





from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block




@dataclass
class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					42




class   UpperCamelCase_       (     nn.Module    ):


							def __init__(							self    :int							,				__A    :str=3							,				__A    :Optional[Any]=3							,				__A    :str=("DownEncoderBlock2D",)							,				__A    :Tuple=(64,)							,				__A    :List[Any]=2							,				__A    :List[str]=32							,				__A    :str="silu"							,				__A    :Optional[int]=True							,				)   ->   List[str]:



									"""simple docstring"""



									super().__init__()
									SCREAMING_SNAKE_CASE__			=      layers_per_block

									SCREAMING_SNAKE_CASE__			=      torch.nn.Convad(
									    __A							,				block_out_channels[0]							,				kernel_size=3							,				stride=1							,				padding=1							,				)

									SCREAMING_SNAKE_CASE__			=      None
									SCREAMING_SNAKE_CASE__			=      nn.ModuleList([]		)

									# down
									SCREAMING_SNAKE_CASE__			=      block_out_channels[0]
									for i, down_block_type in enumerate(__A		):
											SCREAMING_SNAKE_CASE__			=      output_channel
											SCREAMING_SNAKE_CASE__			=      block_out_channels[i]
											SCREAMING_SNAKE_CASE__			=      i == len(__A		) - 1

											SCREAMING_SNAKE_CASE__			=      get_down_block(
											    __A							,				num_layers=self.layers_per_block							,				in_channels=__A							,				out_channels=__A							,				add_downsample=not is_final_block							,				resnet_eps=1E-6							,				downsample_padding=0							,				resnet_act_fn=__A							,				resnet_groups=__A							,				attention_head_dim=__A							,				temb_channels=__A							,				)
											self.down_blocks.append(__A		)

									# mid
									SCREAMING_SNAKE_CASE__			=      UNetMidBlockaD(
									    in_channels=block_out_channels[-1]							,				resnet_eps=1E-6							,				resnet_act_fn=__A							,				output_scale_factor=1							,				resnet_time_scale_shift="""default"""							,				attention_head_dim=block_out_channels[-1]							,				resnet_groups=__A							,				temb_channels=__A							,				)

									# out
									SCREAMING_SNAKE_CASE__			=      nn.GroupNorm(num_channels=block_out_channels[-1]							,				num_groups=__A							,				eps=1E-6		)
									SCREAMING_SNAKE_CASE__			=      nn.SiLU()

									SCREAMING_SNAKE_CASE__			=      2 * out_channels if double_z else out_channels
									SCREAMING_SNAKE_CASE__			=      nn.Convad(block_out_channels[-1]							,				__A							,				3							,				padding=1		)

									SCREAMING_SNAKE_CASE__			=      False


							def      _snake_case  (							self    :str							,				__A    :int		)   ->   Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      x
									SCREAMING_SNAKE_CASE__			=      self.conv_in(__A		)

									if self.training and self.gradient_checkpointing:

											def create_custom_forward(__A    :str		):
													def custom_forward(*__A    :str		):
															return module(*__A		)

													return custom_forward

											# down
											if is_torch_version(""">="""							,				"""1.11.0"""		):
													for down_block in self.down_blocks:
															SCREAMING_SNAKE_CASE__			=      torch.utils.checkpoint.checkpoint(
															    create_custom_forward(__A		)							,				__A							,				use_reentrant=__A		)
													# middle
													SCREAMING_SNAKE_CASE__			=      torch.utils.checkpoint.checkpoint(
													    create_custom_forward(self.mid_block		)							,				__A							,				use_reentrant=__A		)
											else:
													for down_block in self.down_blocks:
															SCREAMING_SNAKE_CASE__			=      torch.utils.checkpoint.checkpoint(create_custom_forward(__A		)							,				__A		)
													# middle
													SCREAMING_SNAKE_CASE__			=      torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block		)							,				__A		)

									else:
											# down
											for down_block in self.down_blocks:
													SCREAMING_SNAKE_CASE__			=      down_block(__A		)

											# middle
											SCREAMING_SNAKE_CASE__			=      self.mid_block(__A		)

									# post-process
									SCREAMING_SNAKE_CASE__			=      self.conv_norm_out(__A		)
									SCREAMING_SNAKE_CASE__			=      self.conv_act(__A		)
									SCREAMING_SNAKE_CASE__			=      self.conv_out(__A		)

									return sample




class   UpperCamelCase_       (     nn.Module    ):


							def __init__(							self    :Dict							,				__A    :Optional[Any]=3							,				__A    :Tuple=3							,				__A    :Optional[int]=("UpDecoderBlock2D",)							,				__A    :int=(64,)							,				__A    :int=2							,				__A    :Dict=32							,				__A    :Dict="silu"							,				__A    :str="group"							,				)   ->   Optional[Any]:



									"""simple docstring"""



									super().__init__()
									SCREAMING_SNAKE_CASE__			=      layers_per_block

									SCREAMING_SNAKE_CASE__			=      nn.Convad(
									    __A							,				block_out_channels[-1]							,				kernel_size=3							,				stride=1							,				padding=1							,				)

									SCREAMING_SNAKE_CASE__			=      None
									SCREAMING_SNAKE_CASE__			=      nn.ModuleList([]		)

									SCREAMING_SNAKE_CASE__			=      in_channels if norm_type == """spatial""" else None

									# mid
									SCREAMING_SNAKE_CASE__			=      UNetMidBlockaD(
									    in_channels=block_out_channels[-1]							,				resnet_eps=1E-6							,				resnet_act_fn=__A							,				output_scale_factor=1							,				resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type							,				attention_head_dim=block_out_channels[-1]							,				resnet_groups=__A							,				temb_channels=__A							,				)

									# up
									SCREAMING_SNAKE_CASE__			=      list(reversed(__A		)		)
									SCREAMING_SNAKE_CASE__			=      reversed_block_out_channels[0]
									for i, up_block_type in enumerate(__A		):
											SCREAMING_SNAKE_CASE__			=      output_channel
											SCREAMING_SNAKE_CASE__			=      reversed_block_out_channels[i]

											SCREAMING_SNAKE_CASE__			=      i == len(__A		) - 1

											SCREAMING_SNAKE_CASE__			=      get_up_block(
											    __A							,				num_layers=self.layers_per_block + 1							,				in_channels=__A							,				out_channels=__A							,				prev_output_channel=__A							,				add_upsample=not is_final_block							,				resnet_eps=1E-6							,				resnet_act_fn=__A							,				resnet_groups=__A							,				attention_head_dim=__A							,				temb_channels=__A							,				resnet_time_scale_shift=__A							,				)
											self.up_blocks.append(__A		)
											SCREAMING_SNAKE_CASE__			=      output_channel

									# out
									if norm_type == "spatial":
											SCREAMING_SNAKE_CASE__			=      SpatialNorm(block_out_channels[0]							,				__A		)
									else:
											SCREAMING_SNAKE_CASE__			=      nn.GroupNorm(num_channels=block_out_channels[0]							,				num_groups=__A							,				eps=1E-6		)
									SCREAMING_SNAKE_CASE__			=      nn.SiLU()
									SCREAMING_SNAKE_CASE__			=      nn.Convad(block_out_channels[0]							,				__A							,				3							,				padding=1		)

									SCREAMING_SNAKE_CASE__			=      False


							def      _snake_case  (							self    :List[str]							,				__A    :Any							,				__A    :Dict=None		)   ->   Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      z
									SCREAMING_SNAKE_CASE__			=      self.conv_in(__A		)

									SCREAMING_SNAKE_CASE__			=      next(iter(self.up_blocks.parameters()		)		).dtype
									if self.training and self.gradient_checkpointing:

											def create_custom_forward(__A    :str		):
													def custom_forward(*__A    :Dict		):
															return module(*__A		)

													return custom_forward

											if is_torch_version(""">="""							,				"""1.11.0"""		):
													# middle
													SCREAMING_SNAKE_CASE__			=      torch.utils.checkpoint.checkpoint(
													    create_custom_forward(self.mid_block		)							,				__A							,				__A							,				use_reentrant=__A		)
													SCREAMING_SNAKE_CASE__			=      sample.to(__A		)

													# up
													for up_block in self.up_blocks:
															SCREAMING_SNAKE_CASE__			=      torch.utils.checkpoint.checkpoint(
															    create_custom_forward(__A		)							,				__A							,				__A							,				use_reentrant=__A		)
											else:
													# middle
													SCREAMING_SNAKE_CASE__			=      torch.utils.checkpoint.checkpoint(
													    create_custom_forward(self.mid_block		)							,				__A							,				__A		)
													SCREAMING_SNAKE_CASE__			=      sample.to(__A		)

													# up
													for up_block in self.up_blocks:
															SCREAMING_SNAKE_CASE__			=      torch.utils.checkpoint.checkpoint(create_custom_forward(__A		)							,				__A							,				__A		)
									else:
											# middle
											SCREAMING_SNAKE_CASE__			=      self.mid_block(__A							,				__A		)
											SCREAMING_SNAKE_CASE__			=      sample.to(__A		)

											# up
											for up_block in self.up_blocks:
													SCREAMING_SNAKE_CASE__			=      up_block(__A							,				__A		)

        # post-process
									if latent_embeds is None:
											SCREAMING_SNAKE_CASE__			=      self.conv_norm_out(__A		)
									else:
											SCREAMING_SNAKE_CASE__			=      self.conv_norm_out(__A							,				__A		)
									SCREAMING_SNAKE_CASE__			=      self.conv_act(__A		)
									SCREAMING_SNAKE_CASE__			=      self.conv_out(__A		)

									return sample




class   UpperCamelCase_       (     nn.Module    ):


							def __init__(							self    :List[str]							,				__A    :Optional[Any]							,				__A    :Tuple							,				__A    :Optional[int]							,				__A    :int=None							,				__A    :Optional[Any]="random"							,				__A    :int=False							,				__A    :Any=True		)   ->   Any:



									"""simple docstring"""



									super().__init__()
									SCREAMING_SNAKE_CASE__			=      n_e
									SCREAMING_SNAKE_CASE__			=      vq_embed_dim
									SCREAMING_SNAKE_CASE__			=      beta
									SCREAMING_SNAKE_CASE__			=      legacy

									SCREAMING_SNAKE_CASE__			=      nn.Embedding(self.n_e							,				self.vq_embed_dim		)
									self.embedding.weight.data.uniform_(-1.0 / self.n_e							,				1.0 / self.n_e		)

									SCREAMING_SNAKE_CASE__			=      remap
									if self.remap is not None:
											self.register_buffer("""used"""							,				torch.tensor(np.load(self.remap		)		)		)
											SCREAMING_SNAKE_CASE__			=      self.used.shape[0]
											SCREAMING_SNAKE_CASE__			=      unknown_index  # "random" or "extra" or integer
											if self.unknown_index == "extra":
													SCREAMING_SNAKE_CASE__			=      self.re_embed
													SCREAMING_SNAKE_CASE__			=      self.re_embed + 1
											print(
											    f'''Remapping {self.n_e} indices to {self.re_embed} indices. '''
											    f'''Using {self.unknown_index} for unknown indices.'''		)
									else:
											SCREAMING_SNAKE_CASE__			=      n_e

									SCREAMING_SNAKE_CASE__			=      sane_index_shape


							def      _snake_case  (							self    :str							,				__A    :List[str]		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      inds.shape
									assert len(__A		) > 1
									SCREAMING_SNAKE_CASE__			=      inds.reshape(ishape[0]							,				-1		)
									SCREAMING_SNAKE_CASE__			=      self.used.to(__A		)
									SCREAMING_SNAKE_CASE__			=      (inds[:, :, None] == used[None, None, ...]).long()
									SCREAMING_SNAKE_CASE__			=      match.argmax(-1		)
									SCREAMING_SNAKE_CASE__			=      match.sum(2		) < 1
									if self.unknown_index == "random":
											SCREAMING_SNAKE_CASE__			=      torch.randint(0							,				self.re_embed							,				size=new[unknown].shape		).to(device=new.device		)
									else:
											SCREAMING_SNAKE_CASE__			=      self.unknown_index
									return new.reshape(__A		)


							def      _snake_case  (							self    :Dict							,				__A    :str		)   ->   List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      inds.shape
									assert len(__A		) > 1
									SCREAMING_SNAKE_CASE__			=      inds.reshape(ishape[0]							,				-1		)
									SCREAMING_SNAKE_CASE__			=      self.used.to(__A		)
									if self.re_embed > self.used.shape[0]:  # extra token
											SCREAMING_SNAKE_CASE__			=      0  # simply set to zero
									SCREAMING_SNAKE_CASE__			=      torch.gather(used[None, :][inds.shape[0] * [0], :]							,				1							,				__A		)
									return back.reshape(__A		)


							def      _snake_case  (							self    :Tuple							,				__A    :Optional[int]		)   ->   Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      z.permute(0							,				2							,				3							,				1		).contiguous()
									SCREAMING_SNAKE_CASE__			=      z.view(-1							,				self.vq_embed_dim		)

									# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
									SCREAMING_SNAKE_CASE__			=      torch.argmin(torch.cdist(__A							,				self.embedding.weight		)							,				dim=1		)

									SCREAMING_SNAKE_CASE__			=      self.embedding(__A		).view(z.shape		)
									SCREAMING_SNAKE_CASE__			=      None
									SCREAMING_SNAKE_CASE__			=      None

									# compute loss for embedding
									if not self.legacy:
											SCREAMING_SNAKE_CASE__			=      self.beta * torch.mean((z_q.detach() - z) ** 2		) + torch.mean((z_q - z.detach()) ** 2		)
									else:
											SCREAMING_SNAKE_CASE__			=      torch.mean((z_q.detach() - z) ** 2		) + self.beta * torch.mean((z_q - z.detach()) ** 2		)

									# preserve gradients
									SCREAMING_SNAKE_CASE__			=      z + (z_q - z).detach()

									# reshape back to match original input shape
									SCREAMING_SNAKE_CASE__			=      z_q.permute(0							,				3							,				1							,				2		).contiguous()

									if self.remap is not None:
											SCREAMING_SNAKE_CASE__			=      min_encoding_indices.reshape(z.shape[0]							,				-1		)  # add batch axis
											SCREAMING_SNAKE_CASE__			=      self.remap_to_used(__A		)
											SCREAMING_SNAKE_CASE__			=      min_encoding_indices.reshape(-1							,				1		)  # flatten

									if self.sane_index_shape:
											SCREAMING_SNAKE_CASE__			=      min_encoding_indices.reshape(z_q.shape[0]							,				z_q.shape[2]							,				z_q.shape[3]		)

									return z_q, loss, (perplexity, min_encodings, min_encoding_indices)


							def      _snake_case  (							self    :List[Any]							,				__A    :Tuple							,				__A    :List[str]		)   ->   List[Any]:



									"""simple docstring"""



									if self.remap is not None:
											SCREAMING_SNAKE_CASE__			=      indices.reshape(shape[0]							,				-1		)  # add batch axis
											SCREAMING_SNAKE_CASE__			=      self.unmap_to_all(__A		)
											SCREAMING_SNAKE_CASE__			=      indices.reshape(-1		)  # flatten again

									# get quantized latent vectors
									SCREAMING_SNAKE_CASE__			=      self.embedding(__A		)

									if shape is not None:
											SCREAMING_SNAKE_CASE__			=      z_q.view(__A		)
											# reshape back to match original input shape
											SCREAMING_SNAKE_CASE__			=      z_q.permute(0							,				3							,				1							,				2		).contiguous()

									return z_q




class   UpperCamelCase_       (     UpperCamelCase__    ):


							def __init__(							self    :List[Any]							,				__A    :List[Any]							,				__A    :int=False		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      parameters
									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      torch.chunk(__A							,				2							,				dim=1		)
									SCREAMING_SNAKE_CASE__			=      torch.clamp(self.logvar							,				-3_0.0							,				2_0.0		)
									SCREAMING_SNAKE_CASE__			=      deterministic
									SCREAMING_SNAKE_CASE__			=      torch.exp(0.5 * self.logvar		)
									SCREAMING_SNAKE_CASE__			=      torch.exp(self.logvar		)
									if self.deterministic:
											SCREAMING_SNAKE_CASE__			=      SCREAMING_SNAKE_CASE__			=      torch.zeros_like(
											    self.mean							,				device=self.parameters.device							,				dtype=self.parameters.dtype		)


							def      _snake_case  (							self    :Any							,				__A    :Optional[torch.Generator] = None		)   ->   torch.FloatTensor:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      randn_tensor(
									    self.mean.shape							,				generator=__A							,				device=self.parameters.device							,				dtype=self.parameters.dtype		)
									SCREAMING_SNAKE_CASE__			=      self.mean + self.std * sample
									return x


							def      _snake_case  (							self    :Union[str, Any]							,				__A    :int=None		)   ->   int:



									"""simple docstring"""



									if self.deterministic:
											return torch.Tensor([0.0]		)
									else:
											if other is None:
													return 0.5 * torch.sum(torch.pow(self.mean							,				2		) + self.var - 1.0 - self.logvar							,				dim=[1, 2, 3]		)
											else:
													return 0.5 * torch.sum(
													    torch.pow(self.mean - other.mean							,				2		) / other.var
													    + self.var / other.var
													    - 1.0
													    - self.logvar
													    + other.logvar							,				dim=[1, 2, 3]							,				)


							def      _snake_case  (							self    :str							,				__A    :Tuple							,				__A    :int=[1, 2, 3]		)   ->   int:



									"""simple docstring"""



									if self.deterministic:
											return torch.Tensor([0.0]		)
									SCREAMING_SNAKE_CASE__			=      np.log(2.0 * np.pi		)
									return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean							,				2		) / self.var							,				dim=__A		)


							def      _snake_case  (							self    :Tuple		)   ->   Optional[int]:



									"""simple docstring"""



									return self.mean






import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowerCamelCase   				=			logging.get_logger(__name__)  # pylint: disable=invalid-name






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[List, PIL.Image.Image, torch.Tensor]    ):
		warnings.warn(
		    """The preprocess method is deprecated and will be removed in a future version. Please"""
		    """ use VaeImageProcessor.preprocess instead"""					,   UpperCamelCase__					,   )
		if isinstance(UpperCamelCase__					,   torch.Tensor    ):
				return image
		elif isinstance(UpperCamelCase__					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__			=      [image]

		if isinstance(image[0]					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      image[0].size
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

				SCREAMING_SNAKE_CASE__			=      [np.array(i.resize((w, h)					,   resample=PIL_INTERPOLATION["""lanczos"""]    )    )[None, :] for i in image]
				SCREAMING_SNAKE_CASE__			=      np.concatenate(UpperCamelCase__					,   axis=0    )
				SCREAMING_SNAKE_CASE__			=      np.array(UpperCamelCase__    ).astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE__			=      image.transpose(0					,   3					,   1					,   2    )
				SCREAMING_SNAKE_CASE__			=      2.0 * image - 1.0
				SCREAMING_SNAKE_CASE__			=      torch.from_numpy(UpperCamelCase__    )
		elif isinstance(image[0]					,   torch.Tensor    ):
				SCREAMING_SNAKE_CASE__			=      torch.cat(UpperCamelCase__					,   dim=0    )
		return image






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[List, PIL.Image.Image, torch.Tensor]    ):
		if isinstance(UpperCamelCase__					,   torch.Tensor    ):
				return mask
		elif isinstance(UpperCamelCase__					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__			=      [mask]

		if isinstance(mask[0]					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      mask[0].size
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				SCREAMING_SNAKE_CASE__			=      [np.array(m.convert("""L"""    ).resize((w, h)					,   resample=PIL_INTERPOLATION["""nearest"""]    )    )[None, :] for m in mask]
				SCREAMING_SNAKE_CASE__			=      np.concatenate(UpperCamelCase__					,   axis=0    )
				SCREAMING_SNAKE_CASE__			=      mask.astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE__			=      0
				SCREAMING_SNAKE_CASE__			=      1
				SCREAMING_SNAKE_CASE__			=      torch.from_numpy(UpperCamelCase__    )
		elif isinstance(mask[0]					,   torch.Tensor    ):
				SCREAMING_SNAKE_CASE__			=      torch.cat(UpperCamelCase__					,   dim=0    )
		return mask




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					42
							lowerCamelCase_           =					42


							def __init__(							self    :Any							,				__A    :List[Any]							,				__A    :Optional[Any]		)   ->   Union[str, Any]:



									"""simple docstring"""



									super().__init__()
									self.register_modules(unet=__A							,				scheduler=__A		)


							@torch.no_grad()
							def __call__(							self    :str							,				__A    :Union[torch.Tensor, PIL.Image.Image]							,				__A    :Union[torch.Tensor, PIL.Image.Image]							,				__A    :int = 250							,				__A    :float = 0.0							,				__A    :int = 10							,				__A    :int = 10							,				__A    :Optional[Union[torch.Generator, List[torch.Generator]]] = None							,				__A    :Optional[str] = "pil"							,				__A    :bool = True							,				)   ->   Union[ImagePipelineOutput, Tuple]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      image

									SCREAMING_SNAKE_CASE__			=      _preprocess_image(__A		)
									SCREAMING_SNAKE_CASE__			=      original_image.to(device=self.device							,				dtype=self.unet.dtype		)
									SCREAMING_SNAKE_CASE__			=      _preprocess_mask(__A		)
									SCREAMING_SNAKE_CASE__			=      mask_image.to(device=self.device							,				dtype=self.unet.dtype		)

									SCREAMING_SNAKE_CASE__			=      original_image.shape[0]

									# sample gaussian noise to begin the loop
									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.'''		)

									SCREAMING_SNAKE_CASE__			=      original_image.shape
									SCREAMING_SNAKE_CASE__			=      randn_tensor(__A							,				generator=__A							,				device=self.device							,				dtype=self.unet.dtype		)

									# set step values
									self.scheduler.set_timesteps(__A							,				__A							,				__A							,				self.device		)
									SCREAMING_SNAKE_CASE__			=      eta

									SCREAMING_SNAKE_CASE__			=      self.scheduler.timesteps[0] + 1
									SCREAMING_SNAKE_CASE__			=      generator[0] if isinstance(__A							,				__A		) else generator
									for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
											if t < t_last:
													# predict the noise residual
													SCREAMING_SNAKE_CASE__			=      self.unet(__A							,				__A		).sample
													# compute previous image: x_t -> x_t-1
													SCREAMING_SNAKE_CASE__			=      self.scheduler.step(__A							,				__A							,				__A							,				__A							,				__A							,				__A		).prev_sample

											else:
													# compute the reverse: x_t-1 -> x_t
													SCREAMING_SNAKE_CASE__			=      self.scheduler.undo_step(__A							,				__A							,				__A		)
											SCREAMING_SNAKE_CASE__			=      t

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

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=__A		)