
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_ (			_a					,			unittest.TestCase    ):
			# FIXME: add fast tests
			pass







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






			@property
			def    UpperCamelCase_				(      self  ):
									return (
									    "CUDAExecutionProvider",
									    {
									        "gpu_mem_limit": "15000000000",  # 15GB
									        "arena_extend_strategy": "kSameAsRequested",
									    },
									)






			@property
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					ort.SessionOptions()
									_lowercase					       =					False
									return options






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

									_lowercase					       =					"""A red cat sitting on a park bench"""

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

									assert images.shape == (1, 512, 512, 3)
									_lowercase					       =					np.array([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64]  )

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






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/in_paint/overture-creations-5sI6fQgYIuo.png"""  )
									_lowercase					       =					load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""  )
									_lowercase					       =					LMSDiscreteScheduler.from_pretrained(
									    """runwayml/stable-diffusion-inpainting"""				,			subfolder="""scheduler"""				,			revision="""onnx"""  )
									_lowercase					       =					OnnxStableDiffusionInpaintPipeline.from_pretrained(
									    """runwayml/stable-diffusion-inpainting"""				,			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  )

									_lowercase					       =					"""A red cat sitting on a park bench"""

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

									assert images.shape == (1, 512, 512, 3)
									_lowercase					       =					np.array([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25]  )

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

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
		from .camera import create_pan_cameras
		from .pipeline_shap_e import ShapEPipeline
		from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
		from .renderer import (
		    BoundingBoxVolume,
		    ImportanceRaySampler,
		    MLPNeRFModelOutput,
		    MLPNeRSTFModel,
		    ShapEParamsProjModel,
		    ShapERenderer,
		    StratifiedRaySampler,
		    VoidNeRFModel,
		)