




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 						_A (  _lowerCamelCase				):






  """simple docstring"""




  _UpperCamelCase       :      Tuple			=      ['''image_processor''', '''tokenizer''']
  _UpperCamelCase       :      Optional[Any]			=      '''LayoutLMv2ImageProcessor'''
  _UpperCamelCase       :      Tuple			=      ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')






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






       """simple docstring"""







       if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''				,						_A				,						)
            lowercase :       Union[str, Any]       =			kwargs.pop('''feature_extractor'''				)

       lowercase :       Dict       =			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		:     Dict				,						_A		:     Union[str, Any]				,						_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] = None				,						_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		:     List[Any]				,						)		->   BatchEncoding:






       """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
       lowercase :       Optional[int]       =			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				):
                 lowercase :       Any       =			[text]  # add batch dimension (as the image processor always adds a batch dimension)
            lowercase :       Optional[Any]       =			features['''words''']

       lowercase :       int       =			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
       lowercase :       List[Any]       =			features.pop('''pixel_values'''				)
       if return_overflowing_tokens is True:
            lowercase :       List[Any]       =			self.get_overflowing_images(_A				,						encoded_inputs['''overflow_to_sample_mapping''']				)
       lowercase :       str       =			images

       return encoded_inputs






  def  __a      (  self		:     List[Any]				,						_A		:     str				,						_A		:     List[Any]				)		->   Union[str, Any]:






       """simple docstring"""







       lowercase :       int       =			[]
       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  __a      (  self		:     int				,						*_A		:     str				,						**_A		:     List[Any]				)		->   List[str]:






       """simple docstring"""







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






  def  __a      (  self		:     Any				,						*_A		:     Tuple				,						**_A		:     List[Any]				)		->   Union[str, Any]:






       """simple docstring"""







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






  @property
  def  __a      (  self		:     Optional[int]				)		->   Optional[int]:






       """simple docstring"""







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






  @property
  def  __a      (  self		:     List[str]				)		->   Optional[int]:






       """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  __a      (  self		:     List[Any]				)		->   int:






       """simple docstring"""







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





from __future__ import annotations

import time

lowerCAmelCase_						=			list[tuple[int, int]]

lowerCAmelCase_						=			[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCAmelCase_						=			[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right
class 						_A :






  def __init__(  self		:     List[Any]				,						_A		:     int				,						_A		:     int				,						_A		:     int				,						_A		:     int				,						_A		:     Node | None				)		->   str:






       """simple docstring"""







       lowercase :       Any       =			pos_x
       lowercase :       Union[str, Any]       =			pos_y
       lowercase :       int       =			(pos_y, pos_x)
       lowercase :       List[str]       =			goal_x
       lowercase :       Optional[int]       =			goal_y
       lowercase :       Union[str, Any]       =			parent


class 						_A :






  def __init__(  self		:     Optional[Any]				,						_A		:     tuple[int, int]				,						_A		:     tuple[int, int]				)		->   List[Any]:






       """simple docstring"""







       lowercase :       int       =			Node(start[1]				,						start[0]				,						goal[1]				,						goal[0]				,						_A				)
       lowercase :       int       =			Node(goal[1]				,						goal[0]				,						goal[1]				,						goal[0]				,						_A				)

       lowercase :       List[str]       =			[self.start]
       lowercase :       Optional[int]       =			False






  def  __a      (  self		:     Dict				)		->   Path | None:






       """simple docstring"""







       while self.node_queue:
            lowercase :       Optional[Any]       =			self.node_queue.pop(0				)

            if current_node.pos == self.target.pos:
                 lowercase :       int       =			True
                 return self.retrace_path(_A				)

            lowercase :       Union[str, Any]       =			self.get_successors(_A				)

            for node in successors:
                 self.node_queue.append(_A				)

       if not self.reached:
            return [self.start.pos]
       return None






  def  __a      (  self		:     str				,						_A		:     Node				)		->   list[Node]:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			[]
       for action in delta:
            lowercase :       List[str]       =			parent.pos_x + action[1]
            lowercase :       str       =			parent.pos_y + action[0]
            if not (0 <= pos_x <= len(grid[0]				) - 1 and 0 <= pos_y <= len(_A				) - 1):
                 continue

            if grid[pos_y][pos_x] != 0:
                 continue

            successors.append(
                Node(_A				,						_A				,						self.target.pos_y				,						self.target.pos_x				,						_A				)				)
       return successors






  def  __a      (  self		:     Any				,						_A		:     Node | None				)		->   Path:






       """simple docstring"""







       lowercase :       Optional[int]       =			node
       lowercase :       Dict       =			[]
       while current_node is not None:
            path.append((current_node.pos_y, current_node.pos_x)				)
            lowercase :       Tuple       =			current_node.parent
       path.reverse()
       return path


class 						_A :






  def __init__(  self		:     Optional[int]				,						_A		:     List[str]				,						_A		:     List[str]				)		->   int:






       """simple docstring"""







       lowercase :       str       =			BreadthFirstSearch(_A				,						_A				)
       lowercase :       Optional[Any]       =			BreadthFirstSearch(_A				,						_A				)
       lowercase :       List[str]       =			False






  def  __a      (  self		:     Tuple				)		->   Path | None:






       """simple docstring"""







       while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
            lowercase :       str       =			self.fwd_bfs.node_queue.pop(0				)
            lowercase :       Tuple       =			self.bwd_bfs.node_queue.pop(0				)

            if current_bwd_node.pos == current_fwd_node.pos:
                 lowercase :       Optional[Any]       =			True
                 return self.retrace_bidirectional_path(
                     _A				,						_A				)

            lowercase :       Optional[int]       =			current_bwd_node
            lowercase :       Optional[int]       =			current_fwd_node

            lowercase :       Union[str, Any]       =			{
                self.fwd_bfs: self.fwd_bfs.get_successors(_A				),
                self.bwd_bfs: self.bwd_bfs.get_successors(_A				),
            }

            for bfs in [self.fwd_bfs, self.bwd_bfs]:
                 for node in successors[bfs]:
                      bfs.node_queue.append(_A				)

       if not self.reached:
            return [self.fwd_bfs.start.pos]
       return None






  def  __a      (  self		:     List[Any]				,						_A		:     Node				,						_A		:     Node				)		->   Path:






       """simple docstring"""







       lowercase :       Optional[Any]       =			self.fwd_bfs.retrace_path(_A				)
       lowercase :       Union[str, Any]       =			self.bwd_bfs.retrace_path(_A				)
       bwd_path.pop()
       bwd_path.reverse()
       lowercase :       Optional[int]       =			fwd_path + bwd_path
       return path


if __name__ == "__main__":
 # all coordinates are given in format [y,x]
 import doctest

 doctest.testmod()
 lowerCAmelCase_						=			(0, 0)
 lowerCAmelCase_						=			(len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 lowerCAmelCase_						=			time.time()
 lowerCAmelCase_						=			BreadthFirstSearch(init, goal)
 lowerCAmelCase_						=			bfs.search()
 lowerCAmelCase_						=			time.time() - start_bfs_time

 print('Unidirectional BFS computation time : ', bfs_time)

 lowerCAmelCase_						=			time.time()
 lowerCAmelCase_						=			BidirectionalBreadthFirstSearch(init, goal)
 lowerCAmelCase_						=			bd_bfs.search()
 lowerCAmelCase_						=			time.time() - start_bd_bfs_time

 print('Bidirectional BFS computation time : ', bd_bfs_time)