
'''simple docstring'''



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__      :		Union[str, Any]       							=  get_tests_dir("fixtures")
UpperCamelCase__      :		Dict       							=  get_tests_dir("fixtures/dummy_feature_extractor_config.json")
UpperCamelCase__      :		int       							=  get_tests_dir("fixtures/dummy-config.json")

class    _a				(unittest.TestCase):





       """simple docstring"""




       def        UpperCamelCase				(							self      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				0






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h"""      )
          self.assertIsInstance(A__       ,							A__      )






       def        UpperCamelCase				(							self      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				AutoFeatureExtractor.from_pretrained(A__      )
          self.assertIsInstance(A__       ,							A__      )






       def        UpperCamelCase				(							self      )    ->			List[str]:
          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        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				AutoFeatureExtractor.from_pretrained(A__      )
          self.assertIsInstance(A__       ,							A__      )






       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          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        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          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        UpperCamelCase				(							self      )    ->			int:
          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        UpperCamelCase				(							self      )    ->			int:
          # 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        UpperCamelCase				(							self      )    ->			Optional[Any]:
          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        UpperCamelCase				(							self      )    ->			str:

          class    _a				(_lowerCamelCase):





                 """simple docstring"""




                 SCREAMING_SNAKE_CASE      =  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]








'''simple docstring'''



UpperCamelCase__      :		Dict       							=  {
    "a": "AAAAA",
    "b": "AAAAB",
    "c": "AAABA",
    "d": "AAABB",
    "e": "AABAA",
    "f": "AABAB",
    "g": "AABBA",
    "h": "AABBB",
    "i": "ABAAA",
    "j": "BBBAA",
    "k": "ABAAB",
    "l": "ABABA",
    "m": "ABABB",
    "n": "ABBAA",
    "o": "ABBAB",
    "p": "ABBBA",
    "q": "ABBBB",
    "r": "BAAAA",
    "s": "BAAAB",
    "t": "BAABA",
    "u": "BAABB",
    "v": "BBBAB",
    "w": "BABAA",
    "x": "BABAB",
    "y": "BABBA",
    "z": "BABBB",
    " ": " ",
}


UpperCamelCase__      :		str       							=  {value: key for key, value in encode_dict.items()}
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				""""""
   for letter in word.lower():
      if letter.isalpha() or letter == " ":
         encoded += encode_dict[letter]
      else:
         raise Exception("""encode() accepts only letters of the alphabet and spaces"""						)
   return encoded
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   if set(SCREAMING_SNAKE_CASE_						) - {"A", "B", " "} != set():
      raise Exception("""decode() accepts only 'A', 'B' and spaces"""						)
   _SCREAMING_SNAKE_CASE      =				""""""
   for word in coded.split():
      while len(SCREAMING_SNAKE_CASE_						) != 0:
         decoded += decode_dict[word[:5]]
         _SCREAMING_SNAKE_CASE      =				word[5:]
      decoded += " "
   return decoded.strip()


if __name__ == "__main__":
     from doctest import testmod

     testmod()






