


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   							=						get_tests_dir('''fixtures''')



class 	__lowerCamelCase			(unittest.TestCase						):







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




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

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







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




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



@is_staging_test
class 	__lowerCamelCase			(unittest.TestCase						):







       @classmethod
       def 		snake_case_	(			cls: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			TOKEN
            HfFolder.save_token(A_					)







       @classmethod
       def 		snake_case_	(			cls: Tuple					):






            '''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 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




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

            __UpperCamelCase =			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					)

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







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




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

            __UpperCamelCase =			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					)

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







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            CustomFeatureExtractor.register_for_auto_class()
            __UpperCamelCase =			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'},)

            __UpperCamelCase =			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'					)






import pytest

import datasets


# Import fixture modules as plugins
__snake_case   							=						['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']






def 		_A		(		_lowercase					,  _lowercase )					-> Tuple:


     """simple docstring"""


     for item in items:
          if any(marker in item.keywords for marker in ['integration', 'unit'] ):
               continue
          item.add_marker(pytest.mark.unit )






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     config.addinivalue_line('markers'					,  'torchaudio_latest: mark test to run with torchaudio>=0.12' )






@pytest.fixture(autouse=_lowercase )
def 		_A		(		_lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


     __UpperCamelCase =			tmp_path_factory.getbasetemp() / 'cache'
     __UpperCamelCase =			test_hf_cache_home / 'datasets'
     __UpperCamelCase =			test_hf_cache_home / 'metrics'
     __UpperCamelCase =			test_hf_cache_home / 'modules'
     monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE'					,  str(_lowercase ) )
     monkeypatch.setattr('datasets.config.HF_METRICS_CACHE'					,  str(_lowercase ) )
     monkeypatch.setattr('datasets.config.HF_MODULES_CACHE'					,  str(_lowercase ) )
     __UpperCamelCase =			test_hf_datasets_cache / 'downloads'
     monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH'					,  str(_lowercase ) )
     __UpperCamelCase =			test_hf_datasets_cache / 'downloads' / 'extracted'
     monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'					,  str(_lowercase ) )






@pytest.fixture(autouse=_lowercase					,  scope='session' )
def 		_A		(		)					-> Dict:


     """simple docstring"""


     datasets.disable_progress_bar()






@pytest.fixture(autouse=_lowercase )
def 		_A		(		_lowercase )					-> Tuple:


     """simple docstring"""


     monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'					,  _lowercase )






@pytest.fixture
def 		_A		(		_lowercase )					-> Any:


     """simple docstring"""


     monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'					,  _lowercase )


