
'''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 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple			,       _a  :				int      ):
      snake_case_    							=				WavLMForSequenceClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["projector.weight"]
      snake_case_    							=				downstream_dict["projector.bias"]
      snake_case_    							=				downstream_dict["model.post_net.linear.weight"]
      snake_case_    							=				downstream_dict["model.post_net.linear.bias"]
      return model
def 	snake_case_							(				_a  :				List[str]			,       _a  :				Optional[Any]			,       _a  :				List[str]      ):
      snake_case_    							=				WavLMForAudioFrameClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["model.linear.weight"]
      snake_case_    							=				downstream_dict["model.linear.bias"]
      return model
def 	snake_case_							(				_a  :				Dict			,       _a  :				Tuple			,       _a  :				Any      ):
      snake_case_    							=				WavLMForXVector.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["connector.weight"]
      snake_case_    							=				downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
            snake_case_    							=				downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            snake_case_    							=				downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      snake_case_    							=				downstream_dict["objective.W"]
      return model
@torch.no_grad()
def 	snake_case_							(				_a  :				Union[str, Any]			,       _a  :				int			,       _a  :				str			,       _a  :				List[Any]      ):
      snake_case_    							=				torch.load(_a			,       map_location="cpu"      )

      snake_case_    							=				checkpoint["Downstream"]

      snake_case_    							=				WavLMConfig.from_pretrained(_a      )
      snake_case_    							=				WavaVecaFeatureExtractor.from_pretrained(
          _a			,       return_attention_mask=_a			,       do_normalize=_a      )

      snake_case_    							=				hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"      ):
            snake_case_    							=				convert_classification(_a			,       _a			,       _a      )
      elif arch.endswith("ForAudioFrameClassification"      ):
            snake_case_    							=				convert_diarization(_a			,       _a			,       _a      )
      elif arch.endswith("ForXVector"      ):
            snake_case_    							=				convert_xvector(_a			,       _a			,       _a      )
      else:
            raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''      )

      if hf_config.use_weighted_layer_sum:
            snake_case_    							=				checkpoint["Featurizer"]["weights"]

      hf_feature_extractor.save_pretrained(_a      )
      hf_model.save_pretrained(_a      )


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)






def 						_lowerCAmelCase    (					__lowerCamelCase :  int  ):



 """simple docstring"""





 if not isinstance(__lowerCamelCase		,						__lowerCamelCase  ):
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						(
      "operation can not be conducted on a object of type "
      F"""{type(__lowerCamelCase  ).__name__}"""
  )
  raise TypeError(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     List[str]							=						""
 for _ in range(0		,						32  ):
  bit_string += str(number % 2  )
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						number >> 1
 return bit_string






def 						_lowerCAmelCase    (					__lowerCamelCase :  int  ):



 """simple docstring"""





 if number < 0:
  raise ValueError("the value of input must be positive"  )
 elif isinstance(__lowerCamelCase		,						__lowerCamelCase  ):
  raise TypeError("Input value must be a 'int' type"  )
 elif isinstance(__lowerCamelCase		,						__lowerCamelCase  ):
  raise TypeError("'<' not supported between instances of 'str' and 'int'"  )
 __SCREAMING_SNAKE_CASE							:     Any							=						0
 # iterator over [1 to 32],since we are dealing with 32 bit integer
 for _ in range(1		,						33  ):
  # left shift the bits by unity
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						result << 1
  # get the end bit
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						number % 2
  # right shift the bits by unity
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						number >> 1
  # add that bit to our ans
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						result | end_bit
 return get_reverse_bit_string(__lowerCamelCase  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()






