



'''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      __magic_name__						(   _UpperCamelCase       ):







							def 							__lowercase      (							self :	Dict		):
									_a					:				Tuple				=							self.config_class(**self.inputs_dict		)
									self.parent.assertTrue(hasattr(_UpperCAmelCase     ,'tf_padding'		)		)
									self.parent.assertTrue(hasattr(_UpperCAmelCase     ,'depth_multiplier'		)		)

class      __magic_name__						:







							def __init__(							self :	Tuple     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	List[str]=13     ,_UpperCAmelCase :	str=3     ,_UpperCAmelCase :	List[str]=32     ,_UpperCAmelCase :	Tuple=0.25     ,_UpperCAmelCase :	List[str]=8     ,_UpperCAmelCase :	int=8     ,_UpperCAmelCase :	str=6     ,_UpperCAmelCase :	List[Any]=32     ,_UpperCAmelCase :	int=True     ,_UpperCAmelCase :	List[str]=True     ,_UpperCAmelCase :	List[Any]=True     ,_UpperCAmelCase :	Dict="relu6"     ,_UpperCAmelCase :	Optional[int]=1280     ,_UpperCAmelCase :	str=0.1     ,_UpperCAmelCase :	Any=0.02     ,_UpperCAmelCase :	int=True     ,_UpperCAmelCase :	Optional[Any]=True     ,_UpperCAmelCase :	int=10     ,_UpperCAmelCase :	List[str]=None     ,):
									_a					:				List[str]				=							parent
									_a					:				Dict				=							batch_size
									_a					:				Union[str, Any]				=							num_channels
									_a					:				Dict				=							image_size
									_a					:				Optional[int]				=							depth_multiplier
									_a					:				int				=							depth_divisible_by
									_a					:				Union[str, Any]				=							min_depth
									_a					:				str				=							expand_ratio
									_a					:				List[str]				=							tf_padding
									_a					:				List[Any]				=							output_stride
									_a					:				Optional[Any]				=							first_layer_is_expansion
									_a					:				Union[str, Any]				=							finegrained_output
									_a					:				Union[str, Any]				=							hidden_act
									_a					:				Optional[int]				=							last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier		)
									_a					:				str				=							classifier_dropout_prob
									_a					:				Dict				=							use_labels
									_a					:				Union[str, Any]				=							is_training
									_a					:				Any				=							num_labels
									_a					:				Union[str, Any]				=							initializer_range
									_a					:				List[str]				=							scope







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				Optional[int]				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

									_a					:				str				=							self.get_config()

									return config, pixel_values, labels, pixel_labels







							def 							__lowercase      (							self :	Dict		):
									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 :	Any     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	int		):
									_a					:				Tuple				=							MobileNetVaModel(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				Dict				=							model(_UpperCAmelCase		)
									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 :	str     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	Optional[Any]		):
									_a					:				str				=							self.num_labels
									_a					:				int				=							MobileNetVaForImageClassification(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				Optional[Any]				=							model(_UpperCAmelCase     ,labels=_UpperCAmelCase		)
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels)		)







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	int		):
									_a					:				Tuple				=							self.num_labels
									_a					:				Optional[int]				=							MobileNetVaForSemanticSegmentation(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				Tuple				=							model(_UpperCAmelCase		)
									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					:				List[Any]				=							model(_UpperCAmelCase     ,labels=_UpperCAmelCase		)
									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 :	Any		):
									_a					:				str				=							self.prepare_config_and_inputs()
									_a	, _a	, _a	, _a					:				List[Any]				=							config_and_inputs
									_a					:				str				=							{'pixel_values': pixel_values}
									return config, inputs_dict

@require_torch
class      __magic_name__						(   _UpperCamelCase      ,						_UpperCamelCase      ,						unittest.TestCase       ):
							lowerCAmelCase	:   List[str]           = (
							    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)
							lowerCAmelCase	:   Tuple           = (
							    {
							        'feature-extraction': MobileNetVaModel,
							        'image-classification': MobileNetVaForImageClassification,
							        'image-segmentation': MobileNetVaForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase	:   List[str]           = False
							lowerCAmelCase	:   Tuple           = False
							lowerCAmelCase	:   List[Any]           = False
							lowerCAmelCase	:   List[Any]           = False







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				List[str]				=							MobileNetVaModelTester(self		)
									_a					:				Dict				=							MobileNetVaConfigTester(self     ,config_class=_UpperCAmelCase     ,has_text_modality=_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[int]		):
									self.config_tester.run_common_tests()







							@unittest.skip(reason='MobileNetV2 does not use inputs_embeds'		)
							def 							__lowercase      (							self :	str		):
									pass







							@unittest.skip(reason='MobileNetV2 does not support input and output embeddings'		)
							def 							__lowercase      (							self :	Union[str, Any]		):
									pass







							@unittest.skip(reason='MobileNetV2 does not output attentions'		)
							def 							__lowercase      (							self :	Union[str, Any]		):
									pass







							def 							__lowercase      (							self :	List[Any]		):
									_a	, _a					:				Tuple				=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a					:				Union[str, Any]				=							model_class(_UpperCAmelCase		)
											_a					:				Optional[Any]				=							inspect.signature(model.forward		)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_a					:				int				=							[*signature.parameters.keys()]

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







							def 							__lowercase      (							self :	int		):
									_a					:				str				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	int		):
									def check_hidden_states_output(_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Optional[Any]		):
											_a					:				List[Any]				=							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.eval()

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

											_a					:				List[Any]				=							outputs.hidden_states

											_a					:				List[str]				=							16
											self.assertEqual(len(_UpperCAmelCase		)     ,_UpperCAmelCase		)

									_a	, _a					:				Dict				=							self.model_tester.prepare_config_and_inputs_for_common()

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

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											_a					:				List[Any]				=							True

											check_hidden_states_output(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	str		):
									_a					:				str				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[int]		):
									_a					:				Optional[Any]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase		)







							@slow
							def 							__lowercase      (							self :	Optional[Any]		):
									for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_a					:				List[str]				=							MobileNetVaModel.from_pretrained(_UpperCAmelCase		)
											self.assertIsNotNone(_UpperCAmelCase		)



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

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







							@cached_property
							def 							__lowercase      (							self :	List[Any]		):
									return (
									    MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'		) if is_vision_available() else None
									)







							@slow
							def 							__lowercase      (							self :	str		):
									_a					:				Tuple				=							MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'		).to(_UpperCAmelCase		)

									_a					:				str				=							self.default_image_processor
									_a					:				Optional[int]				=							prepare_img()
									_a					:				List[str]				=							image_processor(images=_UpperCAmelCase     ,return_tensors='pt'		).to(_UpperCAmelCase		)

									# forward pass
									with torch.no_grad():
											_a					:				List[str]				=							model(**_UpperCAmelCase		)

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

									_a					:				Tuple				=							torch.tensor([0.24_45, -1.19_93, 0.19_05]		).to(_UpperCAmelCase		)

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







							@slow
							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				List[str]				=							MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'		)
									_a					:				Optional[Any]				=							model.to(_UpperCAmelCase		)

									_a					:				Optional[int]				=							MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'		)

									_a					:				int				=							prepare_img()
									_a					:				int				=							image_processor(images=_UpperCAmelCase     ,return_tensors='pt'		).to(_UpperCAmelCase		)

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

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

									_a					:				List[str]				=							torch.tensor(
									    [
									        [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
									        [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
									        [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
									    ]     ,device=_UpperCAmelCase     ,)

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









'''simple docstring'''

import heapq



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				set[int]:
		_a					:				list[list]				=							[]

		# for each node and his adjacency list add them and the rank of the node to queue
		# using heapq module the queue will be filled like a Priority Queue
		# heapq works with a min priority queue, so I used -1*len(v) to build it
		for key, value in graph.items():
				# O(log(n))
				heapq.heappush(lowerCAmelCase_						,      [-1 * len(lowerCAmelCase_  ), (key, value)]  )

		# chosen_vertices = set of chosen vertices
		_a					:				str				=							set()

		# while queue isn't empty and there are still edges
		#   (queue[0][0] is the rank of the node with max rank)
		while queue and queue[0][0] != 0:
				# extract vertex with max rank from queue and add it to chosen_vertices
				_a					:				Optional[int]				=							heapq.heappop(lowerCAmelCase_  )[1][0]
				chosen_vertices.add(lowerCAmelCase_  )

				# Remove all arcs adjacent to argmax
				for elem in queue:
						# if v haven't adjacent node, skip
						if elem[0] == 0:
								continue
						# if argmax is reachable from elem
						# remove argmax from elem's adjacent list and update his rank
						if argmax in elem[1][1]:
								_a					:				Dict				=							elem[1][1].index(lowerCAmelCase_  )
								del elem[1][1][index]
								elem[0] += 1
        # re-order the queue
				heapq.heapify(lowerCAmelCase_  )
		return chosen_vertices


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							__lowerCAmelCase						      =       {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
							print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




