



import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()






def      _A				(UpperCamelCase		:      List[str]      ,			UpperCamelCase		:      Union[str, Any]      ,			UpperCamelCase		:      Optional[int]      ,			UpperCamelCase		:      Optional[int] )  ->Any:



			'''simple docstring'''







			lowerCamelCase__ :      Union[str, Any]								=		BigBirdConfig.from_json_file(UpperCamelCase )
			print(f"Building PyTorch model from configuration: {config}" )

			if is_trivia_qa:
						lowerCamelCase__ :      Optional[Any]								=		BigBirdForQuestionAnswering(UpperCamelCase )
			else:
						lowerCamelCase__ :      Optional[Any]								=		BigBirdForPreTraining(UpperCamelCase )

			# Load weights from tf checkpoint
			load_tf_weights_in_big_bird(UpperCamelCase      ,			UpperCamelCase      ,			is_trivia_qa=UpperCamelCase )

			# Save pytorch-model
			print(f"Save PyTorch model to {pytorch_dump_path}" )
			model.save_pretrained(UpperCamelCase )


if __name__ == "__main__":
				_lowercase     							=						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(
				    '''--big_bird_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.'''
				)
				parser.add_argument(
				    '''--is_trivia_qa''', action='''store_true''', help='''Whether to convert a model with a trivia_qa head.'''
				)
				_lowercase     							=						parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
				)






from string import ascii_uppercase

_lowercase     							=						{char: i for i, char in enumerate(ascii_uppercase)}
_lowercase     							=						dict(enumerate(ascii_uppercase))






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		len(UpperCamelCase )
			lowerCamelCase__ :      int								=		0
			while True:
						if x == i:
									lowerCamelCase__ :      Union[str, Any]								=		0
						if len(UpperCamelCase ) == len(UpperCamelCase ):
									break
						key += key[i]
						i += 1
			return key






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      int								=		""""""
			lowerCamelCase__ :      str								=		0
			for letter in message:
						if letter == " ":
									cipher_text += " "
						else:
									lowerCamelCase__ :      Optional[int]								=		(dicta[letter] - dicta[key_new[i]]) % 26
									i += 1
									cipher_text += dicta[x]
			return cipher_text






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Dict								=		""""""
			lowerCamelCase__ :      Tuple								=		0
			for letter in cipher_text:
						if letter == " ":
									or_txt += " "
						else:
									lowerCamelCase__ :      List[Any]								=		(dicta[letter] + dicta[key_new[i]] + 26) % 26
									i += 1
									or_txt += dicta[x]
			return or_txt






def      _A				()  ->None:



			'''simple docstring'''







			lowerCamelCase__ :      Union[str, Any]								=		"""THE GERMAN ATTACK"""
			lowerCamelCase__ :      Union[str, Any]								=		"""SECRET"""
			lowerCamelCase__ :      List[str]								=		generate_key(UpperCamelCase      ,			UpperCamelCase )
			lowerCamelCase__ :      List[Any]								=		cipher_text(UpperCamelCase      ,			UpperCamelCase )
			print(f"Encrypted Text = {s}" )
			print(f"Original Text = {original_text(UpperCamelCase      ,			UpperCamelCase )}" )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()



