




"""simple docstring"""







import random
import sys

SCREAMING_SNAKE_CASE_      =					'ABCDEFGHIJKLMNOPQRSTUVWXYZ'





def     __snake_case   (							):


				"""simple docstring"""







				UpperCamelCase										=  input('''Enter message: '''						)
				UpperCamelCase										=  '''LFWOAYUISVKMNXPBDCRJTQEGHZ'''
				UpperCamelCase										=  input('''Encrypt/Decrypt [e/d]: '''						)

				check_valid_key(__snake_case						)

				if resp.lower().startswith('''e'''						):
								UpperCamelCase										=  '''encrypt'''
								UpperCamelCase										=  encrypt_message(__snake_case ,__snake_case						)
				elif resp.lower().startswith('''d'''						):
								UpperCamelCase										=  '''decrypt'''
								UpperCamelCase										=  decrypt_message(__snake_case ,__snake_case						)

				print(f'\n{mode.title()}ion: \n{translated}'						)





def     __snake_case   (							_lowercase						):


				"""simple docstring"""







				UpperCamelCase										=  list(__snake_case						)
				UpperCamelCase										=  list(__snake_case						)
				key_list.sort()
				letters_list.sort()

				if key_list != letters_list:
								sys.exit('''Error in the key or symbol set.'''						)





def     __snake_case   (							_lowercase ,_lowercase						):


				"""simple docstring"""







				return translate_message(__snake_case ,__snake_case ,'''encrypt'''						)





def     __snake_case   (							_lowercase ,_lowercase						):


				"""simple docstring"""







				return translate_message(__snake_case ,__snake_case ,'''decrypt'''						)





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase						):


				"""simple docstring"""







				UpperCamelCase										=  ''''''
				UpperCamelCase										=  LETTERS
				UpperCamelCase										=  key

				if mode == "decrypt":
								UpperCamelCase					,  UpperCamelCase										=  chars_b, chars_a

				for symbol in message:
								if symbol.upper() in chars_a:
												UpperCamelCase										=  chars_a.find(symbol.upper()						)
												if symbol.isupper():
																translated += chars_b[sym_index].upper()
												else:
																translated += chars_b[sym_index].lower()
								else:
												translated += symbol

				return translated





def     __snake_case   (							):


				"""simple docstring"""







				UpperCamelCase										=  list(__snake_case						)
				random.shuffle(__snake_case						)
				return "".join(__snake_case						)


if __name__ == "__main__":
		main()





"""simple docstring"""







import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  BertConfig.from_pretrained(
        '''bert-large-cased''' ,vocab_size=_lowercase ,max_position_embeddings=512 ,is_decoder=_lowercase ,add_cross_attention=_lowercase ,)
    UpperCamelCase										=  bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    UpperCamelCase										=  BertGenerationConfig(**_lowercase						)
    if is_encoder:
        UpperCamelCase										=  BertGenerationEncoder(_lowercase						)
    else:
        UpperCamelCase										=  BertGenerationDecoder(_lowercase						)
    print(f'Building PyTorch model from configuration: {config}'						)

    # Load weights from tf checkpoint
    load_tf_weights_in_bert_generation(
        _lowercase ,_lowercase ,model_class='''bert''' ,is_encoder_named_decoder=_lowercase ,is_encoder=_lowercase ,)

    # Save pytorch-model
    print(f'Save PyTorch model and config to {pytorch_dump_path}'						)
    model.save_pretrained(_lowercase						)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_      =					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  parser.add_argument(
      '--is_encoder_named_decoder',
      action='store_true',
      help='If decoder has to be renamed to encoder in PyTorch model.',
  )
  parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
  parser.add_argument('--vocab_size', default=50358, type=int, help='Vocab size of model')
  SCREAMING_SNAKE_CASE_      =					parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_hub_path,
      args.pytorch_dump_path,
      args.is_encoder_named_decoder,
      args.vocab_size,
      is_encoder=args.is_encoder,
  )