



import argparse

import torch

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


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

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Tuple	,   _snake_case	:List[Any]	,   _snake_case	:Optional[Any]				)  ->     List[str]:
   _A      =       UniSpeechSatForSequenceClassification.from_pretrained(_snake_case	,   config=_snake_case				)
   _A      =       downstream_dict['''projector.weight''']
   _A      =       downstream_dict['''projector.bias''']
   _A      =       downstream_dict['''model.post_net.linear.weight''']
   _A      =       downstream_dict['''model.post_net.linear.bias''']
   return model

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Dict	,   _snake_case	:Union[str, Any]	,   _snake_case	:List[str]				)  ->     str:
   _A      =       UniSpeechSatForAudioFrameClassification.from_pretrained(_snake_case	,   config=_snake_case				)
   _A      =       downstream_dict['''model.linear.weight''']
   _A      =       downstream_dict['''model.linear.bias''']
   return model

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]	,   _snake_case	:str	,   _snake_case	:str				)  ->     List[str]:
   _A      =       UniSpeechSatForXVector.from_pretrained(_snake_case	,   config=_snake_case				)
   _A      =       downstream_dict['''connector.weight''']
   _A      =       downstream_dict['''connector.bias''']
   for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
      _A      =       downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      _A      =       downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   _A      =       downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
   _A      =       downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
   _A      =       downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
   _A      =       downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
   _A      =       downstream_dict['''objective.W''']
   return model

@torch.no_grad()
def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Any	,   _snake_case	:List[str]	,   _snake_case	:Union[str, Any]	,   _snake_case	:List[str]				)  ->     Any:
   _A      =       torch.load(_snake_case	,   map_location='''cpu'''				)

   _A      =       checkpoint['''Downstream''']

   _A      =       UniSpeechSatConfig.from_pretrained(_snake_case				)
   _A      =       WavaVecaFeatureExtractor.from_pretrained(
       _snake_case	,   return_attention_mask=_snake_case	,   do_normalize=_snake_case				)

   _A      =       hf_config.architectures[0]
   if arch.endswith('''ForSequenceClassification'''				):
      _A      =       convert_classification(_snake_case	,   _snake_case	,   _snake_case				)
   elif arch.endswith('''ForAudioFrameClassification'''				):
      _A      =       convert_diarization(_snake_case	,   _snake_case	,   _snake_case				)
   elif arch.endswith('''ForXVector'''				):
      _A      =       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:
      _A      =       checkpoint['''Featurizer''']['''weights''']

   hf_feature_extractor.save_pretrained(_snake_case				)
   hf_model.save_pretrained(_snake_case				)


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








import math





def 		a__      (		_UpperCamelCase      :			float			,_UpperCamelCase      :			float				):
   if (
       not isinstance(_UpperCamelCase			,(int, float)				)
       or power_factor < -1
       or power_factor > 1
   ):
      raise ValueError('''power_factor must be a valid float value between -1 and 1.'''				)
   return apparent_power * power_factor





def 		a__      (		_UpperCamelCase      :			float			,_UpperCamelCase      :			float				):
   if (
       not isinstance(_UpperCamelCase			,(int, float)				)
       or power_factor < -1
       or power_factor > 1
   ):
      raise ValueError('''power_factor must be a valid float value between -1 and 1.'''				)
   return apparent_power * math.sqrt(1 - power_factor**2				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






