


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_      (							__snake_case  ,		__snake_case  ,		__snake_case  ,		__snake_case		)  ->    Union[str, Any]:







 """simple docstring"""


 _lowercase							   =torch.load(snake_case_  ,		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(snake_case_		)
 _lowercase							   =HubertForSequenceClassification.from_pretrained(snake_case_  ,		config=snake_case_		)
 _lowercase							   =WavaVecaFeatureExtractor.from_pretrained(
     snake_case_  ,		return_attention_mask=snake_case_  ,		do_normalize=snake_case_		)

 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(snake_case_		)
 hf_model.save_pretrained(snake_case_		)


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)





from ..utils import DummyObject, requires_backends







class    lowerCamelCase__     ( metaclass=lowerCAmelCase):
     SCREAMING_SNAKE_CASE__       =    ['''tensorflow_text''']





     def __init__(self	,  *UpperCAmelCase	,  **UpperCAmelCase )  ->     Any:
      requires_backends(self	,  ['''tensorflow_text'''] )

