No more Tuple, List, Dict (#38797)

* No more Tuple, List, Dict

* make fixup

* More style fixes

* Docstring fixes with regex replacement

* Trigger tests

* Redo fixes after rebase

* Fix copies

* [test all]

* update

* [test all]

* update

* [test all]

* make style after rebase

* Patch the hf_argparser test

* Patch the hf_argparser test

* style fixes

* style fixes

* style fixes

* Fix docstrings in Cohere test

* [test all]

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

docs/source/en/tasks/asr.md

@@ -170,7 +170,7 @@ Unlike other data collators, this specific data collator needs to apply a differ
 ...     processor: AutoProcessor
 ...     padding: Union[bool, str] = "longest"
 
-...     def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+...     def __call__(self, features: list[dict[str, Union[list[int], torch.Tensor]]]) -> dict[str, torch.Tensor]:
 ...         # split inputs and labels since they have to be of different lengths and need
 ...         # different padding methods
 ...         input_features = [{"input_values": feature["input_values"][0]} for feature in features]

docs/source/en/tasks/object_detection.md

@@ -243,7 +243,7 @@ and it uses the exact same dataset as an example. Apply some geometric and color
 ... )
 ```
 
-The `image_processor` expects the annotations to be in the following format: `{'image_id': int, 'annotations': List[Dict]}`,
+The `image_processor` expects the annotations to be in the following format: `{'image_id': int, 'annotations': list[Dict]}`,
  where each dictionary is a COCO object annotation. Let's add a function to reformat annotations for a single example:
 
 ```py
@@ -252,9 +252,9 @@ The `image_processor` expects the annotations to be in the following format: `{'
 
 ...     Args:
 ...         image_id (str): image id. e.g. "0001"
-...         categories (List[int]): list of categories/class labels corresponding to provided bounding boxes
-...         areas (List[float]): list of corresponding areas to provided bounding boxes
-...         bboxes (List[Tuple[float]]): list of bounding boxes provided in COCO format
+...         categories (list[int]): list of categories/class labels corresponding to provided bounding boxes
+...         areas (list[float]): list of corresponding areas to provided bounding boxes
+...         bboxes (list[tuple[float]]): list of bounding boxes provided in COCO format
 ...             ([center_x, center_y, width, height] in absolute coordinates)
 
 ...     Returns:
@@ -397,7 +397,7 @@ Intermediate format of boxes used for training is `YOLO` (normalized) but we wil
 
 ...     Args:
 ...         boxes (torch.Tensor): Bounding boxes in YOLO format
-...         image_size (Tuple[int, int]): Image size in format (height, width)
+...         image_size (tuple[int, int]): Image size in format (height, width)
 
 ...     Returns:
 ...         torch.Tensor: Bounding boxes in Pascal VOC format (x_min, y_min, x_max, y_max)

docs/source/en/tasks/text-to-speech.md

@@ -408,7 +408,7 @@ instructs the model to ignore that part of the spectrogram when calculating the
 ... class TTSDataCollatorWithPadding:
 ...     processor: Any
 
-...     def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+...     def __call__(self, features: list[dict[str, Union[list[int], torch.Tensor]]]) -> dict[str, torch.Tensor]:
 ...         input_ids = [{"input_ids": feature["input_ids"]} for feature in features]
 ...         label_features = [{"input_values": feature["labels"]} for feature in features]
 ...         speaker_features = [feature["speaker_embeddings"] for feature in features]