
import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    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'''





						snake_case_    =					"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"






						def     UpperCamelCase_  (	self :			List[str]							,A :			Dict=0			):
										__A             =			floats_tensor((1, 3, 1_28, 1_28)							,rng=random.Random(A			)			)
										__A             =			np.random.RandomState(A			)
										__A             =			{
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": image,
										    "generator": generator,
										    "num_inference_steps": 3,
										    "strength": 0.75,
										    "guidance_scale": 7.5,
										    "output_type": "numpy",
										}
										return inputs






						def     UpperCamelCase_  (	self :			int			):
										__A             =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
										pipe.set_progress_bar_config(disable=A			)

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__A             =			np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]			)
										assert np.abs(image_slice - expected_slice			).max() < 1E-1






						def     UpperCamelCase_  (	self :			Optional[Any]			):
										__A             =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
										__A             =			PNDMScheduler.from_config(pipe.scheduler.config							,skip_prk_steps=A			)
										pipe.set_progress_bar_config(disable=A			)

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__A             =			np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]			)

										assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-1






						def     UpperCamelCase_  (	self :			Union[str, Any]			):
										__A             =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
										__A             =			LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
										pipe.set_progress_bar_config(disable=A			)

										# warmup pass to apply optimizations
										__A             =			pipe(**self.get_dummy_inputs()			)

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__A             =			np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]			)

										assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-1






						def     UpperCamelCase_  (	self :			Optional[Any]			):
										__A             =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
										__A             =			EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
										pipe.set_progress_bar_config(disable=A			)

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__A             =			np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]			)

										assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-1






						def     UpperCamelCase_  (	self :			str			):
										__A             =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
										__A             =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
										pipe.set_progress_bar_config(disable=A			)

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__A             =			np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]			)

										assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-1







						def     UpperCamelCase_  (	self :			Optional[int]			):
										__A             =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
										__A             =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
										pipe.set_progress_bar_config(disable=A			)

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__A             =			np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]			)

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






						@property
						def     UpperCamelCase_  (	self :			str			):
										__A             =			ort.SessionOptions()
										__A             =			False
										return options






						def     UpperCamelCase_  (	self :			Optional[Any]			):
										__A             =			load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg"			)
										__A             =			init_image.resize((7_68, 5_12)			)
										# using the PNDM scheduler by default
										__A             =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
										    "CompVis/stable-diffusion-v1-4"							,revision="onnx"							,safety_checker=A							,feature_extractor=A							,provider=self.gpu_provider							,sess_options=self.gpu_options							,)
										pipe.set_progress_bar_config(disable=A			)

										__A             =			"A fantasy landscape, trending on artstation"

										__A             =			np.random.RandomState(0			)
										__A             =			pipe(
										    prompt=A							,image=A							,strength=0.75							,guidance_scale=7.5							,num_inference_steps=10							,generator=A							,output_type="np"							,)
										__A             =			output.images
										__A             =			images[0, 2_55:2_58, 3_83:3_86, -1]

										assert images.shape == (1, 5_12, 7_68, 3)
										__A             =			np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]			)
										# 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 :			List[Any]			):
										__A             =			load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg"			)
										__A             =			init_image.resize((7_68, 5_12)			)
										__A             =			LMSDiscreteScheduler.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"							,subfolder="scheduler"							,revision="onnx"			)
										__A             =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"							,revision="onnx"							,scheduler=A							,safety_checker=A							,feature_extractor=A							,provider=self.gpu_provider							,sess_options=self.gpu_options							,)
										pipe.set_progress_bar_config(disable=A			)

										__A             =			"A fantasy landscape, trending on artstation"

										__A             =			np.random.RandomState(0			)
										__A             =			pipe(
										    prompt=A							,image=A							,strength=0.75							,guidance_scale=7.5							,num_inference_steps=20							,generator=A							,output_type="np"							,)
										__A             =			output.images
										__A             =			images[0, 2_55:2_58, 3_83:3_86, -1]

										assert images.shape == (1, 5_12, 7_68, 3)
										__A             =			np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]			)
										# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

										assert np.abs(image_slice.flatten() - expected_slice			).max() < 2E-2


from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			Optional[Any]							,A :			List[Any]			):
					super().__init__()

					# make sure scheduler can always be converted to DDIM
					__A             =			DDIMScheduler.from_config(scheduler.config			)

					self.register_modules(unet=A							,scheduler=A			)







	@torch.no_grad()
	def __call__(	self :			Tuple							,A :			int = 1							,A :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,A :			float = 0.0							,A :			int = 50							,A :			Optional[bool] = None							,A :			Optional[str] = "pil"							,A :			bool = True							,):

					# Sample gaussian noise to begin loop
					if isinstance(self.unet.config.sample_size							,A			):
									__A             =			(
									    batch_size,
									    self.unet.config.in_channels,
									    self.unet.config.sample_size,
									    self.unet.config.sample_size,
									)
					else:
									__A             =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					if isinstance(A							,A			) and len(A			) != batch_size:
									raise ValueError(
									    f'''You have passed a list of generators of length {len(A			)}, but requested an effective batch'''
									    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''			)

					__A             =			randn_tensor(A							,generator=A							,device=self.device							,dtype=self.unet.dtype			)

					# set step values
					self.scheduler.set_timesteps(A			)

					for t in self.progress_bar(self.scheduler.timesteps			):
									# 1. predict noise model_output
									__A             =			self.unet(A							,A			).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									__A             =			self.scheduler.step(
									    A							,A							,A							,eta=A							,use_clipped_model_output=A							,generator=A			).prev_sample

					__A             =			(image / 2 + 0.5).clamp(0							,1			)
					__A             =			image.cpu().permute(0							,2							,3							,1			).numpy()
					if output_type == "pil":
									__A             =			self.numpy_to_pil(A			)

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=A			)
