"""simple docstring"""





import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


__A  =				get_tests_dir('''fixtures''')
class  _snake_case     (					unittest.TestCase		):






						def       lowerCamelCase__     (       self      :			List[str]    ):
													# A mock response for an HTTP head request to emulate server down
													__lowerCamelCase					:		Union[str, Any]		   =     mock.Mock()
													__lowerCamelCase					:		Union[str, Any]		   =     500
													__lowerCamelCase					:		str		   =     {}
													__lowerCamelCase					:		Dict		   =     HTTPError
													__lowerCamelCase					:		List[str]		   =     {}

													# Download this model to make sure it's in the cache.
													__lowerCamelCase					:		List[str]		   =     WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2"    )
													# Under the mock environment we get a 500 error when trying to reach the model.
													with mock.patch("requests.Session.request"							,					return_value=lowercase__    ) as mock_head:
																				__lowerCamelCase					:		Union[str, Any]		   =     WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2"    )
																				# This check we did call the fake head request
																				mock_head.assert_called()







						def       lowerCamelCase__     (       self      :			Dict    ):
													# This test is for deprecated behavior and can be removed in v5
													__lowerCamelCase					:		List[str]		   =     WavaVecaFeatureExtractor.from_pretrained(
													    "https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"    )

@is_staging_test
class  _snake_case     (					unittest.TestCase		):






						@classmethod
						def       lowerCamelCase__     (       cls      :			List[str]    ):
													__lowerCamelCase					:		List[Any]		   =     TOKEN
													HfFolder.save_token(lowercase__    )






						@classmethod
						def       lowerCamelCase__     (       cls      :			Optional[int]    ):
													try:
																				delete_repo(token=cls._token							,					repo_id="test-feature-extractor"    )
													except HTTPError:
																				pass

													try:
																				delete_repo(token=cls._token							,					repo_id="valid_org/test-feature-extractor-org"    )
													except HTTPError:
																				pass

													try:
																				delete_repo(token=cls._token							,					repo_id="test-dynamic-feature-extractor"    )
													except HTTPError:
																				pass






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Tuple		   =     WavaVecaFeatureExtractor.from_pretrained(lowercase__    )
													feature_extractor.push_to_hub("test-feature-extractor"							,					use_auth_token=self._token    )

													__lowerCamelCase					:		Optional[int]		   =     WavaVecaFeatureExtractor.from_pretrained(F"""{USER}/test-feature-extractor"""    )
													for k, v in feature_extractor.__dict__.items():
																				self.assertEqual(lowercase__							,					getattr(lowercase__							,					lowercase__    )    )

													# Reset repo
													delete_repo(token=self._token							,					repo_id="test-feature-extractor"    )

													# Push to hub via save_pretrained
													with tempfile.TemporaryDirectory() as tmp_dir:
																				feature_extractor.save_pretrained(
																				    lowercase__							,					repo_id="test-feature-extractor"							,					push_to_hub=lowercase__							,					use_auth_token=self._token    )

													__lowerCamelCase					:		List[str]		   =     WavaVecaFeatureExtractor.from_pretrained(F"""{USER}/test-feature-extractor"""    )
													for k, v in feature_extractor.__dict__.items():
																				self.assertEqual(lowercase__							,					getattr(lowercase__							,					lowercase__    )    )






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     WavaVecaFeatureExtractor.from_pretrained(lowercase__    )
													feature_extractor.push_to_hub("valid_org/test-feature-extractor"							,					use_auth_token=self._token    )

													__lowerCamelCase					:		Any		   =     WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor"    )
													for k, v in feature_extractor.__dict__.items():
																				self.assertEqual(lowercase__							,					getattr(lowercase__							,					lowercase__    )    )

													# Reset repo
													delete_repo(token=self._token							,					repo_id="valid_org/test-feature-extractor"    )

													# Push to hub via save_pretrained
													with tempfile.TemporaryDirectory() as tmp_dir:
																				feature_extractor.save_pretrained(
																				    lowercase__							,					repo_id="valid_org/test-feature-extractor-org"							,					push_to_hub=lowercase__							,					use_auth_token=self._token    )

													__lowerCamelCase					:		Union[str, Any]		   =     WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org"    )
													for k, v in feature_extractor.__dict__.items():
																				self.assertEqual(lowercase__							,					getattr(lowercase__							,					lowercase__    )    )







						def       lowerCamelCase__     (       self      :			Any    ):
													CustomFeatureExtractor.register_for_auto_class()
													__lowerCamelCase					:		List[Any]		   =     CustomFeatureExtractor.from_pretrained(lowercase__    )

													feature_extractor.push_to_hub("test-dynamic-feature-extractor"							,					use_auth_token=self._token    )

													# This has added the proper auto_map field to the config
													self.assertDictEqual(
													    feature_extractor.auto_map							,					{"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"}							,					)

													__lowerCamelCase					:		str		   =     AutoFeatureExtractor.from_pretrained(
													    F"""{USER}/test-dynamic-feature-extractor"""							,					trust_remote_code=lowercase__    )
													# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
													self.assertEqual(new_feature_extractor.__class__.__name__							,					"CustomFeatureExtractor"    )
"""simple docstring"""





import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__A  =				4
__A  =				3
class  _snake_case     (					a__		):
						pass


def    lowercase_   (   _lowerCamelCase:	List[str]      ) ->      List[str]:



							'''simple docstring'''

							for shard in shards:
														for i in range(_lowerCamelCase      ):
																					yield {"i": i, "shard": shard}


def    lowercase_   (   ) ->      List[Any]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     int(os.environ["RANK"]      )
							__lowerCamelCase					:		Optional[int]		   =     int(os.environ["WORLD_SIZE"]      )

							__lowerCamelCase					:		Any		   =     ArgumentParser()
							parser.add_argument("--streaming"						,   type=_lowerCamelCase      )
							parser.add_argument("--local_rank"						,   type=_lowerCamelCase      )
							parser.add_argument("--num_workers"						,   type=_lowerCamelCase						,   default=0      )
							__lowerCamelCase					:		Dict		   =     parser.parse_args()
							__lowerCamelCase					:		str		   =     args.streaming
							__lowerCamelCase					:		List[Any]		   =     args.num_workers

							__lowerCamelCase					:		Optional[Any]		   =     {"shards": [F"""shard_{shard_idx}""" for shard_idx in range(_lowerCamelCase      )]}
							__lowerCamelCase					:		int		   =     IterableDataset.from_generator(_lowerCamelCase						,   gen_kwargs=_lowerCamelCase      )
							if not streaming:
														__lowerCamelCase					:		Optional[int]		   =     Dataset.from_list(list(_lowerCamelCase      )      )

							__lowerCamelCase					:		Union[str, Any]		   =     split_dataset_by_node(_lowerCamelCase						,   rank=_lowerCamelCase						,   world_size=_lowerCamelCase      )
							__lowerCamelCase					:		Optional[Any]		   =     torch.utils.data.DataLoader(_lowerCamelCase						,   num_workers=_lowerCamelCase      )

							__lowerCamelCase					:		List[Any]		   =     NUM_SHARDS * NUM_ITEMS_PER_SHARD
							__lowerCamelCase					:		Optional[Any]		   =     full_size // world_size
							expected_local_size += int(rank < (full_size % world_size)      )

							__lowerCamelCase					:		Optional[Any]		   =     sum(1 for _ in dataloader      )
							if local_size != expected_local_size:
														raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""      )


if __name__ == "__main__":
				main()