



'''simple docstring'''





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_UpperCamelCase							    =						['UtteranceLevel']

@torch.no_grad()
def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)				->				str:
  __lowerCamelCase	:	int   	=			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}'	)

  __lowerCamelCase	:	str   	=			checkpoint['Downstream']

  __lowerCamelCase	:	Union[str, Any]   	=			HubertConfig.from_pretrained(_lowerCAmelCase	)
  __lowerCamelCase	:	Any   	=			HubertForSequenceClassification.from_pretrained(_lowerCAmelCase	,config=_lowerCAmelCase	)
  __lowerCamelCase	:	List[Any]   	=			WavaVecaFeatureExtractor.from_pretrained(
      _lowerCAmelCase	,return_attention_mask=_lowerCAmelCase	,do_normalize=_lowerCAmelCase	)

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

  __lowerCamelCase	:	int   	=			downstream_dict['projector.weight']
  __lowerCamelCase	:	str   	=			downstream_dict['projector.bias']
  __lowerCamelCase	:	Any   	=			downstream_dict['model.post_net.linear.weight']
  __lowerCamelCase	:	List[str]   	=			downstream_dict['model.post_net.linear.bias']

  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'''





def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				int:
  # The longer word should come first
  if len(_lowerCAmelCase	) < len(_lowerCAmelCase	):
    return levenshtein_distance(_lowerCAmelCase	,_lowerCAmelCase	)

  if len(_lowerCAmelCase	) == 0:
    return len(_lowerCAmelCase	)

  __lowerCamelCase	:	int   	=			list(range(len(_lowerCAmelCase	) + 1	)	)

  for i, ca in enumerate(_lowerCAmelCase	):
    __lowerCamelCase	:	Union[str, Any]   	=			[i + 1]

    for j, ca in enumerate(_lowerCAmelCase	):
      # Calculate insertions, deletions and substitutions
      __lowerCamelCase	:	Any   	=			previous_row[j + 1] + 1
      __lowerCamelCase	:	int   	=			current_row[j] + 1
      __lowerCamelCase	:	Any   	=			previous_row[j] + (ca != ca)

      # Get the minimum to append to the current row
      current_row.append(min(_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)	)

    # Store the previous row
    __lowerCamelCase	:	Optional[int]   	=			current_row

  # Returns the last element (distance)
  return previous_row[-1]


if __name__ == "__main__":
   _UpperCamelCase							    =						input('Enter the first word:\n').strip()
   _UpperCamelCase							    =						input('Enter the second word:\n').strip()

   _UpperCamelCase							    =						levenshtein_distance(first_word, second_word)
   print(f'''Levenshtein distance between {first_word} and {second_word} is {result}''')






