

def       __snake_case						( __UpperCamelCase	: List[str]    ):



      """simple docstring"""

      A_						  =				[0] * len(__UpperCamelCase    )
      A_						  =				[]
      A_						  =				[1] * len(__UpperCamelCase    )

      for values in graph.values():
            for i in values:
                  indegree[i] += 1

      for i in range(len(__UpperCamelCase    )    ):
            if indegree[i] == 0:
                  queue.append(__UpperCamelCase    )

      while queue:
            A_						  =				queue.pop(0    )
            for x in graph[vertex]:
                  indegree[x] -= 1

                  if long_dist[vertex] + 1 > long_dist[x]:
                        A_						  =				long_dist[vertex] + 1

                  if indegree[x] == 0:
                        queue.append(__UpperCamelCase    )

      print(max(__UpperCamelCase    )    )


# Adjacency list of Graph
__a    :int      =			{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}



longest_distance(graph)




'''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    lowerCamelCase_       :
     """simple docstring"""




     @staticmethod
     def 		_lowercase	(					*_a :      Optional[Any]     ,      **_a :      Optional[Any]  )   ->   Tuple:
       pass






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    lowerCamelCase_       (							unittest.TestCase	):
  """simple docstring"""




  a_				   =MODEL_FOR_OBJECT_DETECTION_MAPPING




  def 		_lowercase	(					self :      List[Any]     ,      _a :      int     ,      _a :      str     ,      _a :      Tuple  )   ->   List[Any]:
    __lowerCamelCase			:			List[str]       			=					ObjectDetectionPipeline(model=_a     ,      image_processor=_a  )
    return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




  def 		_lowercase	(					self :      Optional[int]     ,      _a :      int     ,      _a :      Optional[Any]  )   ->   Any:
    __lowerCamelCase			:			int       			=					object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'     ,      threshold=0.0  )

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

    import datasets

    __lowerCamelCase			:			Optional[int]       			=					datasets.load_dataset('hf-internal-testing/fixtures_image_utils'     ,      'image'     ,      split='test'  )

    __lowerCamelCase			:			List[str]       			=					[
        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			:			Dict       			=					object_detector(_a     ,      threshold=0.0  )

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




  @require_tf
  @unittest.skip('Object detection not implemented in TF'  )
  def 		_lowercase	(					self :      Optional[Any]  )   ->   Dict:
    pass




  @require_torch
  def 		_lowercase	(					self :      int  )   ->   Optional[int]:
    __lowerCamelCase			:			Dict       			=					'hf-internal-testing/tiny-detr-mobilenetsv3'

    __lowerCamelCase			:			str       			=					AutoModelForObjectDetection.from_pretrained(_a  )
    __lowerCamelCase			:			Optional[Any]       			=					AutoFeatureExtractor.from_pretrained(_a  )
    __lowerCamelCase			:			str       			=					ObjectDetectionPipeline(model=_a     ,      feature_extractor=_a  )

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

    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
            {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
        ]     ,      )

    __lowerCamelCase			:			Optional[int]       			=					object_detector(
        [
            'http://images.cocodataset.org/val2017/000000039769.jpg',
            'http://images.cocodataset.org/val2017/000000039769.jpg',
        ]     ,      threshold=0.0     ,      )

    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            [
                {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
            ],
            [
                {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
            ],
        ]     ,      )




  @require_torch
  @slow
  def 		_lowercase	(					self :      Union[str, Any]  )   ->   Dict:
    __lowerCamelCase			:			Tuple       			=					'facebook/detr-resnet-50'

    __lowerCamelCase			:			Optional[int]       			=					AutoModelForObjectDetection.from_pretrained(_a  )
    __lowerCamelCase			:			List[str]       			=					AutoFeatureExtractor.from_pretrained(_a  )
    __lowerCamelCase			:			str       			=					ObjectDetectionPipeline(model=_a     ,      feature_extractor=_a  )

    __lowerCamelCase			:			int       			=					object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
            {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
            {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
            {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
            {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
        ]     ,      )

    __lowerCamelCase			:			List[Any]       			=					object_detector(
        [
            'http://images.cocodataset.org/val2017/000000039769.jpg',
            'http://images.cocodataset.org/val2017/000000039769.jpg',
        ]  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            [
                {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
            ],
            [
                {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
            ],
        ]     ,      )




  @require_torch
  @slow
  def 		_lowercase	(					self :      Tuple  )   ->   Optional[Any]:
    __lowerCamelCase			:			Any       			=					'facebook/detr-resnet-50'

    __lowerCamelCase			:			List[str]       			=					pipeline('object-detection'     ,      model=_a  )

    __lowerCamelCase			:			int       			=					object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
            {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
            {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
            {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
            {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
        ]     ,      )

    __lowerCamelCase			:			List[Any]       			=					object_detector(
        [
            'http://images.cocodataset.org/val2017/000000039769.jpg',
            'http://images.cocodataset.org/val2017/000000039769.jpg',
        ]  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            [
                {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
            ],
            [
                {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
            ],
        ]     ,      )




  @require_torch
  @slow
  def 		_lowercase	(					self :      int  )   ->   Tuple:
    __lowerCamelCase			:			Optional[Any]       			=					0.9985
    __lowerCamelCase			:			Optional[Any]       			=					'facebook/detr-resnet-50'

    __lowerCamelCase			:			Optional[int]       			=					pipeline('object-detection'     ,      model=_a  )

    __lowerCamelCase			:			Dict       			=					object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'     ,      threshold=_a  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
            {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
        ]     ,      )




  @require_torch
  @require_pytesseract
  @slow
  def 		_lowercase	(					self :      int  )   ->   Any:
    __lowerCamelCase			:			Optional[int]       			=					'Narsil/layoutlmv3-finetuned-funsd'
    __lowerCamelCase			:			Tuple       			=					0.9993

    __lowerCamelCase			:			List[str]       			=					pipeline('object-detection'     ,      model=_a     ,      threshold=_a  )

    __lowerCamelCase			:			Tuple       			=					object_detector(
        'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
            {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
        ]     ,      )






