





'''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 				__SCREAMING_SNAKE_CASE  :






									"""simple docstring"""







									@staticmethod
									def   UpperCamelCase__   (  *__a     :					List[Any]					,				**__a     :					Any					):
													pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				__a            =MODEL_FOR_OBJECT_DETECTION_MAPPING




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




				def   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					Optional[Any]					,				__a     :					List[Any]					):
								_a								= 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

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

								_a								= [
								    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								= 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     :					Any					):
								pass




				@require_torch
				def   UpperCamelCase__   (  self     :					str					):
								_a								= "hf-internal-testing/tiny-detr-mobilenetsv3"

								_a								= AutoModelForObjectDetection.from_pretrained(__a					)
								_a								= AutoFeatureExtractor.from_pretrained(__a					)
								_a								= ObjectDetectionPipeline(model=__a					,				feature_extractor=__a					)

								_a								= 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": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
								        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
								    ]					,				)

								_a								= 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": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
								            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
								        ],
								        [
								            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
								            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
								        ],
								    ]					,				)




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

								_a								= AutoModelForObjectDetection.from_pretrained(__a					)
								_a								= AutoFeatureExtractor.from_pretrained(__a					)
								_a								= ObjectDetectionPipeline(model=__a					,				feature_extractor=__a					)

								_a								= 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": 1_75, "ymax": 1_17}},
								        {"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
								        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
								        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
								        {"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
								    ]					,				)

								_a								= 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": 1_75, "ymax": 1_17}},
								            {"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
								            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
								            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
								            {"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
								        ],
								        [
								            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
								            {"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
								            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
								            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
								            {"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
								        ],
								    ]					,				)




				@require_torch
				@slow
				def   UpperCamelCase__   (  self     :					int					):
								_a								= "facebook/detr-resnet-50"

								_a								= pipeline("object-detection"					,				model=__a					)

								_a								= 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": 1_75, "ymax": 1_17}},
								        {"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
								        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
								        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
								        {"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
								    ]					,				)

								_a								= 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": 1_75, "ymax": 1_17}},
								            {"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
								            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
								            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
								            {"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
								        ],
								        [
								            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
								            {"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
								            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
								            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
								            {"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
								        ],
								    ]					,				)




				@require_torch
				@slow
				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= 0.9985
								_a								= "facebook/detr-resnet-50"

								_a								= pipeline("object-detection"					,				model=__a					)

								_a								= 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": 3_14, "ymax": 4_70}},
								        {"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
								    ]					,				)




				@require_torch
				@require_pytesseract
				@slow
				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= "Narsil/layoutlmv3-finetuned-funsd"
								_a								= 0.9993

								_a								= pipeline("object-detection"					,				model=__a					,				threshold=__a					)

								_a								= 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": 2_94, "ymin": 2_54, "xmax": 3_43, "ymax": 2_64}},
								        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 2_94, "ymin": 2_54, "xmax": 3_43, "ymax": 2_64}},
								    ]					,				)







'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      int = 100						)	->	int:
				_a								= 0
				_a								= 0
				for i in range(1    ,							n + 1						):
								sum_of_squares += i**2
								sum_of_ints += i
				return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
					print(f"""{solution() = }""")
