





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        snake_case_    (		__lowercase						):
  A_      							=   'char'
  A_      							=   'bpe'
  A_      							=   'wp'



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






class        snake_case_    (		__lowercase						):
  A_      							=   ['image_processor', 'char_tokenizer']
  A_      							=   'ViTImageProcessor'
  A_      							=   'MgpstrTokenizer'






  def __init__(							self			:					List[str]       ,  _snake_case			:					Optional[Any]=None       ,  _snake_case			:					Any=None       ,  **_snake_case			:					List[str]      )->Optional[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       None
   if "feature_extractor" in kwargs:
    warnings.warn(
        """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
        """ instead."""       ,  _snake_case       ,  )
    __lowerCAmelCase					:	List[Any]      =       kwargs.pop("""feature_extractor"""      )

   __lowerCAmelCase					:	List[Any]      =       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					:	Any      =       tokenizer
   __lowerCAmelCase					:	Dict      =       AutoTokenizer.from_pretrained("""gpt2"""      )
   __lowerCAmelCase					:	str      =       AutoTokenizer.from_pretrained("""bert-base-uncased"""      )

   super().__init__(_snake_case       ,  _snake_case      )






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






   '''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:
    __lowerCAmelCase					:	Optional[int]      =       self.image_processor(_snake_case       ,  return_tensors=_snake_case       ,  **_snake_case      )
   if text is not None:
    __lowerCAmelCase					:	Union[str, Any]      =       self.char_tokenizer(_snake_case       ,  return_tensors=_snake_case       ,  **_snake_case      )

   if text is None:
    return inputs
   elif images is None:
    return encodings
   else:
    __lowerCAmelCase					:	Union[str, Any]      =       encodings["""input_ids"""]
    return inputs






  def 				UpperCAmelCase__						(							self			:					str       ,  _snake_case			:					List[Any]      )->Optional[Any]:






   '''simple docstring'''

   __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       sequences
   __lowerCAmelCase					:	Any      =       char_preds.size(0      )

   __lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       self._decode_helper(_snake_case       ,  """char"""      )
   __lowerCAmelCase ,					__lowerCAmelCase					:	Tuple      =       self._decode_helper(_snake_case       ,  """bpe"""      )
   __lowerCAmelCase ,					__lowerCAmelCase					:	Tuple      =       self._decode_helper(_snake_case       ,  """wp"""      )

   __lowerCAmelCase					:	List[Any]      =       []
   __lowerCAmelCase					:	Optional[int]      =       []
   for i in range(_snake_case      ):
    __lowerCAmelCase					:	Optional[int]      =       [char_scores[i], bpe_scores[i], wp_scores[i]]
    __lowerCAmelCase					:	Optional[int]      =       [char_strs[i], bpe_strs[i], wp_strs[i]]
    __lowerCAmelCase					:	Tuple      =       scores.index(max(_snake_case      )      )
    final_strs.append(strs[max_score_index]      )
    final_scores.append(scores[max_score_index]      )

   __lowerCAmelCase					:	int      =       {}
   __lowerCAmelCase					:	Union[str, Any]      =       final_strs
   __lowerCAmelCase					:	int      =       final_scores
   __lowerCAmelCase					:	int      =       char_strs
   __lowerCAmelCase					:	List[Any]      =       bpe_strs
   __lowerCAmelCase					:	List[Any]      =       wp_strs
   return out






  def 				UpperCAmelCase__						(							self			:					Any       ,  _snake_case			:					str       ,  _snake_case			:					int      )->Union[str, Any]:






   '''simple docstring'''

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

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

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

   return dec_strs, conf_scores






  def 				UpperCAmelCase__						(							self			:					List[str]       ,  _snake_case			:					int      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       [seq.replace(""" """       ,  """"""      ) for seq in self.char_tokenizer.batch_decode(_snake_case      )]
   return decode_strs






  def 				UpperCAmelCase__						(							self			:					Any       ,  _snake_case			:					str      )->Union[str, Any]:






   '''simple docstring'''

   return self.bpe_tokenizer.batch_decode(_snake_case      )






  def 				UpperCAmelCase__						(							self			:					Optional[Any]       ,  _snake_case			:					Dict      )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       [seq.replace(""" """       ,  """"""      ) for seq in self.wp_tokenizer.batch_decode(_snake_case      )]
   return decode_strs






import random






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:Dict   )      ->   tuple:
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	List[str]      =       [], [], []
 for element in data:
  if element < pivot:
   less.append(SCREAMING_SNAKE_CASE   )
  elif element > pivot:
   greater.append(SCREAMING_SNAKE_CASE   )
  else:
   equal.append(SCREAMING_SNAKE_CASE   )
 return less, equal, greater







def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:int   )      ->   Dict:
 # index = len(items) // 2 when trying to find the median
 #   (value of index when items is sorted)

 # invalid input
 if index >= len(SCREAMING_SNAKE_CASE   ) or index < 0:
  return None

 __lowerCAmelCase					:	Union[str, Any]      =       items[random.randint(0   ,   len(SCREAMING_SNAKE_CASE   ) - 1   )]
 __lowerCAmelCase					:	int      =       0
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Tuple      =       _partition(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       len(SCREAMING_SNAKE_CASE   )

 # index is the pivot
 if m <= index < m + count:
  return pivot
 # must be in smaller
 elif m > index:
  return quick_select(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 # must be in larger
 else:
  return quick_select(SCREAMING_SNAKE_CASE   ,   index - (m + count)   )