






from __future__ import annotations

import numpy as np

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__  ,					__magic_name__		:Optional[int]   =   np.shape(snake_case			)
				if rows != columns:
								__magic_name__		:Dict   =   (
								    '''\'table\' has to be of square shaped array but got a '''
								    f'''{rows}x{columns} array:\n{table}'''
								)
								raise ValueError(snake_case			)

				__magic_name__		:List[str]   =   np.zeros((rows, columns)			)
				__magic_name__		:Union[str, Any]   =   np.zeros((rows, columns)			)
				for i in range(snake_case			):
								for j in range(snake_case			):
												__magic_name__		:List[str]   =   sum(lower[i][k] * upper[k][j] for k in range(snake_case			)			)
												if upper[j][j] == 0:
																raise ArithmeticError('''No LU decomposition exists'''			)
												__magic_name__		:str   =   (table[i][j] - total) / upper[j][j]
								__magic_name__		:int   =   1
								for j in range(snake_case,	snake_case			):
												__magic_name__		:Any   =   sum(lower[i][k] * upper[k][j] for k in range(snake_case			)			)
												__magic_name__		:Dict   =   table[i][j] - total
				return lower, upper


if __name__ == "__main__":
					import doctest

					doctest.testmod()










'''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 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  torch.FloatTensor




class 						a__							(				a__							,							a__    ):





							'''simple docstring'''






							@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_ = 2_56   ,       lowerCamelCase_ = 32   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = 0.18_215   ,       lowerCamelCase_ = "group"   ,       )			->				Union[str, Any]:
														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     __SCREAMING_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     __SCREAMING_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     __SCREAMING_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_  )