






"""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       __A		(   lowerCAmelCase__     ,unittest.TestCase			):
					# TODO: is there an appropriate internal test set?
					UpperCAmelCase__								=				"ssube/stable-diffusion-x4-upscaler-onnx"

					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      Dict=0					)			->							List[str]:
											__magic_name__:	str							=       floats_tensor((1, 3, 1_2_8, 1_2_8)     ,		rng=random.Random(__UpperCAmelCase					)					)
											__magic_name__:	str							=       torch.manual_seed(__UpperCAmelCase					)
											__magic_name__:	str							=       {
											    """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   lowerCamelCase__   (			self  :      int					)			->							Optional[int]:
											__magic_name__:	Dict							=       OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,		provider="""CPUExecutionProvider"""					)
											pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

											__magic_name__:	str							=       self.get_dummy_inputs()
											__magic_name__:	Tuple							=       pipe(**__UpperCAmelCase					).images
											__magic_name__:	int							=       image[0, -3:, -3:, -1].flatten()

											# started as 128, should now be 512
											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__magic_name__:	List[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 - expected_slice					).max() < 1E-1

					def   lowerCamelCase__   (			self  :      Dict					)			->							Any:
											__magic_name__:	Union[str, Any]							=       OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,		provider="""CPUExecutionProvider"""					)
											__magic_name__:	Optional[int]							=       PNDMScheduler.from_config(pipe.scheduler.config     ,		skip_prk_steps=__UpperCAmelCase					)
											pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

											__magic_name__:	List[str]							=       self.get_dummy_inputs()
											__magic_name__:	Any							=       pipe(**__UpperCAmelCase					).images
											__magic_name__:	Dict							=       image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__magic_name__:	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   lowerCamelCase__   (			self  :      List[Any]					)			->							Optional[Any]:
											__magic_name__:	List[Any]							=       OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,		provider="""CPUExecutionProvider"""					)
											__magic_name__:	int							=       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
											pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

											__magic_name__:	Union[str, Any]							=       self.get_dummy_inputs()
											__magic_name__:	str							=       pipe(**__UpperCAmelCase					).images
											__magic_name__:	int							=       image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__magic_name__:	List[str]							=       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   lowerCamelCase__   (			self  :      Optional[Any]					)			->							int:
											__magic_name__:	int							=       OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,		provider="""CPUExecutionProvider"""					)
											__magic_name__:	Union[str, Any]							=       EulerDiscreteScheduler.from_config(pipe.scheduler.config					)
											pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

											__magic_name__:	Tuple							=       self.get_dummy_inputs()
											__magic_name__:	Any							=       pipe(**__UpperCAmelCase					).images
											__magic_name__:	Optional[int]							=       image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__magic_name__:	List[str]							=       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   lowerCamelCase__   (			self  :      str					)			->							Tuple:
											__magic_name__:	Union[str, Any]							=       OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,		provider="""CPUExecutionProvider"""					)
											__magic_name__:	int							=       EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config					)
											pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

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

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__magic_name__:	int							=       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       __A		(   unittest.TestCase			):

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

					@property
					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							List[Any]:
											__magic_name__:	Union[str, Any]							=       ort.SessionOptions()
											__magic_name__:	Optional[int]							=       False
											return options

					def   lowerCamelCase__   (			self  :      List[str]					)			->							Optional[Any]:
											__magic_name__:	Optional[Any]							=       load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/img2img/sketch-mountains-input.jpg"""					)
											__magic_name__:	Dict							=       init_image.resize((1_2_8, 1_2_8)					)
											# using the PNDM scheduler by default
											__magic_name__:	Dict							=       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					)

											__magic_name__:	int							=       """A fantasy landscape, trending on artstation"""

											__magic_name__:	Tuple							=       torch.manual_seed(0					)
											__magic_name__:	Optional[Any]							=       pipe(
											    prompt=__UpperCAmelCase     ,		image=__UpperCAmelCase     ,		guidance_scale=7.5     ,		num_inference_steps=1_0     ,		generator=__UpperCAmelCase     ,		output_type="""np"""     ,		)
											__magic_name__:	Optional[int]							=       output.images
											__magic_name__:	str							=       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)
											__magic_name__:	List[str]							=       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   lowerCamelCase__   (			self  :      Any					)			->							Union[str, Any]:
											__magic_name__:	int							=       load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/img2img/sketch-mountains-input.jpg"""					)
											__magic_name__:	int							=       init_image.resize((1_2_8, 1_2_8)					)
											__magic_name__:	Union[str, Any]							=       LMSDiscreteScheduler.from_pretrained(
											    """ssube/stable-diffusion-x4-upscaler-onnx"""     ,		subfolder="""scheduler"""					)
											__magic_name__:	Optional[int]							=       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					)

											__magic_name__:	Tuple							=       """A fantasy landscape, trending on artstation"""

											__magic_name__:	int							=       torch.manual_seed(0					)
											__magic_name__:	List[str]							=       pipe(
											    prompt=__UpperCAmelCase     ,		image=__UpperCAmelCase     ,		guidance_scale=7.5     ,		num_inference_steps=2_0     ,		generator=__UpperCAmelCase     ,		output_type="""np"""     ,		)
											__magic_name__:	List[Any]							=       output.images
											__magic_name__:	int							=       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)
											__magic_name__:	Optional[Any]							=       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








def 		a__      (		_UpperCamelCase      :			int				):
   __lowerCamelCase    				=     int(_UpperCamelCase				)
   if decimal in (0, 1):  # Exit cases for the recursion
      return str(_UpperCamelCase				)
   __lowerCamelCase				,__lowerCamelCase    				=     divmod(_UpperCamelCase			,2				)
   return binary_recursive(_UpperCamelCase				) + str(_UpperCamelCase				)





def 		a__      (		_UpperCamelCase      :			str				):
   __lowerCamelCase    				=     str(_UpperCamelCase				).strip()
   if not number:
      raise ValueError('''No input value was provided'''				)
   __lowerCamelCase    				=     '''-''' if number.startswith('''-'''				) else ''''''
   __lowerCamelCase    				=     number.lstrip('''-'''				)
   if not number.isnumeric():
      raise ValueError('''Input value is not an integer'''				)
   return F"""{negative}0b{binary_recursive(int(_UpperCamelCase				)				)}"""


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

  testmod()






