"""simple docstring"""




def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							if not all(char in '01' for char in bin_string ):
														raise ValueError('Non-binary value was passed to the function' )
							if not bin_string:
														raise ValueError('Empty string was passed to the function' )
							A__		   =							''
							while len(UpperCamelCase_ ) % 3 != 0:
														A__		   =							'0' + bin_string
							A__		   =							[
							    bin_string[index : index + 3]
							    for index in range(len(UpperCamelCase_ ) )
							    if index % 3 == 0
							]
							for bin_group in bin_string_in_3_list:
														A__		   =							0
														for index, val in enumerate(UpperCamelCase_ ):
																					oct_val += int(2 ** (2 - index) * int(UpperCamelCase_ ) )
														oct_string += str(UpperCamelCase_ )
							return oct_string


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

				testmod()








"""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 					_lowerCAmelCase  (	lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	"ssube/stable-diffusion-x4-upscaler-onnx"


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			List[str]=0	):
    __lowercase								=						floats_tensor((1, 3, 1_2_8, 1_2_8),					rng=random.Random(UpperCAmelCase__	)	)
    __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    __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							:			Any	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						self.get_dummy_inputs()
    __lowercase								=						pipe(**UpperCAmelCase__	).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_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 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						PNDMScheduler.from_config(pipe.scheduler.config,					skip_prk_steps=UpperCAmelCase__	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						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 					_lowercase (			self							:			int	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						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 					_lowercase (			self							:			str	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						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 					_lowercase (			self							:			Any	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						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 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







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


 @property
 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						ort.SessionOptions()
    __lowercase								=						False
    return options


 def 					_lowercase (			self							:			Dict	):
    __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=UpperCAmelCase__	)

    __lowercase								=						"A fantasy landscape, trending on artstation"

    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						pipe(
        prompt=UpperCAmelCase__,					image=UpperCAmelCase__,					guidance_scale=7.5,					num_inference_steps=1_0,					generator=UpperCAmelCase__,					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([0.4_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 					_lowercase (			self							:			str	):
    __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=UpperCAmelCase__,					provider=self.gpu_provider,					sess_options=self.gpu_options,					)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						"A fantasy landscape, trending on artstation"

    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						pipe(
        prompt=UpperCAmelCase__,					image=UpperCAmelCase__,					guidance_scale=7.5,					num_inference_steps=2_0,					generator=UpperCAmelCase__,					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_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




