Big file_utils cleanup (#16396)

* Big file_utils cleanup

* This one still needs to be treated separately

ISSUES.md

@@ -72,7 +72,7 @@ You are not required to read the following guidelines before opening an issue. H
        from . import dependency_versions_check
      File "/transformers/src/transformers/dependency_versions_check.py", line 34, in <module>
        from .utils import is_tokenizers_available
-     File "/transformers/src/transformers/file_utils.py", line 40, in <module>
+     File "/transformers/src/transformers/utils/import_utils.py", line 40, in <module>
        from tqdm.auto import tqdm
     ModuleNotFoundError: No module named 'tqdm.auto'
     ```
@@ -125,7 +125,7 @@ You are not required to read the following guidelines before opening an issue. H
        from . import dependency_versions_check
      File "/transformers/src/transformers/dependency_versions_check.py", line 34, in <module>
        from .utils import is_tokenizers_available
-     File "/transformers/src/transformers/file_utils.py", line 40, in <module>
+     File "/transformers/src/transformers/utils/import_utils.py", line 40, in <module>
        from tqdm.auto import tqdm
    ModuleNotFoundError: No module named 'tqdm.auto'
    ```

docs/README.md

@@ -172,9 +172,9 @@ adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`funct
 function to be in the main package.
 
 If you want to create a link to some internal class or function, you need to
-provide its path. For instance: \[\`file_utils.ModelOutput\`\]. This will be converted into a link with
-`file_utils.ModelOutput` in the description. To get rid of the path and only keep the name of the object you are
-linking to in the description, add a ~: \[\`~file_utils.ModelOutput\`\] will generate a link with `ModelOutput` in the description.
+provide its path. For instance: \[\`utils.ModelOutput\`\]. This will be converted into a link with
+`utils.ModelOutput` in the description. To get rid of the path and only keep the name of the object you are
+linking to in the description, add a ~: \[\`~utils.ModelOutput\`\] will generate a link with `ModelOutput` in the description.
 
 The same works for methods so you can either use \[\`XXXClass.method\`\] or \[~\`XXXClass.method\`\].
 

docs/source/add_new_model.mdx

@@ -381,7 +381,7 @@ important. Here is some advice is to make your debugging environment as efficien
   original code so that you can directly input the ids instead of an input string.
 - Make sure that the model in your debugging setup is **not** in training mode, which often causes the model to yield
   random outputs due to multiple dropout layers in the model. Make sure that the forward pass in your debugging
-  environment is **deterministic** so that the dropout layers are not used. Or use *transformers.file_utils.set_seed*
+  environment is **deterministic** so that the dropout layers are not used. Or use *transformers.utils.set_seed*
   if the old and new implementations are in the same framework.
 
 The following section gives you more specific details/tips on how you can do this for *brand_new_bert*.

docs/source/internal/file_utils.mdx

@@ -12,35 +12,35 @@ specific language governing permissions and limitations under the License.
 
 # General Utilities
 
-This page lists all of Transformers general utility functions that are found in the file `file_utils.py`.
+This page lists all of Transformers general utility functions that are found in the file `utils.py`.
 
 Most of those are only useful if you are studying the general code in the library.
 
 
 ## Enums and namedtuples
 
-[[autodoc]] file_utils.ExplicitEnum
+[[autodoc]] utils.ExplicitEnum
 
-[[autodoc]] file_utils.PaddingStrategy
+[[autodoc]] utils.PaddingStrategy
 
-[[autodoc]] file_utils.TensorType
+[[autodoc]] utils.TensorType
 
 ## Special Decorators
 
-[[autodoc]] file_utils.add_start_docstrings
+[[autodoc]] utils.add_start_docstrings
 
-[[autodoc]] file_utils.add_start_docstrings_to_model_forward
+[[autodoc]] utils.add_start_docstrings_to_model_forward
 
-[[autodoc]] file_utils.add_end_docstrings
+[[autodoc]] utils.add_end_docstrings
 
-[[autodoc]] file_utils.add_code_sample_docstrings
+[[autodoc]] utils.add_code_sample_docstrings
 
-[[autodoc]] file_utils.replace_return_docstrings
+[[autodoc]] utils.replace_return_docstrings
 
 ## Special Properties
 
-[[autodoc]] file_utils.cached_property
+[[autodoc]] utils.cached_property
 
 ## Other Utilities
 
-[[autodoc]] file_utils._LazyModule
+[[autodoc]] utils._LazyModule

docs/source/internal/generation_utils.mdx

@@ -25,7 +25,7 @@ Most of those are only useful if you are studying the code of the generate metho
 ## Generate Outputs
 
 The output of [`~generation_utils.GenerationMixin.generate`] is an instance of a subclass of
-[`~file_utils.ModelOutput`]. This output is a data structure containing all the information returned
+[`~utils.ModelOutput`]. This output is a data structure containing all the information returned
 by [`~generation_utils.GenerationMixin.generate`], but that can also be used as tuple or dictionary.
 
 Here's an example:

docs/source/main_classes/model.mdx

@@ -88,4 +88,4 @@ Due to Pytorch design, this functionality is only available for floating dtypes.
 
 ## Pushing to the Hub
 
-[[autodoc]] file_utils.PushToHubMixin
+[[autodoc]] utils.PushToHubMixin

docs/source/main_classes/output.mdx

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # Model outputs
 
-All models have outputs that are instances of subclasses of [`~file_utils.ModelOutput`]. Those are
+All models have outputs that are instances of subclasses of [`~utils.ModelOutput`]. Those are
 data structures containing all the information returned by the model, but that can also be used as tuples or
 dictionaries.
 
@@ -57,7 +57,7 @@ documented on their corresponding model page.
 
 ## ModelOutput
 
-[[autodoc]] file_utils.ModelOutput
+[[autodoc]] utils.ModelOutput
     - to_tuple
 
 ## BaseModelOutput

docs/source/main_classes/trainer.mdx

@@ -40,7 +40,7 @@ The [`Trainer`] contains the basic training loop which supports the above featur
 The [`Trainer`] class is optimized for 🤗 Transformers models and can have surprising behaviors
 when you use it on other models. When using it on your own model, make sure:
 
-- your model always return tuples or subclasses of [`~file_utils.ModelOutput`].
+- your model always return tuples or subclasses of [`~utils.ModelOutput`].
 - your model can compute the loss if a `labels` argument is provided and that loss is returned as the first
   element of the tuple (if your model returns tuples)
 - your model can accept multiple label arguments (use the `label_names` in your [`TrainingArguments`] to indicate their name to the [`Trainer`]) but none of them should be named `"label"`.

docs/source/performance.mdx

@@ -855,7 +855,7 @@ If you need to switch a tensor to bf16, it's just: `t.to(dtype=torch.bfloat16)`
 Here is how you can check if your setup supports bf16:
 
 ```
-python -c 'import transformers; print(f"BF16 support is {transformers.file_utils.is_torch_bf16_available()}")'
+python -c 'import transformers; print(f"BF16 support is {transformers.utils.is_torch_bf16_available()}")'
 ```
 
 On the other hand bf16 has a much worse precision than fp16, so there are certain situations where you'd still want to use fp16 and not bf16.

examples/pytorch/multiple-choice/run_swag.py

@@ -153,7 +153,7 @@ class DataCollatorForMultipleChoice:
     Args:
         tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
             The tokenizer used for encoding the data.
-        padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `True`):
+        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:
 

examples/pytorch/multiple-choice/run_swag_no_trainer.py

@@ -193,7 +193,7 @@ class DataCollatorForMultipleChoice:
     Args:
         tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
             The tokenizer used for encoding the data.
-        padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `True`):
+        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:
 

examples/tensorflow/multiple-choice/run_swag.py

@@ -74,7 +74,7 @@ class DataCollatorForMultipleChoice:
     Args:
         tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
             The tokenizer used for encoding the data.
-        padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `True`):
+        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:
 

src/transformers/commands/add_new_model_like.py

@@ -784,7 +784,7 @@ def clean_frameworks_in_init(
             indent = find_indent(lines[idx])
             while find_indent(lines[idx]) >= indent or is_empty_line(lines[idx]):
                 idx += 1
-        # Remove the import from file_utils
+        # Remove the import from utils
         elif re_is_xxx_available.search(lines[idx]) is not None:
             line = lines[idx]
             for framework in to_remove:

src/transformers/configuration_utils.py

@@ -93,7 +93,7 @@ class PretrainedConfig(PushToHubMixin):
         output_attentions (`bool`, *optional*, defaults to `False`):
             Whether or not the model should returns all attentions.
         return_dict (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return a [`~transformers.file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not the model should return a [`~transformers.utils.ModelOutput`] instead of a plain tuple.
         is_encoder_decoder (`bool`, *optional*, defaults to `False`):
             Whether the model is used as an encoder/decoder or not.
         is_decoder (`bool`, *optional*, defaults to `False`):
@@ -170,7 +170,7 @@ class PretrainedConfig(PushToHubMixin):
         output_scores (`bool`, *optional*, defaults to `False`):
             Whether the model should return the logits when used for generation.
         return_dict_in_generate (`bool`, *optional*, defaults to `False`):
-            Whether the model should return a [`~transformers.file_utils.ModelOutput`] instead of a `torch.LongTensor`.
+            Whether the model should return a [`~transformers.utils.ModelOutput`] instead of a `torch.LongTensor`.
         forced_bos_token_id (`int`, *optional*):
             The id of the token to force as the first generated token after the `decoder_start_token_id`. Useful for
             multilingual models like [mBART](../model_doc/mbart) where the first generated token needs to be the target
@@ -379,7 +379,7 @@ class PretrainedConfig(PushToHubMixin):
     @property
     def use_return_dict(self) -> bool:
         """
-        `bool`: Whether or not return [`~file_utils.ModelOutput`] instead of tuples.
+        `bool`: Whether or not return [`~utils.ModelOutput`] instead of tuples.
         """
         # If torchscript is set, force `return_dict=False` to avoid jit errors
         return self.return_dict and not self.torchscript
@@ -417,7 +417,7 @@ class PretrainedConfig(PushToHubMixin):
                 </Tip>
 
             kwargs:
-                Additional key word arguments passed along to the [`~file_utils.PushToHubMixin.push_to_hub`] method.
+                Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
         if os.path.isfile(save_directory):
             raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")

src/transformers/data/data_collator.py

@@ -216,7 +216,7 @@ class DataCollatorWithPadding:
     Args:
         tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
             The tokenizer used for encoding the data.
-        padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `True`):
+        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:
 
@@ -268,7 +268,7 @@ class DataCollatorForTokenClassification(DataCollatorMixin):
     Args:
         tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
             The tokenizer used for encoding the data.
-        padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `True`):
+        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:
 
@@ -523,7 +523,7 @@ class DataCollatorForSeq2Seq:
             prepare the *decoder_input_ids*
 
             This is useful when using *label_smoothing* to avoid calculating loss twice.
-        padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `True`):
+        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:
 

src/transformers/feature_extraction_sequence_utils.py

@@ -90,7 +90,7 @@ class SequenceFeatureExtractor(FeatureExtractionMixin):
 
                 Instead of `List[float]` you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors),
                 see the note above for the return type.
-            padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `True`):
+            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:
 
@@ -114,7 +114,7 @@ class SequenceFeatureExtractor(FeatureExtractionMixin):
                 to the specific feature_extractor's default.
 
                 [What are attention masks?](../glossary#attention-mask)
-            return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
+            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.

src/transformers/feature_extraction_utils.py

@@ -117,9 +117,9 @@ class BatchFeature(UserDict):
         Convert the inner content to tensors.
 
         Args:
-            tensor_type (`str` or [`~file_utils.TensorType`], *optional*):
-                The type of tensors to use. If `str`, should be one of the values of the enum
-                [`~file_utils.TensorType`]. If `None`, no modification is done.
+            tensor_type (`str` or [`~utils.TensorType`], *optional*):
+                The type of tensors to use. If `str`, should be one of the values of the enum [`~utils.TensorType`]. If
+                `None`, no modification is done.
         """
         if tensor_type is None:
             return self
@@ -328,7 +328,7 @@ class FeatureExtractionMixin(PushToHubMixin):
                 </Tip>
 
             kwargs:
-                Additional key word arguments passed along to the [`~file_utils.PushToHubMixin.push_to_hub`] method.
+                Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
         if os.path.isfile(save_directory):
             raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")

src/transformers/generation_flax_utils.py

@@ -241,7 +241,7 @@ class FlaxGenerationMixin:
                 should be prefixed with *decoder_*. Also accepts `encoder_outputs` to skip encoder part.
 
         Return:
-            [`~file_utils.ModelOutput`].
+            [`~utils.ModelOutput`].
 
         Examples:
 

src/transformers/generation_tf_utils.py

@@ -469,7 +469,7 @@ class TFGenerationMixin:
             output_scores (`bool`, *optional*, defaults to `False`):
                 Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
             return_dict_in_generate (`bool`, *optional*, defaults to `False`):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
             forced_bos_token_id (`int`, *optional*):
                 The id of the token to force as the first generated token after the `decoder_start_token_id`. Useful
                 for multilingual models like [mBART](../model_doc/mbart) where the first generated token needs to be
@@ -480,11 +480,11 @@ class TFGenerationMixin:
                 Additional model specific kwargs will be forwarded to the `forward` function of the model.
 
         Return:
-            [`~file_utils.ModelOutput`] or `tf.Tensor`: A [`~file_utils.ModelOutput`] (if
-            `return_dict_in_generate=True` or when `config.return_dict_in_generate=True`) or a `tf.Tensor`.
+            [`~utils.ModelOutput`] or `tf.Tensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` or when
+            `config.return_dict_in_generate=True`) or a `tf.Tensor`.
 
                 If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
-                [`~file_utils.ModelOutput`] types are:
+                [`~utils.ModelOutput`] types are:
 
                     - [`~generation_tf_utils.TFGreedySearchDecoderOnlyOutput`],
                     - [`~generation_tf_utils.TFSampleDecoderOnlyOutput`],
@@ -492,7 +492,7 @@ class TFGenerationMixin:
                     - [`~generation_tf_utils.TFBeamSampleDecoderOnlyOutput`]
 
                 If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
-                [`~file_utils.ModelOutput`] types are:
+                [`~utils.ModelOutput`] types are:
 
                     - [`~generation_tf_utils.TFGreedySearchEncoderDecoderOutput`],
                     - [`~generation_tf_utils.TFSampleEncoderDecoderOutput`],
@@ -1370,7 +1370,7 @@ class TFGenerationMixin:
             output_scores (`bool`, *optional*, defaults to `False`):
                 Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
             return_dict_in_generate (`bool`, *optional*, defaults to `False`):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
             forced_bos_token_id (`int`, *optional*):
                 The id of the token to force as the first generated token after the `decoder_start_token_id`. Useful
                 for multilingual models like [mBART](../model_doc/mbart) where the first generated token needs to be
@@ -1381,11 +1381,11 @@ class TFGenerationMixin:
                 Additional model specific kwargs will be forwarded to the `forward` function of the model.
 
         Return:
-            [`~file_utils.ModelOutput`] or `tf.Tensor`: A [`~file_utils.ModelOutput`] (if
-            `return_dict_in_generate=True` or when `config.return_dict_in_generate=True`) or a `tf.Tensor`.
+            [`~utils.ModelOutput`] or `tf.Tensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` or when
+            `config.return_dict_in_generate=True`) or a `tf.Tensor`.
 
                 If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
-                [`~file_utils.ModelOutput`] types are:
+                [`~utils.ModelOutput`] types are:
 
                     - [`~generation_tf_utils.TFGreedySearchDecoderOnlyOutput`],
                     - [`~generation_tf_utils.TFSampleDecoderOnlyOutput`],
@@ -1393,7 +1393,7 @@ class TFGenerationMixin:
                     - [`~generation_tf_utils.TFBeamSampleDecoderOnlyOutput`]
 
                 If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
-                [`~file_utils.ModelOutput`] types are:
+                [`~utils.ModelOutput`] types are:
 
                     - [`~generation_tf_utils.TFGreedySearchEncoderDecoderOutput`],
                     - [`~generation_tf_utils.TFSampleEncoderDecoderOutput`],
@@ -1822,7 +1822,7 @@ class TFGenerationMixin:
             output_scores (`bool`, *optional*, defaults to `False`):
                 Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
             return_dict_in_generate (`bool`, *optional*, defaults to `False`):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
             model_kwargs:
                 Additional model specific keyword arguments will be forwarded to the `call` function of the model. If
                 model is an encoder-decoder model the kwargs should include `encoder_outputs`.
@@ -2085,7 +2085,7 @@ class TFGenerationMixin:
             output_scores (`bool`, *optional*, defaults to `False`):
                 Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
             return_dict_in_generate (`bool`, *optional*, defaults to `False`):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
             model_kwargs:
                 Additional model specific kwargs will be forwarded to the `call` function of the model. If model is an
                 encoder-decoder model the kwargs should include `encoder_outputs`.

src/transformers/generation_utils.py

@@ -1003,7 +1003,7 @@ class GenerationMixin:
             output_scores (`bool`, *optional*, defaults to `False`):
                 Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
             return_dict_in_generate (`bool`, *optional*, defaults to `False`):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
             forced_bos_token_id (`int`, *optional*):
                 The id of the token to force as the first generated token after the `decoder_start_token_id`. Useful
                 for multilingual models like [mBART](../model_doc/mbart) where the first generated token needs to be
@@ -1026,11 +1026,11 @@ class GenerationMixin:
                 should be prefixed with *decoder_*.
 
         Return:
-            [`~file_utils.ModelOutput`] or `torch.LongTensor`: A [`~file_utils.ModelOutput`] (if
-            `return_dict_in_generate=True` or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`.
+            [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True`
+            or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`.
 
                 If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
-                [`~file_utils.ModelOutput`] types are:
+                [`~utils.ModelOutput`] types are:
 
                     - [`~generation_utils.GreedySearchDecoderOnlyOutput`],
                     - [`~generation_utils.SampleDecoderOnlyOutput`],
@@ -1038,7 +1038,7 @@ class GenerationMixin:
                     - [`~generation_utils.BeamSampleDecoderOnlyOutput`]
 
                 If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
-                [`~file_utils.ModelOutput`] types are:
+                [`~utils.ModelOutput`] types are:
 
                     - [`~generation_utils.GreedySearchEncoderDecoderOutput`],
                     - [`~generation_utils.SampleEncoderDecoderOutput`],
@@ -1531,7 +1531,7 @@ class GenerationMixin:
             output_scores (`bool`, *optional*, defaults to `False`):
                 Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
             return_dict_in_generate (`bool`, *optional*, defaults to `False`):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
             synced_gpus (`bool`, *optional*, defaults to `False`):
                 Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
             model_kwargs:
@@ -1767,7 +1767,7 @@ class GenerationMixin:
             output_scores (`bool`, *optional*, defaults to `False`):
                 Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
             return_dict_in_generate (`bool`, *optional*, defaults to `False`):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
             synced_gpus (`bool`, *optional*, defaults to `False`):
                 Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
             model_kwargs:
@@ -2022,7 +2022,7 @@ class GenerationMixin:
             output_scores (`bool`, *optional*, defaults to `False`):
                 Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
             return_dict_in_generate (`bool`, *optional*, defaults to `False`):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
             synced_gpus (`bool`, *optional*, defaults to `False`):
                 Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
             model_kwargs:
@@ -2339,7 +2339,7 @@ class GenerationMixin:
             output_scores (`bool`, *optional*, defaults to `False`):
                 Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
             return_dict_in_generate (`bool`, *optional*, defaults to `False`):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
             synced_gpus (`bool`, *optional*, defaults to `False`):
                 Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
             model_kwargs:
@@ -2656,7 +2656,7 @@ class GenerationMixin:
             output_scores (`bool`, *optional*, defaults to `False`):
                 Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
             return_dict_in_generate (`bool`, *optional*, defaults to `False`):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
             synced_gpus (`bool`, *optional*, defaults to `False`):
                 Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
 
@@ -3026,7 +3026,7 @@ class GenerationMixin:
             output_scores (`bool`, *optional*, defaults to `False`):
                 Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
             return_dict_in_generate (`bool`, *optional*, defaults to `False`):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
             synced_gpus (`bool`, *optional*, defaults to `False`):
                 Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
             model_kwargs:

src/transformers/modeling_flax_utils.py

@@ -681,7 +681,7 @@ class FlaxPreTrainedModel(PushToHubMixin, FlaxGenerationMixin):
                 </Tip>
 
             kwargs:
-                Additional key word arguments passed along to the [`~file_utils.PushToHubMixin.push_to_hub`] method.
+                Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")

src/transformers/modeling_tf_utils.py

@@ -1401,7 +1401,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
                 </Tip>
 
             kwargs:
-                Additional key word arguments passed along to the [`~file_utils.PushToHubMixin.push_to_hub`] method.
+                Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")

src/transformers/modeling_utils.py

@@ -1036,7 +1036,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                 </Tip>
 
             kwargs:
-                Additional key word arguments passed along to the [`~file_utils.PushToHubMixin.push_to_hub`] method.
+                Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
@@ -2129,7 +2129,7 @@ class SQuADHead(nn.Module):
                 Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS). 1.0 means token
                 should be masked.
             return_dict (`bool`, *optional*, defaults to `False`):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 
         Returns:
         """

src/transformers/models/albert/modeling_albert.py

@@ -610,7 +610,7 @@ ALBERT_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/albert/modeling_flax_albert.py

@@ -144,7 +144,7 @@ ALBERT_INPUTS_DOCSTRING = r"""
             Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
             config.max_position_embeddings - 1]`.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 
 """
 

src/transformers/models/albert/modeling_tf_albert.py

@@ -747,8 +747,8 @@ ALBERT_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False`):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).

src/transformers/models/bart/modeling_bart.py

@@ -679,7 +679,7 @@ BART_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 
@@ -770,7 +770,7 @@ class BartEncoder(BartPretrainedModel):
                 Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                 for more detail.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
@@ -993,7 +993,7 @@ class BartDecoder(BartPretrainedModel):
                 Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                 for more detail.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
@@ -1799,7 +1799,7 @@ class BartForCausalLM(BartPretrainedModel):
                 Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                 for more detail.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 
         Returns:
 

src/transformers/models/bart/modeling_flax_bart.py

@@ -138,7 +138,7 @@ BART_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 
@@ -169,7 +169,7 @@ BART_ENCODE_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 BART_DECODE_INPUTS_DOCSTRING = r"""
@@ -215,7 +215,7 @@ BART_DECODE_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/bart/modeling_tf_bart.py

@@ -625,8 +625,8 @@ BART_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False`):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).
@@ -715,7 +715,7 @@ class TFBartEncoder(tf.keras.layers.Layer):
                 Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                 for more detail.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
 
         if input_ids is not None and inputs_embeds is not None:
@@ -894,7 +894,7 @@ class TFBartDecoder(tf.keras.layers.Layer):
                 Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                 for more detail.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
 
         if input_ids is not None and inputs_embeds is not None:

src/transformers/models/beit/feature_extraction_beit.py

@@ -120,7 +120,7 @@ class BeitFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
             segmentation_maps (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`, *optional*):
                 Optionally, the corresponding semantic segmentation maps with the pixel-wise annotations.
 
-            return_tensors (`str` or [`~file_utils.TensorType`], *optional*, defaults to `'np'`):
+            return_tensors (`str` or [`~utils.TensorType`], *optional*, defaults to `'np'`):
                 If set, will return tensors of a particular framework. Acceptable values are:
 
                 - `'tf'`: Return TensorFlow `tf.constant` objects.

src/transformers/models/beit/modeling_beit.py

@@ -618,7 +618,7 @@ BEIT_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/beit/modeling_flax_beit.py

@@ -111,7 +111,7 @@ BEIT_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/bert/modeling_bert.py

@@ -837,7 +837,7 @@ BERT_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/bert/modeling_flax_bert.py

@@ -164,7 +164,7 @@ BERT_INPUTS_DOCSTRING = r"""
             - 0 indicates the head is **masked**.
 
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 
 """
 

src/transformers/models/bert/modeling_tf_bert.py

@@ -1025,8 +1025,8 @@ BERT_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False``):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).

src/transformers/models/bert_generation/modeling_bert_generation.py

@@ -242,7 +242,7 @@ BERT_GENERATION_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/big_bird/modeling_big_bird.py

@@ -1844,7 +1844,7 @@ BIG_BIRD_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/big_bird/modeling_flax_big_bird.py

@@ -181,7 +181,7 @@ BIG_BIRD_INPUTS_DOCSTRING = r"""
             - 0 indicates the head is **masked**.
 
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 
 """
 

src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py

@@ -1724,7 +1724,7 @@ BIGBIRD_PEGASUS_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 BIGBIRD_PEGASUS_STANDALONE_INPUTS_DOCSTRING = r"""
@@ -1751,7 +1751,7 @@ BIGBIRD_PEGASUS_STANDALONE_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 
@@ -1834,7 +1834,7 @@ class BigBirdPegasusEncoder(BigBirdPegasusPreTrainedModel):
                 Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                 for more detail.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
@@ -2188,7 +2188,7 @@ class BigBirdPegasusDecoder(BigBirdPegasusPreTrainedModel):
                 Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                 for more detail.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
@@ -2999,7 +2999,7 @@ class BigBirdPegasusForCausalLM(BigBirdPegasusPreTrainedModel):
                 Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                 for more detail.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 
         Returns:
 

src/transformers/models/blenderbot/modeling_blenderbot.py

@@ -625,7 +625,7 @@ BLENDERBOT_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 
@@ -710,7 +710,7 @@ class BlenderbotEncoder(BlenderbotPreTrainedModel):
                 Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                 for more detail.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
@@ -935,7 +935,7 @@ class BlenderbotDecoder(BlenderbotPreTrainedModel):
                 Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                 for more detail.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
@@ -1518,7 +1518,7 @@ class BlenderbotForCausalLM(BlenderbotPreTrainedModel):
                 Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                 for more detail.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 
         Returns:
 

src/transformers/models/blenderbot/modeling_flax_blenderbot.py

@@ -124,7 +124,7 @@ BLENDERBOT_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 
@@ -155,7 +155,7 @@ BLENDERBOT_ENCODE_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 BLENDERBOT_DECODE_INPUTS_DOCSTRING = r"""
@@ -201,7 +201,7 @@ BLENDERBOT_DECODE_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/blenderbot/modeling_tf_blenderbot.py

@@ -608,8 +608,8 @@ BLENDERBOT_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False`):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).
@@ -700,8 +700,8 @@ class TFBlenderbotEncoder(tf.keras.layers.Layer):
                 for more detail. This argument can be used only in eager mode, in graph mode the value in the config
                 will be used instead.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be
-                used in eager mode, in graph mode the value will always be set to True.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
+                in eager mode, in graph mode the value will always be set to True.
             training (`bool`, *optional*, defaults to `False`):
                 Whether or not to use the model in training mode (some modules like dropout modules have different
                 behaviors between training and evaluation).
@@ -885,8 +885,8 @@ class TFBlenderbotDecoder(tf.keras.layers.Layer):
                 for more detail. This argument can be used only in eager mode, in graph mode the value in the config
                 will be used instead.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be
-                used in eager mode, in graph mode the value will always be set to True.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
+                in eager mode, in graph mode the value will always be set to True.
             training (`bool`, *optional*, defaults to `False`):
                 Whether or not to use the model in training mode (some modules like dropout modules have different
                 behaviors between training and evaluation).

src/transformers/models/blenderbot_small/modeling_blenderbot_small.py

@@ -623,7 +623,7 @@ BLENDERBOT_SMALL_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 
@@ -708,7 +708,7 @@ class BlenderbotSmallEncoder(BlenderbotSmallPreTrainedModel):
                 Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                 for more detail.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
@@ -930,7 +930,7 @@ class BlenderbotSmallDecoder(BlenderbotSmallPreTrainedModel):
                 Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                 for more detail.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
@@ -1489,7 +1489,7 @@ class BlenderbotSmallForCausalLM(BlenderbotSmallPreTrainedModel):
                 Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                 for more detail.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 
         Returns:
 

src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py

@@ -136,7 +136,7 @@ BLENDERBOT_SMALL_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 
@@ -167,7 +167,7 @@ BLENDERBOT_SMALL_ENCODE_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 BLENDERBOT_SMALL_DECODE_INPUTS_DOCSTRING = r"""
@@ -213,7 +213,7 @@ BLENDERBOT_SMALL_DECODE_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py

@@ -613,8 +613,8 @@ BLENDERBOT_SMALL_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False`):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).
@@ -705,8 +705,8 @@ class TFBlenderbotSmallEncoder(tf.keras.layers.Layer):
                 for more detail. This argument can be used only in eager mode, in graph mode the value in the config
                 will be used instead.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be
-                used in eager mode, in graph mode the value will always be set to True.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
+                in eager mode, in graph mode the value will always be set to True.
             training (`bool`, *optional*, defaults to `False`):
                 Whether or not to use the model in training mode (some modules like dropout modules have different
                 behaviors between training and evaluation).
@@ -889,8 +889,8 @@ class TFBlenderbotSmallDecoder(tf.keras.layers.Layer):
                 for more detail. This argument can be used only in eager mode, in graph mode the value in the config
                 will be used instead.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be
-                used in eager mode, in graph mode the value will always be set to True.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
+                in eager mode, in graph mode the value will always be set to True.
             training (`bool`, *optional*, defaults to `False`):
                 Whether or not to use the model in training mode (some modules like dropout modules have different
                 behaviors between training and evaluation).

src/transformers/models/canine/modeling_canine.py

@@ -975,7 +975,7 @@ CANINE_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/clip/feature_extraction_clip.py

@@ -104,7 +104,7 @@ class CLIPFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
                 tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
                 number of channels, H and W are image height and width.
 
-            return_tensors (`str` or [`~file_utils.TensorType`], *optional*, defaults to `'np'`):
+            return_tensors (`str` or [`~utils.TensorType`], *optional*, defaults to `'np'`):
                 If set, will return tensors of a particular framework. Acceptable values are:
 
                 - `'tf'`: Return TensorFlow `tf.constant` objects.

src/transformers/models/clip/modeling_clip.py

@@ -433,7 +433,7 @@ CLIP_TEXT_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 CLIP_VISION_INPUTS_DOCSTRING = r"""
@@ -448,7 +448,7 @@ CLIP_VISION_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 CLIP_INPUTS_DOCSTRING = r"""
@@ -485,7 +485,7 @@ CLIP_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 
@@ -540,7 +540,7 @@ class CLIPEncoder(nn.Module):
                 Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                 for more detail.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (

src/transformers/models/clip/modeling_flax_clip.py

@@ -100,7 +100,7 @@ CLIP_TEXT_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 CLIP_VISION_INPUTS_DOCSTRING = r"""
@@ -115,7 +115,7 @@ CLIP_VISION_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 CLIP_INPUTS_DOCSTRING = r"""
@@ -150,7 +150,7 @@ CLIP_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/clip/modeling_tf_clip.py

@@ -968,8 +968,8 @@ CLIP_TEXT_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False``):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).
@@ -988,8 +988,8 @@ CLIP_VISION_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False``):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).
@@ -1030,8 +1030,8 @@ CLIP_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False``):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).

src/transformers/models/clip/processing_clip.py

@@ -57,7 +57,7 @@ class CLIPProcessor(ProcessorMixin):
                 tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
                 number of channels, H and W are image height and width.
 
-            return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                 If set, will return tensors of a particular framework. Acceptable values are:
 
                 - `'tf'`: Return TensorFlow `tf.constant` objects.

src/transformers/models/convbert/modeling_convbert.py

@@ -755,7 +755,7 @@ CONVBERT_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/convbert/modeling_tf_convbert.py

@@ -736,8 +736,8 @@ CONVBERT_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False`):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).

src/transformers/models/convnext/feature_extraction_convnext.py

@@ -103,7 +103,7 @@ class ConvNextFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMix
                 tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
                 number of channels, H and W are image height and width.
 
-            return_tensors (`str` or [`~file_utils.TensorType`], *optional*, defaults to `'np'`):
+            return_tensors (`str` or [`~utils.TensorType`], *optional*, defaults to `'np'`):
                 If set, will return tensors of a particular framework. Acceptable values are:
 
                 - `'tf'`: Return TensorFlow `tf.constant` objects.

src/transformers/models/convnext/modeling_convnext.py

@@ -357,7 +357,7 @@ CONVNEXT_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/convnext/modeling_tf_convnext.py

@@ -416,8 +416,8 @@ CONVNEXT_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
 """
 
 

src/transformers/models/ctrl/modeling_ctrl.py

@@ -309,7 +309,7 @@ CTRL_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/ctrl/modeling_tf_ctrl.py

@@ -502,8 +502,8 @@ CTRL_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False`):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).

src/transformers/models/data2vec/modeling_data2vec_audio.py

@@ -887,7 +887,7 @@ DATA2VEC_AUDIO_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/data2vec/modeling_data2vec_text.py

@@ -689,7 +689,7 @@ DATA2VECTEXT_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/deberta/modeling_deberta.py

@@ -871,7 +871,7 @@ DEBERTA_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/deberta/modeling_tf_deberta.py

@@ -1063,7 +1063,7 @@ DEBERTA_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~transformers.file_utils.ModelOutput``] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput``] instead of a plain tuple.
 """
 
 

src/transformers/models/deberta_v2/modeling_deberta_v2.py

@@ -965,7 +965,7 @@ DEBERTA_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py

@@ -1164,7 +1164,7 @@ DEBERTA_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~transformers.file_utils.ModelOutput``] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput``] instead of a plain tuple.
 """
 
 

src/transformers/models/decision_transformer/__init__.py

@@ -18,7 +18,7 @@
 from typing import TYPE_CHECKING
 
 # rely on isort to merge the imports
-from ...file_utils import _LazyModule, is_torch_available
+from ...utils import _LazyModule, is_torch_available
 
 
 _import_structure = {

src/transformers/models/decision_transformer/modeling_decision_transformer.py

@@ -25,14 +25,14 @@ from packaging import version
 from torch import nn
 
 from ...activations import ACT2FN
-from ...file_utils import (
+from ...modeling_utils import Conv1D, PreTrainedModel, find_pruneable_heads_and_indices, prune_conv1d_layer
+from ...utils import (
     ModelOutput,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
+    logging,
     replace_return_docstrings,
 )
-from ...modeling_utils import Conv1D, PreTrainedModel, find_pruneable_heads_and_indices, prune_conv1d_layer
-from ...utils import logging
 
 
 if version.parse(torch.__version__) >= version.parse("1.6"):

src/transformers/models/deit/feature_extraction_deit.py

@@ -107,7 +107,7 @@ class DeiTFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
                 tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
                 number of channels, H and W are image height and width.
 
-            return_tensors (`str` or [`~file_utils.TensorType`], *optional*, defaults to `'np'`):
+            return_tensors (`str` or [`~utils.TensorType`], *optional*, defaults to `'np'`):
                 If set, will return tensors of a particular framework. Acceptable values are:
 
                 - `'tf'`: Return TensorFlow `tf.constant` objects.

src/transformers/models/deit/modeling_deit.py

@@ -460,7 +460,7 @@ DEIT_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/detr/feature_extraction_detr.py

@@ -455,7 +455,7 @@ class DetrFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
                 - 1 for pixels that are real (i.e. **not masked**),
                 - 0 for pixels that are padding (i.e. **masked**).
 
-            return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                 If set, will return tensors instead of NumPy arrays. If set to `'pt'`, return PyTorch `torch.Tensor`
                 objects.
 
@@ -638,7 +638,7 @@ class DetrFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
         Args:
             pixel_values_list (`List[torch.Tensor]`):
                 List of images (pixel values) to be padded. Each image should be a tensor of shape (C, H, W).
-            return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                 If set, will return tensors instead of NumPy arrays. If set to `'pt'`, return PyTorch `torch.Tensor`
                 objects.
 

src/transformers/models/detr/modeling_detr.py

@@ -868,7 +868,7 @@ DETR_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 
@@ -932,7 +932,7 @@ class DetrEncoder(DetrPreTrainedModel):
                 Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                 for more detail.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
@@ -1054,7 +1054,7 @@ class DetrDecoder(DetrPreTrainedModel):
                 Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                 for more detail.
             return_dict (`bool`, *optional*):
-                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (

src/transformers/models/distilbert/modeling_distilbert.py

@@ -446,7 +446,7 @@ DISTILBERT_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/distilbert/modeling_flax_distilbert.py

@@ -89,7 +89,7 @@ DISTILBERT_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/distilbert/modeling_tf_distilbert.py

@@ -508,8 +508,8 @@ DISTILBERT_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False`):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).

src/transformers/models/dpr/modeling_dpr.py

@@ -398,7 +398,7 @@ DPR_ENCODERS_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 DPR_READER_INPUTS_DOCSTRING = r"""
@@ -434,7 +434,7 @@ DPR_READER_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/dpr/modeling_tf_dpr.py

@@ -487,8 +487,8 @@ TF_DPR_ENCODERS_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False`):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).
@@ -523,8 +523,8 @@ TF_DPR_READER_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False`):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).

src/transformers/models/dpr/tokenization_dpr.py

@@ -144,7 +144,7 @@ CUSTOM_DPR_READER_DOCSTRING = r"""
             The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
         texts (`str` or `List[str]`):
             The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
-        padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`):
+        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
             Activates and controls padding. Accepts the following values:
 
             - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
@@ -174,7 +174,7 @@ CUSTOM_DPR_READER_DOCSTRING = r"""
                 If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                 is required by one of the truncation/padding parameters. If the model has no specific maximum input
                 length (like XLNet) truncation/padding to a maximum length will be deactivated.
-        return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
+        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.

src/transformers/models/dpr/tokenization_dpr_fast.py

@@ -145,7 +145,7 @@ CUSTOM_DPR_READER_DOCSTRING = r"""
             The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
         texts (`str` or `List[str]`):
             The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
-        padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`):
+        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
             Activates and controls padding. Accepts the following values:
 
             - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
@@ -175,7 +175,7 @@ CUSTOM_DPR_READER_DOCSTRING = r"""
                 If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                 is required by one of the truncation/padding parameters. If the model has no specific maximum input
                 length (like XLNet) truncation/padding to a maximum length will be deactivated.
-        return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
+        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.

src/transformers/models/electra/modeling_electra.py

@@ -794,7 +794,7 @@ ELECTRA_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/electra/modeling_flax_electra.py

@@ -134,7 +134,7 @@ ELECTRA_INPUTS_DOCSTRING = r"""
             - 0 indicates the head is **masked**.
 
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 
 """
 

src/transformers/models/electra/modeling_tf_electra.py

@@ -907,8 +907,8 @@ ELECTRA_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False`):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).

src/transformers/models/encoder_decoder/modeling_encoder_decoder.py

@@ -135,7 +135,7 @@ ENCODER_DECODER_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            If set to `True`, the model will return a [`~file_utils.Seq2SeqLMOutput`] instead of a plain tuple.
+            If set to `True`, the model will return a [`~utils.Seq2SeqLMOutput`] instead of a plain tuple.
         kwargs: (*optional*) Remaining dictionary of keyword arguments. Keyword arguments come in two flavors:
 
             - Without a prefix which will be input as `**encoder_kwargs` for the encoder forward function.

src/transformers/models/encoder_decoder/modeling_flax_encoder_decoder.py

@@ -122,7 +122,7 @@ ENCODER_DECODER_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            If set to `True`, the model will return a [`~file_utils.FlaxSeq2SeqLMOutput`] instead of a plain tuple.
+            If set to `True`, the model will return a [`~utils.FlaxSeq2SeqLMOutput`] instead of a plain tuple.
 """
 
 ENCODER_DECODER_ENCODE_INPUTS_DOCSTRING = r"""
@@ -152,7 +152,7 @@ ENCODER_DECODER_ENCODE_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            If set to `True`, the model will return a [`~file_utils.FlaxBaseModelOutput`] instead of a plain tuple.
+            If set to `True`, the model will return a [`~utils.FlaxBaseModelOutput`] instead of a plain tuple.
 """
 
 ENCODER_DECODER_DECODE_INPUTS_DOCSTRING = r"""
@@ -198,8 +198,8 @@ ENCODER_DECODER_DECODE_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            If set to `True`, the model will return a [`~file_utils.FlaxCausalLMOutputWithCrossAttentions`] instead of
-            a plain tuple.
+            If set to `True`, the model will return a [`~utils.FlaxCausalLMOutputWithCrossAttentions`] instead of a
+            plain tuple.
 """
 
 

src/transformers/models/encoder_decoder/modeling_tf_encoder_decoder.py

@@ -143,7 +143,7 @@ ENCODER_DECODER_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            If set to `True`, the model will return a [`~file_utils.Seq2SeqLMOutput`] instead of a plain tuple.
+            If set to `True`, the model will return a [`~utils.Seq2SeqLMOutput`] instead of a plain tuple.
         training (`bool`, *optional*, defaults to `False`):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).

src/transformers/models/flaubert/modeling_flaubert.py

@@ -123,7 +123,7 @@ FLAUBERT_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/flaubert/modeling_tf_flaubert.py

@@ -165,8 +165,8 @@ FLAUBERT_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False`):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).

src/transformers/models/fnet/modeling_fnet.py

@@ -507,7 +507,7 @@ FNET_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/fsmt/modeling_fsmt.py

@@ -282,7 +282,7 @@ FSMT_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/funnel/modeling_funnel.py

@@ -913,7 +913,7 @@ FUNNEL_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/funnel/modeling_tf_funnel.py

@@ -1079,8 +1079,8 @@ FUNNEL_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False`):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).

src/transformers/models/glpn/feature_extraction_glpn.py

@@ -86,7 +86,7 @@ class GLPNFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
                 tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
                 number of channels, H and W are image height and width.
 
-            return_tensors (`str` or [`~file_utils.TensorType`], *optional*, defaults to `'np'`):
+            return_tensors (`str` or [`~utils.TensorType`], *optional*, defaults to `'np'`):
                 If set, will return tensors of a particular framework. Acceptable values are:
 
                 - `'tf'`: Return TensorFlow `tf.constant` objects.

src/transformers/models/glpn/modeling_glpn.py

@@ -467,7 +467,7 @@ GLPN_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/gpt2/modeling_flax_gpt2.py

@@ -103,7 +103,7 @@ GPT2_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/gpt2/modeling_gpt2.py

@@ -607,7 +607,7 @@ GPT2_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 PARALLELIZE_DOCSTRING = r"""
     This is an experimental feature and is a subject to change at a moment's notice.

src/transformers/models/gpt2/modeling_tf_gpt2.py

@@ -715,8 +715,8 @@ GPT2_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False`):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).

src/transformers/models/gpt_neo/modeling_flax_gpt_neo.py

@@ -101,7 +101,7 @@ GPT_NEO_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/gpt_neo/modeling_gpt_neo.py

@@ -463,7 +463,7 @@ GPT_NEO_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/gptj/modeling_flax_gptj.py

@@ -103,7 +103,7 @@ GPTJ_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/gptj/modeling_gptj.py

@@ -390,7 +390,7 @@ GPTJ_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 PARALLELIZE_DOCSTRING = r"""

src/transformers/models/hubert/modeling_hubert.py

@@ -931,7 +931,7 @@ HUBERT_INPUTS_DOCSTRING = r"""
             Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
             more detail.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
 

src/transformers/models/hubert/modeling_tf_hubert.py

@@ -1387,8 +1387,8 @@ HUBERT_INPUTS_DOCSTRING = r"""
             more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
             used instead.
         return_dict (`bool`, *optional*):
-            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
-            in eager mode, in graph mode the value will always be set to True.
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
         training (`bool`, *optional*, defaults to `False``):
             Whether or not to use the model in training mode (some modules like dropout modules have different
             behaviors between training and evaluation).