



'''simple docstring'''
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    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 							lowercase__	(							lowercase						,					unittest.TestCase   ):
	# TODO: is there an appropriate internal test set?
	lowercase__  				=			"""ssube/stable-diffusion-x4-upscaler-onnx"""





	def 	UpperCamelCase_  ( self					:				List[Any] ,lowerCamelCase__					:				Optional[int]=0 ):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   floats_tensor((1, 3, 128, 128) ,rng=random.Random(lowerCamelCase__ ) )
							_UpperCamelCase					:		List[str] 				=   torch.manual_seed(lowerCamelCase__ )
							_UpperCamelCase					:		int 				=   {
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': image,
							    'generator': generator,
							    'num_inference_steps': 3,
							    'guidance_scale': 7.5,
							    'output_type': 'numpy',
							}
							return inputs





	def 	UpperCamelCase_  ( self					:				Dict ):







							'''simple docstring'''
							_UpperCamelCase					:		Tuple 				=   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
							pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Optional[int] 				=   self.get_dummy_inputs()
							_UpperCamelCase					:		str 				=   pipe(**lowerCamelCase__ ).images
							_UpperCamelCase					:		Optional[Any] 				=   image[0, -3:, -3:, -1].flatten()

							# started as 128, should now be 512
							assert image.shape == (1, 512, 512, 3)
							_UpperCamelCase					:		Dict 				=   np.array(
							    [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
							assert np.abs(image_slice - expected_slice ).max() < 1E-1





	def 	UpperCamelCase_  ( self					:				Optional[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Tuple 				=   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
							_UpperCamelCase					:		Optional[int] 				=   PNDMScheduler.from_config(pipe.scheduler.config ,skip_prk_steps=lowerCamelCase__ )
							pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		int 				=   self.get_dummy_inputs()
							_UpperCamelCase					:		Tuple 				=   pipe(**lowerCamelCase__ ).images
							_UpperCamelCase					:		Dict 				=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							_UpperCamelCase					:		Optional[Any] 				=   np.array(
							    [0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4] )
							assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1





	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							_UpperCamelCase					:		Dict 				=   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
							_UpperCamelCase					:		Any 				=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
							pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Tuple 				=   self.get_dummy_inputs()
							_UpperCamelCase					:		Union[str, Any] 				=   pipe(**lowerCamelCase__ ).images
							_UpperCamelCase					:		Dict 				=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							_UpperCamelCase					:		Dict 				=   np.array(
							    [0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5] )

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





	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							_UpperCamelCase					:		Tuple 				=   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
							_UpperCamelCase					:		Optional[Any] 				=   EulerDiscreteScheduler.from_config(pipe.scheduler.config )
							pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Tuple 				=   self.get_dummy_inputs()
							_UpperCamelCase					:		Optional[int] 				=   pipe(**lowerCamelCase__ ).images
							_UpperCamelCase					:		Optional[int] 				=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							_UpperCamelCase					:		Union[str, Any] 				=   np.array(
							    [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
							assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1





	def 	UpperCamelCase_  ( self					:				Any ):







							'''simple docstring'''
							_UpperCamelCase					:		str 				=   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
							_UpperCamelCase					:		Tuple 				=   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
							pipe.set_progress_bar_config(disable=lowerCamelCase__ )

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

							assert image.shape == (1, 512, 512, 3)
							_UpperCamelCase					:		List[str] 				=   np.array(
							    [0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3] )

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






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





	@property
	def 	UpperCamelCase_  ( self					:				Any ):







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





	@property
	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							_UpperCamelCase					:		List[Any] 				=   ort.SessionOptions()
							_UpperCamelCase					:		Tuple 				=   False
							return options





	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg' )
							_UpperCamelCase					:		Union[str, Any] 				=   init_image.resize((128, 128) )
							# using the PNDM scheduler by default
							_UpperCamelCase					:		Dict 				=   OnnxStableDiffusionUpscalePipeline.from_pretrained(
							    'ssube/stable-diffusion-x4-upscaler-onnx' ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
							pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Dict 				=   'A fantasy landscape, trending on artstation'

							_UpperCamelCase					:		Optional[int] 				=   torch.manual_seed(0 )
							_UpperCamelCase					:		Optional[Any] 				=   pipe(
							    prompt=lowerCamelCase__ ,image=lowerCamelCase__ ,guidance_scale=7.5 ,num_inference_steps=10 ,generator=lowerCamelCase__ ,output_type='np' ,)
							_UpperCamelCase					:		Union[str, Any] 				=   output.images
							_UpperCamelCase					:		Any 				=   images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 512, 3)
							_UpperCamelCase					:		List[Any] 				=   np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2] )
							# 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[str] ):







							'''simple docstring'''
							_UpperCamelCase					:		Tuple 				=   load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg' )
							_UpperCamelCase					:		List[Any] 				=   init_image.resize((128, 128) )
							_UpperCamelCase					:		Optional[Any] 				=   LMSDiscreteScheduler.from_pretrained(
							    'ssube/stable-diffusion-x4-upscaler-onnx' ,subfolder='scheduler' )
							_UpperCamelCase					:		Any 				=   OnnxStableDiffusionUpscalePipeline.from_pretrained(
							    'ssube/stable-diffusion-x4-upscaler-onnx' ,scheduler=lowerCamelCase__ ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
							pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Optional[int] 				=   'A fantasy landscape, trending on artstation'

							_UpperCamelCase					:		List[str] 				=   torch.manual_seed(0 )
							_UpperCamelCase					:		Union[str, Any] 				=   pipe(
							    prompt=lowerCamelCase__ ,image=lowerCamelCase__ ,guidance_scale=7.5 ,num_inference_steps=20 ,generator=lowerCamelCase__ ,output_type='np' ,)
							_UpperCamelCase					:		List[Any] 				=   output.images
							_UpperCamelCase					:		List[str] 				=   images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 512, 3)
							_UpperCamelCase					:		List[str] 				=   np.array(
							    [0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6] )
							# 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 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Image:

      def brightness(_lowerCAmelCase		) -> 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(_lowerCAmelCase		)


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