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/zh/custom_models.md

@@ -39,7 +39,7 @@ class ResnetConfig(PretrainedConfig):
     def __init__(
         self,
         block_type="bottleneck",
-        layers: List[int] = [3, 4, 6, 3],
+        layers: list[int] = [3, 4, 6, 3],
         num_classes: int = 1000,
         input_channels: int = 3,
         cardinality: int = 1,

docs/source/zh/hpo_train.md

@@ -56,7 +56,7 @@ pip install optuna/sigopt/wandb/ray[tune]
 ...     }
 ```
 
-Optuna提供了多目标HPO。您可以在`hyperparameter_search`中传递`direction`参数，并定义自己的`compute_objective`以返回多个目标值。在`hyperparameter_search`中将返回Pareto Front（`List[BestRun]`），您应该参考[test_trainer](https://github.com/huggingface/transformers/blob/main/tests/trainer/test_trainer.py)中的测试用例`TrainerHyperParameterMultiObjectOptunaIntegrationTest`。它类似于以下内容：
+Optuna提供了多目标HPO。您可以在`hyperparameter_search`中传递`direction`参数，并定义自己的`compute_objective`以返回多个目标值。在`hyperparameter_search`中将返回Pareto Front（`list[BestRun]`），您应该参考[test_trainer](https://github.com/huggingface/transformers/blob/main/tests/trainer/test_trainer.py)中的测试用例`TrainerHyperParameterMultiObjectOptunaIntegrationTest`。它类似于以下内容：
 
 ```py
 >>> best_trials = trainer.hyperparameter_search(

docs/source/zh/tasks/asr.md

@@ -181,7 +181,7 @@ Wav2Vec2 分词器仅训练了大写字符，因此您需要确保文本与分
 ...     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]