'''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 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  torch.FloatTensor

class 	lowerCamelCase_	(snake_case__							, snake_case__    ):
      '''simple docstring'''

      @register_to_config
      def __init__(       self						:				Optional[Any]						,					A						:				int = 3						,					A						:				int = 3						,					A						:				Tuple[str] = ("DownEncoderBlock2D",)						,					A						:				Tuple[str] = ("UpDecoderBlock2D",)						,					A						:				Tuple[int] = (64,)						,					A						:				int = 1						,					A						:				str = "silu"						,					A						:				int = 3						,					A						:				int = 32						,					A						:				int = 256						,					A						:				int = 32						,					A						:				Optional[int] = None						,					A						:				float = 0.18_215						,					A						:				str = "group"						,					):
             super().__init__()

             # pass init params to Encoder
             _UpperCAmelCase     :  Any							      =    Encoder(
                 in_channels=A						,					out_channels=A						,					down_block_types=A						,					block_out_channels=A						,					layers_per_block=A						,					act_fn=A						,					norm_num_groups=A						,					double_z=A						,					)

             _UpperCAmelCase     :  Dict							      =    vq_embed_dim if vq_embed_dim is not None else latent_channels

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

             # pass init params to Decoder
             _UpperCAmelCase     :  List[Any]							      =    Decoder(
                 in_channels=A						,					out_channels=A						,					up_block_types=A						,					block_out_channels=A						,					layers_per_block=A						,					act_fn=A						,					norm_num_groups=A						,					norm_type=A						,					)




      @apply_forward_hook
      def      _A	(       self						:				List[str]						,					A						:				torch.FloatTensor						,					A						:				bool = True       ):
             _UpperCAmelCase     :  List[Any]							      =    self.encoder(A       )
             _UpperCAmelCase     :  List[Any]							      =    self.quant_conv(A       )

             if not return_dict:
                    return (h,)

             return VQEncoderOutput(latents=A       )




      @apply_forward_hook
      def      _A	(       self						:				Optional[Any]						,					A						:				torch.FloatTensor						,					A						:				bool = False						,					A						:				bool = True       ):
             # also go through quantization layer
             if not force_not_quantize:
                    _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase     :  Dict							      =    self.quantize(A       )
             else:
                    _UpperCAmelCase     :  Tuple							      =    h
             _UpperCAmelCase     :  Dict							      =    self.post_quant_conv(A       )
             _UpperCAmelCase     :  Tuple							      =    self.decoder(A						,					quant if self.config.norm_type == "spatial" else None       )

             if not return_dict:
                    return (dec,)

             return DecoderOutput(sample=A       )





      def      _A	(       self						:				Union[str, Any]						,					A						:				torch.FloatTensor						,					A						:				bool = True       ):
             _UpperCAmelCase     :  str							      =    sample
             _UpperCAmelCase     :  Optional[Any]							      =    self.encode(A       ).latents
             _UpperCAmelCase     :  List[Any]							      =    self.decode(A       ).sample

             if not return_dict:
                    return (dec,)

             return DecoderOutput(sample=A       )





'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

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





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  42
  _lowerCamelCase		    =  42






  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   super().__init__()
   self.register_modules(unet=UpperCamelCase			,  scheduler=UpperCamelCase     )







  @torch.no_grad()
  def __call__(    self			,  UpperCamelCase = 1			,  UpperCamelCase = 2000			,  UpperCamelCase = None			,  UpperCamelCase = "pil"			,  UpperCamelCase = True			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  self.unet.config.sample_size
   lowerCamelCase_      =  (batch_size, 3, img_size, img_size)

   lowerCamelCase_      =  self.unet

   lowerCamelCase_      =  randn_tensor(UpperCamelCase			,  generator=UpperCamelCase     ) * self.scheduler.init_noise_sigma
   lowerCamelCase_      =  sample.to(self.device     )

   self.scheduler.set_timesteps(UpperCamelCase     )
   self.scheduler.set_sigmas(UpperCamelCase     )

   for i, t in enumerate(self.progress_bar(self.scheduler.timesteps     )     ):
    lowerCamelCase_      =  self.scheduler.sigmas[i] * torch.ones(shape[0]			,  device=self.device     )

    # correction step
    for _ in range(self.scheduler.config.correct_steps     ):
     lowerCamelCase_      =  self.unet(UpperCamelCase			,  UpperCamelCase     ).sample
     lowerCamelCase_      =  self.scheduler.step_correct(UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     ).prev_sample

    # prediction step
    lowerCamelCase_      =  model(UpperCamelCase			,  UpperCamelCase     ).sample
    lowerCamelCase_      =  self.scheduler.step_pred(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     )

    lowerCamelCase_ ,lowerCamelCase_      =  output.prev_sample, output.prev_sample_mean

   lowerCamelCase_      =  sample_mean.clamp(0			,  1     )
   lowerCamelCase_      =  sample.cpu().permute(0			,  2			,  3			,  1     ).numpy()
   if output_type == "pil":
    lowerCamelCase_      =  self.numpy_to_pil(UpperCamelCase     )

   if not return_dict:
    return (sample,)

   return ImagePipelineOutput(images=UpperCamelCase     )



