





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


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





class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     Tuple			=    mock.Mock()
          a__						:     Optional[Any]			=    500
          a__						:     Dict			=    {}
          a__						:     Tuple			=    HTTPError
          a__						:     Any			=    {}

          # Download this model to make sure it's in the cache.
          a__						:     List[Any]			=    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=lowercase) as mock_head:
                 a__						:     Optional[Any]			=    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)      ->						Optional[Any]:







          '''simple docstring'''

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





@is_staging_test
class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   @classmethod
   def 							__lowercase		( cls)      ->						Any:







          '''simple docstring'''

          a__						:     List[str]			=    TOKEN
          HfFolder.save_token(lowercase)

   @classmethod
   def 							__lowercase		( 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 							__lowercase		( self)      ->						str:







          '''simple docstring'''

          a__						:     List[Any]			=    WavaVecaFeatureExtractor.from_pretrained(lowercase)
          feature_extractor.push_to_hub('test-feature-extractor' ,  use_auth_token=self._token)

          a__						:     Optional[int]			=    WavaVecaFeatureExtractor.from_pretrained(F'{USER}/test-feature-extractor')
          for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(lowercase ,  getattr(lowercase ,  lowercase))

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

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

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     Dict			=    WavaVecaFeatureExtractor.from_pretrained(lowercase)
          feature_extractor.push_to_hub('valid_org/test-feature-extractor' ,  use_auth_token=self._token)

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

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

          a__						:     List[Any]			=    WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org')
          for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(lowercase ,  getattr(lowercase ,  lowercase))

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







          '''simple docstring'''

          CustomFeatureExtractor.register_for_auto_class()
          a__						:     Optional[int]			=    CustomFeatureExtractor.from_pretrained(lowercase)

          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__						:     List[Any]			=    AutoFeatureExtractor.from_pretrained(
              F'{USER}/test-dynamic-feature-extractor' ,  trust_remote_code=lowercase)
          # 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')









from __future__ import annotations

from collections.abc import Callable



def A_     (		A__		,				A__		,				A__		,				A__ = 100		,				)  ->		float:
       a__						:     Dict			=    x_start
       a__						:     Any			=    fnc(A__    )
       a__						:     Optional[int]			=    0.0
       for _ in range(A__    ):
              # Approximates small segments of curve as linear and solve
              # for trapezoidal area
              a__						:     Union[str, Any]			=    (x_end - x_start) / steps + xa
              a__						:     str			=    fnc(A__    )
              area += abs(fxa + fxa    ) * (xa - xa) / 2
              # Increment step
              a__						:     Optional[Any]			=    xa
              a__						:     Optional[int]			=    fxa
       return area


if __name__ == "__main__":



    def A_     (		A__    )  ->		List[str]:
           return x**3 + x**2

    print("""f(x) = x^3 + x^2""")
    print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
    lowercase				:	Union[str, Any]      						=  1_0
    while i <= 1_0_0_0_0_0:
        print(F"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
        i *= 1_0


