"""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__    (   _a	):
   snake_case_							=      "char"
   snake_case_							=      "bpe"
   snake_case_							=      "wp"



lowerCAmelCase_:					str     =					(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class    a__    (   _a	):
   snake_case_							=      ["image_processor", "char_tokenizer"]
   snake_case_							=      "ViTImageProcessor"
   snake_case_							=      "MgpstrTokenizer"


   def __init__(     self,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				**_UpperCAmelCase  ):
        '''simple docstring'''



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

        lowercase__				 =							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`."  )

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

        super().__init__(_UpperCAmelCase,				_UpperCAmelCase  )


   def __call__(     self,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				**_UpperCAmelCase  ):
        '''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:
             lowercase__				 =							self.image_processor(_UpperCAmelCase,				return_tensors=_UpperCAmelCase,				**_UpperCAmelCase  )
        if text is not None:
             lowercase__				 =							self.char_tokenizer(_UpperCAmelCase,				return_tensors=_UpperCAmelCase,				**_UpperCAmelCase  )

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


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



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

        lowercase__ ,						lowercase__				 =							self._decode_helper(_UpperCAmelCase,				"char"  )
        lowercase__ ,						lowercase__				 =							self._decode_helper(_UpperCAmelCase,				"bpe"  )
        lowercase__ ,						lowercase__				 =							self._decode_helper(_UpperCAmelCase,				"wp"  )

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

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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



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

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

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

        return dec_strs, conf_scores


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        return self.bpe_tokenizer.batch_decode(_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



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





"""simple docstring"""


from __future__ import annotations







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     if partitions <= 0:
          raise ValueError("partitions must be a positive number!"      )
     if partitions > number_of_bytes:
          raise ValueError("partitions can not > number_of_bytes!"      )
     lowercase__				 =							number_of_bytes // partitions
     lowercase__				 =							[]
     for i in range(A      ):
          lowercase__				 =							i * bytes_per_partition + 1
          lowercase__				 =							(
              number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
          )
          allocation_list.append(f'''{start_bytes}-{end_bytes}'''      )
     return allocation_list


if __name__ == "__main__":
 import doctest

 doctest.testmod()




