

"""simple docstring"""






import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

lowerCamelCase__				:  Optional[int]													=  ['''UtteranceLevel''']
@torch.no_grad()
def       UpperCamelCase					(			_lowerCAmelCase     :						int,  _lowerCAmelCase     :						Any,  _lowerCAmelCase     :						Any,  _lowerCAmelCase     :						Optional[int] )				->			Optional[int]:
 _UpperCAmelCase    :							Dict									=					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    :							int									=					checkpoint["""Downstream"""]

 _UpperCAmelCase    :							Optional[int]									=					HubertConfig.from_pretrained(_lowerCAmelCase )
 _UpperCAmelCase    :							Optional[int]									=					HubertForSequenceClassification.from_pretrained(_lowerCAmelCase,  config=_lowerCAmelCase )
 _UpperCAmelCase    :							Optional[int]									=					WavaVecaFeatureExtractor.from_pretrained(
     _lowerCAmelCase,  return_attention_mask=_lowerCAmelCase,  do_normalize=_lowerCAmelCase )

 if hf_congfig.use_weighted_layer_sum:
  _UpperCAmelCase    :							List[str]									=					checkpoint["""Featurizer"""]["""weights"""]

 _UpperCAmelCase    :							Dict									=					downstream_dict["""projector.weight"""]
 _UpperCAmelCase    :							List[str]									=					downstream_dict["""projector.bias"""]
 _UpperCAmelCase    :							List[Any]									=					downstream_dict["""model.post_net.linear.weight"""]
 _UpperCAmelCase    :							Union[str, Any]									=					downstream_dict["""model.post_net.linear.bias"""]

 hf_feature_extractor.save_pretrained(_lowerCAmelCase )
 hf_model.save_pretrained(_lowerCAmelCase )


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








"""simple docstring"""






import safs






class     _UpperCAmelCase			(		safs.SaFileSystem):
			pass





