import argparse, random, glob, json, math
from collections import Counter
from pathlib import Path

MOVES = ["up", "down", "left", "right"]

def resolve_input_files(inputs:list[str]) -> list[Path]:
  resolved:list[Path] = []
  seen:set[str] = set()

  for item in inputs:
    path = Path(item)
    if path.is_dir():
      for file_path in sorted(path.rglob("*.jsonl")):
        key = str(file_path.resolve())
        if key in seen:
          continue
        seen.add(key)
        resolved.append(file_path)
      continue

    if any(ch in item for ch in "*?[]"):
      for match in sorted(glob.glob(item)):
        file_path = Path(match)
        if not file_path.is_file():
          continue
        key = str(file_path.resolve())
        if key in seen:
          continue
        seen.add(key)
        resolved.append(file_path)
      continue

    if path.is_file():
      key = str(path.resolve())
      if key in seen:
        continue
      seen.add(key)
      resolved.append(path)

  return resolved

def _neighbors(x:int, y:int) -> list[tuple[int, int, str]]:
  return [
    (x, y + 1, "up"),
    (x, y - 1, "down"),
    (x - 1, y, "left"),
    (x + 1, y, "right"),
  ]

def _safe_neighbor_count(point:tuple[int, int], blocked:set[tuple[int, int]], width:int, height:int) -> int:
  count = 0
  for nx, ny, _ in _neighbors(point[0], point[1]):
    if not (0 <= nx < width and 0 <= ny < height):
      continue
    if (nx, ny) in blocked:
      continue
    count += 1
  return count

def _manhattan_to_nearest_food(point: tuple[int, int], food: set[tuple[int, int]]) -> int:
  if not food:
    return 25
  return min(abs(point[0] - fx) + abs(point[1] - fy) for fx, fy in food)

def extract_feature_values(row:dict) -> dict[str, float]:
  board = row.get("game_board", {})
  snakes = board.get("snakes", [])
  if not snakes:
    return {}

  me = snakes[0]
  body = me.get("body", [])
  if not body:
    return {}

  width = int(board.get("width", 0))
  height = int(board.get("height", 0))
  head = body[0]
  hx = int(head.get("x", 0))
  hy = int(head.get("y", 0))
  health = int(me.get("health", 100))
  length = int(me.get("length", len(body)))

  food_set = {(int(f.get("x", 0)), int(f.get("y", 0))) for f in board.get("food", [])}
  hazard_set = {(int(h.get("x", 0)), int(h.get("y", 0))) for h in board.get("hazards", [])}
  blocked = set()
  for snake in snakes:
    for seg in snake.get("body", []):
      blocked.add((int(seg.get("x", 0)), int(seg.get("y", 0))))

  features:dict[str, float] = {
    "bias": 1.0,
    "health_norm": max(0.0, min(1.0, health / 100.0)),
    "length_norm": min(1.0, length / max(1.0, width * height)),
    "turn_norm": min(1.0, int(row.get("turn", 0)) / 100.0),
    "food_count_norm": min(1.0, len(food_set) / 10.0),
    "hazard_count_norm": min(1.0, len(hazard_set) / 20.0),
    "opponent_count_norm": min(1.0, max(0, len(snakes) - 1) / 7.0),
  }

  safe_total = 0
  for nx, ny, move in _neighbors(hx, hy):
    in_bounds = 1.0 if (0 <= nx < width and 0 <= ny < height) else 0.0
    blocked_next = 1.0 if (nx, ny) in blocked else 0.0
    food_next = 1.0 if (nx, ny) in food_set else 0.0
    hazard_next = 1.0 if (nx, ny) in hazard_set else 0.0

    if in_bounds and not blocked_next:
      safe_total += 1
      open_next = float(_safe_neighbor_count((nx, ny), blocked, width, height))
      dist_food = float(_manhattan_to_nearest_food((nx, ny), food_set))
    else:
      open_next = 0.0
      dist_food = 25.0

    prefix = f"m:{move}:"
    features[prefix + "in_bounds"] = in_bounds
    features[prefix + "blocked"] = blocked_next
    features[prefix + "food"] = food_next
    features[prefix + "hazard"] = hazard_next
    features[prefix + "open_next"] = min(4.0, open_next) / 4.0
    features[prefix + "food_dist"] = min(25.0, dist_food) / 25.0

  features["safe_total_norm"] = safe_total / 4.0
  return features

class SoftmaxMoveModel:
  def __init__(self):
    self.weights = {move: {} for move in MOVES}
    self.bias = {move: 0.0 for move in MOVES}

  def _score(self, move:str, features:dict[str, float]) -> float:
    weight_map = self.weights[move]
    value = self.bias[move]
    for name, feat in features.items():
      value += weight_map.get(name, 0.0) * feat
    return value

  def fit(self, rows:list[dict], epochs:int=14, lr:float=0.08, l2:float=1e-6) -> None:
    examples = []
    for row in rows:
      label = row.get("move")
      if label not in MOVES:
        continue
      features = extract_feature_values(row)
      if not features:
        continue
      examples.append((features, label))

    if not examples:
      return

    for _ in range(epochs):
      random.shuffle(examples)
      for features, label in examples:
        scores = {move: self._score(move, features) for move in MOVES}
        max_score = max(scores.values())
        exp_scores = {
          move: math.exp(scores[move] - max_score) for move in MOVES
        }
        z = sum(exp_scores.values())
        probs = {move: exp_scores[move] / z for move in MOVES}

        for move in MOVES:
          target = 1.0 if move == label else 0.0
          gradient = target - probs[move]
          self.bias[move] += lr * gradient

          w = self.weights[move]
          for name, feat in features.items():
            current = w.get(name, 0.0)
            update = lr * ((gradient * feat) - (l2 * current))
            w[name] = current + update

  def predict_scores(self, row:dict) -> dict[str, float]:
    features = extract_feature_values(row)
    if not features:
      return {move: 0.0 for move in MOVES}
    return {move: self._score(move, features) for move in MOVES}

  def predict(self, row:dict) -> str:
    scores = self.predict_scores(row)
    return max(scores, key=lambda move: scores[move])

  def evaluate(self, rows:list[dict]) -> dict:
    total = 0
    correct = 0
    top2 = 0
    confusion = {move: Counter() for move in MOVES}

    for row in rows:
      expected = row.get("move")
      if expected not in MOVES:
        continue

      scores = self.predict_scores(row)
      ranked = sorted(scores.items(), key=lambda item: item[1], reverse=True)
      predicted = ranked[0][0]

      total += 1
      if predicted == expected:
        correct += 1
      if expected in {
        ranked[0][0],
        ranked[1][0] if len(ranked) > 1 else ranked[0][0],
      }:
        top2 += 1

      confusion[expected][predicted] += 1

    return {
      "total": total,
      "correct": correct,
      "accuracy": round((correct / total) if total else 0.0, 4),
      "top2_accuracy": round((top2 / total) if total else 0.0, 4),
      "confusion": {label: dict(confusion[label]) for label in MOVES},
    }

  def to_dict(self) -> dict:
    return {
      "model_type": "softmax_moves_v2",
      "moves": MOVES,
      "weights": self.weights,
      "bias": self.bias,
    }

def read_rows(paths:list[Path]) -> list[dict]:
  rows: list[dict] = []
  for path in paths:
    with path.open("r", encoding="utf-8") as handle:
      for line in handle:
        if not line.strip():
          continue
        row = json.loads(line)
        if row.get("move") not in MOVES:
          continue
        if not row.get("game_board"):
          continue
        rows.append(row)
  return rows

if __name__ == "__main__":
  parser = argparse.ArgumentParser(description="Train Battlesnake move model")
  parser.add_argument("--input", action="append", required=True)
  parser.add_argument("--output", required=True)
  parser.add_argument("--eval-split", type=float, default=0.2)
  parser.add_argument("--seed", type=int, default=42)
  parser.add_argument("--epochs", type=int, default=14)
  parser.add_argument("--lr", type=float, default=0.08)
  args = parser.parse_args()

  paths = resolve_input_files(args.input)
  if not paths:
    raise SystemExit("No input files found")

  rows = read_rows(paths)
  if len(rows) < 50:
    raise SystemExit("Need at least 50 rows for training")

  random.seed(args.seed)
  random.shuffle(rows)
  eval_count = int(len(rows) * max(0.0, min(0.5, args.eval_split)))
  eval_rows = rows[:eval_count]
  train_rows = rows[eval_count:]

  model = SoftmaxMoveModel()
  model.fit(train_rows, epochs=max(1, args.epochs), lr=max(1e-4, args.lr))
  metrics = model.evaluate(eval_rows)

  output = Path(args.output)
  output.parent.mkdir(parents=True, exist_ok=True)
  payload = {
    "input_files": [str(p) for p in paths],
    "train_rows": len(train_rows),
    "eval_rows": len(eval_rows),
    "eval_metrics": metrics,
    "model": model.to_dict(),
  }
  output.write_text(json.dumps(payload, indent=2), encoding="utf-8")

  print(json.dumps({
      "output": str(output),
      "train_rows": len(train_rows),
      "eval_rows": len(eval_rows),
      "accuracy": metrics.get("accuracy"),
      "top2_accuracy": metrics.get("top2_accuracy"),
    },
    indent=2,
  ))