






'''simple docstring'''







import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
   from transformers.models.mobilevitva.modeling_mobilevitva import (
       MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
       make_divisible,
   )


if is_vision_available():
   from PIL import Image

   from transformers import MobileViTImageProcessor

class _lowercase			(       _lowercase	):




       def 	lowerCamelCase_       (							self:       Any  ):
              lowerCamelCase__      :			Dict 						= self.config_class(**self.inputs_dict  )
              self.parent.assertTrue(hasattr(UpperCamelCase__    ,							"""width_multiplier"""  )  )



class _lowercase			:




       def __init__(							self:       str    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       str=13    ,							UpperCamelCase__:       Any=64    ,							UpperCamelCase__:       Optional[Any]=2    ,							UpperCamelCase__:       str=3    ,							UpperCamelCase__:       List[str]="swish"    ,							UpperCamelCase__:       Any=3    ,							UpperCamelCase__:       Optional[int]=32    ,							UpperCamelCase__:       Union[str, Any]=0.1    ,							UpperCamelCase__:       int=0.02    ,							UpperCamelCase__:       Dict=True    ,							UpperCamelCase__:       Dict=True    ,							UpperCamelCase__:       Any=10    ,							UpperCamelCase__:       int=None    ,							UpperCamelCase__:       List[Any]=0.25    ,							UpperCamelCase__:       str=0.0    ,							UpperCamelCase__:       Optional[int]=0.0    ,							):
              lowerCamelCase__      :			Any 						= parent
              lowerCamelCase__      :			Optional[Any] 						= batch_size
              lowerCamelCase__      :			Optional[int] 						= image_size
              lowerCamelCase__      :			str 						= patch_size
              lowerCamelCase__      :			Optional[int] 						= num_channels
              lowerCamelCase__      :			Optional[Any] 						= make_divisible(512 * width_multiplier    ,							divisor=8  )
              lowerCamelCase__      :			List[str] 						= hidden_act
              lowerCamelCase__      :			Any 						= conv_kernel_size
              lowerCamelCase__      :			Any 						= output_stride
              lowerCamelCase__      :			Union[str, Any] 						= classifier_dropout_prob
              lowerCamelCase__      :			List[str] 						= use_labels
              lowerCamelCase__      :			Optional[Any] 						= is_training
              lowerCamelCase__      :			List[str] 						= num_labels
              lowerCamelCase__      :			Dict 						= initializer_range
              lowerCamelCase__      :			List[Any] 						= scope
              lowerCamelCase__      :			Tuple 						= width_multiplier
              lowerCamelCase__      :			List[Any] 						= ffn_dropout
              lowerCamelCase__      :			Any 						= attn_dropout




       def 	lowerCamelCase_       (							self:       Dict  ):
              lowerCamelCase__      :			Dict 						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

              lowerCamelCase__      :			Union[str, Any] 						= self.get_config()

              return config, pixel_values, labels, pixel_labels




       def 	lowerCamelCase_       (							self:       List[Any]  ):
              return MobileViTVaConfig(
                  image_size=self.image_size    ,							patch_size=self.patch_size    ,							num_channels=self.num_channels    ,							hidden_act=self.hidden_act    ,							conv_kernel_size=self.conv_kernel_size    ,							output_stride=self.output_stride    ,							classifier_dropout_prob=self.classifier_dropout_prob    ,							initializer_range=self.initializer_range    ,							width_multiplier=self.width_multiplier    ,							ffn_dropout=self.ffn_dropout_prob    ,							attn_dropout=self.attn_dropout_prob    ,							)




       def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Optional[int]  ):
              lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaModel(config=UpperCamelCase__  )
              model.to(UpperCamelCase__  )
              model.eval()
              lowerCamelCase__      :			str 						= 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,
                  )    ,							)




       def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Tuple  ):
              lowerCamelCase__      :			Tuple 						= self.num_labels
              lowerCamelCase__      :			Dict 						= MobileViTVaForImageClassification(UpperCamelCase__  )
              model.to(UpperCamelCase__  )
              model.eval()
              lowerCamelCase__      :			int 						= model(UpperCamelCase__    ,							labels=UpperCamelCase__  )
              self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels)  )




       def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       str  ):
              lowerCamelCase__      :			List[str] 						= self.num_labels
              lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaForSemanticSegmentation(UpperCamelCase__  )
              model.to(UpperCamelCase__  )
              model.eval()
              lowerCamelCase__      :			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,
                  )    ,							)
              lowerCamelCase__      :			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 	lowerCamelCase_       (							self:       Tuple  ):
              lowerCamelCase__      :			Any 						= self.prepare_config_and_inputs()
              lowerCamelCase__      :			Union[str, Any] 						= config_and_inputs
              lowerCamelCase__      :			Optional[Any] 						= {"""pixel_values""": pixel_values}
              return config, inputs_dict



@require_torch
class _lowercase			(       _lowercase       ,   _lowercase       ,   unittest.TestCase	):
       a          = (
           (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
           if is_torch_available()
           else ()
       )

       a          = (
           {
               """feature-extraction""": MobileViTVaModel,
               """image-classification""": MobileViTVaForImageClassification,
               """image-segmentation""": MobileViTVaForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       a          = False
       a          = False
       a          = False
       a          = False




       def 	lowerCamelCase_       (							self:       Optional[int]  ):
              lowerCamelCase__      :			Tuple 						= MobileViTVaModelTester(self  )
              lowerCamelCase__      :			List[str] 						= MobileViTVaConfigTester(self    ,							config_class=UpperCamelCase__    ,							has_text_modality=UpperCamelCase__  )




       def 	lowerCamelCase_       (							self:       Tuple  ):
              self.config_tester.run_common_tests()




       @unittest.skip(reason="""MobileViTV2 does not use inputs_embeds"""  )
       def 	lowerCamelCase_       (							self:       int  ):
              pass




       @unittest.skip(reason="""MobileViTV2 does not support input and output embeddings"""  )
       def 	lowerCamelCase_       (							self:       List[str]  ):
              pass




       @unittest.skip(reason="""MobileViTV2 does not output attentions"""  )
       def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
              pass




       @require_torch_multi_gpu
       @unittest.skip(reason="""Got `CUDA error: misaligned address` for tests after this one being run."""  )
       def 	lowerCamelCase_       (							self:       int  ):
              pass




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




       def 	lowerCamelCase_       (							self:       Tuple  ):
              lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs_for_common()

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

                     lowerCamelCase__      :			str 						= ["""pixel_values"""]
                     self.assertListEqual(arg_names[:1]    ,							UpperCamelCase__  )




       def 	lowerCamelCase_       (							self:       str  ):
              lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*UpperCamelCase__  )




       def 	lowerCamelCase_       (							self:       List[str]  ):
              def check_hidden_states_output(UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Optional[Any]  ):
                     lowerCamelCase__      :			List[Any] 						= model_class(UpperCamelCase__  )
                     model.to(UpperCamelCase__  )
                     model.eval()

                     with torch.no_grad():
                            lowerCamelCase__      :			Tuple 						= model(**self._prepare_for_class(UpperCamelCase__    ,							UpperCamelCase__  )  )

                     lowerCamelCase__      :			Optional[int] 						= outputs.hidden_states

                     lowerCamelCase__      :			List[Any] 						= 5
                     self.assertEqual(len(UpperCamelCase__  )    ,							UpperCamelCase__  )

                     # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
                     # with the width and height being successively divided by 2.
                     lowerCamelCase__      :			int 						= 2
                     for i in range(len(UpperCamelCase__  )  ):
                            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  )

              lowerCamelCase__      :			Tuple 						= self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     lowerCamelCase__      :			int 						= True
                     check_hidden_states_output(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )

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

                     check_hidden_states_output(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




       def 	lowerCamelCase_       (							self:       Any  ):
              lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__  )




       def 	lowerCamelCase_       (							self:       Dict  ):
              lowerCamelCase__      :			str 						= self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase__  )




       @slow
       def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
              for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaModel.from_pretrained(UpperCamelCase__  )
                     self.assertIsNotNone(UpperCamelCase__  )





def 				SCREAMING_SNAKE_CASE_ ()   -> Optional[int]:
       lowerCamelCase__      :			str 						= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
       return image



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




       @cached_property
       def 	lowerCamelCase_       (							self:       Tuple  ):
              return (
                  MobileViTImageProcessor.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""  )
                  if is_vision_available()
                  else None
              )




       @slow
       def 	lowerCamelCase_       (							self:       Any  ):
              lowerCamelCase__      :			Optional[Any] 						= MobileViTVaForImageClassification.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""  ).to(
                  UpperCamelCase__  )

              lowerCamelCase__      :			Union[str, Any] 						= self.default_image_processor
              lowerCamelCase__      :			List[Any] 						= prepare_img()
              lowerCamelCase__      :			Any 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  ).to(UpperCamelCase__  )

              # forward pass
              with torch.no_grad():
                     lowerCamelCase__      :			int 						= model(**UpperCamelCase__  )

              # verify the logits
              lowerCamelCase__      :			str 						= torch.Size((1, 1_000)  )
              self.assertEqual(outputs.logits.shape    ,							UpperCamelCase__  )

              lowerCamelCase__      :			Optional[int] 						= torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]  ).to(UpperCamelCase__  )

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




       @slow
       def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
              lowerCamelCase__      :			int 						= MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )
              lowerCamelCase__      :			Optional[Any] 						= model.to(UpperCamelCase__  )

              lowerCamelCase__      :			Any 						= MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )

              lowerCamelCase__      :			Union[str, Any] 						= prepare_img()
              lowerCamelCase__      :			Dict 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  ).to(UpperCamelCase__  )

              # forward pass
              with torch.no_grad():
                     lowerCamelCase__      :			Optional[Any] 						= model(**UpperCamelCase__  )
              lowerCamelCase__      :			str 						= outputs.logits

              # verify the logits
              lowerCamelCase__      :			List[str] 						= torch.Size((1, 21, 32, 32)  )
              self.assertEqual(logits.shape    ,							UpperCamelCase__  )

              lowerCamelCase__      :			Any 						= torch.tensor(
                  [
                      [[7.0_863, 7.1_525, 6.8_201], [6.6_931, 6.8_770, 6.8_933], [6.2_978, 7.0_366, 6.9_636]],
                      [[-3.7_134, -3.6_712, -3.6_675], [-3.5_825, -3.3_549, -3.4_777], [-3.3_435, -3.3_979, -3.2_857]],
                      [[-2.9_329, -2.8_003, -2.7_369], [-3.0_564, -2.4_780, -2.0_207], [-2.6_889, -1.9_298, -1.7_640]],
                  ]    ,							device=UpperCamelCase__    ,							)

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




       @slow
       def 	lowerCamelCase_       (							self:       Optional[int]  ):
              lowerCamelCase__      :			Optional[Any] 						= MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )
              lowerCamelCase__      :			List[Any] 						= model.to(UpperCamelCase__  )

              lowerCamelCase__      :			Union[str, Any] 						= MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )

              lowerCamelCase__      :			Optional[Any] 						= prepare_img()
              lowerCamelCase__      :			Dict 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  ).to(UpperCamelCase__  )

              # forward pass
              with torch.no_grad():
                     lowerCamelCase__      :			Dict 						= model(**UpperCamelCase__  )

              lowerCamelCase__      :			List[str] 						= outputs.logits.detach().cpu()

              lowerCamelCase__      :			List[Any] 						= image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase__    ,							target_sizes=[(50, 60)]  )
              lowerCamelCase__      :			int 						= torch.Size((50, 60)  )
              self.assertEqual(segmentation[0].shape    ,							UpperCamelCase__  )

              lowerCamelCase__      :			Optional[Any] 						= image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase__  )
              lowerCamelCase__      :			int 						= torch.Size((32, 32)  )
              self.assertEqual(segmentation[0].shape    ,							UpperCamelCase__  )









'''simple docstring'''







# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Optional[Any]:
							lowerCamelCase__      :			str 						= [False] * len(UpperCamelCase   )
							lowerCamelCase__      :			str 						= [-1] * len(UpperCamelCase   )

							def dfs(UpperCamelCase       ,    UpperCamelCase   ):
														lowerCamelCase__      :			Optional[int] 						= True
														lowerCamelCase__      :			Union[str, Any] 						= c
														for u in graph[v]:
																					if not visited[u]:
																												dfs(UpperCamelCase       ,    1 - c   )

							for i in range(len(UpperCamelCase   )   ):
														if not visited[i]:
																					dfs(UpperCamelCase       ,    0   )

							for i in range(len(UpperCamelCase   )   ):
														for j in graph[i]:
																					if color[i] == color[j]:
																												return False

							return True


# Adjacency list of graph
_A			:    int					  ={0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}



print(check_bipartite_dfs(graph))

