


import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


UpperCAmelCase     =       torch.cuda.is_available()
UpperCAmelCase     =       [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
UpperCAmelCase     =       ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
UpperCAmelCase     =       {
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1e-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    Optional[int]:




      """simple docstring"""

      snake_case_			= '''\n'''.join(SCREAMING_SNAKE_CASE   )
      Path(SCREAMING_SNAKE_CASE   ).open('''w'''   ).writelines(SCREAMING_SNAKE_CASE   )






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    Optional[Any]:




      """simple docstring"""

      for split in ["train", "val", "test"]:
            _dump_articles(os.path.join(SCREAMING_SNAKE_CASE   ,   f'''{split}.source'''   )   ,   SCREAMING_SNAKE_CASE   )
            _dump_articles(os.path.join(SCREAMING_SNAKE_CASE   ,   f'''{split}.target'''   )   ,   SCREAMING_SNAKE_CASE   )
      return tmp_dir







class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 @classmethod
 def 		UpperCamelCase__	(      cls   ):
       return cls



 @require_torch_multi_gpu
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= {
           '''no_teacher''': True,
           '''freeze_encoder''': True,
           '''gpus''': 2,
           '''overwrite_output_dir''': True,
           '''sortish_sampler''': True,
       }
       self._test_distiller_cli_fork(_UpperCAmelCase						, check_contents=_UpperCAmelCase   )





 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=True   ):
       snake_case_			= {
           '''label_smoothing''': 0.0,
           '''early_stopping_patience''': -1,
           '''train_batch_size''': 1,
           '''eval_batch_size''': 2,
           '''max_epochs''': 2,
           '''alpha_mlm''': 0.2,
           '''alpha_ce''': 0.8,
           '''do_predict''': True,
           '''model_name_or_path''': '''sshleifer/tinier_bart''',
           '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
           '''val_check_interval''': 0.5,
       }
       default_updates.update(_UpperCAmelCase   )
       snake_case_			= CHEAP_ARGS.copy()
       snake_case_			= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
       snake_case_			= self.get_auto_remove_tmp_dir()
       args_d.update(data_dir=_UpperCAmelCase						, output_dir=_UpperCAmelCase						, **_UpperCAmelCase   )

       def convert(_UpperCAmelCase						, _UpperCAmelCase   ):
             if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                   return ""
             if v is False or v is None:
                   return ""
             if v is True:  # or len(str(v))==0:
                   return F'''--{k}'''
             return F'''--{k}={v}'''

       snake_case_			= [x for x in (convert(_UpperCAmelCase						, _UpperCAmelCase   ) for k, v in args_d.items()) if len(_UpperCAmelCase   )]
       snake_case_			= [sys.executable, F'''{self.test_file_dir}/distillation.py'''] + cli_args
       execute_subprocess_async(_UpperCAmelCase						, env=self.get_env()   )

       snake_case_			= os.listdir(_UpperCAmelCase   )
       snake_case_			= {os.path.basename(_UpperCAmelCase   ) for p in contents}
       snake_case_			= [p for p in contents if p.endswith('''ckpt'''   )]
       assert len(_UpperCAmelCase   ) > 0

       self.assertIn('''test_generations.txt'''						, _UpperCAmelCase   )
       self.assertIn('''test_results.txt'''						, _UpperCAmelCase   )

       # get the following from the module, (we don't have access to `model` here)
       snake_case_			= os.path.join(_UpperCAmelCase						, '''metrics.json'''   )
       snake_case_			= '''rouge2'''

       snake_case_			= load_json(_UpperCAmelCase   )
       # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
       print(_UpperCAmelCase   )
       snake_case_			= metrics['''val'''][-1]
       self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']						, 0.01   )
       self.assertIsInstance(last_step_stats[F'''val_avg_{val_metric}''']						, _UpperCAmelCase   )
       self.assertEqual(len(metrics['''test''']   )						, 1   )
       snake_case_			= int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1   )
       self.assertEqual(len(metrics['''val''']   )						, _UpperCAmelCase   )



def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE = 1000   )->    int:




      """simple docstring"""

      snake_case_			= [0, 3, 3, 5, 4, 4, 3, 5, 5, 4, 3, 6, 6, 8, 8, 7, 7, 9, 8, 8]
      # number of letters in twenty, thirty, ..., ninety (0 for numbers less than
      # 20 due to inconsistency in teens)
      snake_case_			= [0, 0, 6, 6, 5, 5, 5, 7, 6, 6]

      snake_case_			= 0

      for i in range(1   ,   n + 1   ):
            if i < 1000:
                  if i >= 100:
                        # add number of letters for "n hundred"
                        count += ones_counts[i // 100] + 7

                        if i % 100 != 0:
                              # add number of letters for "and" if number is not multiple
                              # of 100
                              count += 3

                  if 0 < i % 100 < 20:
                        # add number of letters for one, two, three, ..., nineteen
                        # (could be combined with below if not for inconsistency in
                        # teens)
                        count += ones_counts[i % 100]
                  else:
                        # add number of letters for twenty, twenty one, ..., ninety
                        # nine
                        count += ones_counts[i % 10]
                        count += tens_counts[(i % 100 - i % 10) // 10]
            else:
                  count += ones_counts[i // 1000] + 8
      return count


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