



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     :						List[str]				,	A     :						Optional[int]				,	A     :						List[str]=7				,	A     :						List[str]=3				,	A     :						Optional[int]=30				,	A     :						Dict=4_00				,	A     :						Optional[int]=True				,	A     :						int=None				,	A     :						Dict=True				,	A     :						List[Any]=[0.5, 0.5, 0.5]				,	A     :						str=[0.5, 0.5, 0.5]				,	A     :						Dict=True				,	A     :						Tuple=1 / 2_55				,	A     :						Optional[Any]=True				,	)     ->					Optional[int]:



								"""simple docstring"""






								_UpperCAmelCase	 =     size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
								_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 					_lowerCamelCase (		self     :						List[str])     ->					List[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     :						Union[str, Any]				,	A     :						Any				,	A     :						List[Any]=False)     ->					Optional[int]:



								"""simple docstring"""






								if not batched:
											_UpperCAmelCase	 =     image_inputs[0]
											if isinstance(a_				,	Image.Image):
														_UpperCAmelCase	 =     image.size
											else:
														_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	 =     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     __lowerCAmelCase					(    __lowerCamelCase       ,		unittest.TestCase		):
					UpperCamelCase           =  DeformableDetrImageProcessor if is_vision_available() else None



					def 					_lowerCamelCase (		self     :						str)     ->					List[Any]:



								"""simple docstring"""






								_UpperCAmelCase	 =     DeformableDetrImageProcessingTester(self)



					@property
					def 					_lowerCamelCase (		self     :						Tuple)     ->					List[str]:



								"""simple docstring"""






								return self.image_processor_tester.prepare_image_processor_dict()



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



								"""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 					_lowerCamelCase (		self     :						Optional[Any])     ->					Union[str, Any]:



								"""simple docstring"""






								_UpperCAmelCase	 =     self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size				,	{'shortest_edge': 18, 'longest_edge': 13_33})
								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 					_lowerCamelCase (		self     :						Tuple)     ->					str:



								"""simple docstring"""






								pass



					def 					_lowerCamelCase (		self     :						Optional[int])     ->					Tuple:



								"""simple docstring"""






								_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	 =     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	 =     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 					_lowerCamelCase (		self     :						List[str])     ->					Optional[Any]:



								"""simple docstring"""






								_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	 =     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	 =     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 					_lowerCamelCase (		self     :						Optional[Any])     ->					int:



								"""simple docstring"""






								_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	 =     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	 =     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 					_lowerCamelCase (		self     :						Union[str, Any])     ->					List[Any]:



								"""simple docstring"""






								_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": 3_97_69, "annotations": target}

								# encode them
								_UpperCAmelCase	 =     DeformableDetrImageProcessor()
								_UpperCAmelCase	 =     image_processing(images=a_				,	annotations=a_				,	return_tensors='pt')

								# verify pixel values
								_UpperCAmelCase	 =     torch.Size([1, 3, 8_00, 10_66])
								self.assertEqual(encoding['pixel_values'].shape				,	a_)

								_UpperCAmelCase	 =     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1])
								self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,	a_				,	atol=1E-4))

								# verify area
								_UpperCAmelCase	 =     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']				,	a_))
								# verify boxes
								_UpperCAmelCase	 =     torch.Size([6, 4])
								self.assertEqual(encoding['labels'][0]['boxes'].shape				,	a_)
								_UpperCAmelCase	 =     torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5])
								self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,	a_				,	atol=1E-3))
								# verify image_id
								_UpperCAmelCase	 =     torch.tensor([3_97_69])
								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([4_80, 6_40])
								self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,	a_))
								# verify size
								_UpperCAmelCase	 =     torch.tensor([8_00, 10_66])
								self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,	a_))



					@slow
					def 					_lowerCamelCase (		self     :						Optional[int])     ->					List[Any]:



								"""simple docstring"""






								_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": 3_97_69, "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, 8_00, 10_66])
								self.assertEqual(encoding['pixel_values'].shape				,	a_)

								_UpperCAmelCase	 =     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1])
								self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,	a_				,	atol=1E-4))

								# verify area
								_UpperCAmelCase	 =     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']				,	a_))
								# verify boxes
								_UpperCAmelCase	 =     torch.Size([6, 4])
								self.assertEqual(encoding['labels'][0]['boxes'].shape				,	a_)
								_UpperCAmelCase	 =     torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5])
								self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,	a_				,	atol=1E-3))
								# verify image_id
								_UpperCAmelCase	 =     torch.tensor([3_97_69])
								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	 =     82_28_73
								self.assertEqual(encoding['labels'][0]['masks'].sum().item()				,	a_)
								# verify orig_size
								_UpperCAmelCase	 =     torch.tensor([4_80, 6_40])
								self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,	a_))
								# verify size
								_UpperCAmelCase	 =     torch.tensor([8_00, 10_66])
								self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,	a_))





import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class     __lowerCAmelCase					(    A       ,		A       ,		unittest.TestCase		):
			UpperCamelCase           =  IFInpaintingPipeline
			UpperCamelCase           =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
			UpperCamelCase           =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			UpperCamelCase           =  PipelineTesterMixin.required_optional_params - {'''latents'''}



			def 					_lowerCamelCase (		self     :						List[str])     ->					Tuple:



						"""simple docstring"""






						return self._get_dummy_components()



			def 					_lowerCamelCase (		self     :						Any				,	A     :						int				,	A     :						Dict=0)     ->					Tuple:



						"""simple docstring"""






						if str(A).startswith('mps'):
									_UpperCAmelCase	 =     torch.manual_seed(A)
						else:
									_UpperCAmelCase	 =     torch.Generator(device=A).manual_seed(A)

						_UpperCAmelCase	 =     floats_tensor((1, 3, 32, 32)				,	rng=random.Random(A)).to(A)
						_UpperCAmelCase	 =     floats_tensor((1, 3, 32, 32)				,	rng=random.Random(A)).to(A)

						_UpperCAmelCase	 =     {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'image': image,
						    'mask_image': mask_image,
						    'generator': generator,
						    'num_inference_steps': 2,
						    'output_type': 'numpy',
						}

						return inputs



			@unittest.skipIf(
			    torch_device != 'cuda' or not is_xformers_available()				,	reason='XFormers attention is only available with CUDA and `xformers` installed'				,	)
			def 					_lowerCamelCase (		self     :						List[str])     ->					Union[str, Any]:



						"""simple docstring"""






						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)



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



						"""simple docstring"""






						self._test_save_load_optional_components()



			@unittest.skipIf(torch_device != 'cuda'				,	reason='float16 requires CUDA')
			def 					_lowerCamelCase (		self     :						List[str])     ->					Any:



						"""simple docstring"""






						super().test_save_load_floataa(expected_max_diff=1E-1)



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Tuple:



						"""simple docstring"""






						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)



			def 					_lowerCamelCase (		self     :						str)     ->					List[str]:



						"""simple docstring"""






						self._test_save_load_local()



			def 					_lowerCamelCase (		self     :						int)     ->					Tuple:



						"""simple docstring"""






						self._test_inference_batch_single_identical(
						    expected_max_diff=1E-2				,	)
