

"""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 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		"char"
  _UpperCAmelCase          =		"bpe"
  _UpperCAmelCase          =		"wp"





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







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		["image_processor", "char_tokenizer"]
  _UpperCAmelCase          =		"ViTImageProcessor"
  _UpperCAmelCase          =		"MgpstrTokenizer"

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

      _lowerCAmelCase       :   List[str]  =    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    ,)
          _lowerCAmelCase       :   Union[str, Any]  =    kwargs.pop('feature_extractor'      )

      _lowerCAmelCase       :   int  =    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       :   List[Any]  =    tokenizer
      _lowerCAmelCase       :   Tuple  =    AutoTokenizer.from_pretrained('gpt2'      )
      _lowerCAmelCase       :   Union[str, Any]  =    AutoTokenizer.from_pretrained('bert-base-uncased'      )

      super().__init__(_A    ,_A      )

  def __call__(		self    ,_A=None    ,_A=None    ,_A=None    ,**_A      ):
      '''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       :   Union[str, Any]  =    self.image_processor(_A    ,return_tensors=_A    ,**_A      )
      if text is not None:
          _lowerCAmelCase       :   Tuple  =    self.char_tokenizer(_A    ,return_tensors=_A    ,**_A      )

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

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

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

      _lowerCAmelCase       :   Union[str, Any]  =    self._decode_helper(_A    ,'char'      )
      _lowerCAmelCase       :   Tuple  =    self._decode_helper(_A    ,'bpe'      )
      _lowerCAmelCase       :   int  =    self._decode_helper(_A    ,'wp'      )

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

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

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

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

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

      for index in range(_A      ):
          _lowerCAmelCase       :   Any  =    preds_str[index].find(_A      )
          _lowerCAmelCase       :   Dict  =    preds_str[index][:pred_eos]
          _lowerCAmelCase       :   List[Any]  =    preds_index[index].cpu().tolist()
          _lowerCAmelCase       :   Dict  =    pred_index.index(_A      ) if eos_token in pred_index else -1
          _lowerCAmelCase       :   int  =    preds_max_prob[index][: pred_eos_index + 1]
          _lowerCAmelCase       :   Union[str, 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 __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    [seq.replace(' '    ,''      ) for seq in self.char_tokenizer.batch_decode(_A      )]
      return decode_strs

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return self.bpe_tokenizer.batch_decode(_A      )







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    [seq.replace(' '    ,''      ) for seq in self.wp_tokenizer.batch_decode(_A      )]
      return decode_strs








"""simple docstring"""






from collections.abc import Callable







class 	__UpperCamelCase    :

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

      _lowerCAmelCase       :   list  =    []
      # Stores indexes of each item for supporting updates and deletion.
      _lowerCAmelCase       :   dict  =    {}
      # Stores current size of heap.
      _lowerCAmelCase       :   Union[str, Any]  =    0
      # Stores function used to evaluate the score of an item on which basis ordering
      # will be done.
      _lowerCAmelCase       :   Union[str, Any]  =    key or (lambda _A      : x)

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return int((i - 1) / 2      ) if i > 0 else None

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    int(2 * i + 1      )
      return left if 0 < left < self.size else None

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    int(2 * i + 2      )
      return right if 0 < right < self.size else None

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    (
          self.pos_map[self.arr[j][0]],
          self.pos_map[self.arr[i][0]],
      )
      # Then swap the items in the list.
      _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    self.arr[j], self.arr[i]

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      return self.arr[i][1] < self.arr[j][1]

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self._left(_A      )
      _lowerCAmelCase       :   str  =    self._right(_A      )
      _lowerCAmelCase       :   Tuple  =    i

      if left is not None and not self._cmp(_A    ,_A      ):
          _lowerCAmelCase       :   int  =    left
      if right is not None and not self._cmp(_A    ,_A      ):
          _lowerCAmelCase       :   Optional[int]  =    right

      return valid_parent

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    self._parent(_A      )
      while parent is not None and not self._cmp(_A    ,_A      ):
          self._swap(_A    ,_A      )
          _lowerCAmelCase,					_lowerCAmelCase       :   List[str]  =    parent, self._parent(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self._get_valid_parent(_A      )
      while valid_parent != index:
          self._swap(_A    ,_A      )
          _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    valid_parent, self._get_valid_parent(_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      if item not in self.pos_map:
          return
      _lowerCAmelCase       :   int  =    self.pos_map[item]
      _lowerCAmelCase       :   Dict  =    [item, self.key(_A      )]
      # Make sure heap is right in both up and down direction.
      # Ideally only one of them will make any change.
      self._heapify_up(_A      )
      self._heapify_down(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if item not in self.pos_map:
          return
      _lowerCAmelCase       :   List[str]  =    self.pos_map[item]
      del self.pos_map[item]
      _lowerCAmelCase       :   Dict  =    self.arr[self.size - 1]
      _lowerCAmelCase       :   Optional[Any]  =    index
      self.size -= 1
      # Make sure heap is right in both up and down direction. Ideally only one
      # of them will make any change- so no performance loss in calling both.
      if self.size > index:
          self._heapify_up(_A      )
          self._heapify_down(_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    len(self.arr      )
      if arr_len == self.size:
          self.arr.append([item, self.key(_A      )]      )
      else:
          _lowerCAmelCase       :   Any  =    [item, self.key(_A      )]
      _lowerCAmelCase       :   str  =    self.size
      self.size += 1
      self._heapify_up(self.size - 1      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.arr[0] if self.size else None







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.get_top()
      if top_item_tuple:
          self.delete_item(top_item_tuple[0]      )
      return top_item_tuple





def 		lowerCamelCase__		(					):

    '''simple docstring'''





if __name__ == "__main__":
 import doctest

 doctest.testmod()





