



'''simple docstring'''





import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> List[str]:
  __lowerCamelCase			:			Optional[int]       			=					WavaVecaForSequenceClassification.from_pretrained(_lowerCAmelCase			,config=_lowerCAmelCase			)
  __lowerCamelCase			:			List[Any]       			=					downstream_dict['projector.weight']
  __lowerCamelCase			:			Dict       			=					downstream_dict['projector.bias']
  __lowerCamelCase			:			Tuple       			=					downstream_dict['model.post_net.linear.weight']
  __lowerCamelCase			:			Dict       			=					downstream_dict['model.post_net.linear.bias']
  return model

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> Optional[int]:
  __lowerCamelCase			:			Tuple       			=					WavaVecaForAudioFrameClassification.from_pretrained(_lowerCAmelCase			,config=_lowerCAmelCase			)
  __lowerCamelCase			:			List[str]       			=					downstream_dict['model.linear.weight']
  __lowerCamelCase			:			str       			=					downstream_dict['model.linear.bias']
  return model

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> Tuple:
  __lowerCamelCase			:			Tuple       			=					WavaVecaForXVector.from_pretrained(_lowerCAmelCase			,config=_lowerCAmelCase			)
  __lowerCamelCase			:			Optional[Any]       			=					downstream_dict['connector.weight']
  __lowerCamelCase			:			Union[str, Any]       			=					downstream_dict['connector.bias']
  for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
    __lowerCamelCase			:			Optional[Any]       			=					downstream_dict[
        F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
    ]
    __lowerCamelCase			:			Optional[Any]       			=					downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

  __lowerCamelCase			:			Dict       			=					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
  __lowerCamelCase			:			Dict       			=					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
  __lowerCamelCase			:			int       			=					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
  __lowerCamelCase			:			int       			=					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
  __lowerCamelCase			:			Optional[int]       			=					downstream_dict['objective.W']
  return model

@torch.no_grad()
def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> List[str]:
  __lowerCamelCase			:			List[Any]       			=					torch.load(_lowerCAmelCase			,map_location='cpu'			)

  __lowerCamelCase			:			Tuple       			=					checkpoint['Downstream']

  __lowerCamelCase			:			Dict       			=					WavaVecaConfig.from_pretrained(_lowerCAmelCase			)
  __lowerCamelCase			:			Dict       			=					WavaVecaFeatureExtractor.from_pretrained(
      _lowerCAmelCase			,return_attention_mask=_lowerCAmelCase			,do_normalize=_lowerCAmelCase			)

  __lowerCamelCase			:			str       			=					hf_config.architectures[0]
  if arch.endswith('ForSequenceClassification'			):
    __lowerCamelCase			:			Any       			=					convert_classification(_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			)
  elif arch.endswith('ForAudioFrameClassification'			):
    __lowerCamelCase			:			Optional[Any]       			=					convert_diarization(_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			)
  elif arch.endswith('ForXVector'			):
    __lowerCamelCase			:			Tuple       			=					convert_xvector(_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			)
  else:
    raise NotImplementedError(F'S3PRL weights conversion is not supported for {arch}'			)

  if hf_config.use_weighted_layer_sum:
    __lowerCamelCase			:			int       			=					checkpoint['Featurizer']['weights']

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


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











'''simple docstring'''





import functools

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> int:

  # Validation
  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			) or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for day in days			):
    raise ValueError('The parameter days should be a list of integers'			)

  if len(_lowerCAmelCase			) != 3 or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for cost in costs			):
    raise ValueError('The parameter costs should be a list of three integers'			)

  if len(_lowerCAmelCase			) == 0:
    return 0

  if min(_lowerCAmelCase			) <= 0:
    raise ValueError('All days elements should be greater than 0'			)

  if max(_lowerCAmelCase			) >= 366:
    raise ValueError('All days elements should be less than 366'			)

  __lowerCamelCase			:			int       			=					set(_lowerCAmelCase			)

  @functools.cache
  def dynamic_programming(_lowerCAmelCase			) -> int:
    if index > 365:
      return 0

    if index not in days_set:
      return dynamic_programming(index + 1			)

    return min(
        costs[0] + dynamic_programming(index + 1			)			,costs[1] + dynamic_programming(index + 7			)			,costs[2] + dynamic_programming(index + 30			)			,)

  return dynamic_programming(1			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()






