"""GRPO trainer for the Drift Generator.

Uses R-Zero's composite Challenger reward: max(0, uncertainty - repetition).
Each prompt is sampled `group_size` times; for every breakage we run K
independent Repair Agent rollouts to estimate p_hat (success rate).

Heavy and brittle on a single GPU — keep group_size small for hackathon
budgets. Provides a `--dry_run` mode that just exercises the reward function
without any LLM calls.
"""
from __future__ import annotations

import argparse
import json
import os
import random
from pathlib import Path
from typing import Optional

from forgeenv.env.actions import BreakageAction, ForgeAction, RepairAction
from forgeenv.env.forge_environment import ForgeEnvironment
from forgeenv.roles.drift_generator import (
    BaselineDriftGenerator,
    parse_drift_output,
)
from forgeenv.roles.prompts import (
    DRIFT_GENERATOR_SYSTEM_PROMPT,
    render_drift_generator_prompt,
)
from forgeenv.training.rollout import (
    GenerateFn,
    rollout_one_episode,
    baseline_oracle_repair_generate,
    _baseline_repair_generate,
)
from forgeenv.training.reward_functions import (
    compute_drift_gen_reward,
    compute_uncertainty_reward,
    compute_repetition_penalty,
)


def evaluate_drift_batch(
    env_factory,
    breakages: list[dict],
    repair_generate: GenerateFn,
    n_repair_attempts_per_breakage: int = 4,
    seed: int = 0,
) -> list[float]:
    """For each breakage spec, run K Repair-Agent attempts and compute
    R-Zero's composite Challenger reward. Returns one reward per breakage."""

    breakage_texts = [
        f"{b.get('primitive_type','')}::{json.dumps(b.get('params', {}), sort_keys=True)}"
        for b in breakages
    ]

    rewards: list[float] = []
    for idx, breakage_spec in enumerate(breakages):
        successes: list[bool] = []
        for k in range(n_repair_attempts_per_breakage):
            env = env_factory()
            env.reset(seed=seed + idx * 100 + k, difficulty="easy")
            try:
                obs2 = env.step(
                    ForgeAction(
                        breakage=BreakageAction(
                            primitive_type=breakage_spec.get("primitive_type", ""),
                            params=breakage_spec.get("params", {}) or {},
                        )
                    )
                )
            except Exception:
                successes.append(False)
                continue

            from forgeenv.roles.repair_agent import extract_diff
            from forgeenv.roles.prompts import render_repair_agent_prompt

            user = render_repair_agent_prompt(
                broken_script=obs2.script_content,
                error_trace=obs2.error_trace or "",
                library_versions=obs2.library_versions,
                target_category=obs2.target_category,
            )
            raw = repair_generate("", user)
            diff = extract_diff(raw or "")
            obs3 = env.step(ForgeAction(repair=RepairAction(unified_diff=diff)))
            successes.append(
                bool(obs3.held_out_breakdown.get("executed_cleanly", 0.0) > 0.5)
            )

        reward = compute_drift_gen_reward(
            breakage_text=breakage_texts[idx],
            repair_successes=successes,
            batch_breakages=breakage_texts,
        )
        rewards.append(reward)
    return rewards


def run_drift_grpo_dry_run(
    output_dir: str, total_episodes: int = 100, group_size: int = 4, seed: int = 0
) -> None:
    """Pure-CPU exercise of the drift-side reward loop. Writes per-step rewards."""
    rng = random.Random(seed)
    drift_gen = BaselineDriftGenerator(seed=seed)
    rewards_log: list[dict] = []

    for ep in range(total_episodes):
        env = ForgeEnvironment(seed=seed + ep)
        env.reset(difficulty="easy")
        target_category = env.state["target_category"]
        script = env._original_script  # noqa: SLF001 — read-only convenience

        # Sample group_size candidate breakages
        candidates = [
            drift_gen.propose(target_category=target_category, script=script)
            for _ in range(group_size)
        ]

        # Use the oracle as repair (so we get a meaningful uncertainty signal:
        # an "unbreakable" breakage gives p_hat=1, an "always-fails" one gives 0)
        rewards = evaluate_drift_batch(
            env_factory=lambda: ForgeEnvironment(seed=rng.randint(0, 1_000_000)),
            breakages=candidates,
            repair_generate=baseline_oracle_repair_generate(env),
            n_repair_attempts_per_breakage=2,
            seed=seed + ep,
        )
        rewards_log.append(
            {"episode": ep, "rewards": rewards, "candidates": candidates}
        )

        if ep % max(1, total_episodes // 10) == 0:
            mean_r = sum(rewards) / max(1, len(rewards))
            print(f"[drift dry-run] ep={ep} mean_reward={mean_r:.3f}")

    Path(output_dir).mkdir(parents=True, exist_ok=True)
    (Path(output_dir) / "drift_dry_run.json").write_text(
        json.dumps(rewards_log, indent=2)
    )
    print(f"[drift dry-run] wrote {len(rewards_log)} episodes to {output_dir}")


def _parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--output_dir", required=True)
    parser.add_argument("--total_episodes", type=int, default=100)
    parser.add_argument("--group_size", type=int, default=4)
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument("--dry_run", action="store_true", default=True)
    return parser.parse_args()


if __name__ == "__main__":
    args = _parse_args()
    if args.dry_run:
        run_drift_grpo_dry_run(
            output_dir=args.output_dir,
            total_episodes=args.total_episodes,
            group_size=args.group_size,
            seed=args.seed,
        )
    else:
        raise NotImplementedError(
            "Full LLM Drift GRPO requires both roles loaded — use the Colab notebook"
        )