





'''simple docstring'''






from dataclasses import dataclass
from typing import Dict, 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 .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder


@dataclass
class   _lowercase						(							a				):
   _UpperCamelCase       		=   42







class   _lowercase						(							a		,					a				):
   _UpperCamelCase       		=   True


   @register_to_config
   def __init__( self    ,   _UpperCAmelCase = 3    ,   _UpperCAmelCase = 3    ,   _UpperCAmelCase = ("DownEncoderBlock2D",)    ,   _UpperCAmelCase = ("UpDecoderBlock2D",)    ,   _UpperCAmelCase = (64,)    ,   _UpperCAmelCase = 1    ,   _UpperCAmelCase = "silu"    ,   _UpperCAmelCase = 4    ,   _UpperCAmelCase = 32    ,   _UpperCAmelCase = 32    ,   _UpperCAmelCase = 0.18215    ,   ):
       super().__init__()

       # pass init params to Encoder
       A       :       List[Any]							=						Encoder(
           in_channels=_UpperCAmelCase    ,   out_channels=_UpperCAmelCase    ,   down_block_types=_UpperCAmelCase    ,   block_out_channels=_UpperCAmelCase    ,   layers_per_block=_UpperCAmelCase    ,   act_fn=_UpperCAmelCase    ,   norm_num_groups=_UpperCAmelCase    ,   double_z=_UpperCAmelCase    ,   )

       # pass init params to Decoder
       A       :       Tuple							=						Decoder(
           in_channels=_UpperCAmelCase    ,   out_channels=_UpperCAmelCase    ,   up_block_types=_UpperCAmelCase    ,   block_out_channels=_UpperCAmelCase    ,   layers_per_block=_UpperCAmelCase    ,   norm_num_groups=_UpperCAmelCase    ,   act_fn=_UpperCAmelCase    ,   )

       A       :       Union[str, Any]							=						nn.Convad(2 * latent_channels    ,   2 * latent_channels    ,   1					)
       A       :       Union[str, Any]							=						nn.Convad(_UpperCAmelCase    ,   _UpperCAmelCase    ,   1					)

       A       :       Union[str, Any]							=						False
       A       :       Dict							=						False

       # only relevant if vae tiling is enabled
       A       :       int							=						self.config.sample_size
       A       :       Union[str, Any]							=						(
           self.config.sample_size[0]
           if isinstance(self.config.sample_size    ,   (list, tuple)					)
           else self.config.sample_size
       )
       A       :       List[Any]							=						int(sample_size / (2 ** (len(self.config.block_out_channels					) - 1))					)
       A       :       Union[str, Any]							=						0.25


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=False					):
       if isinstance(_UpperCAmelCase    ,   (Encoder, Decoder)					):
           A       :       Any							=						value


   def 					snake_case				( self    ,   _UpperCAmelCase = True					):
       A       :       Tuple							=						use_tiling


   def 					snake_case				( self					):
       self.enable_tiling(_UpperCAmelCase					)


   def 					snake_case				( self					):
       A       :       int							=						True


   def 					snake_case				( self					):
       A       :       Any							=						False


   @property
   # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
   def 					snake_case				( self					):
       A       :       Union[str, Any]							=						{}

       def fn_recursive_add_processors(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
           if hasattr(_UpperCAmelCase    ,   '''set_processor'''					):
               A       :       Tuple							=						module.processor

           for sub_name, child in module.named_children():
               fn_recursive_add_processors(f'''{name}.{sub_name}'''    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)

           return processors

       for name, module in self.named_children():
           fn_recursive_add_processors(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)

       return processors


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       Any							=						len(self.attn_processors.keys()					)

       if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					) and len(_UpperCAmelCase					) != count:
           raise ValueError(
               f'''A dict of processors was passed, but the number of processors {len(_UpperCAmelCase					)} does not match the'''
               f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''					)

       def fn_recursive_attn_processor(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
           if hasattr(_UpperCAmelCase    ,   '''set_processor'''					):
               if not isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
                   module.set_processor(_UpperCAmelCase					)
               else:
                   module.set_processor(processor.pop(f'''{name}.processor'''					)					)

           for sub_name, child in module.named_children():
               fn_recursive_attn_processor(f'''{name}.{sub_name}'''    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)

       for name, module in self.named_children():
           fn_recursive_attn_processor(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)


   def 					snake_case				( self					):
       self.set_attn_processor(AttnProcessor()					)


   @apply_forward_hook
   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = True					):
       if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
           return self.tiled_encode(_UpperCAmelCase    ,   return_dict=_UpperCAmelCase					)

       if self.use_slicing and x.shape[0] > 1:
           A       :       Optional[Any]							=						[self.encoder(_UpperCAmelCase					) for x_slice in x.split(1					)]
           A       :       Union[str, Any]							=						torch.cat(_UpperCAmelCase					)
       else:
           A       :       Optional[int]							=						self.encoder(_UpperCAmelCase					)

       A       :       Dict							=						self.quant_conv(_UpperCAmelCase					)
       A       :       List[str]							=						DiagonalGaussianDistribution(_UpperCAmelCase					)

       if not return_dict:
           return (posterior,)

       return AutoencoderKLOutput(latent_dist=_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = True					):
       if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
           return self.tiled_decode(_UpperCAmelCase    ,   return_dict=_UpperCAmelCase					)

       A       :       Any							=						self.post_quant_conv(_UpperCAmelCase					)
       A       :       Any							=						self.decoder(_UpperCAmelCase					)

       if not return_dict:
           return (dec,)

       return DecoderOutput(sample=_UpperCAmelCase					)


   @apply_forward_hook
   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = True					):
       if self.use_slicing and z.shape[0] > 1:
           A       :       Optional[Any]							=						[self._decode(_UpperCAmelCase					).sample for z_slice in z.split(1					)]
           A       :       List[str]							=						torch.cat(_UpperCAmelCase					)
       else:
           A       :       Tuple							=						self._decode(_UpperCAmelCase					).sample

       if not return_dict:
           return (decoded,)

       return DecoderOutput(sample=_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       Optional[int]							=						min(a.shape[2]    ,   b.shape[2]    ,   _UpperCAmelCase					)
       for y in range(_UpperCAmelCase					):
           A       :       Dict							=						a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
       return b


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       Tuple							=						min(a.shape[3]    ,   b.shape[3]    ,   _UpperCAmelCase					)
       for x in range(_UpperCAmelCase					):
           A       :       List[str]							=						a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
       return b


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = True					):
       A       :       List[Any]							=						int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)					)
       A       :       str							=						int(self.tile_latent_min_size * self.tile_overlap_factor					)
       A       :       List[Any]							=						self.tile_latent_min_size - blend_extent

       # Split the image into 512x512 tiles and encode them separately.
       A       :       str							=						[]
       for i in range(0    ,   x.shape[2]    ,   _UpperCAmelCase					):
           A       :       Union[str, Any]							=						[]
           for j in range(0    ,   x.shape[3]    ,   _UpperCAmelCase					):
               A       :       List[str]							=						x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
               A       :       Any							=						self.encoder(_UpperCAmelCase					)
               A       :       Optional[Any]							=						self.quant_conv(_UpperCAmelCase					)
               row.append(_UpperCAmelCase					)
           rows.append(_UpperCAmelCase					)
       A       :       Optional[int]							=						[]
       for i, row in enumerate(_UpperCAmelCase					):
           A       :       List[Any]							=						[]
           for j, tile in enumerate(_UpperCAmelCase					):
               # blend the above tile and the left tile
               # to the current tile and add the current tile to the result row
               if i > 0:
                   A       :       List[Any]							=						self.blend_v(rows[i - 1][j]    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)
               if j > 0:
                   A       :       Union[str, Any]							=						self.blend_h(row[j - 1]    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)
               result_row.append(tile[:, :, :row_limit, :row_limit]					)
           result_rows.append(torch.cat(_UpperCAmelCase    ,   dim=3					)					)

       A       :       int							=						torch.cat(_UpperCAmelCase    ,   dim=2					)
       A       :       Optional[Any]							=						DiagonalGaussianDistribution(_UpperCAmelCase					)

       if not return_dict:
           return (posterior,)

       return AutoencoderKLOutput(latent_dist=_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = True					):
       A       :       int							=						int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)					)
       A       :       Any							=						int(self.tile_sample_min_size * self.tile_overlap_factor					)
       A       :       Optional[int]							=						self.tile_sample_min_size - blend_extent

       # Split z into overlapping 64x64 tiles and decode them separately.
       # The tiles have an overlap to avoid seams between tiles.
       A       :       Any							=						[]
       for i in range(0    ,   z.shape[2]    ,   _UpperCAmelCase					):
           A       :       Tuple							=						[]
           for j in range(0    ,   z.shape[3]    ,   _UpperCAmelCase					):
               A       :       List[Any]							=						z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
               A       :       int							=						self.post_quant_conv(_UpperCAmelCase					)
               A       :       Optional[int]							=						self.decoder(_UpperCAmelCase					)
               row.append(_UpperCAmelCase					)
           rows.append(_UpperCAmelCase					)
       A       :       Optional[Any]							=						[]
       for i, row in enumerate(_UpperCAmelCase					):
           A       :       List[str]							=						[]
           for j, tile in enumerate(_UpperCAmelCase					):
               # blend the above tile and the left tile
               # to the current tile and add the current tile to the result row
               if i > 0:
                   A       :       str							=						self.blend_v(rows[i - 1][j]    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)
               if j > 0:
                   A       :       List[str]							=						self.blend_h(row[j - 1]    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)
               result_row.append(tile[:, :, :row_limit, :row_limit]					)
           result_rows.append(torch.cat(_UpperCAmelCase    ,   dim=3					)					)

       A       :       str							=						torch.cat(_UpperCAmelCase    ,   dim=2					)
       if not return_dict:
           return (dec,)

       return DecoderOutput(sample=_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   ):
       A       :       Union[str, Any]							=						sample
       A       :       Any							=						self.encode(_UpperCAmelCase					).latent_dist
       if sample_posterior:
           A       :       Tuple							=						posterior.sample(generator=_UpperCAmelCase					)
       else:
           A       :       List[Any]							=						posterior.mode()
       A       :       List[str]							=						self.decode(_UpperCAmelCase					).sample

       if not return_dict:
           return (dec,)

       return DecoderOutput(sample=_UpperCAmelCase					)







'''simple docstring'''






from __future__ import annotations

import math
def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  list[int]:

    if num <= 0:
        A       :       str							=						F'''{num}: Invalid input, please enter a positive integer.'''
        raise ValueError(UpperCamelCase__		)

    A       :       int							=						[True] * (num + 1)
    A       :       Dict							=						[]
    A       :       str							=						2
    A       :       Any							=						int(math.sqrt(UpperCamelCase__		)		)

    while start <= end:
        # If start is a prime
        if sieve[start] is True:
            prime.append(UpperCamelCase__		)

            # Set multiples of start be False
            for i in range(start * start    ,       num + 1    ,       UpperCamelCase__		):
                if sieve[i] is True:
                    A       :       Union[str, Any]							=						False

        start += 1

    for j in range(end + 1    ,       num + 1		):
        if sieve[j] is True:
            prime.append(UpperCamelCase__		)

    return prime


if __name__ == "__main__":
     print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
