




def 					_a							(					)->	Tuple:
		return 1


def 					_a							(					lowerCAmelCase )->	int:
		return 0 if x < 0 else two_pence(x - 2 ) + one_pence()


def 					_a							(					lowerCAmelCase )->	Optional[int]:
		return 0 if x < 0 else five_pence(x - 5 ) + two_pence(UpperCamelCase_ )


def 					_a							(					lowerCAmelCase )->	Optional[Any]:
		return 0 if x < 0 else ten_pence(x - 10 ) + five_pence(UpperCamelCase_ )


def 					_a							(					lowerCAmelCase )->	Tuple:
		return 0 if x < 0 else twenty_pence(x - 20 ) + ten_pence(UpperCamelCase_ )


def 					_a							(					lowerCAmelCase )->	int:
		return 0 if x < 0 else fifty_pence(x - 50 ) + twenty_pence(UpperCamelCase_ )


def 					_a							(					lowerCAmelCase )->	str:
		return 0 if x < 0 else one_pound(x - 100 ) + fifty_pence(UpperCamelCase_ )


def 					_a							(					lowerCAmelCase )->	str:
		return 0 if x < 0 else two_pound(x - 200 ) + one_pound(UpperCamelCase_ )


def 					_a							(					lowerCAmelCase = 200 )->	Union[str, Any]:
		return two_pound(UpperCamelCase_ )


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



'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel




@require_tf
@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase			(			unittest.TestCase						):


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:List[str]      =      TFCamembertModel.from_pretrained('''jplu/tf-camembert-base''' )

    UpperCAmelCase__	:Optional[Any]      =      tf.convert_to_tensor(
        [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]			,       dtype=tf.intaa			,       )  # J'aime le camembert !"

    UpperCAmelCase__	:Any      =      model(__lowerCamelCase )['''last_hidden_state''']
    UpperCAmelCase__	:Dict      =      tf.TensorShape((1, 1_0, 7_6_8) )
    self.assertEqual(output.shape			,       __lowerCamelCase )
    # compare the actual values for a slice.
    UpperCAmelCase__	:Any      =      tf.convert_to_tensor(
        [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]			,       dtype=tf.floataa			,       )
    # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
    # camembert.eval()
    # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

    self.assertTrue(np.allclose(output[:, :3, :3].numpy()			,       expected_slice.numpy()			,       atol=1e-4 ) )



