



"""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 	snake_case ( *lowerCamelCase							:						Optional[Any],						**lowerCamelCase							:						Any  )->      List[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 	snake_case ( self							:						Tuple,						lowerCamelCase							:						Any,						lowerCamelCase							:						Dict,						lowerCamelCase							:						str  )->      str:
								lowerCamelCase__	:	Dict				=ObjectDetectionPipeline(model=lowerCamelCase,						image_processor=lowerCamelCase  )
								return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]



					def 	snake_case ( self							:						List[str],						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						Tuple  )->      List[str]:
								lowerCamelCase__	:	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

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

								lowerCamelCase__	:	Optional[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'''],
								]
								lowerCamelCase__	:	Dict				=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 	snake_case ( self							:						List[str]  )->      Dict:
								pass



					@require_torch
					def 	snake_case ( self							:						int  )->      Union[str, Any]:
								lowerCamelCase__	:	int				='''hf-internal-testing/tiny-detr-mobilenetsv3'''

								lowerCamelCase__	:	List[Any]				=AutoModelForObjectDetection.from_pretrained(lowerCamelCase  )
								lowerCamelCase__	:	Union[str, Any]				=AutoFeatureExtractor.from_pretrained(lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=ObjectDetectionPipeline(model=lowerCamelCase,						feature_extractor=lowerCamelCase  )

								lowerCamelCase__	:	str				=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}},
								    ],						)

								lowerCamelCase__	:	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 	snake_case ( self							:						Dict  )->      Union[str, Any]:
								lowerCamelCase__	:	List[Any]				='''facebook/detr-resnet-50'''

								lowerCamelCase__	:	Dict				=AutoModelForObjectDetection.from_pretrained(lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=AutoFeatureExtractor.from_pretrained(lowerCamelCase  )
								lowerCamelCase__	:	Any				=ObjectDetectionPipeline(model=lowerCamelCase,						feature_extractor=lowerCamelCase  )

								lowerCamelCase__	:	Tuple				=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}},
								    ],						)

								lowerCamelCase__	:	Tuple				=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 	snake_case ( self							:						Optional[Any]  )->      List[Any]:
								lowerCamelCase__	:	List[Any]				='''facebook/detr-resnet-50'''

								lowerCamelCase__	:	Union[str, Any]				=pipeline('''object-detection''',						model=lowerCamelCase  )

								lowerCamelCase__	:	Tuple				=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}},
								    ],						)

								lowerCamelCase__	:	Dict				=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 	snake_case ( self							:						Any  )->      List[Any]:
								lowerCamelCase__	:	Tuple				=0.9_985
								lowerCamelCase__	:	int				='''facebook/detr-resnet-50'''

								lowerCamelCase__	:	Optional[int]				=pipeline('''object-detection''',						model=lowerCamelCase  )

								lowerCamelCase__	:	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 	snake_case ( self							:						Union[str, Any]  )->      Union[str, Any]:
								lowerCamelCase__	:	Union[str, Any]				='''Narsil/layoutlmv3-finetuned-funsd'''
								lowerCamelCase__	:	Dict				=0.9_993

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

								lowerCamelCase__	:	List[Any]				=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 colorsys

from PIL import Image  # type: ignore






def   snake_case__     (	__lowerCamelCase       :					float							,	__lowerCamelCase       :					float							,	__lowerCamelCase       :					int			):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				=x
			lowerCamelCase__	:	Any				=y
			for step in range(__lowerCamelCase			):  # noqa: B007
						lowerCamelCase__	:	List[Any]				=a * a - b * b + x
						lowerCamelCase__	:	Optional[int]				=2 * a * b + y
						lowerCamelCase__	:	Union[str, Any]				=a_new

						# divergence happens for all complex number with an absolute value
						# greater than 4
						if a * a + b * b > 4:
									break
			return step / (max_step - 1)






def   snake_case__     (	__lowerCamelCase       :					float			):
			"""simple docstring"""
			if distance == 1:
						return (0, 0, 0)
			else:
						return (255, 255, 255)






def   snake_case__     (	__lowerCamelCase       :					float			):
			"""simple docstring"""
			if distance == 1:
						return (0, 0, 0)
			else:
						return tuple(round(i * 255			) for i in colorsys.hsv_to_rgb(__lowerCamelCase							,	1							,	1			)			)






def   snake_case__     (	__lowerCamelCase       :					int = 800							,	__lowerCamelCase       :					int = 600							,	__lowerCamelCase       :					float = -0.6							,	__lowerCamelCase       :					float = 0							,	__lowerCamelCase       :					float = 3.2							,	__lowerCamelCase       :					int = 50							,	__lowerCamelCase       :					bool = True							,	):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				=Image.new('''RGB'''							,	(image_width, image_height)			)
			lowerCamelCase__	:	Optional[int]				=img.load()

			# loop through the image-coordinates
			for image_x in range(__lowerCamelCase			):
						for image_y in range(__lowerCamelCase			):
									# determine the figure-coordinates based on the image-coordinates
									lowerCamelCase__	:	Optional[Any]				=figure_width / image_width * image_height
									lowerCamelCase__	:	Dict				=figure_center_x + (image_x / image_width - 0.5) * figure_width
									lowerCamelCase__	:	Optional[int]				=figure_center_y + (image_y / image_height - 0.5) * figure_height

									lowerCamelCase__	:	Any				=get_distance(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)

									# color the corresponding pixel based on the selected coloring-function
									if use_distance_color_coding:
												lowerCamelCase__	:	int				=get_color_coded_rgb(__lowerCamelCase			)
									else:
												lowerCamelCase__	:	Optional[int]				=get_black_and_white_rgb(__lowerCamelCase			)

			return img


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# colored version, full figure
		_lowercase				:				Optional[Any]   		=		get_image()

		# uncomment for colored version, different section, zoomed in
		# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
		# figure_width = 0.8)

		# uncomment for black and white version, full figure
		# img = get_image(use_distance_color_coding = False)

		# uncomment to save the image
		# img.save("mandelbrot.png")

		img.show()




