

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__							):
  _a									=  42





class  __lowerCAmelCase     (					snake_case__			,			snake_case__							):




  @register_to_config
  def __init__(				self							,   lowerCAmelCase = 3							,   lowerCAmelCase = 3							,   lowerCAmelCase = ("DownEncoderBlock2D",)							,   lowerCAmelCase = ("UpDecoderBlock2D",)							,   lowerCAmelCase = (64,)							,   lowerCAmelCase = 1							,   lowerCAmelCase = "silu"							,   lowerCAmelCase = 3							,   lowerCAmelCase = 32							,   lowerCAmelCase = 256							,   lowerCAmelCase = 32							,   lowerCAmelCase = None							,   lowerCAmelCase = 0.18215							,   lowerCAmelCase = "group"							,   )  -> int:
        '''simple docstring'''






        super().__init__()

        # pass init params to Encoder
        _lowercase	       =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							,   )

        _lowercase	       =vq_embed_dim if vq_embed_dim is not None else latent_channels

        _lowercase	       =nn.Convad(_A							,   _A							,   1					)
        _lowercase	       =VectorQuantizer(_A							,   _A							,   beta=0.25							,   remap=_A							,   sane_index_shape=_A					)
        _lowercase	       =nn.Convad(_A							,   _A							,   1					)

        # pass init params to Decoder
        _lowercase	       =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							,   lowerCAmelCase							,   lowerCAmelCase = True					)  -> Any:
        '''simple docstring'''






        _lowercase	       =self.encoder(_A					)
        _lowercase	       =self.quant_conv(_A					)

        if not return_dict:
              return (h,)

        return VQEncoderOutput(latents=_A					)




  @apply_forward_hook
  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = False							,   lowerCAmelCase = True					)  -> Union[str, Any]:
        '''simple docstring'''






        if not force_not_quantize:
              _lowercase	       =self.quantize(_A					)
        else:
              _lowercase	       =h
        _lowercase	       =self.post_quant_conv(_A					)
        _lowercase	       =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							,   lowerCAmelCase							,   lowerCAmelCase = True					)  -> List[str]:
        '''simple docstring'''






        _lowercase	       =sample
        _lowercase	       =self.encode(_A					).latents
        _lowercase	       =self.decode(_A					).sample

        if not return_dict:
              return (dec,)

        return DecoderOutput(sample=_A					)









def  _SCREAMING_SNAKE_CASE				(   a					,						a = 0  )      ->      list:
					__A	:						int       =				length or len(a  )
					__A	:						str       =				False
					for i in range(length - 1  ):
										if list_data[i] > list_data[i + 1]:
															__A    ,				__A	:						Optional[int]       =				list_data[i + 1], list_data[i]
															__A	:						Union[str, Any]       =				True

					return list_data if not swapped else bubble_sort(a					,						length - 1  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




