
"""simple docstring"""

from itertools import combinations_with_replacement






def 				A		(							snake_case :int = 1_0_0						) ->      int:
 __UpperCamelCase							    =			[*list(range(1							,   2_1						)						), 2_5]
 __UpperCamelCase							    =			[2 * x for x in range(1							,   2_1						)] + [5_0]
 __UpperCamelCase							    =			[3 * x for x in range(1							,   2_1						)]
 __UpperCamelCase							    =			singles + doubles + triples + [0]

 __UpperCamelCase							    =			0
 __UpperCamelCase							    =			42
 __UpperCamelCase							    =			42
 __UpperCamelCase							    =			42
 __UpperCamelCase							    =			42

 for double in doubles:
  for throwa, throwa in combinations_with_replacement(snake_case							,   2						):
   __UpperCamelCase							    =			double + throwa + throwa
   if checkout_total < limit:
    num_checkouts += 1

 return num_checkouts


if __name__ == "__main__":
      print(f'''{solution() = }''')





"""simple docstring"""

from sklearn.neural_network import MLPClassifier

UpperCamelCase      :							Any          =					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
UpperCamelCase      :							Any          =					[0, 1, 0, 0]


UpperCamelCase      :							Optional[Any]          =					MLPClassifier(
    solver="lbfgs", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


UpperCamelCase      :							List[Any]          =					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
UpperCamelCase      :							Optional[Any]          =					clf.predict(test)






def 				A		(							snake_case :int						) ->      int:
 return list(snake_case						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



