from kivy.graphics.vertex_instructions import SmoothLine, Line
from kivy.uix.boxlayout import BoxLayout
from kivy.graphics.context_instructions import Color


class Controls(BoxLayout):
    def __init__(self, **kwargs):
        super(Controls, self).__init__(**kwargs)
        self.status = None
        self.status_node = None

    def set_status(self, msg, at_node=None):
        self.status = msg
        self.status_node = at_node or self.parent.game.current_node
        self.info.text = msg
        self.update_evaluation()

    def select_mode(self, mode):
        if mode == "analyze":
            self.analyze_tab_button.trigger_action(duration=0)
        else:
            self.play_tab_button.trigger_action(duration=0)

    def show_evaluation_stats(self, node):
        if node.analysis_ready:
            self.score.text = node.format_score().replace("-", "\u2013")
            self.win_rate.text = node.format_win_rate()
            if node.points_lost is not None:
                self.points_lost.text = f"{node.points_lost:.1f}"
            else:
                self.points_lost.text = f"?"

    def unlock(self):
        if self.ai_lock.active:
            self.ai_lock.checkbox.trigger_action(duration=0)
        for el in [self.ai_lock.checkbox, self.analyze_tab_button, self.ai_auto.white, self.ai_auto.black, self.ai_move]:
            el.disabled = False

    def on_size(self, *args):
        self.update_evaluation()

    # handles showing completed analysis and score graph
    def update_evaluation(self):
        katrain = self.parent
        current_node = katrain.game.current_node
        move = current_node.single_move
        current_player_is_human_or_both_robots = True  # move not self.ai_auto.active(current_node.player) or self.ai_auto.active(1 - current_node.player) # TODO FIX

        info = ""
        if current_node is self.status_node:
            info += self.status + "\n"
        else:
            self.status_node = None

        if current_player_is_human_or_both_robots and not current_node.is_root and move:
            info += current_node.comment(eval=True, hints=self.hints.active(move.player))
        self.points_lost.text = ""
        if current_player_is_human_or_both_robots:
            self.show_evaluation_stats(current_node)

        self.info.text = info

        game_node = katrain.game.current_node
        scores = [n.score for n in game_node.nodes_from_root]
        # TODO: like redo, what is the node to redo / should we append? cache?
        self.graph.canvas.clear()
        with self.graph.canvas:
            pt = []
            nnscores = [s for s in scores if s is not None] + [-5, 5]
            scale = max(max(*nnscores), -min(*nnscores)) * 1.05
            xscale = self.graph.width * 0.9 / max(len(scores), 20)
            ls = 0
            for i, s in enumerate(scores):
                ls = s or ls
                pt.extend([self.graph.pos[0] + 0.05 * self.graph.width + i * xscale, self.graph.pos[1] + self.graph.height / 2 * (1 + ls / scale)])
            Color(0, 0, 0)
            Line(points=pt, width=1.0)  # just set points?

        if False:  # TODO: UNDO AND AI MOVE
            if current_node.analysis_ready and current_node.parent and current_node.parent.analysis_ready and not current_node.children and not current_node.x_comment.get("undo"):
                # handle automatic undo
                if self.auto_undo.active(move.player) and not self.ai_auto.active(move.player) and not current_node.auto_undid:
                    ts = self.train_settings
                    # TODO: is this overly generous wrt low visit outdated evaluations?
                    evaluation = current_node.evaluation if current_node.evaluation is not None else 1  # assume move is fine if temperature is negative
                    move_eval = max(evaluation, current_node.outdated_evaluation or 0)
                    points_lost = (current_node.parent or current_node).temperature_stats[2] * (1 - move_eval)
                    if move_eval < ts["undo_eval_threshold"] and points_lost >= ts["undo_point_threshold"]:
                        if self.num_undos(current_node) == 0:
                            current_node.x_comment["undid"] = f"Move was below threshold, but no undo granted (probability is {ts['num_undo_prompts']:.0%}).\n"
                            self.update_evaluation()
                        else:
                            current_node.auto_undid = True
                            self.parent.game.undo()
                            if len(current_node.parent.children) >= ts["num_undo_prompts"] + 1:
                                best_move = sorted([m for m in current_node.parent.children], key=lambda m: -(m.evaluation_info[0] or 0))[0]
                                best_move.x_comment["undo_autoplay"] = f"Automatically played as best option after max. {ts['num_undo_prompts']} undo(s).\n"
                                self.parent.game.play(best_move)
                            self.update_evaluation()
                            return
                # ai player doesn't technically need parent ready, but don't want to override waiting for undo
                current_node = self.parent.game.current_node  # this effectively checks undo didn't just happen
                if self.ai_auto.active(move.opponent) and not self.parent.game.game_ended:
                    if current_node.children:
                        self.info.text = "AI paused since moves were undone. Press 'AI Move' or choose a move for the AI to continue playing."
                    else:
                        self._do_aimove()