"""StabilizerForge environment.

Episode loop:
  reset(task_id?)        -> sample/load a task; emit initial observation
  step(action)           -> apply one Clifford gate, or FINALIZE
                            returns dense shaping reward per step,
                            full terminal reward at FINALIZE.
"""
from __future__ import annotations

import json
import os
import random
from pathlib import Path
from typing import Any
from uuid import uuid4

from openenv.core.env_server.interfaces import Environment
from openenv.core.env_server.types import State

try:
    from ..models import StabilizerAction, StabilizerObservation
    from .verifier import match_fraction
except ImportError:  # pragma: no cover  (in-container imports without package context)
    from models import StabilizerAction, StabilizerObservation
    from server.verifier import match_fraction


# Reward weights (keep aligned with README)
W_MATCH = 0.4
W_GATE_EFF = 0.2
W_TWOQ_EFF = 0.2
W_CONN = 0.1
W_FORMAT = 0.1
SHAPING_COEF = 0.05  # per-step Δmatch_fraction shaping
MAX_CONSECUTIVE_VIOLATIONS = 5


def _default_tasks_path() -> str:
    """Resolve the default tasks file. Looks for env var, then sibling tasks.jsonl."""
    env_path = os.environ.get("STABILIZER_FORGE_TASKS")
    if env_path:
        return env_path
    here = Path(__file__).resolve().parent.parent  # stabilizer_forge/
    candidate = here / "tasks.jsonl"
    if candidate.exists():
        return str(candidate)
    # Fallback: project root
    return str(here.parent / "tasks.jsonl")


def _load_tasks(path: str) -> list[dict]:
    p = Path(path)
    if not p.exists():
        return []
    tasks: list[dict] = []
    with p.open() as f:
        for line in f:
            line = line.strip()
            if not line:
                continue
            tasks.append(json.loads(line))
    return tasks


def _gate_to_stim(action: StabilizerAction) -> str:
    """Render a single action as one line of Stim text."""
    if action.op in {"H", "S"}:
        return f"{action.op} {action.qubits[0]}"
    if action.op == "CX":
        return f"CX {action.qubits[0]} {action.qubits[1]}"
    raise ValueError(f"Cannot render gate: {action.op}")


def _validate_action(
    action: StabilizerAction, n_qubits: int
) -> tuple[bool, str]:
    """Schema/range validation. Returns (is_valid, error_msg)."""
    if action.op == "FINALIZE":
        if action.qubits:
            return False, "FINALIZE takes no qubits."
        return True, ""
    if action.op in {"H", "S"}:
        if len(action.qubits) != 1:
            return False, f"{action.op} requires exactly 1 qubit, got {len(action.qubits)}."
        q = action.qubits[0]
        if not (0 <= q < n_qubits):
            return False, f"qubit {q} out of range [0, {n_qubits})."
        return True, ""
    if action.op == "CX":
        if len(action.qubits) != 2:
            return False, f"CX requires exactly 2 qubits, got {len(action.qubits)}."
        c, t = action.qubits
        if c == t:
            return False, "CX control and target must differ."
        for q in (c, t):
            if not (0 <= q < n_qubits):
                return False, f"qubit {q} out of range [0, {n_qubits})."
        return True, ""
    return False, f"unknown op {action.op}"


class StabilizerForgeEnvironment(Environment):
    """Single-agent env: emit Clifford gates to encode a target stabilizer code."""

    SUPPORTS_CONCURRENT_SESSIONS: bool = True

    def __init__(self, tasks_path: str | None = None):
        super().__init__()
        self._tasks_path = tasks_path or _default_tasks_path()
        self._tasks = _load_tasks(self._tasks_path)
        self._task: dict[str, Any] | None = None
        self._gates: list[str] = []
        self._cnot_count = 0
        self._nonadj_cnot_count = 0
        self._format_violations = 0
        self._consecutive_violations = 0
        self._last_match_fraction = 0.0
        self._last_match_results: list[bool] = []
        self._finalized = False
        self._rng = random.Random()
        self._state = State(episode_id=str(uuid4()), step_count=0)

    # ---------- Helpers ----------

    def _circuit_text(self) -> str:
        return "\n".join(self._gates)

    def _is_adjacent(self, a: int, b: int) -> bool:
        edges = self._task.get("connectivity_edges") if self._task else None
        if edges is None:
            return True  # all-to-all
        edge_set = {tuple(sorted(e)) for e in edges}
        return tuple(sorted((a, b))) in edge_set

    def _compute_match(self) -> tuple[float, list[bool]]:
        if not self._task:
            return 0.0, []
        text = self._circuit_text()
        n = self._task["n_qubits"]
        targets = self._task["target_stabilizers"]
        frac, results_dict = match_fraction(text, targets, n)
        ordered = [results_dict[s] for s in targets]
        return frac, ordered

    def _make_obs(
        self,
        *,
        reward: float,
        done: bool,
        last_action_valid: bool = True,
        last_action_error: str = "",
    ) -> StabilizerObservation:
        assert self._task is not None
        bench = int(self._task.get("benchmark_optimum", 0) or 0)
        return StabilizerObservation(
            task_id=self._task["task_id"],
            target_stabilizers=list(self._task["target_stabilizers"]),
            n_qubits=int(self._task["n_qubits"]),
            connectivity_edges=self._task.get("connectivity_edges"),
            gates_so_far=list(self._gates),
            current_circuit=self._circuit_text(),
            current_match=list(self._last_match_results),
            match_fraction=float(self._last_match_fraction),
            gates_emitted=len(self._gates),
            cnot_count=self._cnot_count,
            nonadj_cnot_count=self._nonadj_cnot_count,
            gate_budget=int(self._task.get("gate_budget", 2 * max(1, bench))),
            gate_budget_remaining=max(
                0,
                int(self._task.get("gate_budget", 2 * max(1, bench))) - len(self._gates),
            ),
            benchmark_optimum=bench,
            benchmark_optimum_2q=int(self._task.get("benchmark_optimum_2q", 0) or 0),
            format_violations=self._format_violations,
            consecutive_violations=self._consecutive_violations,
            last_action_valid=last_action_valid,
            last_action_error=last_action_error,
            step_count=self._state.step_count,
            finalized=self._finalized,
            done=done,
            reward=reward,
        )

    def _pick_task(self, task_id: str | None, seed: int | None) -> dict[str, Any]:
        if task_id is not None:
            for t in self._tasks:
                if t.get("task_id") == task_id:
                    return t
            raise ValueError(f"task_id '{task_id}' not found in {self._tasks_path}")
        if not self._tasks:
            raise RuntimeError(
                f"No tasks loaded from {self._tasks_path}. "
                "Set STABILIZER_FORGE_TASKS or place tasks.jsonl next to the env."
            )
        rng = random.Random(seed) if seed is not None else self._rng
        return rng.choice(self._tasks)

    # ---------- Gym API ----------

    def reset(
        self,
        seed: int | None = None,
        episode_id: str | None = None,
        task_id: str | None = None,
        **kwargs: Any,
    ) -> StabilizerObservation:
        if seed is not None:
            self._rng = random.Random(seed)
        self._task = self._pick_task(task_id=task_id, seed=seed)

        self._gates = []
        self._cnot_count = 0
        self._nonadj_cnot_count = 0
        self._format_violations = 0
        self._consecutive_violations = 0
        self._finalized = False
        self._state = State(
            episode_id=episode_id or str(uuid4()), step_count=0
        )

        # Initial match (empty circuit on |0...0>)
        self._last_match_fraction, self._last_match_results = self._compute_match()
        return self._make_obs(reward=0.0, done=False)

    def step(self, action: StabilizerAction, **kwargs: Any) -> StabilizerObservation:  # type: ignore[override]
        if self._task is None:
            raise RuntimeError("step() called before reset().")
        self._state.step_count += 1

        n_qubits = int(self._task["n_qubits"])
        gate_budget = int(
            self._task.get(
                "gate_budget", 2 * max(1, int(self._task.get("benchmark_optimum", 1)))
            )
        )

        # --- Validate ---
        ok, err = _validate_action(action, n_qubits)
        if not ok:
            self._format_violations += 1
            self._consecutive_violations += 1
            done = (
                self._consecutive_violations >= MAX_CONSECUTIVE_VIOLATIONS
                or len(self._gates) >= gate_budget
            )
            return self._make_obs(
                reward=W_FORMAT * -1.0,
                done=done,
                last_action_valid=False,
                last_action_error=err,
            )

        self._consecutive_violations = 0

        # --- FINALIZE: terminal reward ---
        if action.op == "FINALIZE":
            self._finalized = True
            self._last_match_fraction, self._last_match_results = self._compute_match()
            bench = max(1, int(self._task.get("benchmark_optimum", 1)))
            bench_2q = max(1, int(self._task.get("benchmark_optimum_2q", bench)))
            gate_eff = max(0.0, 1.0 - len(self._gates) / (1.5 * bench))
            twoq_eff = max(0.0, 1.0 - self._cnot_count / (1.5 * bench_2q))
            terminal = (
                W_MATCH * self._last_match_fraction
                + W_GATE_EFF * gate_eff
                + W_TWOQ_EFF * twoq_eff
            )
            return self._make_obs(reward=terminal, done=True)

        # --- Apply gate ---
        gate_str = _gate_to_stim(action)
        self._gates.append(gate_str)

        conn_penalty = 0.0
        if action.op == "CX":
            self._cnot_count += 1
            if not self._is_adjacent(action.qubits[0], action.qubits[1]):
                self._nonadj_cnot_count += 1
                conn_penalty = -1.0

        prev_match = self._last_match_fraction
        self._last_match_fraction, self._last_match_results = self._compute_match()
        delta = self._last_match_fraction - prev_match

        # Termination if we exceed budget without finalizing
        done = len(self._gates) >= gate_budget

        step_reward = SHAPING_COEF * delta + W_CONN * conn_penalty
        return self._make_obs(reward=step_reward, done=done)

    @property
    def state(self) -> State:
        return self._state