


import argparse

import torch

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


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






def 						lowercase_(    SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_      ):


  '''simple docstring'''

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






def 						lowercase_(    SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_      ):


  '''simple docstring'''

  lowerCamelCase					:   Any       =							UniSpeechSatForAudioFrameClassification.from_pretrained(A__ ,		config=A__      )
  lowerCamelCase					:   List[Any]       =							downstream_dict["model.linear.weight"]
  lowerCamelCase					:   Optional[int]       =							downstream_dict["model.linear.bias"]
  return model






def 						lowercase_(    SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_      ):


  '''simple docstring'''

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

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






@torch.no_grad()
def 						lowercase_(    SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_      ):


  '''simple docstring'''

  lowerCamelCase					:   Optional[int]       =							torch.load(A__ ,		map_location="cpu"      )

  lowerCamelCase					:   List[Any]       =							checkpoint["Downstream"]

  lowerCamelCase					:   Dict       =							UniSpeechSatConfig.from_pretrained(A__      )
  lowerCamelCase					:   int       =							WavaVecaFeatureExtractor.from_pretrained(
      A__ ,		return_attention_mask=A__ ,		do_normalize=A__      )

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

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

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


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






def 	__a     (      A__			:  int = 1000       ):
    SCREAMING_SNAKE_CASE										=   3
    SCREAMING_SNAKE_CASE										=   0
    while a < n:
        if a % 3 == 0 or a % 5 == 0:
            result += a
        elif a % 15 == 0:
            result -= a
        a += 1
    return result


if __name__ == "__main__":
    print(f'{solution() = }')