




from __future__ import annotations


def    A						(__A   :    int | str       )   ->      bool:







     """simple docstring"""


     UpperCAmelCase_          =							str(__lowerCamelCase       )
     return n == n[::-1]


def    A						(__A   :    int = 1000000       )   ->      Any:







     """simple docstring"""


     UpperCAmelCase_          =							0

     for i in range(1   ,				__lowerCamelCase       ):
          if is_palindrome(__lowerCamelCase       ) and is_palindrome(bin(__lowerCamelCase       ).split('''b'''       )[1]       ):
               total += i
     return total


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








"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__          = logging.get_logger(__name__)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Any ,					__lowerCamelCase   :       Union[str, Any]=False		)     ->					Optional[int]:
  _snake_case					     =       []
  for i in range(config.num_hidden_layers		):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''deit.encoder.layer.{i}.layernorm_before.weight''')		)
    rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''')		)
    rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''')		)
    rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''')		)
    rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''')		)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ('''cls_token''', '''deit.embeddings.cls_token'''),
          ('''dist_token''', '''deit.embeddings.distillation_token'''),
          ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
          ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
          ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
      ]		)

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ('''norm.weight''', '''layernorm.weight'''),
            ('''norm.bias''', '''layernorm.bias'''),
            ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
            ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
        ]		)

    # if just the base model, we should remove "deit" from all keys that start with "deit"
    _snake_case					     =       [(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''		) else pair for pair in rename_keys]
  else:
    # layernorm + classification heads
    rename_keys.extend(
        [
            ('''norm.weight''', '''deit.layernorm.weight'''),
            ('''norm.bias''', '''deit.layernorm.bias'''),
            ('''head.weight''', '''cls_classifier.weight'''),
            ('''head.bias''', '''cls_classifier.bias'''),
            ('''head_dist.weight''', '''distillation_classifier.weight'''),
            ('''head_dist.bias''', '''distillation_classifier.bias'''),
        ]		)

  return rename_keys
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Tuple=False		)     ->					Tuple:
  for i in range(config.num_hidden_layers		):
    if base_model:
      _snake_case					     =       ''''''
    else:
      _snake_case					     =       '''deit.'''
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _snake_case					     =       state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''		)
    _snake_case					     =       state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''		)
    # next, add query, keys and values (in that order) to the state dict
    _snake_case					     =       in_proj_weight[
        : config.hidden_size, :
    ]
    _snake_case					     =       in_proj_bias[: config.hidden_size]
    _snake_case					     =       in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _snake_case					     =       in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _snake_case					     =       in_proj_weight[
        -config.hidden_size :, :
    ]
    _snake_case					     =       in_proj_bias[-config.hidden_size :]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Tuple		)     ->					Tuple:
  _snake_case					     =       dct.pop(__lowerCamelCase		)
  _snake_case					     =       val
def 					_UpperCAmelCase      ( )     ->					Dict:
  _snake_case					     =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case					     =       Image.open(requests.get(__lowerCamelCase ,					stream=__lowerCamelCase		).raw		)
  return im
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       str		)     ->					str:
  _snake_case					     =       DeiTConfig()
  # all deit models have fine-tuned heads
  _snake_case					     =       False
  # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
  _snake_case					     =       10_00
  _snake_case					     =       '''huggingface/label-files'''
  _snake_case					     =       '''imagenet-1k-id2label.json'''
  _snake_case					     =       json.load(open(hf_hub_download(__lowerCamelCase ,					__lowerCamelCase ,					repo_type='''dataset'''		) ,					'''r'''		)		)
  _snake_case					     =       {int(__lowerCamelCase		): v for k, v in idalabel.items()}
  _snake_case					     =       idalabel
  _snake_case					     =       {v: k for k, v in idalabel.items()}
  _snake_case					     =       int(deit_name[-6:-4]		)
  _snake_case					     =       int(deit_name[-3:]		)
  # size of the architecture
  if deit_name[9:].startswith('''tiny'''		):
    _snake_case					     =       1_92
    _snake_case					     =       7_68
    _snake_case					     =       12
    _snake_case					     =       3
  elif deit_name[9:].startswith('''small'''		):
    _snake_case					     =       3_84
    _snake_case					     =       15_36
    _snake_case					     =       12
    _snake_case					     =       6
  if deit_name[9:].startswith('''base'''		):
    pass
  elif deit_name[4:].startswith('''large'''		):
    _snake_case					     =       10_24
    _snake_case					     =       40_96
    _snake_case					     =       24
    _snake_case					     =       16

  # load original model from timm
  _snake_case					     =       timm.create_model(__lowerCamelCase ,					pretrained=__lowerCamelCase		)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  _snake_case					     =       timm_model.state_dict()
  _snake_case					     =       create_rename_keys(__lowerCamelCase ,					__lowerCamelCase		)
  for src, dest in rename_keys:
    rename_key(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)
  read_in_q_k_v(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)

  # load HuggingFace model
  _snake_case					     =       DeiTForImageClassificationWithTeacher(__lowerCamelCase		).eval()
  model.load_state_dict(__lowerCamelCase		)

  # Check outputs on an image, prepared by DeiTImageProcessor
  _snake_case					     =       int(
      (2_56 / 2_24) * config.image_size		)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
  _snake_case					     =       DeiTImageProcessor(size=__lowerCamelCase ,					crop_size=config.image_size		)
  _snake_case					     =       image_processor(images=prepare_img() ,					return_tensors='''pt'''		)
  _snake_case					     =       encoding['''pixel_values''']
  _snake_case					     =       model(__lowerCamelCase		)

  _snake_case					     =       timm_model(__lowerCamelCase		)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(__lowerCamelCase ,					outputs.logits ,					atol=1E-3		)

  Path(__lowerCamelCase		).mkdir(exist_ok=__lowerCamelCase		)
  print(f'''Saving model {deit_name} to {pytorch_dump_folder_path}'''		)
  model.save_pretrained(__lowerCamelCase		)
  print(f'''Saving image processor to {pytorch_dump_folder_path}'''		)
  image_processor.save_pretrained(__lowerCamelCase		)


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--deit_name',
     default='vit_deit_base_distilled_patch16_224',
     type=str,
     help='Name of the DeiT timm model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
 )

 UpperCAmelCase__          = parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



