

'''simple docstring'''

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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer




@dataclass
class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     torch.FloatTensor




class 	A_			(    _snake_case	,       _snake_case							):






     '''simple docstring'''



     @register_to_config
     def __init__(		self			:					Any					,      lowercase_			:					int = 3					,      lowercase_			:					int = 3					,      lowercase_			:					Tuple[str] = ("DownEncoderBlock2D",)					,      lowercase_			:					Tuple[str] = ("UpDecoderBlock2D",)					,      lowercase_			:					Tuple[int] = (64,)					,      lowercase_			:					int = 1					,      lowercase_			:					str = "silu"					,      lowercase_			:					int = 3					,      lowercase_			:					int = 32					,      lowercase_			:					int = 256					,      lowercase_			:					int = 32					,      lowercase_			:					Optional[int] = None					,      lowercase_			:					float = 0.1_8215					,      lowercase_			:					str = "group"					,      )						->     str:
      super().__init__()

      # pass init params to Encoder
      UpperCAmelCase	:     Optional[Any]						 =			Encoder(
          in_channels=lowercase_					,      out_channels=lowercase_					,      down_block_types=lowercase_					,      block_out_channels=lowercase_					,      layers_per_block=lowercase_					,      act_fn=lowercase_					,      norm_num_groups=lowercase_					,      double_z=lowercase_					,      )

      UpperCAmelCase	:     Union[str, Any]						 =			vq_embed_dim if vq_embed_dim is not None else latent_channels

      UpperCAmelCase	:     Tuple						 =			nn.Convad(lowercase_					,      lowercase_					,      1	)
      UpperCAmelCase	:     str						 =			VectorQuantizer(lowercase_					,      lowercase_					,      beta=0.25					,      remap=lowercase_					,      sane_index_shape=lowercase_	)
      UpperCAmelCase	:     Union[str, Any]						 =			nn.Convad(lowercase_					,      lowercase_					,      1	)

      # pass init params to Decoder
      UpperCAmelCase	:     Dict						 =			Decoder(
          in_channels=lowercase_					,      out_channels=lowercase_					,      up_block_types=lowercase_					,      block_out_channels=lowercase_					,      layers_per_block=lowercase_					,      act_fn=lowercase_					,      norm_num_groups=lowercase_					,      norm_type=lowercase_					,      )


     @apply_forward_hook
     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					torch.FloatTensor					,      lowercase_			:					bool = True	)						->     VQEncoderOutput:
      UpperCAmelCase	:     Tuple						 =			self.encoder(lowercase_	)
      UpperCAmelCase	:     Optional[Any]						 =			self.quant_conv(lowercase_	)

      if not return_dict:
       return (h,)

      return VQEncoderOutput(latents=lowercase_	)


     @apply_forward_hook
     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					torch.FloatTensor					,      lowercase_			:					bool = False					,      lowercase_			:					bool = True	)						->     Union[DecoderOutput, torch.FloatTensor]:
      # also go through quantization layer
      if not force_not_quantize:
       UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase	:     List[str]						 =			self.quantize(lowercase_	)
      else:
       UpperCAmelCase	:     Union[str, Any]						 =			h
      UpperCAmelCase	:     Optional[Any]						 =			self.post_quant_conv(lowercase_	)
      UpperCAmelCase	:     Any						 =			self.decoder(lowercase_					,      quant if self.config.norm_type == 'spatial' else None	)

      if not return_dict:
       return (dec,)

      return DecoderOutput(sample=lowercase_	)




     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					torch.FloatTensor					,      lowercase_			:					bool = True	)						->     Union[DecoderOutput, torch.FloatTensor]:
      UpperCAmelCase	:     str						 =			sample
      UpperCAmelCase	:     Union[str, Any]						 =			self.encode(lowercase_	).latents
      UpperCAmelCase	:     Union[str, Any]						 =			self.decode(lowercase_	).sample

      if not return_dict:
       return (dec,)

      return DecoderOutput(sample=lowercase_	)







'''simple docstring'''

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 	A_			(    _snake_case							):






     '''simple docstring'''



     def __init__(		self			:					List[Any]					,      lowercase_			:					VQModel					,      lowercase_			:					UNetaDModel					,      lowercase_			:					DDIMScheduler	)						->     int:
      super().__init__()
      self.register_modules(vqvae=lowercase_					,      unet=lowercase_					,      scheduler=lowercase_	)




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

      # scale the initial noise by the standard deviation required by the scheduler
      UpperCAmelCase	:     Optional[Any]						 =			latents * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(lowercase_	)

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

      UpperCAmelCase	:     Tuple						 =			{}
      if accepts_eta:
       UpperCAmelCase	:     List[str]						 =			eta

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

      # decode the image latents with the VAE
      UpperCAmelCase	:     Any						 =			self.vqvae.decode(lowercase_	).sample

      UpperCAmelCase	:     Union[str, Any]						 =			(image / 2 + 0.5).clamp(0					,      1	)
      UpperCAmelCase	:     Optional[Any]						 =			image.cpu().permute(0					,      2					,      3					,      1	).numpy()
      if output_type == "pil":
       UpperCAmelCase	:     Tuple						 =			self.numpy_to_pil(lowercase_	)

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=lowercase_	)




