
"""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__     (		__SCREAMING_SNAKE_CASE   ):
					_SCREAMING_SNAKE_CASE       :					Any     							=   42







class 							__magic_name__     (		__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 = 0.18215							,						snake_case_   :							str = "group"							,						):
												super().__init__()

												# pass init params to Encoder
												__snake_case     			=       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							,						)

												__snake_case     			=       vq_embed_dim if vq_embed_dim is not None else latent_channels

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

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

												if not return_dict:
																			return (h,)

												return VQEncoderOutput(latents=__snake_case    )





					@apply_forward_hook
					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							bool = False							,						snake_case_   :							bool = True    ):
												# also go through quantization layer
												if not force_not_quantize:
																			__snake_case     			=       self.quantize(__snake_case    )
												else:
																			__snake_case     			=       h
												__snake_case     			=       self.post_quant_conv(__snake_case    )
												__snake_case     			=       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 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							bool = True    ):
												__snake_case     			=       sample
												__snake_case     			=       self.encode(__snake_case    ).latents
												__snake_case     			=       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   __UpperCamelCase   (					)  ->     tuple[list[int], int]:







							"""simple docstring"""


							__snake_case     			=       [randint(-10_00							,    10_00 ) for i in range(10 )]
							__snake_case     			=       randint(-50_00							,    50_00 )
							return (arr, r)


_SCREAMING_SNAKE_CASE	       =      make_dataset()
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     tuple[int, ...]:







							"""simple docstring"""


							for triplet in permutations(SCREAMING_SNAKE_CASE							,    3 ):
														if sum(SCREAMING_SNAKE_CASE ) == target:
																					return tuple(sorted(SCREAMING_SNAKE_CASE ) )
							return (0, 0, 0)
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     tuple[int, int, int]:







							"""simple docstring"""


							arr.sort()
							__snake_case     			=       len(SCREAMING_SNAKE_CASE )
							for i in range(n - 1 ):
														__snake_case			,   __snake_case     			=       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   __UpperCamelCase   (					)  ->     tuple[float, float]:







							"""simple docstring"""


							__snake_case     			=       "\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n"
							__snake_case     			=       "\ntriplet_sum1(*dataset)\n"
							__snake_case     			=       "\ntriplet_sum2(*dataset)\n"
							__snake_case     			=       repeat(setup=SCREAMING_SNAKE_CASE							,    stmt=SCREAMING_SNAKE_CASE							,    repeat=5							,    number=1_00_00 )
							__snake_case     			=       repeat(setup=SCREAMING_SNAKE_CASE							,    stmt=SCREAMING_SNAKE_CASE							,    repeat=5							,    number=1_00_00 )
							return (min(SCREAMING_SNAKE_CASE ), min(SCREAMING_SNAKE_CASE ))


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

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