





from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any



class 				_UpperCamelCase       :
       _UpperCamelCase					:       Optional[int]       = ('''_front''', '''_back''', '''_len''')



       @dataclass
       class 				_UpperCamelCase       :
              _UpperCamelCase					:       Any       = None
              _UpperCamelCase					:       Deque._Node | None       = None
              _UpperCamelCase					:       Deque._Node | None       = None



       class 				_UpperCamelCase       :
              _UpperCamelCase					:       Any       = ('''_cur''',)

              def __init__(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			Deque._Node | None		)							->			None:




                     """simple docstring"""


                     UpperCamelCase_ 							=	cur

              def __iter__(		self:			Tuple		)							->			Deque._Iterator:




                     """simple docstring"""


                     return self



              def lowercase			(		self:			Union[str, Any]		)							->			Any:




                     """simple docstring"""


                     if self._cur is None:
                            # finished iterating
                            raise StopIteration
                     UpperCamelCase_ 							=	self._cur.val
                     UpperCamelCase_ 							=	self._cur.next_node

                     return val

       def __init__(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Iterable[Any] | None = None		)							->			None:




              """simple docstring"""


              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	0

              if iterable is not None:
                     # append every value to the deque
                     for val in iterable:
                            self.append(_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Any		)							->			None:




              """simple docstring"""


              UpperCamelCase_ 							=	self._Node(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              if self.is_empty():
                     # front = back
                     UpperCamelCase_ 							=	UpperCamelCase_ 							=	node
                     UpperCamelCase_ 							=	1
              else:
                     # connect nodes
                     UpperCamelCase_ 							=	node
                     UpperCamelCase_ 							=	self._back
                     UpperCamelCase_ 							=	node  # assign new back to the new node

                     self._len += 1

                     # make sure there were no errors
                     assert not self.is_empty(), "Error on appending value."

       def lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Any		)							->			None:




              """simple docstring"""


              UpperCamelCase_ 							=	self._Node(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              if self.is_empty():
                     # front = back
                     UpperCamelCase_ 							=	UpperCamelCase_ 							=	node
                     UpperCamelCase_ 							=	1
              else:
                     # connect nodes
                     UpperCamelCase_ 							=	self._front
                     UpperCamelCase_ 							=	node
                     UpperCamelCase_ 							=	node  # assign new front to the new node

                     self._len += 1

                     # make sure there were no errors
                     assert not self.is_empty(), "Error on appending value."

       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			Iterable[Any]		)							->			None:




              """simple docstring"""


              for val in iterable:
                     self.append(_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			Iterable[Any]		)							->			None:




              """simple docstring"""


              for val in iterable:
                     self.appendleft(_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			int		)							->			Any:




              """simple docstring"""


              assert not self.is_empty(), "Deque is empty."

              UpperCamelCase_ 							=	self._back
              UpperCamelCase_ 							=	self._back.prev_node  # set new back
              # drop the last node - python will deallocate memory automatically
              UpperCamelCase_ 							=	None

              self._len -= 1

              return topop.val

       def lowercase			(		self:			List[Any]		)							->			Any:




              """simple docstring"""


              assert not self.is_empty(), "Deque is empty."

              UpperCamelCase_ 							=	self._front
              UpperCamelCase_ 							=	self._front.next_node  # set new front and drop the first node
              UpperCamelCase_ 							=	None

              self._len -= 1

              return topop.val

       def lowercase			(		self:			Dict		)							->			bool:




              """simple docstring"""


              return self._front is None

       def __len__(		self:			int		)							->			int:




              """simple docstring"""


              return self._len

       def __eq__(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			object		)							->			bool:




              """simple docstring"""


              if not isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		):
                     return NotImplemented

              UpperCamelCase_ 							=	self._front
              UpperCamelCase_ 							=	other._front

              # if the length of the dequeues are not the same, they are not equal
              if len(self		) != len(_SCREAMING_SNAKE_CASE		):
                     return False

              while me is not None and oth is not None:
                     # compare every value
                     if me.val != oth.val:
                            return False
                     UpperCamelCase_ 							=	me.next_node
                     UpperCamelCase_ 							=	oth.next_node

              return True

       def __iter__(		self:			Union[str, Any]		)							->			Deque._Iterator:




              """simple docstring"""


              return Deque._Iterator(self._front		)



       def __repr__(		self:			Union[str, Any]		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	[]
              UpperCamelCase_ 							=	self._front
              while aux is not None:
                     # append the values in a list to display
                     values_list.append(aux.val		)
                     UpperCamelCase_ 							=	aux.next_node

              return f'''[{", ".join(repr(_SCREAMING_SNAKE_CASE		) for val in values_list		)}]'''


if __name__ == "__main__":
     import doctest

     doctest.testmod()





'''simple docstring'''







import argparse

import torch

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


logging.set_verbosity_info()
a__    :   Optional[Any]								=			logging.get_logger(__name__)







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForSequenceClassification.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["projector.weight"]
      UpperCamelCase__      =							downstream_dict["projector.bias"]
      UpperCamelCase__      =							downstream_dict["model.post_net.linear.weight"]
      UpperCamelCase__      =							downstream_dict["model.post_net.linear.bias"]
      return model







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[str]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForAudioFrameClassification.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["model.linear.weight"]
      UpperCamelCase__      =							downstream_dict["model.linear.bias"]
      return model







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



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

      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      UpperCamelCase__      =							downstream_dict["objective.W"]
      return model







@torch.no_grad()
def _UpperCamelCase			(	__A ,     __A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							torch.load(__A ,     map_location="cpu"       )

      UpperCamelCase__      =							checkpoint["Downstream"]

      UpperCamelCase__      =							WavLMConfig.from_pretrained(__A       )
      UpperCamelCase__      =							WavaVecaFeatureExtractor.from_pretrained(
          __A ,     return_attention_mask=__A ,     do_normalize=__A       )

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

      if hf_config.use_weighted_layer_sum:
            UpperCamelCase__      =							checkpoint["Featurizer"]["weights"]

      hf_feature_extractor.save_pretrained(__A       )
      hf_model.save_pretrained(__A       )


if __name__ == "__main__":
  a__    :   Union[str, 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__    :   Optional[Any]								=			parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





