


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


snake_case							:       str     			=      get_tests_dir('''fixtures''')







class        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Tuple      )					->    Dict:
												# A mock response for an HTTP head request to emulate server down
												a__             =     mock.Mock()
												a__             =     5_00
												a__             =     {}
												a__             =     HTTPError
												a__             =     {}

												# Download this model to make sure it's in the cache.
												a__             =     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=__snake_case      ) as mock_head:
																			a__             =     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   :Optional[Any]      )					->    Optional[int]:
												# This test is for deprecated behavior and can be removed in v5
												a__             =     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   :Union[str, Any]      )					->    Dict:
												a__             =     TOKEN
												HfFolder.save_token(__snake_case      )
					@classmethod
					def  lowerCamelCase__(      cls   :Optional[Any]      )					->    List[Any]:
												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   :Union[str, Any]      )					->    str:
												a__             =     WavaVecaFeatureExtractor.from_pretrained(__snake_case      )
												feature_extractor.push_to_hub('test-feature-extractor'			,use_auth_token=self._token      )

												a__             =     WavaVecaFeatureExtractor.from_pretrained(F'{USER}/test-feature-extractor'      )
												for k, v in feature_extractor.__dict__.items():
																			self.assertEqual(__snake_case			,getattr(__snake_case			,__snake_case      )      )

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

												a__             =     WavaVecaFeatureExtractor.from_pretrained(F'{USER}/test-feature-extractor'      )
												for k, v in feature_extractor.__dict__.items():
																			self.assertEqual(__snake_case			,getattr(__snake_case			,__snake_case      )      )
					def  lowerCamelCase__(      self   :str      )					->    int:
												a__             =     WavaVecaFeatureExtractor.from_pretrained(__snake_case      )
												feature_extractor.push_to_hub('valid_org/test-feature-extractor'			,use_auth_token=self._token      )

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

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

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



					def  lowerCamelCase__(      self   :List[Any]      )					->    Union[str, Any]:
												CustomFeatureExtractor.register_for_auto_class()
												a__             =     CustomFeatureExtractor.from_pretrained(__snake_case      )

												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'}			,)

												a__             =     AutoFeatureExtractor.from_pretrained(
												    F'{USER}/test-dynamic-feature-extractor'			,trust_remote_code=__snake_case      )
												# 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 math






def 							__lowercase   ( __lowerCAmelCase							:				int   ):
							a__             =     [True] * n
							a__             =     False
							a__             =     False
							a__             =     True

							for i in range(3					,    int(n**0.5 + 1   )					,    2   ):
														a__             =     i * 2
														while index < n:
																					a__             =     False
																					a__             =     index + i

							a__             =     [2]

							for i in range(3					,    __lowerCAmelCase					,    2   ):
														if is_prime[i]:
																					primes.append(__lowerCAmelCase   )

							return primes






def 							__lowercase   ( __lowerCAmelCase							:				int = 9_9_9_9_6_6_6_6_3_3_3_3   ):
							a__             =     math.floor(math.sqrt(__lowerCAmelCase   )   ) + 1_0_0
							a__             =     prime_sieve(__lowerCAmelCase   )

							a__             =     0
							a__             =     0
							a__             =     primes[prime_index]

							while (last_prime**2) <= limit:
														a__             =     primes[prime_index + 1]

														a__             =     last_prime**2
														a__             =     next_prime**2

														# Get numbers divisible by lps(current)
														a__             =     lower_bound + last_prime
														while upper_bound > current <= limit:
																					matches_sum += current
																					current += last_prime

														# Reset the upper_bound
														while (upper_bound - next_prime) > limit:
																					upper_bound -= next_prime

														# Add the numbers divisible by ups(current)
														a__             =     upper_bound - next_prime
														while current > lower_bound:
																					matches_sum += current
																					current -= next_prime

														# Remove the numbers divisible by both ups and lps
														a__             =     0
														while upper_bound > current <= limit:
																					if current <= lower_bound:
																												# Increment the current number
																												current += last_prime * next_prime
																												continue

																					if current > limit:
																												break

																					# Remove twice since it was added by both ups and lps
																					matches_sum -= current * 2

																					# Increment the current number
																					current += last_prime * next_prime

														# Setup for next pair
														a__             =     next_prime
														prime_index += 1

							return matches_sum


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




