




def 					__SCREAMING_SNAKE_CASE   (  )       ->	int:

    """simple docstring"""


    return 1




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	int:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	int:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	int:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	int:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	int:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	int:

    """simple docstring"""


    return 0 if x < 0 else one_pound(x - 1_00      ) + fifty_pence(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	int:

    """simple docstring"""


    return 0 if x < 0 else two_pound(x - 2_00      ) + one_pound(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_ = 2_00      )       ->	int:

    """simple docstring"""


    return two_pound(lowercase_      )


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












import os
import time

import numpy as np
import onnxruntime as ort


a_   		=     "1"
a_   		=     "0"
a_   		=     "1"

a_   		=     ort.SessionOptions()
a_   		=     ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
a_   		=     ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
a_   		=     ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
a_   		=     ort.RunOptions()

a_   		=     128
a_   		=     1
a_   		=     np.ones((batch, sequence), dtype=np.intaa)
a_   		=     np.ones((batch, sequence), dtype=np.intaa)
a_   		=     np.ones((batch, sequence), dtype=np.intaa)

print("Warm up phase...")
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print("Start inference...")
a_   		=     time.time()
a_   		=     2000
a_   		=     {}
for iter in range(max_iters):
 a_   		=     sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1000 / max_iters))






