





'''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 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			42




class 					SCREAMING_SNAKE_CASE       (a__   ,						a__				):


     @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 = 0.18215		,      _UpperCAmelCase = "group"		,      ):

          '''simple docstring'''






          super().__init__()

          # pass init params to Encoder
          __A		:					Optional[int]						=			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		,      )

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

          __A		:					Union[str, Any]						=			nn.Convad(_UpperCAmelCase		,      _UpperCAmelCase		,      1)
          __A		:					List[Any]						=			VectorQuantizer(_UpperCAmelCase		,      _UpperCAmelCase		,      beta=0.25		,      remap=_UpperCAmelCase		,      sane_index_shape=_UpperCAmelCase)
          __A		:					Dict						=			nn.Convad(_UpperCAmelCase		,      _UpperCAmelCase		,      1)

          # pass init params to Decoder
          __A		:					Any						=			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 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = True):

          '''simple docstring'''






          __A		:					Optional[int]						=			self.encoder(_UpperCAmelCase)
          __A		:					str						=			self.quant_conv(_UpperCAmelCase)

          if not return_dict:
               return (h,)

          return VQEncoderOutput(latents=_UpperCAmelCase)


     @apply_forward_hook
     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = False		,      _UpperCAmelCase = True):

          '''simple docstring'''






          if not force_not_quantize:
               __A					,__A					,__A		:					Dict						=			self.quantize(_UpperCAmelCase)
          else:
               __A		:					int						=			h
          __A		:					List[Any]						=			self.post_quant_conv(_UpperCAmelCase)
          __A		:					Union[str, Any]						=			self.decoder(_UpperCAmelCase		,      quant if self.config.norm_type == 'spatial' else None)

          if not return_dict:
               return (dec,)

          return DecoderOutput(sample=_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = True):

          '''simple docstring'''






          __A		:					Any						=			sample
          __A		:					Optional[int]						=			self.encode(_UpperCAmelCase).latents
          __A		:					Union[str, Any]						=			self.decode(_UpperCAmelCase).sample

          if not return_dict:
               return (dec,)

          return DecoderOutput(sample=_UpperCAmelCase)

import os
def 							SCREAMING_SNAKE_CASE_	(					)			->						List[str]:
       with open(os.path.dirname(UpperCAmelCase_				) + '''/p022_names.txt'''				) as file:
              SCREAMING_SNAKE_CASE_							:				Any		=str(file.readlines()[0]				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=names.replace('''"'''			,							''''''				).split(''','''				)

       names.sort()

       SCREAMING_SNAKE_CASE_							:				Optional[int]		=0
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=0

       for i, name in enumerate(UpperCAmelCase_				):
              for letter in name:
                     name_score += ord(UpperCAmelCase_				) - 6_4

              total_score += (i + 1) * name_score
              SCREAMING_SNAKE_CASE_							:				Any		=0
       return total_score


if __name__ == "__main__":
 print(solution())
