
import argparse

import torch

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


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


def 		_lowerCamelCase       (   _a			,   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						WavLMForSequenceClassification.from_pretrained(_a			,   config=_a  )
  _lowerCamelCase      =						downstream_dict['''projector.weight''']
  _lowerCamelCase      =						downstream_dict['''projector.bias''']
  _lowerCamelCase      =						downstream_dict['''model.post_net.linear.weight''']
  _lowerCamelCase      =						downstream_dict['''model.post_net.linear.bias''']
  return model


def 		_lowerCamelCase       (   _a			,   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						WavLMForAudioFrameClassification.from_pretrained(_a			,   config=_a  )
  _lowerCamelCase      =						downstream_dict['''model.linear.weight''']
  _lowerCamelCase      =						downstream_dict['''model.linear.bias''']
  return model


def 		_lowerCamelCase       (   _a			,   _a			,   _a  ):
  """simple docstring"""




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

  _lowerCamelCase      =						downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
  _lowerCamelCase      =						downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
  _lowerCamelCase      =						downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
  _lowerCamelCase      =						downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
  _lowerCamelCase      =						downstream_dict['''objective.W''']
  return model


@torch.no_grad()
def 		_lowerCamelCase       (   _a			,   _a			,   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						torch.load(_a			,   map_location='''cpu'''  )

  _lowerCamelCase      =						checkpoint['''Downstream''']

  _lowerCamelCase      =						WavLMConfig.from_pretrained(_a  )
  _lowerCamelCase      =						WavaVecaFeatureExtractor.from_pretrained(
      _a			,   return_attention_mask=_a			,   do_normalize=_a  )

  _lowerCamelCase      =						hf_config.architectures[0]
  if arch.endswith('''ForSequenceClassification'''  ):
    _lowerCamelCase      =						convert_classification(_a			,   _a			,   _a  )
  elif arch.endswith('''ForAudioFrameClassification'''  ):
    _lowerCamelCase      =						convert_diarization(_a			,   _a			,   _a  )
  elif arch.endswith('''ForXVector'''  ):
    _lowerCamelCase      =						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      =						checkpoint['''Featurizer''']['''weights''']

  hf_feature_extractor.save_pretrained(_a  )
  hf_model.save_pretrained(_a  )


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)






class 		__magic_name__				:


  """simple docstring"""





  class 		__magic_name__				:


    """simple docstring"""





    _UpperCamelCase						 = "_prev", "_data", "_next"


    def __init__(					self	,		a__	,		a__	,		a__		):
      _lowerCamelCase      =						link_p
      _lowerCamelCase      =						element
      _lowerCamelCase      =						link_n


    def     _UpperCAmelCase  (					self		):
      return (
          f''' Prev -> {self._prev is not None}, Next -> {self._next is not None}'''
      )







  def __init__(					self		):
    _lowerCamelCase      =						self._Node(a__	,		a__	,		a__		)
    _lowerCamelCase      =						self._Node(a__	,		a__	,		a__		)
    _lowerCamelCase      =						self._trailer
    _lowerCamelCase      =						self._header
    _lowerCamelCase      =						0


  def __len__(					self		):
    return self._size


  def     _UpperCAmelCase  (					self		):
    return self.__len__() == 0


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__		):
    # Create new_node by setting it's prev.link -> header
    # setting it's next.link -> trailer
    _lowerCamelCase      =						self._Node(a__	,		a__	,		a__		)
    _lowerCamelCase      =						new_node
    _lowerCamelCase      =						new_node
    self._size += 1
    return self


  def     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase      =						node._prev
    _lowerCamelCase      =						node._next

    _lowerCamelCase      =						successor
    _lowerCamelCase      =						predecessor
    self._size -= 1
    _lowerCamelCase      =						node._data
    _lowerCamelCase      =						_lowerCamelCase      =						_lowerCamelCase      =						None
    del node
    return temp







class 		__magic_name__				( _DoublyLinkedBase					):


  """simple docstring"""





  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise Exception('''List is empty'''		)
    return self._header._next._data


  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise Exception('''List is empty'''		)
    return self._trailer._prev._data


  def     _UpperCAmelCase  (					self	,		a__		):
    return self._insert(self._header	,		a__	,		self._header._next		)


  def     _UpperCAmelCase  (					self	,		a__		):
    return self._insert(self._trailer._prev	,		a__	,		self._trailer		)


  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise IndexError('''remove_first from empty list'''		)
    return self._delete(self._header._next		)


  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise IndexError('''remove_first from empty list'''		)
    return self._delete(self._trailer._prev		)




