





import argparse

import torch

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


logging.set_verbosity_info()
_UpperCAmelCase             =							logging.get_logger(__name__)






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[int]   ,   SCREAMING_SNAKE_CASE		:str   ,   SCREAMING_SNAKE_CASE		:Optional[int]   )      ->   List[Any]:
 __lowerCAmelCase					:	Union[str, Any]      =       WavaVecaForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE   ,   config=SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	int      =       downstream_dict["""projector.weight"""]
 __lowerCAmelCase					:	Union[str, Any]      =       downstream_dict["""projector.bias"""]
 __lowerCAmelCase					:	Optional[Any]      =       downstream_dict["""model.post_net.linear.weight"""]
 __lowerCAmelCase					:	List[str]      =       downstream_dict["""model.post_net.linear.bias"""]
 return model






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[int]   ,   SCREAMING_SNAKE_CASE		:List[Any]   ,   SCREAMING_SNAKE_CASE		:Optional[int]   )      ->   Union[str, Any]:
 __lowerCAmelCase					:	List[str]      =       WavaVecaForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE   ,   config=SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Union[str, Any]      =       downstream_dict["""model.linear.weight"""]
 __lowerCAmelCase					:	List[Any]      =       downstream_dict["""model.linear.bias"""]
 return model






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Tuple   ,   SCREAMING_SNAKE_CASE		:Dict   ,   SCREAMING_SNAKE_CASE		:Any   )      ->   Union[str, Any]:
 __lowerCAmelCase					:	List[Any]      =       WavaVecaForXVector.from_pretrained(SCREAMING_SNAKE_CASE   ,   config=SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	int      =       downstream_dict["""connector.weight"""]
 __lowerCAmelCase					:	Union[str, Any]      =       downstream_dict["""connector.bias"""]
 for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
  __lowerCAmelCase					:	Any      =       downstream_dict[
      F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
  ]
  __lowerCAmelCase					:	Union[str, Any]      =       downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

 __lowerCAmelCase					:	Dict      =       downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
 __lowerCAmelCase					:	List[Any]      =       downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
 __lowerCAmelCase					:	List[Any]      =       downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
 __lowerCAmelCase					:	Any      =       downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
 __lowerCAmelCase					:	int      =       downstream_dict["""objective.W"""]
 return model






@torch.no_grad()
def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:str   ,   SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:Union[str, Any]   )      ->   Any:
 __lowerCAmelCase					:	List[str]      =       torch.load(SCREAMING_SNAKE_CASE   ,   map_location="""cpu"""   )

 __lowerCAmelCase					:	List[Any]      =       checkpoint["""Downstream"""]

 __lowerCAmelCase					:	str      =       WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Union[str, Any]      =       WavaVecaFeatureExtractor.from_pretrained(
     SCREAMING_SNAKE_CASE   ,   return_attention_mask=SCREAMING_SNAKE_CASE   ,   do_normalize=SCREAMING_SNAKE_CASE   )

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

 if hf_config.use_weighted_layer_sum:
  __lowerCAmelCase					:	List[str]      =       checkpoint["""Featurizer"""]["""weights"""]

 hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE   )
 hf_model.save_pretrained(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      _UpperCAmelCase             =							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.')
      _UpperCAmelCase             =							parser.parse_args()
      convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int | float]]   )      ->   int:
 __lowerCAmelCase					:	str      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(matrix[0]   )
 __lowerCAmelCase					:	List[Any]      =       min(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 for row in range(SCREAMING_SNAKE_CASE   ):
  # 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   ,   SCREAMING_SNAKE_CASE   ):
    __lowerCAmelCase					:	Union[str, Any]      =       matrix[col][row] / matrix[row][row]
    for i in range(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
     matrix[col][i] -= multiplier * matrix[row][i]
  else:
   # Find a non-zero diagonal element to swap rows
   __lowerCAmelCase					:	Optional[int]      =       True
   for i in range(row + 1   ,   SCREAMING_SNAKE_CASE   ):
    if matrix[i][row] != 0:
     __lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       matrix[i], matrix[row]
     __lowerCAmelCase					:	Union[str, Any]      =       False
     break
   if reduce:
    rank -= 1
    for i in range(SCREAMING_SNAKE_CASE   ):
     __lowerCAmelCase					:	int      =       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()