




'''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 (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     torch.FloatTensor







class 					_a (a__,		a__	):





     '''simple docstring'''



     @register_to_config
     def __init__(				self							,__a = 3							,__a = 3							,__a = ("DownEncoderBlock2D",)							,__a = ("UpDecoderBlock2D",)							,__a = (64,)							,__a = 1							,__a = "silu"							,__a = 3							,__a = 32							,__a = 256							,__a = 32							,__a = None							,__a = 0.18_215							,__a = "group"							,)      ->				Union[str, Any]:
          super().__init__()

          # pass init params to Encoder
          snake_case			:      List[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							,)

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

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

          # pass init params to Decoder
          snake_case			:      Union[str, 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 			snake_case_      (				self							,__a							,__a = True							)      ->				VQEncoderOutput:
          snake_case			:      Dict        =			self.encoder(__a							)
          snake_case			:      int        =			self.quant_conv(__a							)

          if not return_dict:
               return (h,)

          return VQEncoderOutput(latents=__a							)







     @apply_forward_hook
     def 			snake_case_      (				self							,__a							,__a = False							,__a = True							)      ->				Union[DecoderOutput, torch.FloatTensor]:
          # also go through quantization layer
          if not force_not_quantize:
               snake_case ,		snake_case ,		snake_case			:      Tuple        =			self.quantize(__a							)
          else:
               snake_case			:      List[Any]        =			h
          snake_case			:      Optional[int]        =			self.post_quant_conv(__a							)
          snake_case			:      Union[str, Any]        =			self.decoder(__a							,quant if self.config.norm_type == """spatial""" else None							)

          if not return_dict:
               return (dec,)

          return DecoderOutput(sample=__a							)







     def 			snake_case_      (				self							,__a							,__a = True							)      ->				Union[DecoderOutput, torch.FloatTensor]:
          snake_case			:      Tuple        =			sample
          snake_case			:      Optional[int]        =			self.encode(__a							).latents
          snake_case			:      Optional[Any]        =			self.decode(__a							).sample

          if not return_dict:
               return (dec,)

          return DecoderOutput(sample=__a							)









'''simple docstring'''







def        lowerCamelCase__				(      __lowercase				):
     if not isinstance(__lowercase     ,   __lowercase				):
          snake_case			:      int        =			F'''Input value of [number={number}] must be an integer'''
          raise TypeError(__lowercase				)
     if number < 0:
          return False
     snake_case			:      Optional[int]        =			number * number
     while number > 0:
          if number % 10 != number_square % 10:
               return False
          number //= 10
          number_square //= 10
     return True


if __name__ == "__main__":
      import doctest

      doctest.testmod()



