


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__      		= get_tests_dir('fixtures')







class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				List[str]:
            # A mock response for an HTTP head request to emulate server down
            _A      :       List[Any]				    = mock.Mock()
            _A      :       int				    = 5_0_0
            _A      :       List[str]				    = {}
            _A      :       Optional[int]				    = HTTPError
            _A      :       Optional[Any]				    = {}

            # Download this model to make sure it's in the cache.
            _A      :       Dict				    = 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:
                   _A      :       Union[str, Any]				    = 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]:
            # This test is for deprecated behavior and can be removed in v5
            _A      :       List[str]				    = WavaVecaFeatureExtractor.from_pretrained(
                "https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json")







@is_staging_test
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     @classmethod
     def  _lowerCamelCase (						cls)			->				Optional[Any]:
            _A      :       Tuple				    = TOKEN
            HfFolder.save_token(__lowerCamelCase)


     @classmethod
     def  _lowerCamelCase (						cls)			->				List[str]:
            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)			->				Optional[Any]:
            _A      :       Tuple				    = WavaVecaFeatureExtractor.from_pretrained(__lowerCamelCase)
            feature_extractor.push_to_hub("test-feature-extractor" ,						use_auth_token=self._token)

            _A      :       List[str]				    = 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)

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


     def  _lowerCamelCase (						self)			->				List[Any]:
            _A      :       Tuple				    = WavaVecaFeatureExtractor.from_pretrained(__lowerCamelCase)
            feature_extractor.push_to_hub("valid_org/test-feature-extractor" ,						use_auth_token=self._token)

            _A      :       Dict				    = 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)

            _A      :       Union[str, Any]				    = WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org")
            for k, v in feature_extractor.__dict__.items():
                   self.assertEqual(__lowerCamelCase ,						getattr(__lowerCamelCase ,						__lowerCamelCase))


     def  _lowerCamelCase (						self)			->				Any:
            CustomFeatureExtractor.register_for_auto_class()
            _A      :       int				    = 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"} ,						)

            _A      :       str				    = 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")


def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			[0 for i in range(r + 1    )]
				# nc0 = 1
				lowerCamelCase_						=			1
				for i in range(1	, n + 1    ):
								# to compute current row from previous row.
								lowerCamelCase_						=			min(lowerCamelCase__	, lowerCamelCase__    )
								while j > 0:
												c[j] += c[j - 1]
												j -= 1
				return c[r]





print(binomial_coefficient(n=1_0, r=5))


