
'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
       _snake_case       =  SwinConfig(image_size=192				)

       if "base" in model_name:
              _snake_case       =  6
              _snake_case       =  128
              _snake_case       =  (2, 2, 18, 2)
              _snake_case       =  (4, 8, 16, 32)
       elif "large" in model_name:
              _snake_case       =  12
              _snake_case       =  192
              _snake_case       =  (2, 2, 18, 2)
              _snake_case       =  (6, 12, 24, 48)
       else:
              raise ValueError("""Model not supported, only supports base and large variants"""				)

       _snake_case       =  window_size
       _snake_case       =  embed_dim
       _snake_case       =  depths
       _snake_case       =  num_heads

       return config






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
       if "encoder.mask_token" in name:
              _snake_case       =  name.replace("""encoder.mask_token"""							, """embeddings.mask_token"""				)
       if "encoder.patch_embed.proj" in name:
              _snake_case       =  name.replace("""encoder.patch_embed.proj"""							, """embeddings.patch_embeddings.projection"""				)
       if "encoder.patch_embed.norm" in name:
              _snake_case       =  name.replace("""encoder.patch_embed.norm"""							, """embeddings.norm"""				)
       if "attn.proj" in name:
              _snake_case       =  name.replace("""attn.proj"""							, """attention.output.dense"""				)
       if "attn" in name:
              _snake_case       =  name.replace("""attn"""							, """attention.self"""				)
       if "norm1" in name:
              _snake_case       =  name.replace("""norm1"""							, """layernorm_before"""				)
       if "norm2" in name:
              _snake_case       =  name.replace("""norm2"""							, """layernorm_after"""				)
       if "mlp.fc1" in name:
              _snake_case       =  name.replace("""mlp.fc1"""							, """intermediate.dense"""				)
       if "mlp.fc2" in name:
              _snake_case       =  name.replace("""mlp.fc2"""							, """output.dense"""				)

       if name == "encoder.norm.weight":
              _snake_case       =  """layernorm.weight"""
       if name == "encoder.norm.bias":
              _snake_case       =  """layernorm.bias"""

       if "decoder" in name:
              pass
       else:
              _snake_case       =  """swin.""" + name

       return name






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
       for key in orig_state_dict.copy().keys():
              _snake_case       =  orig_state_dict.pop(_SCREAMING_SNAKE_CASE				)

              if "attn_mask" in key:
                     pass
              elif "qkv" in key:
                     _snake_case       =  key.split("""."""				)
                     _snake_case       =  int(key_split[2]				)
                     _snake_case       =  int(key_split[4]				)
                     _snake_case       =  model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                     if "weight" in key:
                            _snake_case       =  val[:dim, :]
                            _snake_case       =  val[
                                dim : dim * 2, :
                            ]
                            _snake_case       =  val[-dim:, :]
                     else:
                            _snake_case       =  val[
                                :dim
                            ]
                            _snake_case       =  val[
                                dim : dim * 2
                            ]
                            _snake_case       =  val[
                                -dim:
                            ]
              else:
                     _snake_case       =  val

       return orig_state_dict






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
       _snake_case       =  torch.load(_SCREAMING_SNAKE_CASE							, map_location="""cpu"""				)["""model"""]

       _snake_case       =  get_swin_config(_SCREAMING_SNAKE_CASE				)
       _snake_case       =  SwinForMaskedImageModeling(_SCREAMING_SNAKE_CASE				)
       model.eval()

       _snake_case       =  convert_state_dict(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)
       model.load_state_dict(_SCREAMING_SNAKE_CASE				)

       _snake_case       =  """http://images.cocodataset.org/val2017/000000039769.jpg"""

       _snake_case       =  ViTImageProcessor(size={"""height""": 192, """width""": 192}				)
       _snake_case       =  Image.open(requests.get(_SCREAMING_SNAKE_CASE							, stream=_SCREAMING_SNAKE_CASE				).raw				)
       _snake_case       =  image_processor(images=_SCREAMING_SNAKE_CASE							, return_tensors="""pt"""				)

       with torch.no_grad():
              _snake_case       =  model(**_SCREAMING_SNAKE_CASE				).logits

       print(outputs.keys()				)
       print("""Looks ok!"""				)

       if pytorch_dump_folder_path is not None:
              print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""				)
              model.save_pretrained(_SCREAMING_SNAKE_CASE				)

              print(f"""Saving image processor to {pytorch_dump_folder_path}"""				)
              image_processor.save_pretrained(_SCREAMING_SNAKE_CASE				)

       if push_to_hub:
              print(f"""Pushing model and image processor for {model_name} to hub"""				)
              model.push_to_hub(f"""microsoft/{model_name}"""				)
              image_processor.push_to_hub(f"""microsoft/{model_name}"""				)


if __name__ == "__main__":
    __lowerCAmelCase												=	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default='swin-base-simmim-window6-192',
        type=str,
        choices=['swin-base-simmim-window6-192', 'swin-large-simmim-window12-192'],
        help='Name of the Swin SimMIM model you\'d like to convert.',
    )
    parser.add_argument(
        '--checkpoint_path',
        default='/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth',
        type=str,
        help='Path to the original PyTorch checkpoint (.pth file).',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )
    parser.add_argument(
        '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
    )

    __lowerCAmelCase												=	parser.parse_args()
    convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)

'''simple docstring'''
from math import sqrt






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							_snake_case       =  0
							for i in range(1							, int(sqrt(_SCREAMING_SNAKE_CASE				) + 1				)				):
														if n % i == 0 and i != sqrt(_SCREAMING_SNAKE_CASE				):
																					total += i + n // i
														elif i == sqrt(_SCREAMING_SNAKE_CASE				):
																					total += i
							return total - n






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE = 1_0000				):
							_snake_case       =  sum(
							    i
							    for i in range(1							, _SCREAMING_SNAKE_CASE				)
							    if sum_of_divisors(sum_of_divisors(_SCREAMING_SNAKE_CASE				)				) == i and sum_of_divisors(_SCREAMING_SNAKE_CASE				) != i				)
							return total


if __name__ == "__main__":
				print(solution(int(str(input()).strip())))