
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__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:List[Any]         =	'ssube/stable-diffusion-x4-upscaler-onnx'
	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=0       )       ->     int:
					lowercase_       =					floats_tensor((1, 3, 1_2_8, 1_2_8)       ,							rng=random.Random(SCREAMING_SNAKE_CASE_       )       )
					lowercase_       =					torch.manual_seed(SCREAMING_SNAKE_CASE_       )
					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 						_lowercase  (       self  :			Tuple       )       ->     Optional[Any]:
					lowercase_       =					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,							provider='''CPUExecutionProvider'''       )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

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

					# started as 128, should now be 512
					assert image.shape == (1, 5_1_2, 5_1_2, 3)
					lowercase_       =					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 						_lowercase  (       self  :			int       )       ->     Optional[Any]:
					lowercase_       =					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,							provider='''CPUExecutionProvider'''       )
					lowercase_       =					PNDMScheduler.from_config(pipe.scheduler.config       ,							skip_prk_steps=SCREAMING_SNAKE_CASE_       )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

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

					assert image.shape == (1, 5_1_2, 5_1_2, 3)
					lowercase_       =					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 						_lowercase  (       self  :			Optional[Any]       )       ->     int:
					lowercase_       =					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,							provider='''CPUExecutionProvider'''       )
					lowercase_       =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

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

					assert image.shape == (1, 5_1_2, 5_1_2, 3)
					lowercase_       =					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 						_lowercase  (       self  :			List[str]       )       ->     Union[str, Any]:
					lowercase_       =					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,							provider='''CPUExecutionProvider'''       )
					lowercase_       =					EulerDiscreteScheduler.from_config(pipe.scheduler.config       )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

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

					assert image.shape == (1, 5_1_2, 5_1_2, 3)
					lowercase_       =					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 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[int]:
					lowercase_       =					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,							provider='''CPUExecutionProvider'''       )
					lowercase_       =					EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config       )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

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

					assert image.shape == (1, 5_1_2, 5_1_2, 3)
					lowercase_       =					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  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	@property
	def 						_lowercase  (       self  :			str       )       ->     List[str]:
					return (
					    "CUDAExecutionProvider",
					    {
					        "gpu_mem_limit": "15000000000",  # 15GB
					        "arena_extend_strategy": "kSameAsRequested",
					    },
					)
	@property
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     List[Any]:
					lowercase_       =					ort.SessionOptions()
					lowercase_       =					False
					return options
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     Tuple:
					lowercase_       =					load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/img2img/sketch-mountains-input.jpg'''       )
					lowercase_       =					init_image.resize((1_2_8, 1_2_8)       )
					# 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=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					'''A fantasy landscape, trending on artstation'''

					lowercase_       =					torch.manual_seed(0       )
					lowercase_       =					pipe(
					    prompt=SCREAMING_SNAKE_CASE_       ,							image=SCREAMING_SNAKE_CASE_       ,							guidance_scale=7.5       ,							num_inference_steps=1_0       ,							generator=SCREAMING_SNAKE_CASE_       ,							output_type='''np'''       ,							)
					lowercase_       =					output.images
					lowercase_       =					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)
					lowercase_       =					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 						_lowercase  (       self  :			Dict       )       ->     Optional[Any]:
					lowercase_       =					load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/img2img/sketch-mountains-input.jpg'''       )
					lowercase_       =					init_image.resize((1_2_8, 1_2_8)       )
					lowercase_       =					LMSDiscreteScheduler.from_pretrained(
					    '''ssube/stable-diffusion-x4-upscaler-onnx'''       ,							subfolder='''scheduler'''       )
					lowercase_       =					OnnxStableDiffusionUpscalePipeline.from_pretrained(
					    '''ssube/stable-diffusion-x4-upscaler-onnx'''       ,							scheduler=SCREAMING_SNAKE_CASE_       ,							provider=self.gpu_provider       ,							sess_options=self.gpu_options       ,							)
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					'''A fantasy landscape, trending on artstation'''

					lowercase_       =					torch.manual_seed(0       )
					lowercase_       =					pipe(
					    prompt=SCREAMING_SNAKE_CASE_       ,							image=SCREAMING_SNAKE_CASE_       ,							guidance_scale=7.5       ,							num_inference_steps=2_0       ,							generator=SCREAMING_SNAKE_CASE_       ,							output_type='''np'''       ,							)
					lowercase_       =					output.images
					lowercase_       =					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)
					lowercase_       =					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    a__       (						__UpperCamelCase = 1_0_0_0 ):
      SCREAMING_SNAKE_CASE_      =     -1
      SCREAMING_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
            SCREAMING_SNAKE_CASE_      =     (n * n - 2 * a * n) // (2 * n - 2 * a)
            SCREAMING_SNAKE_CASE_      =     n - a - b
            if c * c == (a * a + b * b):
                  SCREAMING_SNAKE_CASE_      =     a * b * c
                  if candidate >= product:
                        SCREAMING_SNAKE_CASE_      =     candidate
      return product


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


