





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					:				int				=			get_tests_dir("""fixtures""")



class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			mock.Mock()
        SCREAMING_SNAKE_CASE__											=			5_00
        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  lowercase_					(       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 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    @classmethod
    def  lowercase_					(       cls     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TOKEN
        HfFolder.save_token(A_     )



    @classmethod
    def  lowercase_					(       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  lowercase_					(       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  lowercase_					(       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  lowercase_					(       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'''     )







class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			name
        SCREAMING_SNAKE_CASE__											=			value
        SCREAMING_SNAKE_CASE__											=			weight



    def __repr__(       self     ):





        '''simple docstring'''

        return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.value



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.name



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.weight




    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.value / self.weight





def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			[]
    for i in range(len(lowerCAmelCase_	)	):
        menu.append(Things(name[i] ,							value[i] ,							weight[i]	)	)
    return menu


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Dict:
    SCREAMING_SNAKE_CASE__											=			sorted(lowerCAmelCase_ ,							key=lowerCAmelCase_ ,							reverse=lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			0.0, 0.0
    for i in range(len(lowerCAmelCase_	)	):
        if (total_cost + items_copy[i].get_weight()) <= max_cost:
            result.append(items_copy[i]	)
            total_cost += items_copy[i].get_weight()
            total_value += items_copy[i].get_value()
    return (result, total_value)


def 							__snake_case							(    )				->						str:
    pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()
