





from __future__ import annotations

import time

__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 	A_       :
	def __init__(    self							,					_A							,					_A							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				pos_x
				UpperCAmelCase   		=				pos_y
				UpperCAmelCase   		=				(pos_y, pos_x)
				UpperCAmelCase   		=				goal_x
				UpperCAmelCase   		=				goal_y
				UpperCAmelCase   		=				parent

class 	A_       :
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				Node(start[1]							,					start[0]							,					goal[1]							,					goal[0]							,					_A		)
				UpperCAmelCase   		=				Node(goal[1]							,					goal[0]							,					goal[1]							,					goal[0]							,					_A		)

				UpperCAmelCase   		=				[self.start]
				UpperCAmelCase   		=				False
	def      _lowercase				(    self		):






				'''simple docstring'''







				while self.node_queue:
							UpperCAmelCase   		=				self.node_queue.pop(0		)

							if current_node.pos == self.target.pos:
										UpperCAmelCase   		=				True
										return self.retrace_path(_A		)

							UpperCAmelCase   		=				self.get_successors(_A		)

							for node in successors:
										self.node_queue.append(_A		)

				if not self.reached:
							return [self.start.pos]
				return None
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				[]
				for action in delta:
							UpperCAmelCase   		=				parent.pos_x + action[1]
							UpperCAmelCase   		=				parent.pos_y + action[0]
							if not (0 <= pos_x <= len(grid[0]		) - 1 and 0 <= pos_y <= len(_A		) - 1):
										continue

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

							successors.append(
							    Node(_A							,					_A							,					self.target.pos_y							,					self.target.pos_x							,					_A		)		)
				return successors







	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







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

class 	A_       :
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				BreadthFirstSearch(_A							,					_A		)
				UpperCAmelCase   		=				BreadthFirstSearch(_A							,					_A		)
				UpperCAmelCase   		=				False
	def      _lowercase				(    self		):






				'''simple docstring'''







				while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
							UpperCAmelCase   		=				self.fwd_bfs.node_queue.pop(0		)
							UpperCAmelCase   		=				self.bwd_bfs.node_queue.pop(0		)

							if current_bwd_node.pos == current_fwd_node.pos:
										UpperCAmelCase   		=				True
										return self.retrace_bidirectional_path(
										    _A							,					_A		)

							UpperCAmelCase   		=				current_bwd_node
							UpperCAmelCase   		=				current_fwd_node

							UpperCAmelCase   		=				{
							    self.fwd_bfs: self.fwd_bfs.get_successors(_A		),
							    self.bwd_bfs: self.bwd_bfs.get_successors(_A		),
							}

							for bfs in [self.fwd_bfs, self.bwd_bfs]:
										for node in successors[bfs]:
													bfs.node_queue.append(_A		)

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







	def      _lowercase				(    self							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.fwd_bfs.retrace_path(_A		)
				UpperCAmelCase   		=				self.bwd_bfs.retrace_path(_A		)
				bwd_path.pop()
				bwd_path.reverse()
				UpperCAmelCase   		=				fwd_path + bwd_path
				return path


if __name__ == "__main__":
				# all coordinates are given in format [y,x]
				import doctest

				doctest.testmod()
				__A				:    Tuple       =   (0, 0)
				__A				:    Union[str, Any]       =   (len(grid) - 1, len(grid[0]) - 1)
				for elem in grid:
								print(elem)

				__A				:    Dict       =   time.time()
				__A				:    Optional[int]       =   BreadthFirstSearch(init, goal)
				__A				:    Dict       =   bfs.search()
				__A				:    Dict       =   time.time() - start_bfs_time

				print("Unidirectional BFS computation time : ", bfs_time)

				__A				:    List[str]       =   time.time()
				__A				:    int       =   BidirectionalBreadthFirstSearch(init, goal)
				__A				:    Any       =   bd_bfs.search()
				__A				:    Dict       =   time.time() - start_bd_bfs_time

				print("Bidirectional BFS computation time : ", bd_bfs_time)





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     ):
    def __init__(  self		,snake_case		,snake_case=7		,snake_case=3		,snake_case=18		,snake_case=30		,snake_case=400		,snake_case=True		,snake_case=None		,snake_case=True		,snake_case=None		,):



           '''simple docstring'''







           lowercase							:     Dict       	=      size if size is not None else {"""shortest_edge""": 20}
           lowercase							:     Union[str, Any]       	=      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
           lowercase							:     str       	=      parent
           lowercase							:     int       	=      batch_size
           lowercase							:     str       	=      num_channels
           lowercase							:     int       	=      image_size
           lowercase							:     List[str]       	=      min_resolution
           lowercase							:     str       	=      max_resolution
           lowercase							:     Dict       	=      do_resize
           lowercase							:     Dict       	=      size
           lowercase							:     Dict       	=      do_center_crop
           lowercase							:     str       	=      crop_size
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           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		(      lowerCAmelCase					,	unittest.TestCase     ):
    _a		:						Any=				MobileNetVaImageProcessor if is_vision_available() else None
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      MobileNetVaImageProcessingTester(self      )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self.image_processor_tester.prepare_image_processor_dict()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.image_processing_class(**self.image_processor_dict      )
           self.assertTrue(hasattr(snake_case		,"""do_resize"""      )      )
           self.assertTrue(hasattr(snake_case		,"""size"""      )      )
           self.assertTrue(hasattr(snake_case		,"""do_center_crop"""      )      )
           self.assertTrue(hasattr(snake_case		,"""crop_size"""      )      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.image_processing_class.from_dict(self.image_processor_dict      )
           self.assertEqual(image_processor.size		,{"""shortest_edge""": 20}      )
           self.assertEqual(image_processor.crop_size		,{"""height""": 18, """width""": 18}      )

           lowercase							:     int       	=      self.image_processing_class.from_dict(self.image_processor_dict		,size=42		,crop_size=84      )
           self.assertEqual(image_processor.size		,{"""shortest_edge""": 42}      )
           self.assertEqual(image_processor.crop_size		,{"""height""": 84, """width""": 84}      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           pass
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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

           # Test not batched input
           lowercase							:     Dict       	=      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
           lowercase							:     Tuple       	=      image_processing(snake_case		,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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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

           # Test not batched input
           lowercase							:     Optional[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
           lowercase							:     List[str]       	=      image_processing(snake_case		,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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.image_processing_class(**self.image_processor_dict      )
           # create random PyTorch tensors
           lowercase							:     Tuple       	=      prepare_image_inputs(self.image_processor_tester		,equal_resolution=snake_case		,torchify=snake_case      )
           for image in image_inputs:
                  self.assertIsInstance(snake_case		,torch.Tensor      )

           # Test not batched input
           lowercase							:     Optional[int]       	=      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
           lowercase							:     List[str]       	=      image_processing(snake_case		,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"""],
               )		,)
