
'''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       A				(							__UpperCAmelCase		,    unittest.TestCase					):
			lowerCamelCase				:				List[str]									=	"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""



			def A__			(       self							,  lowerCamelCase__=0							)      ->		Optional[int]:




									'''simple docstring'''



									lowercase__							=							floats_tensor((1, 3, 128, 128)							,  rng=random.Random(lowerCamelCase__							)							)
									lowercase__							=							np.random.RandomState(lowerCamelCase__							)
									lowercase__							=							{
									    '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 A__			(       self							)      ->		Tuple:




									'''simple docstring'''



									lowercase__							=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,  provider="""CPUExecutionProvider"""							)
									pipe.set_progress_bar_config(disable=lowerCamelCase__							)

									lowercase__							=							self.get_dummy_inputs()
									lowercase__							=							pipe(**lowerCamelCase__							).images
									lowercase__							=							image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 128, 128, 3)
									lowercase__							=							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 A__			(       self							)      ->		Optional[int]:




									'''simple docstring'''



									lowercase__							=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,  provider="""CPUExecutionProvider"""							)
									lowercase__							=							PNDMScheduler.from_config(pipe.scheduler.config							,  skip_prk_steps=lowerCamelCase__							)
									pipe.set_progress_bar_config(disable=lowerCamelCase__							)

									lowercase__							=							self.get_dummy_inputs()
									lowercase__							=							pipe(**lowerCamelCase__							).images
									lowercase__							=							image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									lowercase__							=							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 A__			(       self							)      ->		Optional[Any]:




									'''simple docstring'''



									lowercase__							=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,  provider="""CPUExecutionProvider"""							)
									lowercase__							=							LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
									pipe.set_progress_bar_config(disable=lowerCamelCase__							)

									# warmup pass to apply optimizations
									lowercase__							=							pipe(**self.get_dummy_inputs()							)

									lowercase__							=							self.get_dummy_inputs()
									lowercase__							=							pipe(**lowerCamelCase__							).images
									lowercase__							=							image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									lowercase__							=							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 A__			(       self							)      ->		int:




									'''simple docstring'''



									lowercase__							=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,  provider="""CPUExecutionProvider"""							)
									lowercase__							=							EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
									pipe.set_progress_bar_config(disable=lowerCamelCase__							)

									lowercase__							=							self.get_dummy_inputs()
									lowercase__							=							pipe(**lowerCamelCase__							).images
									lowercase__							=							image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									lowercase__							=							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 A__			(       self							)      ->		str:




									'''simple docstring'''



									lowercase__							=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,  provider="""CPUExecutionProvider"""							)
									lowercase__							=							EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
									pipe.set_progress_bar_config(disable=lowerCamelCase__							)

									lowercase__							=							self.get_dummy_inputs()
									lowercase__							=							pipe(**lowerCamelCase__							).images
									lowercase__							=							image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									lowercase__							=							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 A__			(       self							)      ->		Tuple:




									'''simple docstring'''



									lowercase__							=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,  provider="""CPUExecutionProvider"""							)
									lowercase__							=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
									pipe.set_progress_bar_config(disable=lowerCamelCase__							)

									lowercase__							=							self.get_dummy_inputs()
									lowercase__							=							pipe(**lowerCamelCase__							).images
									lowercase__							=							image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									lowercase__							=							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       A				(							unittest.TestCase					):



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




									'''simple docstring'''



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



			@property
			def A__			(       self							)      ->		Optional[Any]:




									'''simple docstring'''



									lowercase__							=							ort.SessionOptions()
									lowercase__							=							False
									return options



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




									'''simple docstring'''



									lowercase__							=							load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/img2img/sketch-mountains-input.jpg"""							)
									lowercase__							=							init_image.resize((768, 512)							)
									# using the PNDM scheduler by default
									lowercase__							=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(
									    """CompVis/stable-diffusion-v1-4"""							,  revision="""onnx"""							,  safety_checker=lowerCamelCase__							,  feature_extractor=lowerCamelCase__							,  provider=self.gpu_provider							,  sess_options=self.gpu_options							,  )
									pipe.set_progress_bar_config(disable=lowerCamelCase__							)

									lowercase__							=							'A fantasy landscape, trending on artstation'

									lowercase__							=							np.random.RandomState(0							)
									lowercase__							=							pipe(
									    prompt=lowerCamelCase__							,  image=lowerCamelCase__							,  strength=0.75							,  guidance_scale=7.5							,  num_inference_steps=10							,  generator=lowerCamelCase__							,  output_type="""np"""							,  )
									lowercase__							=							output.images
									lowercase__							=							images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 768, 3)
									lowercase__							=							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 A__			(       self							)      ->		List[Any]:




									'''simple docstring'''



									lowercase__							=							load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/img2img/sketch-mountains-input.jpg"""							)
									lowercase__							=							init_image.resize((768, 512)							)
									lowercase__							=							LMSDiscreteScheduler.from_pretrained(
									    """runwayml/stable-diffusion-v1-5"""							,  subfolder="""scheduler"""							,  revision="""onnx"""							)
									lowercase__							=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(
									    """runwayml/stable-diffusion-v1-5"""							,  revision="""onnx"""							,  scheduler=lowerCamelCase__							,  safety_checker=lowerCamelCase__							,  feature_extractor=lowerCamelCase__							,  provider=self.gpu_provider							,  sess_options=self.gpu_options							,  )
									pipe.set_progress_bar_config(disable=lowerCamelCase__							)

									lowercase__							=							'A fantasy landscape, trending on artstation'

									lowercase__							=							np.random.RandomState(0							)
									lowercase__							=							pipe(
									    prompt=lowerCamelCase__							,  image=lowerCamelCase__							,  strength=0.75							,  guidance_scale=7.5							,  num_inference_steps=20							,  generator=lowerCamelCase__							,  output_type="""np"""							,  )
									lowercase__							=							output.images
									lowercase__							=							images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 768, 3)
									lowercase__							=							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











'''simple docstring'''
from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case_					:      List[Any]    			=    {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      str    			=    [
					    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'VanForImageClassification',
					    'VanModel',
					    'VanPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_van import (
										    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    VanForImageClassification,
										    VanModel,
										    VanPreTrainedModel,
										)

else:
					import sys

					snake_case_					:      List[Any]    			=    _LazyModule(__name__, globals()['__file__'], _import_structure)


