





'''simple docstring'''






import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor




class  _A (					unittest.TestCase		):

		def __init__(   self					:   Tuple  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Dict[str, int] = None  ,      __magic_name__					:   int = 32  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Union[int, float] = 1 / 2_55  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]  ,      __magic_name__					:   Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Any=7  ,      __magic_name__					:   Tuple=30  ,      __magic_name__					:   List[Any]=4_00  ,      __magic_name__					:   Optional[Any]=3  ,      )					->    Tuple:


					"""simple docstring"""

					__snake_case					:      int							      =     parent
					__snake_case					:      Optional[Any]							      =     do_resize
					__snake_case					:      List[str]							      =     size if size is not None else {"""shortest_edge""": 2_88}
					__snake_case					:      Any							      =     size_divisor
					__snake_case					:      List[str]							      =     do_rescale
					__snake_case					:      Optional[int]							      =     rescale_factor
					__snake_case					:      Any							      =     do_normalize
					__snake_case					:      Any							      =     do_center_crop
					__snake_case					:      Tuple							      =     image_mean
					__snake_case					:      Tuple							      =     image_std
					__snake_case					:      int							      =     do_pad
					__snake_case					:      Union[str, Any]							      =     batch_size
					__snake_case					:      Union[str, Any]							      =     num_channels
					__snake_case					:      Optional[int]							      =     min_resolution
					__snake_case					:      str							      =     max_resolution

		def 	lowercase__     (   self					:   List[Any]			)					->    Any:


					"""simple docstring"""

					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "size_divisor": self.size_divisor,
					}

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   Union[str, Any]=False			)					->    Any:


					"""simple docstring"""

					if not batched:
								__snake_case					:      Optional[Any]							      =     self.size["""shortest_edge"""]
								__snake_case					:      Optional[int]							      =     image_inputs[0]
								if isinstance(__magic_name__  ,      Image.Image			):
											__snake_case      ,     __snake_case					:      List[Any]							      =     image.size
								else:
											__snake_case      ,     __snake_case					:      List[str]							      =     image.shape[1], image.shape[2]
								__snake_case					:      Optional[Any]							      =     size / min(__magic_name__  ,      __magic_name__			)
								if h < w:
											__snake_case      ,     __snake_case					:      List[str]							      =     size, scale * w
								else:
											__snake_case      ,     __snake_case					:      int							      =     scale * h, size

								__snake_case					:      str							      =     int((13_33 / 8_00) * size			)
								if max(__magic_name__  ,      __magic_name__			) > max_size:
											__snake_case					:      Dict							      =     max_size / max(__magic_name__  ,      __magic_name__			)
											__snake_case					:      str							      =     newh * scale
											__snake_case					:      List[Any]							      =     neww * scale

								__snake_case      ,     __snake_case					:      int							      =     int(newh + 0.5			), int(neww + 0.5			)
								__snake_case      ,     __snake_case					:      Tuple							      =     (
								    newh // self.size_divisor * self.size_divisor,
								    neww // self.size_divisor * self.size_divisor,
								)

					else:
								__snake_case					:      Tuple							      =     []
								for image in image_inputs:
											__snake_case      ,     __snake_case					:      Tuple							      =     self.get_expected_values([image]			)
											expected_values.append((expected_height, expected_width)			)
								__snake_case					:      Dict							      =     max(__magic_name__  ,      key=lambda __magic_name__			: item[0]			)[0]
								__snake_case					:      Union[str, Any]							      =     max(__magic_name__  ,      key=lambda __magic_name__			: item[1]			)[1]

					return expected_height, expected_width





@require_torch
@require_vision
class  _A (					__lowercase						,					unittest.TestCase		):
		lowercase__:   Any				     =   BridgeTowerImageProcessor if is_vision_available() else None

		def 	lowercase__     (   self					:   List[str]			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     BridgeTowerImageProcessingTester(self			)

		@property
		def 	lowercase__     (   self					:   Optional[Any]			)					->    Any:


					"""simple docstring"""

					return self.image_processor_tester.prepare_image_processor_dict()

		def 	lowercase__     (   self					:   int			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     self.image_processing_class(**self.image_processor_dict			)
					self.assertTrue(hasattr(__magic_name__  ,      """image_mean"""			)			)
					self.assertTrue(hasattr(__magic_name__  ,      """image_std"""			)			)
					self.assertTrue(hasattr(__magic_name__  ,      """do_normalize"""			)			)
					self.assertTrue(hasattr(__magic_name__  ,      """do_resize"""			)			)
					self.assertTrue(hasattr(__magic_name__  ,      """size"""			)			)
					self.assertTrue(hasattr(__magic_name__  ,      """size_divisor"""			)			)

		def 	lowercase__     (   self					:   Any			)					->    Optional[Any]:


					"""simple docstring"""

					pass

		def 	lowercase__     (   self					:   Dict			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     self.image_processing_class(**self.image_processor_dict			)
					# create random PIL images
					__snake_case					:      Optional[Any]							      =     prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=__magic_name__			)
					for image in image_inputs:
								self.assertIsInstance(__magic_name__  ,      Image.Image			)

					# Test not batched input
					__snake_case					:      Any							      =     image_processing(image_inputs[0]  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      int							      =     self.image_processor_tester.get_expected_values(__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

					# Test batched
					__snake_case					:      Dict							      =     image_processing(__magic_name__  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      Optional[int]							      =     self.image_processor_tester.get_expected_values(__magic_name__  ,      batched=__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )  ,      )

		def 	lowercase__     (   self					:   Any			)					->    str:


					"""simple docstring"""

					__snake_case					:      str							      =     self.image_processing_class(**self.image_processor_dict			)
					# create random numpy tensors
					__snake_case					:      Optional[Any]							      =     prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=__magic_name__  ,      numpify=__magic_name__			)
					for image in image_inputs:
								self.assertIsInstance(__magic_name__  ,      np.ndarray			)

					# Test not batched input
					__snake_case					:      int							      =     image_processing(image_inputs[0]  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      Optional[int]							      =     self.image_processor_tester.get_expected_values(__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

					# Test batched
					__snake_case					:      Any							      =     image_processing(__magic_name__  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      Tuple							      =     self.image_processor_tester.get_expected_values(__magic_name__  ,      batched=__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )  ,      )

		def 	lowercase__     (   self					:   str			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     self.image_processing_class(**self.image_processor_dict			)
					# create random PyTorch tensors
					__snake_case					:      Dict							      =     prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=__magic_name__  ,      torchify=__magic_name__			)
					for image in image_inputs:
								self.assertIsInstance(__magic_name__  ,      torch.Tensor			)

					# Test not batched input
					__snake_case					:      str							      =     image_processing(image_inputs[0]  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      List[Any]							      =     self.image_processor_tester.get_expected_values(__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

					# Test batched
					__snake_case					:      Optional[int]							      =     image_processing(__magic_name__  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      List[Any]							      =     self.image_processor_tester.get_expected_values(__magic_name__  ,      batched=__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )  ,      )









'''simple docstring'''




from __future__ import annotations

a__					:			Optional[int]          =     list[tuple[int, int]]

a__					:			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],
]

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






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       )      ->		Any:
		snake_case__	       =   pos_x
		snake_case__	       =   pos_y
		snake_case__	       =   (pos_y, pos_x)
		snake_case__	       =   goal_x
		snake_case__	       =   goal_y
		snake_case__	       =   g_cost
		snake_case__	       =   parent
		snake_case__	       =   self.calculate_heuristic()





	def   _snake_case   (		self		)      ->		float:
		snake_case__	       =   abs(self.pos_x - self.goal_x		)
		snake_case__	       =   abs(self.pos_y - self.goal_y		)
		return dx + dy





	def __lt__(		self      ,       UpperCamelCase_		)      ->		bool:
		return self.f_cost < other.f_cost






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Any:
		snake_case__	       =   Node(start[1]      ,       start[0]      ,       goal[1]      ,       goal[0]      ,       0      ,       UpperCamelCase_		)
		snake_case__	       =   Node(goal[1]      ,       goal[0]      ,       goal[1]      ,       goal[0]      ,       9_9999      ,       UpperCamelCase_		)

		snake_case__	       =   [self.start]
		snake_case__	       =   []

		snake_case__	       =   False





	def   _snake_case   (		self		)      ->		Path | None:
		while self.open_nodes:
			# Open Nodes are sorted using __lt__
			self.open_nodes.sort()
			snake_case__	       =   self.open_nodes.pop(0		)

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

			self.closed_nodes.append(UpperCamelCase_		)
			snake_case__	       =   self.get_successors(UpperCamelCase_		)

			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(UpperCamelCase_		)
				else:
					# retrieve the best current path
					snake_case__	       =   self.open_nodes.pop(self.open_nodes.index(UpperCamelCase_		)		)

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

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





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		list[Node]:
		snake_case__	       =   []
		for action in delta:
			snake_case__	       =   parent.pos_x + action[1]
			snake_case__	       =   parent.pos_y + action[0]

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

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

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





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Path:
		snake_case__	       =   node
		snake_case__	       =   []
		while current_node is not None:
			path.append((current_node.pos_y, current_node.pos_x)		)
			snake_case__	       =   current_node.parent
		path.reverse()
		return path


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

			print('''------''')

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

						for elem in grid:
									print(elem)



