
'''simple docstring'''
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DeformableDetrImageProcessor


class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def __init__(       self						,				A_						,				A_=7						,				A_=3						,				A_=30						,				A_=400						,				A_=True						,				A_=None						,				A_=True						,				A_=[0.5, 0.5, 0.5]						,				A_=[0.5, 0.5, 0.5]						,				A_=True						,				A_=1 / 255						,				A_=True						,				)->		int:







											'''simple docstring'''






											UpperCamelCase   			=     size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
											UpperCamelCase   			=     parent
											UpperCamelCase   			=     batch_size
											UpperCamelCase   			=     num_channels
											UpperCamelCase   			=     min_resolution
											UpperCamelCase   			=     max_resolution
											UpperCamelCase   			=     do_resize
											UpperCamelCase   			=     size
											UpperCamelCase   			=     do_normalize
											UpperCamelCase   			=     image_mean
											UpperCamelCase   			=     image_std
											UpperCamelCase   			=     do_rescale
											UpperCamelCase   			=     rescale_factor
											UpperCamelCase   			=     do_pad







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_rescale": self.do_rescale,
											    "rescale_factor": self.rescale_factor,
											    "do_pad": self.do_pad,
											}







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=False     )->		Union[str, Any]:







											'''simple docstring'''






											if not batched:
																		UpperCamelCase   			=     image_inputs[0]
																		if isinstance(A_						,				Image.Image     ):
																									UpperCamelCase			,	UpperCamelCase   			=     image.size
																		else:
																									UpperCamelCase			,	UpperCamelCase   			=     image.shape[1], image.shape[2]
																		if w < h:
																									UpperCamelCase   			=     int(self.size['shortest_edge'] * h / w     )
																									UpperCamelCase   			=     self.size['shortest_edge']
																		elif w > h:
																									UpperCamelCase   			=     self.size['shortest_edge']
																									UpperCamelCase   			=     int(self.size['shortest_edge'] * w / h     )
																		else:
																									UpperCamelCase   			=     self.size['shortest_edge']
																									UpperCamelCase   			=     self.size['shortest_edge']

											else:
																		UpperCamelCase   			=     []
																		for image in image_inputs:
																									UpperCamelCase			,	UpperCamelCase   			=     self.get_expected_values([image]     )
																									expected_values.append((expected_height, expected_width)     )
																		UpperCamelCase   			=     max(A_						,				key=lambda A_     : item[0]     )[0]
																		UpperCamelCase   			=     max(A_						,				key=lambda A_     : item[1]     )[1]

											return expected_height, expected_width


@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							DeformableDetrImageProcessor if is_vision_available() else None







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     DeformableDetrImageProcessingTester(self     )







				@property
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											return self.image_processor_tester.prepare_image_processor_dict()







				def 						UpperCAmelCase_    (       self     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											self.assertTrue(hasattr(A_						,				'image_mean'     )     )
											self.assertTrue(hasattr(A_						,				'image_std'     )     )
											self.assertTrue(hasattr(A_						,				'do_normalize'     )     )
											self.assertTrue(hasattr(A_						,				'do_resize'     )     )
											self.assertTrue(hasattr(A_						,				'do_rescale'     )     )
											self.assertTrue(hasattr(A_						,				'do_pad'     )     )
											self.assertTrue(hasattr(A_						,				'size'     )     )







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict     )
											self.assertEqual(image_processor.size						,				{'shortest_edge': 18, 'longest_edge': 1333}     )
											self.assertEqual(image_processor.do_pad						,				A_     )

											UpperCamelCase   			=     self.image_processing_class.from_dict(
											    self.image_processor_dict						,				size=42						,				max_size=84						,				pad_and_return_pixel_mask=A_     )
											self.assertEqual(image_processor.size						,				{'shortest_edge': 42, 'longest_edge': 84}     )
											self.assertEqual(image_processor.do_pad						,				A_     )







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											pass







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random PIL images
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				Image.Image     )

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values

											UpperCamelCase			,	UpperCamelCase   			=     self.image_processor_tester.get_expected_values(A_     )

											self.assertEqual(
											    encoded_images.shape						,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,				)

											# Test batched
											UpperCamelCase			,	UpperCamelCase   			=     self.image_processor_tester.get_expected_values(A_						,				batched=A_     )

											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )						,				)







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random numpy tensors
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_						,				numpify=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				np.ndarray     )

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values

											UpperCamelCase			,	UpperCamelCase   			=     self.image_processor_tester.get_expected_values(A_     )

											self.assertEqual(
											    encoded_images.shape						,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values

											UpperCamelCase			,	UpperCamelCase   			=     self.image_processor_tester.get_expected_values(A_						,				batched=A_     )

											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )						,				)







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random PyTorch tensors
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_						,				torchify=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				torch.Tensor     )

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values

											UpperCamelCase			,	UpperCamelCase   			=     self.image_processor_tester.get_expected_values(A_     )

											self.assertEqual(
											    encoded_images.shape						,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values

											UpperCamelCase			,	UpperCamelCase   			=     self.image_processor_tester.get_expected_values(A_						,				batched=A_     )

											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )						,				)







				@slow
				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
											with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'						,				'r'     ) as f:
																		UpperCamelCase   			=     json.loads(f.read()     )

											UpperCamelCase   			=     {'image_id': 39769, 'annotations': target}

											# encode them
											UpperCamelCase   			=     DeformableDetrImageProcessor()
											UpperCamelCase   			=     image_processing(images=A_						,				annotations=A_						,				return_tensors='pt'     )

											# verify pixel values
											UpperCamelCase   			=     torch.Size([1, 3, 800, 1066]     )
											self.assertEqual(encoding['pixel_values'].shape						,				A_     )

											UpperCamelCase   			=     torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,				A_						,				atol=1e-4     )     )

											# verify area
											UpperCamelCase   			=     torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]     )
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,				A_     )     )
											# verify boxes
											UpperCamelCase   			=     torch.Size([6, 4]     )
											self.assertEqual(encoding['labels'][0]['boxes'].shape						,				A_     )
											UpperCamelCase   			=     torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]     )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,				A_						,				atol=1e-3     )     )
											# verify image_id
											UpperCamelCase   			=     torch.tensor([39769]     )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,				A_     )     )
											# verify is_crowd
											UpperCamelCase   			=     torch.tensor([0, 0, 0, 0, 0, 0]     )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,				A_     )     )
											# verify class_labels
											UpperCamelCase   			=     torch.tensor([75, 75, 63, 65, 17, 17]     )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,				A_     )     )
											# verify orig_size
											UpperCamelCase   			=     torch.tensor([480, 640]     )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,				A_     )     )
											# verify size
											UpperCamelCase   			=     torch.tensor([800, 1066]     )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,				A_     )     )







				@slow
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
											with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'						,				'r'     ) as f:
																		UpperCamelCase   			=     json.loads(f.read()     )

											UpperCamelCase   			=     {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

											UpperCamelCase   			=     pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'     )

											# encode them
											UpperCamelCase   			=     DeformableDetrImageProcessor(format='coco_panoptic'     )
											UpperCamelCase   			=     image_processing(images=A_						,				annotations=A_						,				masks_path=A_						,				return_tensors='pt'     )

											# verify pixel values
											UpperCamelCase   			=     torch.Size([1, 3, 800, 1066]     )
											self.assertEqual(encoding['pixel_values'].shape						,				A_     )

											UpperCamelCase   			=     torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,				A_						,				atol=1e-4     )     )

											# verify area
											UpperCamelCase   			=     torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]     )
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,				A_     )     )
											# verify boxes
											UpperCamelCase   			=     torch.Size([6, 4]     )
											self.assertEqual(encoding['labels'][0]['boxes'].shape						,				A_     )
											UpperCamelCase   			=     torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]     )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,				A_						,				atol=1e-3     )     )
											# verify image_id
											UpperCamelCase   			=     torch.tensor([39769]     )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,				A_     )     )
											# verify is_crowd
											UpperCamelCase   			=     torch.tensor([0, 0, 0, 0, 0, 0]     )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,				A_     )     )
											# verify class_labels
											UpperCamelCase   			=     torch.tensor([17, 17, 63, 75, 75, 93]     )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,				A_     )     )
											# verify masks
											UpperCamelCase   			=     822873
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()						,				A_     )
											# verify orig_size
											UpperCamelCase   			=     torch.tensor([480, 640]     )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,				A_     )     )
											# verify size
											UpperCamelCase   			=     torch.tensor([800, 1066]     )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,				A_     )     )






'''simple docstring'''
import colorsys

from PIL import Image  # type: ignore







def 		A_(     A						:		float		,     A						:		float		,     A						:		int):
							UpperCamelCase   			=     x
							UpperCamelCase   			=     y
							for step in range(A):  # noqa: B007
														UpperCamelCase   			=     a * a - b * b + x
														UpperCamelCase   			=     2 * a * b + y
														UpperCamelCase   			=     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 		A_(     A						:		float):
							if distance == 1:
														return (0, 0, 0)
							else:
														return (255, 255, 255)







def 		A_(     A						:		float):
							if distance == 1:
														return (0, 0, 0)
							else:
														return tuple(round(i * 255) for i in colorsys.hsv_to_rgb(A		,     1		,     1))







def 		A_(     A						:		int = 800		,     A						:		int = 600		,     A						:		float = -0.6		,     A						:		float = 0		,     A						:		float = 3.2		,     A						:		int = 50		,     A						:		bool = True		,     ):
							UpperCamelCase   			=     Image.new('RGB'		,     (image_width, image_height))
							UpperCamelCase   			=     img.load()

							# loop through the image-coordinates
							for image_x in range(A):
														for image_y in range(A):
																					# determine the figure-coordinates based on the image-coordinates
																					UpperCamelCase   			=     figure_width / image_width * image_height
																					UpperCamelCase   			=     figure_center_x + (image_x / image_width - 0.5) * figure_width
																					UpperCamelCase   			=     figure_center_y + (image_y / image_height - 0.5) * figure_height

																					UpperCamelCase   			=     get_distance(A		,     A		,     A)

																					# color the corresponding pixel based on the selected coloring-function
																					if use_distance_color_coding:
																												UpperCamelCase   			=     get_color_coded_rgb(A)
																					else:
																												UpperCamelCase   			=     get_black_and_white_rgb(A)

							return img


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						# colored version, full figure
						lowerCAmelCase					:    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()




