"""Local pre-eval chain: microtask + reject-first-submit, then official heuristics."""

from __future__ import annotations

import json
import subprocess
from datetime import datetime, timezone
from pathlib import Path
from uuid import uuid4

from albedo_eval_service.remote.dataset import EvalSample, format_messages
from albedo_eval_service.remote.generation import VllmProcessGenerator
from albedo_eval_service.shared.observation_format import (
    MAX_CONSECUTIVE_BAD_TURNS,
    detect_format,
    first_bash_block,
    unusable_turn,
    wrap,
)
from albedo_eval_service.simulator.prompt_simulator import missing_command_output
from local_eval.constants import DEFAULT_DATA_ROOT, DEFAULT_RUNS_DIR, MAX_NEW_TOKENS, TOKENIZER_DIR
from local_eval.live_protocol import generate_retrying_bad_turns
from local_eval.rollout import build_generator
from local_eval.samples import leftover_observations, load_samples
from sanity_service.chain import (
    SUBMIT_NUDGE,
    followup_instruction,
    micro_instruction,
    segment_has_edit,
)

from .chain_gold import LIVE_CHAIN_IDS
from .chain_heuristics import ChainState, evaluate_chain, is_submit_turn, mid_roll_fatal
from .chain_pack import _REJECTION, infer_micro
from .constants import DEFAULT_RL_EXPORT_DIR

LIVE_SAMPLES = 3
LIVE_TURNS = 32
_OBS_CHARS = 2500
_CHAIN_GPUS = 4
_MIN_FREE_GIB = 100.0


def pick_free_gpus(n: int = _CHAIN_GPUS, min_free_gib: float = _MIN_FREE_GIB) -> list[str]:
    """Prefer cards that can actually satisfy vLLM's 0.8 utilization check."""
    try:
        raw = subprocess.check_output(
            ["nvidia-smi", "--query-gpu=index,memory.free", "--format=csv,noheader,nounits"],
            text=True,
        )
    except (subprocess.CalledProcessError, FileNotFoundError) as exc:
        raise RuntimeError(f"nvidia-smi failed: {exc}") from exc
    free: list[str] = []
    for line in raw.strip().splitlines():
        parts = [p.strip() for p in line.split(",")]
        if len(parts) < 2:
            continue
        idx, mem = parts[0], parts[1]
        try:
            if float(mem) / 1024.0 >= min_free_gib:
                free.append(idx)
        except ValueError:
            continue
    if len(free) < n:
        raise RuntimeError(
            f"need {n} GPUs with >= {min_free_gib:.0f} GiB free, found {free}. "
            "kill leftover VLLM::EngineCore / Worker_TP* processes and retry"
        )
    return free[:n]


def _micro_for(sample) -> dict[str, str]:
    hay = "\n".join(m.get("content") or "" for m in (sample.messages or []))
    return infer_micro(hay)


def _pick_chain_samples(
    dataset_root: Path, *, samples: int, seed: str, live_gate: bool = True
):
    """Use the 3 live-gate files first, then random extras so we test transfer."""
    picked: list[tuple] = []
    seen: set[str] = set()
    forced = []
    if live_gate:
        try:
            forced = load_samples(
                dataset_root,
                sample_ids=list(LIVE_CHAIN_IDS),
                sample_count=len(LIVE_CHAIN_IDS),
                seed=seed,
            )
        except Exception as exc:
            print(f"live-gate samples unavailable ({exc}); falling back to random", flush=True)
            forced = []
    for sample in forced:
        micro = _micro_for(sample)
        if not sample.submit_command or not micro.get("file"):
            continue
        picked.append((sample, micro))
        seen.add(sample.sample_id)
        if len(picked) >= samples:
            return picked
    extras = load_samples(
        dataset_root,
        sample_ids=None,
        sample_count=max(samples * 6, 18),
        seed=seed,
    )
    for sample in extras:
        if sample.sample_id in seen:
            continue
        micro = _micro_for(sample)
        if not sample.submit_command or not micro.get("file"):
            continue
        picked.append((sample, micro))
        seen.add(sample.sample_id)
        if len(picked) >= samples:
            break
    return picked


def run_chain(
    *,
    challenger: Path = DEFAULT_RL_EXPORT_DIR,
    dataset_root: Path = DEFAULT_DATA_ROOT,
    samples: int = LIVE_SAMPLES,
    turns: int = LIVE_TURNS,
    seed: str = "chain-eval",
    live_gate: bool = True,
    gpu_ids: list[str] | None = None,
    runs_dir: Path = DEFAULT_RUNS_DIR,
    reject_first_submit: bool = True,
) -> dict:
    from local_eval.cuda_env import apply as apply_cuda

    apply_cuda()
    dataset_root = Path(dataset_root)
    picked = _pick_chain_samples(
        dataset_root, samples=samples, seed=seed, live_gate=live_gate
    )
    states: list[ChainState] = []
    leftover: dict[str, list[str]] = {}
    leftover_idx: dict[str, int] = {}
    for sample, micro in picked:
        instruction = micro_instruction(micro, sample.submit_command)
        messages = list(sample.messages or []) + [{"role": "user", "content": instruction}]
        turns_so_far = [
            {"role": m.get("role", "user"), "content": m.get("content", "")}
            for m in messages
        ]
        turns_so_far[-1] = {**turns_so_far[-1], "segment": "micro", "injected": True}
        state = ChainState(
            sample_id=sample.sample_id,
            prompt=format_messages(
                messages, tokenizer_path=str(TOKENIZER_DIR), enable_thinking=True
            ),
            messages=messages,
            turns=turns_so_far,
            submit_clause=sample.submit_command,
            submit_marker=sample.submit_marker,
            rewrite_mode=getattr(sample, "rewrite_mode", ""),
            micro=micro,
        )
        states.append(state)
        leftover[sample.sample_id] = leftover_observations(dataset_root, sample.sample_id)
        leftover_idx[sample.sample_id] = 0
    if len(states) < samples:
        raise RuntimeError(f"only {len(states)} chain-able samples, need {samples}")

    gpu_ids = gpu_ids or pick_free_gpus()
    if len(gpu_ids) > _CHAIN_GPUS:
        gpu_ids = gpu_ids[:_CHAIN_GPUS]
    generator = build_generator(
        str(challenger),
        gpu_ids,
        max_new_tokens=MAX_NEW_TOKENS,
    )
    run_id = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") + "-" + uuid4().hex[:8]
    out = Path(runs_dir) / f"{run_id}-chain"
    out.mkdir(parents=True, exist_ok=True)
    print(
        f"local chain run={run_id} samples={len(states)} turns<={turns} chal={challenger}",
        flush=True,
    )
    try:
        _roll(generator, states, leftover, leftover_idx, turns, reject_first_submit)
    finally:
        generator.close()

    evaluate_chain(states, turns)
    report = _report(run_id, challenger, states, turns)
    (out / "chain-report.json").write_text(json.dumps(report, indent=2) + "\n")
    with (out / "chain-samples.jsonl").open("w") as handle:
        for state in states:
            handle.write(
                json.dumps(
                    {
                        "sample_id": state.sample_id,
                        "passed": not state.error and not state.heuristic_reason,
                        "reason": state.error or state.heuristic_reason,
                        "n_submits": len(state.submits),
                        "empty_adjacent": sum(1 for s in state.submits if not s.get("has_edit")),
                        "micro": state.micro,
                        "submit_command": state.submit_clause,
                        "commands": [
                            first_bash_block(str(t.get("content") or ""))
                            for t in state.turns
                            if t.get("role") == "assistant" and t.get("score_target")
                        ],
                    }
                )
                + "\n"
            )
    print(json.dumps(report, indent=2), flush=True)
    print(f"artifacts: {out}", flush=True)
    return report


def _roll(
    generator: VllmProcessGenerator,
    states: list[ChainState],
    leftover: dict[str, list[str]],
    leftover_idx: dict[str, int],
    turn_count: int,
    reject_first_submit: bool,
) -> None:
    for turn_index in range(turn_count):
        active = [s for s in states if not s.stopped and not s.error and not s.heuristic_reason]
        if not active:
            break
        batch = [
            EvalSample(
                sample_id=state.sample_id,
                prompt=state.prompt,
                messages=state.messages,
                submit_command=state.submit_clause,
                submit_marker=state.submit_marker,
            )
            for state in active
        ]
        results = generate_retrying_bad_turns(generator, batch)
        by_id = {r.sample_id: r for r in results}
        for state in active:
            result = by_id.get(state.sample_id)
            if result is None or result.error:
                scored = any(
                    t.get("role") == "assistant" and t.get("score_target") for t in state.turns
                )
                if scored:
                    state.stopped = True
                else:
                    state.error = (
                        (result.error if result else "missing_generation") or "missing_generation"
                    )
                continue
            text = result.text or ""
            reason = unusable_turn(text, truncated=bool(result.truncated))
            if reason:
                state.turns.append(
                    {
                        "role": "assistant",
                        "content": text,
                        "score_target": True,
                        "segment": state.segment,
                    }
                )
                state.heuristic_reason = (
                    f"{reason} on {MAX_CONSECUTIVE_BAD_TURNS} consecutive turns"
                )
                state.stopped = True
                continue
            state.turns.append(
                {
                    "role": "assistant",
                    "content": text,
                    "score_target": True,
                    "segment": state.segment,
                }
            )
            fatal = mid_roll_fatal(state)
            if fatal:
                state.heuristic_reason = fatal
                state.stopped = True
                continue
            if turn_index == turn_count - 1:
                continue
            if is_submit_turn(text, state.submit_clause, state.submit_marker):
                _handle_submit(state, text, turn_index, reject_first_submit)
            elif not first_bash_block(text):
                fmt = detect_format(state.sample_id, state.messages)
                _append_user(state, missing_command_output(fmt))
            else:
                _append_user(state, _env_obs(state, leftover, leftover_idx))
            state.prompt = format_messages(
                state.messages, tokenizer_path=str(TOKENIZER_DIR), enable_thinking=True
            )
        if turn_index >= turn_count - 8 and (turn_count - turn_index) % 4 == 0:
            _nudge([s for s in states if not s.stopped and not s.error and not s.submits])


def _handle_submit(
    state: ChainState, text: str, turn_index: int, reject_first: bool
) -> None:
    rejected = reject_first and not any(s.get("rejected") for s in state.submits)
    record = {
        "turn": turn_index,
        "segment": state.segment,
        "rejected": rejected,
        "format_ok": state.submit_clause.split("&&")[0].strip() in text,
        "post_nudge": bool(state.nudged_at) and turn_index > state.nudged_at,
        "has_edit": segment_has_edit(state.as_ns(), state.segment),
    }
    state.submits.append(record)
    if rejected:
        _append_user(state, followup_instruction(_REJECTION, state.submit_clause, first=False))
        return
    first = state.segment == "micro"
    state.segment_index += not first
    state.segment = "real" if first else f"followup_{state.segment_index}"
    _append_user(
        state,
        followup_instruction(
            "Thanks, received. Please continue with the original issue and any missed call sites.",
            state.submit_clause,
            first=first,
        ),
    )


def _nudge(states: list[ChainState]) -> None:
    for state in states:
        text = SUBMIT_NUDGE.format(clause=state.submit_clause)
        _append_user(state, text)
        state.turns[-1]["injected"] = True
        state.nudged_at = state.nudged_at or len(
            [t for t in state.turns if t.get("score_target")]
        )
        state.prompt = format_messages(
            state.messages, tokenizer_path=str(TOKENIZER_DIR), enable_thinking=True
        )


def _append_user(state: ChainState, observation: str) -> None:
    assistant = str(state.turns[-1].get("content") or "")
    state.messages.extend(
        [
            {"role": "assistant", "content": assistant},
            {"role": "user", "content": observation},
        ]
    )
    state.turns.append(
        {"role": "user", "content": observation, "environment_observation": True}
    )


def _env_obs(
    state: ChainState, leftover: dict[str, list[str]], leftover_idx: dict[str, int]
) -> str:
    gold = leftover.get(state.sample_id) or []
    index = leftover_idx.get(state.sample_id, 0)
    if index < len(gold):
        leftover_idx[state.sample_id] = index + 1
        text = gold[index]
        if len(text) > _OBS_CHARS:
            text = text[:_OBS_CHARS] + "\n...[truncated]"
        return text
    fmt = detect_format(state.sample_id, state.messages)
    return wrap("command completed with no captured output", fmt)


def _report(run_id: str, challenger: Path, states: list[ChainState], turns: int) -> dict:
    rows = []
    for state in states:
        rows.append(
            {
                "sample_id": state.sample_id,
                "passed": not state.error and not state.heuristic_reason,
                "reason": state.error or state.heuristic_reason,
                "n_submits": len(state.submits),
                "unprompted": any(not s.get("post_nudge") for s in state.submits),
                "micro_file": (state.micro or {}).get("file"),
            }
        )
    passed = all(r["passed"] for r in rows) and bool(rows)
    reasons = [r["reason"] for r in rows if r["reason"]]
    if passed:
        reasons = ["go: every sample passed the live chain heuristics"]
    return {
        "run_id": run_id,
        "go": passed,
        "challenger": str(challenger),
        "n": len(rows),
        "turns": turns,
        "pass_rate": round(sum(1 for r in rows if r["passed"]) / max(len(rows), 1), 4),
        "reasons": reasons,
        "samples": rows,
    }