


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 	snake_case_       (lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   Dict   				=      '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :List[Any]=0				) ->   List[Any]:
												a__					=					floats_tensor((1, 3, 1_28, 1_28)      ,rng=random.Random(__snake_case				)				)
												a__					=					np.random.RandomState(__snake_case				)
												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 			lowerCamelCase__(				self       :List[Any]				) ->   str:
												a__					=					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,provider='CPUExecutionProvider'				)
												pipe.set_progress_bar_config(disable=__snake_case				)

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

												assert image.shape == (1, 1_28, 1_28, 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 			lowerCamelCase__(				self       :Optional[Any]				) ->   Tuple:
												a__					=					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,provider='CPUExecutionProvider'				)
												a__					=					PNDMScheduler.from_config(pipe.scheduler.config      ,skip_prk_steps=__snake_case				)
												pipe.set_progress_bar_config(disable=__snake_case				)

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

												assert image.shape == (1, 1_28, 1_28, 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 			lowerCamelCase__(				self       :int				) ->   List[str]:
												a__					=					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,provider='CPUExecutionProvider'				)
												a__					=					LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
												pipe.set_progress_bar_config(disable=__snake_case				)

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

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

												assert image.shape == (1, 1_28, 1_28, 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 			lowerCamelCase__(				self       :List[Any]				) ->   Dict:
												a__					=					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,provider='CPUExecutionProvider'				)
												a__					=					EulerDiscreteScheduler.from_config(pipe.scheduler.config				)
												pipe.set_progress_bar_config(disable=__snake_case				)

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

												assert image.shape == (1, 1_28, 1_28, 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 			lowerCamelCase__(				self       :List[Any]				) ->   Optional[Any]:
												a__					=					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,provider='CPUExecutionProvider'				)
												a__					=					EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config				)
												pipe.set_progress_bar_config(disable=__snake_case				)

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

												assert image.shape == (1, 1_28, 1_28, 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 			lowerCamelCase__(				self       :Optional[Any]				) ->   List[Any]:
												a__					=					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,provider='CPUExecutionProvider'				)
												a__					=					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
												pipe.set_progress_bar_config(disable=__snake_case				)

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

												assert image.shape == (1, 1_28, 1_28, 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 	snake_case_       (unittest.TestCase				):
					@property
					def 			lowerCamelCase__(				self       :Dict				) ->   Union[str, Any]:
												return (
												    "CUDAExecutionProvider",
												    {
												        "gpu_mem_limit": "15000000000",  # 15GB
												        "arena_extend_strategy": "kSameAsRequested",
												    },
												)
					@property
					def 			lowerCamelCase__(				self       :str				) ->   List[str]:
												a__					=					ort.SessionOptions()
												a__					=					False
												return options
					def 			lowerCamelCase__(				self       :List[str]				) ->   Optional[Any]:
												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/img2img/sketch-mountains-input.jpg'				)
												a__					=					init_image.resize((7_68, 5_12)				)
												# using the PNDM scheduler by default
												a__					=					OnnxStableDiffusionImgaImgPipeline.from_pretrained(
												    'CompVis/stable-diffusion-v1-4'      ,revision='onnx'      ,safety_checker=__snake_case      ,feature_extractor=__snake_case      ,provider=self.gpu_provider      ,sess_options=self.gpu_options      ,)
												pipe.set_progress_bar_config(disable=__snake_case				)

												a__					=					'A fantasy landscape, trending on artstation'

												a__					=					np.random.RandomState(0				)
												a__					=					pipe(
												    prompt=__snake_case      ,image=__snake_case      ,strength=0.75      ,guidance_scale=7.5      ,num_inference_steps=10      ,generator=__snake_case      ,output_type='np'      ,)
												a__					=					output.images
												a__					=					images[0, 2_55:2_58, 3_83:3_86, -1]

												assert images.shape == (1, 5_12, 7_68, 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 			lowerCamelCase__(				self       :List[str]				) ->   Optional[Any]:
												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/img2img/sketch-mountains-input.jpg'				)
												a__					=					init_image.resize((7_68, 5_12)				)
												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=__snake_case      ,safety_checker=__snake_case      ,feature_extractor=__snake_case      ,provider=self.gpu_provider      ,sess_options=self.gpu_options      ,)
												pipe.set_progress_bar_config(disable=__snake_case				)

												a__					=					'A fantasy landscape, trending on artstation'

												a__					=					np.random.RandomState(0				)
												a__					=					pipe(
												    prompt=__snake_case      ,image=__snake_case      ,strength=0.75      ,guidance_scale=7.5      ,num_inference_steps=20      ,generator=__snake_case      ,output_type='np'      ,)
												a__					=					output.images
												a__					=					images[0, 2_55:2_58, 3_83:3_86, -1]

												assert images.shape == (1, 5_12, 7_68, 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








snake_case							:	str					      =				{
    0: '''0''',
    1: '''1''',
    2: '''2''',
    3: '''3''',
    4: '''4''',
    5: '''5''',
    6: '''6''',
    7: '''7''',
    8: '''8''',
    9: '''9''',
    10: '''a''',
    11: '''b''',
    12: '''c''',
    13: '''d''',
    14: '''e''',
    15: '''f''',
}






def 							__lowercase       (			__lowerCAmelCase							:    float       ):
							assert type(__lowerCAmelCase       ) in (int, float) and decimal == int(__lowerCAmelCase       )
							a__					=					int(__lowerCAmelCase       )
							a__					=					''
							a__					=					False
							if decimal < 0:
														a__					=					True
														decimal *= -1
							while decimal > 0:
														a__				, a__					=					divmod(__lowerCAmelCase ,   1_6       )
														a__					=					values[remainder] + hexadecimal
							a__					=					'0x' + hexadecimal
							if negative:
														a__					=					'-' + hexadecimal
							return hexadecimal


if __name__ == "__main__":
						import doctest

						doctest.testmod()




