


from __future__ import annotations







def        _lowerCamelCase(	lowercase__	)						->	list[int]:

		'''simple docstring'''







		__lowercase=			[True] * limit
		__lowercase=			False
		__lowercase=			False
		__lowercase=			True

		for i in range(3   , int(limit**0.5 + 1	)   , 2	):
				__lowercase=			i * 2
				while index < limit:
						__lowercase=			False
						__lowercase=			index + i

		__lowercase=			[2]

		for i in range(3   , lowercase__   , 2	):
				if is_prime[i]:
						primes.append(lowercase__	)

		return primes







def        _lowerCamelCase(	lowercase__ = 1_0_0_0_0_0_0	)						->	int:

		'''simple docstring'''







		__lowercase=			prime_sieve(lowercase__	)
		__lowercase=			0
		__lowercase=			0

		for i in range(len(lowercase__	)	):
				for j in range(i + length   , len(lowercase__	)	):
						__lowercase=			sum(primes[i:j]	)
						if sol >= ceiling:
								break

						if sol in primes:
								__lowercase=			j - i
								__lowercase=			sol

		return largest


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










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_ ):
							UpperCamelCase_   :							torch.FloatTensor





class       A    (   A_					,				A_ ):


							@register_to_config
							def __init__(self      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = ("DownEncoderBlock2D",)      ,				lowerCAmelCase = ("UpDecoderBlock2D",)      ,				lowerCAmelCase = (6_4,)      ,				lowerCAmelCase = 1      ,				lowerCAmelCase = "silu"      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = 2_5_6      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = None      ,				lowerCAmelCase = 0.1_82_15      ,				lowerCAmelCase = "group"      ,				):
									super().__init__()

									# pass init params to Encoder
									__lowercase=			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      ,				)

									__lowercase=			vq_embed_dim if vq_embed_dim is not None else latent_channels

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

									# pass init params to Decoder
									__lowercase=			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 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			self.encoder(lowerCAmelCase					)
									__lowercase=			self.quant_conv(lowerCAmelCase					)

									if not return_dict:
											return (h,)

									return VQEncoderOutput(latents=lowerCAmelCase					)


							@apply_forward_hook
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = False      ,				lowerCAmelCase = True					):
									# also go through quantization layer
									if not force_not_quantize:
											__lowercase,							__lowercase,							__lowercase=			self.quantize(lowerCAmelCase					)
									else:
											__lowercase=			h
									__lowercase=			self.post_quant_conv(lowerCAmelCase					)
									__lowercase=			self.decoder(lowerCAmelCase      ,				quant if self.config.norm_type == 'spatial' else None					)

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			sample
									__lowercase=			self.encode(lowerCAmelCase					).latents
									__lowercase=			self.decode(lowerCAmelCase					).sample

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)






