"""Hidden-state game engine for Visual Memory Gym.

Manages in-memory board state, hidden cell contents, move validation,
and win/loss conditions across four task families:

  1. hidden_grid       — deduce hazard locations from signal clues
  2. pattern_memory    — recall briefly-shown cell contents
  3. distractor_search — identify targets among visually similar decoys
  4. fog_of_war        — plan under limited viewport radius
"""

from __future__ import annotations

import copy
from enum import Enum
from typing import Any

import numpy as np
from pydantic import BaseModel, Field


class CellType(str, Enum):
    EMPTY = "empty"
    HAZARD = "hazard"
    SIGNAL = "signal"
    KEY = "key"
    DECOY = "decoy"
    GOAL = "goal"


class CellState(str, Enum):
    HIDDEN = "hidden"
    REVEALED = "revealed"
    FLAGGED = "flagged"
    FADED = "faded"


class ScenarioType(str, Enum):
    HIDDEN_GRID = "hidden_grid"
    PATTERN_MEMORY = "pattern_memory"
    DISTRACTOR_SEARCH = "distractor_search"
    FOG_OF_WAR = "fog_of_war"


class SignalMode(str, Enum):
    COUNT = "count"
    DIRECTIONAL = "directional"
    RANGE = "range"
    PARTIAL = "partial"


class WinCondition(str, Enum):
    FLAG_ALL_HAZARDS = "flag_all_hazards"
    COLLECT_KEYS = "collect_keys"
    IDENTIFY_SAFE = "identify_safe_cells"
    REACH_GOAL = "reach_goal"


class BoardState(BaseModel):
    """Serializable snapshot of the game state (visible portion only)."""

    session_id: str = ""
    scenario_id: str = ""
    scenario_type: str = "hidden_grid"
    step_count: int = 0
    board_width: int = 0
    board_height: int = 0
    visible_cells: list[list[dict]] = Field(default_factory=list)
    discovered_signals: list[dict] = Field(default_factory=list)
    memory_events: list[dict] = Field(default_factory=list)
    game_over: bool = False
    won: bool = False
    flags_remaining: int = 0
    cells_revealed: int = 0
    hazard_hits: int = 0
    keys_collected: int = 0
    max_steps: int = 50


NEIGHBOR_OFFSETS = [
    (-1, -1), (-1, 0), (-1, 1),
    (0, -1),           (0, 1),
    (1, -1),  (1, 0),  (1, 1),
]

DIRECTION_NAMES = {
    (-1, -1): "NW", (-1, 0): "N", (-1, 1): "NE",
    (0, -1): "W",                   (0, 1): "E",
    (1, -1): "SW",  (1, 0): "S",  (1, 1): "SE",
}


class GameEngine:
    """In-memory game engine for the Visual Memory gym.

    Deterministic given a seed. All state lives in Python memory.
    """

    def __init__(self, scenario: dict, seed: int | None = None):
        self.scenario_id: str = scenario["scenario_id"]
        self.scenario_type = ScenarioType(scenario.get("type", "hidden_grid"))
        self.width: int = scenario["board_width"]
        self.height: int = scenario["board_height"]
        self.max_steps: int = scenario.get("max_steps", 50)
        self.max_hazard_reveals: int = scenario.get("max_hazard_reveals", 3)
        self.signal_mode = SignalMode(scenario.get("signal_mode", "count"))
        self.win_condition = WinCondition(
            scenario.get("win_condition", {}).get("type", "flag_all_hazards")
        )

        resolved_seed = seed if seed is not None else scenario.get("seed", 42)
        self._rng = np.random.default_rng(resolved_seed)

        self.step_count: int = 0
        self.hazard_hits: int = 0
        self.keys_collected: int = 0
        self.cells_revealed: int = 0
        self.game_over: bool = False
        self.won: bool = False

        if "layout" in scenario:
            self._hidden = self._load_explicit_layout(scenario["layout"])
        else:
            self._hidden = self._generate_board(scenario)

        self._visible: list[list[dict]] = [
            [{"state": CellState.HIDDEN.value, "content": None} for _ in range(self.width)]
            for _ in range(self.height)
        ]

        total_hazards = sum(
            1
            for r in range(self.height)
            for c in range(self.width)
            if self._hidden[r][c]["type"] == CellType.HAZARD.value
        )
        self.total_flags: int = scenario.get("flags_count", total_hazards + 3)
        self.flags_placed: int = 0

        self.total_keys: int = sum(
            1
            for r in range(self.height)
            for c in range(self.width)
            if self._hidden[r][c]["type"] == CellType.KEY.value
        )

        self._discovered_signals: list[dict] = []
        self._memory_events: list[dict] = []
        self._action_log: list[dict] = []

        self._viewport_center: list[int] | None = scenario.get("start_position")
        self._viewport_radius: int | None = scenario.get("viewport_radius")

        self._flash_cells: list[list[int]] = scenario.get("flash_cells", [])
        self._flash_until_step: int = scenario.get("flash_until_step", 0)
        if self.scenario_type == ScenarioType.PATTERN_MEMORY and self._flash_cells:
            for rc in self._flash_cells:
                r, c = rc[0], rc[1]
                cell = self._hidden[r][c]
                self._visible[r][c] = {
                    "state": CellState.REVEALED.value,
                    "content": copy.deepcopy(cell),
                }
                self._memory_events.append({
                    "step": 0,
                    "event": "flash_shown",
                    "row": r,
                    "col": c,
                    "content": copy.deepcopy(cell),
                })

    # ─── Board Generation ───────────────────────────────────────────

    def _load_explicit_layout(self, layout: list[list[dict]]) -> list[list[dict]]:
        board: list[list[dict]] = []
        for row_data in layout:
            row: list[dict] = []
            for cell in row_data:
                row.append({
                    "type": cell.get("type", CellType.EMPTY.value),
                    "value": cell.get("value"),
                    "properties": cell.get("properties", {}),
                })
            board.append(row)
        return board

    def _generate_board(self, scenario: dict) -> list[list[dict]]:
        hazard_count = scenario.get("hazard_count", 10)
        key_count = scenario.get("key_count", 0)
        decoy_count = scenario.get("decoy_count", 0)
        goal_count = 1 if self.win_condition == WinCondition.REACH_GOAL else 0

        total_cells = self.width * self.height
        total_special = hazard_count + key_count + decoy_count + goal_count
        if total_special > total_cells:
            raise ValueError(
                f"Cannot place {total_special} special cells on a "
                f"{self.width}x{self.height} board ({total_cells} cells)"
            )

        positions = self._rng.permutation(total_cells)
        board: list[list[dict]] = [
            [{"type": CellType.EMPTY.value, "value": None, "properties": {}} for _ in range(self.width)]
            for _ in range(self.height)
        ]

        idx = 0
        for _ in range(hazard_count):
            r, c = divmod(int(positions[idx]), self.width)
            board[r][c] = {"type": CellType.HAZARD.value, "value": None, "properties": {}}
            idx += 1

        for i in range(key_count):
            r, c = divmod(int(positions[idx]), self.width)
            board[r][c] = {"type": CellType.KEY.value, "value": f"key_{i}", "properties": {}}
            idx += 1

        for i in range(decoy_count):
            r, c = divmod(int(positions[idx]), self.width)
            board[r][c] = {"type": CellType.DECOY.value, "value": f"decoy_{i}", "properties": {}}
            idx += 1

        if goal_count:
            r, c = divmod(int(positions[idx]), self.width)
            board[r][c] = {"type": CellType.GOAL.value, "value": None, "properties": {}}
            idx += 1

        self._compute_signals(board)
        return board

    def _compute_signals(self, board: list[list[dict]]) -> None:
        for r in range(self.height):
            for c in range(self.width):
                if board[r][c]["type"] != CellType.EMPTY.value:
                    continue

                if self.signal_mode == SignalMode.COUNT:
                    count = self._count_adjacent_hazards(board, r, c)
                    if count > 0:
                        board[r][c] = {
                            "type": CellType.SIGNAL.value,
                            "value": count,
                            "properties": {"mode": "count"},
                        }

                elif self.signal_mode == SignalMode.DIRECTIONAL:
                    directions = self._get_hazard_directions(board, r, c)
                    if directions:
                        board[r][c] = {
                            "type": CellType.SIGNAL.value,
                            "value": directions,
                            "properties": {"mode": "directional"},
                        }

                elif self.signal_mode == SignalMode.RANGE:
                    count = self._count_adjacent_hazards(board, r, c)
                    if count > 0:
                        noise = int(self._rng.integers(0, 2))
                        low = max(0, count - noise)
                        high = count + int(self._rng.integers(0, 2))
                        board[r][c] = {
                            "type": CellType.SIGNAL.value,
                            "value": {"min": low, "max": high},
                            "properties": {"mode": "range"},
                        }

                elif self.signal_mode == SignalMode.PARTIAL:
                    directions = self._get_hazard_directions(board, r, c)
                    if directions:
                        shown = max(1, len(directions) // 2)
                        indices = self._rng.choice(
                            len(directions), size=shown, replace=False
                        )
                        subset = [directions[i] for i in sorted(indices)]
                        board[r][c] = {
                            "type": CellType.SIGNAL.value,
                            "value": subset,
                            "properties": {
                                "mode": "partial",
                                "total_hint": len(directions),
                            },
                        }

    def _count_adjacent_hazards(self, board: list[list[dict]], r: int, c: int) -> int:
        count = 0
        for dr, dc in NEIGHBOR_OFFSETS:
            nr, nc = r + dr, c + dc
            if 0 <= nr < self.height and 0 <= nc < self.width:
                if board[nr][nc]["type"] == CellType.HAZARD.value:
                    count += 1
        return count

    def _get_hazard_directions(self, board: list[list[dict]], r: int, c: int) -> list[str]:
        dirs: list[str] = []
        for (dr, dc), name in DIRECTION_NAMES.items():
            nr, nc = r + dr, c + dc
            if 0 <= nr < self.height and 0 <= nc < self.width:
                if board[nr][nc]["type"] == CellType.HAZARD.value:
                    dirs.append(name)
        return dirs

    # ─── Pattern Memory Phase ───────────────────────────────────────

    def _tick_pattern_memory(self) -> None:
        if self.scenario_type != ScenarioType.PATTERN_MEMORY:
            return
        if self.step_count != self._flash_until_step:
            return
        for rc in self._flash_cells:
            r, c = rc[0], rc[1]
            if self._visible[r][c]["state"] == CellState.REVEALED.value:
                self._visible[r][c] = {"state": CellState.FADED.value, "content": None}
                self._memory_events.append({
                    "step": self.step_count,
                    "event": "flash_faded",
                    "row": r,
                    "col": c,
                })

    # ─── Core Actions ───────────────────────────────────────────────

    def reveal_cell(self, row: int, col: int) -> dict:
        if self.game_over:
            return {"error": "Game is already over.", "row": row, "col": col}

        if not self._in_bounds(row, col):
            return {"error": f"({row},{col}) is out of bounds.", "row": row, "col": col}

        vis = self._visible[row][col]
        if vis["state"] in (CellState.REVEALED.value, CellState.FLAGGED.value):
            return {
                "error": f"Cell ({row},{col}) is already {vis['state']}.",
                "row": row,
                "col": col,
            }

        if self._viewport_radius is not None and self._viewport_center is not None:
            vr, vc = self._viewport_center
            if abs(row - vr) > self._viewport_radius or abs(col - vc) > self._viewport_radius:
                return {
                    "error": f"({row},{col}) is outside your current viewport.",
                    "row": row,
                    "col": col,
                }

        self.step_count += 1
        self._tick_pattern_memory()

        hidden = self._hidden[row][col]
        cell_type = hidden["type"]

        self._visible[row][col] = {
            "state": CellState.REVEALED.value,
            "content": copy.deepcopy(hidden),
        }
        self.cells_revealed += 1

        result: dict[str, Any] = {
            "row": row,
            "col": col,
            "type": cell_type,
            "value": hidden.get("value"),
            "properties": hidden.get("properties", {}),
        }

        if cell_type == CellType.SIGNAL.value:
            self._discovered_signals.append(result)

        if cell_type == CellType.HAZARD.value:
            self.hazard_hits += 1
            result["hazard_hit"] = True
            if self.hazard_hits >= self.max_hazard_reveals:
                self.game_over = True
                self.won = False
                result["game_over"] = True
                result["message"] = "Too many hazards revealed. Game over."

        if cell_type == CellType.KEY.value:
            self.keys_collected += 1
            result["key_collected"] = True
            if (
                self.win_condition == WinCondition.COLLECT_KEYS
                and self.keys_collected >= self.total_keys
            ):
                self.game_over = True
                self.won = True
                result["game_over"] = True
                result["message"] = "All keys collected. You win!"

        if cell_type == CellType.GOAL.value and self.win_condition == WinCondition.REACH_GOAL:
            self.game_over = True
            self.won = True
            result["game_over"] = True
            result["message"] = "Goal reached. You win!"

        if self.step_count >= self.max_steps and not self.game_over:
            self.game_over = True
            self.won = False
            result["game_over"] = True
            result["message"] = "Max steps exceeded. Game over."

        self._action_log.append({
            "action": "reveal",
            "row": row,
            "col": col,
            "step": self.step_count,
            "result_type": cell_type,
        })
        return result

    def flag_cell(self, row: int, col: int) -> dict:
        if self.game_over:
            return {"error": "Game is already over.", "row": row, "col": col}

        if not self._in_bounds(row, col):
            return {"error": f"({row},{col}) is out of bounds.", "row": row, "col": col}

        vis = self._visible[row][col]
        if vis["state"] == CellState.REVEALED.value:
            return {"error": f"Cell ({row},{col}) is already revealed; cannot flag.", "row": row, "col": col}
        if vis["state"] == CellState.FLAGGED.value:
            return {"error": f"Cell ({row},{col}) is already flagged.", "row": row, "col": col}

        if self.flags_placed >= self.total_flags:
            return {"error": "No flags remaining.", "row": row, "col": col}

        self.step_count += 1
        self._tick_pattern_memory()

        self._visible[row][col] = {"state": CellState.FLAGGED.value, "content": None}
        self.flags_placed += 1

        self._action_log.append({"action": "flag", "row": row, "col": col, "step": self.step_count})
        self._check_flag_win()

        result: dict[str, Any] = {
            "row": row,
            "col": col,
            "flagged": True,
            "flags_remaining": self.total_flags - self.flags_placed,
        }
        if self.game_over and self.won:
            result["game_over"] = True
            result["message"] = "All hazards correctly flagged. You win!"

        if self.step_count >= self.max_steps and not self.game_over:
            self.game_over = True
            self.won = False
            result["game_over"] = True
            result["message"] = "Max steps exceeded. Game over."

        return result

    def unflag_cell(self, row: int, col: int) -> dict:
        if self.game_over:
            return {"error": "Game is already over.", "row": row, "col": col}

        if not self._in_bounds(row, col):
            return {"error": f"({row},{col}) is out of bounds.", "row": row, "col": col}

        if self._visible[row][col]["state"] != CellState.FLAGGED.value:
            return {"error": f"Cell ({row},{col}) is not flagged.", "row": row, "col": col}

        self.step_count += 1
        self._tick_pattern_memory()

        self._visible[row][col] = {"state": CellState.HIDDEN.value, "content": None}
        self.flags_placed -= 1

        self._action_log.append({"action": "unflag", "row": row, "col": col, "step": self.step_count})

        result: dict[str, Any] = {
            "row": row,
            "col": col,
            "unflagged": True,
            "flags_remaining": self.total_flags - self.flags_placed,
        }

        if self.step_count >= self.max_steps and not self.game_over:
            self.game_over = True
            self.won = False
            result["game_over"] = True
            result["message"] = "Max steps exceeded. Game over."

        return result

    def move_viewport(self, row: int, col: int) -> dict:
        if self.scenario_type != ScenarioType.FOG_OF_WAR:
            return {"error": "move_viewport is only available in fog_of_war scenarios."}

        if self.game_over:
            return {"error": "Game is already over."}

        if not self._in_bounds(row, col):
            return {"error": f"({row},{col}) is out of bounds."}

        self.step_count += 1
        self._tick_pattern_memory()

        self._viewport_center = [row, col]
        self._action_log.append({
            "action": "move_viewport",
            "row": row,
            "col": col,
            "step": self.step_count,
        })

        if self.step_count >= self.max_steps and not self.game_over:
            self.game_over = True
            self.won = False

        return {
            "viewport_center": [row, col],
            "viewport_radius": self._viewport_radius,
            "visible_area": self._get_viewport_bounds(),
        }

    def submit_solution(
        self,
        flagged_positions: list[list[int]] | None = None,
        safe_positions: list[list[int]] | None = None,
    ) -> dict:
        if self.game_over:
            return {"error": "Game is already over."}

        self.step_count += 1
        self.game_over = True

        if self.win_condition == WinCondition.FLAG_ALL_HAZARDS:
            return self._judge_flag_solution(flagged_positions or [])
        elif self.win_condition == WinCondition.IDENTIFY_SAFE:
            return self._judge_safe_solution(safe_positions or [])
        elif self.win_condition == WinCondition.COLLECT_KEYS:
            success = self.keys_collected >= self.total_keys
            self.won = success
            return {
                "correct": success,
                "keys_collected": self.keys_collected,
                "keys_required": self.total_keys,
            }
        elif self.win_condition == WinCondition.REACH_GOAL:
            self.won = False
            return {"correct": False, "message": "Goal was not reached before submission."}

        return {"error": "Unknown win condition."}

    # ─── State Queries ──────────────────────────────────────────────

    def get_visible_board(self) -> list[list[dict]]:
        if self._viewport_radius is None or self._viewport_center is None:
            return copy.deepcopy(self._visible)

        vr, vc = self._viewport_center
        rad = self._viewport_radius
        fog_board: list[list[dict]] = [
            [{"state": "fog", "content": None} for _ in range(self.width)]
            for _ in range(self.height)
        ]
        for r in range(max(0, vr - rad), min(self.height, vr + rad + 1)):
            for c in range(max(0, vc - rad), min(self.width, vc + rad + 1)):
                fog_board[r][c] = copy.deepcopy(self._visible[r][c])
        return fog_board

    def get_status(self) -> dict:
        return {
            "scenario_id": self.scenario_id,
            "scenario_type": self.scenario_type.value,
            "step_count": self.step_count,
            "max_steps": self.max_steps,
            "board_size": f"{self.width}x{self.height}",
            "cells_revealed": self.cells_revealed,
            "hazard_hits": self.hazard_hits,
            "max_hazard_reveals": self.max_hazard_reveals,
            "keys_collected": self.keys_collected,
            "total_keys": self.total_keys,
            "flags_placed": self.flags_placed,
            "flags_remaining": self.total_flags - self.flags_placed,
            "game_over": self.game_over,
            "won": self.won,
            "win_condition": self.win_condition.value,
        }

    def get_board_state(self, session_id: str = "") -> BoardState:
        return BoardState(
            session_id=session_id,
            scenario_id=self.scenario_id,
            scenario_type=self.scenario_type.value,
            step_count=self.step_count,
            board_width=self.width,
            board_height=self.height,
            visible_cells=self.get_visible_board(),
            discovered_signals=copy.deepcopy(self._discovered_signals),
            memory_events=copy.deepcopy(self._memory_events),
            game_over=self.game_over,
            won=self.won,
            flags_remaining=self.total_flags - self.flags_placed,
            cells_revealed=self.cells_revealed,
            hazard_hits=self.hazard_hits,
            keys_collected=self.keys_collected,
            max_steps=self.max_steps,
        )

    def get_hidden_board(self) -> list[list[dict]]:
        """Full hidden board — for reward computation only, never sent to agent."""
        return copy.deepcopy(self._hidden)

    def get_action_log(self) -> list[dict]:
        return copy.deepcopy(self._action_log)

    # ─── Internal Helpers ───────────────────────────────────────────

    def _in_bounds(self, row: int, col: int) -> bool:
        return 0 <= row < self.height and 0 <= col < self.width

    def _get_viewport_bounds(self) -> dict:
        if self._viewport_center is None or self._viewport_radius is None:
            return {
                "r_min": 0,
                "r_max": self.height - 1,
                "c_min": 0,
                "c_max": self.width - 1,
            }
        vr, vc = self._viewport_center
        rad = self._viewport_radius
        return {
            "r_min": max(0, vr - rad),
            "r_max": min(self.height - 1, vr + rad),
            "c_min": max(0, vc - rad),
            "c_max": min(self.width - 1, vc + rad),
        }

    def _check_flag_win(self) -> None:
        if self.win_condition != WinCondition.FLAG_ALL_HAZARDS:
            return

        for r in range(self.height):
            for c in range(self.width):
                is_hazard = self._hidden[r][c]["type"] == CellType.HAZARD.value
                is_flagged = self._visible[r][c]["state"] == CellState.FLAGGED.value
                if is_hazard and not is_flagged:
                    return

        wrong_flags = sum(
            1
            for r in range(self.height)
            for c in range(self.width)
            if self._visible[r][c]["state"] == CellState.FLAGGED.value
            and self._hidden[r][c]["type"] != CellType.HAZARD.value
        )
        if wrong_flags == 0:
            self.game_over = True
            self.won = True

    def _judge_flag_solution(self, flagged: list[list[int]]) -> dict:
        actual_hazards: set[tuple[int, int]] = set()
        for r in range(self.height):
            for c in range(self.width):
                if self._hidden[r][c]["type"] == CellType.HAZARD.value:
                    actual_hazards.add((r, c))

        submitted: set[tuple[int, int]] = {(p[0], p[1]) for p in flagged}
        for r in range(self.height):
            for c in range(self.width):
                if self._visible[r][c]["state"] == CellState.FLAGGED.value:
                    submitted.add((r, c))

        correct = submitted & actual_hazards
        missed = actual_hazards - submitted
        wrong = submitted - actual_hazards

        precision = len(correct) / len(submitted) if submitted else 0.0
        recall = len(correct) / len(actual_hazards) if actual_hazards else 1.0

        self.won = len(missed) == 0 and len(wrong) == 0

        return {
            "correct": self.won,
            "hazards_found": len(correct),
            "hazards_total": len(actual_hazards),
            "missed": len(missed),
            "wrong_flags": len(wrong),
            "precision": round(precision, 3),
            "recall": round(recall, 3),
        }

    def _judge_safe_solution(self, safe_positions: list[list[int]]) -> dict:
        actual_safe: set[tuple[int, int]] = set()
        for r in range(self.height):
            for c in range(self.width):
                if self._hidden[r][c]["type"] != CellType.HAZARD.value:
                    actual_safe.add((r, c))

        submitted: set[tuple[int, int]] = {(p[0], p[1]) for p in safe_positions}

        correct = submitted & actual_safe
        false_safe = submitted - actual_safe
        missed_safe = actual_safe - submitted

        precision = len(correct) / len(submitted) if submitted else 0.0
        recall = len(correct) / len(actual_safe) if actual_safe else 1.0

        self.won = len(false_safe) == 0 and len(missed_safe) == 0

        return {
            "correct": self.won,
            "safe_found": len(correct),
            "safe_total": len(actual_safe),
            "false_safe": len(false_safe),
            "missed_safe": len(missed_safe),
            "precision": round(precision, 3),
            "recall": round(recall, 3),
        }