


'''simple docstring'''






def 							UpperCamelCase_     (       A__   :  list[list[int | float]]   ):
  '''simple docstring'''





  lowerCAmelCase_     : List[str]     =  len(A__   )
  lowerCAmelCase_     : Union[str, Any]     =  len(matrix[0]   )
  lowerCAmelCase_     : Optional[int]     =  min(A__				,       A__   )

  for row in range(A__   ):
    # Check if diagonal element is not zero
    if matrix[row][row] != 0:
      # Eliminate all the elements below the diagonal
      for col in range(row + 1				,       A__   ):
        lowerCAmelCase_     : int     =  matrix[col][row] / matrix[row][row]
        for i in range(A__				,       A__   ):
          matrix[col][i] -= multiplier * matrix[row][i]
    else:
      # Find a non-zero diagonal element to swap rows
      lowerCAmelCase_     : int     =  True
      for i in range(row + 1				,       A__   ):
        if matrix[i][row] != 0:
          lowerCAmelCase_,				lowerCAmelCase_     : List[Any]     =  matrix[i], matrix[row]
          lowerCAmelCase_     : Dict     =  False
          break
      if reduce:
        rank -= 1
        for i in range(A__   ):
          lowerCAmelCase_     : List[Any]     =  matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
      row -= 1

  return rank


if __name__ == "__main__":
  import doctest

  doctest.testmod()







'''simple docstring'''






import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
__A   :    str											= logging.get_logger(__name__)







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  List[Any]				,       A__   :  List[str]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  WavaVecaForSequenceClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : str     =  downstream_dict["""projector.weight"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""projector.bias"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""model.post_net.linear.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.post_net.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  Any				,       A__   :  List[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[int]     =  WavaVecaForAudioFrameClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.linear.weight"""]
  lowerCAmelCase_     : List[str]     =  downstream_dict["""model.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  Any				,       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[Any]     =  WavaVecaForXVector.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : List[Any]     =  downstream_dict["""connector.weight"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""connector.bias"""]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
    lowerCAmelCase_     : List[str]     =  downstream_dict[
        f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
    ]
    lowerCAmelCase_     : List[str]     =  downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

  lowerCAmelCase_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  lowerCAmelCase_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  lowerCAmelCase_     : int     =  downstream_dict["""objective.W"""]
  return model







@torch.no_grad()
def 							UpperCamelCase_     (       A__   :  int				,       A__   :  int				,       A__   :  str				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Tuple     =  torch.load(A__				,       map_location="""cpu"""   )

  lowerCAmelCase_     : Optional[int]     =  checkpoint["""Downstream"""]

  lowerCAmelCase_     : int     =  WavaVecaConfig.from_pretrained(A__   )
  lowerCAmelCase_     : Union[str, Any]     =  WavaVecaFeatureExtractor.from_pretrained(
      A__				,       return_attention_mask=A__				,       do_normalize=A__   )

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

  if hf_config.use_weighted_layer_sum:
    lowerCAmelCase_     : Optional[int]     =  checkpoint["""Featurizer"""]["""weights"""]

  hf_feature_extractor.save_pretrained(A__   )
  hf_model.save_pretrained(A__   )


if __name__ == "__main__":
  __A   :    List[Any]											= 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   :    Tuple											= parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



