




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase_       :  Union[str, Any]      =		logging.get_logger(__name__)

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







@torch.no_grad()
def 			_UpperCamelCase (_lowerCamelCase							:     Tuple   ,	_lowerCamelCase							:     Union[str, Any]   ,	_lowerCamelCase							:     List[Any]   ,	_lowerCamelCase							:     Union[str, Any]	)->   Union[str, Any]:




     '''simple docstring'''





     __snake_case    					=  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}'''	)

     __snake_case    					=  checkpoint["Downstream"]

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

     if hf_congfig.use_weighted_layer_sum:
          __snake_case    					=  checkpoint["Featurizer"]["weights"]

     __snake_case    					=  downstream_dict["projector.weight"]
     __snake_case    					=  downstream_dict["projector.bias"]
     __snake_case    					=  downstream_dict["model.post_net.linear.weight"]
     __snake_case    					=  downstream_dict["model.post_net.linear.bias"]

     hf_feature_extractor.save_pretrained(__lowerCAmelCase	)
     hf_model.save_pretrained(__lowerCAmelCase	)


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












'''simple docstring'''



from transformers import ProcessorMixin





class     lowerCAmelCase   ( __lowerCAmelCase):
       __lowercase  :      Union[str, Any]        =			'''AutoFeatureExtractor'''
       __lowercase  :      List[Any]        =			'''AutoTokenizer'''






