





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      (       A_	):
   _UpperCamelCase			:    List[Any]  		=					42







class 	__lowerCAmelCase      (       A_		,A_	):





   @register_to_config
   def __init__(			self       ,      snake_case = 3       ,      snake_case = 3       ,      snake_case = ("DownEncoderBlock2D",)       ,      snake_case = ("UpDecoderBlock2D",)       ,      snake_case = (64,)       ,      snake_case = 1       ,      snake_case = "silu"       ,      snake_case = 3       ,      snake_case = 32       ,      snake_case = 256       ,      snake_case = 32       ,      snake_case = None       ,      snake_case = 0.18_215       ,      snake_case = "group"       ,      )					->	Tuple:
      """simple docstring"""





      super().__init__()

      # pass init params to Encoder
      a__						:    List[str]   		=	Encoder(
          in_channels=snake_case       ,      out_channels=snake_case       ,      down_block_types=snake_case       ,      block_out_channels=snake_case       ,      layers_per_block=snake_case       ,      act_fn=snake_case       ,      norm_num_groups=snake_case       ,      double_z=snake_case       ,      )

      a__						:    Tuple   		=	vq_embed_dim if vq_embed_dim is not None else latent_channels

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

      # pass init params to Decoder
      a__						:    int   		=	Decoder(
          in_channels=snake_case       ,      out_channels=snake_case       ,      up_block_types=snake_case       ,      block_out_channels=snake_case       ,      layers_per_block=snake_case       ,      act_fn=snake_case       ,      norm_num_groups=snake_case       ,      norm_type=snake_case       ,      )





   @apply_forward_hook
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = True   )					->	VQEncoderOutput:
      """simple docstring"""





      a__						:    Optional[Any]   		=	self.encoder(snake_case   )
      a__						:    Dict   		=	self.quant_conv(snake_case   )

      if not return_dict:
         return (h,)

      return VQEncoderOutput(latents=snake_case   )





   @apply_forward_hook
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = False       ,      snake_case = True   )					->	Union[DecoderOutput, torch.FloatTensor]:
      """simple docstring"""





      if not force_not_quantize:
         a__						:    List[str]   		=	self.quantize(snake_case   )
      else:
         a__						:    int   		=	h
      a__						:    List[Any]   		=	self.post_quant_conv(snake_case   )
      a__						:    Optional[int]   		=	self.decoder(snake_case       ,      quant if self.config.norm_type == "spatial" else None   )

      if not return_dict:
         return (dec,)

      return DecoderOutput(sample=snake_case   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = True   )					->	Union[DecoderOutput, torch.FloatTensor]:
      """simple docstring"""





      a__						:    Dict   		=	sample
      a__						:    Any   		=	self.encode(snake_case   ).latents
      a__						:    Optional[int]   		=	self.decode(snake_case   ).sample

      if not return_dict:
         return (dec,)

      return DecoderOutput(sample=snake_case   )





"""simple docstring"""
def 				_snake_case      (   __snake_case							:      str						, __snake_case							:      str   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		len(__snake_case   )
   _lowerCamelCase	:					Union[str, Any]   =		len(__snake_case   )
   _lowerCamelCase	:					int   =		[[False for _ in range(m + 1   )] for _ in range(n + 1   )]
   _lowerCamelCase	:					Union[str, Any]   =		True
   for i in range(__snake_case   ):
      for j in range(m + 1   ):
         if dp[i][j]:
            if j < m and a[i].upper() == b[j]:
               _lowerCamelCase	:					Tuple   =		True
            if a[i].islower():
               _lowerCamelCase	:					Tuple   =		True
   return dp[n][m]


if __name__ == "__main__":
 import doctest

 doctest.testmod()

