


"""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 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      torch.FloatTensor







class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase     ):






       @register_to_config
       def __init__(   self  :    Any  ,     snake_case__  :    int = 3  ,     snake_case__  :    int = 3  ,     snake_case__  :    Tuple[str] = ("DownEncoderBlock2D",)  ,     snake_case__  :    Tuple[str] = ("UpDecoderBlock2D",)  ,     snake_case__  :    Tuple[int] = (6_4,)  ,     snake_case__  :    int = 1  ,     snake_case__  :    str = "silu"  ,     snake_case__  :    int = 3  ,     snake_case__  :    int = 3_2  ,     snake_case__  :    int = 2_5_6  ,     snake_case__  :    int = 3_2  ,     snake_case__  :    Optional[int] = None  ,     snake_case__  :    float = 0.1_82_15  ,     snake_case__  :    str = "group"  ,     ):

              '''simple docstring'''






              super().__init__()

              # pass init params to Encoder
              lowercase		:Dict       							=				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__  ,     )

              lowercase		:int       							=				vq_embed_dim if vq_embed_dim is not None else latent_channels

              lowercase		:List[str]       							=				nn.Convad(snake_case__  ,     snake_case__  ,     1				)
              lowercase		:List[Any]       							=				VectorQuantizer(snake_case__  ,     snake_case__  ,     beta=0.25  ,     remap=snake_case__  ,     sane_index_shape=snake_case__				)
              lowercase		:int       							=				nn.Convad(snake_case__  ,     snake_case__  ,     1				)

              # pass init params to Decoder
              lowercase		:Any       							=				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  :    List[str]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    bool = True				):

              '''simple docstring'''






              lowercase		:List[str]       							=				self.encoder(snake_case__				)
              lowercase		:Any       							=				self.quant_conv(snake_case__				)

              if not return_dict:
                     return (h,)

              return VQEncoderOutput(latents=snake_case__				)






       @apply_forward_hook
       def  __snake_case						(   self  :    int  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    bool = False  ,     snake_case__  :    bool = True				):

              '''simple docstring'''






              if not force_not_quantize:
                     lowercase							,  lowercase							,  lowercase		:str       							=				self.quantize(snake_case__				)
              else:
                     lowercase		:Any       							=				h
              lowercase		:int       							=				self.post_quant_conv(snake_case__				)
              lowercase		:List[str]       							=				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  :    List[Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    bool = True				):

              '''simple docstring'''






              lowercase		:Any       							=				sample
              lowercase		:int       							=				self.encode(snake_case__				).latents
              lowercase		:Optional[int]       							=				self.decode(snake_case__				).sample

              if not return_dict:
                     return (dec,)

              return DecoderOutput(sample=snake_case__				)





"""simple docstring"""



from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat






def       lowerCamelCase					()     ->							tuple[list[int], int]:
       lowercase		:Any       							=				[randint(-1000				,				1000) for i in range(10)]
       lowercase		:Any       							=				randint(-5000				,				5000)
       return (arr, r)


UpperCAmelCase      		=			make_dataset()






def       lowerCamelCase					(a_						:list[int]				,				a_						:int)     ->							tuple[int, ...]:
       for triplet in permutations(a_				,				3):
              if sum(a_) == target:
                     return tuple(sorted(a_))
       return (0, 0, 0)






def       lowerCamelCase					(a_						:list[int]				,				a_						:int)     ->							tuple[int, int, int]:
       arr.sort()
       lowercase		:Union[str, Any]       							=				len(a_)
       for i in range(n - 1):
              lowercase							,  lowercase		:Union[str, Any]       							=				i + 1, n - 1
              while left < right:
                     if arr[i] + arr[left] + arr[right] == target:
                            return (arr[i], arr[left], arr[right])
                     elif arr[i] + arr[left] + arr[right] < target:
                            left += 1
                     elif arr[i] + arr[left] + arr[right] > target:
                            right -= 1
       return (0, 0, 0)






def       lowerCamelCase					()     ->							tuple[float, float]:
       lowercase		:int       							=				'''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
       lowercase		:Optional[Any]       							=				'''
triplet_sum1(*dataset)
'''
       lowercase		:Union[str, Any]       							=				'''
triplet_sum2(*dataset)
'''
       lowercase		:Dict       							=				repeat(setup=a_				,				stmt=a_				,				repeat=5				,				number=1_0000)
       lowercase		:Optional[int]       							=				repeat(setup=a_				,				stmt=a_				,				repeat=5				,				number=1_0000)
       return (min(a_), min(a_))


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 UpperCAmelCase      		=			solution_times()
 print(F"""The time for naive implementation is {times[0]}.""")
 print(F"""The time for optimized implementation is {times[1]}.""")

