

"""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      __lowercase			(    __lowerCamelCase					):
  snake_case_				       =       """char"""
  snake_case_				       =       """bpe"""
  snake_case_				       =       """wp"""







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







class      __lowercase			(    __lowerCamelCase					):
  snake_case_				       =       ["""image_processor""", """char_tokenizer"""]
  snake_case_				       =       """ViTImageProcessor"""
  snake_case_				       =       """MgpstrTokenizer"""

  def __init__(  self		:			str					,A		:			int=None					,A		:			Tuple=None					,**A		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					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__					:  Union[str, Any]					   =					kwargs.pop("""feature_extractor"""    )

   UpperCAmelCase__					:  Tuple					   =					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__					:  int					   =					tokenizer
   UpperCAmelCase__					:  Any					   =					AutoTokenizer.from_pretrained("""gpt2"""    )
   UpperCAmelCase__					:  Tuple					   =					AutoTokenizer.from_pretrained("""bert-base-uncased"""    )

   super().__init__(A					,A    )

  def __call__(  self		:			Dict					,A		:			List[str]=None					,A		:			Optional[Any]=None					,A		:			str=None					,**A		:			Tuple    ):






   '''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__					:  Any					   =					self.image_processor(A					,return_tensors=A					,**A    )
   if text is not None:
    UpperCAmelCase__					:  int					   =					self.char_tokenizer(A					,return_tensors=A					,**A    )

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

  def    __lowercase       (  self		:			int					,A		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					sequences
   UpperCAmelCase__					:  Dict					   =					char_preds.size(0    )

   UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					self._decode_helper(A					,"""char"""    )
   UpperCAmelCase__   ,      UpperCAmelCase__					:  Union[str, Any]					   =					self._decode_helper(A					,"""bpe"""    )
   UpperCAmelCase__   ,      UpperCAmelCase__					:  List[Any]					   =					self._decode_helper(A					,"""wp"""    )

   UpperCAmelCase__					:  Optional[int]					   =					[]
   UpperCAmelCase__					:  Optional[int]					   =					[]
   for i in range(A    ):
    UpperCAmelCase__					:  Optional[int]					   =					[char_scores[i], bpe_scores[i], wp_scores[i]]
    UpperCAmelCase__					:  Union[str, Any]					   =					[char_strs[i], bpe_strs[i], wp_strs[i]]
    UpperCAmelCase__					:  Optional[int]					   =					scores.index(max(A    )    )
    final_strs.append(strs[max_score_index]    )
    final_scores.append(scores[max_score_index]    )

   UpperCAmelCase__					:  int					   =					{}
   UpperCAmelCase__					:  List[str]					   =					final_strs
   UpperCAmelCase__					:  Any					   =					final_scores
   UpperCAmelCase__					:  str					   =					char_strs
   UpperCAmelCase__					:  Optional[Any]					   =					bpe_strs
   UpperCAmelCase__					:  Optional[int]					   =					wp_strs
   return out

  def    __lowercase       (  self		:			Tuple					,A		:			Tuple					,A		:			Optional[Any]    ):






   '''simple docstring'''
   if format == DecodeType.CHARACTER:
    UpperCAmelCase__					:  Optional[Any]					   =					self.char_decode
    UpperCAmelCase__					:  List[str]					   =					1
    UpperCAmelCase__					:  Tuple					   =					"""[s]"""
   elif format == DecodeType.BPE:
    UpperCAmelCase__					:  Dict					   =					self.bpe_decode
    UpperCAmelCase__					:  Dict					   =					2
    UpperCAmelCase__					:  List[Any]					   =					"""#"""
   elif format == DecodeType.WORDPIECE:
    UpperCAmelCase__					:  Optional[int]					   =					self.wp_decode
    UpperCAmelCase__					:  List[Any]					   =					102
    UpperCAmelCase__					:  int					   =					"""[SEP]"""
   else:
    raise ValueError(f"Format {format} is not supported."    )

   UpperCAmelCase__   ,      UpperCAmelCase__					:  List[str]					   =					[], []
   UpperCAmelCase__					:  Union[str, Any]					   =					pred_logits.size(0    )
   UpperCAmelCase__					:  Tuple					   =					pred_logits.size(1    )
   UpperCAmelCase__   ,      UpperCAmelCase__					:  int					   =					pred_logits.topk(1					,dim=-1					,largest=A					,sorted=A    )
   UpperCAmelCase__					:  int					   =					preds_index.view(-1					,A    )[:, 1:]
   UpperCAmelCase__					:  List[Any]					   =					decoder(A    )
   UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[Any]					   =					torch.nn.functional.softmax(A					,dim=2    ).max(dim=2    )
   UpperCAmelCase__					:  List[Any]					   =					preds_max_prob[:, 1:]

   for index in range(A    ):
    UpperCAmelCase__					:  str					   =					preds_str[index].find(A    )
    UpperCAmelCase__					:  Optional[int]					   =					preds_str[index][:pred_eos]
    UpperCAmelCase__					:  Optional[int]					   =					preds_index[index].cpu().tolist()
    UpperCAmelCase__					:  Dict					   =					pred_index.index(A    ) if eos_token in pred_index else -1
    UpperCAmelCase__					:  Optional[Any]					   =					preds_max_prob[index][: pred_eos_index + 1]
    UpperCAmelCase__					:  List[Any]					   =					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    __lowercase       (  self		:			Optional[Any]					,A		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					[seq.replace(""" """					,""""""    ) for seq in self.char_tokenizer.batch_decode(A    )]
   return decode_strs

  def    __lowercase       (  self		:			str					,A		:			Any    ):






   '''simple docstring'''
   return self.bpe_tokenizer.batch_decode(A    )




  def    __lowercase       (  self		:			Dict					,A		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					[seq.replace(""" """					,""""""    ) for seq in self.wp_tokenizer.batch_decode(A    )]
   return decode_strs








"""simple docstring"""




import random
from typing import Any
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 for _ in range(len(__UpperCamelCase      )      ):
  UpperCAmelCase__					:  Dict					   =					random.randint(0							,    len(__UpperCamelCase      ) - 1      )
  UpperCAmelCase__					:  Tuple					   =					random.randint(0							,    len(__UpperCamelCase      ) - 1      )
  UpperCAmelCase__   ,      UpperCAmelCase__					:  int					   =					data[b], data[a]
 return data


if __name__ == "__main__":
      __UpperCAmelCase =				[0, 1, 2, 3, 4, 5, 6, 7]
      __UpperCAmelCase =				['python', 'says', 'hello', '!']
      print('Fisher-Yates Shuffle:')
      print('List', integers, strings)
      print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))





