






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

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							def __init__(  self:   List[str]		,     __A:   VQModel		,     __A:   UNetaDModel		,     __A:   DDIMScheduler   )    ->				Tuple:
												super().__init__()
												self.register_modules(vqvae=__A		,     unet=__A		,     scheduler=__A   )



							@torch.no_grad()
							def __call__(  self:   Optional[Any]		,     __A:   int = 1		,     __A:   Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     __A:   float = 0.0		,     __A:   int = 50		,     __A:   Optional[str] = "pil"		,     __A:   bool = True		,     **__A:   int		,     )    ->				Union[Tuple, ImagePipelineOutput]:
												_A								= randn_tensor(
												    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,     generator=__A		,     )
												_A								= latents.to(self.device   )

												# scale the initial noise by the standard deviation required by the scheduler
												_A								= latents * self.scheduler.init_noise_sigma

												self.scheduler.set_timesteps(__A   )

												# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
												_A								= '''eta''' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )

												_A								= {}
												if accepts_eta:
																	_A								= eta

												for t in self.progress_bar(self.scheduler.timesteps   ):
																	_A								= self.scheduler.scale_model_input(__A		,     __A   )
																	# predict the noise residual
																	_A								= self.unet(__A		,     __A   ).sample
																	# compute the previous noisy sample x_t -> x_t-1
																	_A								= self.scheduler.step(__A		,     __A		,     __A		,     **__A   ).prev_sample

												# decode the image latents with the VAE
												_A								= self.vqvae.decode(__A   ).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   )











import flax.linen as nn
import jax
import jax.numpy as jnp





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					jnp.floataa

							def       __A (  self:   Tuple   )    ->				Tuple:
												_A								= nn.Conv(
												    self.out_channels		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )



							def __call__(  self:   Dict		,     __A:   Dict   )    ->				Tuple:
												_A							,_A							,_A							,_A								= hidden_states.shape
												_A								= jax.image.resize(
												    __A		,     shape=(batch, height * 2, width * 2, channels)		,     method='''nearest'''		,     )
												_A								= self.conv(__A   )
												return hidden_states





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					jnp.floataa

							def       __A (  self:   List[str]   )    ->				Tuple:
												_A								= nn.Conv(
												    self.out_channels		,     kernel_size=(3, 3)		,     strides=(2, 2)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )



							def __call__(  self:   Union[str, Any]		,     __A:   List[Any]   )    ->				Union[str, Any]:
												# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
												# hidden_states = jnp.pad(hidden_states, pad_width=pad)
												_A								= self.conv(__A   )
												return hidden_states





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					None
							A_						  =					0.0
							A_						  =					None
							A_						  =					jnp.floataa

							def       __A (  self:   Dict   )    ->				Dict:
												_A								= self.in_channels if self.out_channels is None else self.out_channels

												_A								= nn.GroupNorm(num_groups=32		,     epsilon=1e-5   )
												_A								= nn.Conv(
												    __A		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )

												_A								= nn.Dense(__A		,     dtype=self.dtype   )

												_A								= nn.GroupNorm(num_groups=32		,     epsilon=1e-5   )
												_A								= nn.Dropout(self.dropout_prob   )
												_A								= nn.Conv(
												    __A		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )

												_A								= self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

												_A								= None
												if use_nin_shortcut:
																	_A								= nn.Conv(
																	    __A		,     kernel_size=(1, 1)		,     strides=(1, 1)		,     padding='''VALID'''		,     dtype=self.dtype		,     )



							def __call__(  self:   Dict		,     __A:   List[Any]		,     __A:   List[Any]		,     __A:   Any=True   )    ->				List[Any]:
												_A								= hidden_states
												_A								= self.norma(__A   )
												_A								= nn.swish(__A   )
												_A								= self.conva(__A   )

												_A								= self.time_emb_proj(nn.swish(__A   )   )
												_A								= jnp.expand_dims(jnp.expand_dims(__A		,     1   )		,     1   )
												_A								= hidden_states + temb

												_A								= self.norma(__A   )
												_A								= nn.swish(__A   )
												_A								= self.dropout(__A		,     __A   )
												_A								= self.conva(__A   )

												if self.conv_shortcut is not None:
																	_A								= self.conv_shortcut(__A   )

												return hidden_states + residual



