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		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     4_2


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   __SCREAMING_SNAKE_CASE				):







							@register_to_config
							def __init__(				self     :    Union[str, Any]	,					__snake_case     :    int = 3	,					__snake_case     :    int = 3	,					__snake_case     :    Tuple[str] = ("DownEncoderBlock2D",)	,					__snake_case     :    Tuple[str] = ("UpDecoderBlock2D",)	,					__snake_case     :    Tuple[int] = (64,)	,					__snake_case     :    int = 1	,					__snake_case     :    str = "silu"	,					__snake_case     :    int = 3	,					__snake_case     :    int = 32	,					__snake_case     :    int = 256	,					__snake_case     :    int = 32	,					__snake_case     :    Optional[int] = None	,					__snake_case     :    float = 0.1_8_2_1_5	,					__snake_case     :    str = "group"	,					):
										super().__init__()

										# pass init params to Encoder
										lowerCamelCase						:Tuple						  =     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	,					)

										lowerCamelCase						:str						  =     vq_embed_dim if vq_embed_dim is not None else latent_channels

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

										# pass init params to Decoder
										lowerCamelCase						:str						  =     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     :    Tuple	,					__snake_case     :    torch.FloatTensor	,					__snake_case     :    bool = True		):
										lowerCamelCase						:str						  =     self.encoder(__snake_case		)
										lowerCamelCase						:int						  =     self.quant_conv(__snake_case		)

										if not return_dict:
													return (h,)

										return VQEncoderOutput(latents=__snake_case		)







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

										if not return_dict:
													return (dec,)

										return DecoderOutput(sample=__snake_case		)




import os
from math import logaa





def  _lowerCamelCase					(   a_				: str = "base_exp.txt"):
			lowerCamelCase						:float						  =     0
			lowerCamelCase						:Optional[int]						  =     0
			for i, line in enumerate(open(os.path.join(os.path.dirname(a_)						,			a_))):
						lowerCamelCase  ,    lowerCamelCase						:Optional[int]						  =     list(map(a_						,			line.split(''',''')))
						if x * logaa(a_) > largest:
									lowerCamelCase						:List[Any]						  =     x * logaa(a_)
									lowerCamelCase						:Any						  =     i + 1
			return result


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



