
from math import pi, sqrt, tan


def 		UpperCAmelCase     (      a_		)						->      Optional[Any]:







				"""simple docstring"""



				if side_length < 0:
								raise ValueError("surface_area_cube() only accepts non-negative values"		)
				return 6 * side_length**2


def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      Any:







				"""simple docstring"""



				if length < 0 or breadth < 0 or height < 0:
								raise ValueError("surface_area_cuboid() only accepts non-negative values"		)
				return 2 * ((length * breadth) + (breadth * height) + (length * height))


def 		UpperCAmelCase     (      a_		)						->      Optional[Any]:







				"""simple docstring"""



				if radius < 0:
								raise ValueError("surface_area_sphere() only accepts non-negative values"		)
				return 4 * pi * radius**2


def 		UpperCAmelCase     (      a_		)						->      Tuple:







				"""simple docstring"""



				if radius < 0:
								raise ValueError("surface_area_hemisphere() only accepts non-negative values"		)
				return 3 * pi * radius**2


def 		UpperCAmelCase     (      a_						,  a_		)						->      Tuple:







				"""simple docstring"""



				if radius < 0 or height < 0:
								raise ValueError("surface_area_cone() only accepts non-negative values"		)
				return pi * radius * (radius + (height**2 + radius**2) ** 0.5)


def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      Optional[int]:







				"""simple docstring"""



				if radius_a < 0 or radius_a < 0 or height < 0:
								raise ValueError(
								    "surface_area_conical_frustum() only accepts non-negative values"		)
				__A       =					(height**2 + (radius_a - radius_a) ** 2) ** 0.5
				return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)


def 		UpperCAmelCase     (      a_						,  a_		)						->      Optional[int]:







				"""simple docstring"""



				if radius < 0 or height < 0:
								raise ValueError("surface_area_cylinder() only accepts non-negative values"		)
				return 2 * pi * radius * (height + radius)


def 		UpperCAmelCase     (      a_						,  a_		)						->      int:







				"""simple docstring"""



				if torus_radius < 0 or tube_radius < 0:
								raise ValueError("surface_area_torus() only accepts non-negative values"		)
				if torus_radius < tube_radius:
								raise ValueError(
								    "surface_area_torus() does not support spindle or self intersecting tori"		)
				return 4 * pow(_a						,  2		) * torus_radius * tube_radius


def 		UpperCAmelCase     (      a_						,  a_		)						->      Dict:







				"""simple docstring"""



				if length < 0 or width < 0:
								raise ValueError("area_rectangle() only accepts non-negative values"		)
				return length * width


def 		UpperCAmelCase     (      a_		)						->      List[Any]:







				"""simple docstring"""



				if side_length < 0:
								raise ValueError("area_square() only accepts non-negative values"		)
				return side_length**2


def 		UpperCAmelCase     (      a_						,  a_		)						->      Union[str, Any]:







				"""simple docstring"""



				if base < 0 or height < 0:
								raise ValueError("area_triangle() only accepts non-negative values"		)
				return (base * height) / 2


def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      str:







				"""simple docstring"""



				if sidea < 0 or sidea < 0 or sidea < 0:
								raise ValueError("area_triangle_three_sides() only accepts non-negative values"		)
				elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
								raise ValueError("Given three sides do not form a triangle"		)
				__A       =					(sidea + sidea + sidea) / 2
				__A       =					sqrt(
				    semi_perimeter
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea)		)
				return area


def 		UpperCAmelCase     (      a_						,  a_		)						->      int:







				"""simple docstring"""



				if base < 0 or height < 0:
								raise ValueError("area_parallelogram() only accepts non-negative values"		)
				return base * height


def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      Union[str, Any]:







				"""simple docstring"""



				if basea < 0 or basea < 0 or height < 0:
								raise ValueError("area_trapezium() only accepts non-negative values"		)
				return 1 / 2 * (basea + basea) * height


def 		UpperCAmelCase     (      a_		)						->      Union[str, Any]:







				"""simple docstring"""



				if radius < 0:
								raise ValueError("area_circle() only accepts non-negative values"		)
				return pi * radius**2


def 		UpperCAmelCase     (      a_						,  a_		)						->      int:







				"""simple docstring"""



				if radius_x < 0 or radius_y < 0:
								raise ValueError("area_ellipse() only accepts non-negative values"		)
				return pi * radius_x * radius_y


def 		UpperCAmelCase     (      a_						,  a_		)						->      Dict:







				"""simple docstring"""



				if diagonal_a < 0 or diagonal_a < 0:
								raise ValueError("area_rhombus() only accepts non-negative values"		)
				return 1 / 2 * diagonal_a * diagonal_a


def 		UpperCAmelCase     (      a_						,  a_		)						->      Dict:







				"""simple docstring"""



				if not isinstance(_a						,  _a		) or sides < 3:
								raise ValueError(
								    "area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"		)
				elif length < 0:
								raise ValueError(
								    "area_reg_polygon() only accepts non-negative values as \
length of a side"		)
				return (sides * length**2) / (4 * tan(pi / sides		))
				return (sides * length**2) / (4 * tan(pi / sides		))


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

					print('[DEMO] Areas of various geometric shapes: \n')
					print(f'''Rectangle: {area_rectangle(10, 20) = }''')
					print(f'''Square: {area_square(10) = }''')
					print(f'''Triangle: {area_triangle(10, 10) = }''')
					print(f'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
					print(f'''Parallelogram: {area_parallelogram(10, 20) = }''')
					print(f'''Rhombus: {area_rhombus(10, 20) = }''')
					print(f'''Trapezium: {area_trapezium(10, 20, 30) = }''')
					print(f'''Circle: {area_circle(20) = }''')
					print(f'''Ellipse: {area_ellipse(10, 20) = }''')
					print('\nSurface Areas of various geometric shapes: \n')
					print(f'''Cube: {surface_area_cube(20) = }''')
					print(f'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
					print(f'''Sphere: {surface_area_sphere(20) = }''')
					print(f'''Hemisphere: {surface_area_hemisphere(20) = }''')
					print(f'''Cone: {surface_area_cone(10, 20) = }''')
					print(f'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
					print(f'''Cylinder: {surface_area_cylinder(10, 20) = }''')
					print(f'''Torus: {surface_area_torus(20, 10) = }''')
					print(f'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
					print(f'''Square: {area_reg_polygon(4, 10) = }''')
					print(f'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''')



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				:
						'''simple docstring'''







						@staticmethod
						def 				__UpperCamelCase			(      *a				:			str   ,   **a				:			int						)				->							str:

							"""simple docstring"""

							pass






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =MODEL_FOR_OBJECT_DETECTION_MAPPING
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			str   ,   a				:			Optional[Any]   ,   a				:			Union[str, Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							ObjectDetectionPipeline(model=a   ,   image_processor=a						)
				return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			Optional[int]   ,   a				:			Optional[int]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							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

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

				SCREAMING_SNAKE_CASE :   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"],
				]
				SCREAMING_SNAKE_CASE :   Tuple					  =							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 				__UpperCamelCase			(      self				:			Optional[int]						)				->							str:

				"""simple docstring"""

				pass
			@require_torch
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							"hf-internal-testing/tiny-detr-mobilenetsv3"

				SCREAMING_SNAKE_CASE :   Dict					  =							AutoModelForObjectDetection.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Any					  =							AutoFeatureExtractor.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							ObjectDetectionPipeline(model=a   ,   feature_extractor=a						)

				SCREAMING_SNAKE_CASE :   int					  =							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}},
				    ]   ,   )

				SCREAMING_SNAKE_CASE :   Dict					  =							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 				__UpperCamelCase			(      self				:			str						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							"facebook/detr-resnet-50"

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							AutoModelForObjectDetection.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							AutoFeatureExtractor.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   int					  =							ObjectDetectionPipeline(model=a   ,   feature_extractor=a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							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}},
				    ]   ,   )

				SCREAMING_SNAKE_CASE :   int					  =							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 				__UpperCamelCase			(      self				:			str						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							"facebook/detr-resnet-50"

				SCREAMING_SNAKE_CASE :   Tuple					  =							pipeline("object-detection"   ,   model=a						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							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}},
				    ]   ,   )

				SCREAMING_SNAKE_CASE :   str					  =							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 				__UpperCamelCase			(      self				:			str						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							0.9985
				SCREAMING_SNAKE_CASE :   int					  =							"facebook/detr-resnet-50"

				SCREAMING_SNAKE_CASE :   List[str]					  =							pipeline("object-detection"   ,   model=a						)

				SCREAMING_SNAKE_CASE :   str					  =							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 				__UpperCamelCase			(      self				:			str						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							"Narsil/layoutlmv3-finetuned-funsd"
				SCREAMING_SNAKE_CASE :   Dict					  =							0.9993

				SCREAMING_SNAKE_CASE :   str					  =							pipeline("object-detection"   ,   model=a   ,   threshold=a						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							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}},
				    ]   ,   )