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>

examples/pytorch/3d_parallel_checks.py

@@ -33,7 +33,7 @@ import logging
 import os
 from collections.abc import Iterable
 from contextlib import nullcontext
-from typing import Dict, Optional
+from typing import Optional
 
 import torch
 import torch.distributed as dist
@@ -589,7 +589,7 @@ class ContextParallelCollator:
     def __init__(self, cp_mesh: Optional[DeviceMesh] = None):
         self.cp_mesh = cp_mesh
 
-    def __call__(self, batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+    def __call__(self, batch: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
         batch = default_collate(batch)
         if self.cp_mesh is not None and self.cp_mesh.size() > 1:
             # Get sequence length from the input batch

examples/pytorch/object-detection/run_object_detection.py

@@ -66,9 +66,9 @@ def format_image_annotations_as_coco(
 
     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:
@@ -101,7 +101,7 @@ def convert_bbox_yolo_to_pascal(boxes: torch.Tensor, image_size: tuple[int, int]
 
     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)

examples/pytorch/object-detection/run_object_detection_no_trainer.py

@@ -67,9 +67,9 @@ def format_image_annotations_as_coco(
 
     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:
@@ -103,7 +103,7 @@ def convert_bbox_yolo_to_pascal(boxes: torch.Tensor, image_size: tuple[int, int]
 
     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)

examples/pytorch/question-answering/utils_qa.py

@@ -47,7 +47,7 @@ def postprocess_qa_predictions(
     Args:
         examples: The non-preprocessed dataset (see the main script for more information).
         features: The processed dataset (see the main script for more information).
-        predictions (:obj:`Tuple[np.ndarray, np.ndarray]`):
+        predictions (:obj:`tuple[np.ndarray, np.ndarray]`):
             The predictions of the model: two arrays containing the start logits and the end logits respectively. Its
             first dimension must match the number of elements of :obj:`features`.
         version_2_with_negative (:obj:`bool`, `optional`, defaults to :obj:`False`):
@@ -270,7 +270,7 @@ def postprocess_qa_predictions_with_beam_search(
     Args:
         examples: The non-preprocessed dataset (see the main script for more information).
         features: The processed dataset (see the main script for more information).
-        predictions (:obj:`Tuple[np.ndarray, np.ndarray]`):
+        predictions (:obj:`tuple[np.ndarray, np.ndarray]`):
             The predictions of the model: two arrays containing the start logits and the end logits respectively. Its
             first dimension must match the number of elements of :obj:`features`.
         version_2_with_negative (:obj:`bool`, `optional`, defaults to :obj:`False`):