
'''simple docstring'''
import argparse

import torch

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


logging.set_verbosity_info()
A__ :		Tuple					=logging.get_logger(__name__)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Dict      ,  __SCREAMING_SNAKE_CASE    :	Tuple      ,  __SCREAMING_SNAKE_CASE    :	Tuple			)					->							Optional[int]:





  """simple docstring"""

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







def 							A_  (    __SCREAMING_SNAKE_CASE    :	int      ,  __SCREAMING_SNAKE_CASE    :	Optional[int]      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any]			)					->							int:





  """simple docstring"""

  __A		:  List[str]       				=		WavLMForAudioFrameClassification.from_pretrained(__SCREAMING_SNAKE_CASE      ,  config=__SCREAMING_SNAKE_CASE			)
  __A		:  Tuple       				=		downstream_dict["""model.linear.weight"""]
  __A		:  int       				=		downstream_dict["""model.linear.bias"""]
  return model







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Dict      ,  __SCREAMING_SNAKE_CASE    :	int      ,  __SCREAMING_SNAKE_CASE    :	str			)					->							List[Any]:





  """simple docstring"""

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

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







@torch.no_grad()
def 							A_  (    __SCREAMING_SNAKE_CASE    :	Dict      ,  __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	List[Any]			)					->							int:





  """simple docstring"""

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

  __A		:  str       				=		checkpoint["""Downstream"""]

  __A		:  int       				=		WavLMConfig.from_pretrained(__SCREAMING_SNAKE_CASE			)
  __A		:  Any       				=		WavaVecaFeatureExtractor.from_pretrained(
      __SCREAMING_SNAKE_CASE      ,  return_attention_mask=__SCREAMING_SNAKE_CASE      ,  do_normalize=__SCREAMING_SNAKE_CASE			)

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

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

  hf_feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE			)
  hf_model.save_pretrained(__SCREAMING_SNAKE_CASE			)


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__ :		Any					=parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												=['''image_processor''', '''tokenizer''']
 lowerCamelCase												='''EfficientNetImageProcessor'''
 lowerCamelCase												=('''BertTokenizer''', '''BertTokenizerFast''')
 def __init__( self      :     str					, lowerCamelCase      :     Optional[int]					, lowerCamelCase      :     Tuple							):



   """simple docstring"""





   super().__init__(lowerCamelCase					, lowerCamelCase							)
 def __call__( self      :     Tuple					, lowerCamelCase      :     str=None					, lowerCamelCase      :     Tuple=None					, lowerCamelCase      :     int="max_length"					, lowerCamelCase      :     int=64					, lowerCamelCase      :     Union[str, Any]=None					, **lowerCamelCase      :     Dict							):



   """simple docstring"""





   if text is None and images is None:
     raise ValueError("""You have to specify either text or images. Both cannot be none."""							)

   if text is not None:
     __A		:  str       				=		self.tokenizer(
         lowerCamelCase					, padding=lowerCamelCase					, max_length=lowerCamelCase					, return_tensors=lowerCamelCase					, **lowerCamelCase							)

   if images is not None:
     __A		:  Optional[Any]       				=		self.image_processor(lowerCamelCase					, return_tensors=lowerCamelCase					, **lowerCamelCase							)

   if text is not None and images is not None:
     __A		:  Optional[int]       				=		image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**lowerCamelCase							)					, tensor_type=lowerCamelCase							)
 def 		lowercase_( self      :     Any					, *lowerCamelCase      :     Optional[Any]					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   return self.tokenizer.batch_decode(*lowerCamelCase					, **lowerCamelCase							)
 def 		lowercase_( self      :     Any					, *lowerCamelCase      :     List[Any]					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   return self.tokenizer.decode(*lowerCamelCase					, **lowerCamelCase							)






 @property
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   __A		:  Dict       				=		self.tokenizer.model_input_names
   __A		:  int       				=		self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



