


'''simple docstring'''







import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
		import torch






class 		_snake_case     (					A_		):






						'''simple docstring'''







						__snake_case				=   "char"
						__snake_case				=   "bpe"
						__snake_case				=   "wp"






_SCREAMING_SNAKE_CASE							: Optional[int]										=			(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class 		_snake_case     (					A_		):






						'''simple docstring'''







						__snake_case				=   ["image_processor", "char_tokenizer"]
						__snake_case				=   "ViTImageProcessor"
						__snake_case				=   "MgpstrTokenizer"







						def __init__(	self:						str   ,				__UpperCamelCase:						Dict=None   ,				__UpperCamelCase:						int=None   ,				**__UpperCamelCase:						Any  )				->						Optional[Any]:
										__magic_name__					:       Tuple    							=							None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
														    " instead."   ,				snake_case__   ,				)
														__magic_name__					:       Optional[Any]    							=							kwargs.pop("feature_extractor"  )

										__magic_name__					:       Tuple    							=							image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError("You need to specify an `image_processor`."  )
										if tokenizer is None:
														raise ValueError("You need to specify a `tokenizer`."  )

										__magic_name__					:       List[str]    							=							tokenizer
										__magic_name__					:       Any    							=							AutoTokenizer.from_pretrained("gpt2"  )
										__magic_name__					:       List[str]    							=							AutoTokenizer.from_pretrained("bert-base-uncased"  )

										super().__init__(snake_case__   ,				snake_case__  )







						def __call__(	self:						Dict   ,				__UpperCamelCase:						int=None   ,				__UpperCamelCase:						Dict=None   ,				__UpperCamelCase:						Optional[int]=None   ,				**__UpperCamelCase:						int  )				->						Optional[Any]:


										if images is None and text is None:
														raise ValueError("You need to specify either an `images` or `text` input to process."  )

										if images is not None:
														__magic_name__					:       List[Any]    							=							self.image_processor(snake_case__   ,				return_tensors=snake_case__   ,				**snake_case__  )
										if text is not None:
														__magic_name__					:       Optional[int]    							=							self.char_tokenizer(snake_case__   ,				return_tensors=snake_case__   ,				**snake_case__  )

										if text is None:
														return inputs
										elif images is None:
														return encodings
										else:
														__magic_name__					:       Dict    							=							encodings["input_ids"]
														return inputs







						def lowerCAmelCase__			(	self:						Optional[Any]   ,				__UpperCamelCase:						Optional[int]  )				->						Optional[int]:
										__magic_name__					:       Dict    							=							sequences
										__magic_name__					:       List[Any]    							=							char_preds.size(0  )

										__magic_name__					:       List[Any]    							=							self._decode_helper(snake_case__   ,				"char"  )
										__magic_name__					:       Optional[int]    							=							self._decode_helper(snake_case__   ,				"bpe"  )
										__magic_name__					:       Any    							=							self._decode_helper(snake_case__   ,				"wp"  )

										__magic_name__					:       List[str]    							=							[]
										__magic_name__					:       List[str]    							=							[]
										for i in range(snake_case__  ):
														__magic_name__					:       Optional[Any]    							=							[char_scores[i], bpe_scores[i], wp_scores[i]]
														__magic_name__					:       Optional[int]    							=							[char_strs[i], bpe_strs[i], wp_strs[i]]
														__magic_name__					:       Dict    							=							scores.index(max(snake_case__  )  )
														final_strs.append(strs[max_score_index]  )
														final_scores.append(scores[max_score_index]  )

										__magic_name__					:       Union[str, Any]    							=							{}
										__magic_name__					:       Optional[int]    							=							final_strs
										__magic_name__					:       str    							=							final_scores
										__magic_name__					:       Optional[Any]    							=							char_strs
										__magic_name__					:       List[str]    							=							bpe_strs
										__magic_name__					:       int    							=							wp_strs
										return out







						def lowerCAmelCase__			(	self:						str   ,				__UpperCamelCase:						List[Any]   ,				__UpperCamelCase:						Optional[int]  )				->						Tuple:


										if format == DecodeType.CHARACTER:
														__magic_name__					:       List[Any]    							=							self.char_decode
														__magic_name__					:       List[str]    							=							1
														__magic_name__					:       int    							=							"[s]"
										elif format == DecodeType.BPE:
														__magic_name__					:       List[str]    							=							self.bpe_decode
														__magic_name__					:       Dict    							=							2
														__magic_name__					:       Union[str, Any]    							=							"#"
										elif format == DecodeType.WORDPIECE:
														__magic_name__					:       Optional[Any]    							=							self.wp_decode
														__magic_name__					:       str    							=							102
														__magic_name__					:       Any    							=							"[SEP]"
										else:
														raise ValueError(f"""Format {format} is not supported."""  )

										__magic_name__					:       Optional[Any]    							=							[], []
										__magic_name__					:       Tuple    							=							pred_logits.size(0  )
										__magic_name__					:       List[Any]    							=							pred_logits.size(1  )
										__magic_name__					:       int    							=							pred_logits.topk(1   ,				dim=-1   ,				largest=snake_case__   ,				sorted=snake_case__  )
										__magic_name__					:       str    							=							preds_index.view(-1   ,				snake_case__  )[:, 1:]
										__magic_name__					:       Tuple    							=							decoder(snake_case__  )
										__magic_name__					:       str    							=							torch.nn.functional.softmax(snake_case__   ,				dim=2  ).max(dim=2  )
										__magic_name__					:       Dict    							=							preds_max_prob[:, 1:]

										for index in range(snake_case__  ):
														__magic_name__					:       Optional[Any]    							=							preds_str[index].find(snake_case__  )
														__magic_name__					:       List[str]    							=							preds_str[index][:pred_eos]
														__magic_name__					:       List[str]    							=							preds_index[index].cpu().tolist()
														__magic_name__					:       Optional[Any]    							=							pred_index.index(snake_case__  ) if eos_token in pred_index else -1
														__magic_name__					:       Tuple    							=							preds_max_prob[index][: pred_eos_index + 1]
														__magic_name__					:       Optional[Any]    							=							pred_max_prob.cumprod(dim=0  )[-1] if pred_max_prob.nelement() != 0 else 0.0
														dec_strs.append(snake_case__  )
														conf_scores.append(snake_case__  )

										return dec_strs, conf_scores







						def lowerCAmelCase__			(	self:						Union[str, Any]   ,				__UpperCamelCase:						Optional[int]  )				->						Tuple:
										__magic_name__					:       str    							=							[seq.replace(" "   ,				""  ) for seq in self.char_tokenizer.batch_decode(snake_case__  )]
										return decode_strs







						def lowerCAmelCase__			(	self:						Optional[Any]   ,				__UpperCamelCase:						int  )				->						List[Any]:
										return self.bpe_tokenizer.batch_decode(snake_case__  )







						def lowerCAmelCase__			(	self:						Optional[int]   ,				__UpperCamelCase:						List[str]  )				->						Any:
										__magic_name__					:       Optional[int]    							=							[seq.replace(" "   ,				""  ) for seq in self.wp_tokenizer.batch_decode(snake_case__  )]
										return decode_strs

import random






class     UpperCAmelCase						:



   @staticmethod
   def 							_SCREAMING_SNAKE_CASE  (snake_case__						:   str					)						->      tuple[list[int], list[int]]:


          '''simple docstring'''


          snake_case			:  int     							= [ord(snake_case__					) for i in text]
          snake_case			:  Optional[int]     							= []
          snake_case			:  int     							= []
          for i in plain:
                 snake_case			:  List[Any]     							= random.randint(1       ,		3_00					)
                 snake_case			:  List[Any]     							= (i + k) * k
                 cipher.append(snake_case__					)
                 key.append(snake_case__					)
          return cipher, key



   @staticmethod
   def 							_SCREAMING_SNAKE_CASE  (snake_case__						:   list[int]       ,		snake_case__						:   list[int]					)						->      str:


          '''simple docstring'''


          snake_case			:  int     							= []
          for i in range(len(snake_case__					)					):
                 snake_case			:  List[str]     							= int((cipher[i] - (key[i]) ** 2) / key[i]					)
                 plain.append(chr(snake_case__					)					)
          return "".join(snake_case__					)


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





