

"""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  _a      (*_lowerCamelCase							,       **_lowerCamelCase	):


            """simple docstring"""

            pass

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  MODEL_FOR_OBJECT_DETECTION_MAPPING



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[Any]							=    ObjectDetectionPipeline(model=_lowerCamelCase							,       image_processor=_lowerCamelCase	)
        return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							,       threshold=0.0	)

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

        import datasets

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

        UpperCAmelCase__			:							Union[str, 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"""],
        ]
        UpperCAmelCase__			:							List[Any]							=    object_detector(_lowerCamelCase							,       threshold=0.0	)

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



  @require_tf
  @unittest.skip("""Object detection not implemented in TF"""	)
  def  _a      (self	):


        """simple docstring"""

        pass



  @require_torch
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    """hf-internal-testing/tiny-detr-mobilenetsv3"""

        UpperCAmelCase__			:							Tuple							=    AutoModelForObjectDetection.from_pretrained(_lowerCamelCase	)
        UpperCAmelCase__			:							List[str]							=    AutoFeatureExtractor.from_pretrained(_lowerCamelCase	)
        UpperCAmelCase__			:							Tuple							=    ObjectDetectionPipeline(model=_lowerCamelCase							,       feature_extractor=_lowerCamelCase	)

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

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

        UpperCAmelCase__			:							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(_lowerCamelCase							,       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  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[Any]							=    """facebook/detr-resnet-50"""

        UpperCAmelCase__			:							Optional[Any]							=    AutoModelForObjectDetection.from_pretrained(_lowerCamelCase	)
        UpperCAmelCase__			:							List[str]							=    AutoFeatureExtractor.from_pretrained(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    ObjectDetectionPipeline(model=_lowerCamelCase							,       feature_extractor=_lowerCamelCase	)

        UpperCAmelCase__			:							Any							=    object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""	)
        self.assertEqual(
            nested_simplify(_lowerCamelCase							,       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}},
            ]							,       )

        UpperCAmelCase__			:							Optional[int]							=    object_detector(
            [
                """http://images.cocodataset.org/val2017/000000039769.jpg""",
                """http://images.cocodataset.org/val2017/000000039769.jpg""",
            ]	)
        self.assertEqual(
            nested_simplify(_lowerCamelCase							,       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  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Any							=    """facebook/detr-resnet-50"""

        UpperCAmelCase__			:							List[str]							=    pipeline("""object-detection"""							,       model=_lowerCamelCase	)

        UpperCAmelCase__			:							List[Any]							=    object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""	)
        self.assertEqual(
            nested_simplify(_lowerCamelCase							,       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}},
            ]							,       )

        UpperCAmelCase__			:							int							=    object_detector(
            [
                """http://images.cocodataset.org/val2017/000000039769.jpg""",
                """http://images.cocodataset.org/val2017/000000039769.jpg""",
            ]	)
        self.assertEqual(
            nested_simplify(_lowerCamelCase							,       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  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    0.9_985
        UpperCAmelCase__			:							Optional[int]							=    """facebook/detr-resnet-50"""

        UpperCAmelCase__			:							Dict							=    pipeline("""object-detection"""							,       model=_lowerCamelCase	)

        UpperCAmelCase__			:							Any							=    object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""							,       threshold=_lowerCamelCase	)
        self.assertEqual(
            nested_simplify(_lowerCamelCase							,       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  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    """Narsil/layoutlmv3-finetuned-funsd"""
        UpperCAmelCase__			:							Union[str, Any]							=    0.9_993

        UpperCAmelCase__			:							Optional[Any]							=    pipeline("""object-detection"""							,       model=_lowerCamelCase							,       threshold=_lowerCamelCase	)

        UpperCAmelCase__			:							Optional[int]							=    object_detector(
            """https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"""	)
        self.assertEqual(
            nested_simplify(_lowerCamelCase							,       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"""
import cmath
import math




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> complex:
      UpperCAmelCase__			:							str							=    math.radians(lowerCAmelCase    )
      UpperCAmelCase__			:							Optional[int]							=    math.radians(lowerCAmelCase    )

      # Convert voltage and current to rectangular form
      UpperCAmelCase__			:							Union[str, Any]							=    cmath.rect(lowerCAmelCase			,	lowerCAmelCase    )
      UpperCAmelCase__			:							List[str]							=    cmath.rect(lowerCAmelCase			,	lowerCAmelCase    )

      # Calculate apparent power
      return voltage_rect * current_rect


if __name__ == "__main__":
    import doctest

    doctest.testmod()

