
'''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_	(						_SCREAMING_SNAKE_CASE			,				unittest.TestCase					):






						'''simple docstring'''






						_lowercase						: Optional[int]										=  '''ssube/stable-diffusion-x4-upscaler-onnx'''




						def    _lowercase      (   self       ,  _lowercase=0 ):

												"""simple docstring"""



												_lowerCAmelCase    		=					floats_tensor((1, 3, 128, 128)       ,  rng=random.Random(_lowercase ) )
												_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												_lowerCAmelCase    		=					{
												    """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 ):

												"""simple docstring"""



												_lowerCAmelCase    		=					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,  provider="""CPUExecutionProvider""" )
												pipe.set_progress_bar_config(disable=_lowercase )

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

												# started as 128, should now be 512
												assert image.shape == (1, 512, 512, 3)
												_lowerCAmelCase    		=					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 ):

												"""simple docstring"""



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

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

												assert image.shape == (1, 512, 512, 3)
												_lowerCAmelCase    		=					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 ):

												"""simple docstring"""



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

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

												assert image.shape == (1, 512, 512, 3)
												_lowerCAmelCase    		=					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 ):

												"""simple docstring"""



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

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

												assert image.shape == (1, 512, 512, 3)
												_lowerCAmelCase    		=					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 ):

												"""simple docstring"""



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

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

												assert image.shape == (1, 512, 512, 3)
												_lowerCAmelCase    		=					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     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



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




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/img2img/sketch-mountains-input.jpg""" )
												_lowerCAmelCase    		=					init_image.resize((128, 128) )
												# using the PNDM scheduler by default
												_lowerCAmelCase    		=					OnnxStableDiffusionUpscalePipeline.from_pretrained(
												    """ssube/stable-diffusion-x4-upscaler-onnx"""       ,  provider=self.gpu_provider       ,  sess_options=self.gpu_options       ,  )
												pipe.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					"""A fantasy landscape, trending on artstation"""

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

												assert images.shape == (1, 512, 512, 3)
												_lowerCAmelCase    		=					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 ):

												"""simple docstring"""



												_lowerCAmelCase    		=					load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/img2img/sketch-mountains-input.jpg""" )
												_lowerCAmelCase    		=					init_image.resize((128, 128) )
												_lowerCAmelCase    		=					LMSDiscreteScheduler.from_pretrained(
												    """ssube/stable-diffusion-x4-upscaler-onnx"""       ,  subfolder="""scheduler""" )
												_lowerCAmelCase    		=					OnnxStableDiffusionUpscalePipeline.from_pretrained(
												    """ssube/stable-diffusion-x4-upscaler-onnx"""       ,  scheduler=_lowercase       ,  provider=self.gpu_provider       ,  sess_options=self.gpu_options       ,  )
												pipe.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					"""A fantasy landscape, trending on artstation"""

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

												assert images.shape == (1, 512, 512, 3)
												_lowerCAmelCase    		=					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


'''simple docstring'''



_lowercase          =						"""
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

_lowercase          =						[{"""type""": """code""", """content""": INSTALL_CONTENT}]
_lowercase          =						{
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}
