






a		:	Union[str, Any]      					=			{
    "0": 1,
    "1": 1,
    "2": 2,
    "3": 6,
    "4": 24,
    "5": 120,
    "6": 720,
    "7": 5_040,
    "8": 40_320,
    "9": 362_880,
}

a		:	str      					=			{145: 145}

a		:	Optional[int]      					=			{
    145: 0,
    169: 3,
    36_301: 3,
    1_454: 3,
    871: 2,
    45_361: 2,
    872: 2,
}






def     lowerCamelCase__				(   __lowerCamelCase      :					int		):
  if n in CACHE_SUM_DIGIT_FACTORIALS:
    return CACHE_SUM_DIGIT_FACTORIALS[n]
  __UpperCAmelCase							:     List[str]									=  sum(DIGIT_FACTORIALS[let] for let in str(__lowerCamelCase		)		)
  __UpperCAmelCase							:     Union[str, Any]									=  ret
  return ret






def     lowerCamelCase__				(   __lowerCamelCase      :					int  ,		__lowerCamelCase      :					set | None = None		):
  __UpperCAmelCase							:     Any									=  previous or set()
  if n in CHAIN_LENGTH_CACHE:
    return CHAIN_LENGTH_CACHE[n]
  __UpperCAmelCase							:     Optional[int]									=  sum_digit_factorials(__lowerCamelCase		)
  if next_number in previous:
    __UpperCAmelCase							:     Tuple									=  0
    return 0
  else:
    previous.add(__lowerCamelCase		)
    __UpperCAmelCase							:     List[str]									=  1 + chain_length(__lowerCamelCase  ,		__lowerCamelCase		)
    __UpperCAmelCase							:     Tuple									=  ret
    return ret






def     lowerCamelCase__				(   __lowerCamelCase      :					int = 60  ,		__lowerCamelCase      :					int = 1000000		):
  return sum(1 for i in range(1  ,		__lowerCamelCase		) if chain_length(__lowerCamelCase		) == num_terms		)


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










from sklearn.neural_network import MLPClassifier

a		:	int      					=			[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
a		:	List[str]      					=			[0, 1, 0, 0]


a		:	str      					=			MLPClassifier(
    solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


a		:	Union[str, Any]      					=			[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
a		:	Optional[int]      					=			clf.predict(test)






def     lowerCamelCase__				(   __lowerCamelCase      :					Dict		):
  return list(__lowerCamelCase		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


