

import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


UpperCamelCase__  :      Optional[Any]			=    get_tests_dir("fixtures")
UpperCamelCase__  :      int			=    get_tests_dir("fixtures/dummy_feature_extractor_config.json")
UpperCamelCase__  :      Optional[Any]			=    get_tests_dir("fixtures/dummy-config.json")



class       __snake_case  (    unittest.TestCase       ):

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						0

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h')
      self.assertIsInstance(_A						,		_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						AutoFeatureExtractor.from_pretrained(_A)
      self.assertIsInstance(_A						,		_A)

  def 						lowerCAmelCase__     (					self):
      with tempfile.TemporaryDirectory() as tmpdirname:
          SCREAMING_SNAKE_CASE_									=						WavaVecaConfig()

          # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
          SCREAMING_SNAKE_CASE_									=						AutoFeatureExtractor.from_pretrained(_A).to_dict()

          config_dict.pop('feature_extractor_type')
          SCREAMING_SNAKE_CASE_									=						WavaVecaFeatureExtractor(**_A)

          # save in new folder
          model_config.save_pretrained(_A)
          config.save_pretrained(_A)

          SCREAMING_SNAKE_CASE_									=						AutoFeatureExtractor.from_pretrained(_A)

          # make sure private variable is not incorrectly saved
          SCREAMING_SNAKE_CASE_									=						json.loads(config.to_json_string())
          self.assertTrue('_processor_class' not in dict_as_saved)

      self.assertIsInstance(_A						,		_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						AutoFeatureExtractor.from_pretrained(_A)
      self.assertIsInstance(_A						,		_A)

  def 						lowerCAmelCase__     (					self):
      with self.assertRaisesRegex(
          _A						,		'bert-base is not a local folder and is not a valid model identifier'):
          SCREAMING_SNAKE_CASE_									=						AutoFeatureExtractor.from_pretrained('bert-base')

  def 						lowerCAmelCase__     (					self):
      with self.assertRaisesRegex(
          _A						,		r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
          SCREAMING_SNAKE_CASE_									=						AutoFeatureExtractor.from_pretrained(_A						,		revision='aaaaaa')

  def 						lowerCAmelCase__     (					self):
      with self.assertRaisesRegex(
          _A						,		'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'						,		):
          SCREAMING_SNAKE_CASE_									=						AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model')

  def 						lowerCAmelCase__     (					self):
      # If remote code is not set, we will time out when asking whether to load the model.
      with self.assertRaises(_A):
          SCREAMING_SNAKE_CASE_									=						AutoFeatureExtractor.from_pretrained(
              'hf-internal-testing/test_dynamic_feature_extractor')
      # If remote code is disabled, we can't load this config.
      with self.assertRaises(_A):
          SCREAMING_SNAKE_CASE_									=						AutoFeatureExtractor.from_pretrained(
              'hf-internal-testing/test_dynamic_feature_extractor'						,		trust_remote_code=_A)

      SCREAMING_SNAKE_CASE_									=						AutoFeatureExtractor.from_pretrained(
          'hf-internal-testing/test_dynamic_feature_extractor'						,		trust_remote_code=_A)
      self.assertEqual(feature_extractor.__class__.__name__						,		'NewFeatureExtractor')

      # Test feature extractor can be reloaded.
      with tempfile.TemporaryDirectory() as tmp_dir:
          feature_extractor.save_pretrained(_A)
          SCREAMING_SNAKE_CASE_									=						AutoFeatureExtractor.from_pretrained(_A						,		trust_remote_code=_A)
      self.assertEqual(reloaded_feature_extractor.__class__.__name__						,		'NewFeatureExtractor')

  def 						lowerCAmelCase__     (					self):
      try:
          AutoConfig.register('custom'						,		_A)
          AutoFeatureExtractor.register(_A						,		_A)
          # Trying to register something existing in the Transformers library will raise an error
          with self.assertRaises(_A):
              AutoFeatureExtractor.register(_A						,		_A)

          # Now that the config is registered, it can be used as any other config with the auto-API
          SCREAMING_SNAKE_CASE_									=						CustomFeatureExtractor.from_pretrained(_A)
          with tempfile.TemporaryDirectory() as tmp_dir:
              feature_extractor.save_pretrained(_A)
              SCREAMING_SNAKE_CASE_									=						AutoFeatureExtractor.from_pretrained(_A)
              self.assertIsInstance(_A						,		_A)

      finally:
          if "custom" in CONFIG_MAPPING._extra_content:
              del CONFIG_MAPPING._extra_content["custom"]
          if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
              del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]


  def 						lowerCAmelCase__     (					self):



      class       __snake_case  (    lowerCAmelCase__       ):
        __lowerCAmelCase     :	Union[str, Any]						 =  True

      try:
          AutoConfig.register('custom'						,		_A)
          AutoFeatureExtractor.register(_A						,		_A)
          # If remote code is not set, the default is to use local
          SCREAMING_SNAKE_CASE_									=						AutoFeatureExtractor.from_pretrained(
              'hf-internal-testing/test_dynamic_feature_extractor')
          self.assertEqual(feature_extractor.__class__.__name__						,		'NewFeatureExtractor')
          self.assertTrue(feature_extractor.is_local)

          # If remote code is disabled, we load the local one.
          SCREAMING_SNAKE_CASE_									=						AutoFeatureExtractor.from_pretrained(
              'hf-internal-testing/test_dynamic_feature_extractor'						,		trust_remote_code=_A)
          self.assertEqual(feature_extractor.__class__.__name__						,		'NewFeatureExtractor')
          self.assertTrue(feature_extractor.is_local)

          # If remote is enabled, we load from the Hub
          SCREAMING_SNAKE_CASE_									=						AutoFeatureExtractor.from_pretrained(
              'hf-internal-testing/test_dynamic_feature_extractor'						,		trust_remote_code=_A)
          self.assertEqual(feature_extractor.__class__.__name__						,		'NewFeatureExtractor')
          self.assertTrue(not hasattr(_A						,		'is_local'))

      finally:
          if "custom" in CONFIG_MAPPING._extra_content:
              del CONFIG_MAPPING._extra_content["custom"]
          if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
              del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int = 200       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[1, 2, 5, 10, 20, 50, 100, 200]
    SCREAMING_SNAKE_CASE_									=						[0] * (pence + 1)
    SCREAMING_SNAKE_CASE_									=						1  # base case: 1 way to make 0 pence

    for coin in coins:
        for i in range(_SCREAMING_SNAKE_CASE ,   pence + 1 ,   1       ):
            number_of_ways[i] += number_of_ways[i - coin]
    return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73_682


