


'''simple docstring'''



import inspect
import unittest

from transformers import ViTMSNConfig
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 torch import nn

	from transformers import ViTMSNForImageClassification, ViTMSNModel
	from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor






class  UpperCamelCase__:




				def __init__(	self    :				Optional[int]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Optional[int]=13   ,					lowerCAmelCase    :				Union[str, Any]=30   ,					lowerCAmelCase    :				Tuple=2   ,					lowerCAmelCase    :				Optional[int]=3   ,					lowerCAmelCase    :				int=True   ,					lowerCAmelCase    :				Dict=True   ,					lowerCAmelCase    :				Any=32   ,					lowerCAmelCase    :				Union[str, Any]=5   ,					lowerCAmelCase    :				List[Any]=4   ,					lowerCAmelCase    :				Optional[int]=37   ,					lowerCAmelCase    :				str="gelu"   ,					lowerCAmelCase    :				Tuple=0.1   ,					lowerCAmelCase    :				List[str]=0.1   ,					lowerCAmelCase    :				List[str]=10   ,					lowerCAmelCase    :				Optional[int]=0.02   ,					lowerCAmelCase    :				Dict=None   ,					)->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			parent
										UpperCAmelCase         =			batch_size
										UpperCAmelCase         =			image_size
										UpperCAmelCase         =			patch_size
										UpperCAmelCase         =			num_channels
										UpperCAmelCase         =			is_training
										UpperCAmelCase         =			use_labels
										UpperCAmelCase         =			hidden_size
										UpperCAmelCase         =			num_hidden_layers
										UpperCAmelCase         =			num_attention_heads
										UpperCAmelCase         =			intermediate_size
										UpperCAmelCase         =			hidden_act
										UpperCAmelCase         =			hidden_dropout_prob
										UpperCAmelCase         =			attention_probs_dropout_prob
										UpperCAmelCase         =			type_sequence_label_size
										UpperCAmelCase         =			initializer_range
										UpperCAmelCase         =			scope

										# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
										UpperCAmelCase         =			(image_size // patch_size) ** 2
										UpperCAmelCase         =			num_patches + 1




				def   a__(	self    :				Tuple   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

										UpperCAmelCase         =			None
										if self.use_labels:
																UpperCAmelCase         =			ids_tensor([self.batch_size]   ,					self.type_sequence_label_size   )

										UpperCAmelCase         =			self.get_config()

										return config, pixel_values, labels




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

										"""simple docstring"""




										return ViTMSNConfig(
										    image_size=self.image_size   ,					patch_size=self.patch_size   ,					num_channels=self.num_channels   ,					hidden_size=self.hidden_size   ,					num_hidden_layers=self.num_hidden_layers   ,					num_attention_heads=self.num_attention_heads   ,					intermediate_size=self.intermediate_size   ,					hidden_act=self.hidden_act   ,					hidden_dropout_prob=self.hidden_dropout_prob   ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,					initializer_range=self.initializer_range   ,					)




				def   a__(	self    :				Tuple   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				List[str]   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			ViTMSNModel(config=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   )
										self.parent.assertEqual(result.last_hidden_state.shape   ,					(self.batch_size, self.seq_length, self.hidden_size)   )




				def   a__(	self    :				Any   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				Tuple   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			self.type_sequence_label_size
										UpperCAmelCase         =			ViTMSNForImageClassification(lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   ,					labels=lowerCAmelCase   )
										print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}'''   )
										print('''Labels: {labels}'''   )
										self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.type_sequence_label_size)   )

										# test greyscale images
										UpperCAmelCase         =			1
										UpperCAmelCase         =			ViTMSNForImageClassification(lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()

										UpperCAmelCase         =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
										UpperCAmelCase         =			model(lowerCAmelCase   )
										self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.type_sequence_label_size)   )





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

										"""simple docstring"""




										UpperCAmelCase         =			self.prepare_config_and_inputs()
										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			config_and_inputs
										UpperCAmelCase         =			{'''pixel_values''': pixel_values}
										return config, inputs_dict







@require_torch
class  UpperCamelCase__(     lowerCAmelCase		,       lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   Union[str, Any]							= (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				__magic_name__       :   Tuple							= (
				    {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__magic_name__       :   List[str]							= False
				__magic_name__       :   int							= False
				__magic_name__       :   Optional[Any]							= False
				__magic_name__       :   Any							= False




				def   a__(	self    :				Optional[int]   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			ViTMSNModelTester(self   )
										UpperCAmelCase         =			ConfigTester(self   ,					config_class=lowerCAmelCase   ,					has_text_modality=lowerCAmelCase   ,					hidden_size=37   )




				def   a__(	self    :				Dict   )->  str:

										"""simple docstring"""




										self.config_tester.run_common_tests()




				@unittest.skip(reason='''ViTMSN does not use inputs_embeds'''   )
				def   a__(	self    :				str   )->  int:

										"""simple docstring"""




										pass




				def   a__(	self    :				Tuple   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																UpperCAmelCase         =			model_class(lowerCAmelCase   )
																self.assertIsInstance(model.get_input_embeddings()   ,					(nn.Module)   )
																UpperCAmelCase         =			model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(lowerCAmelCase   ,					nn.Linear   )   )




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

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

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

																UpperCAmelCase         =			['''pixel_values''']
																self.assertListEqual(arg_names[:1]   ,					lowerCAmelCase   )




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

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase   )




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

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase   )





				@slow
				def   a__(	self    :				Optional[Any]   )->  Tuple:

										"""simple docstring"""




										for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																UpperCAmelCase         =			ViTMSNModel.from_pretrained(lowerCAmelCase   )
																self.assertIsNotNone(lowerCAmelCase   )







def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
						return image






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




				@cached_property
				def   a__(	self    :				str   )->  Optional[Any]:

										"""simple docstring"""




										return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''   ) if is_vision_available() else None





				@slow
				def   a__(	self    :				Tuple   )->  Optional[Any]:

										"""simple docstring"""




										torch.manual_seed(2   )
										UpperCAmelCase         =			ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''   ).to(lowerCAmelCase   )

										UpperCAmelCase         =			self.default_image_processor
										UpperCAmelCase         =			prepare_img()
										UpperCAmelCase         =			image_processor(images=lowerCAmelCase   ,					return_tensors='''pt'''   ).to(lowerCAmelCase   )

										# forward pass
										with torch.no_grad():
																UpperCAmelCase         =			model(**lowerCAmelCase   )

										# verify the logits
										UpperCAmelCase         =			torch.Size((1, 1000)   )
										self.assertEqual(outputs.logits.shape   ,					lowerCAmelCase   )

										UpperCAmelCase         =			torch.tensor([-0.0803, -0.4454, -0.2375]   ).to(lowerCAmelCase   )

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









'''simple docstring'''



import heapq


def    lowerCamelCase__   ( A     :    dict  ):




						'''simple docstring'''






						UpperCAmelCase         =			[]

						# 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(A   ,     [-1 * len(A  ), (key, value)]  )

						# chosen_vertices = set of chosen vertices
						UpperCAmelCase         =			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
												UpperCAmelCase         =			heapq.heappop(A  )[1][0]
												chosen_vertices.add(A  )

												# 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]:
																								UpperCAmelCase         =			elem[1][1].index(A  )
																								del elem[1][1][index]
																								elem[0] += 1
        # re-order the queue
												heapq.heapify(A  )
						return chosen_vertices


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowercase    :    Optional[int]			     =  {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)}""")





