




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 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = "char"
    snake_case__   = "bpe"
    snake_case__   = "wp"






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





class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

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


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

         lowerCAmelCase__            =					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`."  )

         lowerCAmelCase__            =					tokenizer
         lowerCAmelCase__            =					AutoTokenizer.from_pretrained("gpt2"  )
         lowerCAmelCase__            =					AutoTokenizer.from_pretrained("bert-base-uncased"  )

         super().__init__(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )


    def __call__(			self	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	List[str]=None  ,      SCREAMING_SNAKE_CASE__	:	Any=None  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=None  ,      **SCREAMING_SNAKE_CASE__	:	Dict  ) ->			Dict:
         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:
              lowerCAmelCase__            =					self.image_processor(SCREAMING_SNAKE_CASE__  ,      return_tensors=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )
         if text is not None:
              lowerCAmelCase__            =					self.char_tokenizer(SCREAMING_SNAKE_CASE__  ,      return_tensors=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )

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


    def   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	List[str]  ) ->			Any:
         lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__            =					sequences
         lowerCAmelCase__            =					char_preds.size(0  )

         lowerCAmelCase__      ,						lowerCAmelCase__            =					self._decode_helper(SCREAMING_SNAKE_CASE__  ,      "char"  )
         lowerCAmelCase__      ,						lowerCAmelCase__            =					self._decode_helper(SCREAMING_SNAKE_CASE__  ,      "bpe"  )
         lowerCAmelCase__      ,						lowerCAmelCase__            =					self._decode_helper(SCREAMING_SNAKE_CASE__  ,      "wp"  )

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

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


    def   a (			self	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ) ->			str:
         if format == DecodeType.CHARACTER:
              lowerCAmelCase__            =					self.char_decode
              lowerCAmelCase__            =					1
              lowerCAmelCase__            =					"[s]"
         elif format == DecodeType.BPE:
              lowerCAmelCase__            =					self.bpe_decode
              lowerCAmelCase__            =					2
              lowerCAmelCase__            =					"#"
         elif format == DecodeType.WORDPIECE:
              lowerCAmelCase__            =					self.wp_decode
              lowerCAmelCase__            =					102
              lowerCAmelCase__            =					"[SEP]"
         else:
              raise ValueError(f'Format {format} is not supported.'  )

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

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

         return dec_strs, conf_scores


    def   a (			self	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	Any  ) ->			Dict:
         lowerCAmelCase__            =					[seq.replace(" "  ,      ""  ) for seq in self.char_tokenizer.batch_decode(SCREAMING_SNAKE_CASE__  )]
         return decode_strs


    def   a (			self	:	Any  ,      SCREAMING_SNAKE_CASE__	:	int  ) ->			Optional[int]:
         return self.bpe_tokenizer.batch_decode(SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	str  ) ->			List[Any]:
         lowerCAmelCase__            =					[seq.replace(" "  ,      ""  ) for seq in self.wp_tokenizer.batch_decode(SCREAMING_SNAKE_CASE__  )]
         return decode_strs




'''simple docstring'''
def       a_				(	UpperCamelCase_ ):

							if length <= 0 or not isinstance(UpperCamelCase_						,  UpperCamelCase_ ):
														raise ValueError("Length must be a positive integer." )
							return [n * (2 * n - 1) for n in range(UpperCamelCase_ )]


if __name__ == "__main__":
							print(hexagonal_numbers(length=5))
							print(hexagonal_numbers(length=10))





