




'''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   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Dict=0)    ->							List[Any]:
								A_									= floats_tensor((1, 3, 128, 128)      ,       rng=random.Random(_lowercase))
								A_									= np.random.RandomState(_lowercase)
								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     __snake_case						(	self     :						int)    ->							List[str]:
								A_									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,       provider='CPUExecutionProvider')
								pipe.set_progress_bar_config(disable=_lowercase)

								A_									= self.get_dummy_inputs()
								A_									= pipe(**_lowercase).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     __snake_case						(	self     :						Dict)    ->							Optional[int]:
								A_									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,       provider='CPUExecutionProvider')
								A_									= PNDMScheduler.from_config(pipe.scheduler.config      ,       skip_prk_steps=_lowercase)
								pipe.set_progress_bar_config(disable=_lowercase)

								A_									= self.get_dummy_inputs()
								A_									= pipe(**_lowercase).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     __snake_case						(	self     :						Any)    ->							Tuple:
								A_									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,       provider='CPUExecutionProvider')
								A_									= LMSDiscreteScheduler.from_config(pipe.scheduler.config)
								pipe.set_progress_bar_config(disable=_lowercase)

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

								A_									= self.get_dummy_inputs()
								A_									= pipe(**_lowercase).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     __snake_case						(	self     :						List[str])    ->							List[str]:
								A_									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,       provider='CPUExecutionProvider')
								A_									= EulerDiscreteScheduler.from_config(pipe.scheduler.config)
								pipe.set_progress_bar_config(disable=_lowercase)

								A_									= self.get_dummy_inputs()
								A_									= pipe(**_lowercase).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     __snake_case						(	self     :						Optional[int])    ->							Any:
								A_									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,       provider='CPUExecutionProvider')
								A_									= EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
								pipe.set_progress_bar_config(disable=_lowercase)

								A_									= self.get_dummy_inputs()
								A_									= pipe(**_lowercase).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     __snake_case						(	self     :						List[Any])    ->							Any:
								A_									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,       provider='CPUExecutionProvider')
								A_									= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
								pipe.set_progress_bar_config(disable=_lowercase)

								A_									= self.get_dummy_inputs()
								A_									= pipe(**_lowercase).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     __snake_case						(	self     :						Optional[int])    ->							Optional[int]:
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)



					@property
					def     __snake_case						(	self     :						Any)    ->							Any:
								A_									= ort.SessionOptions()
								A_									= False
								return options



					def     __snake_case						(	self     :						int)    ->							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((768, 512))
								# using the PNDM scheduler by default
								A_									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'      ,       revision='onnx'      ,       safety_checker=_lowercase      ,       feature_extractor=_lowercase      ,       provider=self.gpu_provider      ,       sess_options=self.gpu_options      ,       )
								pipe.set_progress_bar_config(disable=_lowercase)

								A_									= 'A fantasy landscape, trending on artstation'

								A_									= np.random.RandomState(0)
								A_									= pipe(
								    prompt=_lowercase      ,       image=_lowercase      ,       strength=0.75      ,       guidance_scale=7.5      ,       num_inference_steps=10      ,       generator=_lowercase      ,       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     __snake_case						(	self     :						Tuple)    ->							List[str]:
								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=_lowercase      ,       safety_checker=_lowercase      ,       feature_extractor=_lowercase      ,       provider=self.gpu_provider      ,       sess_options=self.gpu_options      ,       )
								pipe.set_progress_bar_config(disable=_lowercase)

								A_									= 'A fantasy landscape, trending on artstation'

								A_									= np.random.RandomState(0)
								A_									= pipe(
								    prompt=_lowercase      ,       image=_lowercase      ,       strength=0.75      ,       guidance_scale=7.5      ,       num_inference_steps=20      ,       generator=_lowercase      ,       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










'''simple docstring'''
from PIL import Image





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Image:

			def brightness(SCREAMING_SNAKE_CASE_     ) -> float:
						return 128 + level + (c - 128)

			if not -255.0 <= level <= 255.0:
						raise ValueError('level must be between -255.0 (black) and 255.0 (white)'     )
			return img.point(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
							# Load image
							with Image.open("image_data/lena.jpg") as img:
														# Change brightness to 100
														_SCREAMING_SNAKE_CASE			=						change_brightness(img, 1_00)
														brigt_img.save("image_data/lena_brightness.png", format="png")




