"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		StableDiffusionInstructPixaPixPipeline
					a   	=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
					a   	=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					a   	=		IMAGE_TO_IMAGE_IMAGE_PARAMS
					a   	=		IMAGE_TO_IMAGE_IMAGE_PARAMS



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       str:
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__		  =				UNetaDConditionModel(
									    block_out_channels=(32, 64)   , layers_per_block=2   , sample_size=32   , in_channels=8   , out_channels=4   , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")   , cross_attention_dim=32   , )
									SCREAMING_SNAKE_CASE__		  =				PNDMScheduler(skip_prk_steps=lowercase_		)
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__		  =				AutoencoderKL(
									    block_out_channels=[32, 64]   , in_channels=3   , out_channels=3   , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]   , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]   , latent_channels=4   , )
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__		  =				CLIPTextConfig(
									    bos_token_id=0   , eos_token_id=2   , hidden_size=32   , intermediate_size=37   , layer_norm_eps=1e-05   , num_attention_heads=4   , num_hidden_layers=5   , pad_token_id=1   , vocab_size=1000   , )
									SCREAMING_SNAKE_CASE__		  =				CLIPTextModel(lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

									SCREAMING_SNAKE_CASE__		  =				{
									    "unet": unet,
									    "scheduler": scheduler,
									    "vae": vae,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "safety_checker": None,
									    "feature_extractor": None,
									}
									return components



					def 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Any   , _snake_case			:				List[Any]=0		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				floats_tensor((1, 3, 32, 32)   , rng=random.Random(lowercase_		)		).to(lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				image.cpu().permute(0   , 2   , 3   , 1		)[0]
									SCREAMING_SNAKE_CASE__		  =				Image.fromarray(np.uinta(lowercase_		)		).convert("RGB"		)
									if str(lowercase_		).startswith("mps"		):
													SCREAMING_SNAKE_CASE__		  =				torch.manual_seed(lowercase_		)
									else:
													SCREAMING_SNAKE_CASE__		  =				torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				{
									    "prompt": "A painting of a squirrel eating a burger",
									    "image": image,
									    "generator": generator,
									    "num_inference_steps": 2,
									    "guidance_scale": 6.0,
									    "image_guidance_scale": 1,
									    "output_type": "numpy",
									}
									return inputs



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				"cpu"  # ensure determinism for the device-dependent torch.Generator
									SCREAMING_SNAKE_CASE__		  =				self.get_dummy_components()
									SCREAMING_SNAKE_CASE__		  =				StableDiffusionInstructPixaPixPipeline(**lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				sd_pipe.to(lowercase_		)
									sd_pipe.set_progress_bar_config(disable=lowercase_		)

									SCREAMING_SNAKE_CASE__		  =				self.get_dummy_inputs(lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				sd_pipe(**lowercase_		).images
									SCREAMING_SNAKE_CASE__		  =				image[0, -3:, -3:, -1]
									assert image.shape == (1, 32, 32, 3)
									SCREAMING_SNAKE_CASE__		  =				np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				"cpu"  # ensure determinism for the device-dependent torch.Generator
									SCREAMING_SNAKE_CASE__		  =				self.get_dummy_components()
									SCREAMING_SNAKE_CASE__		  =				StableDiffusionInstructPixaPixPipeline(**lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				sd_pipe.to(lowercase_		)
									sd_pipe.set_progress_bar_config(disable=lowercase_		)

									SCREAMING_SNAKE_CASE__		  =				self.get_dummy_inputs(lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				"french fries"
									SCREAMING_SNAKE_CASE__		  =				sd_pipe(**lowercase_   , negative_prompt=lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				output.images
									SCREAMING_SNAKE_CASE__		  =				image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									SCREAMING_SNAKE_CASE__		  =				np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				"cpu"  # ensure determinism for the device-dependent torch.Generator
									SCREAMING_SNAKE_CASE__		  =				self.get_dummy_components()
									SCREAMING_SNAKE_CASE__		  =				StableDiffusionInstructPixaPixPipeline(**lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				sd_pipe.to(lowercase_		)
									sd_pipe.set_progress_bar_config(disable=lowercase_		)

									SCREAMING_SNAKE_CASE__		  =				self.get_dummy_inputs(lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				[inputs["prompt"]] * 2

									SCREAMING_SNAKE_CASE__		  =				np.array(inputs["image"]		).astype(np.floataa		) / 255.0
									SCREAMING_SNAKE_CASE__		  =				torch.from_numpy(lowercase_		).unsqueeze(0		).to(lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				image / 2 + 0.5
									SCREAMING_SNAKE_CASE__		  =				image.permute(0   , 3   , 1   , 2		)
									SCREAMING_SNAKE_CASE__		  =				image.repeat(2   , 1   , 1   , 1		)

									SCREAMING_SNAKE_CASE__		  =				sd_pipe(**lowercase_		).images
									SCREAMING_SNAKE_CASE__		  =				image[-1, -3:, -3:, -1]

									assert image.shape == (2, 32, 32, 3)
									SCREAMING_SNAKE_CASE__		  =				np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				"cpu"  # ensure determinism for the device-dependent torch.Generator
									SCREAMING_SNAKE_CASE__		  =				self.get_dummy_components()
									SCREAMING_SNAKE_CASE__		  =				EulerAncestralDiscreteScheduler(
									    beta_start=0.0_0085   , beta_end=0.012   , beta_schedule="scaled_linear"		)
									SCREAMING_SNAKE_CASE__		  =				StableDiffusionInstructPixaPixPipeline(**lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				sd_pipe.to(lowercase_		)
									sd_pipe.set_progress_bar_config(disable=lowercase_		)

									SCREAMING_SNAKE_CASE__		  =				self.get_dummy_inputs(lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				sd_pipe(**lowercase_		).images
									SCREAMING_SNAKE_CASE__		  =				image[0, -3:, -3:, -1]

									SCREAMING_SNAKE_CASE__		  =				[round(lowercase_   , 4		) for x in image_slice.flatten().tolist()]
									print(",".join([str(lowercase_		) for x in slice]		)		)

									assert image.shape == (1, 32, 32, 3)
									SCREAMING_SNAKE_CASE__		  =				np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Tuple:
									super().test_inference_batch_single_identical(expected_max_diff=3e-3		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.get_dummy_components()
									SCREAMING_SNAKE_CASE__		  =				StableDiffusionInstructPixaPixPipeline(**lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				VaeImageProcessor(do_resize=lowercase_   , do_normalize=lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				pipe.to(lowercase_		)
									pipe.set_progress_bar_config(disable=lowercase_		)

									SCREAMING_SNAKE_CASE__		  =				pipe(**self.get_dummy_inputs_by_type(lowercase_   , input_image_type="pt"		)		)[0]

									SCREAMING_SNAKE_CASE__		  =				components["vae"]
									SCREAMING_SNAKE_CASE__		  =				self.get_dummy_inputs_by_type(lowercase_   , input_image_type="pt"		)

									for image_param in self.image_latents_params:
													if image_param in inputs.keys():
																	SCREAMING_SNAKE_CASE__		  =				vae.encode(inputs[image_param]		).latent_dist.mode()

									SCREAMING_SNAKE_CASE__		  =				pipe(**lowercase_		)[0]

									SCREAMING_SNAKE_CASE__		  =				np.abs(out - out_latents_inputs		).max()
									self.assertLess(lowercase_   , 1e-4   , "passing latents as image input generate different result from passing image"		)




@slow
@require_torch_gpu
class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				str		)							->       List[Any]:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				Tuple=0		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				torch.manual_seed(lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				load_image(
									    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"		)
									SCREAMING_SNAKE_CASE__		  =				{
									    "prompt": "turn him into a cyborg",
									    "image": image,
									    "generator": generator,
									    "num_inference_steps": 3,
									    "guidance_scale": 7.5,
									    "image_guidance_scale": 1.0,
									    "output_type": "numpy",
									}
									return inputs



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    "timbrooks/instruct-pix2pix"   , safety_checker=lowercase_		)
									pipe.to(lowercase_		)
									pipe.set_progress_bar_config(disable=lowercase_		)
									pipe.enable_attention_slicing()

									SCREAMING_SNAKE_CASE__		  =				self.get_inputs()
									SCREAMING_SNAKE_CASE__		  =				pipe(**lowercase_		).images
									SCREAMING_SNAKE_CASE__		  =				image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE__		  =				np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]		)

									assert np.abs(expected_slice - image_slice		).max() < 1e-3



					def 			lowerCAmelCase_					(						self			:				int		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    "timbrooks/instruct-pix2pix"   , safety_checker=lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
									pipe.to(lowercase_		)
									pipe.set_progress_bar_config(disable=lowercase_		)
									pipe.enable_attention_slicing()

									SCREAMING_SNAKE_CASE__		  =				self.get_inputs()
									SCREAMING_SNAKE_CASE__		  =				pipe(**lowercase_		).images
									SCREAMING_SNAKE_CASE__		  =				image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE__		  =				np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]		)

									assert np.abs(expected_slice - image_slice		).max() < 1e-3



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    "timbrooks/instruct-pix2pix"   , safety_checker=lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				DDIMScheduler.from_config(pipe.scheduler.config		)
									pipe.to(lowercase_		)
									pipe.set_progress_bar_config(disable=lowercase_		)
									pipe.enable_attention_slicing()

									SCREAMING_SNAKE_CASE__		  =				self.get_inputs()
									SCREAMING_SNAKE_CASE__		  =				pipe(**lowercase_		).images
									SCREAMING_SNAKE_CASE__		  =				image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE__		  =				np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]		)

									assert np.abs(expected_slice - image_slice		).max() < 1e-3



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				0

									def callback_fn(_snake_case			:				int   , _snake_case			:				int   , _snake_case			:				torch.FloatTensor		) -> None:
													SCREAMING_SNAKE_CASE__		  =				True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 1:
																	SCREAMING_SNAKE_CASE__		  =				latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	SCREAMING_SNAKE_CASE__		  =				latents[0, -3:, -3:, -1]
																	SCREAMING_SNAKE_CASE__		  =				np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]		)

																	assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5e-2
													elif step == 2:
																	SCREAMING_SNAKE_CASE__		  =				latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	SCREAMING_SNAKE_CASE__		  =				latents[0, -3:, -3:, -1]
																	SCREAMING_SNAKE_CASE__		  =				np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]		)

																	assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5e-2

									SCREAMING_SNAKE_CASE__		  =				False

									SCREAMING_SNAKE_CASE__		  =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    "timbrooks/instruct-pix2pix"   , safety_checker=lowercase_   , torch_dtype=torch.floataa		)
									SCREAMING_SNAKE_CASE__		  =				pipe.to(lowercase_		)
									pipe.set_progress_bar_config(disable=lowercase_		)
									pipe.enable_attention_slicing()

									SCREAMING_SNAKE_CASE__		  =				self.get_inputs()
									pipe(**lowercase_   , callback=lowercase_   , callback_steps=1		)
									assert callback_fn.has_been_called
									assert number_of_steps == 3



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       int:
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									SCREAMING_SNAKE_CASE__		  =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    "timbrooks/instruct-pix2pix"   , safety_checker=lowercase_   , torch_dtype=torch.floataa		)
									SCREAMING_SNAKE_CASE__		  =				pipe.to(lowercase_		)
									pipe.set_progress_bar_config(disable=lowercase_		)
									pipe.enable_attention_slicing(1		)
									pipe.enable_sequential_cpu_offload()

									SCREAMING_SNAKE_CASE__		  =				self.get_inputs()
									SCREAMING_SNAKE_CASE__		  =				pipe(**lowercase_		)

									SCREAMING_SNAKE_CASE__		  =				torch.cuda.max_memory_allocated()
									# make sure that less than 2.2 GB is allocated
									assert mem_bytes < 2.2 * 10**9



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.get_inputs()
									# resize to resolution that is divisible by 8 but not 16 or 32
									SCREAMING_SNAKE_CASE__		  =				inputs["image"].resize((504, 504)		)

									SCREAMING_SNAKE_CASE__		  =				"timbrooks/instruct-pix2pix"
									SCREAMING_SNAKE_CASE__		  =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    lowercase_   , safety_checker=lowercase_   , )
									pipe.to(lowercase_		)
									pipe.set_progress_bar_config(disable=lowercase_		)
									pipe.enable_attention_slicing()

									SCREAMING_SNAKE_CASE__		  =				pipe(**lowercase_		)
									SCREAMING_SNAKE_CASE__		  =				output.images[0]

									SCREAMING_SNAKE_CASE__		  =				image[255:258, 383:386, -1]

									assert image.shape == (504, 504, 3)
									SCREAMING_SNAKE_CASE__		  =				np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 5e-3





"""simple docstring"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput

@dataclass
class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		42
					a   	=		42



class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		1



					@register_to_config
					def __init__(						self			:				Tuple   , _snake_case			:				int = 2000   , _snake_case			:				float = 0.15   , _snake_case			:				float = 0.01   , _snake_case			:				float = 1348.0   , _snake_case			:				float = 1e-5   , _snake_case			:				int = 1   , )							->       str:
									# standard deviation of the initial noise distribution
									SCREAMING_SNAKE_CASE__		  =				sigma_max

									# setable values
									SCREAMING_SNAKE_CASE__		  =				None

									self.set_sigmas(_snake_case   , _snake_case   , _snake_case   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[int] = None		)							->       torch.FloatTensor:
									return sample



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				int   , _snake_case			:				float = None   , _snake_case			:				Union[str, torch.device] = None		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				sampling_eps if sampling_eps is not None else self.config.sampling_eps

									SCREAMING_SNAKE_CASE__		  =				torch.linspace(1   , _snake_case   , _snake_case   , device=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				int   , _snake_case			:				float = None   , _snake_case			:				float = None   , _snake_case			:				float = None		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				sigma_min if sigma_min is not None else self.config.sigma_min
									SCREAMING_SNAKE_CASE__		  =				sigma_max if sigma_max is not None else self.config.sigma_max
									SCREAMING_SNAKE_CASE__		  =				sampling_eps if sampling_eps is not None else self.config.sampling_eps
									if self.timesteps is None:
													self.set_timesteps(_snake_case   , _snake_case		)

									SCREAMING_SNAKE_CASE__		  =				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
									SCREAMING_SNAKE_CASE__		  =				torch.exp(torch.linspace(math.log(_snake_case		)   , math.log(_snake_case		)   , _snake_case		)		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]		)



					def 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Optional[Any]   , _snake_case			:				List[str]		)							->       List[Any]:
									return torch.where(
									    timesteps == 0   , torch.zeros_like(t.to(timesteps.device		)		)   , self.discrete_sigmas[timesteps - 1].to(timesteps.device		)   , )



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				torch.FloatTensor   , _snake_case			:				int   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[torch.Generator] = None   , _snake_case			:				bool = True   , )							->       Union[SdeVeOutput, Tuple]:
									if self.timesteps is None:
													raise ValueError(
													    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

									SCREAMING_SNAKE_CASE__		  =				timestep * torch.ones(
									    sample.shape[0]   , device=sample.device		)  # torch.repeat_interleave(timestep, sample.shape[0])
									SCREAMING_SNAKE_CASE__		  =				(timestep * (len(self.timesteps		) - 1)).long()

									# mps requires indices to be in the same device, so we use cpu as is the default with cuda
									SCREAMING_SNAKE_CASE__		  =				timesteps.to(self.discrete_sigmas.device		)

									SCREAMING_SNAKE_CASE__		  =				self.discrete_sigmas[timesteps].to(sample.device		)
									SCREAMING_SNAKE_CASE__		  =				self.get_adjacent_sigma(_snake_case   , _snake_case		).to(sample.device		)
									SCREAMING_SNAKE_CASE__		  =				torch.zeros_like(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				(sigma**2 - adjacent_sigma**2) ** 0.5

									# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
									# also equation 47 shows the analog from SDE models to ancestral sampling methods
									SCREAMING_SNAKE_CASE__		  =				diffusion.flatten()
									while len(diffusion.shape		) < len(sample.shape		):
													SCREAMING_SNAKE_CASE__		  =				diffusion.unsqueeze(-1		)
									SCREAMING_SNAKE_CASE__		  =				drift - diffusion**2 * model_output

									#  equation 6: sample noise for the diffusion term of
									SCREAMING_SNAKE_CASE__		  =				randn_tensor(
									    sample.shape   , layout=sample.layout   , generator=_snake_case   , device=sample.device   , dtype=sample.dtype		)
									SCREAMING_SNAKE_CASE__		  =				sample - drift  # subtract because `dt` is a small negative timestep
									# TODO is the variable diffusion the correct scaling term for the noise?
									SCREAMING_SNAKE_CASE__		  =				prev_sample_mean + diffusion * noise  # add impact of diffusion field g

									if not return_dict:
													return (prev_sample, prev_sample_mean)

									return SdeVeOutput(prev_sample=_snake_case   , prev_sample_mean=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[torch.Generator] = None   , _snake_case			:				bool = True   , )							->       Union[SchedulerOutput, Tuple]:
									if self.timesteps is None:
													raise ValueError(
													    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

									# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
									# sample noise for correction
									SCREAMING_SNAKE_CASE__		  =				randn_tensor(sample.shape   , layout=sample.layout   , generator=_snake_case		).to(sample.device		)

									# compute step size from the model_output, the noise, and the snr
									SCREAMING_SNAKE_CASE__		  =				torch.norm(model_output.reshape(model_output.shape[0]   , -1		)   , dim=-1		).mean()
									SCREAMING_SNAKE_CASE__		  =				torch.norm(noise.reshape(noise.shape[0]   , -1		)   , dim=-1		).mean()
									SCREAMING_SNAKE_CASE__		  =				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
									SCREAMING_SNAKE_CASE__		  =				step_size * torch.ones(sample.shape[0]		).to(sample.device		)
									# self.repeat_scalar(step_size, sample.shape[0])

									# compute corrected sample: model_output term and noise term
									SCREAMING_SNAKE_CASE__		  =				step_size.flatten()
									while len(step_size.shape		) < len(sample.shape		):
													SCREAMING_SNAKE_CASE__		  =				step_size.unsqueeze(-1		)
									SCREAMING_SNAKE_CASE__		  =				sample + step_size * model_output
									SCREAMING_SNAKE_CASE__		  =				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

									if not return_dict:
													return (prev_sample,)

									return SchedulerOutput(prev_sample=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , )							->       torch.FloatTensor:
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									SCREAMING_SNAKE_CASE__		  =				timesteps.to(original_samples.device		)
									SCREAMING_SNAKE_CASE__		  =				self.discrete_sigmas.to(original_samples.device		)[timesteps]
									SCREAMING_SNAKE_CASE__		  =				(
									    noise * sigmas[:, None, None, None]
									    if noise is not None
									    else torch.randn_like(_snake_case		) * sigmas[:, None, None, None]
									)
									SCREAMING_SNAKE_CASE__		  =				noise + original_samples
									return noisy_samples



					def __len__(						self			:				List[Any]		)							->       int:
									return self.config.num_train_timesteps




