



import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
			from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import MobileViTImageProcessor


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				self.config_class(**self.inputs_dict	)
											self.parent.assertTrue(hasattr(snake_case       ,	'hidden_sizes'	)	)
											self.parent.assertTrue(hasattr(snake_case       ,	'neck_hidden_sizes'	)	)
											self.parent.assertTrue(hasattr(snake_case       ,	'num_attention_heads'	)	)


class 						lowercase    :
						'''simple docstring'''


						def __init__(  self    :				Optional[int]       ,	snake_case    :				str       ,	snake_case    :				List[str]=13       ,	snake_case    :				List[Any]=32       ,	snake_case    :				Tuple=2       ,	snake_case    :				Union[str, Any]=3       ,	snake_case    :				List[str]=640       ,	snake_case    :				List[Any]=4       ,	snake_case    :				List[Any]="silu"       ,	snake_case    :				int=3       ,	snake_case    :				Tuple=32       ,	snake_case    :				List[str]=0.1       ,	snake_case    :				List[Any]=0.1       ,	snake_case    :				int=0.1       ,	snake_case    :				Optional[Any]=0.02       ,	snake_case    :				List[str]=True       ,	snake_case    :				Union[str, Any]=True       ,	snake_case    :				int=10       ,	snake_case    :				List[str]=None       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				parent
											SCREAMING_SNAKE_CASE							:     List[Any]						=				batch_size
											SCREAMING_SNAKE_CASE							:     Tuple						=				image_size
											SCREAMING_SNAKE_CASE							:     str						=				patch_size
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				num_channels
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				last_hidden_size
											SCREAMING_SNAKE_CASE							:     Tuple						=				num_attention_heads
											SCREAMING_SNAKE_CASE							:     str						=				hidden_act
											SCREAMING_SNAKE_CASE							:     Any						=				conv_kernel_size
											SCREAMING_SNAKE_CASE							:     Any						=				output_stride
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				hidden_dropout_prob
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE							:     int						=				classifier_dropout_prob
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				use_labels
											SCREAMING_SNAKE_CASE							:     Any						=				is_training
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				num_labels
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				initializer_range
											SCREAMING_SNAKE_CASE							:     Dict						=				scope







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

											SCREAMING_SNAKE_CASE							:     Any						=				None
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				None
											if self.use_labels:
																SCREAMING_SNAKE_CASE							:     Any						=				ids_tensor([self.batch_size]       ,	self.num_labels	)
																SCREAMING_SNAKE_CASE							:     int						=				ids_tensor([self.batch_size, self.image_size, self.image_size]       ,	self.num_labels	)

											SCREAMING_SNAKE_CASE							:     Tuple						=				self.get_config()

											return config, pixel_values, labels, pixel_labels







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											return MobileViTConfig(
											    image_size=self.image_size       ,	patch_size=self.patch_size       ,	num_channels=self.num_channels       ,	num_attention_heads=self.num_attention_heads       ,	hidden_act=self.hidden_act       ,	conv_kernel_size=self.conv_kernel_size       ,	output_stride=self.output_stride       ,	hidden_dropout_prob=self.hidden_dropout_prob       ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,	classifier_dropout_prob=self.classifier_dropout_prob       ,	initializer_range=self.initializer_range       ,	)







						def       lowerCamelCase_				(  self    :				str       ,	snake_case    :				List[Any]       ,	snake_case    :				List[Any]       ,	snake_case    :				List[str]       ,	snake_case    :				List[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				MobileViTModel(config=snake_case	)
											model.to(snake_case	)
											model.eval()
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				model(snake_case	)
											self.parent.assertEqual(
											    result.last_hidden_state.shape       ,	(
											        self.batch_size,
											        self.last_hidden_size,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )       ,	)







						def       lowerCamelCase_				(  self    :				Any       ,	snake_case    :				Optional[int]       ,	snake_case    :				int       ,	snake_case    :				Any       ,	snake_case    :				Optional[int]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.num_labels
											SCREAMING_SNAKE_CASE							:     List[str]						=				MobileViTForImageClassification(snake_case	)
											model.to(snake_case	)
											model.eval()
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				model(snake_case       ,	labels=snake_case	)
											self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels)	)







						def       lowerCamelCase_				(  self    :				Dict       ,	snake_case    :				List[str]       ,	snake_case    :				Any       ,	snake_case    :				Dict       ,	snake_case    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.num_labels
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				MobileViTForSemanticSegmentation(snake_case	)
											model.to(snake_case	)
											model.eval()
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				model(snake_case	)
											self.parent.assertEqual(
											    result.logits.shape       ,	(
											        self.batch_size,
											        self.num_labels,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )       ,	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				model(snake_case       ,	labels=snake_case	)
											self.parent.assertEqual(
											    result.logits.shape       ,	(
											        self.batch_size,
											        self.num_labels,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )       ,	)







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				self.prepare_config_and_inputs()
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     List[str]						=				config_and_inputs
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				{'pixel_values': pixel_values}
											return config, inputs_dict


@require_torch
class 						lowercase    (							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    unittest.TestCase):
						'''simple docstring'''


						UpperCAmelCase			:		List[str]		=		(
						    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						UpperCAmelCase			:		Any		=		(
						    {
						        'feature-extraction': MobileViTModel,
						        'image-classification': MobileViTForImageClassification,
						        'image-segmentation': MobileViTForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						UpperCAmelCase			:		Optional[Any]		=		False
						UpperCAmelCase			:		List[Any]		=		False
						UpperCAmelCase			:		Optional[int]		=		False
						UpperCAmelCase			:		int		=		False







						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				MobileViTModelTester(self	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				MobileViTConfigTester(self       ,	config_class=snake_case       ,	has_text_modality=snake_case	)







						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											self.config_tester.run_common_tests()







						@unittest.skip(reason='MobileViT does not use inputs_embeds'	)
						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											pass







						@unittest.skip(reason='MobileViT does not support input and output embeddings'	)
						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											pass







						@unittest.skip(reason='MobileViT does not output attentions'	)
						def       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


											pass







						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     List[Any]						=				self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																SCREAMING_SNAKE_CASE							:     Dict						=				model_class(snake_case	)
																SCREAMING_SNAKE_CASE							:     Tuple						=				inspect.signature(model.forward	)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																SCREAMING_SNAKE_CASE							:     int						=				[*signature.parameters.keys()]

																SCREAMING_SNAKE_CASE							:     List[str]						=				['pixel_values']
																self.assertListEqual(arg_names[:1]       ,	snake_case	)







						@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
						def       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											pass







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case	)







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											def check_hidden_states_output(snake_case    :				Tuple       ,	snake_case    :				str       ,	snake_case    :				int	):
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				model_class(snake_case	)
																model.to(snake_case	)
																model.eval()

																with torch.no_grad():
																					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				model(**self._prepare_for_class(snake_case       ,	snake_case	)	)

																SCREAMING_SNAKE_CASE							:     List[str]						=				outputs.hidden_states

																SCREAMING_SNAKE_CASE							:     List[Any]						=				5
																self.assertEqual(len(snake_case	)       ,	snake_case	)

																# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
																# with the width and height being successively divided by 2.
																SCREAMING_SNAKE_CASE							:     Optional[int]						=				2
																for i in range(len(snake_case	)	):
																					self.assertListEqual(
																					    list(hidden_states[i].shape[-2:]	)       ,	[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]       ,	)
																					divisor *= 2

																self.assertEqual(self.model_tester.output_stride       ,	divisor // 2	)

											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				True
																check_hidden_states_output(snake_case       ,	snake_case       ,	snake_case	)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																SCREAMING_SNAKE_CASE							:     Tuple						=				True

																check_hidden_states_output(snake_case       ,	snake_case       ,	snake_case	)







						def       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*snake_case	)







						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*snake_case	)







						@slow
						def       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																SCREAMING_SNAKE_CASE							:     List[Any]						=				MobileViTModel.from_pretrained(snake_case	)
																self.assertIsNotNone(snake_case	)


def        __a			(   )     ->     int:
					SCREAMING_SNAKE_CASE							:     List[str]						=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
					return image



@require_torch
@require_vision
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						@cached_property
						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'	) if is_vision_available() else None







						@slow
						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'	).to(snake_case	)

											SCREAMING_SNAKE_CASE							:     Tuple						=				self.default_image_processor
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				prepare_img()
											SCREAMING_SNAKE_CASE							:     Any						=				image_processor(images=snake_case       ,	return_tensors='pt'	).to(snake_case	)

											# forward pass
											with torch.no_grad():
																SCREAMING_SNAKE_CASE							:     str						=				model(**snake_case	)

											# verify the logits
											SCREAMING_SNAKE_CASE							:     Any						=				torch.Size((1, 1000)	)
											self.assertEqual(outputs.logits.shape       ,	snake_case	)

											SCREAMING_SNAKE_CASE							:     int						=				torch.tensor([-1.9364, -1.2327, -0.4653]	).to(snake_case	)

											self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,	snake_case       ,	atol=1E-4	)	)







						@slow
						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				model.to(snake_case	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'	)

											SCREAMING_SNAKE_CASE							:     List[Any]						=				prepare_img()
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				image_processor(images=snake_case       ,	return_tensors='pt'	).to(snake_case	)

											# forward pass
											with torch.no_grad():
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				model(**snake_case	)
											SCREAMING_SNAKE_CASE							:     str						=				outputs.logits

											# verify the logits
											SCREAMING_SNAKE_CASE							:     int						=				torch.Size((1, 21, 32, 32)	)
											self.assertEqual(logits.shape       ,	snake_case	)

											SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.tensor(
											    [
											        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
											        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
											        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
											    ]       ,	device=snake_case       ,	)

											self.assertTrue(torch.allclose(logits[0, :3, :3, :3]       ,	snake_case       ,	atol=1E-4	)	)







						@slow
						def       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				model.to(snake_case	)

											SCREAMING_SNAKE_CASE							:     int						=				MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'	)

											SCREAMING_SNAKE_CASE							:     Any						=				prepare_img()
											SCREAMING_SNAKE_CASE							:     Any						=				image_processor(images=snake_case       ,	return_tensors='pt'	).to(snake_case	)

											# forward pass
											with torch.no_grad():
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				model(**snake_case	)

											SCREAMING_SNAKE_CASE							:     Any						=				outputs.logits.detach().cpu()

											SCREAMING_SNAKE_CASE							:     Dict						=				image_processor.post_process_semantic_segmentation(outputs=snake_case       ,	target_sizes=[(50, 60)]	)
											SCREAMING_SNAKE_CASE							:     Dict						=				torch.Size((50, 60)	)
											self.assertEqual(segmentation[0].shape       ,	snake_case	)

											SCREAMING_SNAKE_CASE							:     str						=				image_processor.post_process_semantic_segmentation(outputs=snake_case	)
											SCREAMING_SNAKE_CASE							:     Dict						=				torch.Size((32, 32)	)
											self.assertEqual(segmentation[0].shape       ,	snake_case	)




from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
_lowerCamelCase    :		List[str]		      =    0

_lowerCamelCase    :		Tuple		      =    [
    [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],
]

_lowerCamelCase    :		Tuple		      =    [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

_lowerCamelCase    :		Union[str, Any]		      =    tuple[int, int]


class 						lowercase    :
						'''simple docstring'''


						def __init__(  self    :				Optional[Any]       ,	snake_case    :				int       ,	snake_case    :				int       ,	snake_case    :				int       ,	snake_case    :				int       ,	snake_case    :				int       ,	snake_case    :				Node | None       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				pos_x
											SCREAMING_SNAKE_CASE							:     str						=				pos_y
											SCREAMING_SNAKE_CASE							:     Any						=				(pos_y, pos_x)
											SCREAMING_SNAKE_CASE							:     List[str]						=				goal_x
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				goal_y
											SCREAMING_SNAKE_CASE							:     int						=				g_cost
											SCREAMING_SNAKE_CASE							:     Any						=				parent
											SCREAMING_SNAKE_CASE							:     List[Any]						=				self.calculate_heuristic()
											SCREAMING_SNAKE_CASE							:     str						=				self.g_cost + self.h_cost







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				self.pos_x - self.goal_x
											SCREAMING_SNAKE_CASE							:     str						=				self.pos_y - self.goal_y
											if HEURISTIC == 1:
																return abs(snake_case	) + abs(snake_case	)
											else:
																return sqrt(dy**2 + dx**2	)







						def __lt__(  self    :				Any       ,	snake_case    :				Node	):



											'''simple docstring'''


											return self.f_cost < other.f_cost


class 						lowercase    :
						'''simple docstring'''


						def __init__(  self    :				Optional[Any]       ,	snake_case    :				TPosition       ,	snake_case    :				TPosition	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				Node(start[1]       ,	start[0]       ,	goal[1]       ,	goal[0]       ,	0       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     int						=				Node(goal[1]       ,	goal[0]       ,	goal[1]       ,	goal[0]       ,	99999       ,	snake_case	)

											SCREAMING_SNAKE_CASE							:     Any						=				[self.start]
											SCREAMING_SNAKE_CASE							:     list[Node]						=				[]

											SCREAMING_SNAKE_CASE							:     int						=				False







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											while self.open_nodes:
																# Open Nodes are sorted using __lt__
																self.open_nodes.sort()
																SCREAMING_SNAKE_CASE							:     Dict						=				self.open_nodes.pop(0	)

																if current_node.pos == self.target.pos:
																					return self.retrace_path(snake_case	)

																self.closed_nodes.append(snake_case	)
																SCREAMING_SNAKE_CASE							:     int						=				self.get_successors(snake_case	)

																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(snake_case	)
																					else:
																										# retrieve the best current path
																										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.open_nodes.pop(self.open_nodes.index(snake_case	)	)

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

											return [self.start.pos]







						def       lowerCamelCase_				(  self    :				str       ,	snake_case    :				Node	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				[]
											for action in delta:
																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				parent.pos_x + action[1]
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				parent.pos_y + action[0]
																if not (0 <= pos_x <= len(grid[0]	) - 1 and 0 <= pos_y <= len(snake_case	) - 1):
																					continue

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

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







						def       lowerCamelCase_				(  self    :				Optional[int]       ,	snake_case    :				Node | None	):



											'''simple docstring'''


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


class 						lowercase    :
						'''simple docstring'''


						def __init__(  self    :				str       ,	snake_case    :				TPosition       ,	snake_case    :				TPosition	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Any						=				AStar(snake_case       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				AStar(snake_case       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     str						=				False







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
																self.fwd_astar.open_nodes.sort()
																self.bwd_astar.open_nodes.sort()
																SCREAMING_SNAKE_CASE							:     Dict						=				self.fwd_astar.open_nodes.pop(0	)
																SCREAMING_SNAKE_CASE							:     Any						=				self.bwd_astar.open_nodes.pop(0	)

																if current_bwd_node.pos == current_fwd_node.pos:
																					return self.retrace_bidirectional_path(
																					    snake_case       ,	snake_case	)

																self.fwd_astar.closed_nodes.append(snake_case	)
																self.bwd_astar.closed_nodes.append(snake_case	)

																SCREAMING_SNAKE_CASE							:     str						=				current_bwd_node
																SCREAMING_SNAKE_CASE							:     int						=				current_fwd_node

																SCREAMING_SNAKE_CASE							:     str						=				{
																    self.fwd_astar: self.fwd_astar.get_successors(snake_case	),
																    self.bwd_astar: self.bwd_astar.get_successors(snake_case	),
																}

																for astar in [self.fwd_astar, self.bwd_astar]:
																					for child_node in successors[astar]:
																										if child_node in astar.closed_nodes:
																															continue

																										if child_node not in astar.open_nodes:
																															astar.open_nodes.append(snake_case	)
																										else:
																															# retrieve the best current path
																															SCREAMING_SNAKE_CASE							:     List[Any]						=				astar.open_nodes.pop(
																															    astar.open_nodes.index(snake_case	)	)

																															if child_node.g_cost < better_node.g_cost:
																																				astar.open_nodes.append(snake_case	)
																															else:
																																				astar.open_nodes.append(snake_case	)

											return [self.fwd_astar.start.pos]







						def       lowerCamelCase_				(  self    :				str       ,	snake_case    :				Node       ,	snake_case    :				Node	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.fwd_astar.retrace_path(snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.bwd_astar.retrace_path(snake_case	)
											bwd_path.pop()
											bwd_path.reverse()
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				fwd_path + bwd_path
											return path


if __name__ == "__main__":
			# all coordinates are given in format [y,x]
			_lowerCamelCase    :		Tuple		      =    (0, 0)
			_lowerCamelCase    :		Dict		      =    (len(grid) - 1, len(grid[0]) - 1)
			for elem in grid:
						print(elem)

			_lowerCamelCase    :		Union[str, Any]		      =    time.time()
			_lowerCamelCase    :		List[str]		      =    AStar(init, goal)
			_lowerCamelCase    :		Optional[int]		      =    a_star.search()
			_lowerCamelCase    :		str		      =    time.time() - start_time
			print(f"""AStar execution time = {end_time:f} seconds""")

			_lowerCamelCase    :		Optional[Any]		      =    time.time()
			_lowerCamelCase    :		Union[str, Any]		      =    BidirectionalAStar(init, goal)
			_lowerCamelCase    :		List[Any]		      =    time.time() - bd_start_time
			print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")