


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_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       42



class 	A_ (	__UpperCamelCase ,				__UpperCamelCase   ):




      '''simple docstring'''


      @register_to_config
      def __init__(    self:	List[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 = 0.1_8_2_1_5						,				a:	str = "group"						,				):
       super().__init__()

       # pass init params to Encoder
       __lowerCamelCase		:							str		 =						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						,				)

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

       __lowerCamelCase		:							List[str]		 =						nn.Convad(a						,				a						,				1    )
       __lowerCamelCase		:							Optional[Any]		 =						VectorQuantizer(a						,				a						,				beta=0.2_5						,				remap=a						,				sane_index_shape=a    )
       __lowerCamelCase		:							str		 =						nn.Convad(a						,				a						,				1    )

       # pass init params to Decoder
       __lowerCamelCase		:							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:	Optional[int]						,				a:	torch.FloatTensor						,				a:	bool = True    ):
       __lowerCamelCase		:							Any		 =						self.encoder(a    )
       __lowerCamelCase		:							str		 =						self.quant_conv(a    )

       if not return_dict:
        return (h,)

       return VQEncoderOutput(latents=a    )




      @apply_forward_hook
      def 					_snake_case     (    self:	Union[str, Any]						,				a:	torch.FloatTensor						,				a:	bool = False						,				a:	bool = True    ):
       # also go through quantization layer
       if not force_not_quantize:
        __lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		:							Tuple		 =						self.quantize(a    )
       else:
        __lowerCamelCase		:							Optional[Any]		 =						h
       __lowerCamelCase		:							Dict		 =						self.post_quant_conv(a    )
       __lowerCamelCase		:							str		 =						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:	int						,				a:	torch.FloatTensor						,				a:	bool = True    ):
       __lowerCamelCase		:							Tuple		 =						sample
       __lowerCamelCase		:							Union[str, Any]		 =						self.encode(a    ).latents
       __lowerCamelCase		:							str		 =						self.decode(a    ).sample

       if not return_dict:
        return (dec,)

       return DecoderOutput(sample=a    )





from __future__ import annotations
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[Any]		 =						str(SCREAMING_SNAKE_CASE__						)
 return len(SCREAMING_SNAKE_CASE__						) == 9 and set(SCREAMING_SNAKE_CASE__						) == set('123456789'						)
def 				UpperCamelCase__					(  ):
 for base_num in range(9_999      ,	4_999      ,	-1						):
  __lowerCamelCase		:							Tuple		 =						100_002 * base_num
  if is_9_pandigital(SCREAMING_SNAKE_CASE__						):
   return candidate

 for base_num in range(333      ,	99      ,	-1						):
  __lowerCamelCase		:							Union[str, Any]		 =						1_002_003 * base_num
  if is_9_pandigital(SCREAMING_SNAKE_CASE__						):
   return candidate

 return None


if __name__ == "__main__":
       print(F"""{solution() = }""")

