


from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
a			:			Union[str, Any]												=					0

a			:			Dict												=					[
    [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],
]

a			:			Dict												=					[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

a			:			Dict												=					tuple[int, int]
class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							) ->      None:







								'''simple docstring'''

								__lowercase			       = pos_x
								__lowercase			       = pos_y
								__lowercase			       = (pos_y, pos_x)
								__lowercase			       = goal_x
								__lowercase			       = goal_y
								__lowercase			       = g_cost
								__lowercase			       = parent
								__lowercase			       = self.calculate_heuristic()
								__lowercase			       = self.g_cost + self.h_cost






					def A   (					self    ) ->      float:







								'''simple docstring'''

								__lowercase			       = self.pos_x - self.goal_x
								__lowercase			       = self.pos_y - self.goal_y
								if HEURISTIC == 1:
											return abs(snake_case_    ) + abs(snake_case_    )
								else:
											return sqrt(dy**2 + dx**2    )






					def __lt__(					self			,							snake_case_    ) ->      bool:







								'''simple docstring'''

								return self.f_cost < other.f_cost




class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = Node(start[1]			,							start[0]			,							goal[1]			,							goal[0]			,							0			,							snake_case_    )
								__lowercase			       = Node(goal[1]			,							goal[0]			,							goal[1]			,							goal[0]			,							9_9_9_9_9			,							snake_case_    )

								__lowercase			       = [self.start]
								__lowercase			       = []

								__lowercase			       = False






					def A   (					self    ) ->      list[TPosition]:







								'''simple docstring'''

								while self.open_nodes:
											# Open Nodes are sorted using __lt__
											self.open_nodes.sort()
											__lowercase			       = self.open_nodes.pop(0    )

											if current_node.pos == self.target.pos:
														return self.retrace_path(snake_case_    )

											self.closed_nodes.append(snake_case_    )
											__lowercase			       = self.get_successors(snake_case_    )

											for child_node in successors:
														if child_node in self.closed_nodes:
																	continue

														if child_node not in self.open_nodes:
																	self.open_nodes.append(snake_case_    )
														else:
																	# retrieve the best current path
																	__lowercase			       = self.open_nodes.pop(self.open_nodes.index(snake_case_    )    )

																	if child_node.g_cost < better_node.g_cost:
																				self.open_nodes.append(snake_case_    )
																	else:
																				self.open_nodes.append(snake_case_    )

								return [self.start.pos]






					def A   (					self			,							snake_case_    ) ->      list[Node]:







								'''simple docstring'''

								__lowercase			       = []
								for action in delta:
											__lowercase			       = parent.pos_x + action[1]
											__lowercase			       = 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			,							parent.g_cost + 1			,							snake_case_			,							)    )
								return successors






					def A   (					self			,							snake_case_    ) ->      list[TPosition]:







								'''simple docstring'''

								__lowercase			       = node
								__lowercase			       = []
								while current_node is not None:
											path.append((current_node.pos_y, current_node.pos_x)    )
											__lowercase			       = current_node.parent
								path.reverse()
								return path




class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_    ) ->      None:







								'''simple docstring'''

								__lowercase			       = AStar(snake_case_			,							snake_case_    )
								__lowercase			       = AStar(snake_case_			,							snake_case_    )
								__lowercase			       = False






					def A   (					self    ) ->      list[TPosition]:







								'''simple docstring'''

								while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
											self.fwd_astar.open_nodes.sort()
											self.bwd_astar.open_nodes.sort()
											__lowercase			       = self.fwd_astar.open_nodes.pop(0    )
											__lowercase			       = self.bwd_astar.open_nodes.pop(0    )

											if current_bwd_node.pos == current_fwd_node.pos:
														return self.retrace_bidirectional_path(
														    snake_case_			,							snake_case_    )

											self.fwd_astar.closed_nodes.append(snake_case_    )
											self.bwd_astar.closed_nodes.append(snake_case_    )

											__lowercase			       = current_bwd_node
											__lowercase			       = current_fwd_node

											__lowercase			       = {
											    self.fwd_astar: self.fwd_astar.get_successors(snake_case_    ),
											    self.bwd_astar: self.bwd_astar.get_successors(snake_case_    ),
											}

											for astar in [self.fwd_astar, self.bwd_astar]:
														for child_node in successors[astar]:
																	if child_node in astar.closed_nodes:
																				continue

																	if child_node not in astar.open_nodes:
																				astar.open_nodes.append(snake_case_    )
																	else:
																				# retrieve the best current path
																				__lowercase			       = astar.open_nodes.pop(
																				    astar.open_nodes.index(snake_case_    )    )

																				if child_node.g_cost < better_node.g_cost:
																							astar.open_nodes.append(snake_case_    )
																				else:
																							astar.open_nodes.append(snake_case_    )

								return [self.fwd_astar.start.pos]






					def A   (					self			,							snake_case_			,							snake_case_    ) ->      list[TPosition]:







								'''simple docstring'''

								__lowercase			       = self.fwd_astar.retrace_path(snake_case_    )
								__lowercase			       = self.bwd_astar.retrace_path(snake_case_    )
								bwd_path.pop()
								bwd_path.reverse()
								__lowercase			       = fwd_path + bwd_path
								return path


if __name__ == "__main__":
	# all coordinates are given in format [y,x]
	a			:			Any												=					(0, 0)
	a			:			Optional[int]												=					(len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	a			:			Any												=					time.time()
	a			:			Any												=					AStar(init, goal)
	a			:			List[str]												=					a_star.search()
	a			:			List[str]												=					time.time() - start_time
	print(f'''AStar execution time = {end_time:f} seconds''')

	a			:			Optional[int]												=					time.time()
	a			:			int												=					BidirectionalAStar(init, goal)
	a			:			Dict												=					time.time() - bd_start_time
	print(f'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')






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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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


a			:			List[str]												=					logging.get_logger(__name__)
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					["pixel_values"]






					def __init__(					self			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = PILImageResampling.BILINEAR			,							snake_case_ = True			,							snake_case_ = 1 / 2_5_5			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = None			,							**snake_case_			,							) ->      None:







								'''simple docstring'''

								super().__init__(**snake_case_    )
								__lowercase			       = size if size is not None else {'''shortest_edge''': 3_8_4}
								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )

								__lowercase			       = do_resize
								__lowercase			       = size
								# Default value set here for backwards compatibility where the value in config is None
								__lowercase			       = crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
								__lowercase			       = resample
								__lowercase			       = do_rescale
								__lowercase			       = rescale_factor
								__lowercase			       = do_normalize
								__lowercase			       = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__lowercase			       = image_std if image_std is not None else IMAGENET_STANDARD_STD






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = PILImageResampling.BICUBIC			,							snake_case_ = None			,							**snake_case_			,							) ->      np.ndarray:







								'''simple docstring'''

								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )
								if "shortest_edge" not in size:
											raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'    )
								__lowercase			       = size['''shortest_edge''']

								if shortest_edge < 3_8_4:
											# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
											__lowercase			       = int(shortest_edge / crop_pct    )
											__lowercase			       = get_resize_output_image_size(snake_case_			,							size=snake_case_			,							default_to_square=snake_case_    )
											__lowercase			       = resize(image=snake_case_			,							size=snake_case_			,							resample=snake_case_			,							data_format=snake_case_			,							**snake_case_    )
											# then crop to (shortest_edge, shortest_edge)
											return center_crop(image=snake_case_			,							size=(shortest_edge, shortest_edge)			,							data_format=snake_case_			,							**snake_case_    )
								else:
											# warping (no cropping) when evaluated at 384 or larger
											return resize(
											    snake_case_			,							size=(shortest_edge, shortest_edge)			,							resample=snake_case_			,							data_format=snake_case_			,							**snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							**snake_case_			,							) ->      Optional[int]:







								'''simple docstring'''

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






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							**snake_case_			,							) ->      np.ndarray:







								'''simple docstring'''

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






					def A   (					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = ChannelDimension.FIRST			,							**snake_case_			,							) ->      PIL.Image.Image:







								'''simple docstring'''

								__lowercase			       = do_resize if do_resize is not None else self.do_resize
								__lowercase			       = crop_pct if crop_pct is not None else self.crop_pct
								__lowercase			       = resample if resample is not None else self.resample
								__lowercase			       = do_rescale if do_rescale is not None else self.do_rescale
								__lowercase			       = rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowercase			       = do_normalize if do_normalize is not None else self.do_normalize
								__lowercase			       = image_mean if image_mean is not None else self.image_mean
								__lowercase			       = image_std if image_std is not None else self.image_std

								__lowercase			       = size if size is not None else self.size
								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )

								__lowercase			       = make_list_of_images(snake_case_    )

								if not valid_images(snake_case_    ):
											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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
											raise ValueError('''crop_pct must be specified if size < 384.'''    )

								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.'''    )

								# All transformations expect numpy arrays.
								__lowercase			       = [to_numpy_array(snake_case_    ) for image in images]

								if do_resize:
											__lowercase			       = [self.resize(image=snake_case_			,							size=snake_case_			,							crop_pct=snake_case_			,							resample=snake_case_    ) for image in images]

								if do_rescale:
											__lowercase			       = [self.rescale(image=snake_case_			,							scale=snake_case_    ) for image in images]

								if do_normalize:
											__lowercase			       = [self.normalize(image=snake_case_			,							mean=snake_case_			,							std=snake_case_    ) for image in images]

								__lowercase			       = [to_channel_dimension_format(snake_case_			,							snake_case_    ) for image in images]

								__lowercase			       = {'''pixel_values''': images}
								return BatchFeature(data=snake_case_			,							tensor_type=snake_case_    )


