






'''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     a_						(      lowerCamelCase    ):
  lowercase							=				"""char"""
  lowercase							=				"""bpe"""
  lowercase							=				"""wp"""


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






class     a_						(      lowerCamelCase    ):
  lowercase							=				["""image_processor""", """char_tokenizer"""]
  lowercase							=				"""ViTImageProcessor"""
  lowercase							=				"""MgpstrTokenizer"""

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=None      ,		**_SCREAMING_SNAKE_CASE				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = 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      ,		)
              UpperCamelCase      = kwargs.pop("""feature_extractor"""				)

        UpperCamelCase      = 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`."""				)

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

        super().__init__(__a      ,		__a				)

  def __call__(				self      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=None      ,		**_SCREAMING_SNAKE_CASE				)  ->				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:
              UpperCamelCase      = self.image_processor(__a      ,		return_tensors=__a      ,		**__a				)
        if text is not None:
              UpperCamelCase      = self.char_tokenizer(__a      ,		return_tensors=__a      ,		**__a				)

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

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Union[str, Any]:



        """simple docstring"""






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

        UpperCamelCase  ,UpperCamelCase      = self._decode_helper(__a      ,		"""char"""				)
        UpperCamelCase  ,UpperCamelCase      = self._decode_helper(__a      ,		"""bpe"""				)
        UpperCamelCase  ,UpperCamelCase      = self._decode_helper(__a      ,		"""wp"""				)

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

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

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Union[str, Any]:



        """simple docstring"""






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

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

        for index in range(__a				):
              UpperCamelCase      = preds_str[index].find(__a				)
              UpperCamelCase      = preds_str[index][:pred_eos]
              UpperCamelCase      = preds_index[index].cpu().tolist()
              UpperCamelCase      = pred_index.index(__a				) if eos_token in pred_index else -1
              UpperCamelCase      = preds_max_prob[index][: pred_eos_index + 1]
              UpperCamelCase      = 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      ,		_SCREAMING_SNAKE_CASE				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = [seq.replace(""" """      ,		""""""				) for seq in self.char_tokenizer.batch_decode(__a				)]
        return decode_strs

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        return self.bpe_tokenizer.batch_decode(__a				)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = [seq.replace(""" """      ,		""""""				) for seq in self.wp_tokenizer.batch_decode(__a				)]
        return decode_strs








'''simple docstring'''

from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors







def    lowercase__			(				__UpperCamelCase   )->			int:
      UpperCamelCase      = prime_factors(__UpperCamelCase   )
      if is_square_free(__UpperCamelCase   ):
            return -1 if len(__UpperCamelCase   ) % 2 else 1
      return 0


if __name__ == "__main__":
     import doctest

     doctest.testmod()
