




from dataclasses import dataclass
from typing import Dict, 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 .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder

@dataclass
class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    42







class 						__lowerCAmelCase      (      __magic_name__ ,			__magic_name__					):
							UpperCamelCase__   	=    True
							@register_to_config
							def __init__(		self		:List[Any]						, __magic_name__		:int = 3						, __magic_name__		:int = 3						, __magic_name__		:Tuple[str] = ("DownEncoderBlock2D",)						, __magic_name__		:Tuple[str] = ("UpDecoderBlock2D",)						, __magic_name__		:Tuple[int] = (64,)						, __magic_name__		:int = 1						, __magic_name__		:str = "silu"						, __magic_name__		:int = 4						, __magic_name__		:int = 32						, __magic_name__		:int = 32						, __magic_name__		:float = 0.18215						, ):







								'''simple docstring'''







								super().__init__()

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

								# pass init params to Decoder
								a									=				Decoder(
								    in_channels=__magic_name__						, out_channels=__magic_name__						, up_block_types=__magic_name__						, block_out_channels=__magic_name__						, layers_per_block=__magic_name__						, norm_num_groups=__magic_name__						, act_fn=__magic_name__						, )

								a									=				nn.Convad(2 * latent_channels						, 2 * latent_channels						, 1							)
								a									=				nn.Convad(__magic_name__						, __magic_name__						, 1							)

								a									=				False
								a									=				False

								# only relevant if vae tiling is enabled
								a									=				self.config.sample_size
								a									=				(
								    self.config.sample_size[0]
								    if isinstance(self.config.sample_size						, (list, tuple)							)
								    else self.config.sample_size
								)
								a									=				int(sample_size / (2 ** (len(self.config.block_out_channels							) - 1))							)
								a									=				0.25
							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:Union[str, Any]						, __magic_name__		:Optional[int]=False							):







								'''simple docstring'''







								if isinstance(__magic_name__						, (Encoder, Decoder)							):
									a									=				value
							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:bool = True							):







								'''simple docstring'''







								a									=				use_tiling
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								self.enable_tiling(__magic_name__							)
							def  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







								a									=				True
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								a									=				False
							@property
							# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								a									=				{}

								def fn_recursive_add_processors(__magic_name__		:str						, __magic_name__		:torch.nn.Module						, __magic_name__		:Dict[str, AttentionProcessor]							):
									if hasattr(__magic_name__						, """set_processor"""							):
										a									=				module.processor

									for sub_name, child in module.named_children():
										fn_recursive_add_processors(F'{name}.{sub_name}'						, __magic_name__						, __magic_name__							)

									return processors

								for name, module in self.named_children():
									fn_recursive_add_processors(__magic_name__						, __magic_name__						, __magic_name__							)

								return processors
							def  lowerCamelCase__				(		self		:str						, __magic_name__		:Union[AttentionProcessor, Dict[str, AttentionProcessor]]							):







								'''simple docstring'''







								a									=				len(self.attn_processors.keys()							)

								if isinstance(__magic_name__						, __magic_name__							) and len(__magic_name__							) != count:
									raise ValueError(
									    F'A dict of processors was passed, but the number of processors {len(__magic_name__							)} does not match the'
									    F' number of attention layers: {count}. Please make sure to pass {count} processor classes.'							)

								def fn_recursive_attn_processor(__magic_name__		:str						, __magic_name__		:torch.nn.Module						, __magic_name__		:Tuple							):
									if hasattr(__magic_name__						, """set_processor"""							):
										if not isinstance(__magic_name__						, __magic_name__							):
											module.set_processor(__magic_name__							)
										else:
											module.set_processor(processor.pop(F'{name}.processor'							)							)

									for sub_name, child in module.named_children():
										fn_recursive_attn_processor(F'{name}.{sub_name}'						, __magic_name__						, __magic_name__							)

								for name, module in self.named_children():
									fn_recursive_attn_processor(__magic_name__						, __magic_name__						, __magic_name__							)
							def  lowerCamelCase__				(		self		:List[Any]							):







								'''simple docstring'''







								self.set_attn_processor(AttnProcessor()							)
							@apply_forward_hook
							def  lowerCamelCase__				(		self		:List[Any]						, __magic_name__		:torch.FloatTensor						, __magic_name__		:bool = True							):







								'''simple docstring'''







								if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
									return self.tiled_encode(__magic_name__						, return_dict=__magic_name__							)

								if self.use_slicing and x.shape[0] > 1:
									a									=				[self.encoder(__magic_name__							) for x_slice in x.split(1							)]
									a									=				torch.cat(__magic_name__							)
								else:
									a									=				self.encoder(__magic_name__							)

								a									=				self.quant_conv(__magic_name__							)
								a									=				DiagonalGaussianDistribution(__magic_name__							)

								if not return_dict:
									return (posterior,)

								return AutoencoderKLOutput(latent_dist=__magic_name__							)
							def  lowerCamelCase__				(		self		:int						, __magic_name__		:torch.FloatTensor						, __magic_name__		:bool = True							):







								'''simple docstring'''







								if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
									return self.tiled_decode(__magic_name__						, return_dict=__magic_name__							)

								a									=				self.post_quant_conv(__magic_name__							)
								a									=				self.decoder(__magic_name__							)

								if not return_dict:
									return (dec,)

								return DecoderOutput(sample=__magic_name__							)
							@apply_forward_hook
							def  lowerCamelCase__				(		self		:int						, __magic_name__		:torch.FloatTensor						, __magic_name__		:bool = True							):







								'''simple docstring'''







								if self.use_slicing and z.shape[0] > 1:
									a									=				[self._decode(__magic_name__							).sample for z_slice in z.split(1							)]
									a									=				torch.cat(__magic_name__							)
								else:
									a									=				self._decode(__magic_name__							).sample

								if not return_dict:
									return (decoded,)

								return DecoderOutput(sample=__magic_name__							)
							def  lowerCamelCase__				(		self		:Optional[int]						, __magic_name__		:Any						, __magic_name__		:List[str]						, __magic_name__		:str							):







								'''simple docstring'''







								a									=				min(a.shape[2]						, b.shape[2]						, __magic_name__							)
								for y in range(__magic_name__							):
									a									=				a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
								return b
							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:Any						, __magic_name__		:Dict						, __magic_name__		:Tuple							):







								'''simple docstring'''







								a									=				min(a.shape[3]						, b.shape[3]						, __magic_name__							)
								for x in range(__magic_name__							):
									a									=				a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
								return b
							def  lowerCamelCase__				(		self		:int						, __magic_name__		:torch.FloatTensor						, __magic_name__		:bool = True							):







								'''simple docstring'''







								a									=				int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)							)
								a									=				int(self.tile_latent_min_size * self.tile_overlap_factor							)
								a									=				self.tile_latent_min_size - blend_extent

								# Split the image into 512x512 tiles and encode them separately.
								a									=				[]
								for i in range(0						, x.shape[2]						, __magic_name__							):
									a									=				[]
									for j in range(0						, x.shape[3]						, __magic_name__							):
										a									=				x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
										a									=				self.encoder(__magic_name__							)
										a									=				self.quant_conv(__magic_name__							)
										row.append(__magic_name__							)
									rows.append(__magic_name__							)
								a									=				[]
								for i, row in enumerate(__magic_name__							):
									a									=				[]
									for j, tile in enumerate(__magic_name__							):
										# blend the above tile and the left tile
										# to the current tile and add the current tile to the result row
										if i > 0:
											a									=				self.blend_v(rows[i - 1][j]						, __magic_name__						, __magic_name__							)
										if j > 0:
											a									=				self.blend_h(row[j - 1]						, __magic_name__						, __magic_name__							)
										result_row.append(tile[:, :, :row_limit, :row_limit]							)
									result_rows.append(torch.cat(__magic_name__						, dim=3							)							)

								a									=				torch.cat(__magic_name__						, dim=2							)
								a									=				DiagonalGaussianDistribution(__magic_name__							)

								if not return_dict:
									return (posterior,)

								return AutoencoderKLOutput(latent_dist=__magic_name__							)
							def  lowerCamelCase__				(		self		:str						, __magic_name__		:torch.FloatTensor						, __magic_name__		:bool = True							):







								'''simple docstring'''







								a									=				int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)							)
								a									=				int(self.tile_sample_min_size * self.tile_overlap_factor							)
								a									=				self.tile_sample_min_size - blend_extent

								# Split z into overlapping 64x64 tiles and decode them separately.
								# The tiles have an overlap to avoid seams between tiles.
								a									=				[]
								for i in range(0						, z.shape[2]						, __magic_name__							):
									a									=				[]
									for j in range(0						, z.shape[3]						, __magic_name__							):
										a									=				z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
										a									=				self.post_quant_conv(__magic_name__							)
										a									=				self.decoder(__magic_name__							)
										row.append(__magic_name__							)
									rows.append(__magic_name__							)
								a									=				[]
								for i, row in enumerate(__magic_name__							):
									a									=				[]
									for j, tile in enumerate(__magic_name__							):
										# blend the above tile and the left tile
										# to the current tile and add the current tile to the result row
										if i > 0:
											a									=				self.blend_v(rows[i - 1][j]						, __magic_name__						, __magic_name__							)
										if j > 0:
											a									=				self.blend_h(row[j - 1]						, __magic_name__						, __magic_name__							)
										result_row.append(tile[:, :, :row_limit, :row_limit]							)
									result_rows.append(torch.cat(__magic_name__						, dim=3							)							)

								a									=				torch.cat(__magic_name__						, dim=2							)
								if not return_dict:
									return (dec,)

								return DecoderOutput(sample=__magic_name__							)





							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:torch.FloatTensor						, __magic_name__		:bool = False						, __magic_name__		:bool = True						, __magic_name__		:Optional[torch.Generator] = None						, ):







								'''simple docstring'''







								a									=				sample
								a									=				self.encode(__magic_name__							).latent_dist
								if sample_posterior:
									a									=				posterior.sample(generator=__magic_name__							)
								else:
									a									=				posterior.mode()
								a									=				self.decode(__magic_name__							).sample

								if not return_dict:
									return (dec,)

								return DecoderOutput(sample=__magic_name__							)







import os





def 						__A						(      ) ->			Dict:
	with open(os.path.dirname(__lowerCamelCase       ) + """/p022_names.txt"""       ) as file:
		a									=				str(file.readlines()[0]       )
		a									=				names.replace("""\""""				,     """"""       ).split(""","""       )

	names.sort()

	a									=				0
	a									=				0

	for i, name in enumerate(__lowerCamelCase       ):
		for letter in name:
			name_score += ord(__lowerCamelCase       ) - 64

		total_score += (i + 1) * name_score
		a									=				0
	return total_score


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

