



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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase 	=   logging.get_logger(__name__)

lowerCamelCase 	=   {
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Tuple        =  'yoso'



   def __init__(							self,       lowercase_=50265,       lowercase_=768,       lowercase_=12,       lowercase_=12,       lowercase_=3072,       lowercase_="gelu",       lowercase_=0.1,       lowercase_=0.1,       lowercase_=4096,       lowercase_=1,       lowercase_=0.02,       lowercase_=1E-12,       lowercase_="absolute",       lowercase_=True,       lowercase_=9,       lowercase_=64,       lowercase_=None,       lowercase_=True,       lowercase_=True,       lowercase_=1,       lowercase_=0,       lowercase_=2,       **lowercase_,       )				->       List[str]:






     """simple docstring"""






     super().__init__(pad_token_id=lowercase_,       bos_token_id=lowercase_,       eos_token_id=lowercase_,       **lowercase_			)

     a__    =vocab_size
     a__    =max_position_embeddings
     a__    =hidden_size
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =intermediate_size
     a__    =hidden_act
     a__    =hidden_dropout_prob
     a__    =attention_probs_dropout_prob
     a__    =initializer_range
     a__    =type_vocab_size
     a__    =layer_norm_eps
     a__    =position_embedding_type
     a__    =use_expectation
     a__    =hash_code_len
     a__    =num_hash
     a__    =conv_window
     a__    =use_fast_hash
     a__    =lsh_backward
