





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 		SCREAMING_SNAKE_CASE__				(       UpperCAmelCase__  ):
      A_						:				List[Any]							=    """char"""
      A_						:				List[Any]							=    """bpe"""
      A_						:				Dict							=    """wp"""






snake_case_				=			(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)

class 		SCREAMING_SNAKE_CASE__				(       UpperCAmelCase__  ):
      A_						:				Dict							=    ["""image_processor""", """char_tokenizer"""]
      A_						:				Union[str, Any]							=    """ViTImageProcessor"""
      A_						:				str							=    """MgpstrTokenizer"""


      def __init__(self       :      List[str]				,			a__       :      int=None				,			a__       :      Union[str, Any]=None				,			**a__       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''				,			__lowercase				,			)
            __snake_case							       =   kwargs.pop('''feature_extractor'''      )

         __snake_case							       =   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`.'''      )

         __snake_case							       =   tokenizer
         __snake_case							       =   AutoTokenizer.from_pretrained('''gpt2'''      )
         __snake_case							       =   AutoTokenizer.from_pretrained('''bert-base-uncased'''      )

         super().__init__(__lowercase				,			__lowercase      )


      def __call__(self       :      str				,			a__       :      int=None				,			a__       :      Optional[int]=None				,			a__       :      List[Any]=None				,			**a__       :      Optional[Any]      ):





         """simple docstring"""



         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:
            __snake_case							       =   self.image_processor(__lowercase				,			return_tensors=__lowercase				,			**__lowercase      )
         if text is not None:
            __snake_case							       =   self.char_tokenizer(__lowercase				,			return_tensors=__lowercase				,			**__lowercase      )

         if text is None:
            return inputs
         elif images is None:
            return encodings
         else:
            __snake_case							       =   encodings['''input_ids''']
            return inputs


      def    a (self       :      List[Any]				,			a__       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   sequences
         __snake_case							       =   char_preds.size(0      )

         __snake_case							       =   self._decode_helper(__lowercase				,			'''char'''      )
         __snake_case							       =   self._decode_helper(__lowercase				,			'''bpe'''      )
         __snake_case							       =   self._decode_helper(__lowercase				,			'''wp'''      )

         __snake_case							       =   []
         __snake_case							       =   []
         for i in range(__lowercase      ):
            __snake_case							       =   [char_scores[i], bpe_scores[i], wp_scores[i]]
            __snake_case							       =   [char_strs[i], bpe_strs[i], wp_strs[i]]
            __snake_case							       =   scores.index(max(__lowercase      )      )
            final_strs.append(strs[max_score_index]      )
            final_scores.append(scores[max_score_index]      )

         __snake_case							       =   {}
         __snake_case							       =   final_strs
         __snake_case							       =   final_scores
         __snake_case							       =   char_strs
         __snake_case							       =   bpe_strs
         __snake_case							       =   wp_strs
         return out


      def    a (self       :      int				,			a__       :      Union[str, Any]				,			a__       :      Any      ):





         """simple docstring"""



         if format == DecodeType.CHARACTER:
            __snake_case							       =   self.char_decode
            __snake_case							       =   1
            __snake_case							       =   '''[s]'''
         elif format == DecodeType.BPE:
            __snake_case							       =   self.bpe_decode
            __snake_case							       =   2
            __snake_case							       =   '''#'''
         elif format == DecodeType.WORDPIECE:
            __snake_case							       =   self.wp_decode
            __snake_case							       =   102
            __snake_case							       =   '''[SEP]'''
         else:
            raise ValueError(f"""Format {format} is not supported."""      )

         __snake_case							       =   [], []
         __snake_case							       =   pred_logits.size(0      )
         __snake_case							       =   pred_logits.size(1      )
         __snake_case							       =   pred_logits.topk(1				,			dim=-1				,			largest=__lowercase				,			sorted=__lowercase      )
         __snake_case							       =   preds_index.view(-1				,			__lowercase      )[:, 1:]
         __snake_case							       =   decoder(__lowercase      )
         __snake_case							       =   torch.nn.functional.softmax(__lowercase				,			dim=2      ).max(dim=2      )
         __snake_case							       =   preds_max_prob[:, 1:]

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

         return dec_strs, conf_scores


      def    a (self       :      List[Any]				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   [seq.replace(''' '''				,			''''''      ) for seq in self.char_tokenizer.batch_decode(__lowercase      )]
         return decode_strs


      def    a (self       :      str				,			a__       :      str      ):





         """simple docstring"""



         return self.bpe_tokenizer.batch_decode(__lowercase      )


      def    a (self       :      List[Any]				,			a__       :      int      ):





         """simple docstring"""



         __snake_case							       =   [seq.replace(''' '''				,			''''''      ) for seq in self.wp_tokenizer.batch_decode(__lowercase      )]
         return decode_strs





def 	lowerCAmelCase       (						lowerCAmelCase_ = 10**9       )->       int:
				lowerCAmelCase_       :					List[Any]			 = 1
				lowerCAmelCase_       :					Optional[int]			 = 2

				lowerCAmelCase_       :					Any			 = 0
				lowerCAmelCase_       :					str			 = 0
				lowerCAmelCase_       :					str			 = 0
				while perimeter <= max_perimeter:
								perimeters_sum += perimeter

								prev_value += 2 * value
								value += prev_value

								lowerCAmelCase_       :					Any			 = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
								i += 1

				return perimeters_sum


if __name__ == "__main__":
						print(f"""{solution() = }""")