


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

     """simple docstring"""






     lowerCamelCase__       						=				'ssube/stable-diffusion-x4-upscaler-onnx'


     def lowerCAmelCase__  (   self			,     lowerCamelCase=0		):


          '''simple docstring'''





          __A	:       List[Any]      =							floats_tensor((1, 3, 128, 128)			,     rng=random.Random(lowerCamelCase		)		)
          __A	:       Tuple      =							torch.manual_seed(lowerCamelCase		)
          __A	:       Union[str, Any]      =							{
              "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 lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider"		)
          pipe.set_progress_bar_config(disable=lowerCamelCase		)

          __A	:       Optional[int]      =							self.get_dummy_inputs()
          __A	:       Optional[int]      =							pipe(**lowerCamelCase		).images
          __A	:       int      =							image[0, -3:, -3:, -1].flatten()

          # started as 128, should now be 512
          assert image.shape == (1, 512, 512, 3)
          __A	:       List[Any]      =							np.array(
              [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223]		)
          assert np.abs(image_slice - expected_slice		).max() < 1E-1


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider"		)
          __A	:       Any      =							PNDMScheduler.from_config(pipe.scheduler.config			,     skip_prk_steps=lowerCamelCase		)
          pipe.set_progress_bar_config(disable=lowerCamelCase		)

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

          assert image.shape == (1, 512, 512, 3)
          __A	:       Dict      =							np.array(
              [0.689_8892, 0.5924_0556, 0.5249_9527, 0.5886_6215, 0.5225_8235, 0.5257_2715, 0.6241_4473, 0.617_4387, 0.621_4964]		)
          assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Tuple      =							OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider"		)
          __A	:       str      =							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
          pipe.set_progress_bar_config(disable=lowerCamelCase		)

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

          assert image.shape == (1, 512, 512, 3)
          __A	:       Union[str, Any]      =							np.array(
              [0.765_9278, 0.7643_7664, 0.7557_9107, 0.769_1116, 0.7766_6986, 0.772_7672, 0.775_8664, 0.781_2226, 0.7694_2515]		)

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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       str      =							OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider"		)
          __A	:       Dict      =							EulerDiscreteScheduler.from_config(pipe.scheduler.config		)
          pipe.set_progress_bar_config(disable=lowerCamelCase		)

          __A	:       Optional[Any]      =							self.get_dummy_inputs()
          __A	:       int      =							pipe(**lowerCamelCase		).images
          __A	:       Optional[Any]      =							image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          __A	:       Dict      =							np.array(
              [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223]		)
          assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       str      =							OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider"		)
          __A	:       Union[str, Any]      =							EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config		)
          pipe.set_progress_bar_config(disable=lowerCamelCase		)

          __A	:       Any      =							self.get_dummy_inputs()
          __A	:       List[str]      =							pipe(**lowerCamelCase		).images
          __A	:       Optional[Any]      =							image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          __A	:       Union[str, Any]      =							np.array(
              [0.7742_4496, 0.77_3601, 0.764_5288, 0.776_9598, 0.777_2739, 0.773_8688, 0.7818_7233, 0.7787_9584, 0.76_7043]		)

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







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

     """simple docstring"""






     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Dict      =							ort.SessionOptions()
          __A	:       Optional[int]      =							False
          return options


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[int]      =							load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/img2img/sketch-mountains-input.jpg"		)
          __A	:       List[Any]      =							init_image.resize((128, 128)		)
          # using the PNDM scheduler by default
          __A	:       str      =							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		)

          __A	:       List[Any]      =							"A fantasy landscape, trending on artstation"

          __A	:       Tuple      =							torch.manual_seed(0		)
          __A	:       Optional[Any]      =							pipe(
              prompt=lowerCamelCase			,     image=lowerCamelCase			,     guidance_scale=7.5			,     num_inference_steps=10			,     generator=lowerCamelCase			,     output_type="np"			,     )
          __A	:       List[str]      =							output.images
          __A	:       Optional[Any]      =							images[0, 255:258, 383:386, -1]

          assert images.shape == (1, 512, 512, 3)
          __A	:       Any      =							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 lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       str      =							load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/img2img/sketch-mountains-input.jpg"		)
          __A	:       Dict      =							init_image.resize((128, 128)		)
          __A	:       Tuple      =							LMSDiscreteScheduler.from_pretrained(
              "ssube/stable-diffusion-x4-upscaler-onnx"			,     subfolder="scheduler"		)
          __A	:       Optional[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		)

          __A	:       Optional[Any]      =							"A fantasy landscape, trending on artstation"

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

          assert images.shape == (1, 512, 512, 3)
          __A	:       List[Any]      =							np.array(
              [0.5017_3753, 0.5022_3356, 0.50_2039, 0.5023_3036, 0.502_3725, 0.502_2601, 0.501_8758, 0.5023_4085, 0.5024_1566]		)
          # 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'''


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       List[str]      =							1
     __A	:       Union[str, Any]      =							2
     while i * i <= n:
          __A	:       List[Any]      =							0
          while n % i == 0:
               n //= i
               multiplicity += 1
          n_divisors *= multiplicity + 1
          i += 1
     if n > 1:
          n_divisors *= 2
     return n_divisors


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Optional[Any]      =							1
     __A	:       Dict      =							1

     while True:
          i += 1
          t_num += i

          if count_divisors(SCREAMING_SNAKE_CASE							) > 500:
               break

     return t_num


if __name__ == "__main__":
      print(solution())

