
"""simple docstring"""




from __future__ import annotations

UpperCAmelCase__      :    Union[str, Any]    							= list[tuple[int, int]]

UpperCAmelCase__      :    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],
]

UpperCAmelCase__      :    Optional[Any]    							= ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right



class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   pos_x
											SCREAMING_SNAKE_CASE__						:       int           =   pos_y
											SCREAMING_SNAKE_CASE__						:       Tuple           =   (pos_y, pos_x)
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   goal_x
											SCREAMING_SNAKE_CASE__						:       Dict           =   goal_y
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   g_cost
											SCREAMING_SNAKE_CASE__						:       Tuple           =   parent
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.calculate_heuristic()



				def 	__magic_name__    (self    )					->		float:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   abs(self.pos_x - self.goal_x    )
											SCREAMING_SNAKE_CASE__						:       Any           =   abs(self.pos_y - self.goal_y    )
											return dx + dy



				def __lt__(self   ,       SCREAMING_SNAKE_CASE__    )					->		bool:



											"""simple docstring"""




											return self.f_cost < other.f_cost






class 	lowerCAmelCase_						:


				"""simple docstring"""



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   Node(start[1]   ,       start[0]   ,       goal[1]   ,       goal[0]   ,       0   ,       __lowercase    )
											SCREAMING_SNAKE_CASE__						:       str           =   Node(goal[1]   ,       goal[0]   ,       goal[1]   ,       goal[0]   ,       9_99_99   ,       __lowercase    )

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [self.start]
											SCREAMING_SNAKE_CASE__						:       list[Node]           =   []

											SCREAMING_SNAKE_CASE__						:       List[str]           =   False



				def 	__magic_name__    (self    )					->		Path | None:



											"""simple docstring"""




											while self.open_nodes:
																		# Open Nodes are sorted using __lt__
																		self.open_nodes.sort()
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   self.open_nodes.pop(0    )

																		if current_node.pos == self.target.pos:
																									SCREAMING_SNAKE_CASE__						:       Any           =   True
																									return self.retrace_path(__lowercase    )

																		self.closed_nodes.append(__lowercase    )
																		SCREAMING_SNAKE_CASE__						:       Dict           =   self.get_successors(__lowercase    )

																		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(__lowercase    )
																									else:
																																# retrieve the best current path
																																SCREAMING_SNAKE_CASE__						:       int           =   self.open_nodes.pop(self.open_nodes.index(__lowercase    )    )

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

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



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		list[Node]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   []
											for action in delta:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   parent.pos_x + action[1]
																		SCREAMING_SNAKE_CASE__						:       str           =   parent.pos_y + action[0]

																		if not (0 <= pos_x <= len(grid[0]    ) - 1 and 0 <= pos_y <= len(__lowercase    ) - 1):
																									continue

																		if grid[pos_y][pos_x] != 0:
																									continue

																		successors.append(
																		    Node(
																		        __lowercase   ,       __lowercase   ,       self.target.pos_y   ,       self.target.pos_x   ,       parent.g_cost + 1   ,       __lowercase   ,       )    )
											return successors



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Path:



											"""simple docstring"""




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


if __name__ == "__main__":
					UpperCAmelCase__      :    List[Any]    							= (0, 0)
					UpperCAmelCase__      :    str    							= (len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print('------')

					UpperCAmelCase__      :    Tuple    							= GreedyBestFirst(init, goal)
					UpperCAmelCase__      :    Optional[int]    							= greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															UpperCAmelCase__      :    Dict    							= 2

										for elem in grid:
															print(elem)








import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import MobileNetVaImageProcessor







class  snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						def __init__( self      ,  __lowercase      ,  __lowercase=7      ,  __lowercase=3      ,  __lowercase=1_8      ,  __lowercase=3_0      ,  __lowercase=4_0_0      ,  __lowercase=True      ,  __lowercase=None      ,  __lowercase=True      ,  __lowercase=None      ,  )    ->			List[Any]:
										lowerCAmelCase_       :					Optional[Any]			 = size if size is not None else {'''shortest_edge''': 2_0}
										lowerCAmelCase_       :					Any			 = crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
										lowerCAmelCase_       :					Any			 = parent
										lowerCAmelCase_       :					Any			 = batch_size
										lowerCAmelCase_       :					Optional[int]			 = num_channels
										lowerCAmelCase_       :					Tuple			 = image_size
										lowerCAmelCase_       :					List[str]			 = min_resolution
										lowerCAmelCase_       :					Dict			 = max_resolution
										lowerCAmelCase_       :					Tuple			 = do_resize
										lowerCAmelCase_       :					Optional[Any]			 = size
										lowerCAmelCase_       :					Union[str, Any]			 = do_center_crop
										lowerCAmelCase_       :					Optional[Any]			 = crop_size




						def   lowercase_       ( self							)    ->			Union[str, Any]:
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										}







@require_torch
@require_vision
class  snake_case__(							UpperCAmelCase__,  unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Dict    =							MobileNetVaImageProcessor if is_vision_available() else None




						def   lowercase_       ( self							)    ->			List[str]:
										lowerCAmelCase_       :					Union[str, Any]			 = MobileNetVaImageProcessingTester(self							)




						@property
						def   lowercase_       ( self							)    ->			Dict:
										return self.image_processor_tester.prepare_image_processor_dict()




						def   lowercase_       ( self							)    ->			List[Any]:
										lowerCAmelCase_       :					Union[str, Any]			 = self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(__lowercase      ,  '''do_resize'''							)							)
										self.assertTrue(hasattr(__lowercase      ,  '''size'''							)							)
										self.assertTrue(hasattr(__lowercase      ,  '''do_center_crop'''							)							)
										self.assertTrue(hasattr(__lowercase      ,  '''crop_size'''							)							)




						def   lowercase_       ( self							)    ->			int:
										lowerCAmelCase_       :					str			 = self.image_processing_class.from_dict(self.image_processor_dict							)
										self.assertEqual(image_processor.size      ,  {'''shortest_edge''': 2_0}							)
										self.assertEqual(image_processor.crop_size      ,  {'''height''': 1_8, '''width''': 1_8}							)

										lowerCAmelCase_       :					Tuple			 = self.image_processing_class.from_dict(self.image_processor_dict      ,  size=4_2      ,  crop_size=8_4							)
										self.assertEqual(image_processor.size      ,  {'''shortest_edge''': 4_2}							)
										self.assertEqual(image_processor.crop_size      ,  {'''height''': 8_4, '''width''': 8_4}							)




						def   lowercase_       ( self							)    ->			Tuple:
										pass




						def   lowercase_       ( self							)    ->			Union[str, Any]:
										# Initialize image_processing
										lowerCAmelCase_       :					Optional[int]			 = self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										lowerCAmelCase_       :					Union[str, Any]			 = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase      ,  Image.Image							)

										# Test not batched input
										lowerCAmelCase_       :					Tuple			 = image_processing(image_inputs[0]      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )

										# Test batched
										lowerCAmelCase_       :					Tuple			 = image_processing(__lowercase      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )




						def   lowercase_       ( self							)    ->			Optional[int]:
										# Initialize image_processing
										lowerCAmelCase_       :					Tuple			 = self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										lowerCAmelCase_       :					Any			 = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase      ,  numpify=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase      ,  np.ndarray							)

										# Test not batched input
										lowerCAmelCase_       :					Any			 = image_processing(image_inputs[0]      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )

										# Test batched
										lowerCAmelCase_       :					Tuple			 = image_processing(__lowercase      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )




						def   lowercase_       ( self							)    ->			Any:
										# Initialize image_processing
										lowerCAmelCase_       :					Union[str, Any]			 = self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										lowerCAmelCase_       :					int			 = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase      ,  torchify=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase      ,  torch.Tensor							)

										# Test not batched input
										lowerCAmelCase_       :					List[Any]			 = image_processing(image_inputs[0]      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )

										# Test batched
										lowerCAmelCase_       :					Dict			 = image_processing(__lowercase      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )