


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    UpperCAmelCase					(       __snake_case    ):
			lowercase						= 42




class    UpperCAmelCase					(       __snake_case      , __snake_case    ):




			@register_to_config
			def __init__(						self							:		Dict       ,       __magic_name__							:		int = 3       ,       __magic_name__							:		int = 3       ,       __magic_name__							:		Tuple[str] = ("DownEncoderBlock2D",)       ,       __magic_name__							:		Tuple[str] = ("UpDecoderBlock2D",)       ,       __magic_name__							:		Tuple[int] = (6_4,)       ,       __magic_name__							:		int = 1       ,       __magic_name__							:		str = "silu"       ,       __magic_name__							:		int = 3       ,       __magic_name__							:		int = 3_2       ,       __magic_name__							:		int = 2_5_6       ,       __magic_name__							:		int = 3_2       ,       __magic_name__							:		Optional[int] = None       ,       __magic_name__							:		float = 0.18_215       ,       __magic_name__							:		str = "group"       ,       ):







						"""simple docstring"""







						super().__init__()

						# pass init params to Encoder
						UpperCamelCase         =					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__       ,       )

						UpperCamelCase         =					vq_embed_dim if vq_embed_dim is not None else latent_channels

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

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




			@apply_forward_hook
			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		torch.FloatTensor       ,       __magic_name__							:		bool = True						):







						"""simple docstring"""







						UpperCamelCase         =					self.encoder(__magic_name__						)
						UpperCamelCase         =					self.quant_conv(__magic_name__						)

						if not return_dict:
									return (h,)

						return VQEncoderOutput(latents=__magic_name__						)




			@apply_forward_hook
			def   lowerCamelCase_					(						self							:		Dict       ,       __magic_name__							:		torch.FloatTensor       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = True						):







						"""simple docstring"""







						if not force_not_quantize:
									UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					self.quantize(__magic_name__						)
						else:
									UpperCamelCase         =					h
						UpperCamelCase         =					self.post_quant_conv(__magic_name__						)
						UpperCamelCase         =					self.decoder(__magic_name__       ,       quant if self.config.norm_type == """spatial""" else None						)

						if not return_dict:
									return (dec,)

						return DecoderOutput(sample=__magic_name__						)





			def   lowerCamelCase_					(						self							:		Optional[int]       ,       __magic_name__							:		torch.FloatTensor       ,       __magic_name__							:		bool = True						):







						"""simple docstring"""







						UpperCamelCase         =					sample
						UpperCamelCase         =					self.encode(__magic_name__						).latents
						UpperCamelCase         =					self.decode(__magic_name__						).sample

						if not return_dict:
									return (dec,)

						return DecoderOutput(sample=__magic_name__						)








import math






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int = 100	):





			"""simple docstring"""




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


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




