






"""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 					_UpperCAmelCase       ( _lowerCamelCase   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Dict							    =   "ssube/stable-diffusion-x4-upscaler-onnx"




					def   A  (				self     :						Tuple    ,						A     :						Optional[int]=0   )  ->  Optional[Any]:
							lowercase_ : Optional[Any]							       =   floats_tensor((1, 3, 1_28, 1_28)    ,						rng=random.Random(a_   )   )
							lowercase_ : Optional[Any]							       =   torch.manual_seed(a_   )
							lowercase_ : List[Any]							       =   {
							    """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   A  (				self     :						int   )  ->  Union[str, Any]:
							lowercase_ : Tuple							       =   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,						provider='''CPUExecutionProvider'''   )
							pipe.set_progress_bar_config(disable=a_   )

							lowercase_ : List[Any]							       =   self.get_dummy_inputs()
							lowercase_ : Union[str, Any]							       =   pipe(**a_   ).images
							lowercase_ : Optional[Any]							       =   image[0, -3:, -3:, -1].flatten()

							# started as 128, should now be 512
							assert image.shape == (1, 5_12, 5_12, 3)
							lowercase_ : Dict							       =   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   A  (				self     :						int   )  ->  List[str]:
							lowercase_ : Optional[Any]							       =   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,						provider='''CPUExecutionProvider'''   )
							lowercase_ : Dict							       =   PNDMScheduler.from_config(pipe.scheduler.config    ,						skip_prk_steps=a_   )
							pipe.set_progress_bar_config(disable=a_   )

							lowercase_ : Tuple							       =   self.get_dummy_inputs()
							lowercase_ : List[str]							       =   pipe(**a_   ).images
							lowercase_ : Tuple							       =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							lowercase_ : Any							       =   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   A  (				self     :						Optional[Any]   )  ->  Optional[int]:
							lowercase_ : Union[str, Any]							       =   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,						provider='''CPUExecutionProvider'''   )
							lowercase_ : str							       =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=a_   )

							lowercase_ : Optional[Any]							       =   self.get_dummy_inputs()
							lowercase_ : Any							       =   pipe(**a_   ).images
							lowercase_ : Union[str, Any]							       =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							lowercase_ : Union[str, Any]							       =   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   A  (				self     :						str   )  ->  Union[str, Any]:
							lowercase_ : Any							       =   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,						provider='''CPUExecutionProvider'''   )
							lowercase_ : Optional[int]							       =   EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=a_   )

							lowercase_ : Tuple							       =   self.get_dummy_inputs()
							lowercase_ : Dict							       =   pipe(**a_   ).images
							lowercase_ : Optional[Any]							       =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							lowercase_ : Optional[Any]							       =   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   A  (				self     :						Optional[Any]   )  ->  Union[str, Any]:
							lowercase_ : Optional[int]							       =   OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,						provider='''CPUExecutionProvider'''   )
							lowercase_ : Optional[int]							       =   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=a_   )

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

							assert image.shape == (1, 5_12, 5_12, 3)
							lowercase_ : Tuple							       =   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							):




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




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




					def   A  (				self     :						Union[str, Any]   )  ->  Any:
							lowercase_ : str							       =   load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/img2img/sketch-mountains-input.jpg'''   )
							lowercase_ : Dict							       =   init_image.resize((1_28, 1_28)   )
							# using the PNDM scheduler by default
							lowercase_ : Optional[Any]							       =   OnnxStableDiffusionUpscalePipeline.from_pretrained(
							    '''ssube/stable-diffusion-x4-upscaler-onnx'''    ,						provider=self.gpu_provider    ,						sess_options=self.gpu_options    ,						)
							pipe.set_progress_bar_config(disable=a_   )

							lowercase_ : Optional[Any]							       =   """A fantasy landscape, trending on artstation"""

							lowercase_ : List[str]							       =   torch.manual_seed(0   )
							lowercase_ : Union[str, Any]							       =   pipe(
							    prompt=a_    ,						image=a_    ,						guidance_scale=7.5    ,						num_inference_steps=10    ,						generator=a_    ,						output_type='''np'''    ,						)
							lowercase_ : Optional[int]							       =   output.images
							lowercase_ : Tuple							       =   images[0, 2_55:2_58, 3_83:3_86, -1]

							assert images.shape == (1, 5_12, 5_12, 3)
							lowercase_ : Tuple							       =   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   A  (				self     :						int   )  ->  str:
							lowercase_ : Optional[int]							       =   load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/img2img/sketch-mountains-input.jpg'''   )
							lowercase_ : Dict							       =   init_image.resize((1_28, 1_28)   )
							lowercase_ : Union[str, Any]							       =   LMSDiscreteScheduler.from_pretrained(
							    '''ssube/stable-diffusion-x4-upscaler-onnx'''    ,						subfolder='''scheduler'''   )
							lowercase_ : Optional[Any]							       =   OnnxStableDiffusionUpscalePipeline.from_pretrained(
							    '''ssube/stable-diffusion-x4-upscaler-onnx'''    ,						scheduler=a_    ,						provider=self.gpu_provider    ,						sess_options=self.gpu_options    ,						)
							pipe.set_progress_bar_config(disable=a_   )

							lowercase_ : List[str]							       =   """A fantasy landscape, trending on artstation"""

							lowercase_ : Optional[Any]							       =   torch.manual_seed(0   )
							lowercase_ : Tuple							       =   pipe(
							    prompt=a_    ,						image=a_    ,						guidance_scale=7.5    ,						num_inference_steps=20    ,						generator=a_    ,						output_type='''np'''    ,						)
							lowercase_ : str							       =   output.images
							lowercase_ : Optional[Any]							       =   images[0, 2_55:2_58, 3_83:3_86, -1]

							assert images.shape == (1, 5_12, 5_12, 3)
							lowercase_ : Tuple							       =   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








'''simple docstring'''






from numpy import exp, pi, sqrt




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_ = 0.0       ,   lowerCAmelCase_ = 1.0		)->		int:





       '''simple docstring'''


       return 1 / sqrt(2 * pi * sigma**2		) * exp(-((x - mu) ** 2) / (2 * sigma**2)		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



