code: 
  
  
  
  
  
  
  """simple docstring"""
  
  import gc
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection
  
  from diffusers import (
      AutoencoderKL,
      DPMSolverMultistepScheduler,
      PNDMScheduler,
      StableDiffusionImageVariationPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  class 	__lowerCamelCase       (				UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Optional[Any]      =							StableDiffusionImageVariationPipeline
  		a_			:       Optional[int]      =							IMAGE_VARIATION_PARAMS
  		a_			:       Optional[int]      =							IMAGE_VARIATION_BATCH_PARAMS
  		a_			:       Optional[int]      =							frozenset([]    )
  		# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  		a_			:       str      =							frozenset([]    )
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict			):
  				torch.manual_seed(0			)
  				lowerCAmelCase_     :							List[Any]             =    UNetaDConditionModel(
  				    block_out_channels=(32, 64)		,    layers_per_block=2		,    sample_size=32		,    in_channels=4		,    out_channels=4		,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,    cross_attention_dim=32		,    )
  				lowerCAmelCase_     :							Union[str, Any]             =    PNDMScheduler(skip_prk_steps=lowerCAmelCase__			)
  				torch.manual_seed(0			)
  				lowerCAmelCase_     :							int             =    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			)
  				lowerCAmelCase_     :							List[str]             =    CLIPVisionConfig(
  				    hidden_size=32		,    projection_dim=32		,    intermediate_size=37		,    layer_norm_eps=1e-0_5		,    num_attention_heads=4		,    num_hidden_layers=5		,    image_size=32		,    patch_size=4		,    )
  				lowerCAmelCase_     :							Tuple             =    CLIPVisionModelWithProjection(lowerCAmelCase__			)
  				lowerCAmelCase_     :							int             =    CLIPImageProcessor(crop_size=32		,    size=32			)
  
  				lowerCAmelCase_     :							Optional[int]             =    {
  				    "unet": unet,
  				    "scheduler": scheduler,
  				    "vae": vae,
  				    "image_encoder": image_encoder,
  				    "feature_extractor": feature_extractor,
  				    "safety_checker": None,
  				}
  				return components
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict		,    a_							:			Union[str, Any]		,    a_							:			Optional[Any]=0			):
  				lowerCAmelCase_     :							Optional[Any]             =    floats_tensor((1, 3, 32, 32)		,    rng=random.Random(lowerCAmelCase__			)			)
  				lowerCAmelCase_     :							Optional[Any]             =    image.cpu().permute(0		,    2		,    3		,    1			)[0]
  				lowerCAmelCase_     :							int             =    Image.fromarray(np.uinta(lowerCAmelCase__			)			).convert("RGB"			).resize((32, 32)			)
  				if str(lowerCAmelCase__			).startswith("mps"			):
  						lowerCAmelCase_     :							str             =    torch.manual_seed(lowerCAmelCase__			)
  				else:
  						lowerCAmelCase_     :							str             =    torch.Generator(device=lowerCAmelCase__			).manual_seed(lowerCAmelCase__			)
  				lowerCAmelCase_     :							str             =    {
  				    "image": image,
  				    "generator": generator,
  				    "num_inference_steps": 2,
  				    "guidance_scale": 6.0,
  				    "output_type": "numpy",
  				}
  				return inputs
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[str]			):
  				lowerCAmelCase_     :							Tuple             =    "cpu"  # ensure determinism for the device-dependent torch.Generator
  				lowerCAmelCase_     :							Optional[Any]             =    self.get_dummy_components()
  				lowerCAmelCase_     :							List[str]             =    StableDiffusionImageVariationPipeline(**lowerCAmelCase__			)
  				lowerCAmelCase_     :							Optional[Any]             =    sd_pipe.to(lowerCAmelCase__			)
  				sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
  
  				lowerCAmelCase_     :							Any             =    self.get_dummy_inputs(lowerCAmelCase__			)
  				lowerCAmelCase_     :							Optional[Any]             =    sd_pipe(**lowerCAmelCase__			).images
  				lowerCAmelCase_     :							int             =    image[0, -3:, -3:, -1]
  
  				assert image.shape == (1, 64, 64, 3)
  				lowerCAmelCase_     :							int             =    np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921]			)
  
  				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any			):
  				lowerCAmelCase_     :							int             =    "cpu"  # ensure determinism for the device-dependent torch.Generator
  				lowerCAmelCase_     :							Any             =    self.get_dummy_components()
  				lowerCAmelCase_     :							Tuple             =    StableDiffusionImageVariationPipeline(**lowerCAmelCase__			)
  				lowerCAmelCase_     :							Dict             =    sd_pipe.to(lowerCAmelCase__			)
  				sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
  
  				lowerCAmelCase_     :							Union[str, Any]             =    self.get_dummy_inputs(lowerCAmelCase__			)
  				lowerCAmelCase_     :							Optional[int]             =    2 * [inputs["image"]]
  				lowerCAmelCase_     :							Optional[Any]             =    sd_pipe(**lowerCAmelCase__			)
  
  				lowerCAmelCase_     :							Union[str, Any]             =    output.images
  
  				lowerCAmelCase_     :							Any             =    image[-1, -3:, -3:, -1]
  
  				assert image.shape == (2, 64, 64, 3)
  				lowerCAmelCase_     :							List[str]             =    np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289]			)
  
  				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				super().test_inference_batch_single_identical(expected_max_diff=3e-3			)
  
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 	__lowerCamelCase       (				unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any			):
  				super().tearDown()
  				gc.collect()
  				torch.cuda.empty_cache()
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str		,    a_							:			Any		,    a_							:			Union[str, Any]="cpu"		,    a_							:			str=torch.floataa		,    a_							:			Dict=0			):
  				lowerCAmelCase_     :							int             =    torch.Generator(device=lowerCAmelCase__			).manual_seed(lowerCAmelCase__			)
  				lowerCAmelCase_     :							List[Any]             =    load_image(
  				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
  				    "/stable_diffusion_imgvar/input_image_vermeer.png"			)
  				lowerCAmelCase_     :							int             =    np.random.RandomState(lowerCAmelCase__			).standard_normal((1, 4, 64, 64)			)
  				lowerCAmelCase_     :							Any             =    torch.from_numpy(lowerCAmelCase__			).to(device=lowerCAmelCase__		,    dtype=lowerCAmelCase__			)
  				lowerCAmelCase_     :							str             =    {
  				    "image": init_image,
  				    "latents": latents,
  				    "generator": generator,
  				    "num_inference_steps": 3,
  				    "guidance_scale": 7.5,
  				    "output_type": "numpy",
  				}
  				return inputs
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str			):
  				lowerCAmelCase_     :							int             =    StableDiffusionImageVariationPipeline.from_pretrained(
  				    "lambdalabs/sd-image-variations-diffusers"		,    safety_checker=lowerCAmelCase__			)
  				lowerCAmelCase_     :							Union[str, Any]             =    sd_pipe.to(lowerCAmelCase__			)
  				sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
  
  				lowerCAmelCase_     :							Dict             =    self.get_inputs(lowerCAmelCase__			)
  				lowerCAmelCase_     :							Any             =    sd_pipe(**lowerCAmelCase__			).images
  				lowerCAmelCase_     :							List[Any]             =    image[0, -3:, -3:, -1].flatten()
  
  				assert image.shape == (1, 5_12, 5_12, 3)
  				lowerCAmelCase_     :							Any             =    np.array([0.84491, 0.90789, 0.75708, 0.78734, 0.83485, 0.70099, 0.66938, 0.68727, 0.61379]			)
  				assert np.abs(image_slice - expected_slice			).max() < 6e-3
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				lowerCAmelCase_     :							Tuple             =    0
  
  				def callback_fn(a_							:			Optional[int]		,    a_							:			Tuple		,    a_							:			List[str]			) -> None:
  						lowerCAmelCase_     :							int             =    True
  						nonlocal number_of_steps
  						number_of_steps += 1
  						if step == 1:
  								lowerCAmelCase_     :							Union[str, Any]             =    latents.detach().cpu().numpy()
  								assert latents.shape == (1, 4, 64, 64)
  								lowerCAmelCase_     :							Any             =    latents[0, -3:, -3:, -1]
  								lowerCAmelCase_     :							Any             =    np.array(
  								    [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309]			)
  
  								assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5e-2
  						elif step == 2:
  								lowerCAmelCase_     :							List[Any]             =    latents.detach().cpu().numpy()
  								assert latents.shape == (1, 4, 64, 64)
  								lowerCAmelCase_     :							Union[str, Any]             =    latents[0, -3:, -3:, -1]
  								lowerCAmelCase_     :							Dict             =    np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273]			)
  
  								assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5e-2
  
  				lowerCAmelCase_     :							str             =    False
  
  				lowerCAmelCase_     :							List[Any]             =    StableDiffusionImageVariationPipeline.from_pretrained(
  				    "fusing/sd-image-variations-diffusers"		,    safety_checker=lowerCAmelCase__		,    torch_dtype=torch.floataa		,    )
  				pipe.to(lowerCAmelCase__			)
  				pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
  				pipe.enable_attention_slicing()
  
  				lowerCAmelCase_     :							Optional[int]             =    self.get_inputs(lowerCAmelCase__		,    dtype=torch.floataa			)
  				pipe(**lowerCAmelCase__		,    callback=lowerCAmelCase__		,    callback_steps=1			)
  				assert callback_fn.has_been_called
  				assert number_of_steps == inputs["num_inference_steps"]
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict			):
  				torch.cuda.empty_cache()
  				torch.cuda.reset_max_memory_allocated()
  				torch.cuda.reset_peak_memory_stats()
  
  				lowerCAmelCase_     :							Dict             =    "fusing/sd-image-variations-diffusers"
  				lowerCAmelCase_     :							Optional[int]             =    StableDiffusionImageVariationPipeline.from_pretrained(
  				    lowerCAmelCase__		,    safety_checker=lowerCAmelCase__		,    torch_dtype=torch.floataa			)
  				lowerCAmelCase_     :							Optional[Any]             =    pipe.to(lowerCAmelCase__			)
  				pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
  				pipe.enable_attention_slicing(1			)
  				pipe.enable_sequential_cpu_offload()
  
  				lowerCAmelCase_     :							Any             =    self.get_inputs(lowerCAmelCase__		,    dtype=torch.floataa			)
  				lowerCAmelCase_     :							str             =    pipe(**lowerCAmelCase__			)
  
  				lowerCAmelCase_     :							Tuple             =    torch.cuda.max_memory_allocated()
  				# make sure that less than 2.6 GB is allocated
  				assert mem_bytes < 2.6 * 10**9
  
  
  
  
  
  
  
  @nightly
  @require_torch_gpu
  class 	__lowerCamelCase       (				unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[Any]			):
  				super().tearDown()
  				gc.collect()
  				torch.cuda.empty_cache()
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any		,    a_							:			List[Any]		,    a_							:			Any="cpu"		,    a_							:			Tuple=torch.floataa		,    a_							:			Optional[Any]=0			):
  				lowerCAmelCase_     :							Tuple             =    torch.Generator(device=lowerCAmelCase__			).manual_seed(lowerCAmelCase__			)
  				lowerCAmelCase_     :							List[str]             =    load_image(
  				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
  				    "/stable_diffusion_imgvar/input_image_vermeer.png"			)
  				lowerCAmelCase_     :							Tuple             =    np.random.RandomState(lowerCAmelCase__			).standard_normal((1, 4, 64, 64)			)
  				lowerCAmelCase_     :							Any             =    torch.from_numpy(lowerCAmelCase__			).to(device=lowerCAmelCase__		,    dtype=lowerCAmelCase__			)
  				lowerCAmelCase_     :							str             =    {
  				    "image": init_image,
  				    "latents": latents,
  				    "generator": generator,
  				    "num_inference_steps": 50,
  				    "guidance_scale": 7.5,
  				    "output_type": "numpy",
  				}
  				return inputs
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[str]			):
  				lowerCAmelCase_     :							Optional[int]             =    StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers"			)
  				sd_pipe.to(lowerCAmelCase__			)
  				sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
  
  				lowerCAmelCase_     :							Union[str, Any]             =    self.get_inputs(lowerCAmelCase__			)
  				lowerCAmelCase_     :							Dict             =    sd_pipe(**lowerCAmelCase__			).images[0]
  
  				lowerCAmelCase_     :							Any             =    load_numpy(
  				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
  				    "/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy"			)
  				lowerCAmelCase_     :							Tuple             =    np.abs(expected_image - image			).max()
  				assert max_diff < 1e-3
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				lowerCAmelCase_     :							List[Any]             =    StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers"			)
  				lowerCAmelCase_     :							Union[str, Any]             =    DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config			)
  				sd_pipe.to(lowerCAmelCase__			)
  				sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
  
  				lowerCAmelCase_     :							Any             =    self.get_inputs(lowerCAmelCase__			)
  				lowerCAmelCase_     :							Union[str, Any]             =    25
  				lowerCAmelCase_     :							List[str]             =    sd_pipe(**lowerCAmelCase__			).images[0]
  
  				lowerCAmelCase_     :							int             =    load_numpy(
  				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
  				    "/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"			)
  				lowerCAmelCase_     :							Any             =    np.abs(expected_image - image			).max()
  				assert max_diff < 1e-3
  
  
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  import math
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, randn_tensor
  from .scheduling_utils import SchedulerMixin
  
  
  @dataclass
  class 	__lowerCamelCase       (				A__    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       torch.FloatTensor
  		a_			:       torch.FloatTensor
  
  
  
  
  
  
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase=0.9_99   , __UpperCamelCase="cosine"   , )		->					Any:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		if alpha_transform_type == "cosine":
  
  				def alpha_bar_fn(__UpperCamelCase ):
  						return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2
  
  		elif alpha_transform_type == "exp":
  
  				def alpha_bar_fn(__UpperCamelCase ):
  						return math.exp(t * -12.0 )
  
  		else:
  				raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
  
  		lowerCAmelCase_     :							List[str]             =    []
  		for i in range(__UpperCamelCase ):
  				lowerCAmelCase_     :							int             =    i / num_diffusion_timesteps
  				lowerCAmelCase_     :							Optional[Any]             =    (i + 1) / num_diffusion_timesteps
  				betas.append(min(1 - alpha_bar_fn(__UpperCamelCase ) / alpha_bar_fn(__UpperCamelCase )   , __UpperCamelCase ) )
  		return torch.tensor(__UpperCamelCase   , dtype=torch.floataa )
  
  
  
  class 	__lowerCamelCase       (				A__				,							A__    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Tuple      =							1
  
  
  
  
  
  		@register_to_config
  		def __init__(     self							:			Optional[int]		,    a_							:			int = 10_00		,    a_							:			float = 0.0001		,    a_							:			float = 0.02		,    a_							:			str = "linear"		,    a_							:			float = 0.0		,    a_							:			Optional[np.ndarray] = None		,    a_							:			bool = True		,    ):
  				if trained_betas is not None:
  						lowerCAmelCase_     :							Tuple             =    torch.from_numpy(a_			)
  				elif beta_schedule == "linear":
  						lowerCAmelCase_     :							int             =    torch.linspace(a_		,    a_		,    a_		,    dtype=torch.floataa			)
  				elif beta_schedule == "scaled_linear":
  						# this schedule is very specific to the latent diffusion model.
  						lowerCAmelCase_     :							Optional[Any]             =    (
  						    torch.linspace(beta_start**0.5		,    beta_end**0.5		,    a_		,    dtype=torch.floataa			) ** 2
  						)
  				elif beta_schedule == "squaredcos_cap_v2":
  						# Glide cosine schedule
  						lowerCAmelCase_     :							Tuple             =    betas_for_alpha_bar(a_			)
  				elif beta_schedule == "sigmoid":
  						# GeoDiff sigmoid schedule
  						lowerCAmelCase_     :							List[str]             =    torch.linspace(-6		,    6		,    a_			)
  						lowerCAmelCase_     :							int             =    torch.sigmoid(a_			) * (beta_end - beta_start) + beta_start
  				else:
  						raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)
  
  				lowerCAmelCase_     :							Union[str, Any]             =    1.0 - self.betas
  				lowerCAmelCase_     :							Optional[int]             =    torch.cumprod(self.alphas		,    dim=0			)
  				lowerCAmelCase_     :							int             =    torch.tensor(1.0			)
  
  				lowerCAmelCase_     :							int             =    torch.tensor(1.0			)
  
  				# standard deviation of the initial noise distribution
  				lowerCAmelCase_     :							List[str]             =    1.0
  
  				# setable values
  				lowerCAmelCase_     :							Dict             =    None
  				lowerCAmelCase_     :							List[str]             =    torch.from_numpy(np.arange(0		,    a_			)[::-1].copy()			)
  
  				lowerCAmelCase_     :							int             =    eta
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			torch.FloatTensor		,    a_							:			Optional[int] = None			):
  				return sample
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict		,    a_							:			int		,    a_							:			int = 10		,    a_							:			int = 10		,    a_							:			Union[str, torch.device] = None		,    ):
  				lowerCAmelCase_     :							Union[str, Any]             =    min(self.config.num_train_timesteps		,    a_			)
  				lowerCAmelCase_     :							Dict             =    num_inference_steps
  
  				lowerCAmelCase_     :							List[str]             =    []
  
  				lowerCAmelCase_     :							Dict             =    {}
  				for j in range(0		,    num_inference_steps - jump_length		,    a_			):
  						lowerCAmelCase_     :							Dict             =    jump_n_sample - 1
  
  				lowerCAmelCase_     :							List[Any]             =    num_inference_steps
  				while t >= 1:
  						lowerCAmelCase_     :							int             =    t - 1
  						timesteps.append(a_			)
  
  						if jumps.get(a_		,    0			) > 0:
  								lowerCAmelCase_     :							Optional[int]             =    jumps[t] - 1
  								for _ in range(a_			):
  										lowerCAmelCase_     :							Any             =    t + 1
  										timesteps.append(a_			)
  
  				lowerCAmelCase_     :							List[Any]             =    np.array(a_			) * (self.config.num_train_timesteps // self.num_inference_steps)
  				lowerCAmelCase_     :							int             =    torch.from_numpy(a_			).to(a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[int]		,    a_							:			Optional[Any]			):
  				lowerCAmelCase_     :							Optional[Any]             =    t - self.config.num_train_timesteps // self.num_inference_steps
  
  				lowerCAmelCase_     :							List[Any]             =    self.alphas_cumprod[t]
  				lowerCAmelCase_     :							Dict             =    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
  				lowerCAmelCase_     :							List[str]             =    1 - alpha_prod_t
  				lowerCAmelCase_     :							str             =    1 - alpha_prod_t_prev
  
  				# For t > 0, compute predicted variance βt (see formula (6) and (7) from
  				# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
  				# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
  				# variance to pred_sample
  				# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
  				# without eta.
  				# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
  				lowerCAmelCase_     :							Dict             =    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  				return variance
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			torch.FloatTensor		,    a_							:			int		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			Optional[torch.Generator] = None		,    a_							:			bool = True		,    ):
  				lowerCAmelCase_     :							Any             =    timestep
  				lowerCAmelCase_     :							str             =    timestep - self.config.num_train_timesteps // self.num_inference_steps
  
  				# 1. compute alphas, betas
  				lowerCAmelCase_     :							Any             =    self.alphas_cumprod[t]
  				lowerCAmelCase_     :							List[str]             =    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
  				lowerCAmelCase_     :							Optional[Any]             =    1 - alpha_prod_t
  
  				# 2. compute predicted original sample from predicted noise also called
  				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
  				lowerCAmelCase_     :							List[Any]             =    (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
  
  				# 3. Clip "predicted x_0"
  				if self.config.clip_sample:
  						lowerCAmelCase_     :							Optional[int]             =    torch.clamp(a_		,    -1		,    1			)
  
  				# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
  				# substitute formula (7) in the algorithm coming from DDPM paper
  				# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
  				# DDIM schedule gives the same results as DDPM with eta = 1.0
  				# Noise is being reused in 7. and 8., but no impact on quality has
  				# been observed.
  
  				# 5. Add noise
  				lowerCAmelCase_     :							Optional[Any]             =    model_output.device
  				lowerCAmelCase_     :							List[str]             =    randn_tensor(model_output.shape		,    generator=a_		,    device=a_		,    dtype=model_output.dtype			)
  				lowerCAmelCase_     :							List[Any]             =    self.eta * self._get_variance(a_			) ** 0.5
  
  				lowerCAmelCase_     :							Optional[int]             =    0
  				if t > 0 and self.eta > 0:
  						lowerCAmelCase_     :							Optional[int]             =    std_dev_t * noise
  
  				# 6. compute "direction pointing to x_t" of formula (12)
  				# from https://arxiv.org/pdf/2010.02502.pdf
  				lowerCAmelCase_     :							Union[str, Any]             =    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
  
  				# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  				lowerCAmelCase_     :							Dict             =    alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance
  
  				# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
  				lowerCAmelCase_     :							Union[str, Any]             =    (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise
  
  				# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
  				lowerCAmelCase_     :							Tuple             =    mask * prev_known_part + (1.0 - mask) * prev_unknown_part
  
  				if not return_dict:
  						return (
  						    pred_prev_sample,
  						    pred_original_sample,
  						)
  
  				return RePaintSchedulerOutput(prev_sample=a_		,    pred_original_sample=a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict		,    a_							:			Any		,    a_							:			Dict		,    a_							:			Optional[int]=None			):
  				lowerCAmelCase_     :							Dict             =    self.config.num_train_timesteps // self.num_inference_steps
  
  				for i in range(a_			):
  						lowerCAmelCase_     :							Optional[Any]             =    self.betas[timestep + i]
  						if sample.device.type == "mps":
  								# randn does not work reproducibly on mps
  								lowerCAmelCase_     :							str             =    randn_tensor(sample.shape		,    dtype=sample.dtype		,    generator=a_			)
  								lowerCAmelCase_     :							List[str]             =    noise.to(sample.device			)
  						else:
  								lowerCAmelCase_     :							Tuple             =    randn_tensor(sample.shape		,    generator=a_		,    device=sample.device		,    dtype=sample.dtype			)
  
  						# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
  						lowerCAmelCase_     :							int             =    (1 - beta) ** 0.5 * sample + beta**0.5 * noise
  
  				return sample
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			torch.IntTensor		,    ):
  				raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."			)
  
  
  
  
  
  		def __len__(     self							:			Union[str, Any]			):
  				return self.config.num_train_timesteps
  