# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""The strategy to play each move with MCTS."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import random
import sys
import time

import coords
import go
from mcts import MCTSNode
import numpy as np
import sgf_wrapper


def time_recommendation(move_num, seconds_per_move=5, time_limit=15*60,
                        decay_factor=0.98):
  """
  Given current move number and "desired" seconds per move,
  return how much time should actually be used. To be used specifically
  for CGOS time controls, which are absolute 15 minute time.

  The strategy is to spend the maximum time possible using seconds_per_move,
  and then switch to an exponentially decaying time usage, calibrated so that
  we have enough time for an infinite number of moves.
  """

  # divide by two since you only play half the moves in a game.
  player_move_num = move_num / 2

  # sum of geometric series maxes out at endgame_time seconds.
  endgame_time = seconds_per_move / (1 - decay_factor)

  if endgame_time > time_limit:
    # there is so little main time that we're already in "endgame" mode.
    base_time = time_limit * (1 - decay_factor)
    return base_time * decay_factor ** player_move_num

  # leave over endgame_time seconds for the end, and play at seconds_per_move
  # for as long as possible
  core_time = time_limit - endgame_time
  core_moves = core_time / seconds_per_move

  if player_move_num < core_moves:
    return seconds_per_move
  else:
    return seconds_per_move * decay_factor ** (player_move_num - core_moves)


def _get_temperature_cutoff(board_size):
  # When to do deterministic move selection.  ~30 moves on a 19x19, ~8 on 9x9
  return int((board_size * board_size) / 12)


class MCTSPlayerMixin(object):
  # If 'simulations_per_move' is nonzero, it will perform that many reads
  # before playing. Otherwise, it uses 'seconds_per_move' of wall time'
  def __init__(self, board_size, network, seconds_per_move=5,
               simulations_per_move=0, resign_threshold=-0.90,
               verbosity=0, two_player_mode=False, num_parallel=8):
    self.board_size = board_size
    self.network = network
    self.seconds_per_move = seconds_per_move
    self.simulations_per_move = simulations_per_move
    self.verbosity = verbosity
    self.two_player_mode = two_player_mode
    if two_player_mode:
      self.temp_threshold = -1
    else:
      self.temp_threshold = _get_temperature_cutoff(board_size)
    self.num_parallel = num_parallel
    self.qs = []
    self.comments = []
    self.searches_pi = []
    self.root = None
    self.result = 0
    self.result_string = None
    self.resign_threshold = -abs(resign_threshold)
    super(MCTSPlayerMixin, self).__init__(board_size)

  def initialize_game(self, position=None):
    if position is None:
      position = go.Position(self.board_size)
    self.root = MCTSNode(self.board_size, position)
    self.result = 0
    self.result_string = None
    self.comments = []
    self.searches_pi = []
    self.qs = []

  def suggest_move(self, position):
    """ Used for playing a single game.

    For parallel play, use initialize_move, select_leaf,
    incorporate_results, and pick_move
    """
    start = time.time()

    if self.simulations_per_move == 0:
      while time.time() - start < self.seconds_per_move:
        self.tree_search()
    else:
      current_readouts = self.root.N
      while self.root.N < current_readouts + self.simulations_per_move:
        self.tree_search()
      if self.verbosity > 0:
        print("%d: Searched %d times in %s seconds\n\n" % (
            position.n, self.simulations_per_move, time.time() - start),
              file=sys.stderr)

    # print some stats on anything with probability > 1%
    if self.verbosity > 2:
      print(self.root.describe(), file=sys.stderr)
      print('\n\n', file=sys.stderr)
    if self.verbosity > 3:
      print(self.root.position, file=sys.stderr)

    return self.pick_move()

  def play_move(self, c):
    """
    Notable side effects:
      - finalizes the probability distribution according to
      this roots visit counts into the class' running tally, `searches_pi`
      - Makes the node associated with this move the root, for future
      `inject_noise` calls.
    """
    if not self.two_player_mode:
      self.searches_pi.append(
          self.root.children_as_pi(self.root.position.n < self.temp_threshold))
    self.qs.append(self.root.Q)  # Save our resulting Q.
    self.comments.append(self.root.describe())
    self.root = self.root.maybe_add_child(coords.to_flat(self.board_size, c))
    self.position = self.root.position  # for showboard
    del self.root.parent.children
    return True  # GTP requires positive result.

  def pick_move(self):
    """Picks a move to play, based on MCTS readout statistics.

    Highest N is most robust indicator. In the early stage of the game, pick
    a move weighted by visit count; later on, pick the absolute max."""
    if self.root.position.n > self.temp_threshold:
      fcoord = np.argmax(self.root.child_N)
    else:
      cdf = self.root.child_N.cumsum()
      cdf /= cdf[-1]
      selection = random.random()
      fcoord = cdf.searchsorted(selection)
      assert self.root.child_N[fcoord] != 0
    return coords.from_flat(self.board_size, fcoord)

  def tree_search(self, num_parallel=None):
    if num_parallel is None:
      num_parallel = self.num_parallel
    leaves = []
    failsafe = 0
    while len(leaves) < num_parallel and failsafe < num_parallel * 2:
      failsafe += 1
      leaf = self.root.select_leaf()
      if self.verbosity >= 4:
        print(self.show_path_to_root(leaf))
      # if game is over, override the value estimate with the true score
      if leaf.is_done():
        value = 1 if leaf.position.score() > 0 else -1
        leaf.backup_value(value, up_to=self.root)
        continue
      leaf.add_virtual_loss(up_to=self.root)
      leaves.append(leaf)
    if leaves:
      move_probs, values = self.network.run_many(
          [leaf.position for leaf in leaves])
      for leaf, move_prob, value in zip(leaves, move_probs, values):
        leaf.revert_virtual_loss(up_to=self.root)
        leaf.incorporate_results(move_prob, value, up_to=self.root)

  def show_path_to_root(self, node):
    MAX_DEPTH = (self.board_size ** 2) * 1.4  # 505 moves for 19x19, 113 for 9x9
    pos = node.position
    diff = node.position.n - self.root.position.n
    if len(pos.recent) == 0:
      return

    def fmt(move):
      return "{}-{}".format('b' if move.color == 1 else 'w',
                            coords.to_kgs(self.board_size, move.move))
    path = " ".join(fmt(move) for move in pos.recent[-diff:])
    if node.position.n >= MAX_DEPTH:
      path += " (depth cutoff reached) %0.1f" % node.position.score()
    elif node.position.is_game_over():
      path += " (game over) %0.1f" % node.position.score()
    return path

  def should_resign(self):
    """Returns true if the player resigned.

    No further moves should be played.
    """
    return self.root.Q_perspective < self.resign_threshold

  def set_result(self, winner, was_resign):
    self.result = winner
    if was_resign:
      string = "B+R" if winner == go.BLACK else "W+R"
    else:
      string = self.root.position.result_string()
    self.result_string = string

  def to_sgf(self, use_comments=True):
    assert self.result_string is not None
    pos = self.root.position
    if use_comments:
      comments = self.comments or ['No comments.']
      comments[0] = ("Resign Threshold: %0.3f\n" %
                     self.resign_threshold) + comments[0]
    else:
      comments = []
    return sgf_wrapper.make_sgf(
        self.board_size, pos.recent, self.result_string,
        white_name=os.path.basename(self.network.save_file) or "Unknown",
        black_name=os.path.basename(self.network.save_file) or "Unknown",
        comments=comments)

  def is_done(self):
    return self.result != 0 or self.root.is_done()

  def extract_data(self):
    assert len(self.searches_pi) == self.root.position.n
    assert self.result != 0
    for pwc, pi in zip(go.replay_position(
        self.board_size, self.root.position, self.result), self.searches_pi):
      yield pwc.position, pi, pwc.result

  def chat(self, msg_type, sender, text):
    default_response = (
        "Supported commands are 'winrate', 'nextplay', 'fortune', and 'help'.")
    if self.root is None or self.root.position.n == 0:
      return "I'm not playing right now.  " + default_response

    if 'winrate' in text.lower():
      wr = (abs(self.root.Q) + 1.0) / 2.0
      color = "Black" if self.root.Q > 0 else "White"
      return "{:s} {:.2f}%".format(color, wr * 100.0)
    elif 'nextplay' in text.lower():
      return "I'm thinking... " + self.root.most_visited_path()
    elif 'fortune' in text.lower():
      return "You're feeling lucky!"
    elif 'help' in text.lower():
      return "I can't help much with go -- try ladders!  Otherwise: {}".format(
          default_response)
    else:
      return default_response


class CGOSPlayerMixin(MCTSPlayerMixin):
  def suggest_move(self, position):
    self.seconds_per_move = time_recommendation(position.n)
    return super().suggest_move(position)