"""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 DetaImageProcessor

class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					def __init__(						self			:				Any   , _snake_case			:				Optional[Any]   , _snake_case			:				Optional[int]=7   , _snake_case			:				Dict=3   , _snake_case			:				List[Any]=30   , _snake_case			:				Union[str, Any]=400   , _snake_case			:				str=True   , _snake_case			:				str=None   , _snake_case			:				Dict=True   , _snake_case			:				List[Any]=[0.5, 0.5, 0.5]   , _snake_case			:				int=[0.5, 0.5, 0.5]   , _snake_case			:				Dict=True   , _snake_case			:				Any=1 / 255   , _snake_case			:				Optional[int]=True   , )							->       List[str]:
									# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
									SCREAMING_SNAKE_CASE__		  =				size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
									SCREAMING_SNAKE_CASE__		  =				parent
									SCREAMING_SNAKE_CASE__		  =				batch_size
									SCREAMING_SNAKE_CASE__		  =				num_channels
									SCREAMING_SNAKE_CASE__		  =				min_resolution
									SCREAMING_SNAKE_CASE__		  =				max_resolution
									SCREAMING_SNAKE_CASE__		  =				do_resize
									SCREAMING_SNAKE_CASE__		  =				size
									SCREAMING_SNAKE_CASE__		  =				do_normalize
									SCREAMING_SNAKE_CASE__		  =				image_mean
									SCREAMING_SNAKE_CASE__		  =				image_std
									SCREAMING_SNAKE_CASE__		  =				do_rescale
									SCREAMING_SNAKE_CASE__		  =				rescale_factor
									SCREAMING_SNAKE_CASE__		  =				do_pad



					def 			lowerCAmelCase_					(						self			:				Any		)							->       Tuple:
									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 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				Optional[int]   , _snake_case			:				Union[str, Any]=False		)							->       Optional[int]:
									if not batched:
													SCREAMING_SNAKE_CASE__		  =				image_inputs[0]
													if isinstance(__lowerCamelCase   , Image.Image		):
																	SCREAMING_SNAKE_CASE__		  =				image.size
													else:
																	SCREAMING_SNAKE_CASE__		  =				image.shape[1], image.shape[2]
													if w < h:
																	SCREAMING_SNAKE_CASE__		  =				int(self.size["shortest_edge"] * h / w		)
																	SCREAMING_SNAKE_CASE__		  =				self.size["shortest_edge"]
													elif w > h:
																	SCREAMING_SNAKE_CASE__		  =				self.size["shortest_edge"]
																	SCREAMING_SNAKE_CASE__		  =				int(self.size["shortest_edge"] * w / h		)
													else:
																	SCREAMING_SNAKE_CASE__		  =				self.size["shortest_edge"]
																	SCREAMING_SNAKE_CASE__		  =				self.size["shortest_edge"]

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

									return expected_height, expected_width



@require_torch
@require_vision
class lowerCamelCase		(_A    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		DetaImageProcessor if is_vision_available() else None



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				DetaImageProcessingTester(self		)



					@property
					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       int:
									return self.image_processor_tester.prepare_image_processor_dict()



					def 			lowerCAmelCase_					(						self			:				Any		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				self.image_processing_class(**self.image_processor_dict		)
									self.assertTrue(hasattr(__lowerCamelCase   , "image_mean"		)		)
									self.assertTrue(hasattr(__lowerCamelCase   , "image_std"		)		)
									self.assertTrue(hasattr(__lowerCamelCase   , "do_normalize"		)		)
									self.assertTrue(hasattr(__lowerCamelCase   , "do_resize"		)		)
									self.assertTrue(hasattr(__lowerCamelCase   , "do_rescale"		)		)
									self.assertTrue(hasattr(__lowerCamelCase   , "do_pad"		)		)
									self.assertTrue(hasattr(__lowerCamelCase   , "size"		)		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				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   , __lowerCamelCase		)



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Optional[Any]:
									pass



					def 			lowerCAmelCase_					(						self			:				int		)							->       Optional[int]:
									# Initialize image_processing
									SCREAMING_SNAKE_CASE__		  =				self.image_processing_class(**self.image_processor_dict		)
									# create random PIL images
									SCREAMING_SNAKE_CASE__		  =				prepare_image_inputs(self.image_processor_tester   , equal_resolution=__lowerCamelCase		)
									for image in image_inputs:
													self.assertIsInstance(__lowerCamelCase   , Image.Image		)

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

									SCREAMING_SNAKE_CASE__		  =				self.image_processor_tester.get_expected_values(__lowerCamelCase		)

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

									# Test batched
									SCREAMING_SNAKE_CASE__		  =				self.image_processor_tester.get_expected_values(__lowerCamelCase   , batched=__lowerCamelCase		)

									SCREAMING_SNAKE_CASE__		  =				image_processing(__lowerCamelCase   , 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 			lowerCAmelCase_					(						self			:				Dict		)							->       str:
									# Initialize image_processing
									SCREAMING_SNAKE_CASE__		  =				self.image_processing_class(**self.image_processor_dict		)
									# create random numpy tensors
									SCREAMING_SNAKE_CASE__		  =				prepare_image_inputs(self.image_processor_tester   , equal_resolution=__lowerCamelCase   , numpify=__lowerCamelCase		)
									for image in image_inputs:
													self.assertIsInstance(__lowerCamelCase   , np.ndarray		)

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

									SCREAMING_SNAKE_CASE__		  =				self.image_processor_tester.get_expected_values(__lowerCamelCase		)

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

									# Test batched
									SCREAMING_SNAKE_CASE__		  =				image_processing(__lowerCamelCase   , return_tensors="pt"		).pixel_values

									SCREAMING_SNAKE_CASE__		  =				self.image_processor_tester.get_expected_values(__lowerCamelCase   , batched=__lowerCamelCase		)

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



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       List[Any]:
									# Initialize image_processing
									SCREAMING_SNAKE_CASE__		  =				self.image_processing_class(**self.image_processor_dict		)
									# create random PyTorch tensors
									SCREAMING_SNAKE_CASE__		  =				prepare_image_inputs(self.image_processor_tester   , equal_resolution=__lowerCamelCase   , torchify=__lowerCamelCase		)
									for image in image_inputs:
													self.assertIsInstance(__lowerCamelCase   , torch.Tensor		)

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

									SCREAMING_SNAKE_CASE__		  =				self.image_processor_tester.get_expected_values(__lowerCamelCase		)

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

									# Test batched
									SCREAMING_SNAKE_CASE__		  =				image_processing(__lowerCamelCase   , return_tensors="pt"		).pixel_values

									SCREAMING_SNAKE_CASE__		  =				self.image_processor_tester.get_expected_values(__lowerCamelCase   , batched=__lowerCamelCase		)

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



					@slow
					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Union[str, Any]:
									# prepare image and target
									SCREAMING_SNAKE_CASE__		  =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
									with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"   , "r"		) as f:
													SCREAMING_SNAKE_CASE__		  =				json.loads(f.read()		)

									SCREAMING_SNAKE_CASE__		  =				{"image_id": 39769, "annotations": target}

									# encode them
									SCREAMING_SNAKE_CASE__		  =				DetaImageProcessor()
									SCREAMING_SNAKE_CASE__		  =				image_processing(images=__lowerCamelCase   , annotations=__lowerCamelCase   , return_tensors="pt"		)

									# verify pixel values
									SCREAMING_SNAKE_CASE__		  =				torch.Size([1, 3, 800, 1066]		)
									self.assertEqual(encoding["pixel_values"].shape   , __lowerCamelCase		)

									SCREAMING_SNAKE_CASE__		  =				torch.tensor([0.2796, 0.3138, 0.3481]		)
									self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]   , __lowerCamelCase   , atol=1e-4		)		)

									# verify area
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
									self.assertTrue(torch.allclose(encoding["labels"][0]["area"]   , __lowerCamelCase		)		)
									# verify boxes
									SCREAMING_SNAKE_CASE__		  =				torch.Size([6, 4]		)
									self.assertEqual(encoding["labels"][0]["boxes"].shape   , __lowerCamelCase		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
									self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   , __lowerCamelCase   , atol=1e-3		)		)
									# verify image_id
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([39769]		)
									self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   , __lowerCamelCase		)		)
									# verify is_crowd
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([0, 0, 0, 0, 0, 0]		)
									self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]   , __lowerCamelCase		)		)
									# verify class_labels
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([75, 75, 63, 65, 17, 17]		)
									self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   , __lowerCamelCase		)		)
									# verify orig_size
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([480, 640]		)
									self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   , __lowerCamelCase		)		)
									# verify size
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([800, 1066]		)
									self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   , __lowerCamelCase		)		)



					@slow
					def 			lowerCAmelCase_					(						self			:				str		)							->       List[str]:
									# prepare image, target and masks_path
									SCREAMING_SNAKE_CASE__		  =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
									with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"   , "r"		) as f:
													SCREAMING_SNAKE_CASE__		  =				json.loads(f.read()		)

									SCREAMING_SNAKE_CASE__		  =				{"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

									SCREAMING_SNAKE_CASE__		  =				pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"		)

									# encode them
									SCREAMING_SNAKE_CASE__		  =				DetaImageProcessor(format="coco_panoptic"		)
									SCREAMING_SNAKE_CASE__		  =				image_processing(images=__lowerCamelCase   , annotations=__lowerCamelCase   , masks_path=__lowerCamelCase   , return_tensors="pt"		)

									# verify pixel values
									SCREAMING_SNAKE_CASE__		  =				torch.Size([1, 3, 800, 1066]		)
									self.assertEqual(encoding["pixel_values"].shape   , __lowerCamelCase		)

									SCREAMING_SNAKE_CASE__		  =				torch.tensor([0.2796, 0.3138, 0.3481]		)
									self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]   , __lowerCamelCase   , atol=1e-4		)		)

									# verify area
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]		)
									self.assertTrue(torch.allclose(encoding["labels"][0]["area"]   , __lowerCamelCase		)		)
									# verify boxes
									SCREAMING_SNAKE_CASE__		  =				torch.Size([6, 4]		)
									self.assertEqual(encoding["labels"][0]["boxes"].shape   , __lowerCamelCase		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
									self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   , __lowerCamelCase   , atol=1e-3		)		)
									# verify image_id
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([39769]		)
									self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   , __lowerCamelCase		)		)
									# verify is_crowd
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([0, 0, 0, 0, 0, 0]		)
									self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]   , __lowerCamelCase		)		)
									# verify class_labels
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([17, 17, 63, 75, 75, 93]		)
									self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   , __lowerCamelCase		)		)
									# verify masks
									SCREAMING_SNAKE_CASE__		  =				822873
									self.assertEqual(encoding["labels"][0]["masks"].sum().item()   , __lowerCamelCase		)
									# verify orig_size
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([480, 640]		)
									self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   , __lowerCamelCase		)		)
									# verify size
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([800, 1066]		)
									self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   , __lowerCamelCase		)		)





"""simple docstring"""






import math




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   float:

				if initial_intensity < 0:
								raise ValueError("The value of intensity cannot be negative"       )
								# handling of negative values of initial intensity
				if angle < 0 or angle > 360:
								raise ValueError("In Malus Law, the angle is in the range 0-360 degrees"       )
								# handling of values out of allowed range
				return initial_intensity * (math.cos(math.radians(__UpperCAmelCase       )       ) ** 2)


if __name__ == "__main__":
						import doctest

						doctest.testmod(name='malus_law')




