






'''simple docstring'''



from __future__ import annotations

import time

SCREAMING_SNAKE_CASE						=	list[tuple[int, int]]

SCREAMING_SNAKE_CASE						=	[
    [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],
]

SCREAMING_SNAKE_CASE						=	[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right
class 					UpperCAmelCase_					:




      """simple docstring"""




      def __init__( self			:		Dict     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Node | None		)  ->  Tuple:


       '''simple docstring'''

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





class 					UpperCAmelCase_					:




      """simple docstring"""




      def __init__( self			:		Any     ,      UpperCAmelCase			:		tuple[int, int]     ,      UpperCAmelCase			:		tuple[int, int]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =Node(start[1]     ,      start[0]     ,      goal[1]     ,      goal[0]     ,      _snake_case		)
       lowercase					:   Optional[int]				  =Node(goal[1]     ,      goal[0]     ,      goal[1]     ,      goal[0]     ,      _snake_case		)

       lowercase					:   Any				  =[self.start]
       lowercase					:   Union[str, Any]				  =False






      def 				A__      ( self			:		Tuple		)  ->  Any:


       '''simple docstring'''

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

        if current_node.pos == self.target.pos:
         lowercase					:   Union[str, Any]				  =True
         return self.retrace_path(_snake_case		)

        lowercase					:   List[Any]				  =self.get_successors(_snake_case		)

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

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






      def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		Node		)  ->  Dict:


       '''simple docstring'''

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

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

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






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		Node | None		)  ->  Union[str, Any]:


       '''simple docstring'''

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





class 					UpperCAmelCase_					:




      """simple docstring"""




      def __init__( self			:		str     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		int		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   int				  =BreadthFirstSearch(_snake_case     ,      _snake_case		)
       lowercase					:   Optional[int]				  =BreadthFirstSearch(_snake_case     ,      _snake_case		)
       lowercase					:   Any				  =False






      def 				A__      ( self			:		List[str]		)  ->  List[Any]:


       '''simple docstring'''

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

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

        lowercase					:   str				  =current_bwd_node
        lowercase					:   int				  =current_fwd_node

        lowercase					:   Any				  ={
            self.fwd_bfs: self.fwd_bfs.get_successors(_snake_case		),
            self.bwd_bfs: self.bwd_bfs.get_successors(_snake_case		),
        }

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

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






      def 				A__      ( self			:		Optional[Any]     ,      UpperCAmelCase			:		Node     ,      UpperCAmelCase			:		Node		)  ->  Any:


       '''simple docstring'''

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


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

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

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

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

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

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











'''simple docstring'''



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_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			['''image_processor''', '''tokenizer''']
      UpperCamelCase_								=			'''LayoutLMv2ImageProcessor'''
      UpperCamelCase_								=			('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')






      def __init__( self			:		List[str]     ,      UpperCAmelCase			:		Tuple=None     ,      UpperCAmelCase			:		str=None     ,      **UpperCAmelCase			:		Dict		)  ->  Optional[int]:


       '''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.'''     ,      UpperCAmelCase     ,      )
        lowercase					:   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__(UpperCAmelCase     ,      UpperCAmelCase		)






      def __call__( self			:		Any     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,      UpperCAmelCase			:		Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None     ,      UpperCAmelCase			:		Union[List[List[int]], List[List[List[int]]]] = None     ,      UpperCAmelCase			:		Optional[Union[List[int], List[List[int]]]] = None     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Union[bool, str, PaddingStrategy] = False     ,      UpperCAmelCase			:		Union[bool, str, TruncationStrategy] = None     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		int = 0     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		Optional[bool] = None     ,      UpperCAmelCase			:		Optional[bool] = None     ,      UpperCAmelCase			:		bool = False     ,      UpperCAmelCase			:		bool = False     ,      UpperCAmelCase			:		bool = False     ,      UpperCAmelCase			:		bool = False     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Optional[Union[str, TensorType]] = None     ,      **UpperCAmelCase			:		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					:   Tuple				  =self.image_processor(images=UpperCAmelCase     ,      return_tensors=UpperCAmelCase		)

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

       lowercase					:   Optional[Any]				  =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=UpperCAmelCase     ,      add_special_tokens=UpperCAmelCase     ,      padding=UpperCAmelCase     ,      truncation=UpperCAmelCase     ,      max_length=UpperCAmelCase     ,      stride=UpperCAmelCase     ,      pad_to_multiple_of=UpperCAmelCase     ,      return_token_type_ids=UpperCAmelCase     ,      return_attention_mask=UpperCAmelCase     ,      return_overflowing_tokens=UpperCAmelCase     ,      return_special_tokens_mask=UpperCAmelCase     ,      return_offsets_mapping=UpperCAmelCase     ,      return_length=UpperCAmelCase     ,      verbose=UpperCAmelCase     ,      return_tensors=UpperCAmelCase     ,      **UpperCAmelCase     ,      )

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

       return encoded_inputs






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		str		)  ->  str:


       '''simple docstring'''

       lowercase					:   str				  =[]
       for sample_idx in overflow_to_sample_mapping:
        images_with_overflow.append(images[sample_idx]		)

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

       return images_with_overflow






      def 				A__      ( self			:		Union[str, Any]     ,      *UpperCAmelCase			:		Tuple     ,      **UpperCAmelCase			:		Dict		)  ->  int:


       '''simple docstring'''

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






      def 				A__      ( self			:		Dict     ,      *UpperCAmelCase			:		Any     ,      **UpperCAmelCase			:		Dict		)  ->  str:


       '''simple docstring'''

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






      @property
      def 				A__      ( self			:		List[str]		)  ->  List[str]:


       '''simple docstring'''

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






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


       '''simple docstring'''

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






      @property
      def 				A__      ( self			:		Dict		)  ->  List[str]:


       '''simple docstring'''

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



