






"""simple docstring"""



import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
_UpperCamelCase						:    Optional[int]		=						logging.get_logger(__name__)
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Any						,   __snake_case     :    Union[str, Any]						,   __snake_case     :    str       ):





  '''simple docstring'''





  lowercase				       =			WavLMForSequenceClassification.from_pretrained(__snake_case						,   config=__snake_case       )
  lowercase				       =			downstream_dict['projector.weight']
  lowercase				       =			downstream_dict['projector.bias']
  lowercase				       =			downstream_dict['model.post_net.linear.weight']
  lowercase				       =			downstream_dict['model.post_net.linear.bias']
  return model
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    Optional[int]						,   __snake_case     :    Optional[Any]       ):





  '''simple docstring'''





  lowercase				       =			WavLMForAudioFrameClassification.from_pretrained(__snake_case						,   config=__snake_case       )
  lowercase				       =			downstream_dict['model.linear.weight']
  lowercase				       =			downstream_dict['model.linear.bias']
  return model
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    List[str]						,   __snake_case     :    str       ):





  '''simple docstring'''





  lowercase				       =			WavLMForXVector.from_pretrained(__snake_case						,   config=__snake_case       )
  lowercase				       =			downstream_dict['connector.weight']
  lowercase				       =			downstream_dict['connector.bias']
  for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
    lowercase				       =			downstream_dict[
        f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
    ]
    lowercase				       =			downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

  lowercase				       =			downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
  lowercase				       =			downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
  lowercase				       =			downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
  lowercase				       =			downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
  lowercase				       =			downstream_dict['objective.W']
  return model
@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[Any]						,   __snake_case     :    Optional[int]						,   __snake_case     :    List[Any]						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  lowercase				       =			torch.load(__snake_case						,   map_location='cpu'       )

  lowercase				       =			checkpoint['Downstream']

  lowercase				       =			WavLMConfig.from_pretrained(__snake_case       )
  lowercase				       =			WavaVecaFeatureExtractor.from_pretrained(
      __snake_case						,   return_attention_mask=__snake_case						,   do_normalize=__snake_case       )

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

  if hf_config.use_weighted_layer_sum:
    lowercase				       =			checkpoint['Featurizer']['weights']

  hf_feature_extractor.save_pretrained(__snake_case       )
  hf_model.save_pretrained(__snake_case       )


if __name__ == "__main__":
    _UpperCamelCase						:    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.')
    _UpperCamelCase						:    List[str]		=						parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)










"""simple docstring"""


from __future__ import annotations




def      snake_case    (A_ :list[int | float]      ,     A_ :int      ,     A_ :int ):







  '''simple docstring'''

  if len(A_ ) == 0:
    raise ValueError('find_min() arg is an empty sequence' )
  if (
      left >= len(A_ )
      or left < -len(A_ )
      or right >= len(A_ )
      or right < -len(A_ )
  ):
    raise IndexError('list index out of range' )
  if left == right:
    return nums[left]
  a	:     Optional[Any]							=  (left + right) >> 1  # the middle
  a	:     List[str]							=  find_min(A_      ,     A_      ,     A_ )  # find min in range[left, mid]
  a	:     Union[str, Any]							=  find_min(A_      ,     mid + 1      ,     A_ )  # find min in range[mid + 1, right]

  return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)




