
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     lowercase (		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    ,				)  -> str:
				"""simple docstring"""
				# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
				UpperCamelCase          =					size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
				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	)  -> Tuple:
				"""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	)  -> int:
				"""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     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	str     					=    DeformableDetrImageProcessor if is_vision_available() else None



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					DeformableDetrImageProcessingTester(self	)



		@property
		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				return self.image_processor_tester.prepare_image_processor_dict()



		def __UpperCamelCase			(			self	)  -> Dict:
				"""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	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.image_processing_class.from_dict(self.image_processor_dict	)
				self.assertEqual(image_processor.size    ,				{'shortest_edge': 18, 'longest_edge': 1_333}	)
				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	)  -> Union[str, Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				# Initialize image_processing
				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	)  -> Optional[int]:
				"""simple docstring"""
				# Initialize image_processing
				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	)  -> Tuple:
				"""simple docstring"""
				# Initialize image_processing
				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	)  -> List[str]:
				"""simple docstring"""
				# prepare image and target
				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': 39_769, '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, 1_066]	)
				self.assertEqual(encoding['pixel_values'].shape    ,				A_	)

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

				# verify area
				UpperCamelCase          =					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']    ,				A_	)	)
				# verify boxes
				UpperCamelCase          =					torch.Size([6, 4]	)
				self.assertEqual(encoding['labels'][0]['boxes'].shape    ,				A_	)
				UpperCamelCase          =					torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,				A_    ,				atol=1e-3	)	)
				# verify image_id
				UpperCamelCase          =					torch.tensor([39_769]	)
				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, 1_066]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,				A_	)	)



		@slow
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				# prepare image, target and masks_path
				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': 39_769, '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, 1_066]	)
				self.assertEqual(encoding['pixel_values'].shape    ,				A_	)

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

				# verify area
				UpperCamelCase          =					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']    ,				A_	)	)
				# verify boxes
				UpperCamelCase          =					torch.Size([6, 4]	)
				self.assertEqual(encoding['labels'][0]['boxes'].shape    ,				A_	)
				UpperCamelCase          =					torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,				A_    ,				atol=1e-3	)	)
				# verify image_id
				UpperCamelCase          =					torch.tensor([39_769]	)
				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          =					822_873
				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, 1_066]	)
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,				A_	)	)


_UpperCAmelCase :			str              =	[
    999,
    800,
    799,
    600,
    599,
    500,
    400,
    399,
    377,
    355,
    333,
    311,
    288,
    266,
    244,
    222,
    200,
    199,
    177,
    155,
    133,
    111,
    88,
    66,
    44,
    22,
    0,
]

_UpperCAmelCase :			Any              =	[
    999,
    976,
    952,
    928,
    905,
    882,
    858,
    857,
    810,
    762,
    715,
    714,
    572,
    429,
    428,
    286,
    285,
    238,
    190,
    143,
    142,
    118,
    95,
    71,
    47,
    24,
    0,
]

_UpperCAmelCase :			List[Any]              =	[
    999,
    988,
    977,
    966,
    955,
    944,
    933,
    922,
    911,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    350,
    300,
    299,
    266,
    233,
    200,
    199,
    179,
    159,
    140,
    120,
    100,
    99,
    88,
    77,
    66,
    55,
    44,
    33,
    22,
    11,
    0,
]

_UpperCAmelCase :			Tuple              =	[
    999,
    995,
    992,
    989,
    985,
    981,
    978,
    975,
    971,
    967,
    964,
    961,
    957,
    956,
    951,
    947,
    942,
    937,
    933,
    928,
    923,
    919,
    914,
    913,
    908,
    903,
    897,
    892,
    887,
    881,
    876,
    871,
    870,
    864,
    858,
    852,
    846,
    840,
    834,
    828,
    827,
    820,
    813,
    806,
    799,
    792,
    785,
    784,
    777,
    770,
    763,
    756,
    749,
    742,
    741,
    733,
    724,
    716,
    707,
    699,
    698,
    688,
    677,
    666,
    656,
    655,
    645,
    634,
    623,
    613,
    612,
    598,
    584,
    570,
    569,
    555,
    541,
    527,
    526,
    505,
    484,
    483,
    462,
    440,
    439,
    396,
    395,
    352,
    351,
    308,
    307,
    264,
    263,
    220,
    219,
    176,
    132,
    88,
    44,
    0,
]

_UpperCAmelCase :			Union[str, Any]              =	[
    999,
    997,
    995,
    992,
    990,
    988,
    986,
    984,
    981,
    979,
    977,
    975,
    972,
    970,
    968,
    966,
    964,
    961,
    959,
    957,
    956,
    954,
    951,
    949,
    946,
    944,
    941,
    939,
    936,
    934,
    931,
    929,
    926,
    924,
    921,
    919,
    916,
    914,
    913,
    910,
    907,
    905,
    902,
    899,
    896,
    893,
    891,
    888,
    885,
    882,
    879,
    877,
    874,
    871,
    870,
    867,
    864,
    861,
    858,
    855,
    852,
    849,
    846,
    843,
    840,
    837,
    834,
    831,
    828,
    827,
    824,
    821,
    817,
    814,
    811,
    808,
    804,
    801,
    798,
    795,
    791,
    788,
    785,
    784,
    780,
    777,
    774,
    770,
    766,
    763,
    760,
    756,
    752,
    749,
    746,
    742,
    741,
    737,
    733,
    730,
    726,
    722,
    718,
    714,
    710,
    707,
    703,
    699,
    698,
    694,
    690,
    685,
    681,
    677,
    673,
    669,
    664,
    660,
    656,
    655,
    650,
    646,
    641,
    636,
    632,
    627,
    622,
    618,
    613,
    612,
    607,
    602,
    596,
    591,
    586,
    580,
    575,
    570,
    569,
    563,
    557,
    551,
    545,
    539,
    533,
    527,
    526,
    519,
    512,
    505,
    498,
    491,
    484,
    483,
    474,
    466,
    457,
    449,
    440,
    439,
    428,
    418,
    407,
    396,
    395,
    381,
    366,
    352,
    351,
    330,
    308,
    307,
    286,
    264,
    263,
    242,
    220,
    219,
    176,
    175,
    132,
    131,
    88,
    44,
    0,
]

_UpperCAmelCase :			List[str]              =	[
    999,
    991,
    982,
    974,
    966,
    958,
    950,
    941,
    933,
    925,
    916,
    908,
    900,
    899,
    874,
    850,
    825,
    800,
    799,
    700,
    600,
    500,
    400,
    300,
    200,
    100,
    0,
]

_UpperCAmelCase :			Tuple              =	[
    999,
    992,
    985,
    978,
    971,
    964,
    957,
    949,
    942,
    935,
    928,
    921,
    914,
    907,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    300,
    299,
    200,
    199,
    100,
    99,
    0,
]

_UpperCAmelCase :			Dict              =	[
    999,
    996,
    992,
    989,
    985,
    982,
    979,
    975,
    972,
    968,
    965,
    961,
    958,
    955,
    951,
    948,
    944,
    941,
    938,
    934,
    931,
    927,
    924,
    920,
    917,
    914,
    910,
    907,
    903,
    900,
    899,
    891,
    884,
    876,
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