





'''simple docstring'''

import inspect
import unittest

from transformers import MobileNetVaConfig
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 MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
		from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import MobileNetVaImageProcessor






class       _UpperCamelCase      (			SCREAMING_SNAKE_CASE_			):






				'''simple docstring'''





				def        lowercase__   (				self    ):
											"""simple docstring"""
											a__            =							self.config_class(**self.inputs_dict    )
											self.parent.assertTrue(hasattr(_a       ,					'tf_padding'    )    )
											self.parent.assertTrue(hasattr(_a       ,					'depth_multiplier'    )    )






class       _UpperCamelCase      :






				'''simple docstring'''





				def __init__(				self       ,					_a       ,					_a=13       ,					_a=3       ,					_a=32       ,					_a=0.25       ,					_a=8       ,					_a=8       ,					_a=6       ,					_a=32       ,					_a=True       ,					_a=True       ,					_a=True       ,					_a="relu6"       ,					_a=1280       ,					_a=0.1       ,					_a=0.02       ,					_a=True       ,					_a=True       ,					_a=10       ,					_a=None       ,					):
											"""simple docstring"""
											a__            =							parent
											a__            =							batch_size
											a__            =							num_channels
											a__            =							image_size
											a__            =							depth_multiplier
											a__            =							depth_divisible_by
											a__            =							min_depth
											a__            =							expand_ratio
											a__            =							tf_padding
											a__            =							output_stride
											a__            =							first_layer_is_expansion
											a__            =							finegrained_output
											a__            =							hidden_act
											a__            =							last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier    )
											a__            =							classifier_dropout_prob
											a__            =							use_labels
											a__            =							is_training
											a__            =							num_labels
											a__            =							initializer_range
											a__            =							scope






				def        lowercase__   (				self    ):
											"""simple docstring"""
											a__            =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

											a__            =							None
											a__            =							None
											if self.use_labels:
																		a__            =							ids_tensor([self.batch_size]       ,					self.num_labels    )
																		a__            =							ids_tensor([self.batch_size, self.image_size, self.image_size]       ,					self.num_labels    )

											a__            =							self.get_config()

											return config, pixel_values, labels, pixel_labels






				def        lowercase__   (				self    ):
											"""simple docstring"""
											return MobileNetVaConfig(
											    num_channels=self.num_channels       ,					image_size=self.image_size       ,					depth_multiplier=self.depth_multiplier       ,					depth_divisible_by=self.depth_divisible_by       ,					min_depth=self.min_depth       ,					expand_ratio=self.expand_ratio       ,					output_stride=self.output_stride       ,					first_layer_is_expansion=self.first_layer_is_expansion       ,					finegrained_output=self.finegrained_output       ,					hidden_act=self.hidden_act       ,					tf_padding=self.tf_padding       ,					classifier_dropout_prob=self.classifier_dropout_prob       ,					initializer_range=self.initializer_range       ,					)






				def        lowercase__   (				self       ,					_a       ,					_a       ,					_a       ,					_a    ):
											"""simple docstring"""
											a__            =							MobileNetVaModel(config=_a    )
											model.to(_a    )
											model.eval()
											a__            =							model(_a    )
											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,
											    )       ,					)
											self.parent.assertEqual(
											    result.pooler_output.shape       ,					(self.batch_size, self.last_hidden_size)       ,					)






				def        lowercase__   (				self       ,					_a       ,					_a       ,					_a       ,					_a    ):
											"""simple docstring"""
											a__            =							self.num_labels
											a__            =							MobileNetVaForImageClassification(_a    )
											model.to(_a    )
											model.eval()
											a__            =							model(_a       ,					labels=_a    )
											self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)    )






				def        lowercase__   (				self       ,					_a       ,					_a       ,					_a       ,					_a    ):
											"""simple docstring"""
											a__            =							self.num_labels
											a__            =							MobileNetVaForSemanticSegmentation(_a    )
											model.to(_a    )
											model.eval()
											a__            =							model(_a    )
											self.parent.assertEqual(
											    result.logits.shape       ,					(
											        self.batch_size,
											        self.num_labels,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )       ,					)
											a__            =							model(_a       ,					labels=_a    )
											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        lowercase__   (				self    ):
											"""simple docstring"""
											a__            =							self.prepare_config_and_inputs()
											a__            =							config_and_inputs
											a__            =							{'pixel_values': pixel_values}
											return config, inputs_dict






@require_torch
class       _UpperCamelCase      (			SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    unittest.TestCase			):






				'''simple docstring'''





				SCREAMING_SNAKE_CASE:Union[str, Any]         =	(
				    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE:Optional[Any]         =	(
				    {
				        'feature-extraction': MobileNetVaModel,
				        'image-classification': MobileNetVaForImageClassification,
				        'image-segmentation': MobileNetVaForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				SCREAMING_SNAKE_CASE:Optional[Any]         =	False
				SCREAMING_SNAKE_CASE:Tuple         =	False
				SCREAMING_SNAKE_CASE:Union[str, Any]         =	False
				SCREAMING_SNAKE_CASE:Union[str, Any]         =	False






				def        lowercase__   (				self    ):
											"""simple docstring"""
											a__            =							MobileNetVaModelTester(self    )
											a__            =							MobileNetVaConfigTester(self       ,					config_class=_a       ,					has_text_modality=_a    )






				def        lowercase__   (				self    ):
											"""simple docstring"""
											self.config_tester.run_common_tests()






				@unittest.skip(reason='MobileNetV2 does not use inputs_embeds'    )
				def        lowercase__   (				self    ):
											"""simple docstring"""
											pass






				@unittest.skip(reason='MobileNetV2 does not support input and output embeddings'    )
				def        lowercase__   (				self    ):
											"""simple docstring"""
											pass






				@unittest.skip(reason='MobileNetV2 does not output attentions'    )
				def        lowercase__   (				self    ):
											"""simple docstring"""
											pass






				def        lowercase__   (				self    ):
											"""simple docstring"""
											a__            =							self.model_tester.prepare_config_and_inputs_for_common()

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

																		a__            =							['pixel_values']
																		self.assertListEqual(arg_names[:1]       ,					_a    )






				def        lowercase__   (				self    ):
											"""simple docstring"""
											a__            =							self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_a    )






				def        lowercase__   (				self    ):
											"""simple docstring"""


											def check_hidden_states_output(_a       ,					_a       ,					_a    ):
																		a__            =							model_class(_a    )
																		model.to(_a    )
																		model.eval()

																		with torch.no_grad():
																									a__            =							model(**self._prepare_for_class(_a       ,					_a    )    )

																		a__            =							outputs.hidden_states

																		a__            =							16
																		self.assertEqual(len(_a    )       ,					_a    )

											a__            =							self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		a__            =							True
																		check_hidden_states_output(_a       ,					_a       ,					_a    )

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		a__            =							True

																		check_hidden_states_output(_a       ,					_a       ,					_a    )






				def        lowercase__   (				self    ):
											"""simple docstring"""
											a__            =							self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_a    )






				def        lowercase__   (				self    ):
											"""simple docstring"""
											a__            =							self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*_a    )






				@slow
				def        lowercase__   (				self    ):
											"""simple docstring"""


											for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		a__            =							MobileNetVaModel.from_pretrained(_a    )
																		self.assertIsNotNone(_a    )





def 			lowerCAmelCase_						(    ):
							a__            =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
							return image






@require_torch
@require_vision
class       _UpperCamelCase      (			unittest.TestCase			):






				'''simple docstring'''





				@cached_property
				def        lowercase__   (				self    ):
											"""simple docstring"""
											return (
											    MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'    ) if is_vision_available() else None
											)






				@slow
				def        lowercase__   (				self    ):
											"""simple docstring"""
											a__            =							MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'    ).to(_a    )

											a__            =							self.default_image_processor
											a__            =							prepare_img()
											a__            =							image_processor(images=_a       ,					return_tensors='pt'    ).to(_a    )

											# forward pass
											with torch.no_grad():
																		a__            =							model(**_a    )

											# verify the logits
											a__            =							torch.Size((1, 1001)    )
											self.assertEqual(outputs.logits.shape       ,					_a    )

											a__            =							torch.tensor([0.2445, -1.1993, 0.1905]    ).to(_a    )

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






				@slow
				def        lowercase__   (				self    ):
											"""simple docstring"""
											a__            =							MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'    )
											a__            =							model.to(_a    )

											a__            =							MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'    )

											a__            =							prepare_img()
											a__            =							image_processor(images=_a       ,					return_tensors='pt'    ).to(_a    )

											# forward pass
											with torch.no_grad():
																		a__            =							model(**_a    )
											a__            =							outputs.logits

											# verify the logits
											a__            =							torch.Size((1, 21, 65, 65)    )
											self.assertEqual(logits.shape       ,					_a    )

											a__            =							torch.tensor(
											    [
											        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
											        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
											        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
											    ]       ,					device=_a       ,					)

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










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""")