
    Given a customer's credit history spanning n assessment periods and a maximum possible FICO score of m, determine the number of possible ways their credit score could have improved over time, where each new score must be at least double the previous score but not exceed m. The analysis aims to identify feasible credit score progression patterns that demonstrate significant and sustained improvement in creditworthiness.
    

    
def get_total_number_of_sequences(m, n):
    """
    Given a customer's credit history spanning n assessment periods and a maximum possible FICO score of m, determine the number of possible ways their credit score could have improved over time, where each new score must be at least double the previous score but not exceed m. The analysis aims to identify feasible credit score progression patterns that demonstrate significant and sustained improvement in creditworthiness.
    

    """
def get_total_number_of_sequences(m, n):
    """
    Given a customer's credit history spanning n assessment periods and a maximum possible FICO score of m, determine the number of possible ways their credit score could have improved over time, where each new score must be at least double the previous score but not exceed m. The analysis aims to identify feasible credit score progression patterns that demonstrate significant and sustained improvement in creditworthiness.
    

    """
    T = [[0 for i in range(n + 1)] for i in range(m + 1)]
    for i in range(m + 1):
        for j in range(n + 1):
            if i == 0 or j == 0:
                T[i][j] = 0
            elif i < j:
                T[i][j] = 0
            elif j == 1:
                T[i][j] = i
            else:
                T[i][j] = T[i - 1][j] + T[i // 2][j - 1]
    return T[m][n]