code: 
  
  
  
  
  """simple docstring"""
  
  
  
  import unittest
  
  from cer import CER
  
  
  SCREAMING_SNAKE_CASE       = CER()
  
  
  
  
  class 			UpperCAmelCase_						(		unittest.TestCase							):
  
  
  
  
      def 							__magic_name__							(					self		: List[str]   )   ->							str:
  
  
  
  
       '''simple docstring'''
  
       A__ =       ["White House"]
       A__ =       ["white house"]
       # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
       A__ =       cer.compute(predictions=snake_case_			,				references=snake_case_   )
       self.assertTrue(abs(char_error_rate - 0.18_1818_1818   ) < 1e-6   )
  
  
  
  
      def 							__magic_name__							(					self		: Any   )   ->							Union[str, Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       ["were wolf"]
       A__ =       ["werewolf"]
       # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
       A__ =       cer.compute(predictions=snake_case_			,				references=snake_case_   )
       self.assertTrue(abs(char_error_rate - 0.111_1111   ) < 1e-6   )
  
       A__ =       ["werewolf"]
       A__ =       ["weae     wolf"]
       # S = 1, D = 1, I = 0, N = 8, CER = 0.25
       A__ =       cer.compute(predictions=snake_case_			,				references=snake_case_   )
       self.assertTrue(abs(char_error_rate - 0.25   ) < 1e-6   )
  
       # consecutive whitespaces case 1
       A__ =       ["were wolf"]
       A__ =       ["were               wolf"]
       # S = 0, D = 0, I = 0, N = 9, CER = 0
       A__ =       cer.compute(predictions=snake_case_			,				references=snake_case_   )
       self.assertTrue(abs(char_error_rate - 0.0   ) < 1e-6   )
  
       # consecutive whitespaces case 2
       A__ =       ["were   wolf"]
       A__ =       ["were               wolf"]
       # S = 0, D = 0, I = 0, N = 9, CER = 0
       A__ =       cer.compute(predictions=snake_case_			,				references=snake_case_   )
       self.assertTrue(abs(char_error_rate - 0.0   ) < 1e-6   )
  
  
  
  
      def 							__magic_name__							(					self		: int   )   ->							List[Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       ["werewolf"]
       A__ =       ["weaewolf"]
       # S = 1, D = 0, I = 0, N = 8, CER = 0.125
       A__ =       cer.compute(predictions=snake_case_			,				references=snake_case_   )
       self.assertTrue(abs(char_error_rate - 0.125   ) < 1e-6   )
  
  
  
  
      def 							__magic_name__							(					self		: str   )   ->							Union[str, Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       ["werewolf"]
       A__ =       ["wereawolf"]
       # S = 0, D = 1, I = 0, N = 8, CER = 0.125
       A__ =       cer.compute(predictions=snake_case_			,				references=snake_case_   )
       self.assertTrue(abs(char_error_rate - 0.125   ) < 1e-6   )
  
  
  
  
      def 							__magic_name__							(					self		: Tuple   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       ["werewolf"]
       A__ =       ["wereolf"]
       # S = 0, D = 0, I = 1, N = 8, CER = 0.125
       A__ =       cer.compute(predictions=snake_case_			,				references=snake_case_   )
       self.assertTrue(abs(char_error_rate - 0.125   ) < 1e-6   )
  
  
  
  
      def 							__magic_name__							(					self		: str   )   ->							List[str]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       ["werewolf"]
       A__ =       cer.compute(predictions=snake_case_			,				references=snake_case_   )
       self.assertEqual(snake_case_			,				0.0   )
  
  
  
  
      def 							__magic_name__							(					self		: Optional[int]   )   ->							str:
  
  
  
  
       '''simple docstring'''
  
       A__ =       ["werewolf", "I am your father"]
       A__ =       cer.compute(predictions=snake_case_			,				references=snake_case_   )
       self.assertEqual(snake_case_			,				0.0   )
  
       A__ =       ["werewolf", "I am your father", "doge"]
       A__ =       ["werxwolf", "I       am your father", "doge"]
       # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
       A__ =       cer.compute(predictions=snake_case_			,				references=snake_case_   )
       self.assertTrue(abs(char_error_rate - 0.0357_1428   ) < 1e-6   )
  
  
  
  
      def 							__magic_name__							(					self		: Any   )   ->							Optional[int]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       ["My hovercraft", "is full of eels"]
       A__ =       ["My hovercraft is full", " of eels"]
       # S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
       # whitespace at the front of " of eels" will be strip during preporcessing
       # so need to insert 2 whitespaces
       A__ =       cer.compute(predictions=snake_case_			,				references=snake_case_			,				concatenate_texts=snake_case_   )
       self.assertTrue(abs(char_error_rate - 0.07_1428   ) < 1e-6   )
  
  
  
  
      def 							__magic_name__							(					self		: Optional[Any]   )   ->							Union[str, Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       ["我能吞下玻璃而不伤身体"]
       A__ =       [" 能吞虾玻璃而 不霜身体啦"]
       # S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
       A__ =       cer.compute(predictions=snake_case_			,				references=snake_case_   )
       self.assertTrue(abs(char_error_rate - 0.45_4545_4545   ) < 1e-6   )
  
       A__ =       ["我能吞下玻璃", "而不伤身体"]
       A__ =       ["我    能 吞 下 玻 璃", "而不伤身体"]
       # S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
       A__ =       cer.compute(predictions=snake_case_			,				references=snake_case_   )
       self.assertTrue(abs(char_error_rate - 0.4545_4545_4545   ) < 1e-6   )
  
       A__ =       ["我能吞下玻璃而不伤身体"]
       A__ =       cer.compute(predictions=snake_case_			,				references=snake_case_   )
       self.assertFalse(snake_case_			,				0.0   )
  
  
  
  
      def 							__magic_name__							(					self		: Union[str, Any]   )   ->							Tuple:
  
  
  
  
       '''simple docstring'''
  
       A__ =       [""]
       A__ =       ["Hypothesis"]
       with self.assertRaises(snake_case_   ):
        cer.compute(predictions=snake_case_			,				references=snake_case_   )
  
  
  if __name__ == "__main__":
        unittest.main()
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  SCREAMING_SNAKE_CASE       = logging.get_logger(__name__)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
   A__ =       WavLMForSequenceClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["projector.weight"]
   A__ =       downstream_dict["projector.bias"]
   A__ =       downstream_dict["model.post_net.linear.weight"]
   A__ =       downstream_dict["model.post_net.linear.bias"]
   return model
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Optional[Any]:
   A__ =       WavLMForAudioFrameClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["model.linear.weight"]
   A__ =       downstream_dict["model.linear.bias"]
   return model
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
   A__ =       WavLMForXVector.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["connector.weight"]
   A__ =       downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
    A__ =       downstream_dict[
        f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
    ]
    A__ =       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
  
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   A__ =       downstream_dict["objective.W"]
   return model
  
  
  
  
  
  
  
  @torch.no_grad()
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	Union[str, Any]:
   A__ =       torch.load(lowercase_    ,				map_location="cpu"		)
  
   A__ =       checkpoint["Downstream"]
  
   A__ =       WavLMConfig.from_pretrained(lowercase_		)
   A__ =       WavaVecaFeatureExtractor.from_pretrained(
       lowercase_    ,				return_attention_mask=lowercase_    ,				do_normalize=lowercase_		)
  
   A__ =       hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"		):
    A__ =       convert_classification(lowercase_    ,				lowercase_    ,				lowercase_		)
   elif arch.endswith("ForAudioFrameClassification"		):
    A__ =       convert_diarization(lowercase_    ,				lowercase_    ,				lowercase_		)
   elif arch.endswith("ForXVector"		):
    A__ =       convert_xvector(lowercase_    ,				lowercase_    ,				lowercase_		)
   else:
    raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""		)
  
   if hf_config.use_weighted_layer_sum:
    A__ =       checkpoint["Featurizer"]["weights"]
  
   hf_feature_extractor.save_pretrained(lowercase_		)
   hf_model.save_pretrained(lowercase_		)
  
  
  if __name__ == "__main__":
        SCREAMING_SNAKE_CASE       = argparse.ArgumentParser()
        parser.add_argument(
            "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
        )
        parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
        parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
        parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
        SCREAMING_SNAKE_CASE       = parser.parse_args()
        convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
  
  
  
  
  