

'''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 ConditionalDetrImageProcessor





class 		__SCREAMING_SNAKE_CASE       ( unittest.TestCase			):







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







				def    lowerCamelCase_  (					self    :		str      ):
										'''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    lowerCamelCase_  (					self    :		str		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		Optional[Any]=False      ):
										'''simple docstring'''
										if not batched:
																lowercase   :       List[str]								=image_inputs[0]
																if isinstance(UpperCAmelCase__		,				Image.Image      ):
																						lowercase		,  lowercase   :       str								=image.size
																else:
																						lowercase		,  lowercase   :       List[str]								=image.shape[1], image.shape[2]
																if w < h:
																						lowercase   :       Optional[Any]								=int(self.size['''shortest_edge'''] * h / w      )
																						lowercase   :       Dict								=self.size['''shortest_edge''']
																elif w > h:
																						lowercase   :       str								=self.size['''shortest_edge''']
																						lowercase   :       Optional[int]								=int(self.size['''shortest_edge'''] * w / h      )
																else:
																						lowercase   :       str								=self.size['''shortest_edge''']
																						lowercase   :       Union[str, Any]								=self.size['''shortest_edge''']

										else:
																lowercase   :       Union[str, Any]								=[]
																for image in image_inputs:
																						lowercase		,  lowercase   :       Any								=self.get_expected_values([image]      )
																						expected_values.append((expected_height, expected_width)      )
																lowercase   :       Optional[int]								=max(UpperCAmelCase__		,				key=lambda UpperCAmelCase__      : item[0]      )[0]
																lowercase   :       Dict								=max(UpperCAmelCase__		,				key=lambda UpperCAmelCase__      : item[1]      )[1]

										return expected_height, expected_width





@require_torch
@require_vision
class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						ConditionalDetrImageProcessor if is_vision_available() else None







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Any								=ConditionalDetrImageProcessingTester(self      )







				@property
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										return self.image_processor_tester.prepare_image_processor_dict()







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=self.image_processing_class(**self.image_processor_dict      )
										self.assertTrue(hasattr(UpperCAmelCase__		,				'''image_mean'''      )      )
										self.assertTrue(hasattr(UpperCAmelCase__		,				'''image_std'''      )      )
										self.assertTrue(hasattr(UpperCAmelCase__		,				'''do_normalize'''      )      )
										self.assertTrue(hasattr(UpperCAmelCase__		,				'''do_resize'''      )      )
										self.assertTrue(hasattr(UpperCAmelCase__		,				'''size'''      )      )







				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=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		,				UpperCAmelCase__      )

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







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										pass







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase   :       Union[str, Any]								=self.image_processing_class(**self.image_processor_dict      )
										# create random PIL images
										lowercase   :       List[Any]								=prepare_image_inputs(self.image_processor_tester		,				equal_resolution=UpperCAmelCase__      )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,				Image.Image      )

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

										lowercase		,  lowercase   :       Optional[int]								=self.image_processor_tester.get_expected_values(UpperCAmelCase__      )

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

										# Test batched
										lowercase		,  lowercase   :       Optional[int]								=self.image_processor_tester.get_expected_values(UpperCAmelCase__		,				batched=UpperCAmelCase__      )

										lowercase   :       Any								=image_processing(UpperCAmelCase__		,				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    :		List[Any]      ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase   :       Dict								=self.image_processing_class(**self.image_processor_dict      )
										# create random numpy tensors
										lowercase   :       Union[str, Any]								=prepare_image_inputs(self.image_processor_tester		,				equal_resolution=UpperCAmelCase__		,				numpify=UpperCAmelCase__      )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,				np.ndarray      )

										# Test not batched input
										lowercase   :       List[str]								=image_processing(image_inputs[0]		,				return_tensors='''pt'''      ).pixel_values

										lowercase		,  lowercase   :       Dict								=self.image_processor_tester.get_expected_values(UpperCAmelCase__      )

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

										# Test batched
										lowercase   :       Optional[Any]								=image_processing(UpperCAmelCase__		,				return_tensors='''pt'''      ).pixel_values

										lowercase		,  lowercase   :       Optional[int]								=self.image_processor_tester.get_expected_values(UpperCAmelCase__		,				batched=UpperCAmelCase__      )

										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]      ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase   :       Optional[int]								=self.image_processing_class(**self.image_processor_dict      )
										# create random PyTorch tensors
										lowercase   :       Dict								=prepare_image_inputs(self.image_processor_tester		,				equal_resolution=UpperCAmelCase__		,				torchify=UpperCAmelCase__      )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,				torch.Tensor      )

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

										lowercase		,  lowercase   :       List[str]								=self.image_processor_tester.get_expected_values(UpperCAmelCase__      )

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

										# Test batched
										lowercase   :       Any								=image_processing(UpperCAmelCase__		,				return_tensors='''pt'''      ).pixel_values

										lowercase		,  lowercase   :       Optional[int]								=self.image_processor_tester.get_expected_values(UpperCAmelCase__		,				batched=UpperCAmelCase__      )

										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    :		Optional[Any]      ):
										'''simple docstring'''
										# prepare image and target
										lowercase   :       Optional[Any]								=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
										with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''		,				'''r'''      ) as f:
																lowercase   :       Tuple								=json.loads(f.read()      )

										lowercase   :       List[str]								={'''image_id''': 39769, '''annotations''': target}

										# encode them
										lowercase   :       Optional[Any]								=ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''      )
										lowercase   :       Dict								=image_processing(images=UpperCAmelCase__		,				annotations=UpperCAmelCase__		,				return_tensors='''pt'''      )

										# verify pixel values
										lowercase   :       Union[str, Any]								=torch.Size([1, 3, 800, 1066]      )
										self.assertEqual(encoding['''pixel_values'''].shape		,				UpperCAmelCase__      )

										lowercase   :       str								=torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
										self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		,				UpperCAmelCase__		,				atol=1E-4      )      )

										# verify area
										lowercase   :       int								=torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		,				UpperCAmelCase__      )      )
										# verify boxes
										lowercase   :       List[Any]								=torch.Size([6, 4]      )
										self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		,				UpperCAmelCase__      )
										lowercase   :       List[str]								=torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		,				UpperCAmelCase__		,				atol=1E-3      )      )
										# verify image_id
										lowercase   :       Tuple								=torch.tensor([39769]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		,				UpperCAmelCase__      )      )
										# verify is_crowd
										lowercase   :       Tuple								=torch.tensor([0, 0, 0, 0, 0, 0]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		,				UpperCAmelCase__      )      )
										# verify class_labels
										lowercase   :       Union[str, Any]								=torch.tensor([75, 75, 63, 65, 17, 17]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		,				UpperCAmelCase__      )      )
										# verify orig_size
										lowercase   :       int								=torch.tensor([480, 640]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		,				UpperCAmelCase__      )      )
										# verify size
										lowercase   :       List[str]								=torch.tensor([800, 1066]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		,				UpperCAmelCase__      )      )







				@slow
				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										# prepare image, target and masks_path
										lowercase   :       List[Any]								=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
										with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''		,				'''r'''      ) as f:
																lowercase   :       Dict								=json.loads(f.read()      )

										lowercase   :       List[str]								={'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

										lowercase   :       Any								=pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''      )

										# encode them
										lowercase   :       Union[str, Any]								=ConditionalDetrImageProcessor(format='''coco_panoptic'''      )
										lowercase   :       Optional[Any]								=image_processing(images=UpperCAmelCase__		,				annotations=UpperCAmelCase__		,				masks_path=UpperCAmelCase__		,				return_tensors='''pt'''      )

										# verify pixel values
										lowercase   :       List[Any]								=torch.Size([1, 3, 800, 1066]      )
										self.assertEqual(encoding['''pixel_values'''].shape		,				UpperCAmelCase__      )

										lowercase   :       int								=torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
										self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		,				UpperCAmelCase__		,				atol=1E-4      )      )

										# verify area
										lowercase   :       int								=torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		,				UpperCAmelCase__      )      )
										# verify boxes
										lowercase   :       str								=torch.Size([6, 4]      )
										self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		,				UpperCAmelCase__      )
										lowercase   :       List[Any]								=torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		,				UpperCAmelCase__		,				atol=1E-3      )      )
										# verify image_id
										lowercase   :       Union[str, Any]								=torch.tensor([39769]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		,				UpperCAmelCase__      )      )
										# verify is_crowd
										lowercase   :       Dict								=torch.tensor([0, 0, 0, 0, 0, 0]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		,				UpperCAmelCase__      )      )
										# verify class_labels
										lowercase   :       Any								=torch.tensor([17, 17, 63, 75, 75, 93]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		,				UpperCAmelCase__      )      )
										# verify masks
										lowercase   :       Optional[Any]								=822873
										self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()		,				UpperCAmelCase__      )
										# verify orig_size
										lowercase   :       Optional[Any]								=torch.tensor([480, 640]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		,				UpperCAmelCase__      )      )
										# verify size
										lowercase   :       Optional[int]								=torch.tensor([800, 1066]      )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		,				UpperCAmelCase__      )      )








'''simple docstring'''





import math






def 							_lowerCAmelCase (      __magic_name__	:    float      ,   __magic_name__	:    float	)							->     float:
						if (
						    not isinstance(__magic_name__      ,   (int, float)	)
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError('''power_factor must be a valid float value between -1 and 1.'''	)
						return apparent_power * power_factor






def 							_lowerCAmelCase (      __magic_name__	:    float      ,   __magic_name__	:    float	)							->     float:
						if (
						    not isinstance(__magic_name__      ,   (int, float)	)
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError('''power_factor must be a valid float value between -1 and 1.'''	)
						return apparent_power * math.sqrt(1 - power_factor**2	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()





