






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


__UpperCAmelCase								=						get_tests_dir('fixtures')



class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self   )	->							int:
							# A mock response for an HTTP head request to emulate server down
							UpperCAmelCase_    :      Optional[int]            =						mock.Mock()
							UpperCAmelCase_    :      Any            =						5_0_0
							UpperCAmelCase_    :      str            =						{}
							UpperCAmelCase_    :      Any            =						HTTPError
							UpperCAmelCase_    :      List[str]            =						{}

							# Download this model to make sure it's in the cache.
							UpperCAmelCase_    :      Union[str, Any]            =						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=_UpperCamelCase   ) as mock_head:
												UpperCAmelCase_    :      Tuple            =						WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'   )
												# This check we did call the fake head request
												mock_head.assert_called()



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							# This test is for deprecated behavior and can be removed in v5
							UpperCAmelCase_    :      int            =						WavaVecaFeatureExtractor.from_pretrained(
							    'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'   )





@is_staging_test
class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		@classmethod
		def 					__UpperCAmelCase			(							cls   )	->							Tuple:
							UpperCAmelCase_    :      Any            =						TOKEN
							HfFolder.save_token(_UpperCamelCase   )



		@classmethod
		def 					__UpperCAmelCase			(							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 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_    :      Any            =						WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase   )
							feature_extractor.push_to_hub('test-feature-extractor'				,		use_auth_token=self._token   )

							UpperCAmelCase_    :      List[Any]            =						WavaVecaFeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor"   )
							for k, v in feature_extractor.__dict__.items():
												self.assertEqual(_UpperCamelCase				,		getattr(_UpperCamelCase				,		_UpperCamelCase   )   )

							# 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(
												    _UpperCamelCase				,		repo_id='test-feature-extractor'				,		push_to_hub=_UpperCamelCase				,		use_auth_token=self._token   )

							UpperCAmelCase_    :      Optional[Any]            =						WavaVecaFeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor"   )
							for k, v in feature_extractor.__dict__.items():
												self.assertEqual(_UpperCamelCase				,		getattr(_UpperCamelCase				,		_UpperCamelCase   )   )



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							UpperCAmelCase_    :      Union[str, Any]            =						WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase   )
							feature_extractor.push_to_hub('valid_org/test-feature-extractor'				,		use_auth_token=self._token   )

							UpperCAmelCase_    :      Dict            =						WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'   )
							for k, v in feature_extractor.__dict__.items():
												self.assertEqual(_UpperCamelCase				,		getattr(_UpperCamelCase				,		_UpperCamelCase   )   )

							# 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(
												    _UpperCamelCase				,		repo_id='valid_org/test-feature-extractor-org'				,		push_to_hub=_UpperCamelCase				,		use_auth_token=self._token   )

							UpperCAmelCase_    :      List[str]            =						WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org'   )
							for k, v in feature_extractor.__dict__.items():
												self.assertEqual(_UpperCamelCase				,		getattr(_UpperCamelCase				,		_UpperCamelCase   )   )



		def 					__UpperCAmelCase			(							self   )	->							int:
							CustomFeatureExtractor.register_for_auto_class()
							UpperCAmelCase_    :      Optional[int]            =						CustomFeatureExtractor.from_pretrained(_UpperCamelCase   )

							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'}				,		)

							UpperCAmelCase_    :      List[Any]            =						AutoFeatureExtractor.from_pretrained(
							    f"{USER}/test-dynamic-feature-extractor"				,		trust_remote_code=_UpperCamelCase   )
							# 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'   )








import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save


def     __lowerCAmelCase			(			a__						,    a__						,    a__=1024						,    a__=1024						,    a__=False						,    **a__  )       ->  Optional[Any]:
				__a										=    AutoTokenizer.from_pretrained(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''train'''						,    **a__  )
				__a										=    tok.pad_token_id

				def get_lens(a__  ):
								__a										=    tqdm(
								    DataLoader(a__						,    batch_size=512						,    num_workers=8						,    shuffle=a__						,    collate_fn=ds.collate_fn  )						,    desc=str(ds.len_file  )						,    )
								__a										=    []
								for batch in dl:
												__a										=    batch['''input_ids'''].ne(a__  ).sum(1  ).tolist()
												__a										=    batch['''labels'''].ne(a__  ).sum(1  ).tolist()
												if consider_target:
																for src, tgt in zip(a__						,    a__  ):
																				max_lens.append(max(a__						,    a__  )  )
												else:
																max_lens.extend(a__  )
								return max_lens

				__a										=    get_lens(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''val'''						,    **a__  )
				__a										=    get_lens(a__  )
				pickle_save(a__						,    train_ds.len_file  )
				pickle_save(a__						,    val_ds.len_file  )


if __name__ == "__main__":
					fire.Fire(save_len_file)