




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase_						=			logging.get_logger(__name__)


if is_vision_available():
 import PIL
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      int			=      ['''pixel_values''']






  def __init__(  self		:     Union[str, Any]				,						_A		:     bool = True				,						_A		:     Dict[str, int] = None				,						_A		:     PILImageResampling = PILImageResampling.BICUBIC				,						_A		:     bool = True				,						_A		:     Dict[str, int] = None				,						_A		:     bool = True				,						_A		:     Union[int, float] = 1 / 255				,						_A		:     bool = True				,						_A		:     Optional[Union[float, List[float]]] = None				,						_A		:     Optional[Union[float, List[float]]] = None				,						_A		:     bool = True				,						**_A		:     Any				,						)		->   None:






       """simple docstring"""







       super().__init__(**_A				)
       lowercase :       Dict       =			size if size is not None else {'''shortest_edge''': 224}
       lowercase :       List[Any]       =			get_size_dict(_A				,						default_to_square=_A				)
       lowercase :       Dict       =			crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       lowercase :       Dict       =			get_size_dict(_A				,						default_to_square=_A				,						param_name='''crop_size'''				)

       lowercase :       Tuple       =			do_resize
       lowercase :       Optional[int]       =			size
       lowercase :       Union[str, Any]       =			resample
       lowercase :       Optional[Any]       =			do_center_crop
       lowercase :       int       =			crop_size
       lowercase :       int       =			do_rescale
       lowercase :       Dict       =			rescale_factor
       lowercase :       Union[str, Any]       =			do_normalize
       lowercase :       Optional[Any]       =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       lowercase :       Tuple       =			image_std if image_std is not None else OPENAI_CLIP_STD
       lowercase :       int       =			do_convert_rgb






  def  __a      (  self		:     Union[str, Any]				,						_A		:     np.ndarray				,						_A		:     Dict[str, int]				,						_A		:     PILImageResampling = PILImageResampling.BICUBIC				,						_A		:     Optional[Union[str, ChannelDimension]] = None				,						**_A		:     Tuple				,						)		->   np.ndarray:






       """simple docstring"""







       lowercase :       Tuple       =			get_size_dict(_A				,						default_to_square=_A				)
       if "shortest_edge" not in size:
            raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
       lowercase :       List[str]       =			get_resize_output_image_size(_A				,						size=size['''shortest_edge''']				,						default_to_square=_A				)
       return resize(_A				,						size=_A				,						resample=_A				,						data_format=_A				,						**_A				)






  def  __a      (  self		:     List[str]				,						_A		:     np.ndarray				,						_A		:     Dict[str, int]				,						_A		:     Optional[Union[str, ChannelDimension]] = None				,						**_A		:     int				,						)		->   np.ndarray:






       """simple docstring"""







       lowercase :       Optional[Any]       =			get_size_dict(_A				)
       if "height" not in size or "width" not in size:
            raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""				)
       return center_crop(_A				,						size=(size['''height'''], size['''width'''])				,						data_format=_A				,						**_A				)






  def  __a      (  self		:     Dict				,						_A		:     np.ndarray				,						_A		:     Union[int, float]				,						_A		:     Optional[Union[str, ChannelDimension]] = None				,						**_A		:     Any				,						)		->   Optional[Any]:






       """simple docstring"""







       return rescale(_A				,						scale=_A				,						data_format=_A				,						**_A				)






  def  __a      (  self		:     Dict				,						_A		:     np.ndarray				,						_A		:     Union[float, List[float]]				,						_A		:     Union[float, List[float]]				,						_A		:     Optional[Union[str, ChannelDimension]] = None				,						**_A		:     int				,						)		->   np.ndarray:






       """simple docstring"""







       return normalize(_A				,						mean=_A				,						std=_A				,						data_format=_A				,						**_A				)






  def  __a      (  self		:     str				,						_A		:     ImageInput				,						_A		:     bool = None				,						_A		:     Dict[str, int] = None				,						_A		:     PILImageResampling = None				,						_A		:     bool = None				,						_A		:     int = None				,						_A		:     bool = None				,						_A		:     float = None				,						_A		:     bool = None				,						_A		:     Optional[Union[float, List[float]]] = None				,						_A		:     Optional[Union[float, List[float]]] = None				,						_A		:     bool = None				,						_A		:     Optional[Union[str, TensorType]] = None				,						_A		:     Optional[ChannelDimension] = ChannelDimension.FIRST				,						**_A		:     Optional[Any]				,						)		->   PIL.Image.Image:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			do_resize if do_resize is not None else self.do_resize
       lowercase :       Optional[Any]       =			size if size is not None else self.size
       lowercase :       Union[str, Any]       =			get_size_dict(_A				,						param_name='''size'''				,						default_to_square=_A				)
       lowercase :       List[str]       =			resample if resample is not None else self.resample
       lowercase :       Any       =			do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase :       Any       =			crop_size if crop_size is not None else self.crop_size
       lowercase :       str       =			get_size_dict(_A				,						param_name='''crop_size'''				,						default_to_square=_A				)
       lowercase :       Optional[int]       =			do_rescale if do_rescale is not None else self.do_rescale
       lowercase :       str       =			rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase :       List[str]       =			do_normalize if do_normalize is not None else self.do_normalize
       lowercase :       Optional[int]       =			image_mean if image_mean is not None else self.image_mean
       lowercase :       Optional[int]       =			image_std if image_std is not None else self.image_std
       lowercase :       Dict       =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       lowercase :       Optional[int]       =			make_list_of_images(_A				)

       if not valid_images(_A				):
            raise ValueError(
                '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)

       if do_resize and size is None:
            raise ValueError('''Size must be specified if do_resize is True.'''				)

       if do_center_crop and crop_size is None:
            raise ValueError('''Crop size must be specified if do_center_crop is True.'''				)

       if do_rescale and rescale_factor is None:
            raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

       if do_normalize and (image_mean is None or image_std is None):
            raise ValueError('''Image mean and std must be specified if do_normalize is True.'''				)

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
            lowercase :       Optional[Any]       =			[convert_to_rgb(_A				) for image in images]

       # All transformations expect numpy arrays.
       lowercase :       str       =			[to_numpy_array(_A				) for image in images]

       if do_resize:
            lowercase :       Any       =			[self.resize(image=_A				,						size=_A				,						resample=_A				) for image in images]

       if do_center_crop:
            lowercase :       Any       =			[self.center_crop(image=_A				,						size=_A				) for image in images]

       if do_rescale:
            lowercase :       List[Any]       =			[self.rescale(image=_A				,						scale=_A				) for image in images]

       if do_normalize:
            lowercase :       Optional[int]       =			[self.normalize(image=_A				,						mean=_A				,						std=_A				) for image in images]

       lowercase :       Any       =			[to_channel_dimension_format(_A				,						_A				) for image in images]

       lowercase :       Optional[Any]       =			{'''pixel_values''': images}
       return BatchFeature(data=_A				,						tensor_type=_A				)





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)