





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  lowerCAmelCase__(		lowercase    :      Optional[int]				,       lowercase    :      Tuple				,       lowercase    :      Tuple				,       lowercase    :      Tuple			)	->      str:
  __snake_case				:			int            =    torch.load(lowercase				,       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				:			Dict            =    checkpoint["Downstream"]

  __snake_case				:			int            =    HubertConfig.from_pretrained(lowercase			)
  __snake_case				:			Union[str, Any]            =    HubertForSequenceClassification.from_pretrained(lowercase				,       config=lowercase			)
  __snake_case				:			Dict            =    WavaVecaFeatureExtractor.from_pretrained(
      lowercase				,       return_attention_mask=lowercase				,       do_normalize=lowercase			)

  if hf_congfig.use_weighted_layer_sum:
    __snake_case				:			Union[str, Any]            =    checkpoint["Featurizer"]["weights"]

  __snake_case				:			Union[str, Any]            =    downstream_dict["projector.weight"]
  __snake_case				:			Any            =    downstream_dict["projector.bias"]
  __snake_case				:			Optional[int]            =    downstream_dict["model.post_net.linear.weight"]
  __snake_case				:			int            =    downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(lowercase			)
  hf_model.save_pretrained(lowercase			)


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)








_UpperCamelCase    =	[chr(i) for i in range(32, 126)]
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(reversed(range(len(alphabets))))
_UpperCamelCase    =	[]
_UpperCamelCase    =	_UpperCamelCase    =	_UpperCamelCase    =	0







def  lowerCAmelCase__(		)	->      int:
  global gear_one_pos
  global gear_two_pos
  global gear_three_pos
  __snake_case				:			int            =    gear_one[0]
  gear_one.append(lowercase			)
  del gear_one[0]
  gear_one_pos += 1
  if gear_one_pos % int(len(lowercase			)			) == 0:
    __snake_case				:			List[str]            =    gear_two[0]
    gear_two.append(lowercase			)
    del gear_two[0]
    gear_two_pos += 1
    if gear_two_pos % int(len(lowercase			)			) == 0:
      __snake_case				:			Any            =    gear_three[0]
      gear_three.append(lowercase			)
      del gear_three[0]
      gear_three_pos += 1







def  lowerCAmelCase__(		lowercase    :      Tuple			)	->      Union[str, Any]:
  __snake_case				:			Union[str, Any]            =    alphabets.index(lowercase			)
  __snake_case				:			Tuple            =    gear_one[target]
  __snake_case				:			Union[str, Any]            =    gear_two[target]
  __snake_case				:			int            =    gear_three[target]
  __snake_case				:			Optional[int]            =    reflector[target]
  __snake_case				:			Union[str, Any]            =    gear_three.index(lowercase			)
  __snake_case				:			Optional[Any]            =    gear_two.index(lowercase			)
  __snake_case				:			int            =    gear_one.index(lowercase			)
  code.append(alphabets[target]			)
  rotator()


if __name__ == "__main__":
     _UpperCamelCase    =	list(input('''Type your message:\n'''))
     while True:
          try:
               _UpperCamelCase    =	int(input('''Please set token:(must be only digits)\n'''))
               break
          except Exception as error:
               print(error)
     for _ in range(token):
          rotator()
     for j in decode:
          engine(j)
     print('''\n''' + ''''''.join(code))
     print(
         F'''\nYour Token is {token} please write it down.\nIf you want to decode '''
         '''this message again you should input same digits as token!'''
     )

