



"""simple docstring"""

import heapq
import sys

import numpy as np

a_			   =					tuple[int, int]







class    __lowercase     :







						"""simple docstring"""







						def __init__(self       ):
											snake_case_					:		Union[str, Any] 					=					[]
											snake_case_					:		List[str] 					=					set()







						def 						__UpperCamelCase							(self       ):
											if not self.empty():
																return self.elements[0][0]
											else:
																return float("""inf"""       )







						def 						__UpperCamelCase							(self       ):
											return len(self.elements       ) == 0







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											if item not in self.set:
																heapq.heappush(self.elements   ,		(priority, item)       )
																self.set.add(lowercase__       )
											else:
																# update
																# print("update", item)
																snake_case_					:		Optional[int] 					=					[]
																((snake_case_)					,			(snake_case_))					:		int 					=					heapq.heappop(self.elements       )
																while x != item:
																					temp.append((pri, x)       )
																					((snake_case_)					,			(snake_case_))					:		Dict 					=					heapq.heappop(self.elements       )
																temp.append((priority, item)       )
																for pro, xxx in temp:
																					heapq.heappush(self.elements   ,		(pro, xxx)       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if item in self.set:
																self.set.remove(lowercase__       )
																snake_case_					:		List[str] 					=					[]
																((snake_case_)					,			(snake_case_))					:		List[Any] 					=					heapq.heappop(self.elements       )
																while x != item:
																					temp.append((pro, x)       )
																					((snake_case_)					,			(snake_case_))					:		int 					=					heapq.heappop(self.elements       )
																for prito, yyy in temp:
																					heapq.heappush(self.elements   ,		(prito, yyy)       )







						def 						__UpperCamelCase							(self       ):
											return self.elements[0][1]







						def 						__UpperCamelCase							(self       ):
											((snake_case_)					,			(snake_case_))					:		Dict 					=					heapq.heappop(self.elements       )
											self.set.remove(lowercase__       )
											return (priority, item)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  TPos				,    SCREAMING_SNAKE_CASE__ :  TPos			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					np.array(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Tuple 					=					np.array(SCREAMING_SNAKE_CASE__			)
					return np.linalg.norm(a - b			)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  TPos				,    SCREAMING_SNAKE_CASE__ :  TPos			):



					"""simple docstring"""





					return consistent_heuristic(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			) // t







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  TPos				,    SCREAMING_SNAKE_CASE__ :  TPos			):



					"""simple docstring"""





					return abs(p[0] - goal[0]			) + abs(p[1] - goal[1]			)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  TPos				,    SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  TPos				,    SCREAMING_SNAKE_CASE__ :  dict[TPos, float]			):



					"""simple docstring"""





					snake_case_					:		int 					=					g_function[start] + Wa * heuristics[i](SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					return ans







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					np.chararray((n, n)			)
					for i in range(SCREAMING_SNAKE_CASE__			):
										for j in range(SCREAMING_SNAKE_CASE__			):
															snake_case_					:		Dict 					=					"""*"""

					for i in range(SCREAMING_SNAKE_CASE__			):
										for j in range(SCREAMING_SNAKE_CASE__			):
															if (j, (n - 1) - i) in blocks:
																				snake_case_					:		Dict 					=					"""#"""

					snake_case_					:		Dict 					=					"""-"""
					snake_case_					:		List[Any] 					=					back_pointer[goal]
					while x != start:
										((snake_case_)					,			(snake_case_))					:		Union[str, Any] 					=					x
										# print(x)
										snake_case_					:		List[Any] 					=					"""-"""
										snake_case_					:		Optional[Any] 					=					back_pointer[x]
					snake_case_					:		Optional[int] 					=					"""-"""

					for i in range(SCREAMING_SNAKE_CASE__			):
										for j in range(SCREAMING_SNAKE_CASE__			):
															if (i, j) == (0, n - 1):
																				print(grid[i][j]				,    end=""" """			)
																				print("""<-- End position"""				,    end=""" """			)
															else:
																				print(grid[i][j]				,    end=""" """			)
										print()
					print("""^"""			)
					print("""Start position"""			)
					print()
					print("""# is an obstacle"""			)
					print("""- is the path taken by algorithm"""			)
					print("""PATH TAKEN BY THE ALGORITHM IS:-"""			)
					snake_case_					:		int 					=					back_pointer[goal]
					while x != start:
										print(SCREAMING_SNAKE_CASE__				,    end=""" """			)
										snake_case_					:		int 					=					back_pointer[x]
					print(SCREAMING_SNAKE_CASE__			)
					sys.exit()







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  TPos			):



					"""simple docstring"""





					if p[0] < 0 or p[0] > n - 1:
										return False
					if p[1] < 0 or p[1] > n - 1:
										return False
					return True







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  Optional[int]				,    ):



					"""simple docstring"""





					for itera in range(SCREAMING_SNAKE_CASE__			):
										open_list[itera].remove_element(SCREAMING_SNAKE_CASE__			)
					# print("s", s)
					# print("j", j)
					((snake_case_)					,			(snake_case_))					:		Optional[Any] 					=					s
					snake_case_					:		Union[str, Any] 					=					(x - 1, y)
					snake_case_					:		Any 					=					(x + 1, y)
					snake_case_					:		Tuple 					=					(x, y + 1)
					snake_case_					:		int 					=					(x, y - 1)

					for neighbours in [left, right, up, down]:
										if neighbours not in blocks:
															if valid(SCREAMING_SNAKE_CASE__			) and neighbours not in visited:
																				# print("neighbour", neighbours)
																				visited.add(SCREAMING_SNAKE_CASE__			)
																				snake_case_					:		Dict 					=					-1
																				snake_case_					:		Union[str, Any] 					=					float("""inf"""			)

															if valid(SCREAMING_SNAKE_CASE__			) and g_function[neighbours] > g_function[s] + 1:
																				snake_case_					:		Optional[Any] 					=					g_function[s] + 1
																				snake_case_					:		Tuple 					=					s
																				if neighbours not in close_list_anchor:
																									open_list[0].put(SCREAMING_SNAKE_CASE__				,    key(SCREAMING_SNAKE_CASE__				,    0				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)			)
																									if neighbours not in close_list_inad:
																														for var in range(1				,    SCREAMING_SNAKE_CASE__			):
																																			if key(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			) <= Wa * key(
																																			    SCREAMING_SNAKE_CASE__				,    0				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
																																								open_list[j].put(
																																								    SCREAMING_SNAKE_CASE__				,    key(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)			)







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		int 					=					[]
					for x in range(1				,    5			):
										for y in range(1				,    6			):
															some_list.append((x, y)			)

					for x in range(1_5				,    2_0			):
										some_list.append((x, 1_7)			)

					for x in range(1_0				,    1_9			):
										for y in range(1				,    1_5			):
															some_list.append((x, y)			)

    # L block
					for x in range(1				,    4			):
										for y in range(1_2				,    1_9			):
															some_list.append((x, y)			)
					for x in range(3				,    1_3			):
										for y in range(1_6				,    1_9			):
															some_list.append((x, y)			)
					return some_list


a_			   =					{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

a_			   =					[
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
a_			   =					make_common_ground()


a_			   =					blocks_blk
# hyper parameters
a_			   =					1
a_			   =					1
a_			   =					20
a_			   =					3  # one consistent and two other inconsistent

# start and end destination
a_			   =					(0, 0)
a_			   =					(n - 1, n - 1)

a_			   =					1







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  TPos				,    SCREAMING_SNAKE_CASE__ :  TPos				,    SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					{start: 0, goal: float("""inf"""			)}
					snake_case_					:		Tuple 					=					{start: -1, goal: -1}
					snake_case_					:		int 					=					[]
					snake_case_					:		str 					=					set()

					for i in range(SCREAMING_SNAKE_CASE__			):
										open_list.append(PriorityQueue()			)
										open_list[i].put(SCREAMING_SNAKE_CASE__				,    key(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)			)

					snake_case_					:		list[int] 					=					[]
					snake_case_					:		list[int] 					=					[]
					while open_list[0].minkey() < float("""inf"""			):
										for i in range(1				,    SCREAMING_SNAKE_CASE__			):
															# print(open_list[0].minkey(), open_list[i].minkey())
															if open_list[i].minkey() <= Wa * open_list[0].minkey():
																				global t
																				t += 1
																				if g_function[goal] <= open_list[i].minkey():
																									if g_function[goal] < float("""inf"""			):
																														do_something(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
																				else:
																									snake_case_					,			snake_case_					:		Dict 					=					open_list[i].top_show()
																									visited.add(SCREAMING_SNAKE_CASE__			)
																									expand_state(
																									    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    )
																									close_list_inad.append(SCREAMING_SNAKE_CASE__			)
															else:
																				if g_function[goal] <= open_list[0].minkey():
																									if g_function[goal] < float("""inf"""			):
																														do_something(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
																				else:
																									snake_case_					:		Optional[int] 					=					open_list[0].top_show()
																									visited.add(SCREAMING_SNAKE_CASE__			)
																									expand_state(
																									    SCREAMING_SNAKE_CASE__				,    0				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    )
																									close_list_anchor.append(SCREAMING_SNAKE_CASE__			)
					print("""No path found to goal"""			)
					print()
					for i in range(n - 1				,    -1				,    -1			):
										for j in range(SCREAMING_SNAKE_CASE__			):
															if (j, i) in blocks:
																				print("""#"""				,    end=""" """			)
															elif (j, i) in back_pointer:
																				if (j, i) == (n - 1, n - 1):
																									print("""*"""				,    end=""" """			)
																				else:
																									print("""-"""				,    end=""" """			)
															else:
																				print("""*"""				,    end=""" """			)
															if (j, i) == (n - 1, n - 1):
																				print("""<-- End position"""				,    end=""" """			)
										print()
					print("""^"""			)
					print("""Start position"""			)
					print()
					print("""# is an obstacle"""			)
					print("""- is the path taken by algorithm"""			)


if __name__ == "__main__":
			multi_a_star(start, goal, n_heuristic)









"""simple docstring"""

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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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_			   =					logging.get_logger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							int							       =		["""pixel_values"""]







						def __init__(self   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = 0.9   ,		lowercase__ = PILImageResampling.BICUBIC   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = 1 / 2_55   ,		lowercase__ = True   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = None   ,		**lowercase__   ,		):
											super().__init__(**lowercase__       )
											snake_case_					:		Tuple 					=					size if size is not None else {"""shortest_edge""": 2_24}
											snake_case_					:		Union[str, Any] 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											snake_case_					:		str 					=					crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
											snake_case_					:		Dict 					=					get_size_dict(lowercase__   ,		param_name="""crop_size"""       )

											snake_case_					:		Union[str, Any] 					=					do_resize
											snake_case_					:		List[str] 					=					size
											snake_case_					:		str 					=					crop_pct
											snake_case_					:		str 					=					resample
											snake_case_					:		Optional[Any] 					=					do_center_crop
											snake_case_					:		Dict 					=					crop_size
											snake_case_					:		int 					=					do_rescale
											snake_case_					:		Optional[int] 					=					rescale_factor
											snake_case_					:		str 					=					do_normalize
											snake_case_					:		str 					=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
											snake_case_					:		List[str] 					=					image_std if image_std is not None else IMAGENET_DEFAULT_STD







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		lowercase__ = PILImageResampling.BICUBIC   ,		lowercase__ = None   ,		**lowercase__   ,		):
											snake_case_					:		Tuple 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											if "shortest_edge" not in size and ("height" not in size or "width" not in size):
																raise ValueError(f'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'       )
											if crop_pct is not None:
																if "shortest_edge" in size:
																					snake_case_					:		Optional[int] 					=					int(size["""shortest_edge"""] / crop_pct       )
																elif "height" in size and "width" in size:
																					if size["height"] == size["width"]:
																										snake_case_					:		Dict 					=					int(size["""height"""] / crop_pct       )
																					else:
																										snake_case_					:		List[str] 					=					(int(size["""height"""] / crop_pct       ), int(size["""width"""] / crop_pct       ))
																else:
																					raise ValueError("""Invalid size for resize: {}""".format(lowercase__       )       )

																snake_case_					:		List[Any] 					=					get_resize_output_image_size(lowercase__   ,		size=lowercase__   ,		default_to_square=lowercase__       )
											else:
																if "shortest_edge" in size:
																					snake_case_					:		Optional[int] 					=					get_resize_output_image_size(lowercase__   ,		size=size["""shortest_edge"""]   ,		default_to_square=lowercase__       )
																elif "height" in size and "width" in size:
																					snake_case_					:		int 					=					(size["""height"""], size["""width"""])
																else:
																					raise ValueError("""Invalid size for resize: {}""".format(lowercase__       )       )

											return resize(lowercase__   ,		size=lowercase__   ,		resample=lowercase__   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		**lowercase__   ,		):
											snake_case_					:		int 					=					get_size_dict(lowercase__       )
											if "height" not in size or "width" not in size:
																raise ValueError(f'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'       )
											return center_crop(lowercase__   ,		size=(size["""height"""], size["""width"""])   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		**lowercase__   ,		):
											return rescale(lowercase__   ,		scale=lowercase__   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		**lowercase__   ,		):
											return normalize(lowercase__   ,		mean=lowercase__   ,		std=lowercase__   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = ChannelDimension.FIRST   ,		**lowercase__   ,		):
											snake_case_					:		str 					=					do_resize if do_resize is not None else self.do_resize
											snake_case_					:		Any 					=					crop_pct if crop_pct is not None else self.crop_pct
											snake_case_					:		List[Any] 					=					resample if resample is not None else self.resample
											snake_case_					:		str 					=					do_center_crop if do_center_crop is not None else self.do_center_crop
											snake_case_					:		str 					=					do_rescale if do_rescale is not None else self.do_rescale
											snake_case_					:		Any 					=					rescale_factor if rescale_factor is not None else self.rescale_factor
											snake_case_					:		str 					=					do_normalize if do_normalize is not None else self.do_normalize
											snake_case_					:		List[Any] 					=					image_mean if image_mean is not None else self.image_mean
											snake_case_					:		int 					=					image_std if image_std is not None else self.image_std

											snake_case_					:		List[Any] 					=					size if size is not None else self.size
											snake_case_					:		Optional[Any] 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											snake_case_					:		List[Any] 					=					crop_size if crop_size is not None else self.crop_size
											snake_case_					:		int 					=					get_size_dict(lowercase__   ,		param_name="""crop_size"""       )

											snake_case_					:		List[str] 					=					make_list_of_images(lowercase__       )

											if not valid_images(lowercase__       ):
																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_center_crop and crop_pct is None:
																raise ValueError("""Crop_pct 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."""       )

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

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

											if do_center_crop:
																snake_case_					:		Optional[int] 					=					[self.center_crop(image=lowercase__   ,		size=lowercase__       ) for image in images]

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

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

											snake_case_					:		List[Any] 					=					[to_channel_dimension_format(lowercase__   ,		lowercase__       ) for image in images]

											snake_case_					:		Dict 					=					{"""pixel_values""": images}
											return BatchFeature(data=lowercase__   ,		tensor_type=lowercase__       )




