


import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer
class 			SCREAMING_SNAKE_CASE__  (    tf.keras.layers.Layer      ):
      def __init__(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Dict[str, int]					,							SCREAMING_SNAKE_CASE__    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	int = None					,							SCREAMING_SNAKE_CASE__    :	int = None					)		->      str:
             super().__init__()
             a_ :		Optional[int]							=  pad_token_id
             a_ :		List[Any]							=  max_length
             a_ :		int							=  vocab
             a_ :		Union[str, Any]							=  merges
             a_ :		int							=  BytePairTokenizer(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							sequence_length=SCREAMING_SNAKE_CASE__					)
      @classmethod
      def 	SCREAMING_SNAKE_CASE							(	cls    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	GPTaTokenizer					,							*SCREAMING_SNAKE_CASE__    :	int					,							**SCREAMING_SNAKE_CASE__    :	Dict					)		->      List[Any]:
             a_ :		int							=  [' '.join(SCREAMING_SNAKE_CASE__					) for m in tokenizer.bpe_ranks.keys()]
             a_ :		Union[str, Any]							=  tokenizer.get_vocab()
             return cls(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							*SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
      @classmethod
      def 	SCREAMING_SNAKE_CASE							(	cls    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Union[str, os.PathLike]					,							*SCREAMING_SNAKE_CASE__    :	Dict					,							**SCREAMING_SNAKE_CASE__    :	int					)		->      Tuple:
             a_ :		List[Any]							=  GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__					,							*SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
             return cls.from_tokenizer(SCREAMING_SNAKE_CASE__					,							*SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
      @classmethod
      def 	SCREAMING_SNAKE_CASE							(	cls    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	str					)		->      Union[str, Any]:
             return cls(**SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      Optional[int]:
             return {
                 "vocab": self.vocab,
                 "merges": self.merges,
                 "max_length": self.max_length,
                 "pad_token_id": self.pad_token_id,
             }

      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	int = None					)		->      str:
             a_ :		List[Any]							=  self.tf_tokenizer(SCREAMING_SNAKE_CASE__					)
             a_ :		List[Any]							=  tf.ones_like(SCREAMING_SNAKE_CASE__					)

             if self.pad_token_id is not None:
                    # pad the tokens up to max length
                    a_ :		Any							=  max_length if max_length is not None else self.max_length

                    if max_length is not None:
                           a_							,		a_ :		Optional[Any]							=  pad_model_inputs(
                               SCREAMING_SNAKE_CASE__					,							max_seq_length=SCREAMING_SNAKE_CASE__					,							pad_value=self.pad_token_id					)

             return {"attention_mask": attention_mask, "input_ids": input_ids}






from string import ascii_uppercase

UpperCAmelCase_				:			Dict   =	{char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_				:			Optional[int]   =	dict(enumerate(ascii_uppercase))







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Tuple							=  len(__A    )
       a_ :		int							=  0
       while True:
              if x == i:
                     a_ :		Tuple							=  0
              if len(__A    ) == len(__A    ):
                     break
              key += key[i]
              i += 1
       return key







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

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







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Any							=  ''
       a_ :		Optional[Any]							=  0
       for letter in cipher_text:
              if letter == " ":
                     or_txt += " "
              else:
                     a_ :		Union[str, Any]							=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
                     i += 1
                     or_txt += dicta[x]
       return or_txt







def 	SCREAMING_SNAKE_CASE_						(      )     ->		None:

       """simple docstring"""

       a_ :		Tuple							=  'THE GERMAN ATTACK'
       a_ :		Dict							=  'SECRET'
       a_ :		Optional[Any]							=  generate_key(__A  ,   __A    )
       a_ :		Union[str, Any]							=  cipher_text(__A  ,   __A    )
       print(F"""Encrypted Text = {s}"""    )
       print(F"""Original Text = {original_text(__A  ,   __A    )}"""    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()


