


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_     (			UpperCamelCase      ,  unittest.TestCase):

	"""simple docstring"""





	snake_case__    :  List[Any]    =      "ssube/stable-diffusion-x4-upscaler-onnx"




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

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

						# started as 128, should now be 512
						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     Optional[Any]:
						__SCREAMING_SNAKE_CASE						       =    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint      ,      provider="CPUExecutionProvider"       )
						__SCREAMING_SNAKE_CASE						       =    PNDMScheduler.from_config(pipe.scheduler.config      ,      skip_prk_steps=UpperCAmelCase__       )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__       )

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

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			self      :    Dict       )	->     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=UpperCAmelCase__       )

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

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			self      :    List[str]       )	->     Optional[int]:
						__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=UpperCAmelCase__       )

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

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     Union[str, 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=UpperCAmelCase__       )

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

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__SCREAMING_SNAKE_CASE						       =    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 			UpperCamelCase_     (			unittest.TestCase):

	"""simple docstring"""





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




	@property
	def   UpperCAmelCase_				(			self      :    Any       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    ort.SessionOptions()
						__SCREAMING_SNAKE_CASE						       =    False
						return options




	def   UpperCAmelCase_				(			self      :    int       )	->     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((1_2_8, 1_2_8)       )
						# 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=UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    "A fantasy landscape, trending on artstation"

						__SCREAMING_SNAKE_CASE						       =    torch.manual_seed(0       )
						__SCREAMING_SNAKE_CASE						       =    pipe(
						    prompt=UpperCAmelCase__      ,      image=UpperCAmelCase__      ,      guidance_scale=7.5      ,      num_inference_steps=1_0      ,      generator=UpperCAmelCase__      ,      output_type="np"      ,      )
						__SCREAMING_SNAKE_CASE						       =    output.images
						__SCREAMING_SNAKE_CASE						       =    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)
						__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     Optional[int]:
						__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((1_2_8, 1_2_8)       )
						__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=UpperCAmelCase__      ,      provider=self.gpu_provider      ,      sess_options=self.gpu_options      ,      )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    "A fantasy landscape, trending on artstation"

						__SCREAMING_SNAKE_CASE						       =    torch.manual_seed(0       )
						__SCREAMING_SNAKE_CASE						       =    pipe(
						    prompt=UpperCAmelCase__      ,      image=UpperCAmelCase__      ,      guidance_scale=7.5      ,      num_inference_steps=2_0      ,      generator=UpperCAmelCase__      ,      output_type="np"      ,      )
						__SCREAMING_SNAKE_CASE						       =    output.images
						__SCREAMING_SNAKE_CASE						       =    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)
						__SCREAMING_SNAKE_CASE						       =    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




"""simple docstring"""







def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					if digit_amount > 0:
										return round(number - int(lowerCAmelCase_       )  ,						lowerCAmelCase_       )
					return number - int(lowerCAmelCase_       )


if __name__ == "__main__":
		print(decimal_isolate(1.53, 0))
		print(decimal_isolate(35.3_45, 1))
		print(decimal_isolate(35.3_45, 2))
		print(decimal_isolate(35.3_45, 3))
		print(decimal_isolate(-14.7_89, 3))
		print(decimal_isolate(0, 2))
		print(decimal_isolate(-14.1_23, 1))
		print(decimal_isolate(-14.1_23, 2))
		print(decimal_isolate(-14.1_23, 3))
