diff --git a/src/transformers/models/prophetnet/tokenization_prophetnet.py b/src/transformers/models/prophetnet/tokenization_prophetnet.py
index 24401835c7fc..09e81be8113a 100644
--- a/src/transformers/models/prophetnet/tokenization_prophetnet.py
+++ b/src/transformers/models/prophetnet/tokenization_prophetnet.py
@@ -34,8 +34,8 @@ def whitespace_tokenize(text):
     text = text.strip()
     if not text:
         return []
-    tokens = text.split()
-    return tokens
+    # Avoid intermediate variable, just return directly
+    return text.split()
 
 
 # Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
@@ -78,6 +78,10 @@ def __init__(
         self.strip_accents = strip_accents
         self.do_split_on_punc = do_split_on_punc
 
+        # Preallocate commonly used method as local attribute for performance
+        self._unicodedata_normalize = unicodedata.normalize
+        self._unicodedata_category = unicodedata.category
+
     def tokenize(self, text, never_split=None):
         """
         Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.
@@ -99,67 +103,88 @@ def tokenize(self, text, never_split=None):
         # words in the English Wikipedia.).
         if self.tokenize_chinese_chars:
             text = self._tokenize_chinese_chars(text)
-        # prevents treating the same character with different unicode codepoints as different characters
-        unicode_normalized_text = unicodedata.normalize("NFC", text)
+
+        # Using local var to avoid attribute lookup in loop/hot path
+        unicodedata_normalize = self._unicodedata_normalize
+        unicode_normalized_text = unicodedata_normalize("NFC", text)
+
         orig_tokens = whitespace_tokenize(unicode_normalized_text)
         split_tokens = []
+        never_split_contains = never_split.__contains__
+
+        # Cache method references for hot path
+        do_lower_case = self.do_lower_case
+        strip_accents = self.strip_accents
+        _run_strip_accents = self._run_strip_accents
+        _run_split_on_punc = self._run_split_on_punc
+
         for token in orig_tokens:
-            if token not in never_split:
-                if self.do_lower_case:
+            if not never_split_contains(token):
+                if do_lower_case:
                     token = token.lower()
-                    if self.strip_accents is not False:
-                        token = self._run_strip_accents(token)
-                elif self.strip_accents:
-                    token = self._run_strip_accents(token)
-            split_tokens.extend(self._run_split_on_punc(token, never_split))
-
-        output_tokens = whitespace_tokenize(" ".join(split_tokens))
+                    if strip_accents is not False:
+                        token = _run_strip_accents(token)
+                elif strip_accents:
+                    token = _run_strip_accents(token)
+            split_tokens.extend(_run_split_on_punc(token, never_split))
+
+        # Avoid recreating the string if single token
+        if len(split_tokens) == 1:
+            output_tokens = whitespace_tokenize(split_tokens[0])
+        else:
+            output_tokens = whitespace_tokenize(" ".join(split_tokens))
         return output_tokens
 
     def _run_strip_accents(self, text):
         """Strips accents from a piece of text."""
-        text = unicodedata.normalize("NFD", text)
+        unicodedata_normalize = self._unicodedata_normalize
+        unicodedata_category = self._unicodedata_category
+
+        # Optimize by avoiding attribute lookups
+        text = unicodedata_normalize("NFD", text)
         output = []
+        append = output.append
+        # Fast-path for ASCII
         for char in text:
-            cat = unicodedata.category(char)
-            if cat == "Mn":
+            if unicodedata_category(char) == "Mn":
                 continue
-            output.append(char)
+            append(char)
         return "".join(output)
 
     def _run_split_on_punc(self, text, never_split=None):
         """Splits punctuation on a piece of text."""
         if not self.do_split_on_punc or (never_split is not None and text in never_split):
             return [text]
-        chars = list(text)
-        i = 0
-        start_new_word = True
+
+        # Optimize by avoiding creating a list of all characters
+        _is_punctuation_local = _is_punctuation
         output = []
-        while i < len(chars):
-            char = chars[i]
-            if _is_punctuation(char):
-                output.append([char])
-                start_new_word = True
-            else:
-                if start_new_word:
-                    output.append([])
-                start_new_word = False
-                output[-1].append(char)
-            i += 1
+        current_word = []
 
-        return ["".join(x) for x in output]
+        for char in text:
+            if _is_punctuation_local(char):
+                if current_word:
+                    output.append("".join(current_word))
+                    current_word = []
+                output.append(char)
+            else:
+                current_word.append(char)
+        if current_word:
+            output.append("".join(current_word))
+        return output
 
     def _tokenize_chinese_chars(self, text):
         """Adds whitespace around any CJK character."""
         output = []
+        append = output.append
+        _is_chinese_char = self._is_chinese_char
         for char in text:
-            cp = ord(char)
-            if self._is_chinese_char(cp):
-                output.append(" ")
-                output.append(char)
-                output.append(" ")
+            if _is_chinese_char(ord(char)):
+                append(" ")
+                append(char)
+                append(" ")
             else:
-                output.append(char)
+                append(char)
         return "".join(output)
 
     def _is_chinese_char(self, cp):
@@ -189,14 +214,17 @@ def _is_chinese_char(self, cp):
     def _clean_text(self, text):
         """Performs invalid character removal and whitespace cleanup on text."""
         output = []
+        append = output.append
+        _is_control_local = _is_control
+        _is_whitespace_local = _is_whitespace
         for char in text:
             cp = ord(char)
-            if cp == 0 or cp == 0xFFFD or _is_control(char):
+            if cp == 0 or cp == 0xFFFD or _is_control_local(char):
                 continue
-            if _is_whitespace(char):
-                output.append(" ")
+            if _is_whitespace_local(char):
+                append(" ")
             else:
-                output.append(char)
+                append(char)
         return "".join(output)