





"""simple docstring"""




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase_     :						List[Any]						=					logging.get_logger(__name__)





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Tuple										=  UniSpeechSatForSequenceClassification.from_pretrained(_UpperCAmelCase ,					config=_UpperCAmelCase					)
  A_  :       List[Any]										=  downstream_dict['projector.weight']
  A_  :       Union[str, Any]										=  downstream_dict['projector.bias']
  A_  :       List[str]										=  downstream_dict['model.post_net.linear.weight']
  A_  :       Optional[Any]										=  downstream_dict['model.post_net.linear.bias']
  return model





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[Any]										=  UniSpeechSatForAudioFrameClassification.from_pretrained(_UpperCAmelCase ,					config=_UpperCAmelCase					)
  A_  :       Optional[int]										=  downstream_dict['model.linear.weight']
  A_  :       Optional[Any]										=  downstream_dict['model.linear.bias']
  return model





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  UniSpeechSatForXVector.from_pretrained(_UpperCAmelCase ,					config=_UpperCAmelCase					)
  A_  :       Tuple										=  downstream_dict['connector.weight']
  A_  :       Dict										=  downstream_dict['connector.bias']
  for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
    A_  :       str										=  downstream_dict[
        f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
    ]
    A_  :       str										=  downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

  A_  :       Union[str, Any]										=  downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
  A_  :       Union[str, Any]										=  downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
  A_  :       List[Any]										=  downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
  A_  :       str										=  downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
  A_  :       str										=  downstream_dict['objective.W']
  return model





@torch.no_grad()
def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  torch.load(_UpperCAmelCase ,					map_location='cpu'					)

  A_  :       Dict										=  checkpoint['Downstream']

  A_  :       Optional[Any]										=  UniSpeechSatConfig.from_pretrained(_UpperCAmelCase					)
  A_  :       Tuple										=  WavaVecaFeatureExtractor.from_pretrained(
      _UpperCAmelCase ,					return_attention_mask=_UpperCAmelCase ,					do_normalize=_UpperCAmelCase					)

  A_  :       Optional[Any]										=  hf_config.architectures[0]
  if arch.endswith('ForSequenceClassification'					):
    A_  :       Any										=  convert_classification(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)
  elif arch.endswith('ForAudioFrameClassification'					):
    A_  :       List[str]										=  convert_diarization(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)
  elif arch.endswith('ForXVector'					):
    A_  :       List[Any]										=  convert_xvector(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)
  else:
    raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""					)

  if hf_config.use_weighted_layer_sum:
    A_  :       List[Any]										=  checkpoint['Featurizer']['weights']

  hf_feature_extractor.save_pretrained(_UpperCAmelCase					)
  hf_model.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
   lowerCamelCase_     :						int						=					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.')
   lowerCamelCase_     :						Union[str, Any]						=					parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






"""simple docstring"""




def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  if isinstance(_UpperCAmelCase ,					_UpperCAmelCase					):
    raise TypeError('\'float\' object cannot be interpreted as an integer'					)
  if isinstance(_UpperCAmelCase ,					_UpperCAmelCase					):
    raise TypeError('\'str\' object cannot be interpreted as an integer'					)

  if num == 0:
    return "0b0"

  A_  :       str										=  False

  if num < 0:
    A_  :       Dict										=  True
    A_  :       Union[str, Any]										=  -num

  A_  :       list[int]										=  []
  while num > 0:
    binary.insert(0 ,					num % 2					)
    num >>= 1

  if negative:
    return "-0b" + "".join(str(_UpperCAmelCase					) for e in binary					)

  return "0b" + "".join(str(_UpperCAmelCase					) for e in binary					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()