

from math import pi
def      _lowerCAmelCase(	__A   ,       __A			):
  return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
      print(arc_length(90, 10))






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 						__magic_name__    (       unittest.TestCase    ):


 def _UpperCamelCase	(						self		: Tuple    )    ->      Union[str, Any]:
   # A mock response for an HTTP head request to emulate server down
   UpperCAmelCase					      =  mock.Mock()
   UpperCAmelCase					      =  5_0_0
   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=lowerCAmelCase__    ) 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 _UpperCamelCase	(						self		: List[Any]    )    ->      Dict:
   # This test is for deprecated behavior and can be removed in v5
   UpperCAmelCase					      =  WavaVecaFeatureExtractor.from_pretrained(
       "https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"    )






@is_staging_test
class 						__magic_name__    (       unittest.TestCase    ):


 @classmethod
 def _UpperCamelCase	(						cls		: List[str]    )    ->      List[Any]:
   UpperCAmelCase					      =  TOKEN
   HfFolder.save_token(lowerCAmelCase__    )


 @classmethod
 def _UpperCamelCase	(						cls		: Optional[int]    )    ->      Union[str, 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 _UpperCamelCase	(						self		: Any    )    ->      Any:
   UpperCAmelCase					      =  WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase__    )
   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(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    )

   UpperCAmelCase					      =  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		: List[Any]    )    ->      Tuple:
   UpperCAmelCase					      =  WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase__    )
   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(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    )

   UpperCAmelCase					      =  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		: Dict    )    ->      List[str]:
   CustomFeatureExtractor.register_for_auto_class()
   UpperCAmelCase					      =  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"}			,						)

   UpperCAmelCase					      =  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"    )



