




"""simple docstring"""






import random






class lowerCAmelCase__	:






			'''simple docstring'''



			@staticmethod
			def 					A_    (  lowercase  ):
							_lowerCamelCase     :			str    =    [ord(UpperCamelCase__  ) for i in text]
							_lowerCamelCase     :			Any    =    []
							_lowerCamelCase     :			Optional[Any]    =    []
							for i in plain:
											_lowerCamelCase     :			Optional[int]    =    random.randint(1  ,      300  )
											_lowerCamelCase     :			Any    =    (i + k) * k
											cipher.append(UpperCamelCase__  )
											key.append(UpperCamelCase__  )
							return cipher, key






			@staticmethod
			def 					A_    (  lowercase  ,      lowercase  ):
							_lowerCamelCase     :			int    =    []
							for i in range(len(UpperCamelCase__  )  ):
											_lowerCamelCase     :			List[Any]    =    int((cipher[i] - (key[i]) ** 2) / key[i]  )
											plain.append(chr(UpperCamelCase__  )  )
							return "".join(UpperCamelCase__  )


if __name__ == "__main__":
			lowercase__     						=						Onepad().encrypt("""Hello""")
			print(c, k)
			print(Onepad().decrypt(c, k))





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,)	-> List[str]:
       if config_name_or_path is None:
              lowerCamelCase   :      Any			 =					"facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

       if generator_tokenizer_name_or_path is None:
              lowerCamelCase   :      Dict			 =					generator_name_or_path

       if question_encoder_tokenizer_name_or_path is None:
              lowerCamelCase   :      Any			 =					question_encoder_name_or_path

       lowerCamelCase   :      str			 =					RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

       # Save model.
       lowerCamelCase   :      List[Any]			 =					RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[int]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Optional[Any]			 =					gen_config
       lowerCamelCase   :      Optional[Any]			 =					question_encoder_config

       lowerCamelCase   :      List[Any]			 =					model_class.from_pretrained_question_encoder_generator(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,config=_SCREAMING_SNAKE_CASE    )
       rag_model.save_pretrained(_SCREAMING_SNAKE_CASE    )

       # Sanity check.
       model_class.from_pretrained(_SCREAMING_SNAKE_CASE    )

       # Save tokenizers.
       lowerCamelCase   :      List[str]			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"    )
       lowerCamelCase   :      int			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Any  =   argparse.ArgumentParser()
       parser.add_argument(
           '--model_type',
           choices=['rag_sequence', 'rag_token'],
           required=True,
           type=str,
           help='RAG model type: rag_sequence, rag_token',
       )
       parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
       parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
       parser.add_argument(
           '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
       )

       parser.add_argument(
           '--generator_tokenizer_name_or_path',
           type=str,
           help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
       )
       parser.add_argument(
           '--question_encoder_tokenizer_name_or_path',
           type=str,
           help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
       )
       parser.add_argument(
           '--config_name_or_path',
           type=str,
           help=(
               'Identifier of the model config to use, if not provided, resolves to a base config for a given'
               ' ``model_type``'
           ),
       )

       SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   parser.parse_args()

       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   Path(args.dest)
       dest_dir.mkdir(exist_ok=True)

       consolidate(
           args.model_type,
           args.generator_name_or_path,
           args.question_encoder_name_or_path,
           dest_dir,
           args.config_name_or_path,
           args.generator_tokenizer_name_or_path,
           args.question_encoder_tokenizer_name_or_path,
       )






