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>

tests/models/markuplm/test_modeling_markuplm.py

@@ -309,7 +309,7 @@ class MarkupLMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase
         feature_extractor_name,
         processor_name,
     ):
-        # ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
+        # ValueError: Nodes must be of type `list[str]` (single pretokenized example), or `list[list[str]]`
         # (batch of pretokenized examples).
         return True
 

tests/models/timesfm/test_modeling_timesfm.py

@@ -15,7 +15,6 @@
 
 import inspect
 import unittest
-from typing import List
 
 import numpy as np
 import torch
@@ -52,7 +51,7 @@ class TimesFmModelTester:
         num_heads: int = 2,
         tolerance: float = 1e-6,
         rms_norm_eps: float = 1e-6,
-        quantiles: List[float] = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
+        quantiles: list[float] = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
         pad_val: float = 1123581321.0,
         use_positional_embedding: bool = True,
         initializer_factor: float = 0.0,

tests/models/univnet/test_feature_extraction_univnet.py

@@ -248,7 +248,7 @@ class UnivNetFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.
         for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
             self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
 
-        # Test np.ndarray vs List[np.ndarray]
+        # Test np.ndarray vs list[np.ndarray]
         encoded_sequences_1 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
         encoded_sequences_2 = feature_extractor([np_speech_inputs], return_tensors="np").input_features
         for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):

tests/test_pipeline_mixin.py

@@ -280,13 +280,13 @@ class PipelineTesterMixin:
                 A model repository id on the Hub.
             model_architecture (`type`):
                 A subclass of `PretrainedModel` or `PretrainedModel`.
-            tokenizer_names (`List[str]`):
+            tokenizer_names (`list[str]`):
                 A list of names of a subclasses of `PreTrainedTokenizerFast` or `PreTrainedTokenizer`.
-            image_processor_names (`List[str]`):
+            image_processor_names (`list[str]`):
                 A list of names of subclasses of `BaseImageProcessor`.
-            feature_extractor_names (`List[str]`):
+            feature_extractor_names (`list[str]`):
                 A list of names of subclasses of `FeatureExtractionMixin`.
-            processor_names (`List[str]`):
+            processor_names (`list[str]`):
                 A list of names of subclasses of `ProcessorMixin`.
             commit (`str`):
                 The commit hash of the model repository on the Hub.

tests/test_sequence_feature_extraction_common.py

@@ -123,7 +123,7 @@ class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
         batch_size = self.feat_extract_tester.batch_size
         feature_size = self.feat_extract_tester.feature_size
 
-        # test padding for List[int] + numpy
+        # test padding for list[int] + numpy
         input_1 = feat_extract.pad(processed_features, padding=False)
         input_1 = input_1[input_name]
 
@@ -157,7 +157,7 @@ class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
         if feature_size > 1:
             self.assertTrue(input_4.shape[2] == input_5.shape[2] == feature_size)
 
-        # test padding for `pad_to_multiple_of` for List[int] + numpy
+        # test padding for `pad_to_multiple_of` for list[int] + numpy
         input_6 = feat_extract.pad(processed_features, pad_to_multiple_of=10)
         input_6 = input_6[input_name]
 
@@ -319,7 +319,7 @@ class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
         with self.assertRaises(ValueError):
             feat_extract.pad(processed_features, padding="max_length", truncation=True)[input_name]
 
-        # test truncation for `pad_to_multiple_of` for List[int] + numpy
+        # test truncation for `pad_to_multiple_of` for list[int] + numpy
         pad_to_multiple_of = 12
         input_8 = feat_extract.pad(
             processed_features,

tests/utils/test_hf_argparser.py

@@ -22,7 +22,7 @@ from argparse import Namespace
 from dataclasses import dataclass, field
 from enum import Enum
 from pathlib import Path
-from typing import List, Literal, Optional, Union, get_args, get_origin
+from typing import Literal, Optional, Union, get_args, get_origin
 
 import yaml
 
@@ -121,7 +121,7 @@ class StringLiteralAnnotationExample:
     required_enum: "BasicEnum" = field()
     opt: "Optional[bool]" = None
     baz: "str" = field(default="toto", metadata={"help": "help message"})
-    foo_str: "List[str]" = list_field(default=["Hallo", "Bonjour", "Hello"])
+    foo_str: "list[str]" = list_field(default=["Hallo", "Bonjour", "Hello"])
 
 
 if is_python_no_less_than_3_10:
@@ -435,11 +435,11 @@ class HfArgumentParserTest(unittest.TestCase):
 
         for field in fields.values():
             # First verify raw dict
-            if field.type in (dict, dict):
+            if field.type is dict:
                 raw_dict_fields.append(field)
             # Next check for `Union` or `Optional`
             elif get_origin(field.type) == Union:
-                if any(arg in (dict, dict) for arg in get_args(field.type)):
+                if any(arg is dict for arg in get_args(field.type)):
                     optional_dict_fields.append(field)
 
         # First check: anything in `raw_dict_fields` is very bad
@@ -455,12 +455,15 @@ class HfArgumentParserTest(unittest.TestCase):
             args = get_args(field.type)
             # These should be returned as `dict`, `str`, ...
             # we only care about the first two
-            self.assertIn(args[0], (dict, dict))
-            self.assertEqual(
-                str(args[1]),
-                "<class 'str'>",
-                f"Expected field `{field.name}` to have a type signature of at least `typing.Union[dict,str,...]` for CLI compatibility, "
-                "but `str` not found. Please fix this.",
+            self.assertIn(
+                dict,
+                args,
+                f"Expected field `{field.name}` to have a type signature of at least `typing.Union[dict,str,...]` for CLI compatibility, but `dict` not found. Please fix this.",
+            )
+            self.assertIn(
+                str,
+                args,
+                f"Expected field `{field.name}` to have a type signature of at least `typing.Union[dict,str,...]` for CLI compatibility, but `str` not found. Please fix this.",
             )
 
         # Second check: anything in `optional_dict_fields` is bad if it's not in `base_list`