


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    _UpperCamelCase     (       __snake_case     ,		unittest.TestCase					):

				"""simple docstring"""







				lowerCAmelCase					=			'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'

				def   _UpperCAmelCase       (    self					, a__=0 )						->					Dict:
					A             =					floats_tensor((1, 3, 128, 128)					, rng=random.Random(a__ ) )
					A             =					np.random.RandomState(a__ )
					A             =					{
					    """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   _UpperCAmelCase       (    self )						->					int:
					A             =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					, provider="""CPUExecutionProvider""" )
					pipe.set_progress_bar_config(disable=a__ )

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

					assert image.shape == (1, 128, 128, 3)
					A             =					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   _UpperCAmelCase       (    self )						->					List[Any]:
					A             =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					, provider="""CPUExecutionProvider""" )
					A             =					PNDMScheduler.from_config(pipe.scheduler.config					, skip_prk_steps=a__ )
					pipe.set_progress_bar_config(disable=a__ )

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

					assert image.shape == (1, 128, 128, 3)
					A             =					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   _UpperCAmelCase       (    self )						->					int:
					A             =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					, provider="""CPUExecutionProvider""" )
					A             =					LMSDiscreteScheduler.from_config(pipe.scheduler.config )
					pipe.set_progress_bar_config(disable=a__ )

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

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

					assert image.shape == (1, 128, 128, 3)
					A             =					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   _UpperCAmelCase       (    self )						->					int:
					A             =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					, provider="""CPUExecutionProvider""" )
					A             =					EulerDiscreteScheduler.from_config(pipe.scheduler.config )
					pipe.set_progress_bar_config(disable=a__ )

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

					assert image.shape == (1, 128, 128, 3)
					A             =					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   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					A             =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					, provider="""CPUExecutionProvider""" )
					A             =					EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
					pipe.set_progress_bar_config(disable=a__ )

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

					assert image.shape == (1, 128, 128, 3)
					A             =					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   _UpperCAmelCase       (    self )						->					List[Any]:
					A             =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					, provider="""CPUExecutionProvider""" )
					A             =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
					pipe.set_progress_bar_config(disable=a__ )

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

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

				"""simple docstring"""







				@property
				def   _UpperCAmelCase       (    self )						->					List[str]:
					return (
					    "CUDAExecutionProvider",
					    {
					        "gpu_mem_limit": "15000000000",  # 15GB
					        "arena_extend_strategy": "kSameAsRequested",
					    },
					)

				@property
				def   _UpperCAmelCase       (    self )						->					Dict:
					A             =					ort.SessionOptions()
					A             =					False
					return options

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

					A             =					"""A fantasy landscape, trending on artstation"""

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

					assert images.shape == (1, 512, 768, 3)
					A             =					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   _UpperCAmelCase       (    self )						->					List[Any]:
					A             =					load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/img2img/sketch-mountains-input.jpg""" )
					A             =					init_image.resize((768, 512) )
					A             =					LMSDiscreteScheduler.from_pretrained(
					    """runwayml/stable-diffusion-v1-5"""					, subfolder="""scheduler"""					, revision="""onnx""" )
					A             =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(
					    """runwayml/stable-diffusion-v1-5"""					, revision="""onnx"""					, scheduler=a__					, safety_checker=a__					, feature_extractor=a__					, provider=self.gpu_provider					, sess_options=self.gpu_options					, )
					pipe.set_progress_bar_config(disable=a__ )

					A             =					"""A fantasy landscape, trending on artstation"""

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

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







_lowercase							:   Dict						=						{
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}



