






'''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


SCREAMING_SNAKE_CASE						=				get_tests_dir('fixtures')
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      def 				A__      ( self			:		Tuple		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =mock.Mock()
       lowercase					:   Dict				  =500
       lowercase					:   Union[str, Any]				  ={}
       lowercase					:   Optional[int]				  =HTTPError
       lowercase					:   List[Any]				  ={}

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






      def 				A__      ( self			:		str		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =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 				A__      ( cls			:		List[str]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   Any				  =TOKEN
       HfFolder.save_token(UpperCAmelCase		)






      @classmethod
      def 				A__      ( cls			:		List[str]		)  ->  Dict:


       '''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 				A__      ( self			:		Any		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Tuple				  =WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase		)
       feature_extractor.push_to_hub('''test-feature-extractor'''     ,      use_auth_token=self._token		)

       lowercase					:   List[Any]				  =WavaVecaFeatureExtractor.from_pretrained(f'{USER}/test-feature-extractor'		)
       for k, v in feature_extractor.__dict__.items():
        self.assertEqual(UpperCAmelCase     ,      getattr(UpperCAmelCase     ,      UpperCAmelCase		)		)

       # 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(
            UpperCAmelCase     ,      repo_id='''test-feature-extractor'''     ,      push_to_hub=UpperCAmelCase     ,      use_auth_token=self._token		)

       lowercase					:   Union[str, Any]				  =WavaVecaFeatureExtractor.from_pretrained(f'{USER}/test-feature-extractor'		)
       for k, v in feature_extractor.__dict__.items():
        self.assertEqual(UpperCAmelCase     ,      getattr(UpperCAmelCase     ,      UpperCAmelCase		)		)






      def 				A__      ( self			:		Tuple		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase		)
       feature_extractor.push_to_hub('''valid_org/test-feature-extractor'''     ,      use_auth_token=self._token		)

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

       # 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(
            UpperCAmelCase     ,      repo_id='''valid_org/test-feature-extractor-org'''     ,      push_to_hub=UpperCAmelCase     ,      use_auth_token=self._token		)

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






      def 				A__      ( self			:		Optional[int]		)  ->  List[Any]:


       '''simple docstring'''

       CustomFeatureExtractor.register_for_auto_class()
       lowercase					:   List[str]				  =CustomFeatureExtractor.from_pretrained(UpperCAmelCase		)

       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'''}     ,      )

       lowercase					:   Dict				  =AutoFeatureExtractor.from_pretrained(
           f'{USER}/test-dynamic-feature-extractor'     ,      trust_remote_code=UpperCAmelCase		)
       # 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 mpmath  # for roots of unity
import numpy as np
class UpperCAmelCase_					:




      """simple docstring"""




      def __init__( self			:		List[Any]     ,      UpperCAmelCase			:		Dict=None     ,      UpperCAmelCase			:		Optional[int]=None		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Any				  =list(poly_a or [0]		)[:]
       lowercase					:   Dict				  =list(poly_b or [0]		)[:]

       # Remove leading zero coefficients
       while self.polyA[-1] == 0:
        self.polyA.pop()
       lowercase					:   int				  =len(self.polyA		)

       while self.polyB[-1] == 0:
        self.polyB.pop()
       lowercase					:   List[str]				  =len(self.polyB		)

       # Add 0 to make lengths equal a power of 2
       lowercase					:   Tuple				  =int(
           2 ** np.ceil(np.loga(len(self.polyA		) + len(self.polyB		) - 1		)		)		)

       while len(self.polyA		) < self.c_max_length:
        self.polyA.append(0		)
       while len(self.polyB		) < self.c_max_length:
        self.polyB.append(0		)
       # A complex root used for the fourier transform
       lowercase					:   Optional[int]				  =complex(mpmath.root(x=1     ,      n=self.c_max_length     ,      k=1		)		)

       # The product
       lowercase					:   str				  =self.__multiply()






      def 				A__      ( self			:		Optional[Any]     ,      UpperCAmelCase			:		Union[str, Any]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Tuple				  =[[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
       # Corner case
       if len(UpperCAmelCase		) <= 1:
        return dft[0]
       #
       lowercase					:   List[Any]				  =self.c_max_length // 2
       while next_ncol > 0:
        lowercase					:   str				  =[[] for i in range(UpperCAmelCase		)]
        lowercase					:   List[str]				  =self.root**next_ncol

        # First half of next step
        lowercase					:   Union[str, Any]				  =1
        for j in range(self.c_max_length // (next_ncol * 2)		):
         for i in range(UpperCAmelCase		):
          new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]		)
         current_root *= root
        # Second half of next step
        lowercase					:   Any				  =1
        for j in range(self.c_max_length // (next_ncol * 2)		):
         for i in range(UpperCAmelCase		):
          new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]		)
         current_root *= root
        # Update
        lowercase					:   Tuple				  =new_dft
        lowercase					:   List[Any]				  =next_ncol // 2
       return dft[0]






      def 				A__      ( self			:		int		)  ->  str:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.__dft('''A'''		)
       lowercase					:   Union[str, Any]				  =self.__dft('''B'''		)
       lowercase					:   Any				  =[[dft_a[i] * dft_b[i] for i in range(self.c_max_length		)]]
       del dft_a
       del dft_b

       # Corner Case
       if len(inverce_c[0]		) <= 1:
        return inverce_c[0]
       # Inverse DFT
       lowercase					:   Tuple				  =2
       while next_ncol <= self.c_max_length:
        lowercase					:   Tuple				  =[[] for i in range(UpperCAmelCase		)]
        lowercase					:   Tuple				  =self.root ** (next_ncol // 2)
        lowercase					:   Optional[int]				  =1
        # First half of next step
        for j in range(self.c_max_length // next_ncol		):
         for i in range(next_ncol // 2		):
          # Even positions
          new_inverse_c[i].append(
              (
                  inverce_c[i][j]
                  + inverce_c[i][j + self.c_max_length // next_ncol]
              )
              / 2		)
          # Odd positions
          new_inverse_c[i + next_ncol // 2].append(
              (
                  inverce_c[i][j]
                  - inverce_c[i][j + self.c_max_length // next_ncol]
              )
              / (2 * current_root)		)
         current_root *= root
        # Update
        lowercase					:   List[Any]				  =new_inverse_c
        next_ncol *= 2
       # Unpack
       lowercase					:   List[str]				  =[round(x[0].real     ,      8		) + round(x[0].imag     ,      8		) * 1J for x in inverce_c]

       # Remove leading 0's
       while inverce_c[-1] == 0:
        inverce_c.pop()
       return inverce_c






      def __str__( self			:		Dict		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  ='''A = ''' + ''' + '''.join(
           f'{coef}*x^{i}' for coef, i in enumerate(self.polyA[: self.len_A]		)		)
       lowercase					:   List[str]				  ='''B = ''' + ''' + '''.join(
           f'{coef}*x^{i}' for coef, i in enumerate(self.polyB[: self.len_B]		)		)
       lowercase					:   Optional[Any]				  ='''A*B = ''' + ''' + '''.join(
           f'{coef}*x^{i}' for coef, i in enumerate(self.product		)		)

       return f'{a}\n{b}\n{c}'


# Unit tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()



