Revert changes in docstring

Author: pco111

src/transformers/tokenization_utils_base.py

@@ -3267,22 +3267,41 @@ def pad(
         verbose: bool = True,
     ) -> BatchEncoding:
         """
-        Pad a single encoded input or a batch of encoded inputs up to the maximum length of the batch or up to a
-        given maximum length. Padding side can be specified on the left or on the right.
+        Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
+        in the batch.
+
+        Padding side (left/right) padding token ids are defined at the tokenizer level (with `self.padding_side`,
+        `self.pad_token_id` and `self.pad_token_type_id`).
+
+        Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the
+        text followed by a call to the `pad` method to get a padded encoding.
+
+        <Tip>
+
+        If the `encoded_inputs` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the
+        result will use the same type unless you provide a different tensor type with `return_tensors`. In the case of
+        PyTorch tensors, you will lose the specific device of your tensors however.
+
+        </Tip>
 
         Args:
-            encoded_inputs ([`BatchEncoding`], list of [`BatchEncoding`], `dict[str, list[int]]`, `dict[str, list[list[int]]]` or `list[dict[str, list[int]]]`):
-                Tokenized inputs. Can be a single batch encoding, a list of batch encodings, a dictionary of entries
-                produced by a `tokenizer.encode_plus` or a list of dictionaries from a `tokenizer.batch_encode_plus`.
+            encoded_inputs ([`BatchEncoding`], list of [`BatchEncoding`], `dict[str, list[int]]`, `dict[str, list[list[int]]` or `list[dict[str, list[int]]]`):
+                Tokenized inputs. Can represent one input ([`BatchEncoding`] or `dict[str, list[int]]`) or a batch of
+                tokenized inputs (list of [`BatchEncoding`], *dict[str, list[list[int]]]* or *list[dict[str,
+                list[int]]]*) so you can use this method during preprocessing as well as in a PyTorch Dataloader
+                collate function.
+
+                Instead of `list[int]` you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors), see
+                the note above for the return type.
             padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
                  Select a strategy to pad the returned sequences (according to the model's padding side and padding
                  index) among:
 
-                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+                - `True` or `'longest'` (default): Pad to the longest sequence in the batch (or no padding if only a single
                   sequence if provided).
                 - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
                   acceptable input length for the model if that argument is not provided.
-                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+                - `False` or `'do_not_pad'`: No padding (i.e., can output a batch with sequences of different
                   lengths).
             max_length (`int`, *optional*):
                 Maximum length of the returned list and optionally padding length (see above).
@@ -3292,18 +3311,19 @@ def pad(
                 This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
                 `>= 7.5` (Volta).
             padding_side (`str`, *optional*):
-                'right' or 'left'. If get_ येणार from the tokenizer, the `tokenizer.padding_side` will be used.
+                The side on which the model should have padding applied. Should be selected between ['right', 'left'].
+                Default value is picked from the class attribute of the same name.
             return_attention_mask (`bool`, *optional*):
                 Whether to return the attention mask. If left to the default, will return the attention mask according
-                to the specific tokenizer's default.
+                to the specific tokenizer's default, defined by the `return_outputs` attribute.
 
                 [What are attention masks?](../glossary#attention-mask)
             return_tensors (`str` or [`~utils.TensorType`], *optional*):
                 If set, will return tensors instead of list of python integers. Acceptable values are:
 
-                - `'tf'`: Return TensorFlow `tf.Tensor` objects.
+                - `'tf'`: Return TensorFlow `tf.constant` objects.
                 - `'pt'`: Return PyTorch `torch.Tensor` objects.
-                - `'np'`: Return NumPy `np.ndarray` objects.
+                - `'np'`: Return Numpy `np.ndarray` objects.
             verbose (`bool`, *optional*, defaults to `True`):
                 Whether or not to print more information and warnings.
         """
@@ -3724,65 +3744,30 @@ def _pad(
         return_attention_mask: Optional[bool] = None,
     ) -> dict:
         """
-        Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
-        in the batch.
-
-        Padding side (left/right) padding token ids are defined at the tokenizer level (with `self.padding_side`,
-        `self.pad_token_id` and `self.pad_token_type_id`).
-
-        Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the
-        text followed by a call to the `pad` method to get a padded encoding.
-
-        <Tip>
-
-        If the `encoded_inputs` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the
-        result will use the same type unless you provide a different tensor type with `return_tensors`. In the case of
-        PyTorch tensors, you will lose the specific device of your tensors however.
-
-        </Tip>
+        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
 
         Args:
-            encoded_inputs ([`BatchEncoding`], list of [`BatchEncoding`], `dict[str, list[int]]`, `dict[str, list[list[int]]` or `list[dict[str, list[int]]]`):
-                Tokenized inputs. Can represent one input ([`BatchEncoding`] or `dict[str, list[int]]`) or a batch of
-                tokenized inputs (list of [`BatchEncoding`], *dict[str, list[list[int]]]* or *list[dict[str,
-                list[int]]]*) so you can use this method during preprocessing as well as in a PyTorch Dataloader
-                collate function.
-
-                Instead of `list[int]` you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors), see
-                the note above for the return type.
-            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
-                 Select a strategy to pad the returned sequences (according to the model's padding side and padding
-                 index) among:
-
-                - `True` or `'longest'` (default): Pad to the longest sequence in the batch (or no padding if only a single
-                  sequence if provided).
-                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
-                  acceptable input length for the model if that argument is not provided.
-                - `False` or `'do_not_pad'`: No padding (i.e., can output a batch with sequences of different
-                  lengths).
-            max_length (`int`, *optional*):
-                Maximum length of the returned list and optionally padding length (see above).
-            pad_to_multiple_of (`int`, *optional*):
-                If set will pad the sequence to a multiple of the provided value.
-
-                This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
+            encoded_inputs:
+                Dictionary of tokenized inputs (`list[int]`) or batch of tokenized inputs (`list[list[int]]`).
+            max_length: maximum length of the returned list and optionally padding length (see below).
+                Will truncate by taking into account the special tokens.
+            padding_strategy: PaddingStrategy to use for padding.
+
+                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+                - PaddingStrategy.DO_NOT_PAD: Do not pad
+                The tokenizer padding sides are defined in `padding_side` argument:
+
+                    - 'left': pads on the left of the sequences
+                    - 'right': pads on the right of the sequences
+            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
                 `>= 7.5` (Volta).
-            padding_side (`str`, *optional*):
+            padding_side:
                 The side on which the model should have padding applied. Should be selected between ['right', 'left'].
                 Default value is picked from the class attribute of the same name.
-            return_attention_mask (`bool`, *optional*):
-                Whether to return the attention mask. If left to the default, will return the attention mask according
-                to the specific tokenizer's default, defined by the `return_outputs` attribute.
-
-                [What are attention masks?](../glossary#attention-mask)
-            return_tensors (`str` or [`~utils.TensorType`], *optional*):
-                If set, will return tensors instead of list of python integers. Acceptable values are:
-
-                - `'tf'`: Return TensorFlow `tf.constant` objects.
-                - `'pt'`: Return PyTorch `torch.Tensor` objects.
-                - `'np'`: Return Numpy `np.ndarray` objects.
-            verbose (`bool`, *optional*, defaults to `True`):
-                Whether or not to print more information and warnings.
+            return_attention_mask:
+                (optional) Set to False to avoid returning attention mask (default: set to model specifics)
         """
         # Load from model defaults
         if return_attention_mask is None: