



'''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      _lowerCAmelCase							(      unittest.TestCase			):


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


	def    _a     (self							):
				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    _a     (self	,				lowercase	,				lowercase=False							):
				if not batched:
							A_     :						str     =					image_inputs[0]
							if isinstance(lowercase	,				Image.Image							):
										A_     :						Dict     =					image.size
							else:
										A_     :						List[str]     =					image.shape[1], image.shape[2]
							if w < h:
										A_     :						List[Any]     =					int(self.size["""shortest_edge"""] * h / w							)
										A_     :						Tuple     =					self.size["""shortest_edge"""]
							elif w > h:
										A_     :						List[str]     =					self.size["""shortest_edge"""]
										A_     :						Dict     =					int(self.size["""shortest_edge"""] * w / h							)
							else:
										A_     :						str     =					self.size["""shortest_edge"""]
										A_     :						Union[str, Any]     =					self.size["""shortest_edge"""]

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

				return expected_height, expected_width







@require_torch
@require_vision
class      _lowerCAmelCase							(      __UpperCAmelCase				,      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		Tuple		      = DeformableDetrImageProcessor if is_vision_available() else None


	def    _a     (self							):
				A_     :						List[Any]     =					DeformableDetrImageProcessingTester(self							)


	@property
	def    _a     (self							):
				return self.image_processor_tester.prepare_image_processor_dict()


	def    _a     (self							):
				A_     :						Union[str, Any]     =					self.image_processing_class(**self.image_processor_dict							)
				self.assertTrue(hasattr(lowercase	,				"""image_mean"""							)							)
				self.assertTrue(hasattr(lowercase	,				"""image_std"""							)							)
				self.assertTrue(hasattr(lowercase	,				"""do_normalize"""							)							)
				self.assertTrue(hasattr(lowercase	,				"""do_resize"""							)							)
				self.assertTrue(hasattr(lowercase	,				"""do_rescale"""							)							)
				self.assertTrue(hasattr(lowercase	,				"""do_pad"""							)							)
				self.assertTrue(hasattr(lowercase	,				"""size"""							)							)


	def    _a     (self							):
				A_     :						str     =					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	,				lowercase							)

				A_     :						Optional[Any]     =					self.image_processing_class.from_dict(
				    self.image_processor_dict	,				size=42	,				max_size=84	,				pad_and_return_pixel_mask=lowercase							)
				self.assertEqual(image_processor.size	,				{"""shortest_edge""": 42, """longest_edge""": 84}							)
				self.assertEqual(image_processor.do_pad	,				lowercase							)


	def    _a     (self							):
				pass


	def    _a     (self							):
				# Initialize image_processing
				A_     :						Any     =					self.image_processing_class(**self.image_processor_dict							)
				# create random PIL images
				A_     :						List[Any]     =					prepare_image_inputs(self.image_processor_tester	,				equal_resolution=lowercase							)
				for image in image_inputs:
							self.assertIsInstance(lowercase	,				Image.Image							)

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

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

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

				# Test batched
				A_     :						List[Any]     =					self.image_processor_tester.get_expected_values(lowercase	,				batched=lowercase							)

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

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

				A_     :						Dict     =					self.image_processor_tester.get_expected_values(lowercase							)

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

				# Test batched
				A_     :						str     =					image_processing(lowercase	,				return_tensors="""pt"""							).pixel_values

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

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


	def    _a     (self							):
				# Initialize image_processing
				A_     :						int     =					self.image_processing_class(**self.image_processor_dict							)
				# create random PyTorch tensors
				A_     :						Union[str, Any]     =					prepare_image_inputs(self.image_processor_tester	,				equal_resolution=lowercase	,				torchify=lowercase							)
				for image in image_inputs:
							self.assertIsInstance(lowercase	,				torch.Tensor							)

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

				A_     :						Tuple     =					self.image_processor_tester.get_expected_values(lowercase							)

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

				# Test batched
				A_     :						Any     =					image_processing(lowercase	,				return_tensors="""pt"""							).pixel_values

				A_     :						Dict     =					self.image_processor_tester.get_expected_values(lowercase	,				batched=lowercase							)

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


	@slow
	def    _a     (self							):
				# prepare image and target
				A_     :						int     =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
				with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""	,				"""r"""							) as f:
							A_     :						Any     =					json.loads(f.read()							)

				A_     :						List[Any]     =					{"""image_id""": 39769, """annotations""": target}

				# encode them
				A_     :						str     =					DeformableDetrImageProcessor()
				A_     :						Optional[int]     =					image_processing(images=lowercase	,				annotations=lowercase	,				return_tensors="""pt"""							)

				# verify pixel values
				A_     :						Dict     =					torch.Size([1, 3, 800, 1066]							)
				self.assertEqual(encoding["""pixel_values"""].shape	,				lowercase							)

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

				# verify area
				A_     :						Tuple     =					torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]							)
				self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]	,				lowercase							)							)
				# verify boxes
				A_     :						List[Any]     =					torch.Size([6, 4]							)
				self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape	,				lowercase							)
				A_     :						Optional[Any]     =					torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]							)
				self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]	,				lowercase	,				atol=1E-3							)							)
				# verify image_id
				A_     :						Any     =					torch.tensor([39769]							)
				self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]	,				lowercase							)							)
				# verify is_crowd
				A_     :						Optional[Any]     =					torch.tensor([0, 0, 0, 0, 0, 0]							)
				self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]	,				lowercase							)							)
				# verify class_labels
				A_     :						Union[str, Any]     =					torch.tensor([75, 75, 63, 65, 17, 17]							)
				self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]	,				lowercase							)							)
				# verify orig_size
				A_     :						int     =					torch.tensor([480, 640]							)
				self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]	,				lowercase							)							)
				# verify size
				A_     :						str     =					torch.tensor([800, 1066]							)
				self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]	,				lowercase							)							)


	@slow
	def    _a     (self							):
				# prepare image, target and masks_path
				A_     :						Any     =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
				with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""	,				"""r"""							) as f:
							A_     :						int     =					json.loads(f.read()							)

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

				A_     :						List[Any]     =					pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

				# encode them
				A_     :						Dict     =					DeformableDetrImageProcessor(format="""coco_panoptic"""							)
				A_     :						List[Any]     =					image_processing(images=lowercase	,				annotations=lowercase	,				masks_path=lowercase	,				return_tensors="""pt"""							)

				# verify pixel values
				A_     :						Tuple     =					torch.Size([1, 3, 800, 1066]							)
				self.assertEqual(encoding["""pixel_values"""].shape	,				lowercase							)

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

				# verify area
				A_     :						int     =					torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]							)
				self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]	,				lowercase							)							)
				# verify boxes
				A_     :						Dict     =					torch.Size([6, 4]							)
				self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape	,				lowercase							)
				A_     :						int     =					torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]							)
				self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]	,				lowercase	,				atol=1E-3							)							)
				# verify image_id
				A_     :						Any     =					torch.tensor([39769]							)
				self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]	,				lowercase							)							)
				# verify is_crowd
				A_     :						int     =					torch.tensor([0, 0, 0, 0, 0, 0]							)
				self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]	,				lowercase							)							)
				# verify class_labels
				A_     :						str     =					torch.tensor([17, 17, 63, 75, 75, 93]							)
				self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]	,				lowercase							)							)
				# verify masks
				A_     :						int     =					822873
				self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()	,				lowercase							)
				# verify orig_size
				A_     :						Optional[Any]     =					torch.tensor([480, 640]							)
				self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]	,				lowercase							)							)
				# verify size
				A_     :						List[Any]     =					torch.tensor([800, 1066]							)
				self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]	,				lowercase							)							)




'''simple docstring'''




def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return " ".join(input_str.split()[::-1]   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()