






import argparse

import torch

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


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






def   UpperCamelCase  (       snake_case__     :   Dict	,snake_case__     :   Any	,snake_case__     :   Tuple  ):




     '''simple docstring'''






     __snake_case			:int          =						UniSpeechSatForSequenceClassification.from_pretrained(snake_case__	,config=snake_case__  )
     __snake_case			:List[str]          =						downstream_dict["""projector.weight"""]
     __snake_case			:List[str]          =						downstream_dict["""projector.bias"""]
     __snake_case			:int          =						downstream_dict["""model.post_net.linear.weight"""]
     __snake_case			:int          =						downstream_dict["""model.post_net.linear.bias"""]
     return model






def   UpperCamelCase  (       snake_case__     :   Union[str, Any]	,snake_case__     :   Optional[int]	,snake_case__     :   List[Any]  ):




     '''simple docstring'''






     __snake_case			:Optional[Any]          =						UniSpeechSatForAudioFrameClassification.from_pretrained(snake_case__	,config=snake_case__  )
     __snake_case			:Optional[Any]          =						downstream_dict["""model.linear.weight"""]
     __snake_case			:str          =						downstream_dict["""model.linear.bias"""]
     return model






def   UpperCamelCase  (       snake_case__     :   Any	,snake_case__     :   Optional[int]	,snake_case__     :   List[Any]  ):




     '''simple docstring'''






     __snake_case			:List[str]          =						UniSpeechSatForXVector.from_pretrained(snake_case__	,config=snake_case__  )
     __snake_case			:Optional[int]          =						downstream_dict["""connector.weight"""]
     __snake_case			:Any          =						downstream_dict["""connector.bias"""]
     for i, kernel_size in enumerate(hf_config.tdnn_kernel  ):
          __snake_case			:Optional[int]          =						downstream_dict[
              f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
          ]
          __snake_case			:List[str]          =						downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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






@torch.no_grad()
def   UpperCamelCase  (       snake_case__     :   List[Any]	,snake_case__     :   Optional[int]	,snake_case__     :   List[str]	,snake_case__     :   Tuple  ):




     '''simple docstring'''






     __snake_case			:Tuple          =						torch.load(snake_case__	,map_location="""cpu"""  )

     __snake_case			:Dict          =						checkpoint["""Downstream"""]

     __snake_case			:int          =						UniSpeechSatConfig.from_pretrained(snake_case__  )
     __snake_case			:Optional[Any]          =						WavaVecaFeatureExtractor.from_pretrained(
         snake_case__	,return_attention_mask=snake_case__	,do_normalize=snake_case__  )

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

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

     hf_feature_extractor.save_pretrained(snake_case__  )
     hf_model.save_pretrained(snake_case__  )


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)










def   UpperCamelCase  (       snake_case__     :   str  ):




     '''simple docstring'''






     __snake_case			:List[Any]          =						hex_num.strip()
     if not hex_num:
          raise ValueError("""No value was passed to the function"""  )

     __snake_case			:str          =						hex_num[0] == """-"""
     if is_negative:
          __snake_case			:List[Any]          =						hex_num[1:]

     try:
          __snake_case			:Tuple          =						int(snake_case__	,16  )
     except ValueError:
          raise ValueError("""Invalid value was passed to the function"""  )

     __snake_case			:int          =						""""""
     while int_num > 0:
          __snake_case			:Optional[int]          =						str(int_num % 2  ) + bin_str
          int_num >>= 1

     return int(("""-""" + bin_str) if is_negative else bin_str  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()


