






from ..utils import DummyObject, requires_backends

class 					snake_case_							(       metaclass=lowercase_      ):







   """simple docstring"""



   __lowerCAmelCase      =						["torch", "librosa"]

   def __init__(    self:	List[str]   ,       *snake_case__:	List[Any]   ,       **snake_case__:	List[str]	)  ->		int:


       """simple docstring"""



       requires_backends(self   ,       ['torch', 'librosa']	)

   @classmethod
   def 			SCREAMING_SNAKE_CASE__				(    cls:	List[str]   ,       *snake_case__:	Dict   ,       **snake_case__:	List[str]	)  ->		List[str]:


       """simple docstring"""



       requires_backends(cls   ,       ['torch', 'librosa']	)

   @classmethod
   def 			SCREAMING_SNAKE_CASE__				(    cls:	Union[str, Any]   ,       *snake_case__:	List[Any]   ,       **snake_case__:	List[Any]	)  ->		Optional[Any]:


       """simple docstring"""



       requires_backends(cls   ,       ['torch', 'librosa']	)






class 					snake_case_							(       metaclass=lowercase_      ):







   """simple docstring"""



   __lowerCAmelCase      =						["torch", "librosa"]

   def __init__(    self:	str   ,       *snake_case__:	Optional[Any]   ,       **snake_case__:	List[Any]	)  ->		Union[str, Any]:


       """simple docstring"""



       requires_backends(self   ,       ['torch', 'librosa']	)

   @classmethod
   def 			SCREAMING_SNAKE_CASE__				(    cls:	int   ,       *snake_case__:	List[str]   ,       **snake_case__:	List[Any]	)  ->		List[str]:


       """simple docstring"""



       requires_backends(cls   ,       ['torch', 'librosa']	)

   @classmethod
   def 			SCREAMING_SNAKE_CASE__				(    cls:	Tuple   ,       *snake_case__:	Optional[int]   ,       **snake_case__:	Union[str, Any]	)  ->		List[Any]:


       """simple docstring"""



       requires_backends(cls   ,       ['torch', 'librosa']	)








import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

lowerCamelCase										=  ['''UtteranceLevel''']
@torch.no_grad()
def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase,      _lowercase      ):


    '''simple docstring'''



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

    _lowercase     =	checkpoint['Downstream']

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

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

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

    hf_feature_extractor.save_pretrained(_lowercase      )
    hf_model.save_pretrained(_lowercase      )


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