






from __future__ import annotations

import time

__lowerCamelCase :      Optional[Any]										=				list[tuple[int, int]]

__lowerCamelCase :      List[Any]										=				[
    [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 :      Any										=				[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  Optional[Any]							,		__A					:  int							,		__A					:  int							,		__A					:  int							,		__A					:  int							,		__A					:  Node | None	):
						snake_case__			:				int       =  pos_x
						snake_case__			:				Tuple       =  pos_y
						snake_case__			:				str       =  (pos_y, pos_x)
						snake_case__			:				Any       =  goal_x
						snake_case__			:				Tuple       =  goal_y
						snake_case__			:				List[Any]       =  parent



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  Tuple							,		__A					:  tuple[int, int]							,		__A					:  tuple[int, int]	):
						snake_case__			:				Any       =  Node(start[1]							,		start[0]							,		goal[1]							,		goal[0]							,		__A	)
						snake_case__			:				Tuple       =  Node(goal[1]							,		goal[0]							,		goal[1]							,		goal[0]							,		__A	)

						snake_case__			:				Optional[Any]       =  [self.start]
						snake_case__			:				str       =  False



					def 	_lowercase					( self					:  Tuple	):
						while self.node_queue:
							snake_case__			:				Optional[int]       =  self.node_queue.pop(0	)

							if current_node.pos == self.target.pos:
								snake_case__			:				str       =  True
								return self.retrace_path(__A	)

							snake_case__			:				int       =  self.get_successors(__A	)

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

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



					def 	_lowercase					( self					:  Optional[int]							,		__A					:  Node	):
						snake_case__			:				str       =  []
						for action in delta:
							snake_case__			:				str       =  parent.pos_x + action[1]
							snake_case__			:				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 	_lowercase					( self					:  Any							,		__A					:  Node | None	):
						snake_case__			:				List[Any]       =  node
						snake_case__			:				str       =  []
						while current_node is not None:
							path.append((current_node.pos_y, current_node.pos_x)	)
							snake_case__			:				Any       =  current_node.parent
						path.reverse()
						return path



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  Tuple							,		__A					:  List[str]							,		__A					:  int	):
						snake_case__			:				Tuple       =  BreadthFirstSearch(__A							,		__A	)
						snake_case__			:				Tuple       =  BreadthFirstSearch(__A							,		__A	)
						snake_case__			:				Optional[Any]       =  False



					def 	_lowercase					( self					:  Dict	):
						while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
							snake_case__			:				str       =  self.fwd_bfs.node_queue.pop(0	)
							snake_case__			:				Optional[Any]       =  self.bwd_bfs.node_queue.pop(0	)

							if current_bwd_node.pos == current_fwd_node.pos:
								snake_case__			:				Dict       =  True
								return self.retrace_bidirectional_path(
								    __A							,		__A	)

							snake_case__			:				Optional[Any]       =  current_bwd_node
							snake_case__			:				Any       =  current_fwd_node

							snake_case__			:				str       =  {
							    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 	_lowercase					( self					:  Union[str, Any]							,		__A					:  Node							,		__A					:  Node	):
						snake_case__			:				Union[str, Any]       =  self.fwd_bfs.retrace_path(__A	)
						snake_case__			:				List[Any]       =  self.bwd_bfs.retrace_path(__A	)
						bwd_path.pop()
						bwd_path.reverse()
						snake_case__			:				Optional[Any]       =  fwd_path + bwd_path
						return path


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

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

	__lowerCamelCase :      Any										=				time.time()
	__lowerCamelCase :      Optional[int]										=				BreadthFirstSearch(init, goal)
	__lowerCamelCase :      List[str]										=				bfs.search()
	__lowerCamelCase :      List[Any]										=				time.time() - start_bfs_time

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

	__lowerCamelCase :      Optional[int]										=				time.time()
	__lowerCamelCase :      str										=				BidirectionalBreadthFirstSearch(init, goal)
	__lowerCamelCase :      Optional[int]										=				bd_bfs.search()
	__lowerCamelCase :      List[str]										=				time.time() - start_bd_bfs_time

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











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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


__lowerCamelCase :      int										=				logging.get_logger(__name__)







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							Any       ):
	snake_case__			:				List[str]       =  b.T
	snake_case__			:				Union[str, Any]       =  np.sum(np.square(snake_case_       )					,  axis=1       )
	snake_case__			:				Dict       =  np.sum(np.square(snake_case_       )					,  axis=0       )
	snake_case__			:				Dict       =  np.matmul(snake_case_					,  snake_case_       )
	snake_case__			:				Any       =  aa[:, None] - 2 * ab + ba[None, :]
	return d







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[int]					,  snake_case_ :							Tuple       ):
	snake_case__			:				Tuple       =  x.reshape(-1					,  3       )
	snake_case__			:				int       =  squared_euclidean_distance(snake_case_					,  snake_case_       )
	return np.argmin(snake_case_					,  axis=1       )





class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["pixel_values"]



					def __init__( self					:  str							,		__A					:  Optional[Union[List[List[int]], np.ndarray]] = None							,		__A					:  bool = True							,		__A					:  Dict[str, int] = None							,		__A					:  PILImageResampling = PILImageResampling.BILINEAR							,		__A					:  bool = True							,		__A					:  bool = True							,		**__A					:  Union[str, Any]							,		):
						super().__init__(**__A	)
						snake_case__			:				Optional[int]       =  size if size is not None else {"height": 2_5_6, "width": 2_5_6}
						snake_case__			:				List[Any]       =  get_size_dict(__A	)
						snake_case__			:				Any       =  np.array(__A	) if clusters is not None else None
						snake_case__			:				Optional[Any]       =  do_resize
						snake_case__			:				Any       =  size
						snake_case__			:				List[Any]       =  resample
						snake_case__			:				List[Any]       =  do_normalize
						snake_case__			:				Dict       =  do_color_quantize



					def 	_lowercase					( self					:  List[Any]							,		__A					:  np.ndarray							,		__A					:  Dict[str, int]							,		__A					:  PILImageResampling = PILImageResampling.BILINEAR							,		__A					:  Optional[Union[str, ChannelDimension]] = None							,		**__A					:  int							,		):
						snake_case__			:				List[Any]       =  get_size_dict(__A	)
						if "height" not in size or "width" not in size:
							raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''	)
						return resize(
						    __A							,		size=(size["height"], size["width"])							,		resample=__A							,		data_format=__A							,		**__A	)



					def 	_lowercase					( self					:  List[Any]							,		__A					:  np.ndarray							,		__A					:  Optional[Union[str, ChannelDimension]] = None							,		):
						snake_case__			:				List[str]       =  rescale(image=__A							,		scale=1 / 1_2_7.5							,		data_format=__A	)
						snake_case__			:				List[Any]       =  image - 1
						return image






					def 	_lowercase					( self					:  Dict							,		__A					:  ImageInput							,		__A					:  bool = None							,		__A					:  Dict[str, int] = None							,		__A					:  PILImageResampling = None							,		__A					:  bool = None							,		__A					:  Optional[bool] = None							,		__A					:  Optional[Union[List[List[int]], np.ndarray]] = None							,		__A					:  Optional[Union[str, TensorType]] = None							,		__A					:  Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST							,		**__A					:  Optional[int]							,		):
						snake_case__			:				Any       =  do_resize if do_resize is not None else self.do_resize
						snake_case__			:				Union[str, Any]       =  size if size is not None else self.size
						snake_case__			:				Union[str, Any]       =  get_size_dict(__A	)
						snake_case__			:				Optional[Any]       =  resample if resample is not None else self.resample
						snake_case__			:				Tuple       =  do_normalize if do_normalize is not None else self.do_normalize
						snake_case__			:				Optional[Any]       =  do_color_quantize if do_color_quantize is not None else self.do_color_quantize
						snake_case__			:				Union[str, Any]       =  clusters if clusters is not None else self.clusters
						snake_case__			:				Union[str, Any]       =  np.array(__A	)

						snake_case__			:				Any       =  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 or resample is None:
							raise ValueError("Size and resample must be specified if do_resize is True."	)

						if do_color_quantize and clusters is None:
							raise ValueError("Clusters must be specified if do_color_quantize is True."	)

						# All transformations expect numpy arrays.
						snake_case__			:				Optional[Any]       =  [to_numpy_array(__A	) for image in images]

						if do_resize:
							snake_case__			:				List[str]       =  [self.resize(image=__A							,		size=__A							,		resample=__A	) for image in images]

						if do_normalize:
							snake_case__			:				Union[str, Any]       =  [self.normalize(image=__A	) for image in images]

						if do_color_quantize:
							snake_case__			:				int       =  [to_channel_dimension_format(__A							,		ChannelDimension.LAST	) for image in images]
							# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
							snake_case__			:				int       =  np.array(__A	)
							snake_case__			:				Dict       =  color_quantize(__A							,		__A	).reshape(images.shape[:-1]	)

							# flatten to (batch_size, height*width)
							snake_case__			:				str       =  images.shape[0]
							snake_case__			:				str       =  images.reshape(__A							,		-1	)

							# We need to convert back to a list of images to keep consistent behaviour across processors.
							snake_case__			:				Union[str, Any]       =  list(__A	)
						else:
							snake_case__			:				Any       =  [to_channel_dimension_format(__A							,		__A	) for image in images]

						snake_case__			:				Optional[int]       =  {"input_ids": images}
						return BatchFeature(data=__A							,		tensor_type=__A	)



