





'''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    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							torch.FloatTensor







class    lowerCAmelCase_		(		__magic_name__						,__magic_name__		):




 @register_to_config
 def __init__(	self			,     _lowerCAmelCase = 3			,     _lowerCAmelCase = 3			,     _lowerCAmelCase = ("DownEncoderBlock2D",)			,     _lowerCAmelCase = ("UpDecoderBlock2D",)			,     _lowerCAmelCase = (64,)			,     _lowerCAmelCase = 1			,     _lowerCAmelCase = "silu"			,     _lowerCAmelCase = 3			,     _lowerCAmelCase = 32			,     _lowerCAmelCase = 256			,     _lowerCAmelCase = 32			,     _lowerCAmelCase = None			,     _lowerCAmelCase = 0.18215			,     _lowerCAmelCase = "group"			,     )    ->							int:
     super().__init__()

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

     _lowerCAmelCase				=      vq_embed_dim if vq_embed_dim is not None else latent_channels

     _lowerCAmelCase				=      nn.Convad(_lowerCAmelCase			,     _lowerCAmelCase			,     1		)
     _lowerCAmelCase				=      VectorQuantizer(_lowerCAmelCase			,     _lowerCAmelCase			,     beta=0.25			,     remap=_lowerCAmelCase			,     sane_index_shape=_lowerCAmelCase		)
     _lowerCAmelCase				=      nn.Convad(_lowerCAmelCase			,     _lowerCAmelCase			,     1		)

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




 @apply_forward_hook
 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = True		)    ->							VQEncoderOutput:
     _lowerCAmelCase				=      self.encoder(_lowerCAmelCase		)
     _lowerCAmelCase				=      self.quant_conv(_lowerCAmelCase		)

     if not return_dict:
         return (h,)

     return VQEncoderOutput(latents=_lowerCAmelCase		)




 @apply_forward_hook
 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = False			,     _lowerCAmelCase = True		)    ->							Union[DecoderOutput, torch.FloatTensor]:
     # also go through quantization layer
     if not force_not_quantize:
         _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      self.quantize(_lowerCAmelCase		)
     else:
         _lowerCAmelCase				=      h
     _lowerCAmelCase				=      self.post_quant_conv(_lowerCAmelCase		)
     _lowerCAmelCase				=      self.decoder(_lowerCAmelCase			,     quant if self.config.norm_type == "spatial" else None		)

     if not return_dict:
         return (dec,)

     return DecoderOutput(sample=_lowerCAmelCase		)





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = True		)    ->							Union[DecoderOutput, torch.FloatTensor]:
     _lowerCAmelCase				=      sample
     _lowerCAmelCase				=      self.encode(_lowerCAmelCase		).latents
     _lowerCAmelCase				=      self.decode(_lowerCAmelCase		).sample

     if not return_dict:
         return (dec,)

     return DecoderOutput(sample=_lowerCAmelCase		)











'''simple docstring'''


import math


def 	__a(SCREAMING_SNAKE_CASE_  :					int = 100    ):



    '''simple docstring'''





    _lowerCAmelCase				=      sum(i * i for i in range(1		,       n + 1    )    )
    _lowerCAmelCase				=      int(math.pow(sum(range(1		,       n + 1    )    )		,       2    )    )
    return square_of_sum - sum_of_squares


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


