






"""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)





from __future__ import annotations



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      int:



  """simple docstring"""






  assert n > 1 and a > 0 and greatest_common_divisor(_UpperCamelCase					,			_UpperCamelCase	) == 1
  ((_snake_case),	(_snake_case),	(_snake_case))		=				extended_gcd(_UpperCamelCase					,			_UpperCamelCase	)  # Implemented below
  _snake_case		=				(b * s) % n
  return x



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      int:



  """simple docstring"""






  ((_snake_case),	(_snake_case))		=				extended_euclid(_UpperCamelCase					,			_UpperCamelCase	)  # Implemented below
  if b < 0:
    _snake_case		=				(b % n + n) % n
  return b



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      int:



  """simple docstring"""






  _snake_case		=				invert_modulo(_UpperCamelCase					,			_UpperCamelCase	)
  _snake_case		=				(b * s) % n
  return x



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      tuple[int, int, int]:



  """simple docstring"""






  assert a >= 0 and b >= 0

  if b == 0:
    _snake_case,	_snake_case,	_snake_case		=				a, 1, 0
  else:
    ((_snake_case),	(_snake_case),	(_snake_case))		=				extended_gcd(_UpperCamelCase					,			a % b	)
    _snake_case		=				q
    _snake_case		=				p - q * (a // b)

  assert a % d == 0 and b % d == 0
  assert d == a * x + b * y

  return (d, x, y)



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      tuple[int, int]:



  """simple docstring"""






  if b == 0:
    return (1, 0)
  ((_snake_case),	(_snake_case))		=				extended_euclid(_UpperCamelCase					,			a % b	)
  _snake_case		=				a // b
  return (y, x - k * y)



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      int:



  """simple docstring"""






  if a < b:
    _snake_case,	_snake_case		=				b, a

  while a % b != 0:
    _snake_case,	_snake_case		=				b, a % b

  return b


if __name__ == "__main__":
 from doctest import testmod

 testmod(name='''modular_division''', verbose=True)
 testmod(name='''modular_division2''', verbose=True)
 testmod(name='''invert_modulo''', verbose=True)
 testmod(name='''extended_gcd''', verbose=True)
 testmod(name='''extended_euclid''', verbose=True)
 testmod(name='''greatest_common_divisor''', verbose=True)

