


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 							A   (	unittest.TestCase							):
					'''simple docstring'''






					def __init__(self   :						Optional[int]				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Optional[int]=7				, _UpperCAmelCase   :						List[str]=3				, _UpperCAmelCase   :						Tuple=30				, _UpperCAmelCase   :						Optional[Any]=400				, _UpperCAmelCase   :						str=True				, _UpperCAmelCase   :						List[Any]=None				, _UpperCAmelCase   :						Optional[int]=True				, _UpperCAmelCase   :						Dict=[0.5, 0.5, 0.5]				, _UpperCAmelCase   :						Any=[0.5, 0.5, 0.5]				, _UpperCAmelCase   :						Optional[int]=True				, _UpperCAmelCase   :						str=1 / 255				, _UpperCAmelCase   :						str=True				, )						->       Union[str, Any]:




							"""simple docstring"""


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

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




							"""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   :						Dict				, _UpperCAmelCase   :						Tuple=False		)						->       List[str]:




							"""simple docstring"""


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

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

							return expected_height, expected_width






@require_torch
@require_vision
class 							A   (	__snake_case			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			ConditionalDetrImageProcessor if is_vision_available() else None

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




							"""simple docstring"""


							lowercase__					      =		ConditionalDetrImageProcessingTester(self		)

					@property
					def   lowerCamelCase__					(self   :						Any		)						->       int:




							"""simple docstring"""


							return self.image_processor_tester.prepare_image_processor_dict()

					def   lowerCamelCase__					(self   :						str		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		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   :						Union[str, Any]		)						->       int:




							"""simple docstring"""


							lowercase__					      =		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__					      =		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   :						Optional[Any]		)						->       List[str]:




							"""simple docstring"""


							pass

					def   lowerCamelCase__					(self   :						Optional[int]		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		self.image_processing_class(**self.image_processor_dict		)
							# create random PIL images
							lowercase__					      =		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__					      =		image_processing(image_inputs[0]				, return_tensors="""pt"""		).pixel_values

							lowercase__      ,     lowercase__					      =		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__					      =		self.image_processor_tester.get_expected_values(_UpperCAmelCase				, batched=_UpperCAmelCase		)

							lowercase__					      =		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]		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		self.image_processing_class(**self.image_processor_dict		)
							# create random numpy tensors
							lowercase__					      =		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__					      =		image_processing(image_inputs[0]				, return_tensors="""pt"""		).pixel_values

							lowercase__      ,     lowercase__					      =		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__					      =		image_processing(_UpperCAmelCase				, return_tensors="""pt"""		).pixel_values

							lowercase__      ,     lowercase__					      =		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   :						Union[str, Any]		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		self.image_processing_class(**self.image_processor_dict		)
							# create random PyTorch tensors
							lowercase__					      =		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__					      =		image_processing(image_inputs[0]				, return_tensors="""pt"""		).pixel_values

							lowercase__      ,     lowercase__					      =		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__					      =		image_processing(_UpperCAmelCase				, return_tensors="""pt"""		).pixel_values

							lowercase__      ,     lowercase__					      =		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[int]		)						->       Any:




							"""simple docstring"""


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

							lowercase__					      =		{"""image_id""": 3_9769, """annotations""": target}

							# encode them
							lowercase__					      =		ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""		)
							lowercase__					      =		image_processing(images=_UpperCAmelCase				, annotations=_UpperCAmelCase				, return_tensors="""pt"""		)

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

							lowercase__					      =		torch.tensor([0.2_796, 0.3_138, 0.3_481]		)
							self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]				, _UpperCAmelCase				, atol=1E-4		)		)

							# verify area
							lowercase__					      =		torch.tensor([5_887.9_600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]				, _UpperCAmelCase		)		)
							# verify boxes
							lowercase__					      =		torch.Size([6, 4]		)
							self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				, _UpperCAmelCase		)
							lowercase__					      =		torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]		)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]				, _UpperCAmelCase				, atol=1E-3		)		)
							# verify image_id
							lowercase__					      =		torch.tensor([3_9769]		)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				, _UpperCAmelCase		)		)
							# verify is_crowd
							lowercase__					      =		torch.tensor([0, 0, 0, 0, 0, 0]		)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				, _UpperCAmelCase		)		)
							# verify class_labels
							lowercase__					      =		torch.tensor([75, 75, 63, 65, 17, 17]		)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				, _UpperCAmelCase		)		)
							# verify orig_size
							lowercase__					      =		torch.tensor([480, 640]		)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				, _UpperCAmelCase		)		)
							# verify size
							lowercase__					      =		torch.tensor([800, 1066]		)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				, _UpperCAmelCase		)		)


					@slow
					def   lowerCamelCase__					(self   :						Any		)						->       Any:




							"""simple docstring"""


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

							lowercase__					      =		{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

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

							# encode them
							lowercase__					      =		ConditionalDetrImageProcessor(format="""coco_panoptic"""		)
							lowercase__					      =		image_processing(images=_UpperCAmelCase				, annotations=_UpperCAmelCase				, masks_path=_UpperCAmelCase				, return_tensors="""pt"""		)

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

							lowercase__					      =		torch.tensor([0.2_796, 0.3_138, 0.3_481]		)
							self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]				, _UpperCAmelCase				, atol=1E-4		)		)

							# verify area
							lowercase__					      =		torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5_879.6_562, 7_634.1_147]		)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]				, _UpperCAmelCase		)		)
							# verify boxes
							lowercase__					      =		torch.Size([6, 4]		)
							self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				, _UpperCAmelCase		)
							lowercase__					      =		torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]		)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]				, _UpperCAmelCase				, atol=1E-3		)		)
							# verify image_id
							lowercase__					      =		torch.tensor([3_9769]		)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				, _UpperCAmelCase		)		)
							# verify is_crowd
							lowercase__					      =		torch.tensor([0, 0, 0, 0, 0, 0]		)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				, _UpperCAmelCase		)		)
							# verify class_labels
							lowercase__					      =		torch.tensor([17, 17, 63, 75, 75, 93]		)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				, _UpperCAmelCase		)		)
							# verify masks
							lowercase__					      =		82_2873
							self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()				, _UpperCAmelCase		)
							# verify orig_size
							lowercase__					      =		torch.tensor([480, 640]		)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				, _UpperCAmelCase		)		)
							# verify size
							lowercase__					      =		torch.tensor([800, 1066]		)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				, _UpperCAmelCase		)		)









class 							A   :
					'''simple docstring'''






					def __init__(self   :						int				, _UpperCAmelCase   :						list		)						->       None:




							"""simple docstring"""


							lowercase__					      =		set_counts
							lowercase__					      =		max(_UpperCAmelCase		)
							lowercase__					      =		len(_UpperCAmelCase		)
							lowercase__					      =		[1] * num_sets
							lowercase__					      =		list(range(_UpperCAmelCase		)		)

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						int		)						->       bool:




							"""simple docstring"""


							lowercase__					      =		self.get_parent(_UpperCAmelCase		)
							lowercase__					      =		self.get_parent(_UpperCAmelCase		)

							if src_parent == dst_parent:
									return False

							if self.ranks[dst_parent] >= self.ranks[src_parent]:
									self.set_counts[dst_parent] += self.set_counts[src_parent]
									lowercase__					      =		0
									lowercase__					      =		dst_parent
									if self.ranks[dst_parent] == self.ranks[src_parent]:
											self.ranks[dst_parent] += 1
									lowercase__					      =		self.set_counts[dst_parent]
							else:
									self.set_counts[src_parent] += self.set_counts[dst_parent]
									lowercase__					      =		0
									lowercase__					      =		src_parent
									lowercase__					      =		self.set_counts[src_parent]

							lowercase__					      =		max(self.max_set				, _UpperCAmelCase		)
							return True


					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						int		)						->       int:




							"""simple docstring"""


							if self.parents[disj_set] == disj_set:
									return disj_set
							lowercase__					      =		self.get_parent(self.parents[disj_set]		)
							return self.parents[disj_set]

