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from ortools.sat.python import cp_model

def solve_slant_puzzle(puzzle):
    n = len(puzzle)

    # Create an instance of the CP-SAT model
    model = cp_model.CpModel()

    # Define the decision variables as a matrix representing the grid cells
    x = []
    for i in range(n):
        row = []
        for j in range(n):
            row.append(model.NewIntVar(0, 1, f"x[{i}][{j}]"))
        x.append(row)

    # Circled number constraint
    for i in range(n):
        for j in range(n):
            if isinstance(puzzle[i][j], int):
                k = puzzle[i][j]
                neighbors = []
                if i > 0:
                    neighbors.append(x[i-1][j])
                if j > 0:
                    neighbors.append(x[i][j-1])
                if i < n - 1:
                    neighbors.append(x[i+1][j])
                if j < n - 1:
                    neighbors.append(x[i][j+1])
                model.Add(sum(neighbors) == k)

    # No loop constraint
    for i in range(n-1):
        for j in range(n-1):
            model.Add(x[i][j] + x[i+1][j+1] + x[i][j+1] + x[i+1][j] != 2)

    # Solve the model using the CP-SAT solver
    solver = cp_model.CpSolver()
    status = solver.Solve(model)

    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:
        solution = [[solver.Value(x[i][j]) for j in range(n)] for i in range(n)]

        # Print the solution with diagonal lines for an (N-1)x(N-1) matrix
        for i in range(n-1):
            line = ""
            for j in range(n-1):
                if solution[i][j] == 0:
                    line += "\\ "
                else:
                    line += "/ "
            print(line)
    else:
        print("No solution found.")

# Example puzzle
puzzle = [
    ['*',0, "*", "*", '*',0],
    ["*",3, "*", 3, "*",'*'],
    [1,"*","*","*",3,"*"],
    ["*",3,"*","*",2,2],
    ["*","*", 4, "*", "*","*"],
    ["*","*","*","*",1,"*",]
]

solve_slant_puzzle(puzzle)