import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__lowerCamelCase    :				Tuple							     =	logging.get_logger(__name__)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: int			)							->      List[Any]:







		"""simple docstring"""





		SCREAMING_SNAKE_CASE__    							=   UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase__				,  config=UpperCamelCase__			)
		SCREAMING_SNAKE_CASE__    							=   downstream_dict["""projector.weight"""]
		SCREAMING_SNAKE_CASE__    							=   downstream_dict["""projector.bias"""]
		SCREAMING_SNAKE_CASE__    							=   downstream_dict["""model.post_net.linear.weight"""]
		SCREAMING_SNAKE_CASE__    							=   downstream_dict["""model.post_net.linear.bias"""]
		return model



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str				,  __UpperCamelCase					: Dict				,  __UpperCamelCase					: Optional[Any]			)							->      Optional[Any]:







		"""simple docstring"""





		SCREAMING_SNAKE_CASE__    							=   UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase__				,  config=UpperCamelCase__			)
		SCREAMING_SNAKE_CASE__    							=   downstream_dict["""model.linear.weight"""]
		SCREAMING_SNAKE_CASE__    							=   downstream_dict["""model.linear.bias"""]
		return model



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str				,  __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: Optional[int]			)							->      int:







		"""simple docstring"""





		SCREAMING_SNAKE_CASE__    							=   UniSpeechSatForXVector.from_pretrained(UpperCamelCase__				,  config=UpperCamelCase__			)
		SCREAMING_SNAKE_CASE__    							=   downstream_dict["""connector.weight"""]
		SCREAMING_SNAKE_CASE__    							=   downstream_dict["""connector.bias"""]
		for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
				SCREAMING_SNAKE_CASE__    							=   downstream_dict[
				    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
				]
				SCREAMING_SNAKE_CASE__    							=   downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

		SCREAMING_SNAKE_CASE__    							=   downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
		SCREAMING_SNAKE_CASE__    							=   downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
		SCREAMING_SNAKE_CASE__    							=   downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
		SCREAMING_SNAKE_CASE__    							=   downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
		SCREAMING_SNAKE_CASE__    							=   downstream_dict["""objective.W"""]
		return model



@torch.no_grad()
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: List[str]				,  __UpperCamelCase					: Dict				,  __UpperCamelCase					: int			)							->      List[str]:







		"""simple docstring"""





		SCREAMING_SNAKE_CASE__    							=   torch.load(UpperCamelCase__				,  map_location="""cpu"""			)

		SCREAMING_SNAKE_CASE__    							=   checkpoint["""Downstream"""]

		SCREAMING_SNAKE_CASE__    							=   UniSpeechSatConfig.from_pretrained(UpperCamelCase__			)
		SCREAMING_SNAKE_CASE__    							=   WavaVecaFeatureExtractor.from_pretrained(
		    UpperCamelCase__				,  return_attention_mask=UpperCamelCase__				,  do_normalize=UpperCamelCase__			)

		SCREAMING_SNAKE_CASE__    							=   hf_config.architectures[0]
		if arch.endswith("""ForSequenceClassification"""			):
				SCREAMING_SNAKE_CASE__    							=   convert_classification(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__			)
		elif arch.endswith("""ForAudioFrameClassification"""			):
				SCREAMING_SNAKE_CASE__    							=   convert_diarization(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__			)
		elif arch.endswith("""ForXVector"""			):
				SCREAMING_SNAKE_CASE__    							=   convert_xvector(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__			)
		else:
				raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""			)

		if hf_config.use_weighted_layer_sum:
				SCREAMING_SNAKE_CASE__    							=   checkpoint["""Featurizer"""]["""weights"""]

		hf_feature_extractor.save_pretrained(UpperCamelCase__			)
		hf_model.save_pretrained(UpperCamelCase__			)


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)

def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: list				,  __UpperCamelCase					: list				,  __UpperCamelCase					: int				,  __UpperCamelCase					: int				,  __UpperCamelCase					: int			)							->      int:







  """simple docstring"""





  if index == number_of_items:
    return 0
  SCREAMING_SNAKE_CASE__    							=   0
  SCREAMING_SNAKE_CASE__    							=   0
  SCREAMING_SNAKE_CASE__    							=   knapsack(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  index + 1			)
  if weights[index] <= max_weight:
    SCREAMING_SNAKE_CASE__    							=   values[index] + knapsack(
        __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  max_weight - weights[index]				,  index + 1			)
  return max(__UpperCamelCase				,  __UpperCamelCase			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
