


"""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__       :							Optional[int]        =  get_tests_dir('''fixtures''')




class 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





	def 						UpperCAmelCase_   (				self    :       int     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       mock.Mock()
						__SCREAMING_SNAKE_CASE		     =       5_0_0
						__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=_lowerCamelCase     ) 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 						UpperCAmelCase_   (				self    :       Dict     ) ->			int:
						__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 						UpperCAmelCase_   (				cls    :       List[Any]     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       TOKEN
						HfFolder.save_token(_lowerCamelCase     )




	@classmethod
	def 						UpperCAmelCase_   (				cls    :       Optional[int]     ) ->			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    :       Any     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       WavaVecaFeatureExtractor.from_pretrained(_lowerCamelCase     )
						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(_lowerCamelCase    ,    getattr(_lowerCamelCase    ,    _lowerCamelCase     )     )

						# 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(
											    _lowerCamelCase    ,    repo_id="test-feature-extractor"    ,    push_to_hub=_lowerCamelCase    ,    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(_lowerCamelCase    ,    getattr(_lowerCamelCase    ,    _lowerCamelCase     )     )




	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       WavaVecaFeatureExtractor.from_pretrained(_lowerCamelCase     )
						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(_lowerCamelCase    ,    getattr(_lowerCamelCase    ,    _lowerCamelCase     )     )

						# 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(
											    _lowerCamelCase    ,    repo_id="valid_org/test-feature-extractor-org"    ,    push_to_hub=_lowerCamelCase    ,    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(_lowerCamelCase    ,    getattr(_lowerCamelCase    ,    _lowerCamelCase     )     )




	def 						UpperCAmelCase_   (				self    :       str     ) ->			Any:
						CustomFeatureExtractor.register_for_auto_class()
						__SCREAMING_SNAKE_CASE		     =       CustomFeatureExtractor.from_pretrained(_lowerCamelCase     )

						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=_lowerCamelCase     )
						# 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 functools




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					if not isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) or not all(isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) for day in days     ):
										raise ValueError("The parameter days should be a list of integers"     )

					if len(lowerCAmelCase_     ) != 3 or not all(isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) for cost in costs     ):
										raise ValueError("The parameter costs should be a list of three integers"     )

					if len(lowerCAmelCase_     ) == 0:
										return 0

					if min(lowerCAmelCase_     ) <= 0:
										raise ValueError("All days elements should be greater than 0"     )

					if max(lowerCAmelCase_     ) >= 366:
										raise ValueError("All days elements should be less than 366"     )

					__SCREAMING_SNAKE_CASE		     =       set(lowerCAmelCase_     )

					@functools.cache
					def dynamic_programming(lowerCAmelCase_     ) -> int:
										if index > 365:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1     )

										return min(
										    costs[0] + dynamic_programming(index + 1     )						,		costs[1] + dynamic_programming(index + 7     )						,		costs[2] + dynamic_programming(index + 30     )						,		)

					return dynamic_programming(1     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
