





'''simple docstring'''

def 						__magic_name__       (							)						->					int:

   '''simple docstring'''







   return 1

def 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







   return 0 if x < 0 else two_pence(x - 2      ) + one_pence()

def 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







   return 0 if x < 0 else five_pence(x - 5      ) + two_pence(__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







   return 0 if x < 0 else ten_pence(x - 10      ) + five_pence(__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







   return 0 if x < 0 else twenty_pence(x - 20      ) + ten_pence(__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







   return 0 if x < 0 else fifty_pence(x - 50      ) + twenty_pence(__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







   return 0 if x < 0 else one_pound(x - 100      ) + fifty_pence(__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







   return 0 if x < 0 else two_pound(x - 200      ) + one_pound(__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase = 200      )						->					int:

   '''simple docstring'''







   return two_pound(__UpperCAmelCase      )


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












'''simple docstring'''

import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def 						__magic_name__       (							__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   def wrapper(*__UpperCAmelCase							,	**__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer()
      __SCREAMING_SNAKE_CASE      							=       func(*__UpperCAmelCase							,	**__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer() - starttime
      return delta

   __SCREAMING_SNAKE_CASE      							=       func.__name__

   return wrapper

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       []
   __SCREAMING_SNAKE_CASE      							=       seq_shapes or {}
   for i in range(__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       {}
      for col_id, (k, v) in enumerate(features.items()      ):
         if isinstance(__UpperCAmelCase							,	_ArrayXD      ):
            __SCREAMING_SNAKE_CASE      							=       np.random.rand(*v.shape      ).astype(v.dtype      )
         elif isinstance(__UpperCAmelCase							,	datasets.Value      ):
            if v.dtype == "string":
               __SCREAMING_SNAKE_CASE      							=       """The small grey turtle was surprisingly fast when challenged."""
            else:
               __SCREAMING_SNAKE_CASE      							=       np.random.randint(10							,	size=1      ).astype(v.dtype      ).item()
         elif isinstance(__UpperCAmelCase							,	datasets.Sequence      ):
            while isinstance(__UpperCAmelCase							,	datasets.Sequence      ):
               __SCREAMING_SNAKE_CASE      							=       v.feature
            __SCREAMING_SNAKE_CASE      							=       seq_shapes[k]
            __SCREAMING_SNAKE_CASE      							=       np.random.rand(*__UpperCAmelCase      ).astype(v.dtype      )
         __SCREAMING_SNAKE_CASE      							=       data

      dummy_data.append((i, example)      )

   return dummy_data







def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       generate_examples(__UpperCAmelCase							,	num_examples=__UpperCAmelCase							,	seq_shapes=__UpperCAmelCase      )

   with ArrowWriter(features=__UpperCAmelCase							,	path=__UpperCAmelCase      ) as writer:
      for key, record in dummy_data:
         __SCREAMING_SNAKE_CASE      							=       features.encode_example(__UpperCAmelCase      )
         writer.write(__UpperCAmelCase      )

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       writer.finalize()

   if not num_final_examples == num_examples:
      raise ValueError(
          f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""      )

   __SCREAMING_SNAKE_CASE      							=       datasets.Dataset.from_file(filename=__UpperCAmelCase							,	info=datasets.DatasetInfo(features=__UpperCAmelCase      )      )

   return dataset





