
'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_pytesseract_available():
		from PIL import Image

		from transformers import LayoutLMvaImageProcessor






class  A  (						unittest.TestCase     ):





	def __init__(       self      :							Optional[Any]   ,				__magic_name__      :							Tuple   ,				__magic_name__      :							int=7   ,				__magic_name__      :							List[str]=3   ,				__magic_name__      :							Optional[Any]=18   ,				__magic_name__      :							Optional[Any]=30   ,				__magic_name__      :							Any=400   ,				__magic_name__      :							List[Any]=True   ,				__magic_name__      :							List[Any]=None   ,				__magic_name__      :							List[str]=True   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				size if size is not None else {"height": 18, "width": 18}
				lowerCAmelCase__										=				parent
				lowerCAmelCase__										=				batch_size
				lowerCAmelCase__										=				num_channels
				lowerCAmelCase__										=				image_size
				lowerCAmelCase__										=				min_resolution
				lowerCAmelCase__										=				max_resolution
				lowerCAmelCase__										=				do_resize
				lowerCAmelCase__										=				size
				lowerCAmelCase__										=				apply_ocr







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}






@require_torch
@require_pytesseract
class  A  (						SCREAMING_SNAKE_CASE__	,   unittest.TestCase     ):
	snake_case__   :Union[str, Any]   =  LayoutLMvaImageProcessor if is_pytesseract_available() else None





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				lowerCAmelCase__										=				LayoutLMvaImageProcessingTester(self				)





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.image_processing_class(**self.image_processor_dict				)
				self.assertTrue(hasattr(__magic_name__   ,				"do_resize"				)				)
				self.assertTrue(hasattr(__magic_name__   ,				"size"				)				)
				self.assertTrue(hasattr(__magic_name__   ,				"apply_ocr"				)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.image_processing_class.from_dict(self.image_processor_dict				)
				self.assertEqual(image_processor.size   ,				{"height": 18, "width": 18}				)

				lowerCAmelCase__										=				self.image_processing_class.from_dict(self.image_processor_dict   ,				size=42				)
				self.assertEqual(image_processor.size   ,				{"height": 42, "width": 42}				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				pass





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.image_processing_class(**self.image_processor_dict				)
				# create random PIL images
				lowerCAmelCase__										=				prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=__magic_name__				)
				for image in image_inputs:
							self.assertIsInstance(__magic_name__   ,				Image.Image				)

				# Test not batched input
				lowerCAmelCase__										=				image_processing(image_inputs[0]   ,				return_tensors="pt"				)
				self.assertEqual(
				    encoding.pixel_values.shape   ,				(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )   ,				)

				self.assertIsInstance(encoding.words   ,				__magic_name__				)
				self.assertIsInstance(encoding.boxes   ,				__magic_name__				)

				# Test batched
				lowerCAmelCase__										=				image_processing(__magic_name__   ,				return_tensors="pt"				).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,				(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )   ,				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.image_processing_class(**self.image_processor_dict				)
				# create random numpy tensors
				lowerCAmelCase__										=				prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=__magic_name__   ,				numpify=__magic_name__				)
				for image in image_inputs:
							self.assertIsInstance(__magic_name__   ,				np.ndarray				)

				# Test not batched input
				lowerCAmelCase__										=				image_processing(image_inputs[0]   ,				return_tensors="pt"				).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,				(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )   ,				)

				# Test batched
				lowerCAmelCase__										=				image_processing(__magic_name__   ,				return_tensors="pt"				).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,				(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )   ,				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.image_processing_class(**self.image_processor_dict				)
				# create random PyTorch tensors
				lowerCAmelCase__										=				prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=__magic_name__   ,				torchify=__magic_name__				)
				for image in image_inputs:
							self.assertIsInstance(__magic_name__   ,				torch.Tensor				)

				# Test not batched input
				lowerCAmelCase__										=				image_processing(image_inputs[0]   ,				return_tensors="pt"				).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,				(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )   ,				)

				# Test batched
				lowerCAmelCase__										=				image_processing(__magic_name__   ,				return_tensors="pt"				).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,				(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )   ,				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				lowerCAmelCase__										=				LayoutLMvaImageProcessor()

				from datasets import load_dataset

				lowerCAmelCase__										=				load_dataset("hf-internal-testing/fixtures_docvqa"   ,				split="test"				)

				lowerCAmelCase__										=				Image.open(ds[0]["file"]				).convert("RGB"				)

				lowerCAmelCase__										=				image_processing(__magic_name__   ,				return_tensors="pt"				)

				self.assertEqual(encoding.pixel_values.shape   ,				(1, 3, 224, 224)				)
				self.assertEqual(len(encoding.words				)   ,				len(encoding.boxes				)				)

				# fmt: off
				# the words and boxes were obtained with Tesseract 4.1.1
				lowerCAmelCase__										=				[["11:14", "to", "11:39", "a.m", "11:39", "to", "11:44", "a.m.", "11:44", "a.m.", "to", "12:25", "p.m.", "12:25", "to", "12:58", "p.m.", "12:58", "to", "4:00", "p.m.", "2:00", "to", "5:00", "p.m.", "Coffee", "Break", "Coffee", "will", "be", "served", "for", "men", "and", "women", "in", "the", "lobby", "adjacent", "to", "exhibit", "area.", "Please", "move", "into", "exhibit", "area.", "(Exhibits", "Open)", "TRRF", "GENERAL", "SESSION", "(PART", "|)", "Presiding:", "Lee", "A.", "Waller", "TRRF", "Vice", "President", "“Introductory", "Remarks”", "Lee", "A.", "Waller,", "TRRF", "Vice", "Presi-", "dent", "Individual", "Interviews", "with", "TRRF", "Public", "Board", "Members", "and", "Sci-", "entific", "Advisory", "Council", "Mem-", "bers", "Conducted", "by", "TRRF", "Treasurer", "Philip", "G.", "Kuehn", "to", "get", "answers", "which", "the", "public", "refrigerated", "warehousing", "industry", "is", "looking", "for.", "Plus", "questions", "from", "the", "floor.", "Dr.", "Emil", "M.", "Mrak,", "University", "of", "Cal-", "ifornia,", "Chairman,", "TRRF", "Board;", "Sam", "R.", "Cecil,", "University", "of", "Georgia", "College", "of", "Agriculture;", "Dr.", "Stanley", "Charm,", "Tufts", "University", "School", "of", "Medicine;", "Dr.", "Robert", "H.", "Cotton,", "ITT", "Continental", "Baking", "Company;", "Dr.", "Owen", "Fennema,", "University", "of", "Wis-", "consin;", "Dr.", "Robert", "E.", "Hardenburg,", "USDA.", "Questions", "and", "Answers", "Exhibits", "Open", "Capt.", "Jack", "Stoney", "Room", "TRRF", "Scientific", "Advisory", "Council", "Meeting", "Ballroom", "Foyer"]]  # noqa: E231
				lowerCAmelCase__										=				[[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
				# fmt: on

				self.assertListEqual(encoding.words   ,				__magic_name__				)
				self.assertListEqual(encoding.boxes   ,				__magic_name__				)

				# with apply_OCR = False
				lowerCAmelCase__										=				LayoutLMvaImageProcessor(apply_ocr=__magic_name__				)

				lowerCAmelCase__										=				image_processing(__magic_name__   ,				return_tensors="pt"				)

				self.assertEqual(encoding.pixel_values.shape   ,				(1, 3, 224, 224)				)












"""simple docstring"""





import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
  import onnxruntime as ort






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:int						    =   """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""






       def 	snake_case		(       self       ,					__UpperCAmelCase=0 ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				floats_tensor((1, 3, 1_2_8, 1_2_8)       ,					rng=random.Random(__UpperCAmelCase ) )
         lowerCAmelCase__     :List[str]											=				np.random.RandomState(__UpperCAmelCase )
         lowerCAmelCase__     :List[str]											=				{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'generator': generator,
             'num_inference_steps': 3,
             'strength': 0.75,
             'guidance_scale': 7.5,
             'output_type': 'numpy',
         }
         return inputs






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :Union[str, Any]											=				self.get_dummy_inputs()
         lowerCAmelCase__     :Optional[int]											=				pipe(**__UpperCAmelCase ).images
         lowerCAmelCase__     :Union[str, Any]											=				image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :Union[str, Any]											=				np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87] )
         assert np.abs(image_slice - expected_slice ).max() < 1E-1






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         lowerCAmelCase__     :str											=				PNDMScheduler.from_config(pipe.scheduler.config       ,					skip_prk_steps=__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :Tuple											=				self.get_dummy_inputs()
         lowerCAmelCase__     :Optional[Any]											=				pipe(**__UpperCAmelCase ).images
         lowerCAmelCase__     :Any											=				image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :int											=				np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         lowerCAmelCase__     :List[str]											=				LMSDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         # warmup pass to apply optimizations
         lowerCAmelCase__     :List[Any]											=				pipe(**self.get_dummy_inputs() )

         lowerCAmelCase__     :Tuple											=				self.get_dummy_inputs()
         lowerCAmelCase__     :int											=				pipe(**__UpperCAmelCase ).images
         lowerCAmelCase__     :Any											=				image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :Union[str, Any]											=				np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         lowerCAmelCase__     :Dict											=				EulerDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :Any											=				self.get_dummy_inputs()
         lowerCAmelCase__     :List[str]											=				pipe(**__UpperCAmelCase ).images
         lowerCAmelCase__     :List[str]											=				image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :Optional[int]											=				np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         lowerCAmelCase__     :str											=				EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :Tuple											=				self.get_dummy_inputs()
         lowerCAmelCase__     :Any											=				pipe(**__UpperCAmelCase ).images
         lowerCAmelCase__     :Optional[int]											=				image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :int											=				np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         lowerCAmelCase__     :List[Any]											=				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :Any											=				self.get_dummy_inputs()
         lowerCAmelCase__     :List[Any]											=				pipe(**__UpperCAmelCase ).images
         lowerCAmelCase__     :int											=				image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :Optional[Any]											=				np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1






@nightly
@require_onnxruntime
@require_torch_gpu
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				ort.SessionOptions()
         lowerCAmelCase__     :Optional[int]											=				False
         return options






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/img2img/sketch-mountains-input.jpg' )
         lowerCAmelCase__     :Any											=				init_image.resize((7_6_8, 5_1_2) )
         # using the PNDM scheduler by default
         lowerCAmelCase__     :Optional[int]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             'CompVis/stable-diffusion-v1-4'       ,					revision='onnx'       ,					safety_checker=__UpperCAmelCase       ,					feature_extractor=__UpperCAmelCase       ,					provider=self.gpu_provider       ,					sess_options=self.gpu_options       ,					)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				'A fantasy landscape, trending on artstation'

         lowerCAmelCase__     :Optional[Any]											=				np.random.RandomState(0 )
         lowerCAmelCase__     :List[str]											=				pipe(
             prompt=__UpperCAmelCase       ,					image=__UpperCAmelCase       ,					strength=0.75       ,					guidance_scale=7.5       ,					num_inference_steps=1_0       ,					generator=__UpperCAmelCase       ,					output_type='np'       ,					)
         lowerCAmelCase__     :Any											=				output.images
         lowerCAmelCase__     :List[str]											=				images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 7_6_8, 3)
         lowerCAmelCase__     :List[Any]											=				np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19] )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/img2img/sketch-mountains-input.jpg' )
         lowerCAmelCase__     :Optional[Any]											=				init_image.resize((7_6_8, 5_1_2) )
         lowerCAmelCase__     :List[Any]											=				LMSDiscreteScheduler.from_pretrained(
             'runwayml/stable-diffusion-v1-5'       ,					subfolder='scheduler'       ,					revision='onnx' )
         lowerCAmelCase__     :Optional[Any]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             'runwayml/stable-diffusion-v1-5'       ,					revision='onnx'       ,					scheduler=__UpperCAmelCase       ,					safety_checker=__UpperCAmelCase       ,					feature_extractor=__UpperCAmelCase       ,					provider=self.gpu_provider       ,					sess_options=self.gpu_options       ,					)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				'A fantasy landscape, trending on artstation'

         lowerCAmelCase__     :List[Any]											=				np.random.RandomState(0 )
         lowerCAmelCase__     :List[Any]											=				pipe(
             prompt=__UpperCAmelCase       ,					image=__UpperCAmelCase       ,					strength=0.75       ,					guidance_scale=7.5       ,					num_inference_steps=2_0       ,					generator=__UpperCAmelCase       ,					output_type='np'       ,					)
         lowerCAmelCase__     :Optional[Any]											=				output.images
         lowerCAmelCase__     :int											=				images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 7_6_8, 3)
         lowerCAmelCase__     :List[Any]											=				np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31] )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
