






'''simple docstring'''







import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
   from PIL import Image
else:

   class _lowercase			:




          @staticmethod
          def 	lowerCamelCase_       (							*UpperCamelCase__:       str    ,							**UpperCamelCase__:       Any  ):
                 pass

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _lowercase			(       unittest.TestCase	):
       a          = MODEL_FOR_OBJECT_DETECTION_MAPPING




       def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Optional[Any]  ):
              lowerCamelCase__      :			Dict 						= ObjectDetectionPipeline(model=snake_case_    ,							image_processor=snake_case_  )
              return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




       def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Union[str, Any]  ):
              lowerCamelCase__      :			Union[str, Any] 						= object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    ,							threshold=0.0  )

              self.assertGreater(len(snake_case_  )    ,							0  )
              for detected_object in outputs:
                     self.assertEqual(
                         snake_case_    ,							{
                             """score""": ANY(snake_case_  ),
                             """label""": ANY(snake_case_  ),
                             """box""": {"""xmin""": ANY(snake_case_  ), """ymin""": ANY(snake_case_  ), """xmax""": ANY(snake_case_  ), """ymax""": ANY(snake_case_  )},
                         }    ,							)

              import datasets

              lowerCamelCase__      :			Optional[Any] 						= datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""    ,							"""image"""    ,							split="""test"""  )

              lowerCamelCase__      :			List[Any] 						= [
                  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  ),
                  """http://images.cocodataset.org/val2017/000000039769.jpg""",
                  # RGBA
                  dataset[0]["""file"""],
                  # LA
                  dataset[1]["""file"""],
                  # L
                  dataset[2]["""file"""],
              ]
              lowerCamelCase__      :			int 						= object_detector(snake_case_    ,							threshold=0.0  )

              self.assertEqual(len(snake_case_  )    ,							len(snake_case_  )  )
              for outputs in batch_outputs:
                     self.assertGreater(len(snake_case_  )    ,							0  )
                     for detected_object in outputs:
                            self.assertEqual(
                                snake_case_    ,							{
                                    """score""": ANY(snake_case_  ),
                                    """label""": ANY(snake_case_  ),
                                    """box""": {"""xmin""": ANY(snake_case_  ), """ymin""": ANY(snake_case_  ), """xmax""": ANY(snake_case_  ), """ymax""": ANY(snake_case_  )},
                                }    ,							)




       @require_tf
       @unittest.skip("""Object detection not implemented in TF"""  )
       def 	lowerCamelCase_       (							self:       List[Any]  ):
              pass




       @require_torch
       def 	lowerCamelCase_       (							self:       List[Any]  ):
              lowerCamelCase__      :			str 						= """hf-internal-testing/tiny-detr-mobilenetsv3"""

              lowerCamelCase__      :			Dict 						= AutoModelForObjectDetection.from_pretrained(snake_case_  )
              lowerCamelCase__      :			Tuple 						= AutoFeatureExtractor.from_pretrained(snake_case_  )
              lowerCamelCase__      :			List[str] 						= ObjectDetectionPipeline(model=snake_case_    ,							feature_extractor=snake_case_  )

              lowerCamelCase__      :			Tuple 						= object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""    ,							threshold=0.0  )

              self.assertEqual(
                  nested_simplify(snake_case_    ,							decimals=4  )    ,							[
                      {"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                      {"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                  ]    ,							)

              lowerCamelCase__      :			Union[str, Any] 						= object_detector(
                  [
                      """http://images.cocodataset.org/val2017/000000039769.jpg""",
                      """http://images.cocodataset.org/val2017/000000039769.jpg""",
                  ]    ,							threshold=0.0    ,							)

              self.assertEqual(
                  nested_simplify(snake_case_    ,							decimals=4  )    ,							[
                      [
                          {"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                          {"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                      ],
                      [
                          {"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                          {"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                      ],
                  ]    ,							)




       @require_torch
       @slow
       def 	lowerCamelCase_       (							self:       Optional[int]  ):
              lowerCamelCase__      :			Tuple 						= """facebook/detr-resnet-50"""

              lowerCamelCase__      :			Tuple 						= AutoModelForObjectDetection.from_pretrained(snake_case_  )
              lowerCamelCase__      :			List[Any] 						= AutoFeatureExtractor.from_pretrained(snake_case_  )
              lowerCamelCase__      :			str 						= ObjectDetectionPipeline(model=snake_case_    ,							feature_extractor=snake_case_  )

              lowerCamelCase__      :			Any 						= object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""  )
              self.assertEqual(
                  nested_simplify(snake_case_    ,							decimals=4  )    ,							[
                      {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                      {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                      {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                      {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                      {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
                  ]    ,							)

              lowerCamelCase__      :			Optional[int] 						= object_detector(
                  [
                      """http://images.cocodataset.org/val2017/000000039769.jpg""",
                      """http://images.cocodataset.org/val2017/000000039769.jpg""",
                  ]  )
              self.assertEqual(
                  nested_simplify(snake_case_    ,							decimals=4  )    ,							[
                      [
                          {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                          {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                          {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                          {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                          {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
                      ],
                      [
                          {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                          {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                          {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                          {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                          {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
                      ],
                  ]    ,							)




       @require_torch
       @slow
       def 	lowerCamelCase_       (							self:       List[Any]  ):
              lowerCamelCase__      :			Optional[Any] 						= """facebook/detr-resnet-50"""

              lowerCamelCase__      :			Optional[Any] 						= pipeline("""object-detection"""    ,							model=snake_case_  )

              lowerCamelCase__      :			Dict 						= object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""  )
              self.assertEqual(
                  nested_simplify(snake_case_    ,							decimals=4  )    ,							[
                      {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                      {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                      {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                      {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                      {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
                  ]    ,							)

              lowerCamelCase__      :			Optional[Any] 						= object_detector(
                  [
                      """http://images.cocodataset.org/val2017/000000039769.jpg""",
                      """http://images.cocodataset.org/val2017/000000039769.jpg""",
                  ]  )
              self.assertEqual(
                  nested_simplify(snake_case_    ,							decimals=4  )    ,							[
                      [
                          {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                          {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                          {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                          {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                          {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
                      ],
                      [
                          {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                          {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                          {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                          {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                          {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
                      ],
                  ]    ,							)




       @require_torch
       @slow
       def 	lowerCamelCase_       (							self:       str  ):
              lowerCamelCase__      :			Optional[Any] 						= 0.9_985
              lowerCamelCase__      :			Optional[int] 						= """facebook/detr-resnet-50"""

              lowerCamelCase__      :			Optional[int] 						= pipeline("""object-detection"""    ,							model=snake_case_  )

              lowerCamelCase__      :			List[Any] 						= object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""    ,							threshold=snake_case_  )
              self.assertEqual(
                  nested_simplify(snake_case_    ,							decimals=4  )    ,							[
                      {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                      {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
                  ]    ,							)




       @require_torch
       @require_pytesseract
       @slow
       def 	lowerCamelCase_       (							self:       str  ):
              lowerCamelCase__      :			List[Any] 						= """Narsil/layoutlmv3-finetuned-funsd"""
              lowerCamelCase__      :			Any 						= 0.9_993

              lowerCamelCase__      :			Optional[int] 						= pipeline("""object-detection"""    ,							model=snake_case_    ,							threshold=snake_case_  )

              lowerCamelCase__      :			Dict 						= object_detector(
                  """https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"""  )
              self.assertEqual(
                  nested_simplify(snake_case_    ,							decimals=4  )    ,							[
                      {"""score""": 0.9_993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
                      {"""score""": 0.9_993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
                  ]    ,							)









'''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))

