "7 1_uniform_noise_gaussian_data_experiment linear_regression \n", - "2 123e9cbd-1566-443d-9491-f23b6b9af0e2 linear_regression \n", - "10 64d381ae-08d0-4bae-8e40-f1a68cfb2e97 linear_regression \n", - "8 3_laplace_noise_gaussian_data_experiment linear_regression \n", - "9 3_tstudent_noise_gaussian_data_experiment linear_regression \n", - "35 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", - "14 beta_noise_ar1_data_experiment linear_regression \n", - "15 beta_noisy_linear_regression_40_100k linear_regression \n", - "13 aed365ed-51e2-4a72-8374-ae954b37be14 linear_regression \n", - "0 pretrained decision_tree \n", - "16 exponential_noise_gaussian_data_experiment linear_regression \n", - "17 exponential_w linear_regression \n", - "18 exponential_weighted_experiment_100k linear_regression \n", - "19 exponential_weighted_experiment_150k linear_regression \n", - "20 exponential_weighted_regression linear_regression \n", - "21 laplace_noise_gaussian_data_experiment linear_regression \n", - "22 laplace_w linear_regression \n", - "12 a2fcec3c-8ce5-49bf-a8bc-08136b31ec36 linear_regression \n", - "23 pretrained linear_regression \n", - "24 rayleigh_noise_gaussian_data_experiment linear_regression \n", - "33 pretrained relu_2nn_regression \n", - "11 82e728b0-a061-448e-8d7a-f3c79c0c74e5 linear_regression \n", - "34 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", - "1 03de46b6-429a-4151-92e6-3588231c6cad linear_regression \n", - "36 pretrained sparse_linear_regression \n", - "27 t_student_noise_gaussian_data_experiment linear_regression \n", - "25 sparse_gaussian linear_regression \n", - "26 test_cauchy linear_regression \n", - "29 uniform_hypersphere_regression linear_regression \n", - "28 uniform_hypersphere_experiment_standard linear_regression \n", - "30 uniform_noise_ar1_data_experiment linear_regression \n", - "31 uniform_noise_gaussian_data_experiment_ linear_regression \n", - "32 w_laplace_x_exponential_noise_poisson linear_regression \n", + "6 1_beta_noise_gaussian_data_experiment linear_regression \n", + "7 1_exponential_noise_gaussian_data_experiment linear_regression \n", + "8 1_poisson_noise_gaussian_data_experiment linear_regression \n", + "9 1_t_student_noise_gaussian_data_experiment linear_regression \n", + "10 1_uniform_noise_gaussian_data_experiment linear_regression \n", + "5 123e9cbd-1566-443d-9491-f23b6b9af0e2 linear_regression \n", + "13 64d381ae-08d0-4bae-8e40-f1a68cfb2e97 linear_regression \n", + "11 3_laplace_noise_gaussian_data_experiment linear_regression \n", + "12 3_tstudent_noise_gaussian_data_experiment linear_regression \n", + "40 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", + "17 beta_noise_ar1_data_experiment linear_regression \n", + "18 beta_noisy_linear_regression_40_100k linear_regression \n", + "42 case1_sparse_regression sparse_regression_killer \n", + "2 case2_heavy_tail_t_student heavy_tail_noise_killer \n", + "3 case2_heavy_tail_t_student_1_1 heavy_tail_noise_killer \n", + "0 bounded_support_killer bounded_support_killer \n", + "36 case4_mixture_tasks mixture_tasks_killer \n", + "37 case4_mixture_tasks_1_1 mixture_tasks_killer \n", + "43 case5_transfer_tradeoff transfer_tradeoff_task \n", + "16 aed365ed-51e2-4a72-8374-ae954b37be14 linear_regression \n", + "1 pretrained decision_tree \n", + "19 exponential_noise_gaussian_data_experiment linear_regression \n", + "20 exponential_w linear_regression \n", + "21 exponential_weighted_experiment_100k linear_regression \n", + "22 exponential_weighted_experiment_150k linear_regression \n", + "23 exponential_weighted_regression linear_regression \n", + "24 laplace_noise_gaussian_data_experiment linear_regression \n", + "25 laplace_w linear_regression \n", + "15 a2fcec3c-8ce5-49bf-a8bc-08136b31ec36 linear_regression \n", + "26 pretrained linear_regression \n", + "27 rayleigh_noise_gaussian_data_experiment linear_regression \n", + "38 pretrained relu_2nn_regression \n", + "14 82e728b0-a061-448e-8d7a-f3c79c0c74e5 linear_regression \n", + "39 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", + "4 03de46b6-429a-4151-92e6-3588231c6cad linear_regression \n", + "41 pretrained sparse_linear_regression \n", + "30 t_student_noise_gaussian_data_experiment linear_regression \n", + "28 sparse_gaussian linear_regression \n", + "29 test_cauchy linear_regression \n", + "32 uniform_hypersphere_regression linear_regression \n", + "31 uniform_hypersphere_experiment_standard linear_regression \n", + "33 uniform_noise_ar1_data_experiment linear_regression \n", + "34 uniform_noise_gaussian_data_experiment_ linear_regression \n", + "35 w_laplace_x_exponential_noise_poisson linear_regression \n", + "\n", + " model kwargs num_tasks \\\n", + "6 Transformer -1 \n", + "7 Transformer -1 \n", + "8 Transformer -1 \n", + "9 Transformer -1 \n", + "10 Transformer -1 \n", + "5 Transformer -1 \n", + "13 Transformer -1 \n", + "11 Transformer -1 \n", + "12 Transformer -1 \n", + "40 Transformer sparsity=5 -1 \n", + "17 Transformer -1 \n", + "18 Transformer noise_type=beta -1 \n", + "42 Transformer k_sparse=2_scale=1.0 -1 \n", + "2 Transformer df=3.0_noise_scale=0.5_noise_type=t-student -1 \n", + "3 Transformer df=3.0_noise_scale=0.5_noise_type=t-student -1 \n", + "0 Transformer rate=1.0_scale=1.0 -1 \n", + "36 Transformer scale=1.0 -1 \n", + "37 Transformer scale=1.0 -1 \n", + "43 Transformer mixture_std=2.0_prior_type=mixture_gaussian_sc... -1 \n", + "16 Transformer k=5_sparsity=3 -1 \n", + "1 Transformer depth=4 -1 \n", + "19 Transformer -1 \n", + "20 Transformer rate=1.0_scale=1.0 -1 \n", + "21 Transformer rate=1.0_scale=1.0 -1 \n", + "22 Transformer rate=1.0_scale=1.0 -1 \n", + "23 Transformer rate=1.0_scale=1.0 -1 \n", + "24 Transformer -1 \n", + "25 Transformer scale=1.0_weight_scale=1.0 -1 \n", + "15 Transformer scale=1.0_weight_scale=1.0 -1 \n", + "26 Transformer -1 \n", + "27 Transformer -1 \n", + "38 Transformer hidden_layer_size=100 -1 \n", + "14 Transformer sparsity=5 -1 \n", + "39 Transformer -1 \n", + "4 Transformer -1 \n", + "41 Transformer sparsity=3 -1 \n", + "30 Transformer -1 \n", + "28 Transformer -1 \n", + "29 Transformer noise_type=cauchy -1 \n", + "32 Transformer normalize=True_scale=1.0 -1 \n", + "31 Transformer normalize=True_scale=1.0 -1 \n", + "33 Transformer -1 \n", + "34 Transformer -1 \n", + "35 Transformer -1 \n", "\n", - " model kwargs num_tasks num_examples n_dims \\\n", - "3 Transformer -1 -1 5 \n", - "4 Transformer -1 -1 5 \n", - "5 Transformer -1 -1 5 \n", - "6 Transformer -1 -1 5 \n", - "7 Transformer -1 -1 5 \n", - "2 Transformer -1 -1 20 \n", - "10 Transformer -1 -1 20 \n", - "8 Transformer -1 -1 5 \n", - "9 Transformer -1 -1 5 \n", - "35 Transformer sparsity=5 -1 -1 15 \n", - "14 Transformer -1 -1 5 \n", - "15 Transformer noise_type=beta -1 -1 20 \n", - "13 Transformer k=5_sparsity=3 -1 -1 15 \n", - "0 Transformer depth=4 -1 -1 20 \n", - "16 Transformer -1 -1 5 \n", - "17 Transformer rate=1.0_scale=1.0 -1 -1 20 \n", - "18 Transformer rate=1.0_scale=1.0 -1 -1 20 \n", - "19 Transformer rate=1.0_scale=1.0 -1 -1 20 \n", - "20 Transformer rate=1.0_scale=1.0 -1 -1 20 \n", - "21 Transformer -1 -1 5 \n", - "22 Transformer scale=1.0_weight_scale=1.0 -1 -1 20 \n", - "12 Transformer scale=1.0_weight_scale=1.0 -1 -1 20 \n", - "23 Transformer -1 -1 20 \n", - "24 Transformer -1 -1 5 \n", - "33 Transformer hidden_layer_size=100 -1 -1 20 \n", - "11 Transformer sparsity=5 -1 -1 15 \n", - "34 Transformer -1 -1 5 \n", - "1 Transformer -1 -1 20 \n", - "36 Transformer sparsity=3 -1 -1 20 \n", - "27 Transformer -1 -1 5 \n", - "25 Transformer -1 -1 20 \n", - "26 Transformer noise_type=cauchy -1 -1 20 \n", - "29 Transformer normalize=True_scale=1.0 -1 -1 20 \n", - "28 Transformer normalize=True_scale=1.0 -1 -1 20 \n", - "30 Transformer -1 -1 5 \n", - "31 Transformer -1 -1 5 \n", - "32 Transformer -1 -1 20 \n", + " num_examples n_dims n_layer n_head \\\n", + "6 -1 5 4 8 \n", + "7 -1 5 4 8 \n", + "8 -1 5 4 8 \n", + "9 -1 5 4 8 \n", + "10 -1 5 4 8 \n", + "5 -1 20 4 8 \n", + "13 -1 20 4 8 \n", + "11 -1 5 4 8 \n", + "12 -1 5 4 8 \n", + "40 -1 15 4 8 \n", + "17 -1 5 4 8 \n", + "18 -1 20 4 8 \n", + "42 -1 20 4 8 \n", + "2 -1 20 4 8 \n", + "3 -1 20 12 8 \n", + "0 -1 20 4 8 \n", + "36 -1 20 4 8 \n", + "37 -1 20 12 8 \n", + "43 -1 20 4 8 \n", + "16 -1 15 4 8 \n", + "1 -1 20 12 8 \n", + "19 -1 5 4 8 \n", + "20 -1 20 4 8 \n", + "21 -1 20 4 8 \n", + "22 -1 20 4 8 \n", + "23 -1 20 4 8 \n", + "24 -1 5 4 8 \n", + "25 -1 20 4 8 \n", + "15 -1 20 4 8 \n", + "26 -1 20 12 8 \n", + "27 -1 5 4 8 \n", + "38 -1 20 12 8 \n", + "14 -1 15 4 8 \n", + "39 -1 5 4 8 \n", + "4 -1 20 4 8 \n", + "41 -1 20 12 8 \n", + "30 -1 5 4 8 \n", + "28 -1 20 4 8 \n", + "29 -1 20 4 8 \n", + "32 -1 20 4 8 \n", + "31 -1 20 4 8 \n", + "33 -1 5 4 8 \n", + "34 -1 5 4 8 \n", + "35 -1 20 4 8 \n", "\n", - " n_layer n_head run_name \n", - "3 4 8 1_beta_noise_gaussian_data_experiment \n", - "4 4 8 1_exponential_noise_gaussian_data_experiment \n", - "5 4 8 1_poisson_noise_gaussian_data_experiment \n", - "6 4 8 1_t_student_noise_gaussian_data_experiment \n", - "7 4 8 1_uniform_noise_gaussian_data_experiment \n", - "2 4 8 20_dims_uniform_error_gaussian_data \n", - "10 4 8 20_dims_uniform_error_gaussian_data_ \n", - "8 4 8 3_laplace_noise_gaussian_data_experiment \n", - "9 4 8 3_tstudent_noise_gaussian_data_experiment \n", - "35 4 8 4_std_sparse_linear_regression \n", - "14 4 8 beta_noise_ar1_data_experiment \n", - "15 4 8 beta_noisy_linear_regression_40_100k \n", - "13 4 8 data_sparse_linear_regression \n", - "0 12 8 decision_tree_pretrained \n", - "16 4 8 exponential_noise_gaussian_data_experiment \n", - "17 4 8 exponential_w \n", - "18 4 8 exponential_weighted_experiment_100k \n", - "19 4 8 exponential_weighted_experiment_150k \n", - "20 4 8 exponential_weights_experiment \n", - "21 4 8 laplace_noise_gaussian_data_experiment \n", - "22 4 8 laplace_w \n", - "12 4 8 laplace_weights_experiment \n", - "23 12 8 linear_regression_pretrained \n", - "24 4 8 rayleigh_noise_gaussian_data_experiment \n", - "33 12 8 relu_2nn_regression_pretrained \n", - "11 4 8 rigde_normal_linear_regression_gaussian \n", - "34 4 8 sparse \n", - "1 4 8 sparse_data_experiment \n", - "36 12 8 sparse_regression_pretrained \n", - "27 4 8 t_student_noise_gaussian_data_experiment \n", - "25 4 8 task_sparse_data \n", - "26 4 8 test \n", - "29 4 8 uniform_hypersphere_experiment \n", - "28 4 8 uniform_hypersphere_experiment_standard \n", - "30 4 8 uniform_noise_ar1_data_experiment \n", - "31 4 8 uniform_noise_gaussian_data_experiment \n", - "32 4 8 w_laplace_x_exponential_noise_poisson " + " run_name \n", + "6 1_beta_noise_gaussian_data_experiment \n", + "7 1_exponential_noise_gaussian_data_experiment \n", + "8 1_poisson_noise_gaussian_data_experiment \n", + "9 1_t_student_noise_gaussian_data_experiment \n", + "10 1_uniform_noise_gaussian_data_experiment \n", + "5 20_dims_uniform_error_gaussian_data \n", + "13 20_dims_uniform_error_gaussian_data_ \n", + "11 3_laplace_noise_gaussian_data_experiment \n", + "12 3_tstudent_noise_gaussian_data_experiment \n", + "40 4_std_sparse_linear_regression \n", + "17 beta_noise_ar1_data_experiment \n", + "18 beta_noisy_linear_regression_40_100k \n", + "42 case1_sparse_regression \n", + "2 case2_heavy_tail_t_student \n", + "3 case2_heavy_tail_t_student_1_1 \n", + "0 case3_bounded_support \n", + "36 case4_mixture_tasks \n", + "37 case4_mixture_tasks_1_1 \n", + "43 case5_transfer_tradeoff \n", + "16 data_sparse_linear_regression \n", + "1 decision_tree_pretrained \n", + "19 exponential_noise_gaussian_data_experiment \n", + "20 exponential_w \n", + "21 exponential_weighted_experiment_100k \n", + "22 exponential_weighted_experiment_150k \n", + "23 exponential_weights_experiment \n", + "24 laplace_noise_gaussian_data_experiment \n", + "25 laplace_w \n", + "15 laplace_weights_experiment \n", + "26 linear_regression_pretrained \n", + "27 rayleigh_noise_gaussian_data_experiment \n", + "38 relu_2nn_regression_pretrained \n", + "14 rigde_normal_linear_regression_gaussian \n", + "39 sparse \n", + "4 sparse_data_experiment \n", + "41 sparse_regression_pretrained \n", + "30 t_student_noise_gaussian_data_experiment \n", + "28 task_sparse_data \n", + "29 test \n", + "32 uniform_hypersphere_experiment \n", + "31 uniform_hypersphere_experiment_standard \n", + "33 uniform_noise_ar1_data_experiment \n", + "34 uniform_noise_gaussian_data_experiment \n", + "35 w_laplace_x_exponential_noise_poisson " ] }, - "execution_count": 2, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -689,17 +847,17 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 13, "id": "a9980951", "metadata": {}, "outputs": [], "source": [ - "task = \"relu_2nn_regression\"\n", + "task = \"mixture_tasks_killer\"\n", "# task = \"sparse_linear_regression\"\n", "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"pretrained\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"case4_mixture_tasks_1_1\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -710,7 +868,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "id": "937f1b23", "metadata": {}, "outputs": [ @@ -719,10 +877,10 @@ "output_type": "stream", "text": [ "--- Tên mô hình CÓ TRONG relevant_model_names (models) ---\n", - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)']\n", "\n", "--- Các khóa (Tên mô hình) THỰC TẾ trong metrics['standard'] ---\n", - "dict_keys(['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', 'Ridge Var Adj (alpha=0.5, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging'])\n" + "dict_keys([])\n" ] } ], @@ -751,7 +909,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -761,22 +919,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "relu_2nn_regression_pretrained pretrained\n" + "case4_mixture_tasks_1_1 case4_mixture_tasks_1_1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 2/2 [00:00" ] @@ -807,75 +964,36 @@ "\n", "models = relevant_model_names[task]\n", "basic_plot(metrics[\"standard\"], models=models)\n", - "plt.show()\n", - "\n", - "# # Figure 3 and 4\n", - "# for model_name in models: \n", - "# if \"gradient_alignment\" in metrics[\"standard\"][model_name]: \n", - "# alignments = metrics[\"standard\"][model_name][\"gradient_alignment\"]\n", - "# plt.figure(figsize=(6,4))\n", - "# plt.plot(range(len(alignments)), alignments, label=f\"{model_name} gradient-w alignment\")\n", - "# plt.xlabel(\"# in-context examples\")\n", - "# plt.ylabel(\"normalized inner product\") \n", - "# plt.legend()\n", - "# plt.show()" + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "id": "31b4ecca", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: standard\n", - "Metric keys: ['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', 'Ridge Var Adj (alpha=0.5, ar=0.5)', 'Feasible GLS', 'GLS (ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" - ] - } - ], + "outputs": [], "source": [ "# plot any OOD metrics\n", - "# print(\"Available metrics:\", list(metrics.keys()))\n", "for name, metric in metrics.items():\n", - " print(\"Processing:\", name)\n", - " print(\"Metric keys:\", list(metric.keys()))\n", " if name == \"standard\": continue\n", " \n", " if \"scale\" in name:\n", " scale = float(name.split(\"=\")[-1])**2\n", " else:\n", " scale = 1.0\n", + "\n", " trivial = 1.0 if \"noisy\" not in name else (1+1/n_dims)\n", - " \n", - " # # only plot models that exist in this metric dict\n", - " # models_present = [m for m in models if m in metric]\n", - " # if len(models_present) == 0:\n", - " # print(f\"Skipping {name}: no matching models in metric keys {list(metric.keys())}\")\n", - " # continue\n", - " # fig, ax = basic_plot(metric, models=models, trivial=trivial * scale)\n", - " # ax.set_title(name)\n", + " fig, ax = basic_plot(metric, models=models, trivial=trivial * scale)\n", + " ax.set_title(name)\n", " \n", " if \"ortho\" in name:\n", " ax.set_xlim(-1, n_dims - 1)\n", " ax.set_ylim(-.1 * scale, 1.5 * scale)\n", - " plt.show()\n", - "# std = metrics.get(\"standard\", {})\n", - "# for model_name in models:\n", - "# mres = std.get(model_name, {})\n", - "# if \"gradient_alignment\" in mres:\n", - "# print(\"Plotting gradient alignment for\", model_name)\n", - "# alignments = mres[\"gradient_alignment\"]\n", - "# plt.figure(figsize=(6, 4))\n", - "# plt.plot(range(len(alignments)), alignments, label=f\"{model_name} gradient-w alignment\", lw=2)\n", - "# plt.xlabel(\"# in-context examples\")\n", - "# plt.ylabel(\"normalized inner product\")\n", - "# plt.legend()\n", - "# plt.show()" + "\n", + " plt.show()" ] }, { @@ -890,7 +1008,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 26, "id": "beb327ce", "metadata": {}, "outputs": [], @@ -901,10 +1019,31 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 27, "id": "03523b06", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[27], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m model, conf \u001b[38;5;241m=\u001b[39m \u001b[43mget_model_from_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3\u001b[0m n_dims \u001b[38;5;241m=\u001b[39m conf\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mn_dims\n\u001b[0;32m 4\u001b[0m batch_size \u001b[38;5;241m=\u001b[39m conf\u001b[38;5;241m.\u001b[39mtraining\u001b[38;5;241m.\u001b[39mbatch_size\n", + "File \u001b[1;32md:\\MyBK\\Semester_5\\Programming_Intergration_Project\\in-context-learning\\src\\eval.py:28\u001b[0m, in \u001b[0;36mget_model_from_run\u001b[1;34m(run_path, step, only_conf)\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m 27\u001b[0m state_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(run_path, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstate.pt\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 28\u001b[0m state \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 29\u001b[0m model\u001b[38;5;241m.\u001b[39mload_state_dict(state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_state_dict\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m 30\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\torch\\serialization.py:1462\u001b[0m, in \u001b[0;36mload\u001b[1;34m(f, map_location, pickle_module, weights_only, mmap, **pickle_load_args)\u001b[0m\n\u001b[0;32m 1460\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m weights_only:\n\u001b[0;32m 1461\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1462\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_load\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1463\u001b[0m \u001b[43m \u001b[49m\u001b[43mopened_zipfile\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1464\u001b[0m \u001b[43m \u001b[49m\u001b[43mmap_location\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1465\u001b[0m \u001b[43m \u001b[49m\u001b[43m_weights_only_unpickler\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1466\u001b[0m \u001b[43m \u001b[49m\u001b[43moverall_storage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverall_storage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1467\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpickle_load_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1468\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1469\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m pickle\u001b[38;5;241m.\u001b[39mUnpicklingError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 1470\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m pickle\u001b[38;5;241m.\u001b[39mUnpicklingError(_get_wo_message(\u001b[38;5;28mstr\u001b[39m(e))) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\torch\\serialization.py:1964\u001b[0m, in \u001b[0;36m_load\u001b[1;34m(zip_file, map_location, pickle_module, pickle_file, overall_storage, **pickle_load_args)\u001b[0m\n\u001b[0;32m 1962\u001b[0m \u001b[38;5;28;01mglobal\u001b[39;00m _serialization_tls\n\u001b[0;32m 1963\u001b[0m _serialization_tls\u001b[38;5;241m.\u001b[39mmap_location \u001b[38;5;241m=\u001b[39m map_location\n\u001b[1;32m-> 1964\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43munpickler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1965\u001b[0m _serialization_tls\u001b[38;5;241m.\u001b[39mmap_location \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1967\u001b[0m torch\u001b[38;5;241m.\u001b[39m_utils\u001b[38;5;241m.\u001b[39m_validate_loaded_sparse_tensors()\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\torch\\_weights_only_unpickler.py:512\u001b[0m, in \u001b[0;36mUnpickler.load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 504\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m 505\u001b[0m \u001b[38;5;28mtype\u001b[39m(pid) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m\n\u001b[0;32m 506\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(pid) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 507\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mserialization\u001b[38;5;241m.\u001b[39m_maybe_decode_ascii(pid[\u001b[38;5;241m0\u001b[39m]) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstorage\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 508\u001b[0m ):\n\u001b[0;32m 509\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m UnpicklingError(\n\u001b[0;32m 510\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOnly persistent_load of storage is allowed, but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpid[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 511\u001b[0m )\n\u001b[1;32m--> 512\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpersistent_load\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpid\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m 513\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;129;01min\u001b[39;00m [BINGET[\u001b[38;5;241m0\u001b[39m], LONG_BINGET[\u001b[38;5;241m0\u001b[39m]]:\n\u001b[0;32m 514\u001b[0m idx \u001b[38;5;241m=\u001b[39m (read(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m key[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m BINGET[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01melse\u001b[39;00m unpack(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.persistent_load\u001b[1;34m(saved_id)\u001b[0m\n\u001b[0;32m 1926\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1927\u001b[0m nbytes \u001b[38;5;241m=\u001b[39m numel \u001b[38;5;241m*\u001b[39m torch\u001b[38;5;241m.\u001b[39m_utils\u001b[38;5;241m.\u001b[39m_element_size(dtype)\n\u001b[1;32m-> 1928\u001b[0m typed_storage \u001b[38;5;241m=\u001b[39m \u001b[43mload_tensor\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1929\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnbytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_maybe_decode_ascii\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1930\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1932\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m typed_storage\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\torch\\serialization.py:1900\u001b[0m, in \u001b[0;36m_load..load_tensor\u001b[1;34m(dtype, numel, key, location)\u001b[0m\n\u001b[0;32m 1895\u001b[0m storage\u001b[38;5;241m.\u001b[39mbyteswap(dtype)\n\u001b[0;32m 1897\u001b[0m \u001b[38;5;66;03m# TODO: Once we decide to break serialization FC, we can\u001b[39;00m\n\u001b[0;32m 1898\u001b[0m \u001b[38;5;66;03m# stop wrapping with TypedStorage\u001b[39;00m\n\u001b[0;32m 1899\u001b[0m typed_storage \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mstorage\u001b[38;5;241m.\u001b[39mTypedStorage(\n\u001b[1;32m-> 1900\u001b[0m wrap_storage\u001b[38;5;241m=\u001b[39m\u001b[43mrestore_location\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstorage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[0;32m 1901\u001b[0m dtype\u001b[38;5;241m=\u001b[39mdtype,\n\u001b[0;32m 1902\u001b[0m _internal\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 1903\u001b[0m )\n\u001b[0;32m 1905\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m typed_storage\u001b[38;5;241m.\u001b[39m_data_ptr() \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 1906\u001b[0m loaded_storages[key] \u001b[38;5;241m=\u001b[39m typed_storage\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\torch\\serialization.py:693\u001b[0m, in \u001b[0;36mdefault_restore_location\u001b[1;34m(storage, location)\u001b[0m\n\u001b[0;32m 673\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 674\u001b[0m \u001b[38;5;124;03mRestores `storage` using a deserializer function registered for the `location`.\u001b[39;00m\n\u001b[0;32m 675\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 690\u001b[0m \u001b[38;5;124;03m all matching ones return `None`.\u001b[39;00m\n\u001b[0;32m 691\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 692\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _, _, fn \u001b[38;5;129;01min\u001b[39;00m _package_registry:\n\u001b[1;32m--> 693\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstorage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 694\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 695\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\torch\\serialization.py:631\u001b[0m, in \u001b[0;36m_deserialize\u001b[1;34m(backend_name, obj, location)\u001b[0m\n\u001b[0;32m 629\u001b[0m backend_name \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_get_privateuse1_backend_name()\n\u001b[0;32m 630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m location\u001b[38;5;241m.\u001b[39mstartswith(backend_name):\n\u001b[1;32m--> 631\u001b[0m device \u001b[38;5;241m=\u001b[39m \u001b[43m_validate_device\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbackend_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 632\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39mto(device\u001b[38;5;241m=\u001b[39mdevice)\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\torch\\serialization.py:600\u001b[0m, in \u001b[0;36m_validate_device\u001b[1;34m(location, backend_name)\u001b[0m\n\u001b[0;32m 598\u001b[0m device_index \u001b[38;5;241m=\u001b[39m device\u001b[38;5;241m.\u001b[39mindex \u001b[38;5;28;01mif\u001b[39;00m device\u001b[38;5;241m.\u001b[39mindex \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 599\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(device_module, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis_available\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m device_module\u001b[38;5;241m.\u001b[39mis_available():\n\u001b[1;32m--> 600\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m 601\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAttempting to deserialize object on a \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbackend_name\u001b[38;5;241m.\u001b[39mupper()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 602\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdevice but torch.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbackend_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.is_available() is False. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 603\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIf you are running on a CPU-only machine, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 604\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mplease use torch.load with map_location=torch.device(\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 605\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto map your storages to the CPU.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 606\u001b[0m )\n\u001b[0;32m 607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(device_module, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdevice_count\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m 608\u001b[0m device_count \u001b[38;5;241m=\u001b[39m device_module\u001b[38;5;241m.\u001b[39mdevice_count()\n", + "\u001b[1;31mRuntimeError\u001b[0m: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU." + ] + } + ], "source": [ "model, conf = get_model_from_run(run_path)\n", "\n", @@ -922,7 +1061,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "1d9da7c3", "metadata": {}, "outputs": [], @@ -934,7 +1073,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "cb69ddda", "metadata": {}, "outputs": [], @@ -945,7 +1084,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "2aa97fa5", "metadata": {}, "outputs": [ @@ -990,7 +1129,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "a58e04e4", "metadata": {}, "outputs": [], @@ -1003,7 +1142,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "7ea71ba5", "metadata": {}, "outputs": [ @@ -1048,7 +1187,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "395fe757", "metadata": {}, "outputs": [ @@ -1188,6 +1327,88 @@ "\n", "plot_function_visualizations()\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35e8f229", + "metadata": {}, + "outputs": [], + "source": [ + "metrics = eval_model(\n", + " model,\n", + " task_name=\"dense_test_killer\", \n", + " n_dims=20,\n", + " n_points=10,\n", + " prompting_strategy=\"standard\",\n", + " batch_size=64,\n", + " data_sampler_kwargs={},\n", + " task_sampler_kwargs={} \n", + ")\n", + "for model_name, metric in metrics.items():\n", + " plt.plot(np.mean(metric, axis=0), label=model_name)\n", + "\n", + "plt.xlabel(\"# in-context examples\")\n", + "plt.ylabel(\"squared error\")\n", + "plt.title(\"Dense OOD (Anti-Sparsity Trap)\")\n", + "plt.legend()\n", + "plt.show()\n", + "fig, ax = basic_plot(metrics, models=[...])\n", + "ax.set_title(\"Dense OOD (Anti-Sparsity Trap)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54723747", + "metadata": {}, + "outputs": [], + "source": [ + "metrics = eval_model(\n", + " model,\n", + " task_name=\"scale_mismatch_task\",\n", + " n_dims=20,\n", + " n_points=10,\n", + " prompting_strategy=\"standard\",\n", + " batch_size=64,\n", + " data_sampler_kwargs={},\n", + " task_sampler_kwargs={\"train_mode\": False} # OOD: w ~ N(100, 1)\n", + ")\n", + "for model_name, metric in metrics.items():\n", + " plt.plot(np.mean(metric, axis=0), label=model_name)\n", + "plt.xlabel(\"# in-context examples\")\n", + "plt.ylabel(\"squared error\")\n", + "plt.title(\"Scale Mismatch OOD\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d66e427", + "metadata": {}, + "outputs": [], + "source": [ + "metrics = eval_model(\n", + " model,\n", + " task_name=\"mixed_task_killer\",\n", + " n_dims=20,\n", + " n_points=10,\n", + " prompting_strategy=\"standard\",\n", + " batch_size=64,\n", + " data_sampler_kwargs={},\n", + " task_sampler_kwargs={}\n", + ")\n", + "for model_name, metric in metrics.items():\n", + " plt.plot(np.mean(metric, axis=0), label=model_name)\n", + "plt.xlabel(\"# in-context examples\")\n", + "plt.ylabel(\"squared error\")\n", + "plt.title(\"Mixed Task OOD (Task Confusion)\")\n", + "plt.legend()\n", + "plt.show()" + ] } ], "metadata": { diff --git a/src/eval.py b/src/eval.py index d377a18b..e7ae5a2a 100644 --- a/src/eval.py +++ b/src/eval.py @@ -318,6 +318,52 @@ def build_evals(conf): "task_name": "noisy_linear_regression", } + # Case 1: Scale Mismatch OOD test + if conf.training.task == "scale_mismatch_killer": + evaluation_kwargs = {} + # Standard eval (in-distribution) + evaluation_kwargs["standard"] = base_kwargs.copy() + # OOD eval: w ~ N(100, 1) + ood_kwargs = base_kwargs.copy() + ood_kwargs["task_sampler_kwargs"] = dict(base_kwargs.get("task_sampler_kwargs", {})) + ood_kwargs["task_sampler_kwargs"]["train_mode"] = False + evaluation_kwargs["ood_scale_mismatch"] = ood_kwargs + return evaluation_kwargs + + # Case 2: Over-Skeptic OOD test + if conf.training.task == "noisy_linear_regression" and conf.training.task_kwargs.get("noise_std", 0) >= 20: + evaluation_kwargs = {} + # Standard eval (noisy) + evaluation_kwargs["standard"] = base_kwargs.copy() + # OOD eval: linear regression, no noise + ood_kwargs = base_kwargs.copy() + ood_kwargs["task_name"] = "linear_regression" + ood_kwargs["task_sampler_kwargs"] = {} + evaluation_kwargs["ood_clean"] = ood_kwargs + return evaluation_kwargs + # Case 3: Anti-Sparsity Trap (Train sparse, eval densee) + if conf.training.task == "sparse_linear_regression" and conf.training.task_kwargs.get("sparsity", 0) <= 2: + evaluation_kwargs = {} + evaluation_kwargs = {} + # Standard eval (mixed) + evaluation_kwargs["standard"] = base_kwargs.copy() + # OOD eval: linear regression only + ood_kwargs = base_kwargs.copy() + ood_kwargs["task_name"] = "linear_regression" + ood_kwargs["task_sampler_kwargs"] = {} + evaluation_kwargs["ood_linear"] = ood_kwargs + return evaluation_kwargs + # Case 4: Task Confusion (Train mixed, eval linear) + if conf.training.task == "mixture_tasks_killer": + evaluation_kwargs = {} + # Standard eval (mixed) + evaluation_kwargs["standard"] = base_kwargs.copy() + # OOD eval: linear regression only + ood_kwargs = base_kwargs.copy() + ood_kwargs["task_name"] = "linear_regression" + ood_kwargs["task_sampler_kwargs"] = {} + evaluation_kwargs["ood_linear"] = ood_kwargs + return evaluation_kwargs for name, kwargs in evaluation_kwargs.items(): # allow kwargs to override base_kwargs values evaluation_kwargs[name] = base_kwargs.copy() diff --git a/src/plot_utils.py b/src/plot_utils.py index e90791a6..2364d8de 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -140,7 +140,8 @@ def basic_plot(metrics, models=None, trivial=1.0): ax.set_xlabel("in-context examples") ax.set_ylabel("squared error") ax.set_xlim(-1, len(low) + 0.1) - ax.set_ylim(-0.1, 5) + ax.set_ylim(-0.1, 3) + # legend = ax.legend(loc="upper left", bbox_to_anchor=(1, 1)) diff --git a/src/samplers.py b/src/samplers.py index bc43598d..4efb4355 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -26,6 +26,10 @@ def get_data_sampler(data_name, n_dims, **kwargs): "laplace": LaplaceSampler, "gamma": GammaSampler, "beta": BetaSampler, + "tstudent": TStudentSampler, + "poisson": PoissonSampler, + "rayleigh": RayleighSampler, + "cauchy": CauchySampler, } if data_name in names_to_classes: sampler_cls = names_to_classes[data_name] @@ -187,6 +191,77 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): beta_dist = torch.distributions.Beta(concentration1=self.alpha, concentration0=self.beta) xs_b = _sample_distribution(beta_dist, b_size, (n_points, self.n_dims), seeds) + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b + +class TStudentSampler(DataSampler): + def __init__(self, n_dims, bias=None, scale=None, df=3.0): + super().__init__(n_dims) + self.df = float(df) + self.bias = bias + self.scale = scale + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + t_dist = torch.distributions.StudentT(df=self.df) + xs_b = _sample_distribution(t_dist, b_size, (n_points, self.n_dims), seeds) + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b +class PoissonSampler(DataSampler): + def __init__(self, n_dims, bias=None, scale=None, rate=1.0): + super().__init__(n_dims) + self.rate = float(rate) + self.bias = bias + self.scale = scale + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + poisson_dist = torch.distributions.Poisson(rate=self.rate) + xs_b = _sample_distribution(poisson_dist, b_size, (n_points, self.n_dims), seeds) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b +class RayleighSampler(DataSampler): + def __init__(self, n_dims, bias=None, scale=None, scale_param=1.0): + super().__init__(n_dims) + self.bias = bias + self.scale = scale + self.scale_param = float(scale_param) + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + rayleigh_dist = torch.distributions.Rayleigh(scale=self.scale_param) + xs_b = _sample_distribution(rayleigh_dist, b_size, (n_points, self.n_dims), seeds) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b +class CauchySampler(DataSampler): + def __init__(self, n_dims, bias=None, scale=None, loc=0.0, scale_param=1.0): + super().__init__(n_dims) + self.bias = bias + self.scale = scale + self.loc = float(loc) + self.scale_param = float(scale_param) + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + cauchy_dist = torch.distributions.Cauchy(loc=self.loc, scale=self.scale_param) + xs_b = _sample_distribution(cauchy_dist, b_size, (n_points, self.n_dims), seeds) + if self.scale is not None: xs_b = xs_b @ self.scale if self.bias is not None: diff --git a/src/schema.py b/src/schema.py index ed0351d5..2111423b 100644 --- a/src/schema.py +++ b/src/schema.py @@ -60,7 +60,7 @@ "task_kwargs": merge(tdict, required), "num_tasks": merge(tinteger, nullable, default(None)), "num_training_examples": merge(tinteger, nullable, default(None)), - "data": merge(tstring, allowed(["gaussian","ar1","vr1","ar2",'vr2',"nonstation", "sparse_gaussian", "gamma", "beta", "exponential", "laplace", "uniform"])), + "data": merge(tstring, allowed(["gaussian","ar1","vr1","ar2",'vr2',"nonstation", "sparse_gaussian", "gamma", "beta", "exponential", "laplace", "uniform", "poisson", "tstudent", "rayleigh", "cauchy"])), "data_kwargs": merge(tdict, default({})), # Thêm dòng này "batch_size": merge(tinteger, default(64)), "learning_rate": merge(tfloat, default(3e-4)), diff --git a/src/tasks.py b/src/tasks.py index 30db8a62..fad56a46 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -982,3 +982,64 @@ def get_metric(): @staticmethod def get_training_metric(): return mean_squared_error + +class ScaleMismatchTask(Task): + def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, train_mode=True): + super().__init__(n_dims, batch_size, pool_dict, seeds) + if train_mode: + self.w_b = torch.rand(self.b_size, self.n_dims, 1) * 2 - 1 + else: + self.w_b = torch.randn(self.b_size, self.n_dims, 1) + 100 + + def evaluate(self, xs_b): + w_b = self.w_b.to(xs_b.device) + ys_b = (xs_b @ w_b)[:, :, 0] + return ys_b + + @staticmethod + def get_metric(): + return squared_error + + @staticmethod + def get_training_metric(): + return mean_squared_error + +class DenseTestKiller(Task): + def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None): + # w dense: all dimensions = 0.5 + self.w_b = torch.ones(batch_size, n_dims, 1) * 0.5 + + def evaluate(self, xs_b): + w_b = self.w_b.to(xs_b.device) + ys_b = (xs_b @ w_b)[:, :, 0] + return ys_b + + @staticmethod + def get_metric(): + return squared_error + + @staticmethod + def get_training_metric(): + return mean_squared_error + +class MixedTaskKiller(Task): + def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None): + super().__init__(n_dims, batch_size, pool_dict, seeds) + self.w_b = torch.randn(batch_size, n_dims, 1) + self.is_sin = torch.randint(0, 2, (batch_size,)) + + def evaluate(self, xs_b): + w_b = self.w_b.to(xs_b.device) + ys = xs_b @ w_b[:, :, 0] + for i in range(self.b_size): + if self.is_sin[i]: + ys[i] = torch.sin(ys[i]) + return us + + @staticmethod + def get_metric(): + return squared_error + + @staticmethod + def get_training_metric(): + return mean_squared_error From e8ad91b3dd46d6be65586af41035c35f54c4cfe2 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Sat, 29 Nov 2025 09:36:55 +0700 Subject: [PATCH 70/88] add sampler full --- src/conf/lr_wx.yaml | 0 src/samplers.py | 7 +++++-- 2 files changed, 5 insertions(+), 2 deletions(-) create mode 100644 src/conf/lr_wx.yaml diff --git a/src/conf/lr_wx.yaml b/src/conf/lr_wx.yaml new file mode 100644 index 00000000..e69de29b diff --git a/src/samplers.py b/src/samplers.py index 4efb4355..d2d78a2b 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -205,11 +205,14 @@ def __init__(self, n_dims, bias=None, scale=None, df=3.0): self.df = float(df) self.bias = bias self.scale = scale - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device=None): t_dist = torch.distributions.StudentT(df=self.df) xs_b = _sample_distribution(t_dist, b_size, (n_points, self.n_dims), seeds) + if device is not None: + xs_b = xs_b.to(device) if self.scale is not None: - xs_b = xs_b @ self.scale + xs_b = xs_b * self.scale if self.bias is not None: xs_b += self.bias if n_dims_truncated is not None: From e978a52a822211c3c96f090c3924641b70ab8826 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Sat, 29 Nov 2025 09:39:04 +0700 Subject: [PATCH 71/88] add sampler full --- src/samplers.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/src/samplers.py b/src/samplers.py index d2d78a2b..909d00ed 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -206,11 +206,17 @@ def __init__(self, n_dims, bias=None, scale=None, df=3.0): self.bias = bias self.scale = scale - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device=None): + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): t_dist = torch.distributions.StudentT(df=self.df) - xs_b = _sample_distribution(t_dist, b_size, (n_points, self.n_dims), seeds) - if device is not None: - xs_b = xs_b.to(device) + if seeds is None: + xs_b = t_dist.sample((b_size, n_points, self.n_dims)).to(device) + else: + xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) + generator = torch.Generator() + assert len(seeds) == b_size + for i, seed in enumerate(seeds): + generator.manual_seed(seed) + xs_b[i] = t_dist.sample((n_points, self.n_dims), generator=generator).to(device) if self.scale is not None: xs_b = xs_b * self.scale if self.bias is not None: From 8e9011fb41e9d61789565f833cf7b624d7446f7f Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Sat, 29 Nov 2025 09:40:46 +0700 Subject: [PATCH 72/88] add sampler full --- src/tasks.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/tasks.py b/src/tasks.py index fad56a46..7477245b 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -317,6 +317,8 @@ def get_metric(): def get_training_metric(): return mean_squared_error + + class SparseLinearRegression(LinearRegression): def __init__( self, @@ -459,7 +461,7 @@ def sample_noise(self, shape): def evaluate(self, xs_b): ys_b = super().evaluate(xs_b) noise = self.sample_noise(ys_b.shape) - ys_b_noisy = ys_b + noise + ys_b_noisy = ys_b + noise.to(ys_b.device) if self.renormalize_ys: ys_b_noisy = ys_b_noisy * math.sqrt(self.n_dims) / ys_b_noisy.std() From b508b0ccf9b5c12678a83c0d207d26cf9c4677f6 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Sat, 29 Nov 2025 10:04:16 +0700 Subject: [PATCH 73/88] add sampler full --- src/conf/base.yaml | 2 +- src/conf/lr_wx.yaml | 31 +++++++++++++++++++++++++++++++ src/conf/template.yaml | 18 +++++++++--------- src/train.py | 7 +++++-- 4 files changed, 46 insertions(+), 12 deletions(-) diff --git a/src/conf/base.yaml b/src/conf/base.yaml index 1495bac9..7185a964 100644 --- a/src/conf/base.yaml +++ b/src/conf/base.yaml @@ -9,7 +9,7 @@ model: training: data: gaussian task_kwargs: {} - batch_size: 64 + batch_size: 256 learning_rate: 0.0001 save_every_steps: 1000 keep_every_steps: 100000 diff --git a/src/conf/lr_wx.yaml b/src/conf/lr_wx.yaml index e69de29b..c1269ae7 100644 --- a/src/conf/lr_wx.yaml +++ b/src/conf/lr_wx.yaml @@ -0,0 +1,31 @@ +model: + family: gpt2 + n_dims: 20 + n_embd: 256 + n_head: 12 + n_layer: 8 + n_positions: 101 + +training: + batch_size: 64 + curriculum: + dims: + start: 5 + end: 20 + inc: 1 + interval: 2000 + points: + start: 11 + end: 41 + inc: 2 + interval: 2000 + learning_rate: 0.0001 + train_steps: 500001 + data: tstudent # ví dụ: gaussian, uniform, laplace, tstudent, cauchy, poisson, rayleigh + task: linear_regression + task_kwargs: + w_distribution: ${w_distribution} # ví dụ: gaussian, uniform, laplace, tstudent, cauchy, poisson, rayleigh + +wandb: + project: in-context-training + name: linear_regression_custom \ No newline at end of file diff --git a/src/conf/template.yaml b/src/conf/template.yaml index 3c121f4d..d7bd7519 100644 --- a/src/conf/template.yaml +++ b/src/conf/template.yaml @@ -5,13 +5,13 @@ inherit: model: family: gpt2 n_dims: 20 - n_embd: 128 + n_embd: 256 n_head: 8 - n_layer: 4 + n_layer: 12 n_positions: 101 training: - batch_size: 64 + batch_size: 256 curriculum: dims: start: 5 @@ -25,7 +25,7 @@ training: interval: 2000 # One of: gaussian, sparse_gaussian, ar1, vr1, ar2, vr2, nonstation - data: gaussian + data: tstudent # Data kwargs: # - When data == 'sparse_gaussian': you may set 'k' (number of non-zero coords). @@ -47,9 +47,9 @@ training: # - For other tasks: any 'sparsity' key will be ignored automatically. task_kwargs: { # sparsity: 5 # only when task: sparse_linear_regression - # noise_std: 2.0 # e.g., for noisy_linear_regression + noise_std: 2.0 # e.g., for noisy_linear_regression # renormalize_ys: false - noise_type: cauchy + noise_type: normal } learning_rate: 0.0001 @@ -58,14 +58,14 @@ training: num_training_examples: null resume_id: null save_every_steps: 100 - train_steps: 5001 + train_steps: 500001 -out_dir: +out_dir: /content/drive/MyDrive/models/lr_wx wandb: project: "in-context-training" entity: "hai-trinh220970-ho-chi-minh-city-university-of-technology" - name: "test" + name: "lr_wx" notes: "Training with laplace-distributed weights (non-uniform on hypersphere)" log_every_steps: 100 diff --git a/src/train.py b/src/train.py index 26e409e6..049073da 100644 --- a/src/train.py +++ b/src/train.py @@ -132,6 +132,8 @@ def train(model, args): num_training_examples = args.training.num_training_examples + scaler = torch.cuda.amp.GradScaler() # Mixed precision + for i in pbar: data_sampler_args = {} task_sampler_args = {} @@ -156,11 +158,12 @@ def train(model, args): ys = task.evaluate(xs) loss_func = task.get_training_metric() - loss, output = train_step(model, xs.cuda(), ys.cuda(), optimizer, loss_func) + with torch.cuda.amp.autocast(): + loss, output = train_step(model, xs, ys, optimizer, loss_func) point_wise_tags = list(range(curriculum.n_points)) point_wise_loss_func = task.get_metric() - point_wise_loss = point_wise_loss_func(output, ys.cuda()).mean(dim=0) + point_wise_loss = point_wise_loss_func(output, ys).mean(dim=0) baseline_loss = ( sum( From 2f874fb838d5e62887dfdddfa82d37cab8b6a0bf Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 3 Dec 2025 10:33:40 +0700 Subject: [PATCH 74/88] update noiselr --- src/conf/template.yaml | 5 +- src/eval.ipynb | 412 +++++++++++++++++++++++------------------ src/eval.py | 2 +- src/models.py | 15 +- src/plot_utils.py | 14 +- src/tasks.py | 63 ++++++- src/train.py | 2 +- 7 files changed, 312 insertions(+), 201 deletions(-) diff --git a/src/conf/template.yaml b/src/conf/template.yaml index d7bd7519..09d0ec3c 100644 --- a/src/conf/template.yaml +++ b/src/conf/template.yaml @@ -11,7 +11,7 @@ model: n_positions: 101 training: - batch_size: 256 + batch_size: 128 curriculum: dims: start: 5 @@ -50,6 +50,9 @@ training: noise_std: 2.0 # e.g., for noisy_linear_regression # renormalize_ys: false noise_type: normal + w_distribution: gaussian + w_kwargs: + scale: 1.0 } learning_rate: 0.0001 diff --git a/src/eval.ipynb b/src/eval.ipynb index 2baa5690..a07e6950 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "id": "0e8d018b", "metadata": { "scrolled": true @@ -74,7 +74,7 @@ " \n", " \n", " \n", - " 6\n", + " 7\n", " 1_beta_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -87,7 +87,7 @@ " 1_beta_noise_gaussian_data_experiment\n", " \n", " \n", - " 7\n", + " 8\n", " 1_exponential_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -100,7 +100,7 @@ " 1_exponential_noise_gaussian_data_experiment\n", " \n", " \n", - " 8\n", + " 9\n", " 1_poisson_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -113,7 +113,7 @@ " 1_poisson_noise_gaussian_data_experiment\n", " \n", " \n", - " 9\n", + " 10\n", " 1_t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -126,7 +126,7 @@ " 1_t_student_noise_gaussian_data_experiment\n", " \n", " \n", - " 10\n", + " 11\n", " 1_uniform_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -139,7 +139,7 @@ " 1_uniform_noise_gaussian_data_experiment\n", " \n", " \n", - " 5\n", + " 6\n", " 123e9cbd-1566-443d-9491-f23b6b9af0e2\n", " linear_regression\n", " Transformer\n", @@ -152,7 +152,7 @@ " 20_dims_uniform_error_gaussian_data\n", " \n", " \n", - " 13\n", + " 14\n", " 64d381ae-08d0-4bae-8e40-f1a68cfb2e97\n", " linear_regression\n", " Transformer\n", @@ -165,7 +165,7 @@ " 20_dims_uniform_error_gaussian_data_\n", " \n", " \n", - " 11\n", + " 12\n", " 3_laplace_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -178,7 +178,7 @@ " 3_laplace_noise_gaussian_data_experiment\n", " \n", " \n", - " 12\n", + " 13\n", " 3_tstudent_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -191,7 +191,7 @@ " 3_tstudent_noise_gaussian_data_experiment\n", " \n", " \n", - " 40\n", + " 41\n", " daa2cd45-f1c0-4a0c-9100-e171129624c9\n", " sparse_linear_regression\n", " Transformer\n", @@ -204,7 +204,7 @@ " 4_std_sparse_linear_regression\n", " \n", " \n", - " 17\n", + " 18\n", " beta_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -217,7 +217,7 @@ " beta_noise_ar1_data_experiment\n", " \n", " \n", - " 18\n", + " 19\n", " beta_noisy_linear_regression_40_100k\n", " linear_regression\n", " Transformer\n", @@ -230,7 +230,7 @@ " beta_noisy_linear_regression_40_100k\n", " \n", " \n", - " 42\n", + " 43\n", " case1_sparse_regression\n", " sparse_regression_killer\n", " Transformer\n", @@ -269,6 +269,19 @@ " case2_heavy_tail_t_student_1_1\n", " \n", " \n", + " 4\n", + " case2_heavy_tail_t_student_1_2\n", + " heavy_tail_noise_killer\n", + " Transformer\n", + " df=1.0_noise_scale=2.0_noise_type=t-student\n", + " -1\n", + " -1\n", + " 20\n", + " 12\n", + " 8\n", + " case2_heavy_tail_t_student_1_2\n", + " \n", + " \n", " 0\n", " bounded_support_killer\n", " bounded_support_killer\n", @@ -282,7 +295,7 @@ " case3_bounded_support\n", " \n", " \n", - " 36\n", + " 37\n", " case4_mixture_tasks\n", " mixture_tasks_killer\n", " Transformer\n", @@ -295,7 +308,7 @@ " case4_mixture_tasks\n", " \n", " \n", - " 37\n", + " 38\n", " case4_mixture_tasks_1_1\n", " mixture_tasks_killer\n", " Transformer\n", @@ -308,7 +321,7 @@ " case4_mixture_tasks_1_1\n", " \n", " \n", - " 43\n", + " 44\n", " case5_transfer_tradeoff\n", " transfer_tradeoff_task\n", " Transformer\n", @@ -321,7 +334,20 @@ " case5_transfer_tradeoff\n", " \n", " \n", - " 16\n", + " 45\n", + " case5_transfer_tradeoff_1_1\n", + " transfer_tradeoff_task\n", + " Transformer\n", + " mixture_std=2.0_prior_type=mixture_gaussian_sc...\n", + " -1\n", + " -1\n", + " 20\n", + " 12\n", + " 8\n", + " case5_transfer_tradeoff_1_1\n", + " \n", + " \n", + " 17\n", " aed365ed-51e2-4a72-8374-ae954b37be14\n", " linear_regression\n", " Transformer\n", @@ -347,7 +373,7 @@ " decision_tree_pretrained\n", " \n", " \n", - " 19\n", + " 20\n", " exponential_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -360,7 +386,7 @@ " exponential_noise_gaussian_data_experiment\n", " \n", " \n", - " 20\n", + " 21\n", " exponential_w\n", " linear_regression\n", " Transformer\n", @@ -373,7 +399,7 @@ " exponential_w\n", " \n", " \n", - " 21\n", + " 22\n", " exponential_weighted_experiment_100k\n", " linear_regression\n", " Transformer\n", @@ -386,7 +412,7 @@ " exponential_weighted_experiment_100k\n", " \n", " \n", - " 22\n", + " 23\n", " exponential_weighted_experiment_150k\n", " linear_regression\n", " Transformer\n", @@ -399,7 +425,7 @@ " exponential_weighted_experiment_150k\n", " \n", " \n", - " 23\n", + " 24\n", " exponential_weighted_regression\n", " linear_regression\n", " Transformer\n", @@ -412,7 +438,7 @@ " exponential_weights_experiment\n", " \n", " \n", - " 24\n", + " 25\n", " laplace_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -425,7 +451,7 @@ " laplace_noise_gaussian_data_experiment\n", " \n", " \n", - " 25\n", + " 26\n", " laplace_w\n", " linear_regression\n", " Transformer\n", @@ -438,7 +464,7 @@ " laplace_w\n", " \n", " \n", - " 15\n", + " 16\n", " a2fcec3c-8ce5-49bf-a8bc-08136b31ec36\n", " linear_regression\n", " Transformer\n", @@ -451,7 +477,7 @@ " laplace_weights_experiment\n", " \n", " \n", - " 26\n", + " 27\n", " pretrained\n", " linear_regression\n", " Transformer\n", @@ -464,7 +490,7 @@ " linear_regression_pretrained\n", " \n", " \n", - " 27\n", + " 28\n", " rayleigh_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -477,7 +503,7 @@ " rayleigh_noise_gaussian_data_experiment\n", " \n", " \n", - " 38\n", + " 39\n", " pretrained\n", " relu_2nn_regression\n", " Transformer\n", @@ -490,7 +516,7 @@ " relu_2nn_regression_pretrained\n", " \n", " \n", - " 14\n", + " 15\n", " 82e728b0-a061-448e-8d7a-f3c79c0c74e5\n", " linear_regression\n", " Transformer\n", @@ -503,7 +529,7 @@ " rigde_normal_linear_regression_gaussian\n", " \n", " \n", - " 39\n", + " 40\n", " 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615\n", " sparse_linear_regression\n", " Transformer\n", @@ -516,7 +542,7 @@ " sparse\n", " \n", " \n", - " 4\n", + " 5\n", " 03de46b6-429a-4151-92e6-3588231c6cad\n", " linear_regression\n", " Transformer\n", @@ -529,7 +555,7 @@ " sparse_data_experiment\n", " \n", " \n", - " 41\n", + " 42\n", " pretrained\n", " sparse_linear_regression\n", " Transformer\n", @@ -542,7 +568,7 @@ " sparse_regression_pretrained\n", " \n", " \n", - " 30\n", + " 31\n", " t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -555,7 +581,7 @@ " t_student_noise_gaussian_data_experiment\n", " \n", " \n", - " 28\n", + " 29\n", " sparse_gaussian\n", " linear_regression\n", " Transformer\n", @@ -568,7 +594,7 @@ " task_sparse_data\n", " \n", " \n", - " 29\n", + " 30\n", " test_cauchy\n", " linear_regression\n", " Transformer\n", @@ -581,7 +607,7 @@ " test\n", " \n", " \n", - " 32\n", + " 33\n", " uniform_hypersphere_regression\n", " linear_regression\n", " Transformer\n", @@ -594,7 +620,7 @@ " uniform_hypersphere_experiment\n", " \n", " \n", - " 31\n", + " 32\n", " uniform_hypersphere_experiment_standard\n", " linear_regression\n", " Transformer\n", @@ -607,7 +633,7 @@ " uniform_hypersphere_experiment_standard\n", " \n", " \n", - " 33\n", + " 34\n", " uniform_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -620,7 +646,7 @@ " uniform_noise_ar1_data_experiment\n", " \n", " \n", - " 34\n", + " 35\n", " uniform_noise_gaussian_data_experiment_\n", " linear_regression\n", " Transformer\n", @@ -633,7 +659,7 @@ " uniform_noise_gaussian_data_experiment\n", " \n", " \n", - " 35\n", + " 36\n", " w_laplace_x_exponential_noise_poisson\n", " linear_regression\n", " Transformer\n", @@ -651,191 +677,199 @@ ], "text/plain": [ " run_id task \\\n", - "6 1_beta_noise_gaussian_data_experiment linear_regression \n", - "7 1_exponential_noise_gaussian_data_experiment linear_regression \n", - "8 1_poisson_noise_gaussian_data_experiment linear_regression \n", - "9 1_t_student_noise_gaussian_data_experiment linear_regression \n", - "10 1_uniform_noise_gaussian_data_experiment linear_regression \n", - "5 123e9cbd-1566-443d-9491-f23b6b9af0e2 linear_regression \n", - "13 64d381ae-08d0-4bae-8e40-f1a68cfb2e97 linear_regression \n", - "11 3_laplace_noise_gaussian_data_experiment linear_regression \n", - "12 3_tstudent_noise_gaussian_data_experiment linear_regression \n", - "40 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", - "17 beta_noise_ar1_data_experiment linear_regression \n", - "18 beta_noisy_linear_regression_40_100k linear_regression \n", - "42 case1_sparse_regression sparse_regression_killer \n", + "7 1_beta_noise_gaussian_data_experiment linear_regression \n", + "8 1_exponential_noise_gaussian_data_experiment linear_regression \n", + "9 1_poisson_noise_gaussian_data_experiment linear_regression \n", + "10 1_t_student_noise_gaussian_data_experiment linear_regression \n", + "11 1_uniform_noise_gaussian_data_experiment linear_regression \n", + "6 123e9cbd-1566-443d-9491-f23b6b9af0e2 linear_regression \n", + "14 64d381ae-08d0-4bae-8e40-f1a68cfb2e97 linear_regression \n", + "12 3_laplace_noise_gaussian_data_experiment linear_regression \n", + "13 3_tstudent_noise_gaussian_data_experiment linear_regression \n", + "41 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", + "18 beta_noise_ar1_data_experiment linear_regression \n", + "19 beta_noisy_linear_regression_40_100k linear_regression \n", + "43 case1_sparse_regression sparse_regression_killer \n", "2 case2_heavy_tail_t_student heavy_tail_noise_killer \n", "3 case2_heavy_tail_t_student_1_1 heavy_tail_noise_killer \n", + "4 case2_heavy_tail_t_student_1_2 heavy_tail_noise_killer \n", "0 bounded_support_killer bounded_support_killer \n", - "36 case4_mixture_tasks mixture_tasks_killer \n", - "37 case4_mixture_tasks_1_1 mixture_tasks_killer \n", - "43 case5_transfer_tradeoff transfer_tradeoff_task \n", - "16 aed365ed-51e2-4a72-8374-ae954b37be14 linear_regression \n", + "37 case4_mixture_tasks mixture_tasks_killer \n", + "38 case4_mixture_tasks_1_1 mixture_tasks_killer \n", + "44 case5_transfer_tradeoff transfer_tradeoff_task \n", + "45 case5_transfer_tradeoff_1_1 transfer_tradeoff_task \n", + "17 aed365ed-51e2-4a72-8374-ae954b37be14 linear_regression \n", "1 pretrained decision_tree \n", - "19 exponential_noise_gaussian_data_experiment linear_regression \n", - "20 exponential_w linear_regression \n", - "21 exponential_weighted_experiment_100k linear_regression \n", - "22 exponential_weighted_experiment_150k linear_regression \n", - "23 exponential_weighted_regression linear_regression \n", - "24 laplace_noise_gaussian_data_experiment linear_regression \n", - "25 laplace_w linear_regression \n", - "15 a2fcec3c-8ce5-49bf-a8bc-08136b31ec36 linear_regression \n", - "26 pretrained linear_regression \n", - "27 rayleigh_noise_gaussian_data_experiment linear_regression \n", - "38 pretrained relu_2nn_regression \n", - "14 82e728b0-a061-448e-8d7a-f3c79c0c74e5 linear_regression \n", - "39 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", - "4 03de46b6-429a-4151-92e6-3588231c6cad linear_regression \n", - "41 pretrained sparse_linear_regression \n", - "30 t_student_noise_gaussian_data_experiment linear_regression \n", - "28 sparse_gaussian linear_regression \n", - "29 test_cauchy linear_regression \n", - "32 uniform_hypersphere_regression linear_regression \n", - "31 uniform_hypersphere_experiment_standard linear_regression \n", - "33 uniform_noise_ar1_data_experiment linear_regression \n", - "34 uniform_noise_gaussian_data_experiment_ linear_regression \n", - "35 w_laplace_x_exponential_noise_poisson linear_regression \n", + "20 exponential_noise_gaussian_data_experiment linear_regression \n", + "21 exponential_w linear_regression \n", + "22 exponential_weighted_experiment_100k linear_regression \n", + "23 exponential_weighted_experiment_150k linear_regression \n", + "24 exponential_weighted_regression linear_regression \n", + "25 laplace_noise_gaussian_data_experiment linear_regression \n", + "26 laplace_w linear_regression \n", + "16 a2fcec3c-8ce5-49bf-a8bc-08136b31ec36 linear_regression \n", + "27 pretrained linear_regression \n", + "28 rayleigh_noise_gaussian_data_experiment linear_regression \n", + "39 pretrained relu_2nn_regression \n", + "15 82e728b0-a061-448e-8d7a-f3c79c0c74e5 linear_regression \n", + "40 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", + "5 03de46b6-429a-4151-92e6-3588231c6cad linear_regression \n", + "42 pretrained sparse_linear_regression \n", + "31 t_student_noise_gaussian_data_experiment linear_regression \n", + "29 sparse_gaussian linear_regression \n", + "30 test_cauchy linear_regression \n", + "33 uniform_hypersphere_regression linear_regression \n", + "32 uniform_hypersphere_experiment_standard linear_regression \n", + "34 uniform_noise_ar1_data_experiment linear_regression \n", + "35 uniform_noise_gaussian_data_experiment_ linear_regression \n", + "36 w_laplace_x_exponential_noise_poisson linear_regression \n", "\n", " model kwargs num_tasks \\\n", - "6 Transformer -1 \n", "7 Transformer -1 \n", "8 Transformer -1 \n", "9 Transformer -1 \n", "10 Transformer -1 \n", - "5 Transformer -1 \n", - "13 Transformer -1 \n", "11 Transformer -1 \n", + "6 Transformer -1 \n", + "14 Transformer -1 \n", "12 Transformer -1 \n", - "40 Transformer sparsity=5 -1 \n", - "17 Transformer -1 \n", - "18 Transformer noise_type=beta -1 \n", - "42 Transformer k_sparse=2_scale=1.0 -1 \n", + "13 Transformer -1 \n", + "41 Transformer sparsity=5 -1 \n", + "18 Transformer -1 \n", + "19 Transformer noise_type=beta -1 \n", + "43 Transformer k_sparse=2_scale=1.0 -1 \n", "2 Transformer df=3.0_noise_scale=0.5_noise_type=t-student -1 \n", "3 Transformer df=3.0_noise_scale=0.5_noise_type=t-student -1 \n", + "4 Transformer df=1.0_noise_scale=2.0_noise_type=t-student -1 \n", "0 Transformer rate=1.0_scale=1.0 -1 \n", - "36 Transformer scale=1.0 -1 \n", "37 Transformer scale=1.0 -1 \n", - "43 Transformer mixture_std=2.0_prior_type=mixture_gaussian_sc... -1 \n", - "16 Transformer k=5_sparsity=3 -1 \n", + "38 Transformer scale=1.0 -1 \n", + "44 Transformer mixture_std=2.0_prior_type=mixture_gaussian_sc... -1 \n", + "45 Transformer mixture_std=2.0_prior_type=mixture_gaussian_sc... -1 \n", + "17 Transformer k=5_sparsity=3 -1 \n", "1 Transformer depth=4 -1 \n", - "19 Transformer -1 \n", - "20 Transformer rate=1.0_scale=1.0 -1 \n", + "20 Transformer -1 \n", "21 Transformer rate=1.0_scale=1.0 -1 \n", "22 Transformer rate=1.0_scale=1.0 -1 \n", "23 Transformer rate=1.0_scale=1.0 -1 \n", - "24 Transformer -1 \n", - "25 Transformer scale=1.0_weight_scale=1.0 -1 \n", - "15 Transformer scale=1.0_weight_scale=1.0 -1 \n", - "26 Transformer -1 \n", + "24 Transformer rate=1.0_scale=1.0 -1 \n", + "25 Transformer -1 \n", + "26 Transformer scale=1.0_weight_scale=1.0 -1 \n", + "16 Transformer scale=1.0_weight_scale=1.0 -1 \n", "27 Transformer -1 \n", - "38 Transformer hidden_layer_size=100 -1 \n", - "14 Transformer sparsity=5 -1 \n", - "39 Transformer -1 \n", - "4 Transformer -1 \n", - "41 Transformer sparsity=3 -1 \n", - "30 Transformer -1 \n", "28 Transformer -1 \n", - "29 Transformer noise_type=cauchy -1 \n", + "39 Transformer hidden_layer_size=100 -1 \n", + "15 Transformer sparsity=5 -1 \n", + "40 Transformer -1 \n", + "5 Transformer -1 \n", + "42 Transformer sparsity=3 -1 \n", + "31 Transformer -1 \n", + "29 Transformer -1 \n", + "30 Transformer noise_type=cauchy -1 \n", + "33 Transformer normalize=True_scale=1.0 -1 \n", "32 Transformer normalize=True_scale=1.0 -1 \n", - "31 Transformer normalize=True_scale=1.0 -1 \n", - "33 Transformer -1 \n", "34 Transformer -1 \n", "35 Transformer -1 \n", + "36 Transformer -1 \n", "\n", " num_examples n_dims n_layer n_head \\\n", - "6 -1 5 4 8 \n", "7 -1 5 4 8 \n", "8 -1 5 4 8 \n", "9 -1 5 4 8 \n", "10 -1 5 4 8 \n", - "5 -1 20 4 8 \n", - "13 -1 20 4 8 \n", "11 -1 5 4 8 \n", + "6 -1 20 4 8 \n", + "14 -1 20 4 8 \n", "12 -1 5 4 8 \n", - "40 -1 15 4 8 \n", - "17 -1 5 4 8 \n", - "18 -1 20 4 8 \n", - "42 -1 20 4 8 \n", + "13 -1 5 4 8 \n", + "41 -1 15 4 8 \n", + "18 -1 5 4 8 \n", + "19 -1 20 4 8 \n", + "43 -1 20 4 8 \n", "2 -1 20 4 8 \n", "3 -1 20 12 8 \n", + "4 -1 20 12 8 \n", "0 -1 20 4 8 \n", - "36 -1 20 4 8 \n", - "37 -1 20 12 8 \n", - "43 -1 20 4 8 \n", - "16 -1 15 4 8 \n", + "37 -1 20 4 8 \n", + "38 -1 20 12 8 \n", + "44 -1 20 4 8 \n", + "45 -1 20 12 8 \n", + "17 -1 15 4 8 \n", "1 -1 20 12 8 \n", - "19 -1 5 4 8 \n", - "20 -1 20 4 8 \n", + "20 -1 5 4 8 \n", "21 -1 20 4 8 \n", "22 -1 20 4 8 \n", "23 -1 20 4 8 \n", - "24 -1 5 4 8 \n", - "25 -1 20 4 8 \n", - "15 -1 20 4 8 \n", - "26 -1 20 12 8 \n", - "27 -1 5 4 8 \n", - "38 -1 20 12 8 \n", - "14 -1 15 4 8 \n", - "39 -1 5 4 8 \n", - "4 -1 20 4 8 \n", - "41 -1 20 12 8 \n", - "30 -1 5 4 8 \n", - "28 -1 20 4 8 \n", + "24 -1 20 4 8 \n", + "25 -1 5 4 8 \n", + "26 -1 20 4 8 \n", + "16 -1 20 4 8 \n", + "27 -1 20 12 8 \n", + "28 -1 5 4 8 \n", + "39 -1 20 12 8 \n", + "15 -1 15 4 8 \n", + "40 -1 5 4 8 \n", + "5 -1 20 4 8 \n", + "42 -1 20 12 8 \n", + "31 -1 5 4 8 \n", "29 -1 20 4 8 \n", + "30 -1 20 4 8 \n", + "33 -1 20 4 8 \n", "32 -1 20 4 8 \n", - "31 -1 20 4 8 \n", - "33 -1 5 4 8 \n", "34 -1 5 4 8 \n", - "35 -1 20 4 8 \n", + "35 -1 5 4 8 \n", + "36 -1 20 4 8 \n", "\n", " run_name \n", - "6 1_beta_noise_gaussian_data_experiment \n", - "7 1_exponential_noise_gaussian_data_experiment \n", - "8 1_poisson_noise_gaussian_data_experiment \n", - "9 1_t_student_noise_gaussian_data_experiment \n", - "10 1_uniform_noise_gaussian_data_experiment \n", - "5 20_dims_uniform_error_gaussian_data \n", - "13 20_dims_uniform_error_gaussian_data_ \n", - "11 3_laplace_noise_gaussian_data_experiment \n", - "12 3_tstudent_noise_gaussian_data_experiment \n", - "40 4_std_sparse_linear_regression \n", - "17 beta_noise_ar1_data_experiment \n", - "18 beta_noisy_linear_regression_40_100k \n", - "42 case1_sparse_regression \n", + "7 1_beta_noise_gaussian_data_experiment \n", + "8 1_exponential_noise_gaussian_data_experiment \n", + "9 1_poisson_noise_gaussian_data_experiment \n", + "10 1_t_student_noise_gaussian_data_experiment \n", + "11 1_uniform_noise_gaussian_data_experiment \n", + "6 20_dims_uniform_error_gaussian_data \n", + "14 20_dims_uniform_error_gaussian_data_ \n", + "12 3_laplace_noise_gaussian_data_experiment \n", + "13 3_tstudent_noise_gaussian_data_experiment \n", + "41 4_std_sparse_linear_regression \n", + "18 beta_noise_ar1_data_experiment \n", + "19 beta_noisy_linear_regression_40_100k \n", + "43 case1_sparse_regression \n", "2 case2_heavy_tail_t_student \n", "3 case2_heavy_tail_t_student_1_1 \n", + "4 case2_heavy_tail_t_student_1_2 \n", "0 case3_bounded_support \n", - "36 case4_mixture_tasks \n", - "37 case4_mixture_tasks_1_1 \n", - "43 case5_transfer_tradeoff \n", - "16 data_sparse_linear_regression \n", + "37 case4_mixture_tasks \n", + "38 case4_mixture_tasks_1_1 \n", + "44 case5_transfer_tradeoff \n", + "45 case5_transfer_tradeoff_1_1 \n", + "17 data_sparse_linear_regression \n", "1 decision_tree_pretrained \n", - "19 exponential_noise_gaussian_data_experiment \n", - "20 exponential_w \n", - "21 exponential_weighted_experiment_100k \n", - "22 exponential_weighted_experiment_150k \n", - "23 exponential_weights_experiment \n", - "24 laplace_noise_gaussian_data_experiment \n", - "25 laplace_w \n", - "15 laplace_weights_experiment \n", - "26 linear_regression_pretrained \n", - "27 rayleigh_noise_gaussian_data_experiment \n", - "38 relu_2nn_regression_pretrained \n", - "14 rigde_normal_linear_regression_gaussian \n", - "39 sparse \n", - "4 sparse_data_experiment \n", - "41 sparse_regression_pretrained \n", - "30 t_student_noise_gaussian_data_experiment \n", - "28 task_sparse_data \n", - "29 test \n", - "32 uniform_hypersphere_experiment \n", - "31 uniform_hypersphere_experiment_standard \n", - "33 uniform_noise_ar1_data_experiment \n", - "34 uniform_noise_gaussian_data_experiment \n", - "35 w_laplace_x_exponential_noise_poisson " + "20 exponential_noise_gaussian_data_experiment \n", + "21 exponential_w \n", + "22 exponential_weighted_experiment_100k \n", + "23 exponential_weighted_experiment_150k \n", + "24 exponential_weights_experiment \n", + "25 laplace_noise_gaussian_data_experiment \n", + "26 laplace_w \n", + "16 laplace_weights_experiment \n", + "27 linear_regression_pretrained \n", + "28 rayleigh_noise_gaussian_data_experiment \n", + "39 relu_2nn_regression_pretrained \n", + "15 rigde_normal_linear_regression_gaussian \n", + "40 sparse \n", + "5 sparse_data_experiment \n", + "42 sparse_regression_pretrained \n", + "31 t_student_noise_gaussian_data_experiment \n", + "29 task_sparse_data \n", + "30 test \n", + "33 uniform_hypersphere_experiment \n", + "32 uniform_hypersphere_experiment_standard \n", + "34 uniform_noise_ar1_data_experiment \n", + "35 uniform_noise_gaussian_data_experiment \n", + "36 w_laplace_x_exponential_noise_poisson " ] }, - "execution_count": 8, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -847,17 +881,17 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "id": "a9980951", "metadata": {}, "outputs": [], "source": [ - "task = \"mixture_tasks_killer\"\n", + "task = \"transfer_tradeoff_task\"\n", "# task = \"sparse_linear_regression\"\n", "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"case4_mixture_tasks_1_1\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"case5_transfer_tradeoff_1_1\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -868,7 +902,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "id": "937f1b23", "metadata": {}, "outputs": [ @@ -879,8 +913,18 @@ "--- Tên mô hình CÓ TRONG relevant_model_names (models) ---\n", "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)']\n", "\n", - "--- Các khóa (Tên mô hình) THỰC TẾ trong metrics['standard'] ---\n", - "dict_keys([])\n" + "--- Các khóa (Tên mô hình) THỰC TẾ trong metrics['standard'] ---\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'metrics' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[5], line 9\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(models)\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m--- Các khóa (Tên mô hình) THỰC TẾ trong metrics[\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstandard\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m] ---\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 9\u001b[0m pprint\u001b[38;5;241m.\u001b[39mpprint(\u001b[43mmetrics\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstandard\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mkeys()) \n\u001b[0;32m 11\u001b[0m \u001b[38;5;66;03m# basic_plot(metrics[\"standard\"], models=models) \u001b[39;00m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# ...\u001b[39;00m\n", + "\u001b[1;31mNameError\u001b[0m: name 'metrics' is not defined" ] } ], @@ -909,7 +953,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -919,7 +963,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "case4_mixture_tasks_1_1 case4_mixture_tasks_1_1\n" + "case5_transfer_tradeoff_1_1 case5_transfer_tradeoff_1_1\n" ] }, { @@ -945,7 +989,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] diff --git a/src/eval.py b/src/eval.py index e7ae5a2a..e6c81237 100644 --- a/src/eval.py +++ b/src/eval.py @@ -234,7 +234,7 @@ def build_evals(conf): "linear_classification": {"scale", "uniform"}, "relu_2nn_regression": {"scale", "hidden_layer_size"}, "decision_tree": {"depth"}, - "noisy_linear_regression": {"scale", "noise_std", "renormalize_ys", "noise_type", "uniform"}, + "noisy_linear_regression": {"scale", "noise_std", "renormalize_ys", "noise_type", "uniform", "w_distribution", "w_kwargs"}, "ar1_linear_regression": {"scale", "ar_coef", "noise_std", "compute_gradient"}, "uniform_hypersphere_regression": {"scale"}, "linear_regression": {"scale", "uniform"}, diff --git a/src/models.py b/src/models.py index dc9dd7db..167c710a 100644 --- a/src/models.py +++ b/src/models.py @@ -119,14 +119,17 @@ def get_relevant_baselines(task_name): ], "noisy_linear_regression": [ (LeastSquaresModel, {}), - # (RidgeModel, {"alpha": 0.1}), + (RidgeModel, {"alpha": 0.1}), (RidgeModel, {"alpha": 0.5}), + (RidgeModel, {"alpha": 1.0}), + (RidgeModel, {"alpha": 2.0}), + (RidgeModel, {"alpha": 3.0}), (RidgeModelWithVarianceAdjustment, {"alpha": 0.5, "ar_coef": 0.5}), - (FeasibleGLSModel, {"ar_coef": None}), - (GLSModel, {"ar_coef": 0.5}), - (LADModel, {}), # L1 Regression - (HuberRegressionModel, {"epsilon": 1.35}), # Huber Regression - (CauchyMLEModel, {}), # MLE cho Cauchy + # (FeasibleGLSModel, {"ar_coef": None}), + # (GLSModel, {"ar_coef": 0.5}), + # (LADModel, {}), # L1 Regression + # (HuberRegressionModel, {"epsilon": 1.35}), # Huber Regression + # (CauchyMLEModel, {}), # MLE cho Cauchy (NNModel, {"n_neighbors": 3}), (AveragingModel, {}), ], diff --git a/src/plot_utils.py b/src/plot_utils.py index 2364d8de..b9806217 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -61,13 +61,13 @@ "noisy_linear_regression": [ "Transformer", "Least Squares", + "Ridge (alpha=0.1)", "Ridge (alpha=0.5)", - "Ridge Var Adj (alpha=0.5, ar=0.5)", - "Feasible GLS", - "GLS (ar=0.5)", - "LAD (L1 Regression)", - "Huber Regression (ε=1.35)", - "Cauchy MLE", + "Ridge (alpha=1.0)", + "Ridge (alpha=2.0)", + "Ridge (alpha=3.0)", + "3-Nearest Neighbors", + "Averaging" ], "linear_regression": [ "Transformer", @@ -140,7 +140,7 @@ def basic_plot(metrics, models=None, trivial=1.0): ax.set_xlabel("in-context examples") ax.set_ylabel("squared error") ax.set_xlim(-1, len(low) + 0.1) - ax.set_ylim(-0.1, 3) + ax.set_ylim(-0.1, 4) diff --git a/src/tasks.py b/src/tasks.py index 7477245b..09eb96e8 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -389,6 +389,8 @@ def __init__( noise_std=2.0, renormalize_ys=False, noise_type="cauchy", # "normal", "uniform", "laplace", "t-student", "cauchy", "exponential", "rayleigh", "beta", "poisson" + w_distribution="gaussian", + w_kwargs=None, uniform=False, ): super(NoisyLinearRegression, self).__init__( @@ -397,7 +399,66 @@ def __init__( self.noise_std = noise_std self.renormalize_ys = renormalize_ys self.noise_type = noise_type.lower() - + self.w_distribution = w_distribution.lower() + self.w_kwargs = w_kwargs or {} + self.w_b = self._compose_weights(pool_dict, seeds) + + def _compose_weights(self, pool_dict, seeds): + target_shape = (self.b_size, self.n_dims, 1) + if pool_dict is not None: + indices = torch.randperm(len(pool_dict["w"]))[: self.b_size] + return pool_dict["w"][indices] + + if seeds is None: + return self._sample_distribution(target_shape, generator=None) + w_b = torch.zeros(target_shape) + for i, seed in enumerate(seeds): + gen = torch.Generator().manual_seed(int(seed)) + w_b[i] = self._sample_distribution((1, self.n_dims, 1), generator=gen).squeeze(0) + return w_b + + def _sample_distribution(self, shape, generator=None): + if self.w_distribution == "gaussian": + return torch.randn(shape, generator=generator) + elif self.w_distribution == "uniform": + low = self.w_kwargs.get("low", -1.0) + high = self.w_kwargs.get("high", 1.0) + return torch.empty(shape, generator=generator).uniform_(low, high) + elif self.w_distribution == "laplace": + scale = self.w_kwargs.get("scale", 1.0) + laplace_dist = torch.distributions.Laplace(loc=0.0, scale=scale) + return laplace_dist.sample(shape, generator=generator) + elif self.w_distribution == "exponential": + rate = self.w_kwargs.get("rate", 1.0) + exp_dist = torch.distributions.Exponential(rate=rate) + return exp_dist.sample(shape, generator=generator) + elif self.w_distribution == "beta": + alpha = self.w_kwargs.get("alpha", 2.0) + beta = self.w_kwargs.get("beta", 5.0) + beta_dist = torch.distributions.Beta(concentration1=alpha, concentration0=beta) + return beta_dist.sample(shape, generator=generator) + elif self.w_distribution == "poisson": + rate = self.w_kwargs.get("rate", 3.0) + dist = torch.distributions.Poisson(rate=rate) + return dist.sample(shape, generator=generator) + elif self.w_distribution == "cauchy": + scale = self.w_kwargs.get("scale", 1.0) + cauchy_dist = torch.distributions.StudentT(df=1, loc=0.0, scale=scale) + return cauchy_dist.sample(shape, generator=generator) + elif self.w_distribution == "t-student": + df = self.w_kwargs.get("df", 3.0) + scale = self.w_kwargs.get("scale", 1.0) + t_dist = torch.distributions.StudentT(df=df, loc=0.0, scale=scale) + return t_dist.sample(shape, generator=generator) + elif self.w_distribution == "rayleigh": + lambda_param = self.w_kwargs.get("lambda_param", 1.0) + sigma = lambda_param + X = torch.randn(shape, generator=generator) * sigma + Y = torch.randn(shape, generator=generator) * sigma + R = torch.sqrt(X**2 + Y**2) + return R + else: + raise ValueError(f"Unsupported weight distribution: {self.w_distribution}") def sample_noise(self, shape): # 1. if self.noise_type == "normal": diff --git a/src/train.py b/src/train.py index 049073da..f94b1d89 100644 --- a/src/train.py +++ b/src/train.py @@ -78,7 +78,7 @@ def _sanitize_training_kwargs(args): "linear_classification": {"scale", "uniform"}, "relu_2nn_regression": {"scale", "hidden_layer_size"}, "decision_tree": {"depth"}, - "noisy_linear_regression": {"scale", "noise_std", "renormalize_ys", "noise_type", "uniform"}, + "noisy_linear_regression": {"scale", "noise_std", "renormalize_ys", "noise_type", "uniform", "w_distribution", "w_kwargs"}, "ar1_linear_regression": {"scale", "ar_coef", "noise_std", "compute_gradient"}, "uniform_hypersphere_regression": {"scale"}, "wlaplace_noisypoisson": {"scale", "weight_scale", "poisson_rate"}, From aae530f37ac61238e32da15bb35f48434c2576eb Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 3 Dec 2025 18:42:13 +0700 Subject: [PATCH 75/88] update wxe --- src/conf/template.yaml | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/src/conf/template.yaml b/src/conf/template.yaml index 09d0ec3c..1d4b33b7 100644 --- a/src/conf/template.yaml +++ b/src/conf/template.yaml @@ -45,15 +45,12 @@ training: # Task kwargs: # - When task == 'sparse_linear_regression': you may set 'sparsity'. # - For other tasks: any 'sparsity' key will be ignored automatically. - task_kwargs: { - # sparsity: 5 # only when task: sparse_linear_regression - noise_std: 2.0 # e.g., for noisy_linear_regression - # renormalize_ys: false + task_kwargs: + noise_std: 2.0 noise_type: normal w_distribution: gaussian w_kwargs: scale: 1.0 - } learning_rate: 0.0001 keep_every_steps: 10000 From 211cfa34000cfee7d40d398f6d0b1c73d0240f6d Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Wed, 3 Dec 2025 18:54:01 +0700 Subject: [PATCH 76/88] update --- src/plot_utils.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/src/plot_utils.py b/src/plot_utils.py index b9806217..d3e397bc 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -72,8 +72,11 @@ "linear_regression": [ "Transformer", "Least Squares", - # "Ridge (alpha=0.1)", + "Ridge (alpha=0.1)", "Ridge (alpha=0.5)", + "Ridge (alpha=1.0)", + "Ridge (alpha=2.0)", + "Ridge (alpha=3.0)", "3-Nearest Neighbors", "Averaging" ], @@ -144,8 +147,8 @@ def basic_plot(metrics, models=None, trivial=1.0): - # legend = ax.legend(loc="upper left", bbox_to_anchor=(1, 1)) - legend = ax.legend(loc="best") + legend = ax.legend(loc="upper left", bbox_to_anchor=(1, 1)) + # legend = ax.legend(loc="best") fig.set_size_inches(4, 3) for line in legend.get_lines(): From 511f4cda4fe48fca5e79913b2963ccc6596960c2 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Thu, 4 Dec 2025 08:33:49 +0700 Subject: [PATCH 77/88] update wx --- src/eval.ipynb | 252 +++++++++++++++++++++++++++++-------------------- 1 file changed, 151 insertions(+), 101 deletions(-) diff --git a/src/eval.ipynb b/src/eval.ipynb index a07e6950..f0dc4f28 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 30, "id": "0e8d018b", "metadata": { "scrolled": true @@ -191,7 +191,7 @@ " 3_tstudent_noise_gaussian_data_experiment\n", " \n", " \n", - " 41\n", + " 42\n", " daa2cd45-f1c0-4a0c-9100-e171129624c9\n", " sparse_linear_regression\n", " Transformer\n", @@ -230,7 +230,7 @@ " beta_noisy_linear_regression_40_100k\n", " \n", " \n", - " 43\n", + " 44\n", " case1_sparse_regression\n", " sparse_regression_killer\n", " Transformer\n", @@ -295,7 +295,7 @@ " case3_bounded_support\n", " \n", " \n", - " 37\n", + " 38\n", " case4_mixture_tasks\n", " mixture_tasks_killer\n", " Transformer\n", @@ -308,7 +308,7 @@ " case4_mixture_tasks\n", " \n", " \n", - " 38\n", + " 39\n", " case4_mixture_tasks_1_1\n", " mixture_tasks_killer\n", " Transformer\n", @@ -321,7 +321,7 @@ " case4_mixture_tasks_1_1\n", " \n", " \n", - " 44\n", + " 45\n", " case5_transfer_tradeoff\n", " transfer_tradeoff_task\n", " Transformer\n", @@ -334,7 +334,7 @@ " case5_transfer_tradeoff\n", " \n", " \n", - " 45\n", + " 46\n", " case5_transfer_tradeoff_1_1\n", " transfer_tradeoff_task\n", " Transformer\n", @@ -477,7 +477,7 @@ " laplace_weights_experiment\n", " \n", " \n", - " 27\n", + " 28\n", " pretrained\n", " linear_regression\n", " Transformer\n", @@ -490,7 +490,20 @@ " linear_regression_pretrained\n", " \n", " \n", - " 28\n", + " 27\n", + " lr_wx_mixed\n", + " linear_regression\n", + " Transformer\n", + " noise_std=2.0_noise_type=normal_w_distribution...\n", + " -1\n", + " -1\n", + " 20\n", + " 12\n", + " 8\n", + " lr_wx_mixed\n", + " \n", + " \n", + " 29\n", " rayleigh_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -503,7 +516,7 @@ " rayleigh_noise_gaussian_data_experiment\n", " \n", " \n", - " 39\n", + " 40\n", " pretrained\n", " relu_2nn_regression\n", " Transformer\n", @@ -529,7 +542,7 @@ " rigde_normal_linear_regression_gaussian\n", " \n", " \n", - " 40\n", + " 41\n", " 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615\n", " sparse_linear_regression\n", " Transformer\n", @@ -555,7 +568,7 @@ " sparse_data_experiment\n", " \n", " \n", - " 42\n", + " 43\n", " pretrained\n", " sparse_linear_regression\n", " Transformer\n", @@ -568,7 +581,7 @@ " sparse_regression_pretrained\n", " \n", " \n", - " 31\n", + " 32\n", " t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -581,7 +594,7 @@ " t_student_noise_gaussian_data_experiment\n", " \n", " \n", - " 29\n", + " 30\n", " sparse_gaussian\n", " linear_regression\n", " Transformer\n", @@ -594,7 +607,7 @@ " task_sparse_data\n", " \n", " \n", - " 30\n", + " 31\n", " test_cauchy\n", " linear_regression\n", " Transformer\n", @@ -607,7 +620,7 @@ " test\n", " \n", " \n", - " 33\n", + " 34\n", " uniform_hypersphere_regression\n", " linear_regression\n", " Transformer\n", @@ -620,7 +633,7 @@ " uniform_hypersphere_experiment\n", " \n", " \n", - " 32\n", + " 33\n", " uniform_hypersphere_experiment_standard\n", " linear_regression\n", " Transformer\n", @@ -633,7 +646,7 @@ " uniform_hypersphere_experiment_standard\n", " \n", " \n", - " 34\n", + " 35\n", " uniform_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -646,7 +659,7 @@ " uniform_noise_ar1_data_experiment\n", " \n", " \n", - " 35\n", + " 36\n", " uniform_noise_gaussian_data_experiment_\n", " linear_regression\n", " Transformer\n", @@ -659,7 +672,7 @@ " uniform_noise_gaussian_data_experiment\n", " \n", " \n", - " 36\n", + " 37\n", " w_laplace_x_exponential_noise_poisson\n", " linear_regression\n", " Transformer\n", @@ -686,18 +699,18 @@ "14 64d381ae-08d0-4bae-8e40-f1a68cfb2e97 linear_regression \n", "12 3_laplace_noise_gaussian_data_experiment linear_regression \n", "13 3_tstudent_noise_gaussian_data_experiment linear_regression \n", - "41 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", + "42 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", "18 beta_noise_ar1_data_experiment linear_regression \n", "19 beta_noisy_linear_regression_40_100k linear_regression \n", - "43 case1_sparse_regression sparse_regression_killer \n", + "44 case1_sparse_regression sparse_regression_killer \n", "2 case2_heavy_tail_t_student heavy_tail_noise_killer \n", "3 case2_heavy_tail_t_student_1_1 heavy_tail_noise_killer \n", "4 case2_heavy_tail_t_student_1_2 heavy_tail_noise_killer \n", "0 bounded_support_killer bounded_support_killer \n", - "37 case4_mixture_tasks mixture_tasks_killer \n", - "38 case4_mixture_tasks_1_1 mixture_tasks_killer \n", - "44 case5_transfer_tradeoff transfer_tradeoff_task \n", - "45 case5_transfer_tradeoff_1_1 transfer_tradeoff_task \n", + "38 case4_mixture_tasks mixture_tasks_killer \n", + "39 case4_mixture_tasks_1_1 mixture_tasks_killer \n", + "45 case5_transfer_tradeoff transfer_tradeoff_task \n", + "46 case5_transfer_tradeoff_1_1 transfer_tradeoff_task \n", "17 aed365ed-51e2-4a72-8374-ae954b37be14 linear_regression \n", "1 pretrained decision_tree \n", "20 exponential_noise_gaussian_data_experiment linear_regression \n", @@ -708,21 +721,22 @@ "25 laplace_noise_gaussian_data_experiment linear_regression \n", "26 laplace_w linear_regression \n", "16 a2fcec3c-8ce5-49bf-a8bc-08136b31ec36 linear_regression \n", - "27 pretrained linear_regression \n", - "28 rayleigh_noise_gaussian_data_experiment linear_regression \n", - "39 pretrained relu_2nn_regression \n", + "28 pretrained linear_regression \n", + "27 lr_wx_mixed linear_regression \n", + "29 rayleigh_noise_gaussian_data_experiment linear_regression \n", + "40 pretrained relu_2nn_regression \n", "15 82e728b0-a061-448e-8d7a-f3c79c0c74e5 linear_regression \n", - "40 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", + "41 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", "5 03de46b6-429a-4151-92e6-3588231c6cad linear_regression \n", - "42 pretrained sparse_linear_regression \n", - "31 t_student_noise_gaussian_data_experiment linear_regression \n", - "29 sparse_gaussian linear_regression \n", - "30 test_cauchy linear_regression \n", - "33 uniform_hypersphere_regression linear_regression \n", - "32 uniform_hypersphere_experiment_standard linear_regression \n", - "34 uniform_noise_ar1_data_experiment linear_regression \n", - "35 uniform_noise_gaussian_data_experiment_ linear_regression \n", - "36 w_laplace_x_exponential_noise_poisson linear_regression \n", + "43 pretrained sparse_linear_regression \n", + "32 t_student_noise_gaussian_data_experiment linear_regression \n", + "30 sparse_gaussian linear_regression \n", + "31 test_cauchy linear_regression \n", + "34 uniform_hypersphere_regression linear_regression \n", + "33 uniform_hypersphere_experiment_standard linear_regression \n", + "35 uniform_noise_ar1_data_experiment linear_regression \n", + "36 uniform_noise_gaussian_data_experiment_ linear_regression \n", + "37 w_laplace_x_exponential_noise_poisson linear_regression \n", "\n", " model kwargs num_tasks \\\n", "7 Transformer -1 \n", @@ -734,18 +748,18 @@ "14 Transformer -1 \n", "12 Transformer -1 \n", "13 Transformer -1 \n", - "41 Transformer sparsity=5 -1 \n", + "42 Transformer sparsity=5 -1 \n", "18 Transformer -1 \n", "19 Transformer noise_type=beta -1 \n", - "43 Transformer k_sparse=2_scale=1.0 -1 \n", + "44 Transformer k_sparse=2_scale=1.0 -1 \n", "2 Transformer df=3.0_noise_scale=0.5_noise_type=t-student -1 \n", "3 Transformer df=3.0_noise_scale=0.5_noise_type=t-student -1 \n", "4 Transformer df=1.0_noise_scale=2.0_noise_type=t-student -1 \n", "0 Transformer rate=1.0_scale=1.0 -1 \n", - "37 Transformer scale=1.0 -1 \n", "38 Transformer scale=1.0 -1 \n", - "44 Transformer mixture_std=2.0_prior_type=mixture_gaussian_sc... -1 \n", + "39 Transformer scale=1.0 -1 \n", "45 Transformer mixture_std=2.0_prior_type=mixture_gaussian_sc... -1 \n", + "46 Transformer mixture_std=2.0_prior_type=mixture_gaussian_sc... -1 \n", "17 Transformer k=5_sparsity=3 -1 \n", "1 Transformer depth=4 -1 \n", "20 Transformer -1 \n", @@ -756,21 +770,22 @@ "25 Transformer -1 \n", "26 Transformer scale=1.0_weight_scale=1.0 -1 \n", "16 Transformer scale=1.0_weight_scale=1.0 -1 \n", - "27 Transformer -1 \n", "28 Transformer -1 \n", - "39 Transformer hidden_layer_size=100 -1 \n", + "27 Transformer noise_std=2.0_noise_type=normal_w_distribution... -1 \n", + "29 Transformer -1 \n", + "40 Transformer hidden_layer_size=100 -1 \n", "15 Transformer sparsity=5 -1 \n", - "40 Transformer -1 \n", + "41 Transformer -1 \n", "5 Transformer -1 \n", - "42 Transformer sparsity=3 -1 \n", - "31 Transformer -1 \n", - "29 Transformer -1 \n", - "30 Transformer noise_type=cauchy -1 \n", + "43 Transformer sparsity=3 -1 \n", + "32 Transformer -1 \n", + "30 Transformer -1 \n", + "31 Transformer noise_type=cauchy -1 \n", + "34 Transformer normalize=True_scale=1.0 -1 \n", "33 Transformer normalize=True_scale=1.0 -1 \n", - "32 Transformer normalize=True_scale=1.0 -1 \n", - "34 Transformer -1 \n", "35 Transformer -1 \n", "36 Transformer -1 \n", + "37 Transformer -1 \n", "\n", " num_examples n_dims n_layer n_head \\\n", "7 -1 5 4 8 \n", @@ -782,18 +797,18 @@ "14 -1 20 4 8 \n", "12 -1 5 4 8 \n", "13 -1 5 4 8 \n", - "41 -1 15 4 8 \n", + "42 -1 15 4 8 \n", "18 -1 5 4 8 \n", "19 -1 20 4 8 \n", - "43 -1 20 4 8 \n", + "44 -1 20 4 8 \n", "2 -1 20 4 8 \n", "3 -1 20 12 8 \n", "4 -1 20 12 8 \n", "0 -1 20 4 8 \n", - "37 -1 20 4 8 \n", - "38 -1 20 12 8 \n", - "44 -1 20 4 8 \n", - "45 -1 20 12 8 \n", + "38 -1 20 4 8 \n", + "39 -1 20 12 8 \n", + "45 -1 20 4 8 \n", + "46 -1 20 12 8 \n", "17 -1 15 4 8 \n", "1 -1 20 12 8 \n", "20 -1 5 4 8 \n", @@ -804,21 +819,22 @@ "25 -1 5 4 8 \n", "26 -1 20 4 8 \n", "16 -1 20 4 8 \n", + "28 -1 20 12 8 \n", "27 -1 20 12 8 \n", - "28 -1 5 4 8 \n", - "39 -1 20 12 8 \n", + "29 -1 5 4 8 \n", + "40 -1 20 12 8 \n", "15 -1 15 4 8 \n", - "40 -1 5 4 8 \n", + "41 -1 5 4 8 \n", "5 -1 20 4 8 \n", - "42 -1 20 12 8 \n", - "31 -1 5 4 8 \n", - "29 -1 20 4 8 \n", + "43 -1 20 12 8 \n", + "32 -1 5 4 8 \n", "30 -1 20 4 8 \n", + "31 -1 20 4 8 \n", + "34 -1 20 4 8 \n", "33 -1 20 4 8 \n", - "32 -1 20 4 8 \n", - "34 -1 5 4 8 \n", "35 -1 5 4 8 \n", - "36 -1 20 4 8 \n", + "36 -1 5 4 8 \n", + "37 -1 20 4 8 \n", "\n", " run_name \n", "7 1_beta_noise_gaussian_data_experiment \n", @@ -830,18 +846,18 @@ "14 20_dims_uniform_error_gaussian_data_ \n", "12 3_laplace_noise_gaussian_data_experiment \n", "13 3_tstudent_noise_gaussian_data_experiment \n", - "41 4_std_sparse_linear_regression \n", + "42 4_std_sparse_linear_regression \n", "18 beta_noise_ar1_data_experiment \n", "19 beta_noisy_linear_regression_40_100k \n", - "43 case1_sparse_regression \n", + "44 case1_sparse_regression \n", "2 case2_heavy_tail_t_student \n", "3 case2_heavy_tail_t_student_1_1 \n", "4 case2_heavy_tail_t_student_1_2 \n", "0 case3_bounded_support \n", - "37 case4_mixture_tasks \n", - "38 case4_mixture_tasks_1_1 \n", - "44 case5_transfer_tradeoff \n", - "45 case5_transfer_tradeoff_1_1 \n", + "38 case4_mixture_tasks \n", + "39 case4_mixture_tasks_1_1 \n", + "45 case5_transfer_tradeoff \n", + "46 case5_transfer_tradeoff_1_1 \n", "17 data_sparse_linear_regression \n", "1 decision_tree_pretrained \n", "20 exponential_noise_gaussian_data_experiment \n", @@ -852,24 +868,25 @@ "25 laplace_noise_gaussian_data_experiment \n", "26 laplace_w \n", "16 laplace_weights_experiment \n", - "27 linear_regression_pretrained \n", - "28 rayleigh_noise_gaussian_data_experiment \n", - "39 relu_2nn_regression_pretrained \n", + "28 linear_regression_pretrained \n", + "27 lr_wx_mixed \n", + "29 rayleigh_noise_gaussian_data_experiment \n", + "40 relu_2nn_regression_pretrained \n", "15 rigde_normal_linear_regression_gaussian \n", - "40 sparse \n", + "41 sparse \n", "5 sparse_data_experiment \n", - "42 sparse_regression_pretrained \n", - "31 t_student_noise_gaussian_data_experiment \n", - "29 task_sparse_data \n", - "30 test \n", - "33 uniform_hypersphere_experiment \n", - "32 uniform_hypersphere_experiment_standard \n", - "34 uniform_noise_ar1_data_experiment \n", - "35 uniform_noise_gaussian_data_experiment \n", - "36 w_laplace_x_exponential_noise_poisson " + "43 sparse_regression_pretrained \n", + "32 t_student_noise_gaussian_data_experiment \n", + "30 task_sparse_data \n", + "31 test \n", + "34 uniform_hypersphere_experiment \n", + "33 uniform_hypersphere_experiment_standard \n", + "35 uniform_noise_ar1_data_experiment \n", + "36 uniform_noise_gaussian_data_experiment \n", + "37 w_laplace_x_exponential_noise_poisson " ] }, - "execution_count": 3, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -881,17 +898,17 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 31, "id": "a9980951", "metadata": {}, "outputs": [], "source": [ - "task = \"transfer_tradeoff_task\"\n", + "task = \"linear_regression\"\n", "# task = \"sparse_linear_regression\"\n", "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"case5_transfer_tradeoff_1_1\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"lr_wx_mixed\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -902,7 +919,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 27, "id": "937f1b23", "metadata": {}, "outputs": [ @@ -911,20 +928,20 @@ "output_type": "stream", "text": [ "--- Tên mô hình CÓ TRONG relevant_model_names (models) ---\n", - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)']\n", + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", "\n", "--- Các khóa (Tên mô hình) THỰC TẾ trong metrics['standard'] ---\n" ] }, { - "ename": "NameError", - "evalue": "name 'metrics' is not defined", + "ename": "KeyError", + "evalue": "'standard'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[5], line 9\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(models)\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m--- Các khóa (Tên mô hình) THỰC TẾ trong metrics[\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstandard\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m] ---\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 9\u001b[0m pprint\u001b[38;5;241m.\u001b[39mpprint(\u001b[43mmetrics\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstandard\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mkeys()) \n\u001b[0;32m 11\u001b[0m \u001b[38;5;66;03m# basic_plot(metrics[\"standard\"], models=models) \u001b[39;00m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# ...\u001b[39;00m\n", - "\u001b[1;31mNameError\u001b[0m: name 'metrics' is not defined" + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[27], line 9\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(models)\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m--- Các khóa (Tên mô hình) THỰC TẾ trong metrics[\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstandard\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m] ---\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 9\u001b[0m pprint\u001b[38;5;241m.\u001b[39mpprint(\u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstandard\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mkeys()) \n\u001b[0;32m 11\u001b[0m \u001b[38;5;66;03m# basic_plot(metrics[\"standard\"], models=models) \u001b[39;00m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# ...\u001b[39;00m\n", + "\u001b[1;31mKeyError\u001b[0m: 'standard'" ] } ], @@ -953,7 +970,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 34, "id": "cd8e02c5", "metadata": { "scrolled": false @@ -963,7 +980,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "case5_transfer_tradeoff_1_1 case5_transfer_tradeoff_1_1\n" + "lr_wx_mixed lr_wx_mixed\n" ] }, { @@ -977,7 +994,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)']\n" + "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { @@ -989,7 +1006,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -1011,6 +1028,39 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 35, + "id": "4379fea1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available models: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=0.5)', 'Ridge (alpha=1.0)', 'Ridge (alpha=2.0)', 'Ridge (alpha=3.0)', 'Ridge Var Adj (alpha=0.5, ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n", + "['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=0.5)', 'Ridge (alpha=1.0)', 'Ridge (alpha=2.0)', 'Ridge (alpha=3.0)', 'Ridge Var Adj (alpha=0.5, ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eval_key = \"standard\"\n", + "models_to_plot = list(metrics[eval_key].keys())\n", + "print(f\"Available models: {models_to_plot}\")\n", + "basic_plot(metrics[eval_key], models=models_to_plot)\n", + "plt.show()" + ] + }, { "cell_type": "code", "execution_count": 25, From 9dc1ee0518520753e9411c67d1c5df5eb973d644 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Thu, 11 Dec 2025 21:13:58 +0700 Subject: [PATCH 78/88] add all x, y to GPU --- src/conf/template.yaml | 6 +- src/eval.ipynb | 346 ++++++++++++++++++++++++++--------------- src/models.py | 6 - src/plot_utils.py | 2 +- src/samplers.py | 4 +- src/tasks.py | 50 +++--- 6 files changed, 254 insertions(+), 160 deletions(-) diff --git a/src/conf/template.yaml b/src/conf/template.yaml index 1d4b33b7..7fc3ee7c 100644 --- a/src/conf/template.yaml +++ b/src/conf/template.yaml @@ -46,7 +46,7 @@ training: # - When task == 'sparse_linear_regression': you may set 'sparsity'. # - For other tasks: any 'sparsity' key will be ignored automatically. task_kwargs: - noise_std: 2.0 + noise_std: 1.0 noise_type: normal w_distribution: gaussian w_kwargs: @@ -60,12 +60,12 @@ training: save_every_steps: 100 train_steps: 500001 -out_dir: /content/drive/MyDrive/models/lr_wx +out_dir: .../models/noisy_linear_regression/ wandb: project: "in-context-training" entity: "hai-trinh220970-ho-chi-minh-city-university-of-technology" - name: "lr_wx" + name: "noisy_linear_regression" notes: "Training with laplace-distributed weights (non-uniform on hypersphere)" log_every_steps: 100 diff --git a/src/eval.ipynb b/src/eval.ipynb index f0dc4f28..2aef9b2a 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "id": "0e8d018b", "metadata": { "scrolled": true @@ -191,7 +191,7 @@ " 3_tstudent_noise_gaussian_data_experiment\n", " \n", " \n", - " 42\n", + " 43\n", " daa2cd45-f1c0-4a0c-9100-e171129624c9\n", " sparse_linear_regression\n", " Transformer\n", @@ -230,7 +230,7 @@ " beta_noisy_linear_regression_40_100k\n", " \n", " \n", - " 44\n", + " 45\n", " case1_sparse_regression\n", " sparse_regression_killer\n", " Transformer\n", @@ -295,7 +295,7 @@ " case3_bounded_support\n", " \n", " \n", - " 38\n", + " 39\n", " case4_mixture_tasks\n", " mixture_tasks_killer\n", " Transformer\n", @@ -308,7 +308,7 @@ " case4_mixture_tasks\n", " \n", " \n", - " 39\n", + " 40\n", " case4_mixture_tasks_1_1\n", " mixture_tasks_killer\n", " Transformer\n", @@ -321,7 +321,7 @@ " case4_mixture_tasks_1_1\n", " \n", " \n", - " 45\n", + " 46\n", " case5_transfer_tradeoff\n", " transfer_tradeoff_task\n", " Transformer\n", @@ -334,7 +334,7 @@ " case5_transfer_tradeoff\n", " \n", " \n", - " 46\n", + " 47\n", " case5_transfer_tradeoff_1_1\n", " transfer_tradeoff_task\n", " Transformer\n", @@ -516,7 +516,7 @@ " rayleigh_noise_gaussian_data_experiment\n", " \n", " \n", - " 40\n", + " 41\n", " pretrained\n", " relu_2nn_regression\n", " Transformer\n", @@ -542,7 +542,7 @@ " rigde_normal_linear_regression_gaussian\n", " \n", " \n", - " 41\n", + " 42\n", " 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615\n", " sparse_linear_regression\n", " Transformer\n", @@ -568,7 +568,7 @@ " sparse_data_experiment\n", " \n", " \n", - " 43\n", + " 44\n", " pretrained\n", " sparse_linear_regression\n", " Transformer\n", @@ -673,6 +673,19 @@ " \n", " \n", " 37\n", + " w_exp_x_gamma_e_uni\n", + " linear_regression\n", + " Transformer\n", + " noise_std=1.0_noise_type=uniform_w_distributio...\n", + " -1\n", + " -1\n", + " 20\n", + " 12\n", + " 8\n", + " w_expo x_gamma e uni\n", + " \n", + " \n", + " 38\n", " w_laplace_x_exponential_noise_poisson\n", " linear_regression\n", " Transformer\n", @@ -699,18 +712,18 @@ "14 64d381ae-08d0-4bae-8e40-f1a68cfb2e97 linear_regression \n", "12 3_laplace_noise_gaussian_data_experiment linear_regression \n", "13 3_tstudent_noise_gaussian_data_experiment linear_regression \n", - "42 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", + "43 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", "18 beta_noise_ar1_data_experiment linear_regression \n", "19 beta_noisy_linear_regression_40_100k linear_regression \n", - "44 case1_sparse_regression sparse_regression_killer \n", + "45 case1_sparse_regression sparse_regression_killer \n", "2 case2_heavy_tail_t_student heavy_tail_noise_killer \n", "3 case2_heavy_tail_t_student_1_1 heavy_tail_noise_killer \n", "4 case2_heavy_tail_t_student_1_2 heavy_tail_noise_killer \n", "0 bounded_support_killer bounded_support_killer \n", - "38 case4_mixture_tasks mixture_tasks_killer \n", - "39 case4_mixture_tasks_1_1 mixture_tasks_killer \n", - "45 case5_transfer_tradeoff transfer_tradeoff_task \n", - "46 case5_transfer_tradeoff_1_1 transfer_tradeoff_task \n", + "39 case4_mixture_tasks mixture_tasks_killer \n", + "40 case4_mixture_tasks_1_1 mixture_tasks_killer \n", + "46 case5_transfer_tradeoff transfer_tradeoff_task \n", + "47 case5_transfer_tradeoff_1_1 transfer_tradeoff_task \n", "17 aed365ed-51e2-4a72-8374-ae954b37be14 linear_regression \n", "1 pretrained decision_tree \n", "20 exponential_noise_gaussian_data_experiment linear_regression \n", @@ -724,11 +737,11 @@ "28 pretrained linear_regression \n", "27 lr_wx_mixed linear_regression \n", "29 rayleigh_noise_gaussian_data_experiment linear_regression \n", - "40 pretrained relu_2nn_regression \n", + "41 pretrained relu_2nn_regression \n", "15 82e728b0-a061-448e-8d7a-f3c79c0c74e5 linear_regression \n", - "41 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", + "42 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", "5 03de46b6-429a-4151-92e6-3588231c6cad linear_regression \n", - "43 pretrained sparse_linear_regression \n", + "44 pretrained sparse_linear_regression \n", "32 t_student_noise_gaussian_data_experiment linear_regression \n", "30 sparse_gaussian linear_regression \n", "31 test_cauchy linear_regression \n", @@ -736,7 +749,8 @@ "33 uniform_hypersphere_experiment_standard linear_regression \n", "35 uniform_noise_ar1_data_experiment linear_regression \n", "36 uniform_noise_gaussian_data_experiment_ linear_regression \n", - "37 w_laplace_x_exponential_noise_poisson linear_regression \n", + "37 w_exp_x_gamma_e_uni linear_regression \n", + "38 w_laplace_x_exponential_noise_poisson linear_regression \n", "\n", " model kwargs num_tasks \\\n", "7 Transformer -1 \n", @@ -748,18 +762,18 @@ "14 Transformer -1 \n", "12 Transformer -1 \n", "13 Transformer -1 \n", - "42 Transformer sparsity=5 -1 \n", + "43 Transformer sparsity=5 -1 \n", "18 Transformer -1 \n", "19 Transformer noise_type=beta -1 \n", - "44 Transformer k_sparse=2_scale=1.0 -1 \n", + "45 Transformer k_sparse=2_scale=1.0 -1 \n", "2 Transformer df=3.0_noise_scale=0.5_noise_type=t-student -1 \n", "3 Transformer df=3.0_noise_scale=0.5_noise_type=t-student -1 \n", "4 Transformer df=1.0_noise_scale=2.0_noise_type=t-student -1 \n", "0 Transformer rate=1.0_scale=1.0 -1 \n", - "38 Transformer scale=1.0 -1 \n", "39 Transformer scale=1.0 -1 \n", - "45 Transformer mixture_std=2.0_prior_type=mixture_gaussian_sc... -1 \n", + "40 Transformer scale=1.0 -1 \n", "46 Transformer mixture_std=2.0_prior_type=mixture_gaussian_sc... -1 \n", + "47 Transformer mixture_std=2.0_prior_type=mixture_gaussian_sc... -1 \n", "17 Transformer k=5_sparsity=3 -1 \n", "1 Transformer depth=4 -1 \n", "20 Transformer -1 \n", @@ -773,11 +787,11 @@ "28 Transformer -1 \n", "27 Transformer noise_std=2.0_noise_type=normal_w_distribution... -1 \n", "29 Transformer -1 \n", - "40 Transformer hidden_layer_size=100 -1 \n", + "41 Transformer hidden_layer_size=100 -1 \n", "15 Transformer sparsity=5 -1 \n", - "41 Transformer -1 \n", + "42 Transformer -1 \n", "5 Transformer -1 \n", - "43 Transformer sparsity=3 -1 \n", + "44 Transformer sparsity=3 -1 \n", "32 Transformer -1 \n", "30 Transformer -1 \n", "31 Transformer noise_type=cauchy -1 \n", @@ -785,7 +799,8 @@ "33 Transformer normalize=True_scale=1.0 -1 \n", "35 Transformer -1 \n", "36 Transformer -1 \n", - "37 Transformer -1 \n", + "37 Transformer noise_std=1.0_noise_type=uniform_w_distributio... -1 \n", + "38 Transformer -1 \n", "\n", " num_examples n_dims n_layer n_head \\\n", "7 -1 5 4 8 \n", @@ -797,18 +812,18 @@ "14 -1 20 4 8 \n", "12 -1 5 4 8 \n", "13 -1 5 4 8 \n", - "42 -1 15 4 8 \n", + "43 -1 15 4 8 \n", "18 -1 5 4 8 \n", "19 -1 20 4 8 \n", - "44 -1 20 4 8 \n", + "45 -1 20 4 8 \n", "2 -1 20 4 8 \n", "3 -1 20 12 8 \n", "4 -1 20 12 8 \n", "0 -1 20 4 8 \n", - "38 -1 20 4 8 \n", - "39 -1 20 12 8 \n", - "45 -1 20 4 8 \n", - "46 -1 20 12 8 \n", + "39 -1 20 4 8 \n", + "40 -1 20 12 8 \n", + "46 -1 20 4 8 \n", + "47 -1 20 12 8 \n", "17 -1 15 4 8 \n", "1 -1 20 12 8 \n", "20 -1 5 4 8 \n", @@ -822,11 +837,11 @@ "28 -1 20 12 8 \n", "27 -1 20 12 8 \n", "29 -1 5 4 8 \n", - "40 -1 20 12 8 \n", + "41 -1 20 12 8 \n", "15 -1 15 4 8 \n", - "41 -1 5 4 8 \n", + "42 -1 5 4 8 \n", "5 -1 20 4 8 \n", - "43 -1 20 12 8 \n", + "44 -1 20 12 8 \n", "32 -1 5 4 8 \n", "30 -1 20 4 8 \n", "31 -1 20 4 8 \n", @@ -834,7 +849,8 @@ "33 -1 20 4 8 \n", "35 -1 5 4 8 \n", "36 -1 5 4 8 \n", - "37 -1 20 4 8 \n", + "37 -1 20 12 8 \n", + "38 -1 20 4 8 \n", "\n", " run_name \n", "7 1_beta_noise_gaussian_data_experiment \n", @@ -846,18 +862,18 @@ "14 20_dims_uniform_error_gaussian_data_ \n", "12 3_laplace_noise_gaussian_data_experiment \n", "13 3_tstudent_noise_gaussian_data_experiment \n", - "42 4_std_sparse_linear_regression \n", + "43 4_std_sparse_linear_regression \n", "18 beta_noise_ar1_data_experiment \n", "19 beta_noisy_linear_regression_40_100k \n", - "44 case1_sparse_regression \n", + "45 case1_sparse_regression \n", "2 case2_heavy_tail_t_student \n", "3 case2_heavy_tail_t_student_1_1 \n", "4 case2_heavy_tail_t_student_1_2 \n", "0 case3_bounded_support \n", - "38 case4_mixture_tasks \n", - "39 case4_mixture_tasks_1_1 \n", - "45 case5_transfer_tradeoff \n", - "46 case5_transfer_tradeoff_1_1 \n", + "39 case4_mixture_tasks \n", + "40 case4_mixture_tasks_1_1 \n", + "46 case5_transfer_tradeoff \n", + "47 case5_transfer_tradeoff_1_1 \n", "17 data_sparse_linear_regression \n", "1 decision_tree_pretrained \n", "20 exponential_noise_gaussian_data_experiment \n", @@ -871,11 +887,11 @@ "28 linear_regression_pretrained \n", "27 lr_wx_mixed \n", "29 rayleigh_noise_gaussian_data_experiment \n", - "40 relu_2nn_regression_pretrained \n", + "41 relu_2nn_regression_pretrained \n", "15 rigde_normal_linear_regression_gaussian \n", - "41 sparse \n", + "42 sparse \n", "5 sparse_data_experiment \n", - "43 sparse_regression_pretrained \n", + "44 sparse_regression_pretrained \n", "32 t_student_noise_gaussian_data_experiment \n", "30 task_sparse_data \n", "31 test \n", @@ -883,10 +899,11 @@ "33 uniform_hypersphere_experiment_standard \n", "35 uniform_noise_ar1_data_experiment \n", "36 uniform_noise_gaussian_data_experiment \n", - "37 w_laplace_x_exponential_noise_poisson " + "37 w_expo x_gamma e uni \n", + "38 w_laplace_x_exponential_noise_poisson " ] }, - "execution_count": 30, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -898,17 +915,17 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 29, "id": "a9980951", "metadata": {}, "outputs": [], "source": [ - "task = \"linear_regression\"\n", + "task = \"noisy_linear_regression\"\n", "# task = \"sparse_linear_regression\"\n", "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"lr_wx_mixed\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"lr_wx_1\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -917,96 +934,100 @@ " get_run_metrics(run_path) # these are normally precomputed at the end of training" ] }, + { + "cell_type": "markdown", + "id": "f6d09964", + "metadata": {}, + "source": [ + "# Plot pre-computed metrics" + ] + }, { "cell_type": "code", - "execution_count": 27, - "id": "937f1b23", + "execution_count": 31, + "id": "8a7aec35", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "--- Tên mô hình CÓ TRONG relevant_model_names (models) ---\n", - "['Transformer', 'Least Squares', 'Ridge (alpha=0.5)', '3-Nearest Neighbors', 'Averaging']\n", - "\n", - "--- Các khóa (Tên mô hình) THỰC TẾ trong metrics['standard'] ---\n" + "['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=0.5)', 'Ridge (alpha=1.0)', 'Ridge (alpha=2.0)', 'Ridge (alpha=3.0)', '3-Nearest Neighbors', 'Averaging']\n", + "Missing metrics for: ['Ridge (alpha=0.5)', 'Ridge (alpha=2.0)', 'Ridge (alpha=3.0)']\n" ] }, { - "ename": "KeyError", - "evalue": "'standard'", + "ename": "TypeError", + "evalue": "list indices must be integers or slices, not str", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[27], line 9\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(models)\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m--- Các khóa (Tên mô hình) THỰC TẾ trong metrics[\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstandard\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m] ---\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 9\u001b[0m pprint\u001b[38;5;241m.\u001b[39mpprint(\u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstandard\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mkeys()) \n\u001b[0;32m 11\u001b[0m \u001b[38;5;66;03m# basic_plot(metrics[\"standard\"], models=models) \u001b[39;00m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# ...\u001b[39;00m\n", - "\u001b[1;31mKeyError\u001b[0m: 'standard'" + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[31], line 28\u001b[0m\n\u001b[0;32m 25\u001b[0m n_dims \u001b[38;5;241m=\u001b[39m conf\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mn_dims\n\u001b[0;32m 27\u001b[0m models \u001b[38;5;241m=\u001b[39m relevant_model_names[task]\n\u001b[1;32m---> 28\u001b[0m \u001b[43mbasic_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstandard\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 29\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", + "File \u001b[1;32md:\\MyBK\\Semester_5\\Programming_Intergration_Project\\in-context-learning\\src\\plot_utils.py:138\u001b[0m, in \u001b[0;36mbasic_plot\u001b[1;34m(metrics, models, trivial)\u001b[0m\n\u001b[0;32m 136\u001b[0m ax\u001b[38;5;241m.\u001b[39maxhline(trivial, ls\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m--\u001b[39m\u001b[38;5;124m\"\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgray\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 137\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, vs \u001b[38;5;129;01min\u001b[39;00m metrics\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m--> 138\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(\u001b[43mvs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m\"\u001b[39m, label\u001b[38;5;241m=\u001b[39mname, color\u001b[38;5;241m=\u001b[39mpalette[color \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m10\u001b[39m], lw\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m 139\u001b[0m low \u001b[38;5;241m=\u001b[39m vs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbootstrap_low\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 140\u001b[0m high \u001b[38;5;241m=\u001b[39m vs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbootstrap_high\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "\u001b[1;31mTypeError\u001b[0m: list indices must be integers or slices, not str" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# Cell In[26], trước dòng 9\n", + "import json\n", + "from eval import baseline_names\n", "\n", - "import pprint # Dùng để in dictionary đẹp hơn\n", - "# ...\n", - "models = relevant_model_names[task]\n", - "print(\"--- Tên mô hình CÓ TRONG relevant_model_names (models) ---\")\n", - "print(models)\n", - "print(\"\\n--- Các khóa (Tên mô hình) THỰC TẾ trong metrics['standard'] ---\")\n", - "pprint.pprint(metrics[\"standard\"].keys()) \n", + "# Load metrics trực tiếp từ file JSON\n", + "run_path = os.path.join(run_dir, task, run_id)\n", + "metrics_file = os.path.join(run_path, \"metrics.json\")\n", "\n", - "# basic_plot(metrics[\"standard\"], models=models) \n", - "# ..." - ] - }, - { - "cell_type": "markdown", - "id": "f6d09964", - "metadata": {}, - "source": [ - "# Plot pre-computed metrics" + "with open(metrics_file, 'r') as f:\n", + " raw_metrics = json.load(f)\n", + "\n", + "# Chuyển đổi tên model từ \"ridge_alpha=0.1\" -> \"Ridge (alpha=0.1)\"\n", + "metrics = {}\n", + "for eval_key, models_dict in raw_metrics.items():\n", + " metrics[eval_key] = {}\n", + " for model_name, values in models_dict.items():\n", + " # Chuyển đổi tên model\n", + " if \"gpt2\" in model_name:\n", + " display_name = \"Transformer\"\n", + " else:\n", + " display_name = baseline_names(model_name)\n", + " metrics[eval_key][display_name] = values\n", + "\n", + "# Giờ dùng basic_plot như bình thường\n", + "_, conf = get_model_from_run(run_path, only_conf=True)\n", + "n_dims = conf.model.n_dims\n", + "\n", + "models = relevant_model_names[task]\n", + "basic_plot(metrics[\"standard\"], models=models)\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 34, - "id": "cd8e02c5", - "metadata": { - "scrolled": false - }, + "execution_count": 32, + "id": "8d983d7f", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "lr_wx_mixed lr_wx_mixed\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:00" ] @@ -1015,6 +1036,88 @@ "output_type": "display_data" } ], + "source": [ + "import json\n", + "import numpy as np\n", + "from eval import baseline_names, get_model_from_run\n", + "\n", + "# Load metrics trực tiếp từ file JSON\n", + "run_path = os.path.join(run_dir, task, run_id)\n", + "metrics_file = os.path.join(run_path, \"metrics.json\")\n", + "\n", + "with open(metrics_file, 'r') as f:\n", + " raw_metrics = json.load(f)\n", + "\n", + "# Chuyển đổi tên model và xử lý cấu trúc khác nhau\n", + "metrics = {}\n", + "for eval_key, models_dict in raw_metrics.items():\n", + " metrics[eval_key] = {}\n", + " for model_name, values in models_dict.items():\n", + " # Convert model name\n", + " if \"gpt2\" in model_name:\n", + " display_name = \"Transformer\"\n", + " else:\n", + " display_name = baseline_names(model_name)\n", + " \n", + " # Handle different data structures\n", + " if isinstance(values, dict) and \"mean\" in values:\n", + " # Format: {\"mean\": [...], \"std\": [...], \"bootstrap_low\": [...], \"bootstrap_high\": [...]}\n", + " metrics[eval_key][display_name] = values\n", + " elif isinstance(values, list) and len(values) > 0:\n", + " # Format: [[...], [...], ...] - raw batches, need to aggregate\n", + " if isinstance(values[0], list):\n", + " # Convert list of lists to mean/std\n", + " values_array = np.array(values)\n", + " metrics[eval_key][display_name] = {\n", + " \"mean\": np.mean(values_array, axis=0).tolist(),\n", + " \"std\": np.std(values_array, axis=0).tolist(),\n", + " \"bootstrap_low\": np.percentile(values_array, 2.5, axis=0).tolist(),\n", + " \"bootstrap_high\": np.percentile(values_array, 97.5, axis=0).tolist()\n", + " }\n", + " else:\n", + " # Single array\n", + " metrics[eval_key][display_name] = {\n", + " \"mean\": values,\n", + " \"std\": [0] * len(values),\n", + " \"bootstrap_low\": values,\n", + " \"bootstrap_high\": values\n", + " }\n", + " else:\n", + " # Empty or unknown format - skip\n", + " continue\n", + "\n", + "# Get config & plot\n", + "_, conf = get_model_from_run(run_path, only_conf=True)\n", + "n_dims = conf.model.n_dims\n", + "\n", + "# Remove empty models\n", + "metrics[\"standard\"] = {k: v for k, v in metrics[\"standard\"].items() if v}\n", + "\n", + "models = relevant_model_names[task]\n", + "basic_plot(metrics[\"standard\"], models=models)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cd8e02c5", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'standard'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[9], line 9\u001b[0m\n\u001b[0;32m 6\u001b[0m n_dims \u001b[38;5;241m=\u001b[39m conf\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mn_dims\n\u001b[0;32m 8\u001b[0m models \u001b[38;5;241m=\u001b[39m relevant_model_names[task]\n\u001b[1;32m----> 9\u001b[0m basic_plot(\u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstandard\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m, models\u001b[38;5;241m=\u001b[39mmodels)\n\u001b[0;32m 10\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", + "\u001b[1;31mKeyError\u001b[0m: 'standard'" + ] + } + ], "source": [ "def valid_row(r):\n", " return r.task == task and r.run_id == run_id\n", @@ -1030,27 +1133,20 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 6, "id": "4379fea1", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available models: ['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=0.5)', 'Ridge (alpha=1.0)', 'Ridge (alpha=2.0)', 'Ridge (alpha=3.0)', 'Ridge Var Adj (alpha=0.5, ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n", - "['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=0.5)', 'Ridge (alpha=1.0)', 'Ridge (alpha=2.0)', 'Ridge (alpha=3.0)', 'Ridge Var Adj (alpha=0.5, ar=0.5)', '3-Nearest Neighbors', 'Averaging']\n" + "ename": "KeyError", + "evalue": "'standard'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[6], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m eval_key \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstandard\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m----> 2\u001b[0m models_to_plot \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[43meval_key\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable models: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodels_to_plot\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 4\u001b[0m basic_plot(metrics[eval_key], models\u001b[38;5;241m=\u001b[39mmodels_to_plot)\n", + "\u001b[1;31mKeyError\u001b[0m: 'standard'" ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ diff --git a/src/models.py b/src/models.py index 167c710a..da243f3d 100644 --- a/src/models.py +++ b/src/models.py @@ -124,12 +124,6 @@ def get_relevant_baselines(task_name): (RidgeModel, {"alpha": 1.0}), (RidgeModel, {"alpha": 2.0}), (RidgeModel, {"alpha": 3.0}), - (RidgeModelWithVarianceAdjustment, {"alpha": 0.5, "ar_coef": 0.5}), - # (FeasibleGLSModel, {"ar_coef": None}), - # (GLSModel, {"ar_coef": 0.5}), - # (LADModel, {}), # L1 Regression - # (HuberRegressionModel, {"epsilon": 1.35}), # Huber Regression - # (CauchyMLEModel, {}), # MLE cho Cauchy (NNModel, {"n_neighbors": 3}), (AveragingModel, {}), ], diff --git a/src/plot_utils.py b/src/plot_utils.py index 73773c42..99c05467 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -145,7 +145,7 @@ def basic_plot(metrics, models=None, trivial=1.0): ax.set_xlabel("in-context examples") ax.set_ylabel("squared error") ax.set_xlim(-1, len(low) + 0.1) - ax.set_ylim(-0.1, 4) + ax.set_ylim(-0.1, 2) diff --git a/src/samplers.py b/src/samplers.py index aaab95a7..1895a7c7 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -187,7 +187,9 @@ def __init__(self, n_dims, bias=None, scale=None, alpha=2.0, beta=5.0): self.alpha = float(alpha) self.beta = float(beta) - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): + alpha_t = torch.tensor(self.alpha, device=device) + beta_t = torch.tensor(self.beta, device=device) beta_dist = torch.distributions.Beta(concentration1=self.alpha, concentration0=self.beta) xs_b = _sample_distribution(beta_dist, b_size, (n_points, self.n_dims), seeds) diff --git a/src/tasks.py b/src/tasks.py index 0e198d23..b0e5b495 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -417,76 +417,78 @@ def _compose_weights(self, pool_dict, seeds): w_b[i] = self._sample_distribution((1, self.n_dims, 1), generator=gen).squeeze(0) return w_b - def _sample_distribution(self, shape, generator=None): + def _sample_distribution(self, shape, generator=None, device='cpu'): + def to_val(val): + return torch.tensor(val, device=device) if not torch.is_tensor(val) else val.to(device) if self.w_distribution == "gaussian": - return torch.randn(shape, generator=generator) + return torch.randn(shape, generator=generator, device=device) elif self.w_distribution == "uniform": low = self.w_kwargs.get("low", -1.0) high = self.w_kwargs.get("high", 1.0) - return torch.empty(shape, generator=generator).uniform_(low, high) + return torch.empty(shape, generator=generator, device=device).uniform_(low, high) elif self.w_distribution == "laplace": scale = self.w_kwargs.get("scale", 1.0) laplace_dist = torch.distributions.Laplace(loc=0.0, scale=scale) - return laplace_dist.sample(shape, generator=generator) + return laplace_dist.sample(shape, generator=generator, device=device) elif self.w_distribution == "exponential": rate = self.w_kwargs.get("rate", 1.0) exp_dist = torch.distributions.Exponential(rate=rate) - return exp_dist.sample(shape, generator=generator) + return exp_dist.sample(shape, generator=generator, device=device) elif self.w_distribution == "beta": alpha = self.w_kwargs.get("alpha", 2.0) beta = self.w_kwargs.get("beta", 5.0) beta_dist = torch.distributions.Beta(concentration1=alpha, concentration0=beta) - return beta_dist.sample(shape, generator=generator) + return beta_dist.sample(shape, generator=generator, device=device) elif self.w_distribution == "poisson": rate = self.w_kwargs.get("rate", 3.0) dist = torch.distributions.Poisson(rate=rate) - return dist.sample(shape, generator=generator) + return dist.sample(shape, generator=generator, device=device) elif self.w_distribution == "cauchy": scale = self.w_kwargs.get("scale", 1.0) cauchy_dist = torch.distributions.StudentT(df=1, loc=0.0, scale=scale) - return cauchy_dist.sample(shape, generator=generator) + return cauchy_dist.sample(shape, generator=generator, device=device) elif self.w_distribution == "t-student": df = self.w_kwargs.get("df", 3.0) scale = self.w_kwargs.get("scale", 1.0) t_dist = torch.distributions.StudentT(df=df, loc=0.0, scale=scale) - return t_dist.sample(shape, generator=generator) + return t_dist.sample(shape, generator=generator, device=device) elif self.w_distribution == "rayleigh": lambda_param = self.w_kwargs.get("lambda_param", 1.0) sigma = lambda_param - X = torch.randn(shape, generator=generator) * sigma - Y = torch.randn(shape, generator=generator) * sigma + X = torch.randn(shape, generator=generator, device=device) * sigma + Y = torch.randn(shape, generator=generator, device=device) * sigma R = torch.sqrt(X**2 + Y**2) return R else: raise ValueError(f"Unsupported weight distribution: {self.w_distribution}") - def sample_noise(self, shape): + def sample_noise(self, shape, device='cpu'): # 1. if self.noise_type == "normal": - noise = torch.randn(shape) * self.noise_std + noise = torch.randn(shape, device=device) * self.noise_std # 2. elif self.noise_type == "uniform": a = math.sqrt(3) * self.noise_std - noise = torch.empty(shape).uniform_(-a, a) + noise = torch.empty(shape, device=device).uniform_(-a, a) # 3. elif self.noise_type == "laplace": scale_param = self.noise_std / math.sqrt(2.0) laplace_dist = torch.distributions.Laplace(loc=0, scale=scale_param) - noise = laplace_dist.sample(shape) + noise = laplace_dist.sample(shape, device=device) # 4. elif self.noise_type == "t-student": df = 3.0 scale_param = self.noise_std / math.sqrt(df / (df-2.0)) t_dist = torch.distributions.StudentT(df=df, loc=0, scale=scale_param) - noise = t_dist.sample(shape) + noise = t_dist.sample(shape, device=device) # 5. elif self.noise_type == "cauchy": scale_param = self.noise_std cauchy_dist = torch.distributions.StudentT(df=1, loc=0, scale=scale_param) - noise = cauchy_dist.sample(shape) + noise = cauchy_dist.sample(shape, device=device) # 6. elif self.noise_type == "exponential": exp_noise = torch.distributions.Exponential(rate=1.0 / self.noise_std) - noise = exp_noise.sample(shape) - self.noise_std + noise = exp_noise.sample(shape, device=device) - self.noise_std # 7. elif self.noise_type == "rayleigh": lambda_param = self.noise_std / math.sqrt(2.0 - math.pi / 2.0) @@ -494,8 +496,8 @@ def sample_noise(self, shape): # where sigma = lambda_param. sigma = lambda_param - X = torch.randn(shape) * sigma - Y = torch.randn(shape) * sigma + X = torch.randn(shape, device=device) * sigma + Y = torch.randn(shape, device=device) * sigma R = torch.sqrt(X**2 + Y**2) mean = lambda_param * math.sqrt(math.pi / 2.0) noise = R - mean @@ -506,13 +508,13 @@ def sample_noise(self, shape): var = (alpha * beta) / (((alpha + beta) ** 2) * (alpha + beta + 1)) std = math.sqrt(var) beta_dist = torch.distributions.Beta(concentration1=alpha, concentration0=beta) - X = beta_dist.sample(shape) + X = beta_dist.sample(shape, device=device) noise = (X - mean) / std * self.noise_std # 9. elif self.noise_type == "poisson": lam = 3.0 poisson_noise = torch.distributions.Poisson(lam) - X = poisson_noise.sample(shape) + X = poisson_noise.sample(shape, device=device) scale_factor = self.noise_std / math.sqrt(lam) noise = (X - lam) * scale_factor else: @@ -521,8 +523,8 @@ def sample_noise(self, shape): def evaluate(self, xs_b): ys_b = super().evaluate(xs_b) - noise = self.sample_noise(ys_b.shape) - ys_b_noisy = ys_b + noise.to(ys_b.device) + noise = self.sample_noise(ys_b.shape, device=ys_b.device) + ys_b_noisy = ys_b + noise if self.renormalize_ys: ys_b_noisy = ys_b_noisy * math.sqrt(self.n_dims) / ys_b_noisy.std() From c6b519e3f363b40709281b27ed8578c225700123 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Fri, 12 Dec 2025 00:35:04 +0700 Subject: [PATCH 79/88] fix error --- src/eval.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/src/eval.py b/src/eval.py index 529a1957..d5dd2853 100644 --- a/src/eval.py +++ b/src/eval.py @@ -277,14 +277,14 @@ def build_evals(conf): # task_name =["linear_regression" if task_name == "ar1_linear_regression" else task_name][0] if task_name not in ["linear_regression", "ar1_linear_regression"]: - if task_name != "linear_regression": - if task_name in ["relu_2nn_regression"]: - evaluation_kwargs["linear_regression"] = {"task_name": "linear_regression"} - for name, kwargs in evaluation_kwargs.items(): - # allow kwargs to override base_kwargs values - evaluation_kwargs[name] = base_kwargs.copy() - evaluation_kwargs[name].update(kwargs) - return evaluation_kwargs + if task_name != "linear_regression": + if task_name in ["relu_2nn_regression"]: + evaluation_kwargs["linear_regression"] = {"task_name": "linear_regression"} + for name, kwargs in evaluation_kwargs.items(): + # allow kwargs to override base_kwargs values + evaluation_kwargs[name] = base_kwargs.copy() + evaluation_kwargs[name].update(kwargs) + return evaluation_kwargs for strategy in [ "random_quadrants", From a1762379ac3555e778c313c6e08db2186ace52bf Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Fri, 12 Dec 2025 00:41:48 +0700 Subject: [PATCH 80/88] fix error --- src/models.py | 415 ++++++++++++++++++++++++++++++++++++++++++++++++ src/samplers.py | 200 ++++++++++++----------- 2 files changed, 518 insertions(+), 97 deletions(-) diff --git a/src/models.py b/src/models.py index da243f3d..527cf354 100644 --- a/src/models.py +++ b/src/models.py @@ -1193,3 +1193,418 @@ def _init_single(j): print(f"[{self.name}] Completed!") return torch.stack(preds, dim=1) + + xs_b[i] = torch.randn(n_points, self.n_dims, generator=generator, device=device) + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b + + +class BetaSampler(DataSampler): + def __init__(self, n_dims, bias=None, scale=None, alpha=2.0, beta=5.0): + super().__init__(n_dims) + if alpha <= 0 or beta <= 0: + raise ValueError("alpha and beta must be positive for Beta distribution.") + self.bias = bias + self.scale = scale + self.alpha = float(alpha) + self.beta = float(beta) + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): + beta_dist = torch.distributions.Beta(concentration1=self.alpha, concentration0=self.beta) + xs_b = _sample_distribution(beta_dist, b_size, (n_points, self.n_dims), seeds, device) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b + +class TStudentSampler(DataSampler): + def __init__(self, n_dims, bias=None, scale=None, df=3.0): + super().__init__(n_dims) + self.df = float(df) + self.bias = bias + self.scale = scale + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): + t_dist = torch.distributions.StudentT(df=self.df) + xs_b = _sample_distribution(t_dist, b_size, (n_points, self.n_dims), seeds, device) + + if self.scale is not None: + xs_b = xs_b * self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b + +class PoissonSampler(DataSampler): + def __init__(self, n_dims, bias=None, scale=None, rate=1.0): + super().__init__(n_dims) + self.rate = float(rate) + self.bias = bias + self.scale = scale + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): + poisson_dist = torch.distributions.Poisson(rate=self.rate) + xs_b = _sample_distribution(poisson_dist, b_size, (n_points, self.n_dims), seeds, device) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b + +class RayleighSampler(DataSampler): + def __init__(self, n_dims, bias=None, scale=None, scale_param=1.0): + super().__init__(n_dims) + self.bias = bias + self.scale = scale + self.scale_param = float(scale_param) + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): + rayleigh_dist = torch.distributions.Rayleigh(scale=self.scale_param) + xs_b = _sample_distribution(rayleigh_dist, b_size, (n_points, self.n_dims), seeds, device) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b + +class CauchySampler(DataSampler): + def __init__(self, n_dims, bias=None, scale=None, loc=0.0, scale_param=1.0): + super().__init__(n_dims) + self.bias = bias + self.scale = scale + self.loc = float(loc) + self.scale_param = float(scale_param) + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): + cauchy_dist = torch.distributions.Cauchy(loc=self.loc, scale=self.scale_param) + xs_b = _sample_distribution(cauchy_dist, b_size, (n_points, self.n_dims), seeds, device) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + + return xs_b + +class SparseGaussianSampler(DataSampler): + def __init__(self, n_dims, k, bias=None, scale=None): + super().__init__(n_dims) + if not (0 < k <= n_dims): + raise ValueError(f"k must be in range (0, {n_dims}]") + self.k = int(k) + self.bias = bias + # Store scale as float + self.scale = float(scale) if isinstance(scale, (int, float)) else 1.0 + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): + if seeds is None: + xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) + values = torch.randn(b_size, n_points, self.k, device=device) + rand_scores = torch.rand(b_size, n_points, self.n_dims, device=device) + _, indices = torch.topk(rand_scores, self.k, dim=-1) + xs_b.scatter_(dim=2, index=indices, src=values) + else: + xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) + assert len(seeds) == b_size + for i in range(b_size): + generator = torch.Generator(device=device).manual_seed(int(seeds[i])) + values = torch.randn(n_points, self.k, generator=generator, device=device) + rand_scores = torch.rand(n_points, self.n_dims, generator=generator, device=device) + _, indices = torch.topk(rand_scores, self.k, dim=-1) + xs_b[i].scatter_(dim=1, index=indices, src=values) + + if self.scale is not None: + # Simple scalar multiplication + xs_b = xs_b * self.scale + + if self.bias is not None: + xs_b += self.bias + + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + + return xs_b + + +class AR1Sampler(DataSampler): + def __init__(self, n_dims, rho=0.9, noise_std=1.0, bias=None, scale=None, compute_gradient=False): + super().__init__(n_dims) + assert 0 <= abs(rho) < 1, "|rho| must be < 1 for a stable AR(1)" + self.rho = float(rho) + self.noise_std = float(noise_std) + self.bias = bias + self.scale = scale + self.compute_gradient = compute_gradient + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): + # Shape: (batch, time, dims) + xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) + + generators = None + if seeds is not None: + assert len(seeds) == b_size + generators = [] + for seed in seeds: + g = torch.Generator(device=device) + g.manual_seed(int(seed)) + generators.append(g) + + # Initialize x_0 ~ N(0, I) + if generators is None: + xs_b[:, 0, :] = torch.randn(b_size, self.n_dims, device=device) + else: + for i in range(b_size): + xs_b[i, 0, :] = torch.randn(self.n_dims, generator=generators[i], device=device) + + # AR(1): x_t = rho * x_{t-1} + eps_t, eps_t ~ N(0, noise_std^2 I) + for t in range(1, n_points): + if generators is None: + eps_t = self.noise_std * torch.randn(b_size, self.n_dims, device=device) + else: + eps_t = torch.zeros(b_size, self.n_dims, device=device) + for i in range(b_size): + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i], device=device) + xs_b[:, t, :] = self.rho * xs_b[:, t - 1, :] + eps_t + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + + return xs_b + +class AR2Sampler(DataSampler): + def __init__(self, n_dims, ar1_coef=0.5, ar2_coef=0.3, noise_std=1.0, bias=None, scale=None): + super().__init__(n_dims) + assert abs(ar2_coef) < 1, "|ar2_coef| must be < 1 for a stable AR(2)" + + self.ar1_coef = float(ar1_coef) + self.ar2_coef = float(ar2_coef) + self.noise_std = float(noise_std) + self.bias = bias + self.scale = scale + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): + # Shape: (batch, time, dims) + xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) + + generators = None + if seeds is not None: + assert len(seeds) == b_size + generators = [] + for seed in seeds: + g = torch.Generator(device=device) + g.manual_seed(int(seed)) + generators.append(g) + + # Initialize first two time steps + for t in range(2): + if generators is None: + xs_b[:, t, :] = torch.randn(b_size, self.n_dims, device=device) + else: + for i in range(b_size): + xs_b[i, t, :] = torch.randn(self.n_dims, generator=generators[i], device=device) + + # AR(2): x_t = ar1_coef * x_{t-1} + ar2_coef * x_{t-2} + eps_t + for t in range(2, n_points): + if generators is None: + eps_t = self.noise_std * torch.randn(b_size, self.n_dims, device=device) + else: + eps_t = torch.zeros(b_size, self.n_dims, device=device) + for i in range(b_size): + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i], device=device) + xs_b[:, t, :] = ( + self.ar1_coef * xs_b[:, t - 1, :] + + self.ar2_coef * xs_b[:, t - 2, :] + + eps_t + ) + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + + return xs_b + +class VR2Sampler(DataSampler): + def __init__(self, n_dims, ar1_mat=None, ar2_mat=None, noise_std=1.0, bias=None, scale=None): + super().__init__(n_dims) + + if ar1_mat is None: + ar1_mat = 0.5 * torch.eye(n_dims) + if ar2_mat is None: + ar2_mat = 0.3 * torch.eye(n_dims) + + # Check + assert ar1_mat.shape == (n_dims, n_dims), "ar1_mat must be n_dims x n_dims" + assert ar2_mat.shape == (n_dims, n_dims), "ar2_mat must be n_dims x n_dims" + + self.ar1_mat = torch.tensor(ar1_mat, dtype=torch.float32) + self.ar2_mat = torch.tensor(ar2_mat, dtype=torch.float32) + self.noise_std = float(noise_std) + self.bias = bias + self.scale = scale + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): + xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) + + generators = None + if seeds is not None: + generators = [torch.Generator(device=device).manual_seed(int(seed)) for seed in seeds] + + # Initialize first two time points + for t in range(2): + if generators is None: + xs_b[:, t, :] = torch.randn(b_size, self.n_dims, device=device) + else: + for i in range(b_size): + xs_b[i, t, :] = torch.randn(self.n_dims, generator=generators[i], device=device) + + # VR(2): x_t = A1 * x_{t-1} + A2 * x_{t-2} + eps_t + for t in range(2, n_points): + if generators is None: + eps_t = self.noise_std * torch.randn(b_size, self.n_dims, device=device) + else: + eps_t = torch.zeros(b_size, self.n_dims, device=device) + for i in range(b_size): + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i], device=device) + + # Matrix multiplication for each sample in batch + ar1_mat_device = self.ar1_mat.to(device) + ar2_mat_device = self.ar2_mat.to(device) + xs_b[:, t, :] = (torch.matmul(xs_b[:, t-1, :], ar1_mat_device.T) + + torch.matmul(xs_b[:, t-2, :], ar2_mat_device.T) + + eps_t) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + + return xs_b + +class NonStationarySampler(DataSampler): + def __init__(self, n_dims, coef_base=0.5, coef_amplitude=0.4, noise_std=0.1, bias=None, scale=None): + super().__init__(n_dims) + self.coef_base = float(coef_base) + self.coef_amplitude = float(coef_amplitude) + self.noise_std = float(noise_std) + self.scale = scale + self.bias = bias + + def get_transition_matrix(self, t, n_points): + t_norm = t / (n_points - 1) if n_points > 1 else 0.0 + time_varying_factor = self.coef_base + self.coef_amplitude * math.sin(2 * math.pi * t_norm) + A_t = time_varying_factor * torch.eye(self.n_dims) + return A_t + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): + xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) + generators = None + if seeds is not None: + assert len(seeds) == b_size + generators = [torch.Generator(device=device).manual_seed(int(seed)) for seed in seeds] + + if generators is None: + xs_b[:,0,:] = torch.randn(b_size, self.n_dims, device=device) * self.noise_std + else: + for i in range(b_size): + xs_b[i, 0, :] = torch.randn(self.n_dims, generator=generators[i], device=device) * self.noise_std + + for t in range(1, n_points): + A_t = self.get_transition_matrix(t, n_points).to(device) + + if generators is None: + eps_t = self.noise_std * torch.randn(b_size, self.n_dims, device=device) + else: + eps_t = torch.zeros(b_size, self.n_dims, device=device) + for i in range(b_size): + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i], device=device) + xs_b[:, t, :] = (torch.matmul(xs_b[:, t-1, :], A_t) + eps_t) + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + + return xs_b + +class VAR1Sampler(DataSampler): + def __init__(self, n_dims, ar1_mat=None, noise_std=1.0, bias=None, scale=None): + super().__init__(n_dims) + + if ar1_mat is None: + ar1_mat = 0.9 * torch.eye(n_dims) + + assert ar1_mat.shape == (n_dims, n_dims), "ar1_mat must be n_dims x n_dims" + + if isinstance(ar1_mat, torch.Tensor): + self.ar1_mat = ar1_mat.float() + else: + self.ar1_mat = torch.tensor(ar1_mat, dtype=torch.float32) + + + + self.noise_std = float(noise_std) + self.bias = bias + self.scale = scale + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): + xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) + + generators = None + if seeds is not None: + assert len(seeds) == b_size + generators = [torch.Generator(device=device).manual_seed(int(seed)) for seed in seeds] + + if generators is None: + xs_b[:, 0, :] = torch.randn(b_size, self.n_dims, device=device) + else: + for i in range(b_size): + xs_b[i, 0, :] = torch.randn(self.n_dims, generator=generators[i], device=device) + + for t in range(1, n_points): + if generators is None: + eps_t = self.noise_std * torch.randn(b_size, self.n_dims, device=device) + else: + eps_t = torch.zeros(b_size, self.n_dims, device=device) + for i in range(b_size): + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i], device=device) + + ar1_mat_device = self.ar1_mat.to(device) + xs_b[:, t, :] = torch.matmul(xs_b[:, t - 1, :], ar1_mat_device.T) + eps_t + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 + return xs_b + diff --git a/src/samplers.py b/src/samplers.py index 1895a7c7..88f3b02a 100644 --- a/src/samplers.py +++ b/src/samplers.py @@ -54,18 +54,19 @@ def sample_transformation(eigenvalues, normalize=False): t *= math.sqrt(n_dims / norm_subspace) return t -def _sample_distribution(dist, b_size, inner_shape, seeds=None): +def _sample_distribution(dist, b_size, inner_shape, seeds=None, device="cpu"): sample_shape = (b_size, *inner_shape) if seeds is None: - return dist.sample(sample_shape) + samples = dist.sample(sample_shape) + return samples.to(device) if device != "cpu" else samples assert len(seeds) == b_size template = dist.mean - xs_b = torch.empty(sample_shape, dtype=template.dtype, device=template.device) + xs_b = torch.empty(sample_shape, dtype=template.dtype, device=device) for i, seed in enumerate(seeds): with torch.random.fork_rng(): torch.manual_seed(int(seed)) - xs_b[i] = dist.sample(inner_shape) + xs_b[i] = dist.sample(inner_shape).to(device) return xs_b class UniformSampler(DataSampler): @@ -76,9 +77,9 @@ def __init__(self, n_dims, bias=None, scale=None, low=0.0, high=1.0): self.low = low self.high = high - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): uni_dist = torch.distributions.Uniform(self.low, self.high) - xs_b = _sample_distribution(uni_dist, b_size, (n_points, self.n_dims), seeds) + xs_b = _sample_distribution(uni_dist, b_size, (n_points, self.n_dims), seeds, device) if self.scale is not None: xs_b = xs_b @ self.scale @@ -95,9 +96,9 @@ def __init__(self, n_dims, bias=None, scale=None, rate=1.0): self.scale = scale self.rate = float(rate) - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): exp_dist = torch.distributions.Exponential(rate=self.rate) - xs_b = _sample_distribution(exp_dist, b_size, (n_points, self.n_dims), seeds) + xs_b = _sample_distribution(exp_dist, b_size, (n_points, self.n_dims), seeds, device) if self.scale is not None: xs_b = xs_b @ self.scale @@ -116,9 +117,9 @@ def __init__(self, n_dims, bias=None, scale=None, loc=0.0, laplace_scale=1.0): self.loc = float(loc) self.laplace_scale = float(laplace_scale) - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): laplace_dist = torch.distributions.Laplace(loc=self.loc, scale=self.laplace_scale) - xs_b = _sample_distribution(laplace_dist, b_size, (n_points, self.n_dims), seeds) + xs_b = _sample_distribution(laplace_dist, b_size, (n_points, self.n_dims), seeds, device) if self.scale is not None: xs_b = xs_b @ self.scale @@ -139,9 +140,9 @@ def __init__(self, n_dims, bias=None, scale=None, concentration=2.0, rate=1.0): self.concentration = float(concentration) self.rate = float(rate) - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): gamma_dist = torch.distributions.Gamma(concentration=self.concentration, rate=self.rate) - xs_b = _sample_distribution(gamma_dist, b_size, (n_points, self.n_dims), seeds) + xs_b = _sample_distribution(gamma_dist, b_size, (n_points, self.n_dims), seeds, device) if self.scale is not None: xs_b = xs_b @ self.scale @@ -163,11 +164,11 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device= xs_b = torch.randn(b_size, n_points, self.n_dims, device=device) else: xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) - generator = torch.Generator() + generator = torch.Generator(device=device) assert len(seeds) == b_size for i, seed in enumerate(seeds): generator.manual_seed(seed) - xs_b[i] = torch.randn(n_points, self.n_dims, generator=generator) + xs_b[i] = torch.randn(n_points, self.n_dims, generator=generator, device=device) if self.scale is not None: xs_b = xs_b @ self.scale if self.bias is not None: @@ -188,10 +189,8 @@ def __init__(self, n_dims, bias=None, scale=None, alpha=2.0, beta=5.0): self.beta = float(beta) def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): - alpha_t = torch.tensor(self.alpha, device=device) - beta_t = torch.tensor(self.beta, device=device) beta_dist = torch.distributions.Beta(concentration1=self.alpha, concentration0=self.beta) - xs_b = _sample_distribution(beta_dist, b_size, (n_points, self.n_dims), seeds) + xs_b = _sample_distribution(beta_dist, b_size, (n_points, self.n_dims), seeds, device) if self.scale is not None: xs_b = xs_b @ self.scale @@ -210,15 +209,8 @@ def __init__(self, n_dims, bias=None, scale=None, df=3.0): def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): t_dist = torch.distributions.StudentT(df=self.df) - if seeds is None: - xs_b = t_dist.sample((b_size, n_points, self.n_dims)).to(device) - else: - xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) - generator = torch.Generator() - assert len(seeds) == b_size - for i, seed in enumerate(seeds): - generator.manual_seed(seed) - xs_b[i] = t_dist.sample((n_points, self.n_dims), generator=generator).to(device) + xs_b = _sample_distribution(t_dist, b_size, (n_points, self.n_dims), seeds, device) + if self.scale is not None: xs_b = xs_b * self.scale if self.bias is not None: @@ -226,15 +218,17 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device= if n_dims_truncated is not None: xs_b[:, :, n_dims_truncated:] = 0 return xs_b + class PoissonSampler(DataSampler): def __init__(self, n_dims, bias=None, scale=None, rate=1.0): super().__init__(n_dims) self.rate = float(rate) self.bias = bias self.scale = scale - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): poisson_dist = torch.distributions.Poisson(rate=self.rate) - xs_b = _sample_distribution(poisson_dist, b_size, (n_points, self.n_dims), seeds) + xs_b = _sample_distribution(poisson_dist, b_size, (n_points, self.n_dims), seeds, device) if self.scale is not None: xs_b = xs_b @ self.scale @@ -243,6 +237,7 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): if n_dims_truncated is not None: xs_b[:, :, n_dims_truncated:] = 0 return xs_b + class RayleighSampler(DataSampler): def __init__(self, n_dims, bias=None, scale=None, scale_param=1.0): super().__init__(n_dims) @@ -250,9 +245,9 @@ def __init__(self, n_dims, bias=None, scale=None, scale_param=1.0): self.scale = scale self.scale_param = float(scale_param) - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): rayleigh_dist = torch.distributions.Rayleigh(scale=self.scale_param) - xs_b = _sample_distribution(rayleigh_dist, b_size, (n_points, self.n_dims), seeds) + xs_b = _sample_distribution(rayleigh_dist, b_size, (n_points, self.n_dims), seeds, device) if self.scale is not None: xs_b = xs_b @ self.scale @@ -261,6 +256,7 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): if n_dims_truncated is not None: xs_b[:, :, n_dims_truncated:] = 0 return xs_b + class CauchySampler(DataSampler): def __init__(self, n_dims, bias=None, scale=None, loc=0.0, scale_param=1.0): super().__init__(n_dims) @@ -269,9 +265,9 @@ def __init__(self, n_dims, bias=None, scale=None, loc=0.0, scale_param=1.0): self.loc = float(loc) self.scale_param = float(scale_param) - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): cauchy_dist = torch.distributions.Cauchy(loc=self.loc, scale=self.scale_param) - xs_b = _sample_distribution(cauchy_dist, b_size, (n_points, self.n_dims), seeds) + xs_b = _sample_distribution(cauchy_dist, b_size, (n_points, self.n_dims), seeds, device) if self.scale is not None: xs_b = xs_b @ self.scale @@ -281,7 +277,7 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): xs_b[:, :, n_dims_truncated:] = 0 return xs_b -# code này là thêm: + class SparseGaussianSampler(DataSampler): def __init__(self, n_dims, k, bias=None, scale=None): super().__init__(n_dims) @@ -292,20 +288,20 @@ def __init__(self, n_dims, k, bias=None, scale=None): # Store scale as float self.scale = float(scale) if isinstance(scale, (int, float)) else 1.0 - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): if seeds is None: - xs_b = torch.zeros(b_size, n_points, self.n_dims) - values = torch.randn(b_size, n_points, self.k) - rand_scores = torch.rand(b_size, n_points, self.n_dims) + xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) + values = torch.randn(b_size, n_points, self.k, device=device) + rand_scores = torch.rand(b_size, n_points, self.n_dims, device=device) _, indices = torch.topk(rand_scores, self.k, dim=-1) xs_b.scatter_(dim=2, index=indices, src=values) else: - xs_b = torch.zeros(b_size, n_points, self.n_dims) + xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) assert len(seeds) == b_size for i in range(b_size): - generator = torch.Generator().manual_seed(int(seeds[i])) - values = torch.randn(n_points, self.k, generator=generator) - rand_scores = torch.rand(n_points, self.n_dims, generator=generator) + generator = torch.Generator(device=device).manual_seed(int(seeds[i])) + values = torch.randn(n_points, self.k, generator=generator, device=device) + rand_scores = torch.rand(n_points, self.n_dims, generator=generator, device=device) _, indices = torch.topk(rand_scores, self.k, dim=-1) xs_b[i].scatter_(dim=1, index=indices, src=values) @@ -323,7 +319,7 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): class AR1Sampler(DataSampler): - def __init__(self, n_dims, rho=0.9, noise_std=1.0, bias=None, scale=None,compute_gradient=False): + def __init__(self, n_dims, rho=0.9, noise_std=1.0, bias=None, scale=None, compute_gradient=False): super().__init__(n_dims) assert 0 <= abs(rho) < 1, "|rho| must be < 1 for a stable AR(1)" self.rho = float(rho) @@ -332,36 +328,34 @@ def __init__(self, n_dims, rho=0.9, noise_std=1.0, bias=None, scale=None,compute self.scale = scale self.compute_gradient = compute_gradient -# if __name__ == "__main__": -# test_var1_sampler() - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): # Shape: (batch, time, dims) - xs_b = torch.zeros(b_size, n_points, self.n_dims) + xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) generators = None if seeds is not None: assert len(seeds) == b_size generators = [] for seed in seeds: - g = torch.Generator() + g = torch.Generator(device=device) g.manual_seed(int(seed)) generators.append(g) # Initialize x_0 ~ N(0, I) if generators is None: - xs_b[:, 0, :] = torch.randn(b_size, self.n_dims) + xs_b[:, 0, :] = torch.randn(b_size, self.n_dims, device=device) else: for i in range(b_size): - xs_b[i, 0, :] = torch.randn(self.n_dims, generator=generators[i]) + xs_b[i, 0, :] = torch.randn(self.n_dims, generator=generators[i], device=device) # AR(1): x_t = rho * x_{t-1} + eps_t, eps_t ~ N(0, noise_std^2 I) for t in range(1, n_points): if generators is None: - eps_t = self.noise_std * torch.randn(b_size, self.n_dims) + eps_t = self.noise_std * torch.randn(b_size, self.n_dims, device=device) else: - eps_t = torch.zeros(b_size, self.n_dims) + eps_t = torch.zeros(b_size, self.n_dims, device=device) for i in range(b_size): - eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i]) + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i], device=device) xs_b[:, t, :] = self.rho * xs_b[:, t - 1, :] + eps_t if self.scale is not None: @@ -373,6 +367,7 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): xs_b[:, :, n_dims_truncated:] = 0 return xs_b + class AR2Sampler(DataSampler): def __init__(self, n_dims, ar1_coef=0.5, ar2_coef=0.3, noise_std=1.0, bias=None, scale=None): super().__init__(n_dims) @@ -384,35 +379,35 @@ def __init__(self, n_dims, ar1_coef=0.5, ar2_coef=0.3, noise_std=1.0, bias=None, self.bias = bias self.scale = scale - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): # Shape: (batch, time, dims) - xs_b = torch.zeros(b_size, n_points, self.n_dims) + xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) generators = None if seeds is not None: assert len(seeds) == b_size generators = [] for seed in seeds: - g = torch.Generator() + g = torch.Generator(device=device) g.manual_seed(int(seed)) generators.append(g) # Initialize first two time steps for t in range(2): if generators is None: - xs_b[:, t, :] = torch.randn(b_size, self.n_dims) + xs_b[:, t, :] = torch.randn(b_size, self.n_dims, device=device) else: for i in range(b_size): - xs_b[i, t, :] = torch.randn(self.n_dims, generator=generators[i]) + xs_b[i, t, :] = torch.randn(self.n_dims, generator=generators[i], device=device) # AR(2): x_t = ar1_coef * x_{t-1} + ar2_coef * x_{t-2} + eps_t for t in range(2, n_points): if generators is None: - eps_t = self.noise_std * torch.randn(b_size, self.n_dims) + eps_t = self.noise_std * torch.randn(b_size, self.n_dims, device=device) else: - eps_t = torch.zeros(b_size, self.n_dims) + eps_t = torch.zeros(b_size, self.n_dims, device=device) for i in range(b_size): - eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i]) + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i], device=device) xs_b[:, t, :] = ( self.ar1_coef * xs_b[:, t - 1, :] + self.ar2_coef * xs_b[:, t - 2, :] + @@ -427,6 +422,7 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): xs_b[:, :, n_dims_truncated:] = 0 return xs_b + class VR2Sampler(DataSampler): def __init__(self, n_dims, ar1_mat=None, ar2_mat=None, noise_std=1.0, bias=None, scale=None): super().__init__(n_dims) @@ -446,33 +442,35 @@ def __init__(self, n_dims, ar1_mat=None, ar2_mat=None, noise_std=1.0, bias=None, self.bias = bias self.scale = scale - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): - xs_b = torch.zeros(b_size, n_points, self.n_dims) + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): + xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) generators = None if seeds is not None: - generators = [torch.Generator().manual_seed(int(seed)) for seed in seeds] + generators = [torch.Generator(device=device).manual_seed(int(seed)) for seed in seeds] # Initialize first two time points for t in range(2): if generators is None: - xs_b[:, t, :] = torch.randn(b_size, self.n_dims) + xs_b[:, t, :] = torch.randn(b_size, self.n_dims, device=device) else: for i in range(b_size): - xs_b[i, t, :] = torch.randn(self.n_dims, generator=generators[i]) + xs_b[i, t, :] = torch.randn(self.n_dims, generator=generators[i], device=device) # VR(2): x_t = A1 * x_{t-1} + A2 * x_{t-2} + eps_t for t in range(2, n_points): if generators is None: - eps_t = self.noise_std * torch.randn(b_size, self.n_dims) + eps_t = self.noise_std * torch.randn(b_size, self.n_dims, device=device) else: - eps_t = torch.zeros(b_size, self.n_dims) + eps_t = torch.zeros(b_size, self.n_dims, device=device) for i in range(b_size): - eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i]) + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i], device=device) # Matrix multiplication for each sample in batch - xs_b[:, t, :] = (torch.matmul(xs_b[:, t-1, :], self.ar1_mat.T) + - torch.matmul(xs_b[:, t-2, :], self.ar2_mat.T) + + ar1_mat_device = self.ar1_mat.to(device) + ar2_mat_device = self.ar2_mat.to(device) + xs_b[:, t, :] = (torch.matmul(xs_b[:, t-1, :], ar1_mat_device.T) + + torch.matmul(xs_b[:, t-2, :], ar2_mat_device.T) + eps_t) if self.scale is not None: @@ -484,41 +482,44 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): xs_b[:, :, n_dims_truncated:] = 0 return xs_b - + class NonStationarySampler(DataSampler): - def __init__(self, n_dims, coef_base=0.5, coef_amplitude=0.4, noise_std = 0.1, bias=None, scale=None): + def __init__(self, n_dims, coef_base=0.5, coef_amplitude=0.4, noise_std=0.1, bias=None, scale=None): super().__init__(n_dims) self.coef_base = float(coef_base) self.coef_amplitude = float(coef_amplitude) self.noise_std = float(noise_std) self.scale = scale self.bias = bias + def get_transition_matrix(self, t, n_points): t_norm = t / (n_points - 1) if n_points > 1 else 0.0 time_varying_factor = self.coef_base + self.coef_amplitude * math.sin(2 * math.pi * t_norm) A_t = time_varying_factor * torch.eye(self.n_dims) return A_t - def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): - xs_b = torch.zeros(b_size, n_points, self.n_dims) + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): + xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) generators = None if seeds is not None: assert len(seeds) == b_size - generators = [torch.Generator().manual_seed(int(seed)) for seed in seeds] + generators = [torch.Generator(device=device).manual_seed(int(seed)) for seed in seeds] + if generators is None: - xs_b[:,0,:] = torch.randn(b_size, self.n_dims) * self.noise_std + xs_b[:,0,:] = torch.randn(b_size, self.n_dims, device=device) * self.noise_std else: for i in range(b_size): - xs_b[i, 0, :] = torch.randn(self.n_dims, generator=generators[i]) * self.noise_std + xs_b[i, 0, :] = torch.randn(self.n_dims, generator=generators[i], device=device) * self.noise_std + for t in range(1, n_points): - A_t = self.get_transition_matrix(t, n_points) + A_t = self.get_transition_matrix(t, n_points).to(device) if generators is None: - eps_t = self.noise_std * torch.randn(b_size, self.n_dims) + eps_t = self.noise_std * torch.randn(b_size, self.n_dims, device=device) else: - eps_t = torch.zeros(b_size, self.n_dims) + eps_t = torch.zeros(b_size, self.n_dims, device=device) for i in range(b_size): - eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i]) + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i], device=device) xs_b[:, t, :] = (torch.matmul(xs_b[:, t-1, :], A_t) + eps_t) if self.scale is not None: @@ -527,6 +528,7 @@ def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None): xs_b += self.bias return xs_b + class VAR1Sampler(DataSampler): def __init__(self, n_dims, ar1_mat=None, noise_std=1.0, bias=None, scale=None): super().__init__(n_dims) @@ -544,32 +546,36 @@ def __init__(self, n_dims, ar1_mat=None, noise_std=1.0, bias=None, scale=None): self.noise_std = float(noise_std) self.bias = bias self.scale = scale - def sample(self, n_points, b_size, n_dims_truncated=None, seeds=None): - xs_b = torch.zeros(b_size, n_points, self.n_dims) + + def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None, device="cpu"): + xs_b = torch.zeros(b_size, n_points, self.n_dims, device=device) generators = None if seeds is not None: assert len(seeds) == b_size - generators = [torch.Generator().manual_seed(int(seed)) for seed in seeds] + generators = [torch.Generator(device=device).manual_seed(int(seed)) for seed in seeds] if generators is None: - xs_b[:, 0, :] = torch.randn(b_size, self.n_dims) + xs_b[:, 0, :] = torch.randn(b_size, self.n_dims, device=device) else: for i in range(b_size): - xs_b[i, 0, i] = torch.randn(self.n_dims, generator=generators[i]) + xs_b[i, 0, :] = torch.randn(self.n_dims, generator=generators[i], device=device) + for t in range(1, n_points): if generators is None: - eps_t = self.noise_std * torch.randn(b_size, self.n_dims) + eps_t = self.noise_std * torch.randn(b_size, self.n_dims, device=device) else: - eps_t = torch.zeros(b_size, self.n_dims) + eps_t = torch.zeros(b_size, self.n_dims, device=device) for i in range(b_size): - eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i]) - xs_b[:, t, :] = torch.matmul(xs_b[:, t - 1, :], self.ar1_mat.T) + eps_t - - if self.scale is not None: - xs_b = xs_b @ self.scale - if self.bias is not None: - xs_b += self.bias - if n_dims_truncated is not None: - xs_b[:, :, n_dims_truncated:] = 0 + eps_t[i] = self.noise_std * torch.randn(self.n_dims, generator=generators[i], device=device) + + ar1_mat_device = self.ar1_mat.to(device) + xs_b[:, t, :] = torch.matmul(xs_b[:, t - 1, :], ar1_mat_device.T) + eps_t + + if self.scale is not None: + xs_b = xs_b @ self.scale + if self.bias is not None: + xs_b += self.bias + if n_dims_truncated is not None: + xs_b[:, :, n_dims_truncated:] = 0 return xs_b From 63115280aeecc0ce0df980b55aa71387bc0a17ac Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 15 Dec 2025 14:42:08 +0700 Subject: [PATCH 81/88] fix conflict device --- src/conf/template.yaml | 5 +- src/conf/uniform_hypersphere_regression.yaml | 1 - src/eval.ipynb | 433 +++++++++---------- src/eval.py | 6 +- src/figure3_4.py | 185 ++++++++ src/models.py | 7 +- src/plot_utils.py | 4 +- src/tasks.py | 4 +- src/train.py | 6 +- 9 files changed, 417 insertions(+), 234 deletions(-) diff --git a/src/conf/template.yaml b/src/conf/template.yaml index 7fc3ee7c..ac2a5df4 100644 --- a/src/conf/template.yaml +++ b/src/conf/template.yaml @@ -25,7 +25,7 @@ training: interval: 2000 # One of: gaussian, sparse_gaussian, ar1, vr1, ar2, vr2, nonstation - data: tstudent + data: gaussian # Data kwargs: # - When data == 'sparse_gaussian': you may set 'k' (number of non-zero coords). @@ -60,7 +60,8 @@ training: save_every_steps: 100 train_steps: 500001 -out_dir: .../models/noisy_linear_regression/ +out_dir: D:\Henry-Projects\ChestXray\data\in-context-learning\models\noisy_linear_regression +# out_dir: ../models/noisy_linear_regression wandb: project: "in-context-training" diff --git a/src/conf/uniform_hypersphere_regression.yaml b/src/conf/uniform_hypersphere_regression.yaml index 99c091fc..cb25e79d 100644 --- a/src/conf/uniform_hypersphere_regression.yaml +++ b/src/conf/uniform_hypersphere_regression.yaml @@ -13,7 +13,6 @@ training: task: uniform_hypersphere_regression task_kwargs: scale: 1.0 - normalize: true data: gaussian data_kwargs: {} curriculum: diff --git a/src/eval.ipynb b/src/eval.ipynb index 2aef9b2a..2f3972c3 100644 --- a/src/eval.ipynb +++ b/src/eval.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "id": "0e8d018b", "metadata": { "scrolled": true @@ -74,7 +74,7 @@ " \n", " \n", " \n", - " 7\n", + " 6\n", " 1_beta_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -87,7 +87,7 @@ " 1_beta_noise_gaussian_data_experiment\n", " \n", " \n", - " 8\n", + " 7\n", " 1_exponential_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -100,7 +100,7 @@ " 1_exponential_noise_gaussian_data_experiment\n", " \n", " \n", - " 9\n", + " 8\n", " 1_poisson_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -113,7 +113,7 @@ " 1_poisson_noise_gaussian_data_experiment\n", " \n", " \n", - " 10\n", + " 9\n", " 1_t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -126,7 +126,7 @@ " 1_t_student_noise_gaussian_data_experiment\n", " \n", " \n", - " 11\n", + " 10\n", " 1_uniform_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -139,7 +139,7 @@ " 1_uniform_noise_gaussian_data_experiment\n", " \n", " \n", - " 6\n", + " 5\n", " 123e9cbd-1566-443d-9491-f23b6b9af0e2\n", " linear_regression\n", " Transformer\n", @@ -152,7 +152,7 @@ " 20_dims_uniform_error_gaussian_data\n", " \n", " \n", - " 14\n", + " 13\n", " 64d381ae-08d0-4bae-8e40-f1a68cfb2e97\n", " linear_regression\n", " Transformer\n", @@ -165,7 +165,7 @@ " 20_dims_uniform_error_gaussian_data_\n", " \n", " \n", - " 12\n", + " 11\n", " 3_laplace_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -178,7 +178,7 @@ " 3_laplace_noise_gaussian_data_experiment\n", " \n", " \n", - " 13\n", + " 12\n", " 3_tstudent_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -204,7 +204,7 @@ " 4_std_sparse_linear_regression\n", " \n", " \n", - " 18\n", + " 17\n", " beta_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -217,7 +217,7 @@ " beta_noise_ar1_data_experiment\n", " \n", " \n", - " 19\n", + " 18\n", " beta_noisy_linear_regression_40_100k\n", " linear_regression\n", " Transformer\n", @@ -243,7 +243,7 @@ " case1_sparse_regression\n", " \n", " \n", - " 2\n", + " 1\n", " case2_heavy_tail_t_student\n", " heavy_tail_noise_killer\n", " Transformer\n", @@ -256,7 +256,7 @@ " case2_heavy_tail_t_student\n", " \n", " \n", - " 3\n", + " 2\n", " case2_heavy_tail_t_student_1_1\n", " heavy_tail_noise_killer\n", " Transformer\n", @@ -269,7 +269,7 @@ " case2_heavy_tail_t_student_1_1\n", " \n", " \n", - " 4\n", + " 3\n", " case2_heavy_tail_t_student_1_2\n", " heavy_tail_noise_killer\n", " Transformer\n", @@ -295,7 +295,7 @@ " case3_bounded_support\n", " \n", " \n", - " 39\n", + " 37\n", " case4_mixture_tasks\n", " mixture_tasks_killer\n", " Transformer\n", @@ -308,7 +308,7 @@ " case4_mixture_tasks\n", " \n", " \n", - " 40\n", + " 38\n", " case4_mixture_tasks_1_1\n", " mixture_tasks_killer\n", " Transformer\n", @@ -347,7 +347,7 @@ " case5_transfer_tradeoff_1_1\n", " \n", " \n", - " 17\n", + " 16\n", " aed365ed-51e2-4a72-8374-ae954b37be14\n", " linear_regression\n", " Transformer\n", @@ -360,20 +360,7 @@ " data_sparse_linear_regression\n", " \n", " \n", - " 1\n", - " pretrained\n", - " decision_tree\n", - " Transformer\n", - " depth=4\n", - " -1\n", - " -1\n", - " 20\n", - " 12\n", - " 8\n", - " decision_tree_pretrained\n", - " \n", - " \n", - " 20\n", + " 19\n", " exponential_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -386,7 +373,7 @@ " exponential_noise_gaussian_data_experiment\n", " \n", " \n", - " 21\n", + " 20\n", " exponential_w\n", " linear_regression\n", " Transformer\n", @@ -399,7 +386,7 @@ " exponential_w\n", " \n", " \n", - " 22\n", + " 21\n", " exponential_weighted_experiment_100k\n", " linear_regression\n", " Transformer\n", @@ -412,7 +399,7 @@ " exponential_weighted_experiment_100k\n", " \n", " \n", - " 23\n", + " 22\n", " exponential_weighted_experiment_150k\n", " linear_regression\n", " Transformer\n", @@ -425,7 +412,7 @@ " exponential_weighted_experiment_150k\n", " \n", " \n", - " 24\n", + " 23\n", " exponential_weighted_regression\n", " linear_regression\n", " Transformer\n", @@ -438,7 +425,7 @@ " exponential_weights_experiment\n", " \n", " \n", - " 25\n", + " 24\n", " laplace_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -451,7 +438,7 @@ " laplace_noise_gaussian_data_experiment\n", " \n", " \n", - " 26\n", + " 25\n", " laplace_w\n", " linear_regression\n", " Transformer\n", @@ -464,7 +451,7 @@ " laplace_w\n", " \n", " \n", - " 16\n", + " 15\n", " a2fcec3c-8ce5-49bf-a8bc-08136b31ec36\n", " linear_regression\n", " Transformer\n", @@ -477,7 +464,7 @@ " laplace_weights_experiment\n", " \n", " \n", - " 28\n", + " 27\n", " pretrained\n", " linear_regression\n", " Transformer\n", @@ -490,7 +477,33 @@ " linear_regression_pretrained\n", " \n", " \n", - " 27\n", + " 39\n", + " lr_wx\n", + " noisy_linear_regression\n", + " Transformer\n", + " noise_std=1.0_noise_type=laplace_w_distributio...\n", + " -1\n", + " -1\n", + " 20\n", + " 12\n", + " 8\n", + " lr_wx\n", + " \n", + " \n", + " 40\n", + " lr_wx_1\n", + " noisy_linear_regression\n", + " Transformer\n", + " noise_std=1.0_noise_type=uniform_w_distributio...\n", + " -1\n", + " -1\n", + " 20\n", + " 12\n", + " 8\n", + " lr_wx_1\n", + " \n", + " \n", + " 26\n", " lr_wx_mixed\n", " linear_regression\n", " Transformer\n", @@ -503,7 +516,7 @@ " lr_wx_mixed\n", " \n", " \n", - " 29\n", + " 28\n", " rayleigh_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -516,20 +529,7 @@ " rayleigh_noise_gaussian_data_experiment\n", " \n", " \n", - " 41\n", - " pretrained\n", - " relu_2nn_regression\n", - " Transformer\n", - " hidden_layer_size=100\n", - " -1\n", - " -1\n", - " 20\n", - " 12\n", - " 8\n", - " relu_2nn_regression_pretrained\n", - " \n", - " \n", - " 15\n", + " 14\n", " 82e728b0-a061-448e-8d7a-f3c79c0c74e5\n", " linear_regression\n", " Transformer\n", @@ -555,7 +555,7 @@ " sparse\n", " \n", " \n", - " 5\n", + " 4\n", " 03de46b6-429a-4151-92e6-3588231c6cad\n", " linear_regression\n", " Transformer\n", @@ -581,7 +581,7 @@ " sparse_regression_pretrained\n", " \n", " \n", - " 32\n", + " 31\n", " t_student_noise_gaussian_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -594,7 +594,7 @@ " t_student_noise_gaussian_data_experiment\n", " \n", " \n", - " 30\n", + " 29\n", " sparse_gaussian\n", " linear_regression\n", " Transformer\n", @@ -607,7 +607,7 @@ " task_sparse_data\n", " \n", " \n", - " 31\n", + " 30\n", " test_cauchy\n", " linear_regression\n", " Transformer\n", @@ -620,7 +620,7 @@ " test\n", " \n", " \n", - " 34\n", + " 33\n", " uniform_hypersphere_regression\n", " linear_regression\n", " Transformer\n", @@ -633,7 +633,7 @@ " uniform_hypersphere_experiment\n", " \n", " \n", - " 33\n", + " 32\n", " uniform_hypersphere_experiment_standard\n", " linear_regression\n", " Transformer\n", @@ -646,7 +646,7 @@ " uniform_hypersphere_experiment_standard\n", " \n", " \n", - " 35\n", + " 34\n", " uniform_noise_ar1_data_experiment\n", " linear_regression\n", " Transformer\n", @@ -659,7 +659,7 @@ " uniform_noise_ar1_data_experiment\n", " \n", " \n", - " 36\n", + " 35\n", " uniform_noise_gaussian_data_experiment_\n", " linear_regression\n", " Transformer\n", @@ -672,9 +672,9 @@ " uniform_noise_gaussian_data_experiment\n", " \n", " \n", - " 37\n", + " 41\n", " w_exp_x_gamma_e_uni\n", - " linear_regression\n", + " noisy_linear_regression\n", " Transformer\n", " noise_std=1.0_noise_type=uniform_w_distributio...\n", " -1\n", @@ -685,7 +685,7 @@ " w_expo x_gamma e uni\n", " \n", " \n", - " 38\n", + " 36\n", " w_laplace_x_exponential_noise_poisson\n", " linear_regression\n", " Transformer\n", @@ -703,207 +703,207 @@ ], "text/plain": [ " run_id task \\\n", - "7 1_beta_noise_gaussian_data_experiment linear_regression \n", - "8 1_exponential_noise_gaussian_data_experiment linear_regression \n", - "9 1_poisson_noise_gaussian_data_experiment linear_regression \n", - "10 1_t_student_noise_gaussian_data_experiment linear_regression \n", - "11 1_uniform_noise_gaussian_data_experiment linear_regression \n", - "6 123e9cbd-1566-443d-9491-f23b6b9af0e2 linear_regression \n", - "14 64d381ae-08d0-4bae-8e40-f1a68cfb2e97 linear_regression \n", - "12 3_laplace_noise_gaussian_data_experiment linear_regression \n", - "13 3_tstudent_noise_gaussian_data_experiment linear_regression \n", + "6 1_beta_noise_gaussian_data_experiment linear_regression \n", + "7 1_exponential_noise_gaussian_data_experiment linear_regression \n", + "8 1_poisson_noise_gaussian_data_experiment linear_regression \n", + "9 1_t_student_noise_gaussian_data_experiment linear_regression \n", + "10 1_uniform_noise_gaussian_data_experiment linear_regression \n", + "5 123e9cbd-1566-443d-9491-f23b6b9af0e2 linear_regression \n", + "13 64d381ae-08d0-4bae-8e40-f1a68cfb2e97 linear_regression \n", + "11 3_laplace_noise_gaussian_data_experiment linear_regression \n", + "12 3_tstudent_noise_gaussian_data_experiment linear_regression \n", "43 daa2cd45-f1c0-4a0c-9100-e171129624c9 sparse_linear_regression \n", - "18 beta_noise_ar1_data_experiment linear_regression \n", - "19 beta_noisy_linear_regression_40_100k linear_regression \n", + "17 beta_noise_ar1_data_experiment linear_regression \n", + "18 beta_noisy_linear_regression_40_100k linear_regression \n", "45 case1_sparse_regression sparse_regression_killer \n", - "2 case2_heavy_tail_t_student heavy_tail_noise_killer \n", - "3 case2_heavy_tail_t_student_1_1 heavy_tail_noise_killer \n", - "4 case2_heavy_tail_t_student_1_2 heavy_tail_noise_killer \n", + "1 case2_heavy_tail_t_student heavy_tail_noise_killer \n", + "2 case2_heavy_tail_t_student_1_1 heavy_tail_noise_killer \n", + "3 case2_heavy_tail_t_student_1_2 heavy_tail_noise_killer \n", "0 bounded_support_killer bounded_support_killer \n", - "39 case4_mixture_tasks mixture_tasks_killer \n", - "40 case4_mixture_tasks_1_1 mixture_tasks_killer \n", + "37 case4_mixture_tasks mixture_tasks_killer \n", + "38 case4_mixture_tasks_1_1 mixture_tasks_killer \n", "46 case5_transfer_tradeoff transfer_tradeoff_task \n", "47 case5_transfer_tradeoff_1_1 transfer_tradeoff_task \n", - "17 aed365ed-51e2-4a72-8374-ae954b37be14 linear_regression \n", - "1 pretrained decision_tree \n", - "20 exponential_noise_gaussian_data_experiment linear_regression \n", - "21 exponential_w linear_regression \n", - "22 exponential_weighted_experiment_100k linear_regression \n", - "23 exponential_weighted_experiment_150k linear_regression \n", - "24 exponential_weighted_regression linear_regression \n", - "25 laplace_noise_gaussian_data_experiment linear_regression \n", - "26 laplace_w linear_regression \n", - "16 a2fcec3c-8ce5-49bf-a8bc-08136b31ec36 linear_regression \n", - "28 pretrained linear_regression \n", - "27 lr_wx_mixed linear_regression \n", - "29 rayleigh_noise_gaussian_data_experiment linear_regression \n", - "41 pretrained relu_2nn_regression \n", - "15 82e728b0-a061-448e-8d7a-f3c79c0c74e5 linear_regression \n", + "16 aed365ed-51e2-4a72-8374-ae954b37be14 linear_regression \n", + "19 exponential_noise_gaussian_data_experiment linear_regression \n", + "20 exponential_w linear_regression \n", + "21 exponential_weighted_experiment_100k linear_regression \n", + "22 exponential_weighted_experiment_150k linear_regression \n", + "23 exponential_weighted_regression linear_regression \n", + "24 laplace_noise_gaussian_data_experiment linear_regression \n", + "25 laplace_w linear_regression \n", + "15 a2fcec3c-8ce5-49bf-a8bc-08136b31ec36 linear_regression \n", + "27 pretrained linear_regression \n", + "39 lr_wx noisy_linear_regression \n", + "40 lr_wx_1 noisy_linear_regression \n", + "26 lr_wx_mixed linear_regression \n", + "28 rayleigh_noise_gaussian_data_experiment linear_regression \n", + "14 82e728b0-a061-448e-8d7a-f3c79c0c74e5 linear_regression \n", "42 5bb54dbc-0f41-4f33-a0b2-7af35d8d1615 sparse_linear_regression \n", - "5 03de46b6-429a-4151-92e6-3588231c6cad linear_regression \n", + "4 03de46b6-429a-4151-92e6-3588231c6cad linear_regression \n", "44 pretrained sparse_linear_regression \n", - "32 t_student_noise_gaussian_data_experiment linear_regression \n", - "30 sparse_gaussian linear_regression \n", - "31 test_cauchy linear_regression \n", - "34 uniform_hypersphere_regression linear_regression \n", - "33 uniform_hypersphere_experiment_standard linear_regression \n", - "35 uniform_noise_ar1_data_experiment linear_regression \n", - "36 uniform_noise_gaussian_data_experiment_ linear_regression \n", - "37 w_exp_x_gamma_e_uni linear_regression \n", - "38 w_laplace_x_exponential_noise_poisson linear_regression \n", + "31 t_student_noise_gaussian_data_experiment linear_regression \n", + "29 sparse_gaussian linear_regression \n", + "30 test_cauchy linear_regression \n", + "33 uniform_hypersphere_regression linear_regression \n", + "32 uniform_hypersphere_experiment_standard linear_regression \n", + "34 uniform_noise_ar1_data_experiment linear_regression \n", + "35 uniform_noise_gaussian_data_experiment_ linear_regression \n", + "41 w_exp_x_gamma_e_uni noisy_linear_regression \n", + "36 w_laplace_x_exponential_noise_poisson linear_regression \n", "\n", " model kwargs num_tasks \\\n", + "6 Transformer -1 \n", "7 Transformer -1 \n", "8 Transformer -1 \n", "9 Transformer -1 \n", "10 Transformer -1 \n", + "5 Transformer -1 \n", + "13 Transformer -1 \n", "11 Transformer -1 \n", - "6 Transformer -1 \n", - "14 Transformer -1 \n", "12 Transformer -1 \n", - "13 Transformer -1 \n", "43 Transformer sparsity=5 -1 \n", - "18 Transformer -1 \n", - "19 Transformer noise_type=beta -1 \n", + "17 Transformer -1 \n", + "18 Transformer noise_type=beta -1 \n", "45 Transformer k_sparse=2_scale=1.0 -1 \n", + "1 Transformer df=3.0_noise_scale=0.5_noise_type=t-student -1 \n", "2 Transformer df=3.0_noise_scale=0.5_noise_type=t-student -1 \n", - "3 Transformer df=3.0_noise_scale=0.5_noise_type=t-student -1 \n", - "4 Transformer df=1.0_noise_scale=2.0_noise_type=t-student -1 \n", + "3 Transformer df=1.0_noise_scale=2.0_noise_type=t-student -1 \n", "0 Transformer rate=1.0_scale=1.0 -1 \n", - "39 Transformer scale=1.0 -1 \n", - "40 Transformer scale=1.0 -1 \n", + "37 Transformer scale=1.0 -1 \n", + "38 Transformer scale=1.0 -1 \n", "46 Transformer mixture_std=2.0_prior_type=mixture_gaussian_sc... -1 \n", "47 Transformer mixture_std=2.0_prior_type=mixture_gaussian_sc... -1 \n", - "17 Transformer k=5_sparsity=3 -1 \n", - "1 Transformer depth=4 -1 \n", - "20 Transformer -1 \n", + "16 Transformer k=5_sparsity=3 -1 \n", + "19 Transformer -1 \n", + "20 Transformer rate=1.0_scale=1.0 -1 \n", "21 Transformer rate=1.0_scale=1.0 -1 \n", "22 Transformer rate=1.0_scale=1.0 -1 \n", "23 Transformer rate=1.0_scale=1.0 -1 \n", - "24 Transformer rate=1.0_scale=1.0 -1 \n", - "25 Transformer -1 \n", - "26 Transformer scale=1.0_weight_scale=1.0 -1 \n", - "16 Transformer scale=1.0_weight_scale=1.0 -1 \n", + "24 Transformer -1 \n", + "25 Transformer scale=1.0_weight_scale=1.0 -1 \n", + "15 Transformer scale=1.0_weight_scale=1.0 -1 \n", + "27 Transformer -1 \n", + "39 Transformer noise_std=1.0_noise_type=laplace_w_distributio... -1 \n", + "40 Transformer noise_std=1.0_noise_type=uniform_w_distributio... -1 \n", + "26 Transformer noise_std=2.0_noise_type=normal_w_distribution... -1 \n", "28 Transformer -1 \n", - "27 Transformer noise_std=2.0_noise_type=normal_w_distribution... -1 \n", - "29 Transformer -1 \n", - "41 Transformer hidden_layer_size=100 -1 \n", - "15 Transformer sparsity=5 -1 \n", + "14 Transformer sparsity=5 -1 \n", "42 Transformer -1 \n", - "5 Transformer -1 \n", + "4 Transformer -1 \n", "44 Transformer sparsity=3 -1 \n", - "32 Transformer -1 \n", - "30 Transformer -1 \n", - "31 Transformer noise_type=cauchy -1 \n", - "34 Transformer normalize=True_scale=1.0 -1 \n", + "31 Transformer -1 \n", + "29 Transformer -1 \n", + "30 Transformer noise_type=cauchy -1 \n", "33 Transformer normalize=True_scale=1.0 -1 \n", + "32 Transformer normalize=True_scale=1.0 -1 \n", + "34 Transformer -1 \n", "35 Transformer -1 \n", + "41 Transformer noise_std=1.0_noise_type=uniform_w_distributio... -1 \n", "36 Transformer -1 \n", - "37 Transformer noise_std=1.0_noise_type=uniform_w_distributio... -1 \n", - "38 Transformer -1 \n", "\n", " num_examples n_dims n_layer n_head \\\n", + "6 -1 5 4 8 \n", "7 -1 5 4 8 \n", "8 -1 5 4 8 \n", "9 -1 5 4 8 \n", "10 -1 5 4 8 \n", + "5 -1 20 4 8 \n", + "13 -1 20 4 8 \n", "11 -1 5 4 8 \n", - "6 -1 20 4 8 \n", - "14 -1 20 4 8 \n", "12 -1 5 4 8 \n", - "13 -1 5 4 8 \n", "43 -1 15 4 8 \n", - "18 -1 5 4 8 \n", - "19 -1 20 4 8 \n", + "17 -1 5 4 8 \n", + "18 -1 20 4 8 \n", "45 -1 20 4 8 \n", - "2 -1 20 4 8 \n", + "1 -1 20 4 8 \n", + "2 -1 20 12 8 \n", "3 -1 20 12 8 \n", - "4 -1 20 12 8 \n", "0 -1 20 4 8 \n", - "39 -1 20 4 8 \n", - "40 -1 20 12 8 \n", + "37 -1 20 4 8 \n", + "38 -1 20 12 8 \n", "46 -1 20 4 8 \n", "47 -1 20 12 8 \n", - "17 -1 15 4 8 \n", - "1 -1 20 12 8 \n", - "20 -1 5 4 8 \n", + "16 -1 15 4 8 \n", + "19 -1 5 4 8 \n", + "20 -1 20 4 8 \n", "21 -1 20 4 8 \n", "22 -1 20 4 8 \n", "23 -1 20 4 8 \n", - "24 -1 20 4 8 \n", - "25 -1 5 4 8 \n", - "26 -1 20 4 8 \n", - "16 -1 20 4 8 \n", - "28 -1 20 12 8 \n", + "24 -1 5 4 8 \n", + "25 -1 20 4 8 \n", + "15 -1 20 4 8 \n", "27 -1 20 12 8 \n", - "29 -1 5 4 8 \n", - "41 -1 20 12 8 \n", - "15 -1 15 4 8 \n", + "39 -1 20 12 8 \n", + "40 -1 20 12 8 \n", + "26 -1 20 12 8 \n", + "28 -1 5 4 8 \n", + "14 -1 15 4 8 \n", "42 -1 5 4 8 \n", - "5 -1 20 4 8 \n", + "4 -1 20 4 8 \n", "44 -1 20 12 8 \n", - "32 -1 5 4 8 \n", + "31 -1 5 4 8 \n", + "29 -1 20 4 8 \n", "30 -1 20 4 8 \n", - "31 -1 20 4 8 \n", - "34 -1 20 4 8 \n", "33 -1 20 4 8 \n", + "32 -1 20 4 8 \n", + "34 -1 5 4 8 \n", "35 -1 5 4 8 \n", - "36 -1 5 4 8 \n", - "37 -1 20 12 8 \n", - "38 -1 20 4 8 \n", + "41 -1 20 12 8 \n", + "36 -1 20 4 8 \n", "\n", " run_name \n", - "7 1_beta_noise_gaussian_data_experiment \n", - "8 1_exponential_noise_gaussian_data_experiment \n", - "9 1_poisson_noise_gaussian_data_experiment \n", - "10 1_t_student_noise_gaussian_data_experiment \n", - "11 1_uniform_noise_gaussian_data_experiment \n", - "6 20_dims_uniform_error_gaussian_data \n", - "14 20_dims_uniform_error_gaussian_data_ \n", - "12 3_laplace_noise_gaussian_data_experiment \n", - "13 3_tstudent_noise_gaussian_data_experiment \n", + "6 1_beta_noise_gaussian_data_experiment \n", + "7 1_exponential_noise_gaussian_data_experiment \n", + "8 1_poisson_noise_gaussian_data_experiment \n", + "9 1_t_student_noise_gaussian_data_experiment \n", + "10 1_uniform_noise_gaussian_data_experiment \n", + "5 20_dims_uniform_error_gaussian_data \n", + "13 20_dims_uniform_error_gaussian_data_ \n", + "11 3_laplace_noise_gaussian_data_experiment \n", + "12 3_tstudent_noise_gaussian_data_experiment \n", "43 4_std_sparse_linear_regression \n", - "18 beta_noise_ar1_data_experiment \n", - "19 beta_noisy_linear_regression_40_100k \n", + "17 beta_noise_ar1_data_experiment \n", + "18 beta_noisy_linear_regression_40_100k \n", "45 case1_sparse_regression \n", - "2 case2_heavy_tail_t_student \n", - "3 case2_heavy_tail_t_student_1_1 \n", - "4 case2_heavy_tail_t_student_1_2 \n", + "1 case2_heavy_tail_t_student \n", + "2 case2_heavy_tail_t_student_1_1 \n", + "3 case2_heavy_tail_t_student_1_2 \n", "0 case3_bounded_support \n", - "39 case4_mixture_tasks \n", - "40 case4_mixture_tasks_1_1 \n", + "37 case4_mixture_tasks \n", + "38 case4_mixture_tasks_1_1 \n", "46 case5_transfer_tradeoff \n", "47 case5_transfer_tradeoff_1_1 \n", - "17 data_sparse_linear_regression \n", - "1 decision_tree_pretrained \n", - "20 exponential_noise_gaussian_data_experiment \n", - "21 exponential_w \n", - "22 exponential_weighted_experiment_100k \n", - "23 exponential_weighted_experiment_150k \n", - "24 exponential_weights_experiment \n", - "25 laplace_noise_gaussian_data_experiment \n", - "26 laplace_w \n", - "16 laplace_weights_experiment \n", - "28 linear_regression_pretrained \n", - "27 lr_wx_mixed \n", - "29 rayleigh_noise_gaussian_data_experiment \n", - "41 relu_2nn_regression_pretrained \n", - "15 rigde_normal_linear_regression_gaussian \n", + "16 data_sparse_linear_regression \n", + "19 exponential_noise_gaussian_data_experiment \n", + "20 exponential_w \n", + "21 exponential_weighted_experiment_100k \n", + "22 exponential_weighted_experiment_150k \n", + "23 exponential_weights_experiment \n", + "24 laplace_noise_gaussian_data_experiment \n", + "25 laplace_w \n", + "15 laplace_weights_experiment \n", + "27 linear_regression_pretrained \n", + "39 lr_wx \n", + "40 lr_wx_1 \n", + "26 lr_wx_mixed \n", + "28 rayleigh_noise_gaussian_data_experiment \n", + "14 rigde_normal_linear_regression_gaussian \n", "42 sparse \n", - "5 sparse_data_experiment \n", + "4 sparse_data_experiment \n", "44 sparse_regression_pretrained \n", - "32 t_student_noise_gaussian_data_experiment \n", - "30 task_sparse_data \n", - "31 test \n", - "34 uniform_hypersphere_experiment \n", - "33 uniform_hypersphere_experiment_standard \n", - "35 uniform_noise_ar1_data_experiment \n", - "36 uniform_noise_gaussian_data_experiment \n", - "37 w_expo x_gamma e uni \n", - "38 w_laplace_x_exponential_noise_poisson " + "31 t_student_noise_gaussian_data_experiment \n", + "29 task_sparse_data \n", + "30 test \n", + "33 uniform_hypersphere_experiment \n", + "32 uniform_hypersphere_experiment_standard \n", + "34 uniform_noise_ar1_data_experiment \n", + "35 uniform_noise_gaussian_data_experiment \n", + "41 w_expo x_gamma e uni \n", + "36 w_laplace_x_exponential_noise_poisson " ] }, - "execution_count": 13, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -915,7 +915,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 3, "id": "a9980951", "metadata": {}, "outputs": [], @@ -925,7 +925,7 @@ "#task = \"decision_tree\"\n", "#task = \"relu_2nn_regression\"\n", "\n", - "run_id = \"lr_wx_1\" # if you train more models, replace with the run_id from the table above\n", + "run_id = \"w_exp_x_gamma_e_uni\" # if you train more models, replace with the run_id from the table above\n", "\n", "run_path = os.path.join(run_dir, task, run_id)\n", "recompute_metrics = False\n", @@ -944,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 4, "id": "8a7aec35", "metadata": {}, "outputs": [ @@ -952,27 +952,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=0.5)', 'Ridge (alpha=1.0)', 'Ridge (alpha=2.0)', 'Ridge (alpha=3.0)', '3-Nearest Neighbors', 'Averaging']\n", - "Missing metrics for: ['Ridge (alpha=0.5)', 'Ridge (alpha=2.0)', 'Ridge (alpha=3.0)']\n" - ] - }, - { - "ename": "TypeError", - "evalue": "list indices must be integers or slices, not str", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[31], line 28\u001b[0m\n\u001b[0;32m 25\u001b[0m n_dims \u001b[38;5;241m=\u001b[39m conf\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mn_dims\n\u001b[0;32m 27\u001b[0m models \u001b[38;5;241m=\u001b[39m relevant_model_names[task]\n\u001b[1;32m---> 28\u001b[0m \u001b[43mbasic_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmetrics\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstandard\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 29\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", - "File \u001b[1;32md:\\MyBK\\Semester_5\\Programming_Intergration_Project\\in-context-learning\\src\\plot_utils.py:138\u001b[0m, in \u001b[0;36mbasic_plot\u001b[1;34m(metrics, models, trivial)\u001b[0m\n\u001b[0;32m 136\u001b[0m ax\u001b[38;5;241m.\u001b[39maxhline(trivial, ls\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m--\u001b[39m\u001b[38;5;124m\"\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgray\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 137\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, vs \u001b[38;5;129;01min\u001b[39;00m metrics\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m--> 138\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(\u001b[43mvs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m\"\u001b[39m, label\u001b[38;5;241m=\u001b[39mname, color\u001b[38;5;241m=\u001b[39mpalette[color \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m10\u001b[39m], lw\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m 139\u001b[0m low \u001b[38;5;241m=\u001b[39m vs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbootstrap_low\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 140\u001b[0m high \u001b[38;5;241m=\u001b[39m vs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbootstrap_high\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", - "\u001b[1;31mTypeError\u001b[0m: list indices must be integers or slices, not str" + "['Transformer', 'Least Squares', 'Ridge (alpha=0.1)', 'Ridge (alpha=0.5)', 'Ridge (alpha=1.0)', 'Ridge (alpha=2.0)', 'Ridge (alpha=3.0)', '3-Nearest Neighbors', 'Averaging']\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, diff --git a/src/eval.py b/src/eval.py index d5dd2853..85021e3f 100644 --- a/src/eval.py +++ b/src/eval.py @@ -505,7 +505,11 @@ def read_run_dir(run_dir): task_dir = os.path.join(run_dir, task) for run_id in os.listdir(task_dir): run_path = os.path.join(task_dir, run_id) - _, conf = get_model_from_run(run_path, only_conf=True) + try: + _, conf = get_model_from_run(run_path, only_conf=True) + except FileNotFoundError: + print(f"Skipping run {run_id} - config.yaml not found") + continue params = {} params["run_id"] = run_id params["task"] = task diff --git a/src/figure3_4.py b/src/figure3_4.py index 5f683f88..0ecd74bc 100644 --- a/src/figure3_4.py +++ b/src/figure3_4.py @@ -240,6 +240,181 @@ def plot_gradient_alignment( plt.show() +def plot_learning_curve(run_path: str, use_log_scale: bool = True): + """ + Plot learning curve: MSE vs context length k for Transformer, OLS, Ridge. + Load metrics from metrics.json file. + """ + import json + import os + + metrics_path = os.path.join(run_path, "metrics.json") + if not os.path.exists(metrics_path): + print(f"Error: metrics.json not found at {metrics_path}") + return + + with open(metrics_path, "r") as f: + metrics = json.load(f) + + plt.figure(figsize=(10, 6)) + + # Extract models from "standard" evaluation + if "standard" in metrics: + standard_eval = metrics["standard"] + ks = list(range(1, len(next(iter(standard_eval.values()))["mean"]) + 1)) + + for model_name, data in standard_eval.items(): + if isinstance(data, dict) and "mean" in data: + means = data["mean"] + plt.plot(ks, means, marker="o", label=model_name, lw=2, markersize=4) + + plt.xlabel("# in-context examples (k)") + plt.ylabel("MSE") + if use_log_scale: + plt.yscale("log") + plt.xscale("log") + plt.legend() + plt.grid(True, alpha=0.3) + plt.tight_layout() + plt.show() + + +def plot_prediction_scatter(run_path: str, k: Optional[int] = None, num_samples: int = 500, seed: Optional[int] = None): + """ + Plot prediction vs ground truth scatter plot. + Shows bias/shrinkage effects: Transformer vs OLS. + Generates predictions on-the-fly by evaluating on test data. + """ + if seed is not None: + np.random.seed(seed) + torch.manual_seed(seed) + + model, conf, data_sampler, task_sampler, device = _prepare(run_path) + + d = conf.model.n_dims + if k is None: + k = d # Use k = d for visualization + + # Collect predictions from both Transformer and OLS + transformer_preds = [] + ols_preds = [] + y_true_list = [] + + for i in range(num_samples): + task = task_sampler() + xs = data_sampler.sample_xs(n_points=k + 1, b_size=1).to(device) + ys = task.evaluate(xs).to(device) + + ctx_xs = xs[:, :k, :] + ctx_ys = ys[:, :k] + x_query = xs[:, k : k + 1, :] + y_query = ys[:, k : k + 1, 0] + + # Transformer prediction + xs_in = torch.cat([ctx_xs, x_query], dim=1) + ys_in = torch.cat([ctx_ys, torch.zeros_like(ctx_ys[:, :1])], dim=1) + with torch.no_grad(): + transformer_pred = model(xs_in, ys_in, inds=[k]).cpu().numpy().flatten() + + # OLS prediction + X = ctx_xs[0].cpu().numpy() + y = ctx_ys[0, :, 0].cpu().numpy() + try: + w_ols = np.linalg.lstsq(X, y, rcond=None)[0] + x_q = x_query[0, 0].cpu().numpy() + ols_pred = np.dot(w_ols, x_q) + except: + ols_pred = np.array([0.0]) + + transformer_preds.append(transformer_pred[0]) + ols_preds.append(ols_pred if isinstance(ols_pred, (int, float)) else ols_pred[0]) + y_true_list.append(y_query[0, 0].cpu().item()) + + transformer_preds = np.array(transformer_preds) + ols_preds = np.array(ols_preds) + y_true = np.array(y_true_list) + + fig, axes = plt.subplots(1, 2, figsize=(12, 5)) + + models = [(transformer_preds, "Transformer", "red"), (ols_preds, "OLS", "blue")] + + for idx, (preds, name, color) in enumerate(models): + ax = axes[idx] + ax.scatter(y_true, preds, alpha=0.5, s=20, color=color) + + # Perfect prediction line + lim = [min(y_true.min(), preds.min()), max(y_true.max(), preds.max())] + ax.plot(lim, lim, "k--", lw=2, label="perfect") + + ax.set_xlabel("Ground Truth") + ax.set_ylabel("Prediction") + ax.set_title(f"{name} (k={k})") + ax.legend() + ax.grid(True, alpha=0.3) + + plt.tight_layout() + plt.show() + + +def plot_weight_recovery(run_path: str, num_prompts: int = 1280, seed: Optional[int] = None): + """ + Plot histogram of cosine similarity between predicted weight and true weight. + Compares Transformer vs OLS weight recovery. + """ + if seed is not None: + torch.manual_seed(seed) + + model, conf, data_sampler, task_sampler, device = _prepare(run_path) + + d = conf.model.n_dims + max_pts = conf.training.curriculum.points.end + k = d # Use k = d for comparison + + transformer_sims = [] + ols_sims = [] + + for _ in range(num_prompts): + task = task_sampler() + w_true = _get_true_w(task) + if w_true is None: + continue + w_true = w_true.to(device) + + xs = data_sampler.sample_xs(n_points=k + 1, b_size=1).to(device) + ys = task.evaluate(xs).to(device) + + ctx_xs = xs[:, :k, :] + ctx_ys = ys[:, :k] + x_query = xs[:, k : k + 1, :].clone().detach().requires_grad_(True) + + # Transformer weight estimate via gradient + xs_in = torch.cat([ctx_xs, x_query], dim=1) + ys_in = torch.cat([ctx_ys, torch.zeros_like(ctx_ys[:, :1])], dim=1) + + pred = model(xs_in, ys_in, inds=[k]) + grad_transformer = torch.autograd.grad(pred.sum(), x_query, retain_graph=False)[0].view(-1) + + # OLS weight estimate + X = ctx_xs[0] + y = ctx_ys[0, :, 0] + w_ols = torch.linalg.lstsq(X, y.unsqueeze(1)).solution.view(-1) + + transformer_sims.append(_cosine(grad_transformer, w_true)) + ols_sims.append(_cosine(w_ols, w_true)) + + plt.figure(figsize=(10, 6)) + plt.hist(transformer_sims, bins=30, alpha=0.6, label="Transformer", color="red", density=True) + plt.hist(ols_sims, bins=30, alpha=0.6, label="OLS", color="blue", density=True) + + plt.xlabel("Cosine Similarity with true weight") + plt.ylabel("Density") + plt.title("Weight Recovery: Transformer vs OLS") + plt.legend() + plt.grid(True, alpha=0.3) + plt.tight_layout() + plt.show() + + def main(args: Optional[Sequence[str]] = None): parser = argparse.ArgumentParser(description="Reproduce Figure 3 diagnostics.") parser.add_argument("run_path", type=str, help="Path to a trained run directory.") @@ -248,15 +423,25 @@ def main(args: Optional[Sequence[str]] = None): parser.add_argument("--seed", type=int, default=None, help="random seed") parser.add_argument("--no_fig3a", action="store_true", help="skip prefix-conditioned function plot") parser.add_argument("--no_fig3b", action="store_true", help="skip gradient alignment plot") + parser.add_argument("--learning_curve", action="store_true", help="plot learning curve vs context length") + parser.add_argument("--scatter", action="store_true", help="plot prediction vs ground truth scatter") + parser.add_argument("--weight_recovery", action="store_true", help="plot weight recovery histogram") parsed = parser.parse_args(args=args) if not parsed.no_fig3a: plot_prefix_conditioned_function(parsed.run_path, num_dirs=parsed.num_dirs, seed=parsed.seed) if not parsed.no_fig3b: plot_gradient_alignment(parsed.run_path, num_prompts=parsed.num_prompts, seed=parsed.seed) + if parsed.learning_curve: + plot_learning_curve(parsed.run_path) + if parsed.scatter: + plot_prediction_scatter(parsed.run_path, num_samples=parsed.num_prompts, seed=parsed.seed) + if parsed.weight_recovery: + plot_weight_recovery(parsed.run_path, num_prompts=parsed.num_prompts, seed=parsed.seed) if __name__ == "__main__": main() +# python figure3_4.py --learning_curve --scatter --weight_recovery \ No newline at end of file diff --git a/src/models.py b/src/models.py index 527cf354..a16967d4 100644 --- a/src/models.py +++ b/src/models.py @@ -13,6 +13,7 @@ import numpy as np from base_models import NeuralNetwork, ParallelNetworks +from samplers import DataSampler def build_model(conf): @@ -167,6 +168,8 @@ def __init__(self, n_dims, n_positions, n_embd=128, n_layer=12, n_head=4): @staticmethod def _combine(xs_b, ys_b): # Create sequence context by interleaving x's and y's """Interleaves the x's and the y's into a single sequence.""" + # Ensure both xs_b and ys_b are on the same device + xs_b = xs_b.to(ys_b.device) bsize, points, dim = xs_b.shape ys_b_wide = torch.cat( ( @@ -186,6 +189,8 @@ def forward(self, xs, ys, inds=None): inds = torch.tensor(inds) if max(inds) >= ys.shape[1] or min(inds) < 0: raise ValueError("inds contain indices where xs and ys are not defined") + # Ensure inds is on the same device as xs + inds = inds.to(xs.device) zs = self._combine(xs, ys) embeds = self._read_in(zs) output = self._backbone(inputs_embeds=embeds).last_hidden_state @@ -1194,7 +1199,7 @@ def _init_single(j): return torch.stack(preds, dim=1) - xs_b[i] = torch.randn(n_points, self.n_dims, generator=generator, device=device) + xs_b[i] = torch.randn(n_points, self.n_dims, generator=generator, device=device) if self.scale is not None: xs_b = xs_b @ self.scale if self.bias is not None: diff --git a/src/plot_utils.py b/src/plot_utils.py index 99c05467..e10354e6 100644 --- a/src/plot_utils.py +++ b/src/plot_utils.py @@ -119,7 +119,7 @@ "GLS (ar=0.5)", "Averaging" ] - ], + , } @@ -145,7 +145,7 @@ def basic_plot(metrics, models=None, trivial=1.0): ax.set_xlabel("in-context examples") ax.set_ylabel("squared error") ax.set_xlim(-1, len(low) + 0.1) - ax.set_ylim(-0.1, 2) + ax.set_ylim(-0.1, 5) diff --git a/src/tasks.py b/src/tasks.py index b0e5b495..414b702c 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -124,8 +124,8 @@ def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, scale=1): def evaluate(self, xs_b): w_b = self.w_b.to(xs_b.device) ys_linear = self.scale * (xs_b @ w_b)[:, :, 0] - ys_b = ys_linear + torch.randn_like(ys_linear) - return ys_b + # ys_b = ys_linear + torch.randn_like(ys_linear) + return ys_linear @staticmethod def generate_pool_dict(n_dims, num_tasks): diff --git a/src/train.py b/src/train.py index f94b1d89..a7d16cc6 100644 --- a/src/train.py +++ b/src/train.py @@ -132,7 +132,7 @@ def train(model, args): num_training_examples = args.training.num_training_examples - scaler = torch.cuda.amp.GradScaler() # Mixed precision + scaler = torch.amp.GradScaler('cuda') # Mixed precision for i in pbar: data_sampler_args = {} @@ -156,9 +156,11 @@ def train(model, args): ) task = task_sampler(**task_sampler_args) ys = task.evaluate(xs) + # Ensure ys is on the same device as xs + ys = ys.to(xs.device) loss_func = task.get_training_metric() - with torch.cuda.amp.autocast(): + with torch.amp.autocast('cuda'): loss, output = train_step(model, xs, ys, optimizer, loss_func) point_wise_tags = list(range(curriculum.n_points)) From 0d66e4883000a45fb958fcd929e7a0db02580946 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 15 Dec 2025 14:50:34 +0700 Subject: [PATCH 82/88] FIX conflict device --- src/conf/uniform_hypersphere_regression.yaml | 1 + src/models.py | 8 +++----- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/src/conf/uniform_hypersphere_regression.yaml b/src/conf/uniform_hypersphere_regression.yaml index cb25e79d..99c091fc 100644 --- a/src/conf/uniform_hypersphere_regression.yaml +++ b/src/conf/uniform_hypersphere_regression.yaml @@ -13,6 +13,7 @@ training: task: uniform_hypersphere_regression task_kwargs: scale: 1.0 + normalize: true data: gaussian data_kwargs: {} curriculum: diff --git a/src/models.py b/src/models.py index a16967d4..d05cb59d 100644 --- a/src/models.py +++ b/src/models.py @@ -184,13 +184,11 @@ def _combine(xs_b, ys_b): # Create sequence context by interleaving x's and y's def forward(self, xs, ys, inds=None): if inds is None: - inds = torch.arange(ys.shape[1]) + inds = torch.arange(ys.shape[1], device=xs.device) else: - inds = torch.tensor(inds) - if max(inds) >= ys.shape[1] or min(inds) < 0: + inds = torch.tensor(inds, device=xs.device) + if inds.max().item() >= ys.shape[1] or inds.min().item() < 0: raise ValueError("inds contain indices where xs and ys are not defined") - # Ensure inds is on the same device as xs - inds = inds.to(xs.device) zs = self._combine(xs, ys) embeds = self._read_in(zs) output = self._backbone(inputs_embeds=embeds).last_hidden_state From 5bda6005b20f1bc06d0dceb9e750cd56a6eff132 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 15 Dec 2025 14:55:11 +0700 Subject: [PATCH 83/88] FIX conflict device --- src/schema.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/schema.py b/src/schema.py index 2111423b..4a3ac0b7 100644 --- a/src/schema.py +++ b/src/schema.py @@ -57,11 +57,11 @@ training_schema = { "task": merge(tstring, allowed(TASK_LIST)), - "task_kwargs": merge(tdict, required), + "task_kwargs": merge(stdict({}), required), "num_tasks": merge(tinteger, nullable, default(None)), "num_training_examples": merge(tinteger, nullable, default(None)), "data": merge(tstring, allowed(["gaussian","ar1","vr1","ar2",'vr2',"nonstation", "sparse_gaussian", "gamma", "beta", "exponential", "laplace", "uniform", "poisson", "tstudent", "rayleigh", "cauchy"])), - "data_kwargs": merge(tdict, default({})), # Thêm dòng này + "data_kwargs": merge(stdict({}), default({})), "batch_size": merge(tinteger, default(64)), "learning_rate": merge(tfloat, default(3e-4)), "train_steps": merge(tinteger, default(1000)), From c0edda9b207f849abb0b875395c4f2764bc7d60f Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 15 Dec 2025 16:26:00 +0700 Subject: [PATCH 84/88] FIX: Remove stdict wrapper from task_kwargs and data_kwargs to prevent gin parse error --- src/schema.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/schema.py b/src/schema.py index 4a3ac0b7..07b70a1a 100644 --- a/src/schema.py +++ b/src/schema.py @@ -57,11 +57,11 @@ training_schema = { "task": merge(tstring, allowed(TASK_LIST)), - "task_kwargs": merge(stdict({}), required), + "task_kwargs": default({}), "num_tasks": merge(tinteger, nullable, default(None)), "num_training_examples": merge(tinteger, nullable, default(None)), "data": merge(tstring, allowed(["gaussian","ar1","vr1","ar2",'vr2',"nonstation", "sparse_gaussian", "gamma", "beta", "exponential", "laplace", "uniform", "poisson", "tstudent", "rayleigh", "cauchy"])), - "data_kwargs": merge(stdict({}), default({})), + "data_kwargs": default({}), "batch_size": merge(tinteger, default(64)), "learning_rate": merge(tfloat, default(3e-4)), "train_steps": merge(tinteger, default(1000)), From 7d271f94e712cedaf140c5fc4f4899a4e9ad758e Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 15 Dec 2025 16:28:16 +0700 Subject: [PATCH 85/88] FIX: Merge stdict with nullable for task_kwargs and data_kwargs --- src/schema.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/schema.py b/src/schema.py index 07b70a1a..5fb6d25d 100644 --- a/src/schema.py +++ b/src/schema.py @@ -57,11 +57,11 @@ training_schema = { "task": merge(tstring, allowed(TASK_LIST)), - "task_kwargs": default({}), + "task_kwargs": merge(stdict({}), nullable), "num_tasks": merge(tinteger, nullable, default(None)), "num_training_examples": merge(tinteger, nullable, default(None)), "data": merge(tstring, allowed(["gaussian","ar1","vr1","ar2",'vr2',"nonstation", "sparse_gaussian", "gamma", "beta", "exponential", "laplace", "uniform", "poisson", "tstudent", "rayleigh", "cauchy"])), - "data_kwargs": default({}), + "data_kwargs": merge(stdict({}), nullable), "batch_size": merge(tinteger, default(64)), "learning_rate": merge(tfloat, default(3e-4)), "train_steps": merge(tinteger, default(1000)), From a9f6d5054aef32f1d04eb2c5808a2a5449768026 Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 15 Dec 2025 16:41:12 +0700 Subject: [PATCH 86/88] DEBUG: Add print statements to track where training gets stuck --- src/train.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/train.py b/src/train.py index a7d16cc6..efe0debf 100644 --- a/src/train.py +++ b/src/train.py @@ -103,8 +103,10 @@ def _sanitize_training_kwargs(args): def train(model, args): + print("[TRAIN] Starting train function") optimizer = torch.optim.Adam(model.parameters(), lr=args.training.learning_rate) curriculum = Curriculum(args.training.curriculum) + print("[TRAIN] Curriculum initialized") starting_step = 0 state_path = os.path.join(args.out_dir, "state.pt") @@ -118,9 +120,11 @@ def train(model, args): n_dims = model.n_dims bsize = args.training.batch_size + print(f"[TRAIN] Getting data sampler for {args.training.data}") data_sampler = get_data_sampler( args.training.data, n_dims=n_dims, **getattr(args.training, "data_kwargs", {}) ) + print(f"[TRAIN] Getting task sampler for {args.training.task}") task_sampler = get_task_sampler( args.training.task, n_dims, @@ -128,11 +132,13 @@ def train(model, args): num_tasks=args.training.num_tasks, **getattr(args.training, "task_kwargs", {}) ) + print("[TRAIN] Creating tqdm progress bar") pbar = tqdm(range(starting_step, args.training.train_steps)) num_training_examples = args.training.num_training_examples scaler = torch.amp.GradScaler('cuda') # Mixed precision + print("[TRAIN] Starting training loop") for i in pbar: data_sampler_args = {} From 51d3c254bdee7282e1b808141a30108c9e88f46f Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 15 Dec 2025 17:53:08 +0700 Subject: [PATCH 87/88] Add cpu_only option to schema --- src/conf/linear_regression.yaml | 4 +++- src/conf/template.yaml | 2 +- src/schema.py | 5 +++-- src/tasks.py | 3 ++- src/train.py | 28 +++++++++++++++++----------- 5 files changed, 26 insertions(+), 16 deletions(-) diff --git a/src/conf/linear_regression.yaml b/src/conf/linear_regression.yaml index 9d027794..3b883fba 100644 --- a/src/conf/linear_regression.yaml +++ b/src/conf/linear_regression.yaml @@ -10,7 +10,9 @@ training: inc: 2 interval: 2000 -out_dir: ../models/linear_regression +# out_dir: ../models/linear_regression +out_dir: D:\Henry-Projects\ChestXray\data\in-context-learning\models\linear_regression + wandb: name: "linear_regression_standard" diff --git a/src/conf/template.yaml b/src/conf/template.yaml index ac2a5df4..bd0fe1bb 100644 --- a/src/conf/template.yaml +++ b/src/conf/template.yaml @@ -46,7 +46,7 @@ training: # - When task == 'sparse_linear_regression': you may set 'sparsity'. # - For other tasks: any 'sparsity' key will be ignored automatically. task_kwargs: - noise_std: 1.0 + noise_std: 0.0 noise_type: normal w_distribution: gaussian w_kwargs: diff --git a/src/schema.py b/src/schema.py index 5fb6d25d..a20435b4 100644 --- a/src/schema.py +++ b/src/schema.py @@ -57,11 +57,11 @@ training_schema = { "task": merge(tstring, allowed(TASK_LIST)), - "task_kwargs": merge(stdict({}), nullable), + "task_kwargs": merge(tdict, nullable), "num_tasks": merge(tinteger, nullable, default(None)), "num_training_examples": merge(tinteger, nullable, default(None)), "data": merge(tstring, allowed(["gaussian","ar1","vr1","ar2",'vr2',"nonstation", "sparse_gaussian", "gamma", "beta", "exponential", "laplace", "uniform", "poisson", "tstudent", "rayleigh", "cauchy"])), - "data_kwargs": merge(stdict({}), nullable), + "data_kwargs": merge(tdict, nullable), "batch_size": merge(tinteger, default(64)), "learning_rate": merge(tfloat, default(3e-4)), "train_steps": merge(tinteger, default(1000)), @@ -85,4 +85,5 @@ "training": stdict(training_schema), "wandb": stdict(wandb_schema), "test_run": merge(tboolean, default(False)), + "cpu_only": merge(tboolean, default(False)), } diff --git a/src/tasks.py b/src/tasks.py index 414b702c..2b6c8fa8 100644 --- a/src/tasks.py +++ b/src/tasks.py @@ -421,7 +421,8 @@ def _sample_distribution(self, shape, generator=None, device='cpu'): def to_val(val): return torch.tensor(val, device=device) if not torch.is_tensor(val) else val.to(device) if self.w_distribution == "gaussian": - return torch.randn(shape, generator=generator, device=device) + scale = self.w_kwargs.get("scale", 1.0) + return scale * torch.randn(shape, generator=generator, device=device) elif self.w_distribution == "uniform": low = self.w_kwargs.get("low", -1.0) high = self.w_kwargs.get("high", 1.0) diff --git a/src/train.py b/src/train.py index efe0debf..d625183a 100644 --- a/src/train.py +++ b/src/train.py @@ -103,10 +103,13 @@ def _sanitize_training_kwargs(args): def train(model, args): - print("[TRAIN] Starting train function") + # Determine device - can override with --cpu_only flag + use_cpu = getattr(args, 'cpu_only', False) + device = "cpu" if use_cpu else ("cuda" if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}") + optimizer = torch.optim.Adam(model.parameters(), lr=args.training.learning_rate) curriculum = Curriculum(args.training.curriculum) - print("[TRAIN] Curriculum initialized") starting_step = 0 state_path = os.path.join(args.out_dir, "state.pt") @@ -121,25 +124,24 @@ def train(model, args): n_dims = model.n_dims bsize = args.training.batch_size print(f"[TRAIN] Getting data sampler for {args.training.data}") + data_kwargs = getattr(args.training, "data_kwargs", {}) or {} data_sampler = get_data_sampler( - args.training.data, n_dims=n_dims, **getattr(args.training, "data_kwargs", {}) + args.training.data, n_dims=n_dims, **data_kwargs ) print(f"[TRAIN] Getting task sampler for {args.training.task}") + task_kwargs = getattr(args.training, "task_kwargs", {}) or {} task_sampler = get_task_sampler( args.training.task, n_dims, bsize, num_tasks=args.training.num_tasks, - **getattr(args.training, "task_kwargs", {}) + **task_kwargs ) print("[TRAIN] Creating tqdm progress bar") pbar = tqdm(range(starting_step, args.training.train_steps)) num_training_examples = args.training.num_training_examples - scaler = torch.amp.GradScaler('cuda') # Mixed precision - print("[TRAIN] Starting training loop") - for i in pbar: data_sampler_args = {} task_sampler_args = {} @@ -158,7 +160,7 @@ def train(model, args): bsize, curriculum.n_dims_truncated, **data_sampler_args, - device="cuda" + device=device ) task = task_sampler(**task_sampler_args) ys = task.evaluate(xs) @@ -166,8 +168,8 @@ def train(model, args): ys = ys.to(xs.device) loss_func = task.get_training_metric() - with torch.amp.autocast('cuda'): - loss, output = train_step(model, xs, ys, optimizer, loss_func) + # Disable mixed precision for now - testing numeric stability + loss, output = train_step(model, xs, ys, optimizer, loss_func) point_wise_tags = list(range(curriculum.n_points)) point_wise_loss_func = task.get_metric() @@ -232,7 +234,11 @@ def main(args): ) model = build_model(args.model) - model.cuda() + + # Check if we should use CUDA + use_cuda = torch.cuda.is_available() and not getattr(args, 'cpu_only', False) + device = "cuda" if use_cuda else "cpu" + model = model.to(device) model.train() train(model, args) From ef3488bb10bd22d01035f49f25a4fc54f120817e Mon Sep 17 00:00:00 2001 From: HoangTimothy Date: Mon, 15 Dec 2025 17:57:46 +0700 Subject: [PATCH 88/88] fix error --- src/conf/linear_regression.yaml | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/src/conf/linear_regression.yaml b/src/conf/linear_regression.yaml index 3b883fba..5a4ed561 100644 --- a/src/conf/linear_regression.yaml +++ b/src/conf/linear_regression.yaml @@ -15,4 +15,10 @@ out_dir: D:\Henry-Projects\ChestXray\data\in-context-learning\models\linear_regr wandb: - name: "linear_regression_standard" + project: "in-context-training" + entity: "hai-trinh220970-ho-chi-minh-city-university-of-technology" + name: "noisy_linear_regression" + notes: "Training with laplace-distributed weights (non-uniform on hypersphere)" + log_every_steps: 100 + +