






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




			def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       Optional[Any]=0  ):
										lowerCamelCase__      :			Optional[Any] 						= floats_tensor((1, 3, 128, 128)    ,							rng=random.Random(UpperCamelCase__  )  )
										lowerCamelCase__      :			Dict 						= torch.manual_seed(UpperCamelCase__  )
										lowerCamelCase__      :			Optional[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 	lowerCamelCase_       (							self:       Tuple  ):
										lowerCamelCase__      :			int 						= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,							provider="""CPUExecutionProvider"""  )
										pipe.set_progress_bar_config(disable=UpperCamelCase__  )

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

										# started as 128, should now be 512
										assert image.shape == (1, 512, 512, 3)
										lowerCamelCase__      :			Any 						= 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 	lowerCamelCase_       (							self:       Union[str, Any]  ):
										lowerCamelCase__      :			Union[str, Any] 						= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,							provider="""CPUExecutionProvider"""  )
										lowerCamelCase__      :			Tuple 						= PNDMScheduler.from_config(pipe.scheduler.config    ,							skip_prk_steps=UpperCamelCase__  )
										pipe.set_progress_bar_config(disable=UpperCamelCase__  )

										lowerCamelCase__      :			int 						= self.get_dummy_inputs()
										lowerCamelCase__      :			int 						= pipe(**UpperCamelCase__  ).images
										lowerCamelCase__      :			List[str] 						= image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										lowerCamelCase__      :			Union[str, Any] 						= 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 	lowerCamelCase_       (							self:       Dict  ):
										lowerCamelCase__      :			Optional[Any] 						= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,							provider="""CPUExecutionProvider"""  )
										lowerCamelCase__      :			str 						= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
										pipe.set_progress_bar_config(disable=UpperCamelCase__  )

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

										assert image.shape == (1, 512, 512, 3)
										lowerCamelCase__      :			List[Any] 						= 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 	lowerCamelCase_       (							self:       List[str]  ):
										lowerCamelCase__      :			Dict 						= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,							provider="""CPUExecutionProvider"""  )
										lowerCamelCase__      :			Tuple 						= EulerDiscreteScheduler.from_config(pipe.scheduler.config  )
										pipe.set_progress_bar_config(disable=UpperCamelCase__  )

										lowerCamelCase__      :			Optional[int] 						= self.get_dummy_inputs()
										lowerCamelCase__      :			int 						= pipe(**UpperCamelCase__  ).images
										lowerCamelCase__      :			List[Any] 						= image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										lowerCamelCase__      :			Dict 						= 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 	lowerCamelCase_       (							self:       Union[str, Any]  ):
										lowerCamelCase__      :			Dict 						= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,							provider="""CPUExecutionProvider"""  )
										lowerCamelCase__      :			int 						= EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config  )
										pipe.set_progress_bar_config(disable=UpperCamelCase__  )

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

										assert image.shape == (1, 512, 512, 3)
										lowerCamelCase__      :			int 						= 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 _lowercase			(       unittest.TestCase	):




			@property
			def 	lowerCamelCase_       (							self:       Optional[Any]  ):
										return (
										    "CUDAExecutionProvider",
										    {
										        "gpu_mem_limit": "15000000000",  # 15GB
										        "arena_extend_strategy": "kSameAsRequested",
										    },
										)




			@property
			def 	lowerCamelCase_       (							self:       Any  ):
										lowerCamelCase__      :			int 						= ort.SessionOptions()
										lowerCamelCase__      :			List[Any] 						= False
										return options




			def 	lowerCamelCase_       (							self:       Any  ):
										lowerCamelCase__      :			Union[str, Any] 						= load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/img2img/sketch-mountains-input.jpg"""  )
										lowerCamelCase__      :			Union[str, Any] 						= init_image.resize((128, 128)  )
										# using the PNDM scheduler by default
										lowerCamelCase__      :			Optional[int] 						= 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__  )

										lowerCamelCase__      :			Optional[int] 						= """A fantasy landscape, trending on artstation"""

										lowerCamelCase__      :			int 						= torch.manual_seed(0  )
										lowerCamelCase__      :			Union[str, Any] 						= pipe(
										    prompt=UpperCamelCase__    ,							image=UpperCamelCase__    ,							guidance_scale=7.5    ,							num_inference_steps=10    ,							generator=UpperCamelCase__    ,							output_type="""np"""    ,							)
										lowerCamelCase__      :			int 						= output.images
										lowerCamelCase__      :			Union[str, Any] 						= images[0, 255:258, 383:386, -1]

										assert images.shape == (1, 512, 512, 3)
										lowerCamelCase__      :			List[Any] 						= 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 	lowerCamelCase_       (							self:       Union[str, Any]  ):
										lowerCamelCase__      :			List[str] 						= load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/img2img/sketch-mountains-input.jpg"""  )
										lowerCamelCase__      :			Union[str, Any] 						= init_image.resize((128, 128)  )
										lowerCamelCase__      :			Optional[int] 						= LMSDiscreteScheduler.from_pretrained(
										    """ssube/stable-diffusion-x4-upscaler-onnx"""    ,							subfolder="""scheduler"""  )
										lowerCamelCase__      :			Dict 						= 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__  )

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

										lowerCamelCase__      :			List[str] 						= torch.manual_seed(0  )
										lowerCamelCase__      :			int 						= pipe(
										    prompt=UpperCamelCase__    ,							image=UpperCamelCase__    ,							guidance_scale=7.5    ,							num_inference_steps=20    ,							generator=UpperCamelCase__    ,							output_type="""np"""    ,							)
										lowerCamelCase__      :			Optional[Any] 						= output.images
										lowerCamelCase__      :			Any 						= images[0, 255:258, 383:386, -1]

										assert images.shape == (1, 512, 512, 3)
										lowerCamelCase__      :			int 						= 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'''







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_A			:    Union[str, Any]					  =False

class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       str=32  ):
														set_seed(0  )
														lowerCamelCase__      :			Optional[int] 						= UNetaDModel(sample_size=UpperCamelCase__    ,							in_channels=3    ,							out_channels=3  )
														lowerCamelCase__      :			List[Any] 						= torch.optim.SGD(model.parameters()    ,							lr=0.0_001  )
														return model, optimizer




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Optional[Any] 						= """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
														lowerCamelCase__      :			List[Any] 						= DDPMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Any 						= DDIMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)

														assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

														# shared batches for DDPM and DDIM
														set_seed(0  )
														lowerCamelCase__      :			str 						= [torch.randn((4, 3, 32, 32)  ).clip(-1    ,							1  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randn((4, 3, 32, 32)  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randint(0    ,							1_000    ,							(4,)  ).long().to(UpperCamelCase__  ) for _ in range(4  )]

														# train with a DDPM scheduler
														lowerCamelCase__						,	lowerCamelCase__      :			Any 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			str 						= ddpm_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			str 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Tuple 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														# recreate the model and optimizer, and retry with DDIM
														lowerCamelCase__						,	lowerCamelCase__      :			Optional[Any] 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			Optional[Any] 						= ddim_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			Dict 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Union[str, Any] 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )
														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )

