



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 				_snake_case			(					lowercase__    ,	unittest.TestCase):
				# TODO: is there an appropriate internal test set?
				UpperCamelCase__				:					List[Any]  		="""ssube/stable-diffusion-x4-upscaler-onnx"""




				def 							A__      (						self					:						List[str],					__lowercase					:						Union[str, Any]=0						):
							lowercase__					  =				floats_tensor((1, 3, 128, 128),					rng=random.Random(__lowercase						)						)
							lowercase__					  =				torch.manual_seed(__lowercase						)
							lowercase__					  =				{
							    "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 							A__      (						self					:						Any						):
							lowercase__					  =				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"						)
							pipe.set_progress_bar_config(disable=__lowercase						)

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

							# started as 128, should now be 512
							assert image.shape == (1, 512, 512, 3)
							lowercase__					  =				np.array(
							    [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]						)
							assert np.abs(image_slice - expected_slice						).max() < 1e-1




				def 							A__      (						self					:						str						):
							lowercase__					  =				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"						)
							lowercase__					  =				PNDMScheduler.from_config(pipe.scheduler.config,					skip_prk_steps=__lowercase						)
							pipe.set_progress_bar_config(disable=__lowercase						)

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

							assert image.shape == (1, 512, 512, 3)
							lowercase__					  =				np.array(
							    [0.6898892, 0.59240556, 0.52499527, 0.58866215, 0.52258235, 0.52572715, 0.62414473, 0.6174387, 0.6214964]						)
							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1




				def 							A__      (						self					:						Tuple						):
							lowercase__					  =				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"						)
							lowercase__					  =				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
							pipe.set_progress_bar_config(disable=__lowercase						)

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

							assert image.shape == (1, 512, 512, 3)
							lowercase__					  =				np.array(
							    [0.7659278, 0.76437664, 0.75579107, 0.7691116, 0.77666986, 0.7727672, 0.7758664, 0.7812226, 0.76942515]						)

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




				def 							A__      (						self					:						Tuple						):
							lowercase__					  =				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"						)
							lowercase__					  =				EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
							pipe.set_progress_bar_config(disable=__lowercase						)

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

							assert image.shape == (1, 512, 512, 3)
							lowercase__					  =				np.array(
							    [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]						)
							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1




				def 							A__      (						self					:						Any						):
							lowercase__					  =				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"						)
							lowercase__					  =				EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config						)
							pipe.set_progress_bar_config(disable=__lowercase						)

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

							assert image.shape == (1, 512, 512, 3)
							lowercase__					  =				np.array(
							    [0.77424496, 0.773601, 0.7645288, 0.7769598, 0.7772739, 0.7738688, 0.78187233, 0.77879584, 0.767043]						)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class 				_snake_case			(					unittest.TestCase):




				@property
				def 							A__      (						self					:						List[Any]						):
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)




				@property
				def 							A__      (						self					:						Dict						):
							lowercase__					  =				ort.SessionOptions()
							lowercase__					  =				False
							return options




				def 							A__      (						self					:						Any						):
							lowercase__					  =				load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/img2img/sketch-mountains-input.jpg"						)
							lowercase__					  =				init_image.resize((128, 128)						)
							# using the PNDM scheduler by default
							lowercase__					  =				OnnxStableDiffusionUpscalePipeline.from_pretrained(
							    "ssube/stable-diffusion-x4-upscaler-onnx",					provider=self.gpu_provider,					sess_options=self.gpu_options,					)
							pipe.set_progress_bar_config(disable=__lowercase						)

							lowercase__					  =				"A fantasy landscape, trending on artstation"

							lowercase__					  =				torch.manual_seed(0						)
							lowercase__					  =				pipe(
							    prompt=__lowercase,					image=__lowercase,					guidance_scale=7.5,					num_inference_steps=10,					generator=__lowercase,					output_type="np",					)
							lowercase__					  =				output.images
							lowercase__					  =				images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 512, 3)
							lowercase__					  =				np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972]						)
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 2e-2




				def 							A__      (						self					:						Any						):
							lowercase__					  =				load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/img2img/sketch-mountains-input.jpg"						)
							lowercase__					  =				init_image.resize((128, 128)						)
							lowercase__					  =				LMSDiscreteScheduler.from_pretrained(
							    "ssube/stable-diffusion-x4-upscaler-onnx",					subfolder="scheduler"						)
							lowercase__					  =				OnnxStableDiffusionUpscalePipeline.from_pretrained(
							    "ssube/stable-diffusion-x4-upscaler-onnx",					scheduler=__lowercase,					provider=self.gpu_provider,					sess_options=self.gpu_options,					)
							pipe.set_progress_bar_config(disable=__lowercase						)

							lowercase__					  =				"A fantasy landscape, trending on artstation"

							lowercase__					  =				torch.manual_seed(0						)
							lowercase__					  =				pipe(
							    prompt=__lowercase,					image=__lowercase,					guidance_scale=7.5,					num_inference_steps=20,					generator=__lowercase,					output_type="np",					)
							lowercase__					  =				output.images
							lowercase__					  =				images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 512, 3)
							lowercase__					  =				np.array(
							    [0.50173753, 0.50223356, 0.502039, 0.50233036, 0.5023725, 0.5022601, 0.5018758, 0.50234085, 0.50241566]						)
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 2e-2









import math



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			if 0 not in (x, y):
						# We use the relation x^y = y*log10(x), where 10 is the base.
						return y * math.logaa(SCREAMING_SNAKE_CASE_  )
			else:
						if x == 0:  # 0 raised to any number is 0
									return 0
						elif y == 0:
									return 1  # any number raised to 0 is 1
			raise AssertionError("This should never happen"  )


if __name__ == "__main__":  # Main function
							# Read two numbers from input and typecast them to int using map function.
							# Here x is the base and y is the power.
							lowercase_								=     """Enter the base and the power separated by a comma: """
							lowercase_				,    lowercase_								=     map(int, input(prompt).split(""","""))
							lowercase_				,    lowercase_								=     map(int, input(prompt).split(""","""))

							# We find the log of each number, using the function res(), which takes two
							# arguments.
							lowercase_								=     res(xa, ya)
							lowercase_								=     res(xa, ya)

							# We check for the largest number
							if resa > resa:
														print("""Largest number is""", xa, """^""", ya)
							elif resa > resa:
														print("""Largest number is""", xa, """^""", ya)
							else:
														print("""Both are equal""")




