
def     a__			(    snake_case   ,	snake_case   ,	snake_case   ,	snake_case    ):





  """simple docstring"""
  return len_board * len_board_column * row + column


def     a__			(    snake_case   ,	snake_case   ,	snake_case   ,	snake_case   ,	snake_case   ,	snake_case   ,	):





  """simple docstring"""

  if board[row][column] != word[word_index]:
    return False

  if word_index == len(snake_case    ) - 1:
    return True

  __SCREAMING_SNAKE_CASE     :					str			=						[(0, 1), (0, -1), (-1, 0), (1, 0)]
  __SCREAMING_SNAKE_CASE     :					Tuple			=						len(snake_case    )
  __SCREAMING_SNAKE_CASE     :					Tuple			=						len(board[0]    )
  for direction in traverts_directions:
    __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						row + direction[0]
    __SCREAMING_SNAKE_CASE     :					Dict			=						column + direction[1]
    if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
      continue

    __SCREAMING_SNAKE_CASE     :					str			=						get_point_key(snake_case   ,	snake_case   ,	snake_case   ,	snake_case    )
    if key in visited_points_set:
      continue

    visited_points_set.add(snake_case    )
    if exits_word(snake_case   ,	snake_case   ,	snake_case   ,	snake_case   ,	word_index + 1   ,	snake_case    ):
      return True

    visited_points_set.remove(snake_case    )

  return False


def     a__			(    snake_case   ,	snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						(
      '''The board should be a non empty matrix of single chars strings.'''
  )

  __SCREAMING_SNAKE_CASE     :					List[str]			=						len(snake_case    )
  if not isinstance(snake_case   ,	snake_case    ) or len(snake_case    ) == 0:
    raise ValueError(snake_case    )

  for row in board:
    if not isinstance(snake_case   ,	snake_case    ) or len(snake_case    ) == 0:
      raise ValueError(snake_case    )

    for item in row:
      if not isinstance(snake_case   ,	snake_case    ) or len(snake_case    ) != 1:
        raise ValueError(snake_case    )

    # Validate word
  if not isinstance(snake_case   ,	snake_case    ) or len(snake_case    ) == 0:
    raise ValueError(
        '''The word parameter should be a string of length greater than 0.'''    )

  __SCREAMING_SNAKE_CASE     :					str			=						len(board[0]    )
  for i in range(snake_case    ):
    for j in range(snake_case    ):
      if exits_word(
          snake_case   ,	snake_case   ,	snake_case   ,	snake_case   ,	0   ,	{get_point_key(snake_case   ,	snake_case   ,	snake_case   ,	snake_case    )}    ):
        return True

  return False


if __name__ == "__main__":
   import doctest

   doctest.testmod()


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	['''image_processor''', '''tokenizer''']
     lowerCAmelCase_     =	'''LayoutLMv2ImageProcessor'''
     lowerCAmelCase_     =	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')



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







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						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      ,		)
         __SCREAMING_SNAKE_CASE     :					Tuple			=						kwargs.pop('''feature_extractor'''				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						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`.'''				)

       super().__init__(_A      ,		_A				)



     def __call__(							self    :    Optional[int]      ,		_A    :    Tuple      ,		_A    :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,		_A    :    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,		_A    :    Union[List[List[int]], List[List[List[int]]]] = None      ,		_A    :    Optional[Union[List[int], List[List[int]]]] = None      ,		_A    :    bool = True      ,		_A    :    Union[bool, str, PaddingStrategy] = False      ,		_A    :    Union[bool, str, TruncationStrategy] = False      ,		_A    :    Optional[int] = None      ,		_A    :    int = 0      ,		_A    :    Optional[int] = None      ,		_A    :    Optional[bool] = None      ,		_A    :    Optional[bool] = None      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = True      ,		_A    :    Optional[Union[str, TensorType]] = None      ,		**_A    :    Tuple      ,		):







       """simple docstring"""




       if self.image_processor.apply_ocr and (boxes is not None):
         raise ValueError(
             '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''				)

       if self.image_processor.apply_ocr and (word_labels is not None):
         raise ValueError(
             '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''				)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
         raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''				)

       # first, apply the image processor
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.image_processor(images=_A      ,		return_tensors=_A				)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
         if isinstance(_A      ,		_A				):
           __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						[text]  # add batch dimension (as the image processor always adds a batch dimension)
         __SCREAMING_SNAKE_CASE     :					Tuple			=						features['''words''']

       __SCREAMING_SNAKE_CASE     :					str			=						self.tokenizer(
           text=text if text is not None else features['''words''']      ,		text_pair=text_pair if text_pair is not None else None      ,		boxes=boxes if boxes is not None else features['''boxes''']      ,		word_labels=_A      ,		add_special_tokens=_A      ,		padding=_A      ,		truncation=_A      ,		max_length=_A      ,		stride=_A      ,		pad_to_multiple_of=_A      ,		return_token_type_ids=_A      ,		return_attention_mask=_A      ,		return_overflowing_tokens=_A      ,		return_special_tokens_mask=_A      ,		return_offsets_mapping=_A      ,		return_length=_A      ,		verbose=_A      ,		return_tensors=_A      ,		**_A      ,		)

       # add pixel values
       __SCREAMING_SNAKE_CASE     :					str			=						features.pop('''pixel_values'''				)
       if return_overflowing_tokens is True:
         __SCREAMING_SNAKE_CASE     :					str			=						self.get_overflowing_images(_A      ,		encoded_inputs['''overflow_to_sample_mapping''']				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						images

       return encoded_inputs



     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    List[Any]      ,		_A    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						[]
       for sample_idx in overflow_to_sample_mapping:
         images_with_overflow.append(images[sample_idx]				)

       if len(_A				) != len(_A				):
         raise ValueError(
             '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
             F''' {len(_A				)} and {len(_A				)}'''				)

       return images_with_overflow



     def 				UpperCAmelCase__    (							self    :    int      ,		*_A    :    Dict      ,		**_A    :    Optional[Any]				):







       """simple docstring"""




       return self.tokenizer.batch_decode(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]      ,		*_A    :    Union[str, Any]      ,		**_A    :    List[str]				):







       """simple docstring"""




       return self.tokenizer.decode(*_A      ,		**_A				)



     @property
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]



     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''      ,		_A      ,		)
       return self.image_processor_class




     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''      ,		_A      ,		)
       return self.image_processor
