





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  UpperCAmelCase__	( A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      "ssube/stable-diffusion-x4-upscaler-onnx"






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Tuple=0      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					floats_tensor((1, 3, 128, 128)			,     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    lowercase_	(							self		:	Tuple      )	->							List[str]:
       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, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]      )
       assert np.abs(image_slice - expected_slice      ).max() < 1e-1






  def    lowercase_	(							self		:	Dict      )	->							Tuple:
       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, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.6898892, 0.59240556, 0.52499527, 0.58866215, 0.52258235, 0.52572715, 0.62414473, 0.6174387, 0.6214964]      )
       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1






  def    lowercase_	(							self		:	Optional[Any]      )	->							Dict:
       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, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.7659278, 0.76437664, 0.75579107, 0.7691116, 0.77666986, 0.7727672, 0.7758664, 0.7812226, 0.76942515]      )

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






  def    lowercase_	(							self		:	List[Any]      )	->							Any:
       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, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]      )
       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1







  def    lowercase_	(							self		:	Dict      )	->							List[Any]:
       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, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.77424496, 0.773601, 0.7645288, 0.7769598, 0.7772739, 0.7738688, 0.78187233, 0.77879584, 0.767043]      )

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






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






  """simple docstring"""


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






  @property
  def    lowercase_	(							self		:	List[Any]      )	->							str:
       SCREAMING_SNAKE_CASE__					=					ort.SessionOptions()
       SCREAMING_SNAKE_CASE__					=					False
       return options






  def    lowercase_	(							self		:	Any      )	->							List[Any]:
       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((128, 128)      )
       # 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=10			,     generator=__lowerCamelCase			,     output_type='''np'''			,     )
       SCREAMING_SNAKE_CASE__					=					output.images
       SCREAMING_SNAKE_CASE__					=					images[0, 255:258, 383:386, -1]

       assert images.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					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		:	Optional[Any]      )	->							List[str]:
       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((128, 128)      )
       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=20			,     generator=__lowerCamelCase			,     output_type='''np'''			,     )
       SCREAMING_SNAKE_CASE__					=					output.images
       SCREAMING_SNAKE_CASE__					=					images[0, 255:258, 383:386, -1]

       assert images.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.50173753, 0.50223356, 0.502039, 0.50233036, 0.5023725, 0.5022601, 0.5018758, 0.50234085, 0.50241566]      )
       # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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





from collections.abc import Callable

import numpy as np
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					int(np.ceil((x_end - xa) / step_size       )       )
     SCREAMING_SNAKE_CASE__					=					np.zeros((n + 1,)       )
     SCREAMING_SNAKE_CASE__					=					ya
     SCREAMING_SNAKE_CASE__					=					xa

     for k in range(_A       ):
          SCREAMING_SNAKE_CASE__					=					y[k] + step_size * ode_func(_A   ,	y[k]       )
          SCREAMING_SNAKE_CASE__					=					y[k] + (
              (step_size / 2) * (ode_func(_A   ,	y[k]       ) + ode_func(x + step_size   ,	_A       ))
          )
          x += step_size

     return y


if __name__ == "__main__":
       import doctest

       doctest.testmod()


