



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


lowerCAmelCase__		:					Optional[int]							=get_tests_dir('''fixtures''')

class        UpperCAmelCase_  (       unittest.TestCase	):

						'''simple docstring'''




						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= mock.Mock()
													__SCREAMING_SNAKE_CASE									= 500
													__SCREAMING_SNAKE_CASE									= {}
													__SCREAMING_SNAKE_CASE									= HTTPError
													__SCREAMING_SNAKE_CASE									= {}

													# Download this model to make sure it's in the cache.
													__SCREAMING_SNAKE_CASE									= 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=_A		) as mock_head:
																				__SCREAMING_SNAKE_CASE									= WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'		)
																				# This check we did call the fake head request
																				mock_head.assert_called()






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= WavaVecaFeatureExtractor.from_pretrained(
													    'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'		)







@is_staging_test
class        UpperCAmelCase_  (       unittest.TestCase	):

						'''simple docstring'''




						@classmethod
						def 	_A     (   cls		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= TOKEN
													HfFolder.save_token(_A		)






						@classmethod
						def 	_A     (   cls		):







													'''simple docstring'''

													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 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= WavaVecaFeatureExtractor.from_pretrained(_A		)
													feature_extractor.push_to_hub('test-feature-extractor'				,							use_auth_token=self._token		)

													__SCREAMING_SNAKE_CASE									= WavaVecaFeatureExtractor.from_pretrained(f"""{USER}/test-feature-extractor"""		)
													for k, v in feature_extractor.__dict__.items():
																				self.assertEqual(_A				,							getattr(_A				,							_A		)		)

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

													__SCREAMING_SNAKE_CASE									= WavaVecaFeatureExtractor.from_pretrained(f"""{USER}/test-feature-extractor"""		)
													for k, v in feature_extractor.__dict__.items():
																				self.assertEqual(_A				,							getattr(_A				,							_A		)		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= WavaVecaFeatureExtractor.from_pretrained(_A		)
													feature_extractor.push_to_hub('valid_org/test-feature-extractor'				,							use_auth_token=self._token		)

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

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

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






						def 	_A     (   self		):







													'''simple docstring'''

													CustomFeatureExtractor.register_for_auto_class()
													__SCREAMING_SNAKE_CASE									= CustomFeatureExtractor.from_pretrained(_A		)

													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'}				,							)

													__SCREAMING_SNAKE_CASE									= AutoFeatureExtractor.from_pretrained(
													    f"""{USER}/test-dynamic-feature-extractor"""				,							trust_remote_code=_A		)
													# 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'		)











from __future__ import annotations

from collections.abc import Generator






def      __lowercase       (			)    ->			Generator[int, None, None]:
							__SCREAMING_SNAKE_CASE									= {}
							__SCREAMING_SNAKE_CASE									= 2
							while True:
														__SCREAMING_SNAKE_CASE									= factor_map.pop(a__							,       a__       )
														if factor:
																					__SCREAMING_SNAKE_CASE									= factor + prime
																					while x in factor_map:
																												x += factor
																					__SCREAMING_SNAKE_CASE									= factor
														else:
																					__SCREAMING_SNAKE_CASE									= prime
																					yield prime
														prime += 1






def      __lowercase       (			a__ = 1E10       )    ->			int:
							__SCREAMING_SNAKE_CASE									= sieve()

							__SCREAMING_SNAKE_CASE									= 1
							while True:
														__SCREAMING_SNAKE_CASE									= next(a__       )
														if (2 * prime * n) > limit:
																					return n
														# Ignore the next prime as the reminder will be 2.
														next(a__       )
														n += 2


if __name__ == "__main__":
			print(solution())






