


def   _UpperCAmelCase       (     UpperCAmelCase      :			int      ):
      """simple docstring"""




      __lowerCamelCase     :      Any       						=						[1]

      __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      Optional[Any]       						=						0, 0, 0
      __lowerCamelCase     :      Tuple       						=						ugly_nums[ia] * 2
      __lowerCamelCase     :      Union[str, Any]       						=						ugly_nums[ia] * 3
      __lowerCamelCase     :      Tuple       						=						ugly_nums[ia] * 5

      for _ in range(1						,	UpperCAmelCase      ):
            __lowerCamelCase     :      str       						=						min(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
            ugly_nums.append(UpperCAmelCase      )
            if next_num == next_a:
                  ia += 1
                  __lowerCamelCase     :      Any       						=						ugly_nums[ia] * 2
            if next_num == next_a:
                  ia += 1
                  __lowerCamelCase     :      List[Any]       						=						ugly_nums[ia] * 3
            if next_num == next_a:
                  ia += 1
                  __lowerCamelCase     :      Optional[int]       						=						ugly_nums[ia] * 5
      return ugly_nums[-1]


if __name__ == "__main__":
    from doctest import testmod

    testmod(verbose=True)
    print(F'''{ugly_numbers(200) = }''')







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 							_UpperCamelCase (						A,unittest.TestCase						):

   '''simple docstring'''







   a_				:							Any    = "ssube/stable-diffusion-x4-upscaler-onnx"

   def   _snake_case    (  self  : Any       ,  _lowerCamelCase  : List[str]=0							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						floats_tensor((1, 3, 1_2_8, 1_2_8)       ,  rng=random.Random(_lowerCamelCase							)							)
         __lowerCamelCase     :      int       						=						torch.manual_seed(_lowerCamelCase							)
         __lowerCamelCase     :      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   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,  provider="""CPUExecutionProvider"""							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      Tuple       						=						self.get_dummy_inputs()
         __lowerCamelCase     :      Dict       						=						pipe(**_lowerCamelCase							).images
         __lowerCamelCase     :      Dict       						=						image[0, -3:, -3:, -1].flatten()

         # started as 128, should now be 512
         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      str       						=						np.array(
             [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]							)
         assert np.abs(image_slice - expected_slice							).max() < 1E-1

   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,  provider="""CPUExecutionProvider"""							)
         __lowerCamelCase     :      str       						=						PNDMScheduler.from_config(pipe.scheduler.config       ,  skip_prk_steps=_lowerCamelCase							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      Tuple       						=						self.get_dummy_inputs()
         __lowerCamelCase     :      Union[str, Any]       						=						pipe(**_lowerCamelCase							).images
         __lowerCamelCase     :      List[Any]       						=						image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      int       						=						np.array(
             [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]							)
         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,  provider="""CPUExecutionProvider"""							)
         __lowerCamelCase     :      int       						=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      str       						=						np.array(
             [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]							)

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

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,  provider="""CPUExecutionProvider"""							)
         __lowerCamelCase     :      Optional[Any]       						=						EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      Dict       						=						self.get_dummy_inputs()
         __lowerCamelCase     :      Dict       						=						pipe(**_lowerCamelCase							).images
         __lowerCamelCase     :      List[str]       						=						image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      List[str]       						=						np.array(
             [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]							)
         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1


   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



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

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      Optional[int]       						=						np.array(
             [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]							)

         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  : str							):






         '''simple docstring'''



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

   @property
   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						ort.SessionOptions()
         __lowerCamelCase     :      str       						=						False
         return options

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/img2img/sketch-mountains-input.jpg"""							)
         __lowerCamelCase     :      Optional[Any]       						=						init_image.resize((1_2_8, 1_2_8)							)
         # using the PNDM scheduler by default
         __lowerCamelCase     :      List[Any]       						=						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							)

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

         __lowerCamelCase     :      str       						=						torch.manual_seed(0							)
         __lowerCamelCase     :      Any       						=						pipe(
             prompt=_lowerCamelCase       ,  image=_lowerCamelCase       ,  guidance_scale=7.5       ,  num_inference_steps=1_0       ,  generator=_lowerCamelCase       ,  output_type="""np"""       ,  )
         __lowerCamelCase     :      List[str]       						=						output.images
         __lowerCamelCase     :      Any       						=						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      int       						=						np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]							)
         # 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  : Any							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/img2img/sketch-mountains-input.jpg"""							)
         __lowerCamelCase     :      Union[str, Any]       						=						init_image.resize((1_2_8, 1_2_8)							)
         __lowerCamelCase     :      str       						=						LMSDiscreteScheduler.from_pretrained(
             """ssube/stable-diffusion-x4-upscaler-onnx"""       ,  subfolder="""scheduler"""							)
         __lowerCamelCase     :      str       						=						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							)

         __lowerCamelCase     :      str       						=						"""A fantasy landscape, trending on artstation"""

         __lowerCamelCase     :      Tuple       						=						torch.manual_seed(0							)
         __lowerCamelCase     :      Dict       						=						pipe(
             prompt=_lowerCamelCase       ,  image=_lowerCamelCase       ,  guidance_scale=7.5       ,  num_inference_steps=2_0       ,  generator=_lowerCamelCase       ,  output_type="""np"""       ,  )
         __lowerCamelCase     :      int       						=						output.images
         __lowerCamelCase     :      List[Any]       						=						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      List[str]       						=						np.array(
             [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]							)
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



