






'''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   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						42





class   snake_case					(					lowercase    ,		lowercase    ):





  """simple docstring"""




  @register_to_config
  def __init__(					self    ,						UpperCamelCase = 3    ,						UpperCamelCase = 3    ,						UpperCamelCase = ("DownEncoderBlock2D",)    ,						UpperCamelCase = ("UpDecoderBlock2D",)    ,						UpperCamelCase = (64,)    ,						UpperCamelCase = 1    ,						UpperCamelCase = "silu"    ,						UpperCamelCase = 3    ,						UpperCamelCase = 32    ,						UpperCamelCase = 256    ,						UpperCamelCase = 32    ,						UpperCamelCase = None    ,						UpperCamelCase = 0.18_215    ,						UpperCamelCase = "group"    ,						):







   """simple docstring"""
   super().__init__()

   # pass init params to Encoder
   lowerCamelCase_  				=						Encoder(
       in_channels=UpperCamelCase_    ,						out_channels=UpperCamelCase_    ,						down_block_types=UpperCamelCase_    ,						block_out_channels=UpperCamelCase_    ,						layers_per_block=UpperCamelCase_    ,						act_fn=UpperCamelCase_    ,						norm_num_groups=UpperCamelCase_    ,						double_z=UpperCamelCase_    ,						)

   lowerCamelCase_  				=						vq_embed_dim if vq_embed_dim is not None else latent_channels

   lowerCamelCase_  				=						nn.Convad(UpperCamelCase_    ,						UpperCamelCase_    ,						1  )
   lowerCamelCase_  				=						VectorQuantizer(UpperCamelCase_    ,						UpperCamelCase_    ,						beta=0.25    ,						remap=UpperCamelCase_    ,						sane_index_shape=UpperCamelCase_  )
   lowerCamelCase_  				=						nn.Convad(UpperCamelCase_    ,						UpperCamelCase_    ,						1  )

   # pass init params to Decoder
   lowerCamelCase_  				=						Decoder(
       in_channels=UpperCamelCase_    ,						out_channels=UpperCamelCase_    ,						up_block_types=UpperCamelCase_    ,						block_out_channels=UpperCamelCase_    ,						layers_per_block=UpperCamelCase_    ,						act_fn=UpperCamelCase_    ,						norm_num_groups=UpperCamelCase_    ,						norm_type=UpperCamelCase_    ,						)






  @apply_forward_hook
  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = True  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.encoder(UpperCamelCase_  )
   lowerCamelCase_  				=						self.quant_conv(UpperCamelCase_  )

   if not return_dict:
    return (h,)

   return VQEncoderOutput(latents=UpperCamelCase_  )






  @apply_forward_hook
  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = False    ,						UpperCamelCase = True  ):







   """simple docstring"""





   if not force_not_quantize:
    lowerCamelCase_  				=						self.quantize(UpperCamelCase_  )
   else:
    lowerCamelCase_  				=						h
   lowerCamelCase_  				=						self.post_quant_conv(UpperCamelCase_  )
   lowerCamelCase_  				=						self.decoder(UpperCamelCase_    ,						quant if self.config.norm_type == "spatial" else None  )

   if not return_dict:
    return (dec,)

   return DecoderOutput(sample=UpperCamelCase_  )







  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = True  ):







   """simple docstring"""
   lowerCamelCase_  				=						sample
   lowerCamelCase_  				=						self.encode(UpperCamelCase_  ).latents
   lowerCamelCase_  				=						self.decode(UpperCamelCase_  ).sample

   if not return_dict:
    return (dec,)

   return DecoderOutput(sample=UpperCamelCase_  )







'''simple docstring'''

def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 return "\n".join(
     F'''{number} * {i} = {number * i}''' for i in range(1      ,       number_of_terms + 1	)	)


if __name__ == "__main__":
     print(multiplication_table(number=5, number_of_terms=10))



