



"""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  lowercase__  (       SCREAMING_SNAKE_CASE							,   unittest.TestCase ):







       '''simple docstring'''


       UpperCamelCase		=	'''ssube/stable-diffusion-x4-upscaler-onnx'''



       def 	lowercase__							(				self     :    str						,							_UpperCAmelCase     :    int=0       )					->		int:


             '''simple docstring'''




             UpperCAmelCase_     					=				floats_tensor((1, 3, 128, 128)						,							rng=random.Random(_UpperCAmelCase       )       )
             UpperCAmelCase_     					=				torch.manual_seed(_UpperCAmelCase       )
             UpperCAmelCase_     					=				{
                 "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     :    List[str]       )					->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,							provider="CPUExecutionProvider"       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

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

             # started as 128, should now be 512
             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_     					=				np.array(
                 [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223]       )
             assert np.abs(image_slice - expected_slice       ).max() < 1e-1



       def 	lowercase__							(				self     :    Optional[int]       )					->		int:


             '''simple docstring'''




             UpperCAmelCase_     					=				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,							provider="CPUExecutionProvider"       )
             UpperCAmelCase_     					=				PNDMScheduler.from_config(pipe.scheduler.config						,							skip_prk_steps=_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

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

             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_     					=				np.array(
                 [0.689_8892, 0.5924_0556, 0.5249_9527, 0.5886_6215, 0.5225_8235, 0.5257_2715, 0.6241_4473, 0.617_4387, 0.621_4964]       )
             assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-1



       def 	lowercase__							(				self     :    Any       )					->		Dict:


             '''simple docstring'''




             UpperCAmelCase_     					=				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,							provider="CPUExecutionProvider"       )
             UpperCAmelCase_     					=				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

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

             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_     					=				np.array(
                 [0.765_9278, 0.7643_7664, 0.7557_9107, 0.769_1116, 0.7766_6986, 0.772_7672, 0.775_8664, 0.781_2226, 0.7694_2515]       )

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



       def 	lowercase__							(				self     :    int       )					->		Dict:


             '''simple docstring'''




             UpperCAmelCase_     					=				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,							provider="CPUExecutionProvider"       )
             UpperCAmelCase_     					=				EulerDiscreteScheduler.from_config(pipe.scheduler.config       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

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

             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_     					=				np.array(
                 [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223]       )
             assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-1







       def 	lowercase__							(				self     :    Dict       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_     					=				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,							provider="CPUExecutionProvider"       )
             UpperCAmelCase_     					=				EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

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

             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_     					=				np.array(
                 [0.7742_4496, 0.77_3601, 0.764_5288, 0.776_9598, 0.777_2739, 0.773_8688, 0.7818_7233, 0.7787_9584, 0.76_7043]       )

             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     :    List[str]       )					->		Dict:


             '''simple docstring'''




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



       @property
       def 	lowercase__							(				self     :    Union[str, Any]       )					->		Dict:


             '''simple docstring'''




             UpperCAmelCase_     					=				ort.SessionOptions()
             UpperCAmelCase_     					=				False
             return options



       def 	lowercase__							(				self     :    Optional[Any]       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_     					=				load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/img2img/sketch-mountains-input.jpg"       )
             UpperCAmelCase_     					=				init_image.resize((128, 128)       )
             # using the PNDM scheduler by default
             UpperCAmelCase_     					=				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       )

             UpperCAmelCase_     					=				"A fantasy landscape, trending on artstation"

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(
                 prompt=_UpperCAmelCase						,							image=_UpperCAmelCase						,							guidance_scale=7.5						,							num_inference_steps=10						,							generator=_UpperCAmelCase						,							output_type="np"						,							)
             UpperCAmelCase_     					=				output.images
             UpperCAmelCase_     					=				images[0, 255:258, 383:386, -1]

             assert images.shape == (1, 512, 512, 3)
             UpperCAmelCase_     					=				np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972]       )
             # 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     :    Any       )					->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/img2img/sketch-mountains-input.jpg"       )
             UpperCAmelCase_     					=				init_image.resize((128, 128)       )
             UpperCAmelCase_     					=				LMSDiscreteScheduler.from_pretrained(
                 "ssube/stable-diffusion-x4-upscaler-onnx"						,							subfolder="scheduler"       )
             UpperCAmelCase_     					=				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       )

             UpperCAmelCase_     					=				"A fantasy landscape, trending on artstation"

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(
                 prompt=_UpperCAmelCase						,							image=_UpperCAmelCase						,							guidance_scale=7.5						,							num_inference_steps=20						,							generator=_UpperCAmelCase						,							output_type="np"						,							)
             UpperCAmelCase_     					=				output.images
             UpperCAmelCase_     					=				images[0, 255:258, 383:386, -1]

             assert images.shape == (1, 512, 512, 3)
             UpperCAmelCase_     					=				np.array(
                 [0.5017_3753, 0.5022_3356, 0.50_2039, 0.5023_3036, 0.502_3725, 0.502_2601, 0.501_8758, 0.5023_4085, 0.5024_1566]       )
             # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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











from __future__ import annotations

from cmath import sqrt

def   __snake_case    (       _lowerCAmelCase			:			int					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			int						)			->    tuple[complex, complex]:

   if a == 0:
      raise ValueError("Coefficient 'a' must not be zero."						)
   A_       :					Tuple        =      b * b - 4 * a * c

   A_       :					Any        =      (-b + sqrt(_lowerCAmelCase						)) / (2 * a)
   A_       :					Union[str, Any]        =      (-b - sqrt(_lowerCAmelCase						)) / (2 * a)

   return (
       root_a.real if not root_a.imag else root_a,
       root_a.real if not root_a.imag else root_a,
   )

def   __snake_case    (       )			->    List[Any]:
   A_   ,    A_       :					Optional[Any]        =      quadratic_roots(a=5					,					b=6					,					c=1						)
   print(f"The solutions are: {solutiona} and {solutiona}"						)


if __name__ == "__main__":
 main()
