






"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
							import torch

if is_vision_available():
							import PIL


_lowerCAmelCase         =							logging.get_logger(__name__)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):
	def constraint_to_multiple_of(snake_case__   ,   snake_case__   ,   snake_case__=0   ,   snake_case__=None					):
		A_		:						int	    =   round(val / multiple					) * multiple

		if max_val is not None and x > max_val:
			A_		:						Any	    =   math.floor(val / multiple					) * multiple

		if x < min_val:
			A_		:						Tuple	    =   math.ceil(val / multiple					) * multiple

		return x

	A_		:						List[str]	    =   (output_size, output_size) if isinstance(snake_case__   ,   snake_case__					) else output_size

	A_		:						List[Any]	    =   get_image_size(snake_case__					)
	A_		:						List[Any]	    =   output_size

	# determine new height and width
	A_		:						Any	    =   output_height / input_height
	A_		:						Optional[int]	    =   output_width / input_width

	if keep_aspect_ratio:
		# scale as little as possible
		if abs(1 - scale_width					) < abs(1 - scale_height					):
			# fit width
			A_		:						Optional[Any]	    =   scale_width
		else:
			# fit height
			A_		:						Any	    =   scale_height

	A_		:						List[Any]	    =   constraint_to_multiple_of(scale_height * input_height   ,   multiple=snake_case__					)
	A_		:						Any	    =   constraint_to_multiple_of(scale_width * input_width   ,   multiple=snake_case__					)

	return (new_height, new_width)




class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Any    =				["""pixel_values"""]





	def __init__(self     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = PILImageResampling.BILINEAR     ,     lowerCAmelCase_ = False     ,     lowerCAmelCase_ = 1     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = 1 / 255     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
		super().__init__(**lowerCAmelCase_							)
		A_		:						Optional[Any]	    =   size if size is not None else {"""height""": 384, """width""": 384}
		A_		:						Union[str, Any]	    =   get_size_dict(lowerCAmelCase_							)
		A_		:						Dict	    =   do_resize
		A_		:						Tuple	    =   size
		A_		:						Optional[Any]	    =   keep_aspect_ratio
		A_		:						Tuple	    =   ensure_multiple_of
		A_		:						List[str]	    =   resample
		A_		:						Tuple	    =   do_rescale
		A_		:						Optional[Any]	    =   rescale_factor
		A_		:						Optional[Any]	    =   do_normalize
		A_		:						Optional[int]	    =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		A_		:						Union[str, Any]	    =   image_std if image_std is not None else IMAGENET_STANDARD_STD





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = False     ,     lowerCAmelCase_ = 1     ,     lowerCAmelCase_ = PILImageResampling.BICUBIC     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
		A_		:						Union[str, Any]	    =   get_size_dict(lowerCAmelCase_							)
		if "height" not in size or "width" not in size:
			raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""							)
		A_		:						List[str]	    =   get_resize_output_image_size(
		    lowerCAmelCase_     ,     output_size=(size["""height"""], size["""width"""])     ,     keep_aspect_ratio=lowerCAmelCase_     ,     multiple=lowerCAmelCase_     ,     )
		return resize(lowerCAmelCase_     ,     size=lowerCAmelCase_     ,     resample=lowerCAmelCase_     ,     data_format=lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
		return rescale(lowerCAmelCase_     ,     scale=lowerCAmelCase_     ,     data_format=lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
		return normalize(lowerCAmelCase_     ,     mean=lowerCAmelCase_     ,     std=lowerCAmelCase_     ,     data_format=lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = ChannelDimension.FIRST     ,     **lowerCAmelCase_     ,     ):
		A_		:						Dict	    =   do_resize if do_resize is not None else self.do_resize
		A_		:						List[Any]	    =   size if size is not None else self.size
		A_		:						str	    =   get_size_dict(lowerCAmelCase_							)
		A_		:						List[Any]	    =   keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
		A_		:						Tuple	    =   ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
		A_		:						List[Any]	    =   resample if resample is not None else self.resample
		A_		:						Optional[int]	    =   do_rescale if do_rescale is not None else self.do_rescale
		A_		:						Dict	    =   rescale_factor if rescale_factor is not None else self.rescale_factor
		A_		:						Optional[int]	    =   do_normalize if do_normalize is not None else self.do_normalize
		A_		:						str	    =   image_mean if image_mean is not None else self.image_mean
		A_		:						str	    =   image_std if image_std is not None else self.image_std

		A_		:						Tuple	    =   make_list_of_images(lowerCAmelCase_							)

		if not valid_images(lowerCAmelCase_							):
			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_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.
		A_		:						Union[str, Any]	    =   [to_numpy_array(lowerCAmelCase_							) for image in images]

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

		if do_rescale:
			A_		:						Any	    =   [self.rescale(image=lowerCAmelCase_     ,     scale=lowerCAmelCase_							) for image in images]

		if do_normalize:
			A_		:						List[Any]	    =   [self.normalize(image=lowerCAmelCase_     ,     mean=lowerCAmelCase_     ,     std=lowerCAmelCase_							) for image in images]

		A_		:						List[str]	    =   [to_channel_dimension_format(lowerCAmelCase_     ,     lowerCAmelCase_							) for image in images]

		A_		:						Tuple	    =   {"""pixel_values""": images}
		return BatchFeature(data=lowerCAmelCase_     ,     tensor_type=lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
		A_		:						Any	    =   outputs.logits

		# Resize logits and compute semantic segmentation maps
		if target_sizes is not None:
			if len(lowerCAmelCase_							) != len(lowerCAmelCase_							):
				raise ValueError(
				    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""							)

			if is_torch_tensor(lowerCAmelCase_							):
				A_		:						Any	    =   target_sizes.numpy()

			A_		:						Optional[int]	    =   []

			for idx in range(len(lowerCAmelCase_							)							):
				A_		:						Optional[Any]	    =   torch.nn.functional.interpolate(
				    logits[idx].unsqueeze(dim=0							)     ,     size=target_sizes[idx]     ,     mode="""bilinear"""     ,     align_corners=lowerCAmelCase_							)
				A_		:						List[Any]	    =   resized_logits[0].argmax(dim=0							)
				semantic_segmentation.append(lowerCAmelCase_							)
		else:
			A_		:						List[Any]	    =   logits.argmax(dim=1							)
			A_		:						Tuple	    =   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

		return semantic_segmentation









"""simple docstring"""
import heapq
import sys

import numpy as np

_lowerCAmelCase         =							tuple[int, int]



class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self							):
		A_		:						Any	    =   []
		A_		:						str	    =   set()





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





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





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		if item not in self.set:
			heapq.heappush(self.elements     ,     (priority, item)							)
			self.set.add(lowerCAmelCase_							)
		else:
			# update
			# print("update", item)
			A_		:						Union[str, Any]	    =   []
			((A_)    ,     (A_))		:						Union[str, Any]	    =   heapq.heappop(self.elements							)
			while x != item:
				temp.append((pri, x)							)
				((A_)    ,     (A_))		:						Any	    =   heapq.heappop(self.elements							)
			temp.append((priority, item)							)
			for pro, xxx in temp:
				heapq.heappush(self.elements     ,     (pro, xxx)							)





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





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





	def    lowerCamelCase(self							):
		((A_)    ,     (A_))		:						Optional[Any]	    =   heapq.heappop(self.elements							)
		self.set.remove(lowerCAmelCase_							)
		return (priority, item)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	# euclidean distance
	A_		:						Optional[Any]	    =   np.array(snake_case__					)
	A_		:						List[str]	    =   np.array(snake_case__					)
	return np.linalg.norm(a - b					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	# integer division by time variable
	return consistent_heuristic(snake_case__   ,   snake_case__					) // t






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	# manhattan distance
	return abs(p[0] - goal[0]					) + abs(p[1] - goal[1]					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Optional[int]	    =   g_function[start] + Wa * heuristics[i](snake_case__   ,   snake_case__					)
	return ans






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Tuple	    =   np.chararray((n, n)					)
	for i in range(snake_case__					):
		for j in range(snake_case__					):
			A_		:						List[Any]	    =   """*"""

	for i in range(snake_case__					):
		for j in range(snake_case__					):
			if (j, (n - 1) - i) in blocks:
				A_		:						Optional[Any]	    =   """#"""

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

	for i in range(snake_case__					):
		for j in range(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:-"""					)
	A_		:						List[str]	    =   back_pointer[goal]
	while x != start:
		print(snake_case__   ,   end=""" """					)
		A_		:						Optional[int]	    =   back_pointer[x]
	print(snake_case__					)
	sys.exit()






def 			__UpperCamelCase	(		snake_case__					):
	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 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   ):
	for itera in range(snake_case__					):
		open_list[itera].remove_element(snake_case__					)
	# print("s", s)
	# print("j", j)
	((A_)    ,     (A_))		:						Any	    =   s
	A_		:						int	    =   (x - 1, y)
	A_		:						List[str]	    =   (x + 1, y)
	A_		:						Union[str, Any]	    =   (x, y + 1)
	A_		:						Any	    =   (x, y - 1)

	for neighbours in [left, right, up, down]:
		if neighbours not in blocks:
			if valid(snake_case__					) and neighbours not in visited:
				# print("neighbour", neighbours)
				visited.add(snake_case__					)
				A_		:						int	    =   -1
				A_		:						Optional[int]	    =   float("""inf"""					)

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






def 			__UpperCamelCase	(		):
	A_		:						str	    =   []
	for x in range(1   ,   5					):
		for y in range(1   ,   6					):
			some_list.append((x, y)					)

	for x in range(15   ,   20					):
		some_list.append((x, 17)					)

	for x in range(10   ,   19					):
		for y in range(1   ,   15					):
			some_list.append((x, y)					)

    # L block
	for x in range(1   ,   4					):
		for y in range(12   ,   19					):
			some_list.append((x, y)					)
	for x in range(3   ,   13					):
		for y in range(16   ,   19					):
			some_list.append((x, y)					)
	return some_list


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

_lowerCAmelCase         =							[
    (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),
]
_lowerCAmelCase         =							make_common_ground()


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

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

_lowerCAmelCase         =							1






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Tuple	    =   {start: 0, goal: float("""inf"""					)}
	A_		:						List[str]	    =   {start: -1, goal: -1}
	A_		:						str	    =   []
	A_		:						int	    =   set()

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

	A_		:						list[int]	    =   []
	A_		:						list[int]	    =   []
	while open_list[0].minkey() < float("""inf"""					):
		for i in range(1   ,   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(snake_case__   ,   snake_case__   ,   snake_case__					)
				else:
					A_    ,     A_		:						Optional[Any]	    =   open_list[i].top_show()
					visited.add(snake_case__					)
					expand_state(
					    snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   )
					close_list_inad.append(snake_case__					)
			else:
				if g_function[goal] <= open_list[0].minkey():
					if g_function[goal] < float("""inf"""					):
						do_something(snake_case__   ,   snake_case__   ,   snake_case__					)
				else:
					A_		:						int	    =   open_list[0].top_show()
					visited.add(snake_case__					)
					expand_state(
					    snake_case__   ,   0   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   )
					close_list_anchor.append(snake_case__					)
	print("""No path found to goal"""					)
	print()
	for i in range(n - 1   ,   -1   ,   -1					):
		for j in range(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)

