A few fixes in the documentation (#10033)

src/transformers/tokenization_utils.py

@@ -101,7 +101,7 @@ def _is_start_of_word(text):
     return bool(_is_control(first_char) | _is_punctuation(first_char) | _is_whitespace(first_char))
 
 
-@add_end_docstrings(INIT_TOKENIZER_DOCSTRING, """    .. automethod:: __call__""")
+@add_end_docstrings(INIT_TOKENIZER_DOCSTRING)
 class PreTrainedTokenizer(PreTrainedTokenizerBase):
     """
     Base class for all slow tokenizers.

src/transformers/tokenization_utils_base.py

@@ -2056,7 +2056,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
                 Whether or not to add the special tokens associated with the corresponding model.
             kwargs (additional keyword arguments, `optional`):
                 Will be passed to the underlying model specific encode method. See details in
-                :meth:`~transformers.PreTrainedTokenizer.__call__`
+                :meth:`~transformers.PreTrainedTokenizerBase.__call__`
 
         Returns:
             :obj:`List[str]`: The list of tokens.

src/transformers/tokenization_utils_fast.py

@@ -56,12 +56,7 @@ TOKENIZER_CONFIG_FILE = "tokenizer_config.json"
 ADDED_TOKENS_FILE = "added_tokens.json"
 
 
-@add_end_docstrings(
-    INIT_TOKENIZER_DOCSTRING,
-    """
-    .. automethod:: __call__
-    """,
-)
+@add_end_docstrings(INIT_TOKENIZER_DOCSTRING)
 class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
     """
     Base class for all fast tokenizers (wrapping HuggingFace tokenizers library).