





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_						:    Tuple							=   ['image_processor', 'char_tokenizer']
      A_						:    Union[str, Any]							=   'ViTImageProcessor'
      A_						:    Optional[int]							=   'MgpstrTokenizer'


      def __init__(self	:				List[Any]    ,      a__	:				List[Any]=None    ,      a__	:				Optional[int]=None    ,      **a__	:				List[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.'''    ,      a__    ,      )
            __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__(a__    ,      a__				)


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





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

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


      def        a			(self	:				Any    ,      a__	:				int				):





         """simple docstring"""



         __snake_case	,     __snake_case	,     __snake_case								=       sequences
         __snake_case								=       char_preds.size(0				)

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

         __snake_case								=       []
         __snake_case								=       []
         for i in range(a__				):
            __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(a__				)				)
            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	:				List[Any]    ,      a__	:				Optional[int]    ,      a__	:				str				):





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

         for index in range(a__				):
            __snake_case								=       preds_str[index].find(a__				)
            __snake_case								=       preds_str[index][:pred_eos]
            __snake_case								=       preds_index[index].cpu().tolist()
            __snake_case								=       pred_index.index(a__				) 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(a__				)
            conf_scores.append(a__				)

         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(a__				)]
         return decode_strs


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





         """simple docstring"""



         return self.bpe_tokenizer.batch_decode(a__				)


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





         """simple docstring"""



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








import math






def lowerCamelCase__   (							snake_case_     :  int	)				->    list[int]:
   __snake_case								=       []
   __snake_case								=       2
   __snake_case								=       int(math.sqrt(snake_case_	)	)  # Size of every segment
   __snake_case								=       [True] * (end + 1)
   __snake_case								=       []

   while start <= end:
      if temp[start] is True:
         in_prime.append(snake_case_	)
         for i in range(start * start	,				end + 1	,				snake_case_	):
            __snake_case								=       False
      start += 1
   prime += in_prime

   __snake_case								=       end + 1
   __snake_case								=       min(2 * end	,				snake_case_	)

   while low <= n:
      __snake_case								=       [True] * (high - low + 1)
      for each in in_prime:
         __snake_case								=       math.floor(low / each	) * each
         if t < low:
            t += each

         for j in range(snake_case_	,				high + 1	,				snake_case_	):
            __snake_case								=       False

      for j in range(len(snake_case_	)	):
         if temp[j] is True:
            prime.append(j + low	)

      __snake_case								=       high + 1
      __snake_case								=       min(high + end	,				snake_case_	)

   return prime







print(sieve(10**6))

