




'''simple docstring'''

import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
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 						a				(   _lowerCamelCase   ,     unittest.TestCase     ):
				# FIXME: add fast tests
				pass







@nightly
@require_onnxruntime
@require_torch_gpu
class 						a				(   unittest.TestCase     ):


				@property
				def   A_ (		self    :					List[Any]					):
									return (
									    "CUDAExecutionProvider",
									    {
									        "gpu_mem_limit": "15000000000",  # 15GB
									        "arena_extend_strategy": "kSameAsRequested",
									    },
									)


				@property
				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				ort.SessionOptions()
									snake_case_									=				False
									return options


				def   A_ (		self    :					Tuple					):
									snake_case_									=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''					)
									snake_case_									=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''					)
									snake_case_									=				OnnxStableDiffusionInpaintPipeline.from_pretrained(
									    '''runwayml/stable-diffusion-inpainting'''       ,				revision='''onnx'''       ,				safety_checker=lowercase_       ,				feature_extractor=lowercase_       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
									pipe.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				'''A red cat sitting on a park bench'''

									snake_case_									=				np.random.RandomState(0					)
									snake_case_									=				pipe(
									    prompt=lowercase_       ,				image=lowercase_       ,				mask_image=lowercase_       ,				guidance_scale=7.5       ,				num_inference_steps=10       ,				generator=lowercase_       ,				output_type='''np'''       ,				)
									snake_case_									=				output.images
									snake_case_									=				images[0, 255:258, 255:258, -1]

									assert images.shape == (1, 512, 512, 3)
									snake_case_									=				np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]					)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3


				def   A_ (		self    :					Tuple					):
									snake_case_									=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''					)
									snake_case_									=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''					)
									snake_case_									=				LMSDiscreteScheduler.from_pretrained(
									    '''runwayml/stable-diffusion-inpainting'''       ,				subfolder='''scheduler'''       ,				revision='''onnx'''					)
									snake_case_									=				OnnxStableDiffusionInpaintPipeline.from_pretrained(
									    '''runwayml/stable-diffusion-inpainting'''       ,				revision='''onnx'''       ,				scheduler=lowercase_       ,				safety_checker=lowercase_       ,				feature_extractor=lowercase_       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
									pipe.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				'''A red cat sitting on a park bench'''

									snake_case_									=				np.random.RandomState(0					)
									snake_case_									=				pipe(
									    prompt=lowercase_       ,				image=lowercase_       ,				mask_image=lowercase_       ,				guidance_scale=7.5       ,				num_inference_steps=20       ,				generator=lowercase_       ,				output_type='''np'''       ,				)
									snake_case_									=				output.images
									snake_case_									=				images[0, 255:258, 255:258, -1]

									assert images.shape == (1, 512, 512, 3)
									snake_case_									=				np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]					)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3







'''simple docstring'''

from ..utils import DummyObject, requires_backends






class 						a				(   metaclass=_lowerCamelCase     ):
				snake_case_      =		["transformers", "torch", "note_seq"]


				def __init__(		self    :					Union[str, Any]       ,				*lowercase_    :					Optional[int]       ,				**lowercase_    :					int					):
									requires_backends(self       ,				['''transformers''', '''torch''', '''note_seq''']					)


				@classmethod
				def   A_ (		cls    :					List[Any]       ,				*lowercase_    :					Optional[Any]       ,				**lowercase_    :					str					):
									requires_backends(cls       ,				['''transformers''', '''torch''', '''note_seq''']					)


				@classmethod
				def   A_ (		cls    :					Tuple       ,				*lowercase_    :					Union[str, Any]       ,				**lowercase_    :					List[Any]					):
									requires_backends(cls       ,				['''transformers''', '''torch''', '''note_seq''']					)

