code: 
  
  """simple docstring"""
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  _A						=     logging.get_logger(__name__)
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   int:
        UpperCAmelCase__ :       Union[str, Any]   				=						WavLMForSequenceClassification.from_pretrained(lowerCAmelCase ,  config=lowerCAmelCase						)
        UpperCAmelCase__ :       List[Any]   				=						downstream_dict["""projector.weight"""]
        UpperCAmelCase__ :       Optional[int]   				=						downstream_dict["""projector.bias"""]
        UpperCAmelCase__ :       Optional[int]   				=						downstream_dict["""model.post_net.linear.weight"""]
        UpperCAmelCase__ :       Optional[Any]   				=						downstream_dict["""model.post_net.linear.bias"""]
        return model
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   int:
        UpperCAmelCase__ :       List[Any]   				=						WavLMForAudioFrameClassification.from_pretrained(lowerCAmelCase ,  config=lowerCAmelCase						)
        UpperCAmelCase__ :       List[Any]   				=						downstream_dict["""model.linear.weight"""]
        UpperCAmelCase__ :       Any   				=						downstream_dict["""model.linear.bias"""]
        return model
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Tuple:
        UpperCAmelCase__ :       Tuple   				=						WavLMForXVector.from_pretrained(lowerCAmelCase ,  config=lowerCAmelCase						)
        UpperCAmelCase__ :       List[str]   				=						downstream_dict["""connector.weight"""]
        UpperCAmelCase__ :       Optional[Any]   				=						downstream_dict["""connector.bias"""]
        for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
              UpperCAmelCase__ :       Any   				=						downstream_dict[
                  F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
              ]
              UpperCAmelCase__ :       List[Any]   				=						downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
  
        UpperCAmelCase__ :       int   				=						downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
        UpperCAmelCase__ :       List[str]   				=						downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
        UpperCAmelCase__ :       int   				=						downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
        UpperCAmelCase__ :       List[str]   				=						downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
        UpperCAmelCase__ :       str   				=						downstream_dict["""objective.W"""]
        return model
  
  
  
  
  @torch.no_grad()
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Optional[int]:
        UpperCAmelCase__ :       Tuple   				=						torch.load(lowerCAmelCase ,  map_location="""cpu"""						)
  
        UpperCAmelCase__ :       Optional[Any]   				=						checkpoint["""Downstream"""]
  
        UpperCAmelCase__ :       int   				=						WavLMConfig.from_pretrained(lowerCAmelCase						)
        UpperCAmelCase__ :       Tuple   				=						WavaVecaFeatureExtractor.from_pretrained(
            lowerCAmelCase ,  return_attention_mask=lowerCAmelCase ,  do_normalize=lowerCAmelCase						)
  
        UpperCAmelCase__ :       Dict   				=						hf_config.architectures[0]
        if arch.endswith("""ForSequenceClassification"""						):
              UpperCAmelCase__ :       Any   				=						convert_classification(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
        elif arch.endswith("""ForAudioFrameClassification"""						):
              UpperCAmelCase__ :       Any   				=						convert_diarization(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
        elif arch.endswith("""ForXVector"""						):
              UpperCAmelCase__ :       str   				=						convert_xvector(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
        else:
              raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""						)
  
        if hf_config.use_weighted_layer_sum:
              UpperCAmelCase__ :       Dict   				=						checkpoint["""Featurizer"""]["""weights"""]
  
        hf_feature_extractor.save_pretrained(lowerCAmelCase						)
        hf_model.save_pretrained(lowerCAmelCase						)
  
  
  if __name__ == "__main__":
      _A						=     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.""")
      _A						=     parser.parse_args()
      convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
  
style_context: 
  
  """simple docstring"""
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   str:
        if not isinstance(lowerCAmelCase ,  lowerCAmelCase						):
              raise TypeError("""plaintext must be a string"""						)
        if not isinstance(lowerCAmelCase ,  lowerCAmelCase						):
              raise TypeError("""key must be a string"""						)
  
        if not plaintext:
              raise ValueError("""plaintext is empty"""						)
        if not key:
              raise ValueError("""key is empty"""						)
  
        key += plaintext
        UpperCAmelCase__ :       Dict						=						plaintext.lower()
        UpperCAmelCase__ :       Tuple						=						key.lower()
        UpperCAmelCase__ :       Optional[int]						=						0
        UpperCAmelCase__ :       Tuple						=						0
        UpperCAmelCase__ :       Optional[int]						=						""""""
        while plaintext_iterator < len(lowerCAmelCase						):
              if (
                  ord(plaintext[plaintext_iterator]						) < 97
                  or ord(plaintext[plaintext_iterator]						) > 1_22
              ):
                    ciphertext += plaintext[plaintext_iterator]
                    plaintext_iterator += 1
              elif ord(key[key_iterator]						) < 97 or ord(key[key_iterator]						) > 1_22:
                    key_iterator += 1
              else:
                    ciphertext += chr(
                        (
                            (ord(plaintext[plaintext_iterator]						) - 97 + ord(key[key_iterator]						))
                            - 97
                        )
                        % 26
                        + 97						)
                    key_iterator += 1
                    plaintext_iterator += 1
        return ciphertext
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   str:
        if not isinstance(lowerCAmelCase ,  lowerCAmelCase						):
              raise TypeError("""ciphertext must be a string"""						)
        if not isinstance(lowerCAmelCase ,  lowerCAmelCase						):
              raise TypeError("""key must be a string"""						)
  
        if not ciphertext:
              raise ValueError("""ciphertext is empty"""						)
        if not key:
              raise ValueError("""key is empty"""						)
  
        UpperCAmelCase__ :       Union[str, Any]						=						key.lower()
        UpperCAmelCase__ :       int						=						0
        UpperCAmelCase__ :       int						=						0
        UpperCAmelCase__ :       int						=						""""""
        while ciphertext_iterator < len(lowerCAmelCase						):
              if (
                  ord(ciphertext[ciphertext_iterator]						) < 97
                  or ord(ciphertext[ciphertext_iterator]						) > 1_22
              ):
                    plaintext += ciphertext[ciphertext_iterator]
              else:
                    plaintext += chr(
                        (ord(ciphertext[ciphertext_iterator]						) - ord(key[key_iterator]						)) % 26
                        + 97						)
                    key += chr(
                        (ord(ciphertext[ciphertext_iterator]						) - ord(key[key_iterator]						)) % 26
                        + 97						)
                    key_iterator += 1
              ciphertext_iterator += 1
        return plaintext
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod()
      _A						=     int(input("""Type 1 to encrypt or 2 to decrypt:"""))
      if operation == 1:
          _A						=     input("""Typeplaintext to be encrypted:\n""")
          _A						=     input("""Type the key:\n""")
          print(encrypt(plaintext, key))
      elif operation == 2:
          _A						=     input("""Type the ciphertext to be decrypted:\n""")
          _A						=     input("""Type the key:\n""")
          print(decrypt(ciphertext, key))
      decrypt("""jsqqs avvwo""", """coffee""")
  
  