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 			UpperCAmelCase__    (		__a					):



  """simple docstring"""






  lowerCAmelCase__		:	Tuple    		= 'char'
  lowerCAmelCase__		:	Optional[int]    		= 'bpe'
  lowerCAmelCase__		:	Optional[int]    		= 'wp'






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


class 			UpperCAmelCase__    (		__a					):



  """simple docstring"""






  lowerCAmelCase__		:	Tuple    		= ['image_processor', 'char_tokenizer']
  lowerCAmelCase__		:	Union[str, Any]    		= 'ViTImageProcessor'
  lowerCAmelCase__		:	Any    		= 'MgpstrTokenizer'






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




    '''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:			int				,				__lowerCAmelCase:			str=None				,				__lowerCAmelCase:			Tuple=None				,				__lowerCAmelCase:			Any=None				,				**__lowerCAmelCase:			Tuple   ) -> List[str]:




    '''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 	_UpperCAmelCase				(				self:			Dict				,				__lowerCAmelCase:			List[Any]   ) -> Dict:




    '''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 	_UpperCAmelCase				(				self:			Optional[Any]				,				__lowerCAmelCase:			Dict				,				__lowerCAmelCase:			str   ) -> Optional[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 	_UpperCAmelCase				(				self:			List[Any]				,				__lowerCAmelCase:			int   ) -> int:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			List[Any]				,				__lowerCAmelCase:			Dict   ) -> List[str]:




    '''simple docstring'''




    return self.bpe_tokenizer.batch_decode(A__   )






  def 	_UpperCAmelCase				(				self:			Dict				,				__lowerCAmelCase:			str   ) -> Union[str, Any]:




    '''simple docstring'''




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




def       __lowerCAmelCase      (						A_   : str					)    ->  Any:
  if not head:
    return True
  # split the list to two parts
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       head.next, head
  while fast and fast.next:
    __UpperCAmelCase						    =       fast.next.next
    __UpperCAmelCase						    =       slow.next
  __UpperCAmelCase						    =       slow.next
  __UpperCAmelCase						    =       None  # Don't forget here! But forget still works!
  # reverse the second part
  __UpperCAmelCase						    =       None
  while second:
    __UpperCAmelCase						    =       second.next
    __UpperCAmelCase						    =       node
    __UpperCAmelCase						    =       second
    __UpperCAmelCase						    =       nxt
  # compare two parts
  # second part has the same or one less node
  while node:
    if node.val != head.val:
      return False
    __UpperCAmelCase						    =       node.next
    __UpperCAmelCase						    =       head.next
  return True




def       __lowerCAmelCase      (						A_   : Optional[Any]					)    ->  Optional[Any]:
  if not head or not head.next:
    return True

  # 1. Get the midpoint (slow)
  __UpperCAmelCase						    =       __UpperCAmelCase						    =       __UpperCAmelCase						    =       head
  while fast and fast.next:
    __UpperCAmelCase    ,		__UpperCAmelCase						    =       fast.next.next, slow.next

  # 2. Push the second half into the stack
  __UpperCAmelCase						    =       [slow.val]
  while slow.next:
    __UpperCAmelCase						    =       slow.next
    stack.append(slow.val					)

  # 3. Comparison
  while stack:
    if stack.pop() != cur.val:
      return False
    __UpperCAmelCase						    =       cur.next

  return True





def       __lowerCAmelCase      (						A_   : int					)    ->  Optional[int]:
  if not head or not head.next:
    return True
  __UpperCAmelCase						    =       {}
  __UpperCAmelCase						    =       0
  while head:
    if head.val in d:
      d[head.val].append(A_					)
    else:
      __UpperCAmelCase						    =       [pos]
    __UpperCAmelCase						    =       head.next
    pos += 1
  __UpperCAmelCase						    =       pos - 1
  __UpperCAmelCase						    =       0
  for v in d.values():
    if len(A_					) % 2 != 0:
      middle += 1
    else:
      __UpperCAmelCase						    =       0
      for i in range(0						,	len(A_					)					):
        if v[i] + v[len(A_					) - 1 - step] != checksum:
          return False
        step += 1
    if middle > 1:
      return False
  return True



