




UpperCamelCase__						=		[chr(i) for i in range(3_2, 1_2_6)]
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    snake_case   (				)				->				Optional[int]:




	'''simple docstring'''


	global gear_one_pos
	global gear_two_pos
	global gear_three_pos
	lowerCamelCase_				:						Optional[Any]				  =				gear_one[0]
	gear_one.append(A     )
	del gear_one[0]
	gear_one_pos += 1
	if gear_one_pos % int(len(A     )     ) == 0:
		lowerCamelCase_				:						Any				  =				gear_two[0]
		gear_two.append(A     )
		del gear_two[0]
		gear_two_pos += 1
		if gear_two_pos % int(len(A     )     ) == 0:
			lowerCamelCase_				:						Optional[int]				  =				gear_three[0]
			gear_three.append(A     )
			del gear_three[0]
			gear_three_pos += 1






def    snake_case   (				A     )				->				List[str]:




	'''simple docstring'''


	lowerCamelCase_				:						Tuple				  =				alphabets.index(A     )
	lowerCamelCase_				:						Union[str, Any]				  =				gear_one[target]
	lowerCamelCase_				:						Optional[int]				  =				gear_two[target]
	lowerCamelCase_				:						Union[str, Any]				  =				gear_three[target]
	lowerCamelCase_				:						str				  =				reflector[target]
	lowerCamelCase_				:						str				  =				gear_three.index(A     )
	lowerCamelCase_				:						Union[str, Any]				  =				gear_two.index(A     )
	lowerCamelCase_				:						Dict				  =				gear_one.index(A     )
	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!'
							)


"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->Dict:

   """simple docstring"""



   a_						   =     RoFormerConfig.from_json_file(UpperCAmelCase     )
   print(F'''Building PyTorch model from configuration: {config}'''     )
   a_						   =     RoFormerForMaskedLM(UpperCAmelCase     )

   # Load weights from tf checkpoint
   load_tf_weights_in_roformer(UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )

   # Save pytorch-model
   print(F'''Save PyTorch model to {pytorch_dump_path}'''     )
   torch.save(model.state_dict() ,   UpperCAmelCase ,   _use_new_zipfile_serialization=UpperCAmelCase     )


if __name__ == "__main__":
      UpperCamelCase_						   =   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
      )
      parser.add_argument(
          '--bert_config_file',
          default=None,
          type=str,
          required=True,
          help=(
              'The config json file corresponding to the pre-trained BERT model. \n'
              'This specifies the model architecture.'
          ),
      )
      parser.add_argument(
          '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      UpperCamelCase_						   =   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)