



import argparse

import torch

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


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






def 						_lowerCAmelCase    (					__lowerCamelCase :  List[str]		,						__lowerCamelCase :  int		,						__lowerCamelCase :  Dict  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     List[str]							=						WavLMForSequenceClassification.from_pretrained(__lowerCamelCase		,						config=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Any							=						downstream_dict["projector.weight"]
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						downstream_dict["projector.bias"]
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						downstream_dict["model.post_net.linear.weight"]
 __SCREAMING_SNAKE_CASE							:     Tuple							=						downstream_dict["model.post_net.linear.bias"]
 return model






def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  Dict		,						__lowerCamelCase :  List[Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						WavLMForAudioFrameClassification.from_pretrained(__lowerCamelCase		,						config=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						downstream_dict["model.linear.weight"]
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						downstream_dict["model.linear.bias"]
 return model






def 						_lowerCAmelCase    (					__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Any		,						__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						WavLMForXVector.from_pretrained(__lowerCamelCase		,						config=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						downstream_dict["connector.weight"]
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						downstream_dict["connector.bias"]
 for i, kernel_size in enumerate(hf_config.tdnn_kernel  ):
  __SCREAMING_SNAKE_CASE							:     str							=						downstream_dict[
      F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
  ]
  __SCREAMING_SNAKE_CASE							:     int							=						downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
 __SCREAMING_SNAKE_CASE							:     List[str]							=						downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
 __SCREAMING_SNAKE_CASE							:     Tuple							=						downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
 __SCREAMING_SNAKE_CASE							:     Any							=						downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
 __SCREAMING_SNAKE_CASE							:     int							=						downstream_dict["objective.W"]
 return model






@torch.no_grad()
def 						_lowerCAmelCase    (					__lowerCamelCase :  Dict		,						__lowerCamelCase :  Tuple		,						__lowerCamelCase :  str		,						__lowerCamelCase :  int  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Any							=						torch.load(__lowerCamelCase		,						map_location="cpu"  )

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

 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						WavLMConfig.from_pretrained(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Dict							=						WavaVecaFeatureExtractor.from_pretrained(
     __lowerCamelCase		,						return_attention_mask=__lowerCamelCase		,						do_normalize=__lowerCamelCase  )

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

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

 hf_feature_extractor.save_pretrained(__lowerCamelCase  )
 hf_model.save_pretrained(__lowerCamelCase  )


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











from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

_lowerCamelCase         =	TypeVar("""T""", bound=bool)

class    _SCREAMING_SNAKE_CASE						(Generic[T]			):





     def __init__(							self			:				List[Any]  ,	UpperCamelCase			:				T					)->None:
      __SCREAMING_SNAKE_CASE							:     T							=						value
      __SCREAMING_SNAKE_CASE							:     SkewNode[T] | None							=						None
      __SCREAMING_SNAKE_CASE							:     SkewNode[T] | None							=						None





     @property
     def 						__snake_case     (							self			:				Dict					)->T:
      return self._value






     @staticmethod
     def 						__snake_case     (							UpperCamelCase			:				SkewNode[T] | None  ,	UpperCamelCase			:				SkewNode[T] | None					)->SkewNode[T] | None:
      if not roota:
       return roota

      if not roota:
       return roota

      if roota.value > roota.value:
       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[Any]							=						roota, roota

      __SCREAMING_SNAKE_CASE							:     int							=						roota
      __SCREAMING_SNAKE_CASE							:     str							=						roota.right
      __SCREAMING_SNAKE_CASE							:     int							=						roota.left
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						SkewNode.merge(UpperCamelCase  ,	UpperCamelCase					)

      return result






class    _SCREAMING_SNAKE_CASE						(Generic[T]			):





     def __init__(							self			:				str  ,	UpperCamelCase			:				Iterable[T] | None = ()					)->None:
      __SCREAMING_SNAKE_CASE							:     SkewNode[T] | None							=						None
      if data:
       for item in data:
        self.insert(UpperCamelCase					)





     def __bool__(							self			:				str					)->bool:
      return self._root is not None





     def __iter__(							self			:				Any					)->Iterator[T]:
      __SCREAMING_SNAKE_CASE							:     list[Any]							=						[]
      while self:
       result.append(self.pop()					)

      # Pushing items back to the heap not to clear it.
      for item in result:
       self.insert(UpperCamelCase					)

      return iter(UpperCamelCase					)





     def 						__snake_case     (							self			:				List[Any]  ,	UpperCamelCase			:				T					)->None:
      __SCREAMING_SNAKE_CASE							:     str							=						SkewNode.merge(self._root  ,	SkewNode(UpperCamelCase					)					)





     def 						__snake_case     (							self			:				List[Any]					)->T | None:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.top()
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						(
          SkewNode.merge(self._root.left  ,	self._root.right					) if self._root else None
      )

      return result





     def 						__snake_case     (							self			:				Optional[int]					)->T:
      if not self._root:
       raise IndexError("Can't get top element for the empty heap."					)
      return self._root.value






     def 						__snake_case     (							self			:				Dict					)->None:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						None


if __name__ == "__main__":
      import doctest

      doctest.testmod()






