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remove tokenizer warning #42483

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Merged
merged 3 commits into from
Dec 1, 2025
Merged

remove tokenizer warning #42483

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1e9cbbd
fix warning
SunMarc Nov 28, 2025
94bcc0c
fix
SunMarc Nov 28, 2025
f4dfdd5
remove
SunMarc Nov 28, 2025
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52 changes: 1 addition & 51 deletions src/transformers/tokenization_utils_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1827,16 +1827,14 @@ def _from_pretrained(
if tokenizer_config_file is not None:
with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle:
init_kwargs = json.load(tokenizer_config_handle)
# First attempt. We get tokenizer_class from tokenizer_config to check mismatch between tokenizers.
config_tokenizer_class = init_kwargs.get("tokenizer_class")
# used in the past to check if the tokenizer class matches the class in the repo
init_kwargs.pop("tokenizer_class", None)
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@SunMarc SunMarc Nov 28, 2025

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left a small comment here to understand why we pop tokenizer_class. Maybe we don't even need to pop it ?

if not has_tokenizer_file:
init_kwargs.get("tokenizer_file", None)
saved_init_inputs = init_kwargs.pop("init_inputs", ())
if not init_inputs:
init_inputs = saved_init_inputs
else:
config_tokenizer_class = None
init_kwargs = init_configuration

# If independent chat template file(s) exist, they take priority over template entries in the tokenizer config
Expand Down Expand Up @@ -1864,54 +1862,6 @@ def _from_pretrained(
if isinstance(init_kwargs["auto_map"], (tuple, list)):
init_kwargs["auto_map"] = {"AutoTokenizer": init_kwargs["auto_map"]}

if config_tokenizer_class is None:
# Matt: This entire block is only used to decide if the tokenizer class matches the class in the repo.
# If not, it raises a warning, but otherwise continues. Since we mostly load tokenizers with
# AutoTokenizer these days, it seems like a lot of work (and a source of bugs) for little gain.
# Maybe we can just remove this entirely?
from .models.auto.configuration_auto import AutoConfig # tests_ignore

# Second attempt. If we have not yet found tokenizer_class, let's try to use the config.
try:
config = AutoConfig.from_pretrained(
pretrained_model_name_or_path,
token=token,
cache_dir=cache_dir,
local_files_only=local_files_only,
trust_remote_code=trust_remote_code,
_commit_hash=_commit_hash,
)
config_tokenizer_class = config.tokenizer_class
except (OSError, ValueError, KeyError):
# skip if an error occurred.
config = None
if config_tokenizer_class is None:
# Third attempt. If we have not yet found the original type of the tokenizer,
# we are loading we see if we can infer it from the type of the configuration file
from .models.auto.tokenization_auto import TOKENIZER_MAPPING_NAMES # tests_ignore

if hasattr(config, "model_type"):
model_type = config.model_type
else:
# Fallback: use pattern matching on the string.
model_type = None
for pattern in TOKENIZER_MAPPING_NAMES:
if pattern in str(pretrained_model_name_or_path):
model_type = pattern
break

if model_type is not None:
config_tokenizer_class = TOKENIZER_MAPPING_NAMES.get(model_type)

if config_tokenizer_class is not None:
if cls.__name__.replace("Fast", "") != config_tokenizer_class.replace("Fast", ""):
logger.warning(
"The tokenizer class you load from this checkpoint is not the same type as the class this"
" function is called from. It may result in unexpected tokenization. \nThe tokenizer class you"
f" load from this checkpoint is '{config_tokenizer_class}'. \nThe class this function is called"
f" from is '{cls.__name__}'."
)

# Preserve extra_special_tokens from tokenizer_config.json before updating with kwargs
# extra_special_tokens should be a list (user-defined extra tokens)
extra_special_tokens_from_config = init_kwargs.get("extra_special_tokens")
Expand Down