





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    _UpperCAmelCase							:
            @staticmethod
            def 		_lowerCamelCase      (		*a__      ,       **a__       ):
               pass

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    _UpperCAmelCase							(		unittest.TestCase     ):
      a						       =							MODEL_FOR_OBJECT_DETECTION_MAPPING
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__       ):
         A_     :				int			      =	ObjectDetectionPipeline(model=_SCREAMING_SNAKE_CASE      ,       image_processor=_SCREAMING_SNAKE_CASE       )
         return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__       ):
         A_     :				Optional[int]			      =	object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      ,       threshold=0.0       )

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

         import datasets

         A_     :				int			      =	datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""      ,       """image"""      ,       split="""test"""       )

         A_     :				Dict			      =	[
             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"""],
         ]
         A_     :				Union[str, Any]			      =	object_detector(_SCREAMING_SNAKE_CASE      ,       threshold=0.0       )

         self.assertEqual(len(_SCREAMING_SNAKE_CASE       )      ,       len(_SCREAMING_SNAKE_CASE       )       )
         for outputs in batch_outputs:
            self.assertGreater(len(_SCREAMING_SNAKE_CASE       )      ,       0       )
            for detected_object in outputs:
               self.assertEqual(
                   _SCREAMING_SNAKE_CASE      ,       {
                       """score""": ANY(_SCREAMING_SNAKE_CASE       ),
                       """label""": ANY(_SCREAMING_SNAKE_CASE       ),
                       """box""": {"""xmin""": ANY(_SCREAMING_SNAKE_CASE       ), """ymin""": ANY(_SCREAMING_SNAKE_CASE       ), """xmax""": ANY(_SCREAMING_SNAKE_CASE       ), """ymax""": ANY(_SCREAMING_SNAKE_CASE       )},
                   }      ,       )
      @require_tf
      @unittest.skip("""Object detection not implemented in TF"""       )
      def 		_lowerCamelCase      (		self       ):
         pass
      @require_torch
      def 		_lowerCamelCase      (		self       ):
         A_     :				Tuple			      =	"""hf-internal-testing/tiny-detr-mobilenetsv3"""

         A_     :				int			      =	AutoModelForObjectDetection.from_pretrained(_SCREAMING_SNAKE_CASE       )
         A_     :				Optional[int]			      =	AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE       )
         A_     :				Optional[int]			      =	ObjectDetectionPipeline(model=_SCREAMING_SNAKE_CASE      ,       feature_extractor=_SCREAMING_SNAKE_CASE       )

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

         self.assertEqual(
             nested_simplify(_SCREAMING_SNAKE_CASE      ,       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}},
             ]      ,       )

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

         self.assertEqual(
             nested_simplify(_SCREAMING_SNAKE_CASE      ,       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 		_lowerCamelCase      (		self       ):
         A_     :				List[Any]			      =	"""facebook/detr-resnet-50"""

         A_     :				Optional[int]			      =	AutoModelForObjectDetection.from_pretrained(_SCREAMING_SNAKE_CASE       )
         A_     :				int			      =	AutoFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE       )
         A_     :				Dict			      =	ObjectDetectionPipeline(model=_SCREAMING_SNAKE_CASE      ,       feature_extractor=_SCREAMING_SNAKE_CASE       )

         A_     :				Optional[Any]			      =	object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""       )
         self.assertEqual(
             nested_simplify(_SCREAMING_SNAKE_CASE      ,       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}},
             ]      ,       )

         A_     :				str			      =	object_detector(
             [
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
             ]       )
         self.assertEqual(
             nested_simplify(_SCREAMING_SNAKE_CASE      ,       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 		_lowerCamelCase      (		self       ):
         A_     :				List[str]			      =	"""facebook/detr-resnet-50"""

         A_     :				Any			      =	pipeline("""object-detection"""      ,       model=_SCREAMING_SNAKE_CASE       )

         A_     :				Optional[Any]			      =	object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""       )
         self.assertEqual(
             nested_simplify(_SCREAMING_SNAKE_CASE      ,       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}},
             ]      ,       )

         A_     :				Optional[int]			      =	object_detector(
             [
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
             ]       )
         self.assertEqual(
             nested_simplify(_SCREAMING_SNAKE_CASE      ,       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 		_lowerCamelCase      (		self       ):
         A_     :				Optional[int]			      =	0.9985
         A_     :				str			      =	"""facebook/detr-resnet-50"""

         A_     :				str			      =	pipeline("""object-detection"""      ,       model=_SCREAMING_SNAKE_CASE       )

         A_     :				Tuple			      =	object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""      ,       threshold=_SCREAMING_SNAKE_CASE       )
         self.assertEqual(
             nested_simplify(_SCREAMING_SNAKE_CASE      ,       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 		_lowerCamelCase      (		self       ):
         A_     :				Optional[Any]			      =	"""Narsil/layoutlmv3-finetuned-funsd"""
         A_     :				Any			      =	0.9993

         A_     :				List[Any]			      =	pipeline("""object-detection"""      ,       model=_SCREAMING_SNAKE_CASE      ,       threshold=_SCREAMING_SNAKE_CASE       )

         A_     :				Dict			      =	object_detector(
             """https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"""       )
         self.assertEqual(
             nested_simplify(_SCREAMING_SNAKE_CASE      ,       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}},
             ]      ,       )













def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	[0] * len(_lowerCAmelCase     )
   A_     :				Optional[int]			      =	[]
   A_     :				str			      =	[]
   A_     :				Dict			      =	0

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

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

   while queue:
      A_     :				List[str]			      =	queue.pop(0     )
      cnt += 1
      topo.append(_lowerCAmelCase     )
      for x in graph[vertex]:
         indegree[x] -= 1
         if indegree[x] == 0:
            queue.append(_lowerCAmelCase     )

   if cnt != len(_lowerCAmelCase     ):
      print("""Cycle exists"""     )
   else:
      print(_lowerCAmelCase     )


# Adjacency List of Graph
_lowerCAmelCase 				=     {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)






