



'''simple docstring'''





import math

def        a_     (							_lowerCAmelCase       )      ->      list[int]:
  __lowerCamelCase       :       Optional[int]           = []
  __lowerCamelCase       :       Tuple           = 2
  __lowerCamelCase       :       Union[str, Any]           = int(math.sqrt(_lowerCAmelCase       )       )  # Size of every segment
  __lowerCamelCase       :       Optional[Any]           = [True] * (end + 1)
  __lowerCamelCase       :       Dict           = []

  while start <= end:
    if temp[start] is True:
      in_prime.append(_lowerCAmelCase       )
      for i in range(start * start       ,end + 1       ,_lowerCAmelCase       ):
        __lowerCamelCase       :       Tuple           = False
    start += 1
  prime += in_prime

  __lowerCamelCase       :       List[Any]           = end + 1
  __lowerCamelCase       :       List[Any]           = min(2 * end       ,_lowerCAmelCase       )

  while low <= n:
    __lowerCamelCase       :       Tuple           = [True] * (high - low + 1)
    for each in in_prime:
      __lowerCamelCase       :       Optional[int]           = math.floor(low / each       ) * each
      if t < low:
        t += each

      for j in range(_lowerCAmelCase       ,high + 1       ,_lowerCAmelCase       ):
        __lowerCamelCase       :       Optional[int]           = False

    for j in range(len(_lowerCAmelCase       )       ):
      if temp[j] is True:
        prime.append(j + low       )

    __lowerCamelCase       :       Optional[Any]           = high + 1
    __lowerCamelCase       :       List[str]           = min(high + end       ,_lowerCAmelCase       )

  return prime


print(sieve(10**6))











'''simple docstring'''





import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Dict:
  __lowerCamelCase       :       Any           = checkpoint

  __lowerCamelCase       :       List[str]           = {}

  __lowerCamelCase       :       List[str]           = vae_state_dict['encoder.conv_in.weight']
  __lowerCamelCase       :       Dict           = vae_state_dict['encoder.conv_in.bias']
  __lowerCamelCase       :       Optional[Any]           = vae_state_dict['encoder.conv_out.weight']
  __lowerCamelCase       :       Optional[int]           = vae_state_dict['encoder.conv_out.bias']
  __lowerCamelCase       :       Optional[int]           = vae_state_dict['encoder.norm_out.weight']
  __lowerCamelCase       :       Optional[int]           = vae_state_dict['encoder.norm_out.bias']

  __lowerCamelCase       :       Dict           = vae_state_dict['decoder.conv_in.weight']
  __lowerCamelCase       :       List[str]           = vae_state_dict['decoder.conv_in.bias']
  __lowerCamelCase       :       Tuple           = vae_state_dict['decoder.conv_out.weight']
  __lowerCamelCase       :       List[str]           = vae_state_dict['decoder.conv_out.bias']
  __lowerCamelCase       :       Dict           = vae_state_dict['decoder.norm_out.weight']
  __lowerCamelCase       :       Union[str, Any]           = vae_state_dict['decoder.norm_out.bias']

  __lowerCamelCase       :       Any           = vae_state_dict['quant_conv.weight']
  __lowerCamelCase       :       Union[str, Any]           = vae_state_dict['quant_conv.bias']
  __lowerCamelCase       :       Tuple           = vae_state_dict['post_quant_conv.weight']
  __lowerCamelCase       :       List[Any]           = vae_state_dict['post_quant_conv.bias']

  # Retrieves the keys for the encoder down blocks only
  __lowerCamelCase       :       str           = len({'.'.join(layer.split('.'       )[:3]       ) for layer in vae_state_dict if 'encoder.down' in layer}       )
  __lowerCamelCase       :       int           = {
      layer_id: [key for key in vae_state_dict if F'down.{layer_id}' in key] for layer_id in range(_lowerCAmelCase       )
  }

  # Retrieves the keys for the decoder up blocks only
  __lowerCamelCase       :       Dict           = len({'.'.join(layer.split('.'       )[:3]       ) for layer in vae_state_dict if 'decoder.up' in layer}       )
  __lowerCamelCase       :       Dict           = {
      layer_id: [key for key in vae_state_dict if F'up.{layer_id}' in key] for layer_id in range(_lowerCAmelCase       )
  }

  for i in range(_lowerCAmelCase       ):
    __lowerCamelCase       :       List[str]           = [key for key in down_blocks[i] if F'down.{i}' in key and F'down.{i}.downsample' not in key]

    if F'encoder.down.{i}.downsample.conv.weight' in vae_state_dict:
      __lowerCamelCase       :       Any           = vae_state_dict.pop(
          F'encoder.down.{i}.downsample.conv.weight'       )
      __lowerCamelCase       :       Any           = vae_state_dict.pop(
          F'encoder.down.{i}.downsample.conv.bias'       )

    __lowerCamelCase       :       Optional[int]           = renew_vae_resnet_paths(_lowerCAmelCase       )
    __lowerCamelCase       :       Any           = {'old': F'down.{i}.block', 'new': F'down_blocks.{i}.resnets'}
    assign_to_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,additional_replacements=[meta_path]       ,config=_lowerCAmelCase       )

  __lowerCamelCase       :       List[Any]           = [key for key in vae_state_dict if 'encoder.mid.block' in key]
  __lowerCamelCase       :       str           = 2
  for i in range(1       ,num_mid_res_blocks + 1       ):
    __lowerCamelCase       :       int           = [key for key in mid_resnets if F'encoder.mid.block_{i}' in key]

    __lowerCamelCase       :       Optional[Any]           = renew_vae_resnet_paths(_lowerCAmelCase       )
    __lowerCamelCase       :       Dict           = {'old': F'mid.block_{i}', 'new': F'mid_block.resnets.{i - 1}'}
    assign_to_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,additional_replacements=[meta_path]       ,config=_lowerCAmelCase       )

  __lowerCamelCase       :       Optional[Any]           = [key for key in vae_state_dict if 'encoder.mid.attn' in key]
  __lowerCamelCase       :       int           = renew_vae_attention_paths(_lowerCAmelCase       )
  __lowerCamelCase       :       Optional[Any]           = {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
  assign_to_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,additional_replacements=[meta_path]       ,config=_lowerCAmelCase       )
  conv_attn_to_linear(_lowerCAmelCase       )

  for i in range(_lowerCAmelCase       ):
    __lowerCamelCase       :       Optional[Any]           = num_up_blocks - 1 - i
    __lowerCamelCase       :       Any           = [
        key for key in up_blocks[block_id] if F'up.{block_id}' in key and F'up.{block_id}.upsample' not in key
    ]

    if F'decoder.up.{block_id}.upsample.conv.weight' in vae_state_dict:
      __lowerCamelCase       :       Optional[int]           = vae_state_dict[
          F'decoder.up.{block_id}.upsample.conv.weight'
      ]
      __lowerCamelCase       :       Optional[Any]           = vae_state_dict[
          F'decoder.up.{block_id}.upsample.conv.bias'
      ]

    __lowerCamelCase       :       Tuple           = renew_vae_resnet_paths(_lowerCAmelCase       )
    __lowerCamelCase       :       Any           = {'old': F'up.{block_id}.block', 'new': F'up_blocks.{i}.resnets'}
    assign_to_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,additional_replacements=[meta_path]       ,config=_lowerCAmelCase       )

  __lowerCamelCase       :       Optional[Any]           = [key for key in vae_state_dict if 'decoder.mid.block' in key]
  __lowerCamelCase       :       Union[str, Any]           = 2
  for i in range(1       ,num_mid_res_blocks + 1       ):
    __lowerCamelCase       :       List[str]           = [key for key in mid_resnets if F'decoder.mid.block_{i}' in key]

    __lowerCamelCase       :       str           = renew_vae_resnet_paths(_lowerCAmelCase       )
    __lowerCamelCase       :       Any           = {'old': F'mid.block_{i}', 'new': F'mid_block.resnets.{i - 1}'}
    assign_to_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,additional_replacements=[meta_path]       ,config=_lowerCAmelCase       )

  __lowerCamelCase       :       Any           = [key for key in vae_state_dict if 'decoder.mid.attn' in key]
  __lowerCamelCase       :       List[str]           = renew_vae_attention_paths(_lowerCAmelCase       )
  __lowerCamelCase       :       Optional[Any]           = {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
  assign_to_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,additional_replacements=[meta_path]       ,config=_lowerCAmelCase       )
  conv_attn_to_linear(_lowerCAmelCase       )
  return new_checkpoint

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,)      ->      List[Any]:
  # Only support V1
  __lowerCamelCase       :       Any           = requests.get(
      ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'       )
  __lowerCamelCase       :       str           = io.BytesIO(r.content       )

  __lowerCamelCase       :       Any           = OmegaConf.load(_lowerCAmelCase       )
  __lowerCamelCase       :       List[Any]           = 512
  __lowerCamelCase       :       Dict           = 'cuda' if torch.cuda.is_available() else 'cpu'
  if checkpoint_path.endswith('safetensors'       ):
    from safetensors import safe_open

    __lowerCamelCase       :       Optional[Any]           = {}
    with safe_open(_lowerCAmelCase       ,framework='pt'       ,device='cpu'       ) as f:
      for key in f.keys():
        __lowerCamelCase       :       int           = f.get_tensor(_lowerCAmelCase       )
  else:
    __lowerCamelCase       :       int           = torch.load(_lowerCAmelCase       ,map_location=_lowerCAmelCase       )['state_dict']

  # Convert the VAE model.
  __lowerCamelCase       :       int           = create_vae_diffusers_config(_lowerCAmelCase       ,image_size=_lowerCAmelCase       )
  __lowerCamelCase       :       int           = custom_convert_ldm_vae_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       )

  __lowerCamelCase       :       Any           = AutoencoderKL(**_lowerCAmelCase       )
  vae.load_state_dict(_lowerCAmelCase       )
  vae.save_pretrained(_lowerCAmelCase       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()

   parser.add_argument('--vae_pt_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
   parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')

   _UpperCamelCase											=						parser.parse_args()

   vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)






