
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   lowerCamelCase	(						A_							,				unittest.TestCase						):
     # TODO: is there an appropriate internal test set?
     UpperCAmelCase__       :							Dict     =	"ssube/stable-diffusion-x4-upscaler-onnx"





     def 	UpperCAmelCase(self   :					str       ,					_A   :					Union[str, Any]=0     )		->		List[Any]:
         snake_case				  =      floats_tensor((1, 3, 1_2_8, 1_2_8)       ,					rng=random.Random(_A     )     )
         snake_case				  =      torch.manual_seed(_A     )
         snake_case				  =      {
             "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     )		->		Any:
         snake_case				  =      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,					provider="CPUExecutionProvider"     )
         pipe.set_progress_bar_config(disable=_A     )

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

         # started as 128, should now be 512
         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         snake_case				  =      np.array(
             [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23]     )
         assert np.abs(image_slice - expected_slice     ).max() < 1E-1





     def 	UpperCAmelCase(self   :					Any     )		->		Any:
         snake_case				  =      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,					provider="CPUExecutionProvider"     )
         snake_case				  =      PNDMScheduler.from_config(pipe.scheduler.config       ,					skip_prk_steps=_A     )
         pipe.set_progress_bar_config(disable=_A     )

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         snake_case				  =      np.array(
             [0.6_89_88_92, 0.59_24_05_56, 0.52_49_95_27, 0.58_86_62_15, 0.52_25_82_35, 0.52_57_27_15, 0.62_41_44_73, 0.6_17_43_87, 0.6_21_49_64]     )
         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-1





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Dict:
         snake_case				  =      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,					provider="CPUExecutionProvider"     )
         snake_case				  =      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
         pipe.set_progress_bar_config(disable=_A     )

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         snake_case				  =      np.array(
             [0.7_65_92_78, 0.76_43_76_64, 0.75_57_91_07, 0.7_69_11_16, 0.77_66_69_86, 0.7_72_76_72, 0.7_75_86_64, 0.7_81_22_26, 0.76_94_25_15]     )

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





     def 	UpperCAmelCase(self   :					List[Any]     )		->		Union[str, Any]:
         snake_case				  =      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,					provider="CPUExecutionProvider"     )
         snake_case				  =      EulerDiscreteScheduler.from_config(pipe.scheduler.config     )
         pipe.set_progress_bar_config(disable=_A     )

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         snake_case				  =      np.array(
             [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23]     )
         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-1





     def 	UpperCAmelCase(self   :					Dict     )		->		Optional[Any]:
         snake_case				  =      OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,					provider="CPUExecutionProvider"     )
         snake_case				  =      EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config     )
         pipe.set_progress_bar_config(disable=_A     )

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         snake_case				  =      np.array(
             [0.77_42_44_96, 0.77_36_01, 0.7_64_52_88, 0.7_76_95_98, 0.7_77_27_39, 0.7_73_86_88, 0.78_18_72_33, 0.77_87_95_84, 0.76_70_43]     )

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







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





     @property
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		int:
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )





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





     def 	UpperCAmelCase(self   :					Any     )		->		Optional[int]:
         snake_case				  =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/img2img/sketch-mountains-input.jpg"     )
         snake_case				  =      init_image.resize((1_2_8, 1_2_8)     )
         # using the PNDM scheduler by default
         snake_case				  =      OnnxStableDiffusionUpscalePipeline.from_pretrained(
             "ssube/stable-diffusion-x4-upscaler-onnx"       ,					provider=self.gpu_provider       ,					sess_options=self.gpu_options       ,					)
         pipe.set_progress_bar_config(disable=_A     )

         snake_case				  =      "A fantasy landscape, trending on artstation"

         snake_case				  =      torch.manual_seed(0     )
         snake_case				  =      pipe(
             prompt=_A       ,					image=_A       ,					guidance_scale=7.5       ,					num_inference_steps=1_0       ,					generator=_A       ,					output_type="np"       ,					)
         snake_case				  =      output.images
         snake_case				  =      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)
         snake_case				  =      np.array([0.48_83, 0.49_47, 0.49_80, 0.49_75, 0.49_82, 0.49_80, 0.50_00, 0.50_06, 0.49_72]     )
         # 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   :					Optional[Any]     )		->		Any:
         snake_case				  =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/img2img/sketch-mountains-input.jpg"     )
         snake_case				  =      init_image.resize((1_2_8, 1_2_8)     )
         snake_case				  =      LMSDiscreteScheduler.from_pretrained(
             "ssube/stable-diffusion-x4-upscaler-onnx"       ,					subfolder="scheduler"     )
         snake_case				  =      OnnxStableDiffusionUpscalePipeline.from_pretrained(
             "ssube/stable-diffusion-x4-upscaler-onnx"       ,					scheduler=_A       ,					provider=self.gpu_provider       ,					sess_options=self.gpu_options       ,					)
         pipe.set_progress_bar_config(disable=_A     )

         snake_case				  =      "A fantasy landscape, trending on artstation"

         snake_case				  =      torch.manual_seed(0     )
         snake_case				  =      pipe(
             prompt=_A       ,					image=_A       ,					guidance_scale=7.5       ,					num_inference_steps=2_0       ,					generator=_A       ,					output_type="np"       ,					)
         snake_case				  =      output.images
         snake_case				  =      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)
         snake_case				  =      np.array(
             [0.50_17_37_53, 0.50_22_33_56, 0.50_20_39, 0.50_23_30_36, 0.5_02_37_25, 0.5_02_26_01, 0.5_01_87_58, 0.50_23_40_85, 0.50_24_15_66]     )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



def 		lowercase_    (     A__ = 1000	)							->							int:
    """simple docstring"""

    snake_case				  =      -1
    snake_case				  =      0
    for a in range(1				,	n // 3	):
        # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
        snake_case				  =      (n * n - 2 * a * n) // (2 * n - 2 * a)
        snake_case				  =      n - a - b
        if c * c == (a * a + b * b):
            snake_case				  =      a * b * c
            if candidate >= product:
                snake_case				  =      candidate
    return product


if __name__ == "__main__":
   print(f"{solution() = }")

