
"""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 						SCREAMING_SNAKE_CASE_ (						__a     ,				unittest.TestCase ):




		"""simple docstring"""





		__lowercase							:  Union[str, Any]						   =		'''ssube/stable-diffusion-x4-upscaler-onnx'''




		def 	snake_case_     (     self							,  lowerCAmelCase__=0):
				__SCREAMING_SNAKE_CASE            = floats_tensor((1, 3, 1_2_8, 1_2_8)							,  rng=random.Random(lowerCAmelCase__))
				__SCREAMING_SNAKE_CASE            = torch.manual_seed(lowerCAmelCase__)
				__SCREAMING_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 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint							,  provider="""CPUExecutionProvider""")
				pipe.set_progress_bar_config(disable=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = self.get_dummy_inputs()
				__SCREAMING_SNAKE_CASE            = pipe(**lowerCAmelCase__).images
				__SCREAMING_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)
				__SCREAMING_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 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint							,  provider="""CPUExecutionProvider""")
				__SCREAMING_SNAKE_CASE            = PNDMScheduler.from_config(pipe.scheduler.config							,  skip_prk_steps=lowerCAmelCase__)
				pipe.set_progress_bar_config(disable=lowerCAmelCase__)

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

				assert image.shape == (1, 5_1_2, 5_1_2, 3)
				__SCREAMING_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 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint							,  provider="""CPUExecutionProvider""")
				__SCREAMING_SNAKE_CASE            = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
				pipe.set_progress_bar_config(disable=lowerCAmelCase__)

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

				assert image.shape == (1, 5_1_2, 5_1_2, 3)
				__SCREAMING_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 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint							,  provider="""CPUExecutionProvider""")
				__SCREAMING_SNAKE_CASE            = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
				pipe.set_progress_bar_config(disable=lowerCAmelCase__)

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

				assert image.shape == (1, 5_1_2, 5_1_2, 3)
				__SCREAMING_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 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint							,  provider="""CPUExecutionProvider""")
				__SCREAMING_SNAKE_CASE            = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
				pipe.set_progress_bar_config(disable=lowerCAmelCase__)

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

				assert image.shape == (1, 5_1_2, 5_1_2, 3)
				__SCREAMING_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 						SCREAMING_SNAKE_CASE_ (						unittest.TestCase ):




		"""simple docstring"""





		@property
		def 	snake_case_     (     self):
				return (
				    "CUDAExecutionProvider",
				    {
				        "gpu_mem_limit": "15000000000",  # 15GB
				        "arena_extend_strategy": "kSameAsRequested",
				    },
				)




		@property
		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = ort.SessionOptions()
				__SCREAMING_SNAKE_CASE            = False
				return options




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = load_image(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/img2img/sketch-mountains-input.jpg""")
				__SCREAMING_SNAKE_CASE            = init_image.resize((1_2_8, 1_2_8))
				# using the PNDM scheduler by default
				__SCREAMING_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=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = """A fantasy landscape, trending on artstation"""

				__SCREAMING_SNAKE_CASE            = torch.manual_seed(0)
				__SCREAMING_SNAKE_CASE            = pipe(
				    prompt=lowerCAmelCase__							,  image=lowerCAmelCase__							,  guidance_scale=7.5							,  num_inference_steps=1_0							,  generator=lowerCAmelCase__							,  output_type="""np"""							,  )
				__SCREAMING_SNAKE_CASE            = output.images
				__SCREAMING_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)
				__SCREAMING_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 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = load_image(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/img2img/sketch-mountains-input.jpg""")
				__SCREAMING_SNAKE_CASE            = init_image.resize((1_2_8, 1_2_8))
				__SCREAMING_SNAKE_CASE            = LMSDiscreteScheduler.from_pretrained(
				    """ssube/stable-diffusion-x4-upscaler-onnx"""							,  subfolder="""scheduler""")
				__SCREAMING_SNAKE_CASE            = 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__)

				__SCREAMING_SNAKE_CASE            = """A fantasy landscape, trending on artstation"""

				__SCREAMING_SNAKE_CASE            = torch.manual_seed(0)
				__SCREAMING_SNAKE_CASE            = pipe(
				    prompt=lowerCAmelCase__							,  image=lowerCAmelCase__							,  guidance_scale=7.5							,  num_inference_steps=2_0							,  generator=lowerCAmelCase__							,  output_type="""np"""							,  )
				__SCREAMING_SNAKE_CASE            = output.images
				__SCREAMING_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)
				__SCREAMING_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






"""simple docstring"""





from __future__ import annotations

from fractions import Fraction



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		return (
		    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
		)



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = 11
		__SCREAMING_SNAKE_CASE            = int("""1""" + """0""" * digit_len				)
		for num in range(UpperCamelCase_  ,				UpperCamelCase_				):
				while den <= 99:
						if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
								if is_digit_cancelling(UpperCamelCase_  ,				UpperCamelCase_				):
										solutions.append(f"{num}/{den}"				)
						den += 1
				num += 1
				__SCREAMING_SNAKE_CASE            = 10
		return solutions



def 						_lowerCAmelCase				(				UpperCamelCase_ = 2				):
		__SCREAMING_SNAKE_CASE            = 1.0
		for fraction in fraction_list(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = Fraction(UpperCamelCase_				)
				result *= frac.denominator / frac.numerator
		return int(UpperCamelCase_				)


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




