

import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__A			:					Union[str, Any]						=			logging.get_logger(__name__)





def 	__a     (      A__			:  str			,  A__			:  Optional[Any]			,  A__			:  Union[str, Any]       ):
    SCREAMING_SNAKE_CASE										=   UniSpeechSatForSequenceClassification.from_pretrained(A__			,  config=A__       )
    SCREAMING_SNAKE_CASE										=   downstream_dict["projector.weight"]
    SCREAMING_SNAKE_CASE										=   downstream_dict["projector.bias"]
    SCREAMING_SNAKE_CASE										=   downstream_dict["model.post_net.linear.weight"]
    SCREAMING_SNAKE_CASE										=   downstream_dict["model.post_net.linear.bias"]
    return model





def 	__a     (      A__			:  Dict			,  A__			:  Dict			,  A__			:  List[Any]       ):
    SCREAMING_SNAKE_CASE										=   UniSpeechSatForAudioFrameClassification.from_pretrained(A__			,  config=A__       )
    SCREAMING_SNAKE_CASE										=   downstream_dict["model.linear.weight"]
    SCREAMING_SNAKE_CASE										=   downstream_dict["model.linear.bias"]
    return model





def 	__a     (      A__			:  List[Any]			,  A__			:  str			,  A__			:  Tuple       ):
    SCREAMING_SNAKE_CASE										=   UniSpeechSatForXVector.from_pretrained(A__			,  config=A__       )
    SCREAMING_SNAKE_CASE										=   downstream_dict["connector.weight"]
    SCREAMING_SNAKE_CASE										=   downstream_dict["connector.bias"]
    for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
        SCREAMING_SNAKE_CASE										=   downstream_dict[
            F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
        ]
        SCREAMING_SNAKE_CASE										=   downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

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





@torch.no_grad()
def 	__a     (      A__			:  Union[str, Any]			,  A__			:  Any			,  A__			:  Dict			,  A__			:  Union[str, Any]       ):
    SCREAMING_SNAKE_CASE										=   torch.load(A__			,  map_location="cpu"       )

    SCREAMING_SNAKE_CASE										=   checkpoint["Downstream"]

    SCREAMING_SNAKE_CASE										=   UniSpeechSatConfig.from_pretrained(A__       )
    SCREAMING_SNAKE_CASE										=   WavaVecaFeatureExtractor.from_pretrained(
        A__			,  return_attention_mask=A__			,  do_normalize=A__       )

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

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

    hf_feature_extractor.save_pretrained(A__       )
    hf_model.save_pretrained(A__       )


if __name__ == "__main__":
    __A			:					Dict						=			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			:					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 ..utils import DummyObject, requires_backends



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       int				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       int				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       Dict				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       Optional[Any]				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)





