"""
AWARI

An ancient African game (see also Kalah, Mancala).

Ported by Dave LeCompte
"""

# PORTING NOTES
#
# This game started out as 70 lines of BASIC, and I have ported it
# before. I find it somewhat amazing how efficient (densely packed) the
# original code is. Of course, the original code has fairly cryptic
# variable names (as was forced by BASIC's limitation on long (2+
# character) variable names). I have done my best here to interpret what
# each variable is doing in context, and rename them appropriately.
#
# I have endeavored to leave the logic of the code in place, as it's
# interesting to see a 2-ply game tree evaluation written in BASIC,
# along with what a reader in 2021 would call "machine learning".
#
# As each game is played, the move history is stored as base-6
# digits stored losing_book[game_number]. If the human player wins or
# draws, the computer increments game_number, effectively "recording"
# that loss to be referred to later. As the computer evaluates moves, it
# checks the potential game state against these losing game records, and
# if the potential move matches with the losing game (up to the current
# number of moves), that move is evaluated at a two point penalty.
#
# Compare this, for example with MENACE, a mechanical device for
# "learning" tic-tac-toe:
# https://en.wikipedia.org/wiki/Matchbox_Educable_Noughts_and_Crosses_Engine
#
# The base-6 representation allows game history to be VERY efficiently
# represented. I considered whether to rewrite this representation to be
# easier to read, but I elected to TRY to document it, instead.
#
# Another place where I have made a difficult decision between accuracy
# and correctness is inside the "wrapping" code where it considers
# "while human_move_end > 13". The original BASIC code reads:
#
# 830 IF L>13 THEN L=L-14:R=1:GOTO 830
#
# I suspect that the intention is not to assign 1 to R, but to increment
# R. I discuss this more in a porting note comment next to the
# translated code. If you wish to play a more accurate version of the
# game as written in the book, you can convert the increment back to an
# assignment.
#
# I continue to be impressed with this jewel of a game; as soon as I had
# the AI playing against me, it was beating me. I've been able to score
# a few wins against the computer, but even at its 2-ply lookahead, it
# beats me nearly always. I would like to become better at this game to
# explore the effectiveness of the "losing book" machine learning.
#
#
# EXERCISES FOR THE READER
# One could go many directions with this game:
# - change the initial number of stones in each pit
# - change the number of pits
# - only allow capturing if you end on your side of the board
# - don't allow capturing at all
# - don't drop a stone into the enemy "home"
# - go clockwise, instead
# - allow the player to choose to go clockwise or counterclockwise
# - instead of a maximum of two moves, allow each move that ends on the
#   "home" to be followed by a free move.
# - increase the AI lookahead
# - make the scoring heuristic a little more nuanced
# - store history to a file on disk (or in the cloud!) to allow the AI
#   to learn over more than a single session


game_number = 0
move_count = 0
losing_book = []
n = 0

MAX_HISTORY = 9
LOSING_BOOK_SIZE = 50


def print_with_tab(space_count, msg):
    if space_count > 0:
        spaces = " " * space_count
    else:
        spaces = ""
    print(spaces + msg)


def draw_pit(line, board, pit_index):
    val = board[pit_index]
    line = line + " "
    if val < 10:
        line = line + " "
    line = line + str(val) + " "
    return line


def draw_board(board):
    print()

    # Draw the top (computer) pits
    line = "   "
    for i in range(12, 6, -1):
        line = draw_pit(line, board, i)
    print(line)

    # Draw the side (home) pits
    line = draw_pit("", board, 13)
    line += " " * 24
    line = draw_pit(line, board, 6)
    print(line)

    # Draw the bottom (player) pits
    line = "   "
    for i in range(0, 6):
        line = draw_pit(line, board, i)
    print(line)
    print()
    print()


def play_game(board):
    # Place the beginning stones
    for i in range(0, 13):
        board[i] = 3

    # Empty the home pits
    board[6] = 0
    board[13] = 0

    global move_count
    move_count = 0

    # clear the history record for this game
    losing_book[game_number] = 0

    while True:
        draw_board(board)

        print("YOUR MOVE")
        landing_spot, is_still_going, home = player_move(board)
        if not is_still_going:
            break
        if landing_spot == home:
            landing_spot, is_still_going, home = player_move_again(board)
        if not is_still_going:
            break

        print("MY MOVE")
        landing_spot, is_still_going, home, msg = computer_move("", board)

        if not is_still_going:
            print(msg)
            break
        if landing_spot == home:
            landing_spot, is_still_going, home, msg = computer_move(msg + " , ", board)
        if not is_still_going:
            print(msg)
            break
        print(msg)

    game_over(board)


def computer_move(msg, board):
    # This function does a two-ply lookahead evaluation; one computer
    # move plus one human move.
    #
    # To do this, it makes a copy (temp_board) of the board, plays
    # each possible computer move and then uses math to work out what
    # the scoring heuristic is for each possible human move.
    #
    # Additionally, if it detects that a potential move puts it on a
    # series of moves that it has recorded in its "losing book", it
    # penalizes that move by two stones.

    best_quality = -99

    # Make a copy of the board, so that we can experiment. We'll put
    # everything back, later.
    temp_board = board[:]

    # For each legal computer move 7-12
    for computer_move in range(7, 13):
        if board[computer_move] == 0:
            continue
        do_move(computer_move, 13, board)  # try the move (1 move lookahead)

        best_player_move_quality = 0
        # for all legal human moves 0-5 (responses to computer move computer_move)
        for human_move_start in range(0, 6):
            if board[human_move_start] == 0:
                continue

            human_move_end = board[human_move_start] + human_move_start
            this_player_move_quality = 0

            # If this move goes around the board, wrap backwards.
            #
            # PORTING NOTE: The careful reader will note that I am
            # incrementing this_player_move_quality for each wrap,
            # while the original code only set it equal to 1.
            #
            # I expect this was a typo or oversight, but I also
            # recognize that you'd have to go around the board more
            # than once for this to be a difference, and even so, it
            # would be a very small difference; there are only 36
            # stones in the game, and going around the board twice
            # requires 24 stones.

            while human_move_end > 13:
                human_move_end = human_move_end - 14
                this_player_move_quality += 1

            if (
                (board[human_move_end] == 0)
                and (human_move_end != 6)
                and (human_move_end != 13)
            ):
                # score the capture
                this_player_move_quality += board[12 - human_move_end]

            if this_player_move_quality > best_player_move_quality:
                best_player_move_quality = this_player_move_quality

        # This is a zero sum game, so the better the human player's
        # move is, the worse it is for the computer player.
        computer_move_quality = board[13] - board[6] - best_player_move_quality

        if move_count < MAX_HISTORY:
            move_digit = computer_move
            if move_digit > 6:
                move_digit = move_digit - 7

            # Calculate the base-6 history representation of the game
            # with this move. If that history is in our "losing book",
            # penalize that move.
            for prev_game_number in range(game_number):
                if losing_book[game_number] * 6 + move_digit == int(
                    losing_book[prev_game_number] / 6 ^ (7 - move_count) + 0.1
                ):
                    computer_move_quality -= 2

        # Copy back from temporary board
        for i in range(14):
            board[i] = temp_board[i]

        if computer_move_quality >= best_quality:
            best_move = computer_move
            best_quality = computer_move_quality

    selected_move = best_move

    move_str = chr(42 + selected_move)
    if msg:
        msg += ", " + move_str
    else:
        msg = move_str

    move_number, is_still_going, home = execute_move(selected_move, 13, board)

    return move_number, is_still_going, home, msg


def game_over(board):
    print()
    print("GAME OVER")

    pit_difference = board[6] - board[13]
    if pit_difference < 0:
        print(f"I WIN BY {-pit_difference} POINTS")

    else:
        global n
        n = n + 1

        if pit_difference == 0:
            print("DRAWN GAME")
        else:
            print(f"YOU WIN BY {pit_difference} POINTS")


def do_capture(m, home, board):
    board[home] += board[12 - m] + 1
    board[m] = 0
    board[12 - m] = 0


def do_move(m, home, board):
    move_stones = board[m]
    board[m] = 0

    for stones in range(move_stones, 0, -1):
        m = m + 1
        if m > 13:
            m = m - 14
        board[m] += 1
    if board[m] == 1:
        # capture
        if (m != 6) and (m != 13) and (board[12 - m] != 0):
            do_capture(m, home, board)
    return m


def player_has_stones(board):
    for i in range(6):
        if board[i] > 0:
            return True
    return False


def computer_has_stones(board):
    for i in range(7, 13):
        if board[i] > 0:
            return True
    return False


def execute_move(move, home, board):
    move_digit = move
    last_location = do_move(move, home, board)

    if move_digit > 6:
        move_digit = move_digit - 7

    global move_count
    move_count += 1
    if move_count < MAX_HISTORY:
        # The computer keeps a chain of moves in losing_book by
        # storing a sequence of moves as digits in a base-6 number.
        #
        # game_number represents the current game,
        # losing_book[game_number] records the history of the ongoing
        # game.  When the computer evaluates moves, it tries to avoid
        # moves that will lead it into paths that have led to previous
        # losses.
        losing_book[game_number] = losing_book[game_number] * 6 + move_digit

    if player_has_stones(board) and computer_has_stones(board):
        is_still_going = True
    else:
        is_still_going = False
    return last_location, is_still_going, home


def player_move_again(board):
    print("AGAIN")
    return player_move(board)


def player_move(board):
    while True:
        print("SELECT MOVE 1-6")
        m = int(input()) - 1

        if m > 5 or m < 0 or board[m] == 0:
            print("ILLEGAL MOVE")
            continue

        break

    ending_spot, is_still_going, home = execute_move(m, 6, board)

    draw_board(board)

    return ending_spot, is_still_going, home


def main():
    print_with_tab(34, "AWARI")
    print_with_tab(15, "CREATIVE COMPUTING  MORRISTOWN, NEW JERSEY")
    print()
    print()

    board = [0] * 14  # clear the board representation
    global losing_book
    losing_book = [0] * LOSING_BOOK_SIZE  # clear the "machine learning" state

    while True:
        play_game(board)


if __name__ == "__main__":
    main()