
'''simple docstring'''





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
a__		: Optional[Any]  							=							logging.get_logger(__name__)

a__		: Optional[Any]  							=							["UtteranceLevel"]





@torch.no_grad()
def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      str:







      """simple docstring"""
      __A													=      torch.load(UpperCAmelCase   ,	map_location='cpu'						)
      if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
            raise NotImplementedError(f'The supported s3prl models are {SUPPORTED_MODELS}'						)

      __A													=      checkpoint['Downstream']

      __A													=      HubertConfig.from_pretrained(UpperCAmelCase						)
      __A													=      HubertForSequenceClassification.from_pretrained(UpperCAmelCase   ,	config=UpperCAmelCase						)
      __A													=      WavaVecaFeatureExtractor.from_pretrained(
          UpperCAmelCase   ,	return_attention_mask=UpperCAmelCase   ,	do_normalize=UpperCAmelCase						)

      if hf_congfig.use_weighted_layer_sum:
            __A													=      checkpoint['Featurizer']['weights']

      __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']

      hf_feature_extractor.save_pretrained(UpperCAmelCase						)
      hf_model.save_pretrained(UpperCAmelCase						)


if __name__ == "__main__":
 a__		: List[str]  							=							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__		: Optional[int]  							=							parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)









def _a					(			SCREAMING_SNAKE_CASE						:     str						)       ->       str:

 """simple docstring"""






 return "".join(chr(ord(SCREAMING_SNAKE_CASE						) + 32						) if 'A' <= char <= 'Z' else char for char in word						)


if __name__ == "__main__":
  from doctest import testmod

  testmod()





