'''simple docstring'''




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_UpperCamelCase				:		List[Any]       			=		logging.get_logger(__name__)

_UpperCamelCase				:		List[str]       			=		['UtteranceLevel']



@torch.no_grad()
def 						__snake_case (       lowerCAmelCase       :     Optional[int]      ,  lowerCAmelCase       :     Optional[Any]      ,  lowerCAmelCase       :     Union[str, Any]      ,  lowerCAmelCase       :     Optional[Any]			):
 __UpperCAmelCase         =					torch.load(lowerCAmelCase      ,  map_location='cpu'			)
 if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
  raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""			)

 __UpperCAmelCase         =					checkpoint['Downstream']

 __UpperCAmelCase         =					HubertConfig.from_pretrained(lowerCAmelCase			)
 __UpperCAmelCase         =					HubertForSequenceClassification.from_pretrained(lowerCAmelCase      ,  config=lowerCAmelCase			)
 __UpperCAmelCase         =					WavaVecaFeatureExtractor.from_pretrained(
     lowerCAmelCase      ,  return_attention_mask=lowerCAmelCase      ,  do_normalize=lowerCAmelCase			)

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

 __UpperCAmelCase         =					downstream_dict['projector.weight']
 __UpperCAmelCase         =					downstream_dict['projector.bias']
 __UpperCAmelCase         =					downstream_dict['model.post_net.linear.weight']
 __UpperCAmelCase         =					downstream_dict['model.post_net.linear.bias']

 hf_feature_extractor.save_pretrained(lowerCAmelCase			)
 hf_model.save_pretrained(lowerCAmelCase			)


if __name__ == "__main__":
     _UpperCamelCase				:		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.')
     _UpperCamelCase				:		Optional[int]       			=		parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




'''simple docstring'''




def 						__snake_case (       lowerCAmelCase       :     str			):
 __UpperCAmelCase         =					5381
 for x in s:
  __UpperCAmelCase         =					((hash_value << 5) + hash_value) + ord(lowerCAmelCase			)
 return hash_value & 0Xffff_ffff



