from axelrod.action import Action

from axelrod.player import Player

C, D = Action.C, Action.D

def create_policy(pCC, pCD, pDC, pDD):
    """
    Creates a dict that represents a Policy. As defined in the reference, a
    Policy is a set of (prev_move, p) where p is the probability to cooperate
    after prev_move, where prev_move can be (C, C), (C, D), (D, C) or (D, D).

    Parameters

    pCC, pCD, pDC, pDD : float
        Must be between 0 and 1.
    """
    return {(C, C): pCC, (C, D): pCD, (D, C): pDC, (D, D): pDD}

def action_to_int(action):
    if action == C:
        return 1
    return 0

def minimax_tree_search(begin_node, policy, max_depth):
    """
    Tree search function (minimax search procedure) for the tree (built by
    recursion) corresponding to the opponent's policy, and solves it.
    Returns a tuple of two floats that are the utility of playing C, and the
    utility of playing D.
    """
    if begin_node.is_stochastic():
        # A stochastic node cannot have the same depth than its parent node
        # hence there is no need to check that its depth is < max_depth.
        siblings = begin_node.get_siblings()
        # The stochastic node value is the expected value of siblings.
        node_value = begin_node.pC * minimax_tree_search(
            siblings[0], policy, max_depth
        ) + (1 - begin_node.pC) * minimax_tree_search(
            siblings[1], policy, max_depth
        )
        return node_value
    else:  # Deterministic node
        if begin_node.depth == max_depth:
            # This is an end node, we just return its outcome value.
            return begin_node.get_value()
        elif begin_node.depth == 0:
            siblings = begin_node.get_siblings(policy)
            # This returns the two max expected values, for choice C or D,
            # as a tuple.
            return (
                minimax_tree_search(siblings[0], policy, max_depth)
                + begin_node.get_value(),
                minimax_tree_search(siblings[1], policy, max_depth)
                + begin_node.get_value(),
            )
        elif begin_node.depth < max_depth:
            siblings = begin_node.get_siblings(policy)
            # The deterministic node value is the max of both siblings values
            # + the score of the outcome of the node.
            a = minimax_tree_search(siblings[0], policy, max_depth)
            b = minimax_tree_search(siblings[1], policy, max_depth)
            node_value = max(a, b) + begin_node.get_value()
            return node_value

def move_gen(outcome, policy, depth_search_tree=5):
    """
    Returns the best move considering opponent's policy and last move,
    using tree-search procedure.
    """
    current_node = DeterministicNode(outcome[0], outcome[1], depth=0)
    values_of_choices = minimax_tree_search(
        current_node, policy, depth_search_tree
    )
    # Returns the Action which correspond to the best choice in terms of
    # expected value. In case value(C) == value(D), returns C.
    actions_tuple = (C, D)
    return actions_tuple[values_of_choices.index(max(values_of_choices))]

class DBS(Player):
    """
    A strategy that learns the opponent's strategy and uses symbolic noise
    detection for detecting whether anomalies in player’s behavior are
    deliberate or accidental. From the learned opponent's strategy, a tree
    search is used to choose the best move.

    Default values for the parameters are the suggested values in the article.
    When noise increases you can try to diminish violation_threshold and
    rejection_threshold.

    Names

    - Derived Belief Strategy: [Au2006]_
    """

    # These are various properties for the strategy
    name = "DBS"
    classifier = {
        "memory_depth": float("inf"),
        "stochastic": False,
        "long_run_time": True,
        "inspects_source": False,
        "manipulates_source": False,
        "manipulates_state": False,
    }

    def __init__(
        self,
        discount_factor=0.75,
        promotion_threshold=3,
        violation_threshold=4,
        reject_threshold=3,
        tree_depth=5,
    ):
        """
        Parameters

        discount_factor: float, optional
            Used when computing discounted frequencies to learn opponent's
            strategy. Must be between 0 and 1. The default is 0.75.
        promotion_threshold: int, optional
            Number of successive observations needed to promote an opponent
            behavior as a deterministic rule. The default is 3.
        violation_threshold: int, optional
            Number of observations needed to considerate opponent's strategy has
            changed. You can lower it when noise increases. The default is 4,
            which is good for a noise level of .1.
        reject_threshold: int, optional
            Number of observations before forgetting opponent's previous
            strategy. You can lower it when noise increases. The default is 3,
             which is good for a noise level of .1.
        tree_depth: int, optional
            Depth of the tree for the tree-search algorithm. Higher depth means
            more time to compute the move. The default is 5.
        """
        super().__init__()

        # The opponent's behavior is represented by a 3 dicts: Rd, Rc, and Rp.
        # Its behavior is modeled by a set of rules. A rule is the move that
        # the opponent will play (C or D or a probability to play C) after a
        # given outcome (for instance after (C, D)).
        # A rule can be deterministic or probabilistic:
        # - Rc is the set of deterministic rules
        # - Rp is the set of probabilistic rules
        # - Rd is the default rule set which is used for initialization but also
        # keeps track of previous policies when change in the opponent behavior
        # happens, in order to have a smooth transition.
        # - Pi is a set of rules that aggregates all above sets of rules in
        # order to fully model the opponent's behavior.

        # Default rule set is Rd.
        # Default opponent's policy is TitForTat.
        self.Rd = create_policy(1, 1, 0, 0)
        # Set of current deterministic rules Rc
        self.Rc = {}
        # Aggregated rule set Pi
        self.Pi = self.Rd
        # For each rule in Rd we need to count the number of successive
        # violations. Those counts are saved in violation_counts.
        self.violation_counts = {}
        self.reject_threshold = reject_threshold
        self.violation_threshold = violation_threshold
        self.promotion_threshold = promotion_threshold
        self.tree_depth = tree_depth
        # v is a violation count used to know when to clean the default rule
        # set Rd
        self.v = 0
        # A discount factor for computing the probabilistic rules
        self.alpha = discount_factor

        # The probabilistic rule set Rp is not saved as an attribute, but each
        # rule is computed only when needed. The rules are computed as
        # discounted frequencies of opponent's past moves. To compute the
        # discounted frequencies, we need to keep up to date an history of what
        # has been played following each outcome (or condition):
        # We save it as a dict history_by_cond; keys are conditions
        # (ex (C, C)) and values are a tuple of 2 lists (G, F)
        # for a condition j and an iteration i in the match:
        # G[i] = 1 if cond j was True at turn i-1 and C has been played
        # by the opponent; else G[i] = 0
        # F[i] = 1 if cond j was True at turn i-1; else F[i] = 0
        # This representation makes the computing of discounted frequencies
        # easy and efficient.
        # The initial hypothesized policy is TitForTat.
        self.history_by_cond = {
            (C, C): ([1], [1]),
            (C, D): ([1], [1]),
            (D, C): ([0], [1]),
            (D, D): ([0], [1]),
        }

    def should_promote(self, r_plus, promotion_threshold=3):
        """
        This function determines if the move r_plus is a deterministic
        behavior of the opponent, and then returns True, or if r_plus
        is due to a random behavior (or noise) which would require a
        probabilistic rule, in which case it returns False.

        To do so it looks into the game history: if the k last times
        when the opponent was in the same situation than in r_plus it
        played the same thing then then r_plus is considered as a
        deterministic rule (where K is the user-defined promotion_threshold).

        Parameters

        r_plus: tuple of (tuple of actions.Action, actions.Action)
            example: ((C, C), D)
            r_plus represents one outcome of the history, and the
            following move played by the opponent.
        promotion_threshold: int, optional
            Number of successive observations needed to promote an
            opponent behavior as a deterministic rule. Default is 3.
        """
        if r_plus[1] == C:
            opposite_action = 0
        elif r_plus[1] == D:
            opposite_action = 1
        k = 1
        count = 0
        # We iterate on the history, while we do not encounter
        # counter-examples of r_plus, i.e. while we do not encounter
        # r_minus
        while k < len(self.history_by_cond[r_plus[0]][0]) and not (
            self.history_by_cond[r_plus[0]][0][1:][-k] == opposite_action
            and self.history_by_cond[r_plus[0]][1][1:][-k] == 1
        ):
            # We count every occurrence of r_plus in history
            if self.history_by_cond[r_plus[0]][1][1:][-k] == 1:
                count += 1
            k += 1
        if count >= promotion_threshold:
            return True
        return False

    def should_demote(self, r_minus, violation_threshold=4):
        """
        Checks if the number of successive violations of a deterministic
        rule (in the opponent's behavior) exceeds the user-defined
        violation_threshold.
        """
        return self.violation_counts[r_minus[0]] >= violation_threshold

    def update_history_by_cond(self, opponent_history):
        """
        Updates self.history_by_cond between each turns of the game.
        """
        two_moves_ago = (self.history[-2], opponent_history[-2])
        for outcome, GF in self.history_by_cond.items():
            G, F = GF
            if outcome == two_moves_ago:
                if opponent_history[-1] == C:
                    G.append(1)
                else:
                    G.append(0)
                F.append(1)
            else:
                G.append(0)
                F.append(0)

    def compute_prob_rule(self, outcome, alpha=1):
        """
        Uses the game history to compute the probability of the opponent
        playing C, in the outcome situation (example: outcome = (C, C)).
        When alpha = 1, the results is approximately equal to the frequency of
        the occurrence of outcome C. alpha is a discount factor that gives more
        weight to recent events than earlier ones.

        Parameters

        outcome: tuple of two actions.Action
        alpha: int, optional. Discount factor. Default is 1.
        """
        G = self.history_by_cond[outcome][0]
        F = self.history_by_cond[outcome][1]
        discounted_g = 0
        discounted_f = 0
        alpha_k = 1
        for g, f in zip(G[::-1], F[::-1]):
            discounted_g += alpha_k * g
            discounted_f += alpha_k * f
            alpha_k = alpha * alpha_k
        p_cond = discounted_g / discounted_f
        return p_cond

    def strategy(self, opponent: Player) -> Action:
        """Actual strategy definition that determines player's action."""
        # First move
        if not self.history:
            return C
        if len(opponent.history) >= 2:
            # We begin by update history_by_cond (i.e. update Rp)
            self.update_history_by_cond(opponent.history)
            two_moves_ago = (self.history[-2], opponent.history[-2])
            # r_plus is the information of what the opponent just played,
            # following the previous outcome two_moves_ago.
            r_plus = (two_moves_ago, opponent.history[-1])
            # r_minus is the opposite move, following the same outcome.
            r_minus = (two_moves_ago, ({C, D} - {opponent.history[-1]}).pop())

            # If r_plus and r_minus are not in the current set of deterministic
            # rules, we check if r_plus should be added to it (following the
            # rule defined in the should_promote function).
            if r_plus[0] not in self.Rc.keys():
                if self.should_promote(r_plus, self.promotion_threshold):
                    self.Rc[r_plus[0]] = action_to_int(r_plus[1])
                    self.violation_counts[r_plus[0]] = 0
                    self.violation_counts[r_plus[0]] = 0

            # If r+ or r- in Rc
            if r_plus[0] in self.Rc.keys():
                to_check = C if self.Rc[r_plus[0]] == 1 else D
                # (if r+ in Rc)
                if r_plus[1] == to_check:
                    # Set the violation count of r+ to 0.
                    self.violation_counts[r_plus[0]] = 0
                # if r- in Rc
                elif r_minus[1] == to_check:
                    # Increment violation count of r-.
                    self.violation_counts[r_plus[0]] += 1
                    # As we observe that the behavior of the opponent is
                    # opposed to a rule modeled in Rc, we check if the number
                    # of consecutive violations of this rule is superior to
                    # a threshold. If it is, we clean Rc, but we keep the rules
                    # of Rc in Rd for smooth transition.
                    if self.should_demote(r_minus, self.violation_threshold):
                        self.Rd.update(self.Rc)
                        self.Rc.clear()
                        self.violation_counts.clear()
                        self.v = 0
            # r+ in Rc.
            r_plus_in_Rc = r_plus[0] in self.Rc.keys() and self.Rc[
                r_plus[0]
            ] == action_to_int(r_plus[1])
            # r- in Rd
            r_minus_in_Rd = r_minus[0] in self.Rd.keys() and self.Rd[
                r_minus[0]
            ] == action_to_int(r_minus[1])

            # Increment number of violations of Rd rules.
            if r_minus_in_Rd:
                self.v += 1
            # If the number of violations is superior to a threshold, clean Rd.
            if (self.v > self.reject_threshold) or (
                r_plus_in_Rc and r_minus_in_Rd
            ):
                self.Rd.clear()
                self.v = 0

            # Compute Rp for conditions that are neither in Rc or Rd.
            Rp = {}
            all_cond = [(C, C), (C, D), (D, C), (D, D)]
            for outcome in all_cond:
                if (outcome not in self.Rc.keys()) and (
                    outcome not in self.Rd.keys()
                ):
                    # Compute opponent's C answer probability.
                    Rp[outcome] = self.compute_prob_rule(outcome, self.alpha)

            # We aggregate the rules of Rc, Rd, and Rp in a set of rule Pi.
            self.Pi = {}
            # The algorithm makes sure that a rule cannot be in two different
            # sets of rules so we do not need to check for duplicates.
            self.Pi.update(self.Rc)
            self.Pi.update(self.Rd)
            self.Pi.update(Rp)

        # React to the opponent's last move
        return move_gen(
            (self.history[-1], opponent.history[-1]),
            self.Pi,
            depth_search_tree=self.tree_depth,
        )