Big file_utils cleanup (#16396)

* Big file_utils cleanup

* This one still needs to be treated separately

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.