"""
Dataset generator for RhythmEnv GRPO training (meta-RL version).

Plays episodes under a continuously-sampled profile per seed and emits
observation prompts at each step, paired with the replay metadata
(seed, step_index, action_history) the reward functions need to
reconstruct env state deterministically.

The system prompt asks for "S M W ACTION_NAME" — three belief digits then
the action. A `hint_fraction` slice of episodes carries a true-belief hint
in the prompt as a curriculum warmup; the rest force pure inference.
"""

import sys
import os
import random

sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))

from models import ActionType, RhythmAction
from server.rhythm_environment import RhythmEnvironment, MAX_STEPS, METERS

SLOT_NAMES = ["Morning", "Afternoon", "Evening", "Night"]
DAY_NAMES = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]

SYSTEM_PROMPT = """You are a life-management agent helping a person whose preferences are HIDDEN.
You see 5 life meters and a rolling history of recent steps. The same action
affects different people differently — you must INFER who you're helping from
rewards, meter changes, and per-meter ANOMALY signals.

Each step, do TWO things:

1. Reason briefly about what the observations imply about the person.
   Focus on:
     - Anomalies (actual delta vs neutral-profile expectation): big positive
       social_serenity / connection responses → high S; big morning cognition
       gains → high M; productive work giving vitality back → high W
     - Current meter state: any meter under 0.15 needs urgent recovery
     - What action best fits BOTH the inferred profile and the current state

2. Output your final answer on the LAST line in this exact format:
       S M W ACTION_NAME
   where S, M, W are belief digits 0-9 (0=low, 9=high) representing your best
   estimate of social_pref, morning_pref, work_pref. ACTION_NAME is one of:
   DEEP_WORK, ADMIN_WORK, LEARN, SLEEP, EXERCISE, MEDITATE, FAMILY_TIME,
   SOCIALIZE, ME_TIME, BINGE_WATCH

Wrap your reasoning in <reasoning>...</reasoning> tags. Keep reasoning under
120 tokens. The final answer line MUST be the last line of your response.

Belief→action quick reference:
- High S (extrovert): SOCIALIZE, FAMILY_TIME boost connection cheaply
- High M (morning person): DEEP_WORK / LEARN in early slots gets bonus cognition
- High W (workaholic): DEEP_WORK, LEARN drive progress and may energize
- Low S (introvert): MEDITATE, ME_TIME for solo recharge; avoid SOCIALIZE
- Low M (night owl): DEEP_WORK / LEARN in evening/night slots
- Watch crashes: any meter under 0.10 = -0.30 penalty per crashed meter
- Connection decays passively — actively maintain via SOCIALIZE/FAMILY_TIME
- Don't repeat the same action 3+ times in a row — repetition penalty applies

Strategy: probe varied actions in the first ~5 steps to gather profile evidence,
then exploit your sharpened belief by picking actions that match the inferred
profile + current meter state.

Example output:
<reasoning>
Last step's socialize gave V-0.12 (anom -0.06, much worse than neutral) — high
social drain, suggests low S. Morning DEEP_WORK earlier gave bonus cognition
(anom +0.04) → high M. Vitality at 0.6 still ok, serenity dropping. With low S +
high M, MEDITATE is the recovery play that fits.
</reasoning>
2 8 5 MEDITATE"""


def format_observation_prompt(obs, profile_hint: dict | None = None) -> str:
    """Format an observation into a user prompt for the LLM.

    If profile_hint is provided (curriculum's "visible" phase), include it in
    the prompt so the agent learns the *skill* of using profile signals
    before having to infer them from scratch.
    """
    day_name = DAY_NAMES[obs.day] if obs.day < 7 else f"Day {obs.day}"
    slot_name = SLOT_NAMES[obs.slot] if obs.slot < 4 else f"Slot {obs.slot}"
    event_str = f"\nActive event: {obs.active_event}" if obs.active_event else ""

    history_lines = []
    for h in (obs.step_history or [])[-5:]:  # last 5 only to fit prompt budget
        # Per-meter anomalies (actual_delta − expected_under_neutral_profile)
        # are the cleanest profile-inference signal — they show how this person's
        # response DEVIATES from the average person. Surfacing them here in the
        # prompt is what gives the agent a fingerprint to learn from.
        anom_str = (
            f" [anom V{h.vitality_anomaly:+.2f} C{h.cognition_anomaly:+.2f} "
            f"P{h.progress_anomaly:+.2f} S{h.serenity_anomaly:+.2f} "
            f"Cn{h.connection_anomaly:+.2f}]"
        )
        history_lines.append(
            f"  step {h.step}: {h.action} -> reward {h.reward:+.2f} "
            f"(V{h.vitality_delta:+.2f} C{h.cognition_delta:+.2f} "
            f"P{h.progress_delta:+.2f} S{h.serenity_delta:+.2f} Cn{h.connection_delta:+.2f})"
            f"{anom_str}"
        )
    history_str = ""
    if history_lines:
        history_str = (
            "\n\nRecent history (anom = how this person deviated from neutral baseline):\n"
            + "\n".join(history_lines)
        )

    hint_str = ""
    if profile_hint is not None:
        hint_str = (
            f"\n\nKnown about this person (training hint):\n"
            f"  social_pref={profile_hint['social_pref']:.2f}, "
            f"morning_pref={profile_hint['morning_pref']:.2f}, "
            f"work_pref={profile_hint['work_pref']:.2f}"
        )

    return (
        f"Step: {obs.timestep}/{MAX_STEPS} ({day_name} {slot_name})\n"
        f"Remaining steps: {obs.remaining_steps}\n\n"
        f"Meters:\n"
        f"  Vitality:   {obs.vitality:.2f}\n"
        f"  Cognition:  {obs.cognition:.2f}\n"
        f"  Progress:   {obs.progress:.2f}\n"
        f"  Serenity:   {obs.serenity:.2f}\n"
        f"  Connection: {obs.connection:.2f}"
        f"{event_str}"
        f"{history_str}"
        f"{hint_str}\n\n"
        f"Output your belief, then your action (format: S M W ACTION_NAME):"
    )


def generate_episode_samples(
    seed: int,
    strategy: str = "random",
    profile_mode: str = "continuous",
    show_profile_hint: bool = False,
) -> list:
    """Play one episode and return a list of training samples.

    Each sample includes the prompt + replay metadata (seed, step_index,
    action_history, profile_mode) so reward functions can deterministically
    reconstruct the env state.

    Args:
        seed: Episode seed (also determines profile when profile_mode=continuous).
        strategy: "random" or "heuristic" — used to roll out the episode for
            state diversity. The agent's training generations replace these
            actions; we only need the prefix history for replay.
        profile_mode: "continuous" (sampled per seed) or "discrete" (1 of 3
            hardcoded profiles).
        show_profile_hint: If True, include the true belief vector in the prompt.
            Use during the curriculum's "visible" warmup phase.
    """
    env = RhythmEnvironment()
    obs = env.reset(seed=seed, profile_mode=profile_mode)
    profile_hint = env.get_profile_hint() if show_profile_hint else None
    rng = random.Random(seed + 1000)
    actions_taken = []
    samples = []
    all_actions = list(ActionType)

    for step in range(MAX_STEPS):
        if obs.done:
            break

        prompt = format_observation_prompt(obs, profile_hint=profile_hint)

        samples.append({
            "prompt": [
                {"role": "system", "content": SYSTEM_PROMPT},
                {"role": "user", "content": prompt},
            ],
            "seed": seed,
            "step_index": step,
            "action_history": list(actions_taken),
            "profile_mode": profile_mode,
            "show_profile_hint": show_profile_hint,
        })

        if strategy == "random":
            action_type = rng.choice(all_actions)
        elif strategy == "heuristic":
            action_type = heuristic_action(obs)
        else:
            action_type = rng.choice(all_actions)

        action = RhythmAction(action_type=action_type)
        actions_taken.append(action_type.value)
        obs = env.step(action)

    return samples


def heuristic_action(obs) -> ActionType:
    """Priority-based heuristic baseline (profile-blind).

    Used both during dataset generation (to roll out diverse states) and
    by inference_eval as the heuristic baseline strategy.
    """
    slot = obs.slot
    v, c, p, s, cn = obs.vitality, obs.cognition, obs.progress, obs.serenity, obs.connection

    if v < 0.15:
        return ActionType.SLEEP
    if s < 0.15:
        return ActionType.MEDITATE
    if cn < 0.15:
        return ActionType.FAMILY_TIME
    if slot == 3:
        return ActionType.SLEEP
    if slot == 0:
        return ActionType.DEEP_WORK if (v > 0.4 and c > 0.3) else ActionType.EXERCISE
    if slot == 1:
        if cn < 0.3:
            return ActionType.FAMILY_TIME
        if p < 0.3 and v > 0.3:
            return ActionType.LEARN
        return ActionType.ADMIN_WORK
    if cn < 0.4:
        return ActionType.SOCIALIZE
    if s < 0.5:
        return ActionType.ME_TIME
    return ActionType.MEDITATE


def generate_dataset(
    num_episodes: int = 200,
    strategy: str = "mixed",
    max_samples: int = 2000,
    profile_mode: str = "continuous",
    hint_fraction: float = 0.2,
) -> list:
    """Generate a training dataset by playing multiple episodes.

    Curriculum is baked into the dataset: hint_fraction of samples have the
    true profile visible (visible-phase warmup). After shuffle, GRPOTrainer
    sees a mix early on; we can sort to put hint samples first if needed.

    Args:
        num_episodes: Number of episodes to play.
        strategy: "random", "heuristic", or "mixed" (alternating).
        max_samples: Maximum samples to return.
        profile_mode: "continuous" (default, meta-RL) or "discrete" (3 profiles).
        hint_fraction: Fraction of episodes to play with profile hint visible.
    """
    all_samples = []
    n_hint_episodes = int(num_episodes * hint_fraction)

    for i in range(num_episodes):
        seed = i
        if strategy == "mixed":
            s = "heuristic" if i % 2 == 0 else "random"
        else:
            s = strategy
        show_hint = i < n_hint_episodes

        episode_samples = generate_episode_samples(
            seed=seed,
            strategy=s,
            profile_mode=profile_mode,
            show_profile_hint=show_hint,
        )
        all_samples.extend(episode_samples)

        if len(all_samples) >= max_samples:
            break

    # Shuffle (curriculum is per-sample via show_profile_hint flag, not order)
    random.shuffle(all_samples)
    all_samples = all_samples[:max_samples]

    n_hint = sum(1 for s in all_samples if s["show_profile_hint"])
    print(
        f"Generated {len(all_samples)} samples from {min(i+1, num_episodes)} episodes "
        f"({n_hint} with profile hint, {len(all_samples) - n_hint} without)"
    )
    return all_samples


if __name__ == "__main__":
    samples = generate_dataset(num_episodes=20, strategy="mixed", max_samples=80, hint_fraction=0.5)
    print(f"\nFirst sample (with hint):")
    hinted = next((s for s in samples if s["show_profile_hint"]), None)
    if hinted:
        print(hinted["prompt"][1]["content"])
        print(f"\nseed={hinted['seed']}, step={hinted['step_index']}, mode={hinted['profile_mode']}")

    print(f"\nFirst sample (without hint):")
    plain = next((s for s in samples if not s["show_profile_hint"]), None)
    if plain:
        print(plain["prompt"][1]["content"])