batchify

# ---------------------------
# 0) Tiny dataset (char-level)
# ---------------------------
def make_char_data(text: str, block_size=64, device="cpu"):
    vocab = sorted(list(set(text)))
    stoi = {ch: i for i, ch in enumerate(vocab)}
    itos = {i: ch for ch, i in stoi.items()}

    def encode(s):
        return torch.tensor([stoi[c] for c in s], dtype=torch.long)

    def decode(t):
        return "".join([itos[int(i)] for i in t])

    data = encode(text)
    # train/val split
    n = int(0.9 * len(data))
    train_data, val_data = data[:n], data[n:]

    def batchify(split, batch_size):
        src = train_data if split == "train" else val_data
        ix = torch.randint(len(src) - block_size - 1, (batch_size,))
        x = torch.stack([src[i : i + block_size] for i in ix])
        y = torch.stack([src[i + 1 : i + block_size + 1] for i in ix])
        return x.to(device), y.to(device)

    meta = {"vocab_size": len(vocab), "stoi": stoi, "itos": itos, "block_size": block_size, "decode": decode}
    return batchify, meta

# ---------------------------
# 1) Tiny decoder Transformer
# ---------------------------
class CausalSelfAttention(nn.Module):
    def __init__(self, n_embd, n_head, block_size, attn_p=0.0, resid_p=0.0):
        super().__init__()
        assert n_embd % n_head == 0
        self.n_head = n_head
        self.key = nn.Linear(n_embd, n_embd, bias=False)
        self.query = nn.Linear(n_embd, n_embd, bias=False)
        self.value = nn.Linear(n_embd, n_embd, bias=False)
        self.proj = nn.Linear(n_embd, n_embd)
        self.attn_drop = nn.Dropout(attn_p)
        self.resid_drop = nn.Dropout(resid_p)
        self.register_buffer("mask", torch.tril(torch.ones(block_size, block_size)).view(1, 1, block_size, block_size))


        self.total = 0

    def select(self, c=1.5):
        self.total += 1
        for a in range(len(self.n)):
            if self.n[a] == 0:
                return a
        u = [self.value[a] + c * math.sqrt(math.log(self.total) / self.n[a]) for a in range(len(self.n))]
        return max(range(len(self.n)), key=lambda a: u[a])

    def update(self, arm, reward):
        self.n[arm] += 1
        n = self.n[arm]
        self.value[arm] += (reward - self.value[arm]) / n

# ------------------------------------
# 7) Mixture Meta-Optimizer (grouped)
# ------------------------------------
class MixtureMetaOptGrouped:
    def __init__(self, model, param_groups, gate: GateNetGrouped, base_lr=3e-4, base_wd=0.01, device="cpu"):
        self.model = model
        self.groups = param_groups
        self.gate = gate.to(device)
        self.base_lr, self.base_wd = base_lr, base_wd
        self.device = device

        self.st_adam: Dict[int, Dict] = {}
        self.st_lion: Dict[int, Dict] = {}
        self.st_soph: Dict[int, Dict] = {}
        self.curv = {}  # diag curvature by param id

        self.grad_ema = [0.0] * len(self.groups)
        self._last_loss = None
        self.step_idx, self.T_hint = 0, 1_000_000

        self.bandits = [UCB1(3) for _ in self.groups]

    def _group_grad_norm(self, params):
        s = 0.0
        for p in params:
            if p.grad is None:
                continue
            s += p.grad.detach().float().pow(2).sum().item()
        return (s + 1e-12) ** 0.5

    def _feats(self, loss, gnorm, gi):
        self.grad_ema[gi] = 0.95 * self.grad_ema[gi] + 0.05 * gnorm
        dloss = 0.0 if self._last_loss is None else (loss - self._last_loss)
        cos_proxy = gnorm / (self.grad_ema[gi] + 1e-8)
        t_feat = float(self.step_idx / max(1, self.T_hint))
        self._last_loss = loss
        return torch.tensor(
            [[math.log(gnorm + 1e-8), math.log(self.grad_ema[gi] + 1e-8), float(loss), float(dloss), float(cos_proxy), t_feat]],
            device=self.device,
        )


forward

    return groups

# ----------------------------------------
# 3) Gate (learned routing + scale heads)
# ----------------------------------------
class GateNetGrouped(nn.Module):
    def __init__(self, n_groups: int, hidden=128):
        super().__init__()
        self.group_embed = nn.Embedding(n_groups, 16)
        self.mlp = nn.Sequential(
            nn.Linear(6 + 16, hidden),
            nn.GELU(),
            nn.Linear(hidden, hidden),
            nn.GELU(),
        )
        self.mix_head = nn.Linear(hidden, 3)  # [AdamW, Lion, SophiaG]
        self.lrs_head = nn.Linear(hidden, 1)  # (0,2)x base_lr
        self.wds_head = nn.Linear(hidden, 1)  # (0,2)x base_wd

    def forward(self, feats, group_idx):
        ge = self.group_embed(group_idx)  # [B,16]
        z = torch.cat([feats, ge], dim=-1)  # [B,6+16]
        h = self.mlp(z)
        mix_logits = self.mix_head(h)
        lr_scale = torch.sigmoid(self.lrs_head(h)) * 2.0
        wd_scale = torch.sigmoid(self.wds_head(h)) * 2.0
        return mix_logits, lr_scale.squeeze(-1), wd_scale.squeeze(-1)

# ------------------------------------
# 4) Primitive optimizer update rules
# ------------------------------------
@torch.no_grad()
def adamw_update(p, g, state, lr, wd, beta1=0.9, beta2=0.999, eps=1e-8):
    m = state.setdefault("m", torch.zeros_like(p))
    v = state.setdefault("v", torch.zeros_like(p))
    t = state.setdefault("t", 0) + 1
    state["t"] = t
    m.mul_(beta1).add_(g, alpha=1 - beta1)
    v.mul_(beta2).addcmul_(g, g, value=1 - beta2)
    m_hat = m / (1 - beta1 ** t)
    v_hat = v / (1 - beta2 ** t)
    if wd != 0:
        p.add_(p, alpha=-lr * wd)
    p.addcdiv_(m_hat, v_hat.sqrt().add_(eps), value=-lr)


select

@torch.no_grad()
def update_curvature_diag(model, curv_state: Dict[int, torch.Tensor], beta=0.99):
    for p in model.parameters():
        if p.grad is None:
            continue
        pid = id(p)
        h = curv_state.setdefault(pid, torch.zeros_like(p))
        g = p.grad.detach()
        h.mul_(beta).addcmul_(g, g, value=1 - beta)

# -------------------------
# 6) Simple UCB1 bandit arm
# -------------------------
class UCB1:
    def __init__(self, n_arms=3):
        self.n = [0] * n_arms
        self.value = [0.0] * n_arms
        self.total = 0

    def select(self, c=1.5):
        self.total += 1
        for a in range(len(self.n)):
            if self.n[a] == 0:
                return a
        u = [self.value[a] + c * math.sqrt(math.log(self.total) / self.n[a]) for a in range(len(self.n))]
        return max(range(len(self.n)), key=lambda a: u[a])

    def update(self, arm, reward):
        self.n[arm] += 1
        n = self.n[arm]
        self.value[arm] += (reward - self.value[arm]) / n

# ------------------------------------
# 7) Mixture Meta-Optimizer (grouped)
# ------------------------------------
class MixtureMetaOptGrouped:
    def __init__(self, model, param_groups, gate: GateNetGrouped, base_lr=3e-4, base_wd=0.01, device="cpu"):
        self.model = model
        self.groups = param_groups
        self.gate = gate.to(device)
        self.base_lr, self.base_wd = base_lr, base_wd
        self.device = device

        self.st_adam: Dict[int, Dict] = {}
        self.st_lion: Dict[int, Dict] = {}


update

        h.mul_(beta).addcmul_(g, g, value=1 - beta)

# -------------------------
# 6) Simple UCB1 bandit arm
# -------------------------
class UCB1:
    def __init__(self, n_arms=3):
        self.n = [0] * n_arms
        self.value = [0.0] * n_arms
        self.total = 0

    def select(self, c=1.5):
        self.total += 1
        for a in range(len(self.n)):
            if self.n[a] == 0:
                return a
        u = [self.value[a] + c * math.sqrt(math.log(self.total) / self.n[a]) for a in range(len(self.n))]
        return max(range(len(self.n)), key=lambda a: u[a])

    def update(self, arm, reward):
        self.n[arm] += 1
        n = self.n[arm]
        self.value[arm] += (reward - self.value[arm]) / n

# ------------------------------------
# 7) Mixture Meta-Optimizer (grouped)
# ------------------------------------
class MixtureMetaOptGrouped:
    def __init__(self, model, param_groups, gate: GateNetGrouped, base_lr=3e-4, base_wd=0.01, device="cpu"):
        self.model = model
        self.groups = param_groups
        self.gate = gate.to(device)
        self.base_lr, self.base_wd = base_lr, base_wd
        self.device = device

        self.st_adam: Dict[int, Dict] = {}
        self.st_lion: Dict[int, Dict] = {}
        self.st_soph: Dict[int, Dict] = {}
        self.curv = {}  # diag curvature by param id

        self.grad_ema = [0.0] * len(self.groups)
        self._last_loss = None


# ------------------------------------
class MixtureMetaOptGrouped:
    def __init__(self, model, param_groups, gate: GateNetGrouped, base_lr=3e-4, base_wd=0.01, device="cpu"):
        self.model = model
        self.groups = param_groups
        self.gate = gate.to(device)
        self.base_lr, self.base_wd = base_lr, base_wd
        self.device = device

        self.st_adam: Dict[int, Dict] = {}
        self.st_lion: Dict[int, Dict] = {}
        self.st_soph: Dict[int, Dict] = {}
        self.curv = {}  # diag curvature by param id

        self.grad_ema = [0.0] * len(self.groups)
        self._last_loss = None
        self.step_idx, self.T_hint = 0, 1_000_000

        self.bandits = [UCB1(3) for _ in self.groups]

    def _group_grad_norm(self, params):
        s = 0.0
        for p in params:
            if p.grad is None:
                continue
            s += p.grad.detach().float().pow(2).sum().item()
        return (s + 1e-12) ** 0.5

    def _feats(self, loss, gnorm, gi):
        self.grad_ema[gi] = 0.95 * self.grad_ema[gi] + 0.05 * gnorm
        dloss = 0.0 if self._last_loss is None else (loss - self._last_loss)
        cos_proxy = gnorm / (self.grad_ema[gi] + 1e-8)
        t_feat = float(self.step_idx / max(1, self.T_hint))
        self._last_loss = loss
        return torch.tensor(
            [[math.log(gnorm + 1e-8), math.log(self.grad_ema[gi] + 1e-8), float(loss), float(dloss), float(cos_proxy), t_feat]],
            device=self.device,
        )

    @torch.no_grad()
    def _apply_blend(self, params, mix, lr, wd):
        w_adam, w_lion, w_soph = mix.tolist()
        for p in params:
            if p.grad is None:
                continue
            pid = id(p)
            g = p.grad


        self.st_adam: Dict[int, Dict] = {}
        self.st_lion: Dict[int, Dict] = {}
        self.st_soph: Dict[int, Dict] = {}
        self.curv = {}  # diag curvature by param id

        self.grad_ema = [0.0] * len(self.groups)
        self._last_loss = None
        self.step_idx, self.T_hint = 0, 1_000_000

        self.bandits = [UCB1(3) for _ in self.groups]

    def _group_grad_norm(self, params):
        s = 0.0
        for p in params:
            if p.grad is None:
                continue
            s += p.grad.detach().float().pow(2).sum().item()
        return (s + 1e-12) ** 0.5

    def _feats(self, loss, gnorm, gi):
        self.grad_ema[gi] = 0.95 * self.grad_ema[gi] + 0.05 * gnorm
        dloss = 0.0 if self._last_loss is None else (loss - self._last_loss)
        cos_proxy = gnorm / (self.grad_ema[gi] + 1e-8)
        t_feat = float(self.step_idx / max(1, self.T_hint))
        self._last_loss = loss
        return torch.tensor(
            [[math.log(gnorm + 1e-8), math.log(self.grad_ema[gi] + 1e-8), float(loss), float(dloss), float(cos_proxy), t_feat]],
            device=self.device,
        )

    @torch.no_grad()
    def _apply_blend(self, params, mix, lr, wd):
        w_adam, w_lion, w_soph = mix.tolist()
        for p in params:
            if p.grad is None:
                continue
            pid = id(p)
            g = p.grad
            p0 = p.detach().clone()

            # AdamW
            stA = self.st_adam.setdefault(pid, {})
            pA = p0.clone()
            adamw_update(pA, g, stA, lr=lr, wd=wd)

            # Lion
            stL = self.st_lion.setdefault(pid, {})
            pL = p0.clone()
            lion_update(pL, g, stL, lr=lr, wd=wd)


    def _group_grad_norm(self, params):
        s = 0.0
        for p in params:
            if p.grad is None:
                continue
            s += p.grad.detach().float().pow(2).sum().item()
        return (s + 1e-12) ** 0.5

    def _feats(self, loss, gnorm, gi):
        self.grad_ema[gi] = 0.95 * self.grad_ema[gi] + 0.05 * gnorm
        dloss = 0.0 if self._last_loss is None else (loss - self._last_loss)
        cos_proxy = gnorm / (self.grad_ema[gi] + 1e-8)
        t_feat = float(self.step_idx / max(1, self.T_hint))
        self._last_loss = loss
        return torch.tensor(
            [[math.log(gnorm + 1e-8), math.log(self.grad_ema[gi] + 1e-8), float(loss), float(dloss), float(cos_proxy), t_feat]],
            device=self.device,
        )

    @torch.no_grad()
    def _apply_blend(self, params, mix, lr, wd):
        w_adam, w_lion, w_soph = mix.tolist()
        for p in params:
            if p.grad is None:
                continue
            pid = id(p)
            g = p.grad
            p0 = p.detach().clone()

            # AdamW
            stA = self.st_adam.setdefault(pid, {})
            pA = p0.clone()
            adamw_update(pA, g, stA, lr=lr, wd=wd)

            # Lion
            stL = self.st_lion.setdefault(pid, {})
            pL = p0.clone()
            lion_update(pL, g, stL, lr=lr, wd=wd)

            # Sophia-G (diag)
            stS = self.st_soph.setdefault(pid, {})
            h = self.curv.get(pid, torch.ones_like(p0))
            pS = p0.clone()
            sophia_update_with_curv(pS, g, stS, lr=lr, wd=wd, curv_diag=h)

            p.copy_(w_adam * pA + w_lion * pL + w_soph * pS)

    @torch.no_grad()
    def step(self, loss_value: float):
        self.step_idx += 1

        # curvature refresh from current grads
        update_curvature_diag(self.model, self.curv, beta=0.99)

        for gi, grp in enumerate(self.groups):
            params = grp["params"]
            gnorm = self._group_grad_norm(params)
            feats = self._feats(loss_value, gnorm, gi)

            mix_logits, lr_s, wd_s = self.gate(feats, torch.tensor([gi], device=self.device))
            # Bandit bias
            arm = self.bandits[gi].select()
            boost = torch.zeros_like(mix_logits)
            boost[0, arm] += 0.75
            mix = (mix_logits + boost).softmax(-1).squeeze(0)


step

            # AdamW
            stA = self.st_adam.setdefault(pid, {})
            pA = p0.clone()
            adamw_update(pA, g, stA, lr=lr, wd=wd)

            # Lion
            stL = self.st_lion.setdefault(pid, {})
            pL = p0.clone()
            lion_update(pL, g, stL, lr=lr, wd=wd)

            # Sophia-G (diag)
            stS = self.st_soph.setdefault(pid, {})
            h = self.curv.get(pid, torch.ones_like(p0))
            pS = p0.clone()
            sophia_update_with_curv(pS, g, stS, lr=lr, wd=wd, curv_diag=h)

            p.copy_(w_adam * pA + w_lion * pL + w_soph * pS)

    @torch.no_grad()
    def step(self, loss_value: float):
        self.step_idx += 1

        # curvature refresh from current grads
        update_curvature_diag(self.model, self.curv, beta=0.99)

        for gi, grp in enumerate(self.groups):
            params = grp["params"]
            gnorm = self._group_grad_norm(params)
            feats = self._feats(loss_value, gnorm, gi)

            mix_logits, lr_s, wd_s = self.gate(feats, torch.tensor([gi], device=self.device))
            # Bandit bias
            arm = self.bandits[gi].select()
            boost = torch.zeros_like(mix_logits)
            boost[0, arm] += 0.75
            mix = (mix_logits + boost).softmax(-1).squeeze(0)

            lr = float(self.base_lr * lr_s.item())
            wd = float(self.base_wd * wd_s.item())

            self._apply_blend(params, mix, lr, wd)

    def bandit_reward(self, gi, reward):
        # Reward is improvement; caller decides cadence.
        self.bandits[gi].update(self.bandits[gi].select(), reward)

# --------------------------------------
# 8) Training utilities (forward/back)
# --------------------------------------
def train_step(model, metaopt: MixtureMetaOptGrouped, xb, yb, max_grad_norm=1.0):
    model.train()
    logits, loss = model(xb, yb)
    for p in model.parameters():
        if p.grad is not None:
            p.grad.zero_()
    loss.backward()
    clip_grad_norm_(model.parameters(), max_grad_norm)
    metaopt.step(float(loss.item()))
    return float(loss.item())


        # curvature refresh from current grads
        update_curvature_diag(self.model, self.curv, beta=0.99)

        for gi, grp in enumerate(self.groups):
            params = grp["params"]
            gnorm = self._group_grad_norm(params)
            feats = self._feats(loss_value, gnorm, gi)

            mix_logits, lr_s, wd_s = self.gate(feats, torch.tensor([gi], device=self.device))
            # Bandit bias
            arm = self.bandits[gi].select()
            boost = torch.zeros_like(mix_logits)
            boost[0, arm] += 0.75
            mix = (mix_logits + boost).softmax(-1).squeeze(0)

            lr = float(self.base_lr * lr_s.item())
            wd = float(self.base_wd * wd_s.item())

            self._apply_blend(params, mix, lr, wd)

    def bandit_reward(self, gi, reward):
        # Reward is improvement; caller decides cadence.
        self.bandits[gi].update(self.bandits[gi].select(), reward)

# --------------------------------------
# 8) Training utilities (forward/back)
# --------------------------------------
def train_step(model, metaopt: MixtureMetaOptGrouped, xb, yb, max_grad_norm=1.0):
    model.train()
    logits, loss = model(xb, yb)
    for p in model.parameters():
        if p.grad is not None:
            p.grad.zero_()
    loss.backward()
    clip_grad_norm_(model.parameters(), max_grad_norm)
    metaopt.step(float(loss.item()))
    return float(loss.item())

@torch.no_grad()
def eval_loss(model, loader, iters=20):
    model.eval()


import logging
import argparse
import json
from pathlib import Path
from typing import Any, Dict, List

def generate_plan_candidates(obs: Dict[str, Any], model: str, num_candidates: int = 3) -> List[Dict[str, Any]]:
	from agi_dw.core.llm.hf_client import HFClient  # type: ignore
	client = HFClient.get_cached(model)
	prompt = (
		f"You are a planner. Given the observation below, propose {num_candidates} diverse candidate plans as a JSON array.\n"
		"Each plan must be an object with keys: subgoals (array of strings), tools (array of strings), constraints (object).\n"
		"Return ONLY JSON (no code fences).\n\nObservation (JSON):\n" + json.dumps(obs, ensure_ascii=False)
	)
	text = client.generate(prompt, max_new_tokens=400, temperature=0.2)
	try:
		arr = json.loads(text)
		if isinstance(arr, list):
			out: List[Dict[str, Any]] = []
			for it in arr:
				if isinstance(it, dict) and all(k in it for k in ("subgoals", "tools", "constraints")):
					out.append(it)
			return out[:num_candidates]
	except Exception:
		pass
	# Fallback: trivial single-plan using centralized planner service
	try:
		from agi_dw.core.planner.service import PlannerConfig, VerifierConfig, WMConfig, ContextAugment, plan_with_context  # type: ignore
		pl = PlannerConfig(model=model, backend="hf", timeout_sec=20, adapter_dir=None, structured_mode="none", candidates=1)
		vf = VerifierConfig(model=model, backend="hf", adapter_dir=None, structured_mode="none")
		wm = WMConfig(enabled=False, model_path=None, horizon=1, plan_rank=False)
		ctx = ContextAugment(use_memory=False, index_k=0, inject_cli_policy=False, inject_dom_policy=False, inject_caps=False)
		p, _info, _obs_aug, _mem, _ms = plan_with_context(obs, obs.get("kind", "cli"), pl, vf, wm, ctx, critic_fallback_threshold=None, log_prompts=False)
		return [p] if isinstance(p, dict) else []
	except Exception:
		return []

def score_by_wm(obs: Dict[str, Any], plans: List[Dict[str, Any]], wm_path: Path) -> List[Dict[str, Any]]:
	from agi_dw.core.world_model.api import WorldModelPrior  # type: ignore
	wm = WorldModelPrior.load(wm_path)
	scored: List[Dict[str, Any]] = []
	for p in plans:
		prior = wm.predict_prior(obs, p, action={}) or {"risk": 0.5, "success_prob": 0.5}
		scored.append({"plan": p, "risk": float(prior.get("risk", 0.5)), "success_prob": float(prior.get("success_prob", 0.5))})
	return scored

def self_eval_score(obs: Dict[str, Any], plan: Dict[str, Any], model: str) -> float:
	"""Return a self-evaluation score in [0,1] estimating plan quality for the given observation."""
	try:
		from agi_dw.core.llm.hf_client import HFClient  # type: ignore
		client = HFClient.get_cached(model)
		prompt = (
			"Rate the suitability of the candidate plan for the given observation on a 0.0 to 1.0 scale.\n"
			"Return ONLY a floating point number.\n\n"
			f"Observation (JSON): {json.dumps(obs, ensure_ascii=False)}\n"
			f"Plan (JSON): {json.dumps(plan, ensure_ascii=False)}\n"
		)
		text = client.generate(prompt, max_new_tokens=8, temperature=0.0)
		try:
			return max(0.0, min(1.0, float(text.strip())))
		except Exception:
			return 0.5
	except Exception:
		return 0.5

def rank_candidates(
	obs: Dict[str, Any],
	plans: List[Dict[str, Any]],
	wm_path: Path,
	use_self_eval: bool,
	model: str,
) -> List[Dict[str, Any]]:
	# Start with WM scoring if available
	if wm_path.exists():
		ranked = score_by_wm(obs, plans, wm_path)
	else:
		ranked = [{"plan": p, "risk": None, "success_prob": None} for p in plans]
	# Optionally add self-eval and use in sorting
	if use_self_eval:
		for r in ranked:
			r["self_eval"] = self_eval_score(obs, r["plan"], model)
		# Sort: lower risk, higher success_prob, higher self_eval
		def key_fn(x: Dict[str, Any]):
			risk = x.get("risk")
			sprob = x.get("success_prob")
			se = x.get("self_eval", 0.0)
			return (
				risk if isinstance(risk, (int, float)) else 1.0,
				-(sprob if isinstance(sprob, (int, float)) else 0.0),
				-se,
			)
		ranked.sort(key=key_fn)
	return ranked

def main() -> int:
	ap = argparse.ArgumentParser()
	root = Path(__file__).resolve().parents[1]
	ap.add_argument("--obs", required=False, default=None, help="Observation JSON string; if omitted uses a DOM example")
	ap.add_argument("--model", default="meta-llama/Llama-3.2-3B")
	ap.add_argument("--wm", default=str(root / "models" / "wm_mlp" / "wm_mlp.joblib"))
	ap.add_argument("--planner-candidates", type=int, default=3, help="Number of candidate plans to generate")
	ap.add_argument("--use-self-eval", action="store_true", help="Use LLM self-evaluation to score plans")
	args = ap.parse_args()

	if args.obs:
		try:
			obs = json.loads(args.obs)
		except Exception:
			print("Invalid --obs JSON")
			return 2
	else:
		obs = {"kind": "dom", "content": "fetch text via selector", "meta": {"url": "https://example.com", "selector": "h1"}}

	plans = generate_plan_candidates(obs, args.model, num_candidates=int(args.planner_candidates))
	if not plans:
		print(json.dumps({"error": "no_candidates"}))
		return 1
	wm_path = Path(args.wm)
	ranked = rank_candidates(obs, plans, wm_path, bool(args.use_self_eval), args.model)
	print(json.dumps({"obs": obs, "candidates": ranked}, ensure_ascii=False))
	return 0

if __name__ == "__main__":
	import sys
	sys.exit(main())


import logging
import argparse
import json
from pathlib import Path
from typing import Any, Dict, List

def generate_plan_candidates(obs: Dict[str, Any], model: str, num_candidates: int = 3) -> List[Dict[str, Any]]:
	from agi_dw.core.llm.hf_client import HFClient  # type: ignore
	client = HFClient.get_cached(model)
	prompt = (
		f"You are a planner. Given the observation below, propose {num_candidates} diverse candidate plans as a JSON array.\n"
		"Each plan must be an object with keys: subgoals (array of strings), tools (array of strings), constraints (object).\n"
		"Return ONLY JSON (no code fences).\n\nObservation (JSON):\n" + json.dumps(obs, ensure_ascii=False)
	)
	text = client.generate(prompt, max_new_tokens=400, temperature=0.2)
	try:
		arr = json.loads(text)
		if isinstance(arr, list):
			out: List[Dict[str, Any]] = []
			for it in arr:
				if isinstance(it, dict) and all(k in it for k in ("subgoals", "tools", "constraints")):
					out.append(it)
			return out[:num_candidates]
	except Exception:
		pass
	# Fallback: trivial single-plan using centralized planner service
	try:
		from agi_dw.core.planner.service import PlannerConfig, VerifierConfig, WMConfig, ContextAugment, plan_with_context  # type: ignore
		pl = PlannerConfig(model=model, backend="hf", timeout_sec=20, adapter_dir=None, structured_mode="none", candidates=1)
		vf = VerifierConfig(model=model, backend="hf", adapter_dir=None, structured_mode="none")
		wm = WMConfig(enabled=False, model_path=None, horizon=1, plan_rank=False)
		ctx = ContextAugment(use_memory=False, index_k=0, inject_cli_policy=False, inject_dom_policy=False, inject_caps=False)
		p, _info, _obs_aug, _mem, _ms = plan_with_context(obs, obs.get("kind", "cli"), pl, vf, wm, ctx, critic_fallback_threshold=None, log_prompts=False)
		return [p] if isinstance(p, dict) else []
	except Exception:
		return []

def score_by_wm(obs: Dict[str, Any], plans: List[Dict[str, Any]], wm_path: Path) -> List[Dict[str, Any]]:
	from agi_dw.core.world_model.api import WorldModelPrior  # type: ignore
	wm = WorldModelPrior.load(wm_path)
	scored: List[Dict[str, Any]] = []
	for p in plans:
		prior = wm.predict_prior(obs, p, action={}) or {"risk": 0.5, "success_prob": 0.5}
		scored.append({"plan": p, "risk": float(prior.get("risk", 0.5)), "success_prob": float(prior.get("success_prob", 0.5))})
	return scored

def self_eval_score(obs: Dict[str, Any], plan: Dict[str, Any], model: str) -> float:
	"""Return a self-evaluation score in [0,1] estimating plan quality for the given observation."""
	try:
		from agi_dw.core.llm.hf_client import HFClient  # type: ignore
		client = HFClient.get_cached(model)
		prompt = (
			"Rate the suitability of the candidate plan for the given observation on a 0.0 to 1.0 scale.\n"
			"Return ONLY a floating point number.\n\n"


			out: List[Dict[str, Any]] = []
			for it in arr:
				if isinstance(it, dict) and all(k in it for k in ("subgoals", "tools", "constraints")):
					out.append(it)
			return out[:num_candidates]
	except Exception:
		pass
	# Fallback: trivial single-plan using centralized planner service
	try:
		from agi_dw.core.planner.service import PlannerConfig, VerifierConfig, WMConfig, ContextAugment, plan_with_context  # type: ignore
		pl = PlannerConfig(model=model, backend="hf", timeout_sec=20, adapter_dir=None, structured_mode="none", candidates=1)
		vf = VerifierConfig(model=model, backend="hf", adapter_dir=None, structured_mode="none")
		wm = WMConfig(enabled=False, model_path=None, horizon=1, plan_rank=False)
		ctx = ContextAugment(use_memory=False, index_k=0, inject_cli_policy=False, inject_dom_policy=False, inject_caps=False)
		p, _info, _obs_aug, _mem, _ms = plan_with_context(obs, obs.get("kind", "cli"), pl, vf, wm, ctx, critic_fallback_threshold=None, log_prompts=False)
		return [p] if isinstance(p, dict) else []
	except Exception:
		return []

def score_by_wm(obs: Dict[str, Any], plans: List[Dict[str, Any]], wm_path: Path) -> List[Dict[str, Any]]:
	from agi_dw.core.world_model.api import WorldModelPrior  # type: ignore
	wm = WorldModelPrior.load(wm_path)
	scored: List[Dict[str, Any]] = []
	for p in plans:
		prior = wm.predict_prior(obs, p, action={}) or {"risk": 0.5, "success_prob": 0.5}
		scored.append({"plan": p, "risk": float(prior.get("risk", 0.5)), "success_prob": float(prior.get("success_prob", 0.5))})
	return scored

def self_eval_score(obs: Dict[str, Any], plan: Dict[str, Any], model: str) -> float:
	"""Return a self-evaluation score in [0,1] estimating plan quality for the given observation."""
	try:
		from agi_dw.core.llm.hf_client import HFClient  # type: ignore
		client = HFClient.get_cached(model)
		prompt = (
			"Rate the suitability of the candidate plan for the given observation on a 0.0 to 1.0 scale.\n"
			"Return ONLY a floating point number.\n\n"
			f"Observation (JSON): {json.dumps(obs, ensure_ascii=False)}\n"
			f"Plan (JSON): {json.dumps(plan, ensure_ascii=False)}\n"
		)
		text = client.generate(prompt, max_new_tokens=8, temperature=0.0)
		try:
			return max(0.0, min(1.0, float(text.strip())))
		except Exception:
			return 0.5
	except Exception:
		return 0.5


		pl = PlannerConfig(model=model, backend="hf", timeout_sec=20, adapter_dir=None, structured_mode="none", candidates=1)
		vf = VerifierConfig(model=model, backend="hf", adapter_dir=None, structured_mode="none")
		wm = WMConfig(enabled=False, model_path=None, horizon=1, plan_rank=False)
		ctx = ContextAugment(use_memory=False, index_k=0, inject_cli_policy=False, inject_dom_policy=False, inject_caps=False)
		p, _info, _obs_aug, _mem, _ms = plan_with_context(obs, obs.get("kind", "cli"), pl, vf, wm, ctx, critic_fallback_threshold=None, log_prompts=False)
		return [p] if isinstance(p, dict) else []
	except Exception:
		return []

def score_by_wm(obs: Dict[str, Any], plans: List[Dict[str, Any]], wm_path: Path) -> List[Dict[str, Any]]:
	from agi_dw.core.world_model.api import WorldModelPrior  # type: ignore
	wm = WorldModelPrior.load(wm_path)
	scored: List[Dict[str, Any]] = []
	for p in plans:
		prior = wm.predict_prior(obs, p, action={}) or {"risk": 0.5, "success_prob": 0.5}
		scored.append({"plan": p, "risk": float(prior.get("risk", 0.5)), "success_prob": float(prior.get("success_prob", 0.5))})
	return scored

def self_eval_score(obs: Dict[str, Any], plan: Dict[str, Any], model: str) -> float:
	"""Return a self-evaluation score in [0,1] estimating plan quality for the given observation."""
	try:
		from agi_dw.core.llm.hf_client import HFClient  # type: ignore
		client = HFClient.get_cached(model)
		prompt = (
			"Rate the suitability of the candidate plan for the given observation on a 0.0 to 1.0 scale.\n"
			"Return ONLY a floating point number.\n\n"
			f"Observation (JSON): {json.dumps(obs, ensure_ascii=False)}\n"
			f"Plan (JSON): {json.dumps(plan, ensure_ascii=False)}\n"
		)
		text = client.generate(prompt, max_new_tokens=8, temperature=0.0)
		try:
			return max(0.0, min(1.0, float(text.strip())))
		except Exception:
			return 0.5
	except Exception:
		return 0.5

def rank_candidates(
	obs: Dict[str, Any],
	plans: List[Dict[str, Any]],
	wm_path: Path,
	use_self_eval: bool,
	model: str,
) -> List[Dict[str, Any]]:
	# Start with WM scoring if available
	if wm_path.exists():
		ranked = score_by_wm(obs, plans, wm_path)
	else:
		ranked = [{"plan": p, "risk": None, "success_prob": None} for p in plans]
	# Optionally add self-eval and use in sorting
	if use_self_eval:
		for r in ranked:
			r["self_eval"] = self_eval_score(obs, r["plan"], model)
		# Sort: lower risk, higher success_prob, higher self_eval
		def key_fn(x: Dict[str, Any]):


def self_eval_score(obs: Dict[str, Any], plan: Dict[str, Any], model: str) -> float:
	"""Return a self-evaluation score in [0,1] estimating plan quality for the given observation."""
	try:
		from agi_dw.core.llm.hf_client import HFClient  # type: ignore
		client = HFClient.get_cached(model)
		prompt = (
			"Rate the suitability of the candidate plan for the given observation on a 0.0 to 1.0 scale.\n"
			"Return ONLY a floating point number.\n\n"
			f"Observation (JSON): {json.dumps(obs, ensure_ascii=False)}\n"
			f"Plan (JSON): {json.dumps(plan, ensure_ascii=False)}\n"
		)
		text = client.generate(prompt, max_new_tokens=8, temperature=0.0)
		try:
			return max(0.0, min(1.0, float(text.strip())))
		except Exception:
			return 0.5
	except Exception:
		return 0.5

def rank_candidates(
	obs: Dict[str, Any],
	plans: List[Dict[str, Any]],
	wm_path: Path,
	use_self_eval: bool,
	model: str,
) -> List[Dict[str, Any]]:
	# Start with WM scoring if available
	if wm_path.exists():
		ranked = score_by_wm(obs, plans, wm_path)
	else:
		ranked = [{"plan": p, "risk": None, "success_prob": None} for p in plans]
	# Optionally add self-eval and use in sorting
	if use_self_eval:
		for r in ranked:
			r["self_eval"] = self_eval_score(obs, r["plan"], model)
		# Sort: lower risk, higher success_prob, higher self_eval
		def key_fn(x: Dict[str, Any]):
			risk = x.get("risk")
			sprob = x.get("success_prob")
			se = x.get("self_eval", 0.0)
			return (
				risk if isinstance(risk, (int, float)) else 1.0,
				-(sprob if isinstance(sprob, (int, float)) else 0.0),
				-se,
			)
		ranked.sort(key=key_fn)
	return ranked

def main() -> int:
	ap = argparse.ArgumentParser()
	root = Path(__file__).resolve().parents[1]
	ap.add_argument("--obs", required=False, default=None, help="Observation JSON string; if omitted uses a DOM example")
	ap.add_argument("--model", default="meta-llama/Llama-3.2-3B")
	ap.add_argument("--wm", default=str(root / "models" / "wm_mlp" / "wm_mlp.joblib"))
	ap.add_argument("--planner-candidates", type=int, default=3, help="Number of candidate plans to generate")
	ap.add_argument("--use-self-eval", action="store_true", help="Use LLM self-evaluation to score plans")
	args = ap.parse_args()

	if args.obs:
		try:
			obs = json.loads(args.obs)
		except Exception:
			print("Invalid --obs JSON")
			return 2
	else:
		obs = {"kind": "dom", "content": "fetch text via selector", "meta": {"url": "https://example.com", "selector": "h1"}}


main

	else:
		ranked = [{"plan": p, "risk": None, "success_prob": None} for p in plans]
	# Optionally add self-eval and use in sorting
	if use_self_eval:
		for r in ranked:
			r["self_eval"] = self_eval_score(obs, r["plan"], model)
		# Sort: lower risk, higher success_prob, higher self_eval
		def key_fn(x: Dict[str, Any]):
			risk = x.get("risk")
			sprob = x.get("success_prob")
			se = x.get("self_eval", 0.0)
			return (
				risk if isinstance(risk, (int, float)) else 1.0,
				-(sprob if isinstance(sprob, (int, float)) else 0.0),
				-se,
			)
		ranked.sort(key=key_fn)
	return ranked

def main() -> int:
	ap = argparse.ArgumentParser()
	root = Path(__file__).resolve().parents[1]
	ap.add_argument("--obs", required=False, default=None, help="Observation JSON string; if omitted uses a DOM example")
	ap.add_argument("--model", default="meta-llama/Llama-3.2-3B")
	ap.add_argument("--wm", default=str(root / "models" / "wm_mlp" / "wm_mlp.joblib"))
	ap.add_argument("--planner-candidates", type=int, default=3, help="Number of candidate plans to generate")
	ap.add_argument("--use-self-eval", action="store_true", help="Use LLM self-evaluation to score plans")
	args = ap.parse_args()

	if args.obs:
		try:
			obs = json.loads(args.obs)
		except Exception:
			print("Invalid --obs JSON")
			return 2
	else:
		obs = {"kind": "dom", "content": "fetch text via selector", "meta": {"url": "https://example.com", "selector": "h1"}}

	plans = generate_plan_candidates(obs, args.model, num_candidates=int(args.planner_candidates))
	if not plans:
		print(json.dumps({"error": "no_candidates"}))
		return 1
	wm_path = Path(args.wm)
	ranked = rank_candidates(obs, plans, wm_path, bool(args.use_self_eval), args.model)
	print(json.dumps({"obs": obs, "candidates": ranked}, ensure_ascii=False))
	return 0

if __name__ == "__main__":
	import sys
	sys.exit(main())


		return 0.5

def rank_candidates(
	obs: Dict[str, Any],
	plans: List[Dict[str, Any]],
	wm_path: Path,
	use_self_eval: bool,
	model: str,
) -> List[Dict[str, Any]]:
	# Start with WM scoring if available
	if wm_path.exists():
		ranked = score_by_wm(obs, plans, wm_path)
	else:
		ranked = [{"plan": p, "risk": None, "success_prob": None} for p in plans]
	# Optionally add self-eval and use in sorting
	if use_self_eval:
		for r in ranked:
			r["self_eval"] = self_eval_score(obs, r["plan"], model)
		# Sort: lower risk, higher success_prob, higher self_eval
		def key_fn(x: Dict[str, Any]):
			risk = x.get("risk")
			sprob = x.get("success_prob")
			se = x.get("self_eval", 0.0)
			return (
				risk if isinstance(risk, (int, float)) else 1.0,
				-(sprob if isinstance(sprob, (int, float)) else 0.0),
				-se,
			)
		ranked.sort(key=key_fn)
	return ranked

def main() -> int:
	ap = argparse.ArgumentParser()
	root = Path(__file__).resolve().parents[1]
	ap.add_argument("--obs", required=False, default=None, help="Observation JSON string; if omitted uses a DOM example")
	ap.add_argument("--model", default="meta-llama/Llama-3.2-3B")
	ap.add_argument("--wm", default=str(root / "models" / "wm_mlp" / "wm_mlp.joblib"))
	ap.add_argument("--planner-candidates", type=int, default=3, help="Number of candidate plans to generate")
	ap.add_argument("--use-self-eval", action="store_true", help="Use LLM self-evaluation to score plans")
	args = ap.parse_args()

	if args.obs:
		try:
			obs = json.loads(args.obs)
		except Exception:
			print("Invalid --obs JSON")
			return 2


import logging
import argparse
import json
import sys
from pathlib import Path
from typing import Any, Dict, List

def ensure_curriculum(root: Path, path: Path) -> None:
	if not path.exists():
		try:
			from scripts.misc.generate_practice_curriculum import main as gen_main  # type: ignore
		except Exception:
			return
		try:
			gen_main()
		except SystemExit:
			pass

def load_tier(path: Path, tier: str) -> List[Dict[str, Any]]:
	try:
		import yaml  # type: ignore
	except Exception:
		return []
	try:
		obj = yaml.safe_load(path.read_text(encoding="utf-8"))
		tiers = (obj.get("tiers", {}) or {})
		items = tiers.get(tier, []) or []
		out: List[Dict[str, Any]] = []
		for it in items:
			if isinstance(it, dict) and it.get("repo"):
				ent: Dict[str, Any] = {"repo": str(it["repo"]), "pytest_args": list(it.get("pytest_args", []) or [])}
				# Optional per-repo success thresholds (min_ok: bool pass expectation)
				if "threshold" in it:
					try:
						ent["threshold"] = float(it.get("threshold"))
					except Exception:
						pass
				out.append(ent)
		return out
	except Exception:
		return []

def run_job(root: Path, repo: str, pytest_args: List[str]) -> Dict[str, Any]:
	from scripts.devtools.task_scheduler import run_dev_repo  # type: ignore
	res = run_dev_repo(root, repo, pytest_args)
	return {"repo": repo, **res}

def main() -> int:
	ap = argparse.ArgumentParser()
	root = Path(__file__).resolve().parents[1]
	ap.add_argument("--tier", default="T1")
	ap.add_argument("--curriculum", default=str(root / "data" / "practice_repos.yaml"))
	ap.add_argument("--out", default=str(root / "data" / "ci" / "matrix_results.json"))
	ap.add_argument("--min-ok", type=int, default=1, help="Minimum passing repos in tier to pass gate")
	args = ap.parse_args()

	cur_path = Path(args.curriculum)
	ensure_curriculum(root, cur_path)
	jobs = load_tier(cur_path, args.tier)
	if not jobs:
		print(json.dumps({"ok": False, "reason": "no_jobs", "tier": args.tier}))
		return 2
	res_list: List[Dict[str, Any]] = []
	passed = 0
	for job in jobs:
		res = run_job(root, job["repo"], job.get("pytest_args", []))
		ok = int(res.get("returncode", 1) == 0)
		# If per-repo threshold is specified, gate by it (placeholder: same as ok since we run tests not metrics)
		thr = job.get("threshold")
		if isinstance(thr, (int, float)):
			# For now, treat threshold as a boolean pass expectation; future: use metrics.json per repo
			ok = ok and 1  # keep structure; allows expansion later
		passed += ok
		res_list.append({**res, "ok": bool(ok), "threshold": float(thr) if isinstance(thr, (int, float)) else None})
		sys.stdout.write(json.dumps({"repo": job["repo"], "ok": bool(ok)}) + "\n")
		sys.stdout.flush()
	pack = {"tier": args.tier, "total": len(jobs), "passed": passed, "min_ok": int(args.min_ok), "results": res_list}
	outp = Path(args.out)
	outp.parent.mkdir(parents=True, exist_ok=True)
	outp.write_text(json.dumps(pack, ensure_ascii=False, indent=2), encoding="utf-8")
	ok_all = bool(passed >= int(args.min_ok))
	print(json.dumps({"ok": ok_all, "summary": {"tier": args.tier, "total": len(jobs), "passed": passed, "min_ok": int(args.min_ok)}, "out": str(outp)}))
	return 0 if ok_all else 1

if __name__ == "__main__":
	raise SystemExit(main())


import logging
import argparse
import json
import sys
from pathlib import Path
from typing import Any, Dict, List

def ensure_curriculum(root: Path, path: Path) -> None:
	if not path.exists():
		try:
			from scripts.misc.generate_practice_curriculum import main as gen_main  # type: ignore
		except Exception:
			return
		try:
			gen_main()
		except SystemExit:
			pass

def load_tier(path: Path, tier: str) -> List[Dict[str, Any]]:
	try:
		import yaml  # type: ignore
	except Exception:
		return []
	try:
		obj = yaml.safe_load(path.read_text(encoding="utf-8"))
		tiers = (obj.get("tiers", {}) or {})
		items = tiers.get(tier, []) or []
		out: List[Dict[str, Any]] = []
		for it in items:
			if isinstance(it, dict) and it.get("repo"):
				ent: Dict[str, Any] = {"repo": str(it["repo"]), "pytest_args": list(it.get("pytest_args", []) or [])}
				# Optional per-repo success thresholds (min_ok: bool pass expectation)
				if "threshold" in it:
					try:
						ent["threshold"] = float(it.get("threshold"))
					except Exception:


import logging
import argparse
import json
import sys
from pathlib import Path
from typing import Any, Dict, List

def ensure_curriculum(root: Path, path: Path) -> None:
	if not path.exists():
		try:
			from scripts.misc.generate_practice_curriculum import main as gen_main  # type: ignore
		except Exception:
			return
		try:
			gen_main()
		except SystemExit:
			pass

def load_tier(path: Path, tier: str) -> List[Dict[str, Any]]:
	try:
		import yaml  # type: ignore
	except Exception:
		return []
	try:
		obj = yaml.safe_load(path.read_text(encoding="utf-8"))
		tiers = (obj.get("tiers", {}) or {})
		items = tiers.get(tier, []) or []
		out: List[Dict[str, Any]] = []
		for it in items:
			if isinstance(it, dict) and it.get("repo"):
				ent: Dict[str, Any] = {"repo": str(it["repo"]), "pytest_args": list(it.get("pytest_args", []) or [])}
				# Optional per-repo success thresholds (min_ok: bool pass expectation)
				if "threshold" in it:
					try:
						ent["threshold"] = float(it.get("threshold"))
					except Exception:
						pass
				out.append(ent)
		return out
	except Exception:
		return []

def run_job(root: Path, repo: str, pytest_args: List[str]) -> Dict[str, Any]:
	from scripts.devtools.task_scheduler import run_dev_repo  # type: ignore
	res = run_dev_repo(root, repo, pytest_args)
	return {"repo": repo, **res}

def main() -> int:
	ap = argparse.ArgumentParser()
	root = Path(__file__).resolve().parents[1]
	ap.add_argument("--tier", default="T1")
	ap.add_argument("--curriculum", default=str(root / "data" / "practice_repos.yaml"))
	ap.add_argument("--out", default=str(root / "data" / "ci" / "matrix_results.json"))
	ap.add_argument("--min-ok", type=int, default=1, help="Minimum passing repos in tier to pass gate")
	args = ap.parse_args()

	cur_path = Path(args.curriculum)
	ensure_curriculum(root, cur_path)
	jobs = load_tier(cur_path, args.tier)


	try:
		obj = yaml.safe_load(path.read_text(encoding="utf-8"))
		tiers = (obj.get("tiers", {}) or {})
		items = tiers.get(tier, []) or []
		out: List[Dict[str, Any]] = []
		for it in items:
			if isinstance(it, dict) and it.get("repo"):
				ent: Dict[str, Any] = {"repo": str(it["repo"]), "pytest_args": list(it.get("pytest_args", []) or [])}
				# Optional per-repo success thresholds (min_ok: bool pass expectation)
				if "threshold" in it:
					try:
						ent["threshold"] = float(it.get("threshold"))
					except Exception:
						pass
				out.append(ent)
		return out
	except Exception:
		return []

def run_job(root: Path, repo: str, pytest_args: List[str]) -> Dict[str, Any]:
	from scripts.devtools.task_scheduler import run_dev_repo  # type: ignore
	res = run_dev_repo(root, repo, pytest_args)
	return {"repo": repo, **res}

def main() -> int:
	ap = argparse.ArgumentParser()
	root = Path(__file__).resolve().parents[1]
	ap.add_argument("--tier", default="T1")
	ap.add_argument("--curriculum", default=str(root / "data" / "practice_repos.yaml"))
	ap.add_argument("--out", default=str(root / "data" / "ci" / "matrix_results.json"))
	ap.add_argument("--min-ok", type=int, default=1, help="Minimum passing repos in tier to pass gate")
	args = ap.parse_args()

	cur_path = Path(args.curriculum)
	ensure_curriculum(root, cur_path)
	jobs = load_tier(cur_path, args.tier)
	if not jobs:
		print(json.dumps({"ok": False, "reason": "no_jobs", "tier": args.tier}))
		return 2
	res_list: List[Dict[str, Any]] = []
	passed = 0
	for job in jobs:


			if isinstance(it, dict) and it.get("repo"):
				ent: Dict[str, Any] = {"repo": str(it["repo"]), "pytest_args": list(it.get("pytest_args", []) or [])}
				# Optional per-repo success thresholds (min_ok: bool pass expectation)
				if "threshold" in it:
					try:
						ent["threshold"] = float(it.get("threshold"))
					except Exception:
						pass
				out.append(ent)
		return out
	except Exception:
		return []

def run_job(root: Path, repo: str, pytest_args: List[str]) -> Dict[str, Any]:
	from scripts.devtools.task_scheduler import run_dev_repo  # type: ignore
	res = run_dev_repo(root, repo, pytest_args)
	return {"repo": repo, **res}

def main() -> int:
	ap = argparse.ArgumentParser()
	root = Path(__file__).resolve().parents[1]
	ap.add_argument("--tier", default="T1")
	ap.add_argument("--curriculum", default=str(root / "data" / "practice_repos.yaml"))
	ap.add_argument("--out", default=str(root / "data" / "ci" / "matrix_results.json"))
	ap.add_argument("--min-ok", type=int, default=1, help="Minimum passing repos in tier to pass gate")
	args = ap.parse_args()

	cur_path = Path(args.curriculum)
	ensure_curriculum(root, cur_path)
	jobs = load_tier(cur_path, args.tier)
	if not jobs:
		print(json.dumps({"ok": False, "reason": "no_jobs", "tier": args.tier}))
		return 2
	res_list: List[Dict[str, Any]] = []
	passed = 0
	for job in jobs:
		res = run_job(root, job["repo"], job.get("pytest_args", []))
		ok = int(res.get("returncode", 1) == 0)
		# If per-repo threshold is specified, gate by it (placeholder: same as ok since we run tests not metrics)
		thr = job.get("threshold")
		if isinstance(thr, (int, float)):
			# For now, treat threshold as a boolean pass expectation; future: use metrics.json per repo
			ok = ok and 1  # keep structure; allows expansion later
		passed += ok
		res_list.append({**res, "ok": bool(ok), "threshold": float(thr) if isinstance(thr, (int, float)) else None})
		sys.stdout.write(json.dumps({"repo": job["repo"], "ok": bool(ok)}) + "\n")
		sys.stdout.flush()
	pack = {"tier": args.tier, "total": len(jobs), "passed": passed, "min_ok": int(args.min_ok), "results": res_list}
	outp = Path(args.out)
	outp.parent.mkdir(parents=True, exist_ok=True)
	outp.write_text(json.dumps(pack, ensure_ascii=False, indent=2), encoding="utf-8")
	ok_all = bool(passed >= int(args.min_ok))
	print(json.dumps({"ok": ok_all, "summary": {"tier": args.tier, "total": len(jobs), "passed": passed, "min_ok": int(args.min_ok)}, "out": str(outp)}))
	return 0 if ok_all else 1

if __name__ == "__main__":
	raise SystemExit(main())


#!/usr/bin/env python3
import logging
import argparse
import json
import os
import tempfile
from pathlib import Path

def parse_args():
	root = Path(__file__).resolve().parents[1]
	ap = argparse.ArgumentParser(description="Run style/type checks on multi-language code samples (if tools installed)")
	ap.add_argument("--samples", nargs="*", default=[
		str(root / "data" / "llm_bench" / "humaneval_samples.jsonl"),
		str(root / "data" / "llm_bench" / "mbpp_samples.jsonl"),
		str(root / "data" / "llm_bench" / "apps_samples.jsonl"),
		str(root / "data" / "llm_bench" / "ds1000_samples.jsonl"),
		str(root / "data" / "llm_bench" / "cruxeval_samples.jsonl"),
	])
	ap.add_argument("--out", default=str(root / "data" / "llm_bench" / "code_style_multi.json"))
	return ap.parse_args()

def tool_exists(cmd: list[str]) -> bool:
	import subprocess
	try:
		subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, timeout=2)
		return True
	except Exception:
		return False

def run_tool(cmd: list[str]) -> tuple[int, str, str]:
	import subprocess
	try:
		p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, timeout=30)
		return p.returncode, p.stdout, p.stderr
	except Exception as e:
		return 1, "", str(e)

def detect_lang(obj: dict) -> str:
	lang = str(obj.get("lang", "")).strip().lower()
	if lang:
		return lang
	# Heuristic: default to Python
	return "py"

def main() -> int:
	args = parse_args()
	# Tool availability
	ruff_ok = tool_exists(["ruff", "--version"])
	flake_ok = tool_exists(["flake8", "--version"])
	mypy_ok = tool_exists(["mypy", "--version"])
	eslint_ok = tool_exists(["eslint", "--version"])
	tsc_ok = tool_exists(["tsc", "--version"])
	cpplint_ok = tool_exists(["cpplint", "--version"]) or tool_exists(["cpplint.py", "--version"]) or tool_exists(["python3", "-m", "cpplint", "--version"])
	javac_ok = tool_exists(["javac", "-version"])

	results = {
		"tools": {
			"ruff": ruff_ok, "flake8": flake_ok, "mypy": mypy_ok,
			"eslint": eslint_ok, "tsc": tsc_ok, "cpplint": cpplint_ok, "javac": javac_ok,
		},
		"files": 0,
		"violations": {"ruff": 0, "flake8": 0, "mypy": 0, "eslint": 0, "tsc": 0, "cpplint": 0, "javac": 0},
		"sample_paths": [],
	}

	for samp_path in args.samples:
		p = Path(samp_path)
		if not p.exists():
			continue
		for line in p.read_text(encoding="utf-8").splitlines():
			line = line.strip()
			if not line:
				continue
			try:
				obj = json.loads(line)
			except Exception:
				continue
			code = obj.get("completion")
			if not code:
				continue
			lang = detect_lang(obj)
			# Write to temp file with extension
			ext = {
				"py": ".py",
				"js": ".js",
				"ts": ".ts",
				"cpp": ".cpp",
				"java": ".java",
			}.get(lang, ".txt")
			with tempfile.NamedTemporaryFile("w", suffix=ext, delete=False) as tf:
				tf.write(code)
				tmpfile = tf.name
			results["files"] += 1
			results["sample_paths"].append(tmpfile)
			# Run per-language tools
			if lang == "py":
				if ruff_ok:
					rc, out, err = run_tool(["ruff", "--quiet", "check", tmpfile])
					if rc != 0:
						results["violations"]["ruff"] += len(out.splitlines())
				if flake_ok:
					rc, out, err = run_tool(["flake8", tmpfile])
					if rc != 0:
						results["violations"]["flake8"] += len(out.splitlines())
				if mypy_ok:
					rc, out, err = run_tool(["mypy", "--ignore-missing-imports", tmpfile])
					if rc != 0:
						results["violations"]["mypy"] += len(out.splitlines())
			elif lang in ("js", "ts"):
				if eslint_ok:
					rc, out, err = run_tool(["eslint", "-f", "unix", tmpfile])
					if rc != 0:
						results["violations"]["eslint"] += len(out.splitlines())
				if lang == "ts" and tsc_ok:
					# Type-check a single file
					rc, out, err = run_tool(["tsc", "--noEmit", tmpfile])
					if rc != 0:
						results["violations"]["tsc"] += len((out + "\n" + err).splitlines())
			elif lang == "cpp" and cpplint_ok:
				# cpplint may be installed as module
				if tool_exists(["cpplint", "--version"]):
					rc, out, err = run_tool(["cpplint", tmpfile])
				else:
					rc, out, err = run_tool(["python3", "-m", "cpplint", tmpfile])
				if rc != 0:
					results["violations"]["cpplint"] += len((out + "\n" + err).splitlines())
			elif lang == "java" and javac_ok:
				rc, out, err = run_tool(["javac", tmpfile])
				if rc != 0:
					results["violations"]["javac"] += len((out + "\n" + err).splitlines())

	outp = Path(args.out)
	outp.parent.mkdir(parents=True, exist_ok=True)
	outp.write_text(json.dumps(results, ensure_ascii=False, indent=2), encoding="utf-8")
	print(json.dumps({"ok": True, "wrote": str(outp), "files": results["files"]}))
	return 0

if __name__ == "__main__":
	raise SystemExit(main())


#!/usr/bin/env python3
import logging
import argparse
import json
import os
import tempfile
from pathlib import Path

def parse_args():
	root = Path(__file__).resolve().parents[1]
	ap = argparse.ArgumentParser(description="Run style/type checks on multi-language code samples (if tools installed)")
	ap.add_argument("--samples", nargs="*", default=[
		str(root / "data" / "llm_bench" / "humaneval_samples.jsonl"),
		str(root / "data" / "llm_bench" / "mbpp_samples.jsonl"),
		str(root / "data" / "llm_bench" / "apps_samples.jsonl"),
		str(root / "data" / "llm_bench" / "ds1000_samples.jsonl"),
		str(root / "data" / "llm_bench" / "cruxeval_samples.jsonl"),
	])
	ap.add_argument("--out", default=str(root / "data" / "llm_bench" / "code_style_multi.json"))
	return ap.parse_args()

def tool_exists(cmd: list[str]) -> bool:
	import subprocess
	try:
		subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, timeout=2)
		return True
	except Exception:
		return False

def run_tool(cmd: list[str]) -> tuple[int, str, str]:
	import subprocess
	try:
		p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, timeout=30)
		return p.returncode, p.stdout, p.stderr
	except Exception as e:
		return 1, "", str(e)


import json
import os
import tempfile
from pathlib import Path

def parse_args():
	root = Path(__file__).resolve().parents[1]
	ap = argparse.ArgumentParser(description="Run style/type checks on multi-language code samples (if tools installed)")
	ap.add_argument("--samples", nargs="*", default=[
		str(root / "data" / "llm_bench" / "humaneval_samples.jsonl"),
		str(root / "data" / "llm_bench" / "mbpp_samples.jsonl"),
		str(root / "data" / "llm_bench" / "apps_samples.jsonl"),
		str(root / "data" / "llm_bench" / "ds1000_samples.jsonl"),
		str(root / "data" / "llm_bench" / "cruxeval_samples.jsonl"),
	])
	ap.add_argument("--out", default=str(root / "data" / "llm_bench" / "code_style_multi.json"))
	return ap.parse_args()

def tool_exists(cmd: list[str]) -> bool:
	import subprocess
	try:
		subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, timeout=2)
		return True
	except Exception:
		return False

def run_tool(cmd: list[str]) -> tuple[int, str, str]:
	import subprocess
	try:
		p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, timeout=30)
		return p.returncode, p.stdout, p.stderr
	except Exception as e:
		return 1, "", str(e)

def detect_lang(obj: dict) -> str:
	lang = str(obj.get("lang", "")).strip().lower()
	if lang:
		return lang
	# Heuristic: default to Python
	return "py"

def main() -> int:


	ap.add_argument("--samples", nargs="*", default=[
		str(root / "data" / "llm_bench" / "humaneval_samples.jsonl"),
		str(root / "data" / "llm_bench" / "mbpp_samples.jsonl"),
		str(root / "data" / "llm_bench" / "apps_samples.jsonl"),
		str(root / "data" / "llm_bench" / "ds1000_samples.jsonl"),
		str(root / "data" / "llm_bench" / "cruxeval_samples.jsonl"),
	])
	ap.add_argument("--out", default=str(root / "data" / "llm_bench" / "code_style_multi.json"))
	return ap.parse_args()

def tool_exists(cmd: list[str]) -> bool:
	import subprocess
	try:
		subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, timeout=2)
		return True
	except Exception:
		return False

def run_tool(cmd: list[str]) -> tuple[int, str, str]:
	import subprocess
	try:
		p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, timeout=30)
		return p.returncode, p.stdout, p.stderr
	except Exception as e:
		return 1, "", str(e)

def detect_lang(obj: dict) -> str:
	lang = str(obj.get("lang", "")).strip().lower()
	if lang:
		return lang
	# Heuristic: default to Python
	return "py"

def main() -> int:
	args = parse_args()
	# Tool availability
	ruff_ok = tool_exists(["ruff", "--version"])
	flake_ok = tool_exists(["flake8", "--version"])
	mypy_ok = tool_exists(["mypy", "--version"])
	eslint_ok = tool_exists(["eslint", "--version"])
	tsc_ok = tool_exists(["tsc", "--version"])
	cpplint_ok = tool_exists(["cpplint", "--version"]) or tool_exists(["cpplint.py", "--version"]) or tool_exists(["python3", "-m", "cpplint", "--version"])
	javac_ok = tool_exists(["javac", "-version"])


def tool_exists(cmd: list[str]) -> bool:
	import subprocess
	try:
		subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, timeout=2)
		return True
	except Exception:
		return False

def run_tool(cmd: list[str]) -> tuple[int, str, str]:
	import subprocess
	try:
		p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, timeout=30)
		return p.returncode, p.stdout, p.stderr
	except Exception as e:
		return 1, "", str(e)

def detect_lang(obj: dict) -> str:
	lang = str(obj.get("lang", "")).strip().lower()
	if lang:
		return lang
	# Heuristic: default to Python
	return "py"

def main() -> int:
	args = parse_args()
	# Tool availability
	ruff_ok = tool_exists(["ruff", "--version"])
	flake_ok = tool_exists(["flake8", "--version"])
	mypy_ok = tool_exists(["mypy", "--version"])
	eslint_ok = tool_exists(["eslint", "--version"])
	tsc_ok = tool_exists(["tsc", "--version"])
	cpplint_ok = tool_exists(["cpplint", "--version"]) or tool_exists(["cpplint.py", "--version"]) or tool_exists(["python3", "-m", "cpplint", "--version"])
	javac_ok = tool_exists(["javac", "-version"])

	results = {
		"tools": {
			"ruff": ruff_ok, "flake8": flake_ok, "mypy": mypy_ok,
			"eslint": eslint_ok, "tsc": tsc_ok, "cpplint": cpplint_ok, "javac": javac_ok,
		},
		"files": 0,
		"violations": {"ruff": 0, "flake8": 0, "mypy": 0, "eslint": 0, "tsc": 0, "cpplint": 0, "javac": 0},


		return False

def run_tool(cmd: list[str]) -> tuple[int, str, str]:
	import subprocess
	try:
		p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, timeout=30)
		return p.returncode, p.stdout, p.stderr
	except Exception as e:
		return 1, "", str(e)

def detect_lang(obj: dict) -> str:
	lang = str(obj.get("lang", "")).strip().lower()
	if lang:
		return lang
	# Heuristic: default to Python
	return "py"

def main() -> int:
	args = parse_args()
	# Tool availability
	ruff_ok = tool_exists(["ruff", "--version"])
	flake_ok = tool_exists(["flake8", "--version"])
	mypy_ok = tool_exists(["mypy", "--version"])
	eslint_ok = tool_exists(["eslint", "--version"])
	tsc_ok = tool_exists(["tsc", "--version"])
	cpplint_ok = tool_exists(["cpplint", "--version"]) or tool_exists(["cpplint.py", "--version"]) or tool_exists(["python3", "-m", "cpplint", "--version"])
	javac_ok = tool_exists(["javac", "-version"])

	results = {
		"tools": {
			"ruff": ruff_ok, "flake8": flake_ok, "mypy": mypy_ok,
			"eslint": eslint_ok, "tsc": tsc_ok, "cpplint": cpplint_ok, "javac": javac_ok,
		},
		"files": 0,
		"violations": {"ruff": 0, "flake8": 0, "mypy": 0, "eslint": 0, "tsc": 0, "cpplint": 0, "javac": 0},
		"sample_paths": [],
	}

	for samp_path in args.samples:
		p = Path(samp_path)
		if not p.exists():
			continue
		for line in p.read_text(encoding="utf-8").splitlines():
			line = line.strip()
			if not line:
				continue
			try:
				obj = json.loads(line)
			except Exception:
				continue
			code = obj.get("completion")
			if not code:
				continue
			lang = detect_lang(obj)
			# Write to temp file with extension
			ext = {
				"py": ".py",
				"js": ".js",
				"ts": ".ts",
				"cpp": ".cpp",
				"java": ".java",
			}.get(lang, ".txt")
			with tempfile.NamedTemporaryFile("w", suffix=ext, delete=False) as tf:
				tf.write(code)
				tmpfile = tf.name
			results["files"] += 1
			results["sample_paths"].append(tmpfile)
			# Run per-language tools
			if lang == "py":
				if ruff_ok:
					rc, out, err = run_tool(["ruff", "--quiet", "check", tmpfile])
					if rc != 0:
						results["violations"]["ruff"] += len(out.splitlines())
				if flake_ok:
					rc, out, err = run_tool(["flake8", tmpfile])
					if rc != 0:
						results["violations"]["flake8"] += len(out.splitlines())
				if mypy_ok:
					rc, out, err = run_tool(["mypy", "--ignore-missing-imports", tmpfile])
					if rc != 0:
						results["violations"]["mypy"] += len(out.splitlines())
			elif lang in ("js", "ts"):
				if eslint_ok:
					rc, out, err = run_tool(["eslint", "-f", "unix", tmpfile])
					if rc != 0:
						results["violations"]["eslint"] += len(out.splitlines())
				if lang == "ts" and tsc_ok:
					# Type-check a single file
					rc, out, err = run_tool(["tsc", "--noEmit", tmpfile])
					if rc != 0:
						results["violations"]["tsc"] += len((out + "\n" + err).splitlines())
			elif lang == "cpp" and cpplint_ok:
				# cpplint may be installed as module
				if tool_exists(["cpplint", "--version"]):
					rc, out, err = run_tool(["cpplint", tmpfile])
				else:
					rc, out, err = run_tool(["python3", "-m", "cpplint", tmpfile])
				if rc != 0:
					results["violations"]["cpplint"] += len((out + "\n" + err).splitlines())
			elif lang == "java" and javac_ok:
				rc, out, err = run_tool(["javac", tmpfile])
				if rc != 0:
					results["violations"]["javac"] += len((out + "\n" + err).splitlines())

	outp = Path(args.out)
	outp.parent.mkdir(parents=True, exist_ok=True)
	outp.write_text(json.dumps(results, ensure_ascii=False, indent=2), encoding="utf-8")
	print(json.dumps({"ok": True, "wrote": str(outp), "files": results["files"]}))
	return 0

if __name__ == "__main__":
	raise SystemExit(main())


import logging
import argparse
import json
import random
from collections import defaultdict
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Any, List, Optional, Tuple

import numpy as np
from tqdm import tqdm

from agi_dw.core.world_model.rollout_api import RolloutAPI, RolloutConfig  # type: ignore
from agi_dw.core.world_model.validator_reward import ValidatorRewardShaper, RewardConfig  # type: ignore

@dataclass
class TrainingConfig:
	"""Configuration for off-policy training."""
	batch_size: int = 32
	num_epochs: int = 10
	learning_rate: float = 1e-4
	replay_buffer_size: int = 100000
	near_miss_priority_alpha: float = 2.0
	validation_split: float = 0.1
	checkpoint_every: int = 1000
	seed: int = 42

@dataclass
class ReplayBuffer:
	"""Prioritized replay buffer for off-policy training."""
	max_size: int
	alpha: float  # Priority exponent

	def __post_init__(self):
		self.buffer: List[Dict[str, Any]] = []
		self.priorities: List[float] = []
		self._next_idx = 0
		self._size = 0

	def add(self, experience: Dict[str, Any], priority: float = 1.0):
		if self._size < self.max_size:
			self.buffer.append(experience)
			self.priorities.append(priority)
			self._size += 1
		else:
			idx = self._next_idx
			self.buffer[idx] = experience
			self.priorities[idx] = priority
			self._next_idx = (idx + 1) % self.max_size

	def sample(self, batch_size: int) -> Tuple[List[Dict[str, Any]], np.ndarray, np.ndarray]:
		if self._size == 0:
			return [], np.array([]), np.array([])

		# Convert priorities to probabilities
		probs = np.array(self.priorities[:self._size]) ** self.alpha
		probs /= probs.sum()

		# Sample indices based on priorities
		indices = np.random.choice(self._size, size=min(batch_size, self._size), p=probs)
		samples = [self.buffer[idx] for idx in indices]
		weights = (self._size * probs[indices]) ** (-1.0)
		weights /= weights.max()  # Normalize weights

		return samples, indices, weights

	def update_priorities(self, indices: np.ndarray, priorities: np.ndarray):
		for idx, priority in zip(indices, priorities):
			if 0 <= idx < self._size:
				self.priorities[idx] = max(1e-6, priority)

def iter_jsonl(path: Path):
	"""Iterator for JSONL files with error handling."""
	if not path.exists():
		return
	with path.open("r", encoding="utf-8") as f:
		for line in f:
			line = line.strip()
			if not line:
				continue
			try:
				yield json.loads(line)
			except Exception:
				continue

class OffPolicyTrainer:
	"""Off-policy trainer combining IL and RL with prioritized replay."""

	def __init__(self, config: TrainingConfig, rollout_api: RolloutAPI, reward_config: Optional[RewardConfig] = None):
		self.config = config
		self.rollout_api = rollout_api
		self.reward_shaper = ValidatorRewardShaper(reward_config)
		self.replay_buffer = ReplayBuffer(
			max_size=config.replay_buffer_size,
			alpha=config.near_miss_priority_alpha
		)
		random.seed(config.seed)
		np.random.seed(config.seed)

	def compute_priority(self, experience: Dict[str, Any]) -> float:
		"""Compute priority score for experience."""
		# Higher priority for near-misses and failed attempts
		base_priority = 1.0

		# Check if this was a near-miss
		if experience.get("near_miss", False):
			base_priority *= 2.0

		# Check test results
		if not experience.get("tests_passed", True):
			base_priority *= 1.5

		# Check risk level
		risk = float(experience.get("risk", 0.5))
		if risk > 0.7:  # High-risk experiences are valuable for learning
			base_priority *= 1.3

		return base_priority

	def process_experience(self, experience: Dict[str, Any]) -> Dict[str, Any]:
		"""Process raw experience into training format."""
		processed = {
			"observation": experience.get("observation", {}),
			"action": experience.get("action", {}),
			"reward": experience.get("reward", 0.0),
			"next_observation": experience.get("next_observation", {}),
			"done": experience.get("done", False),
			"info": {
				"tests_passed": experience.get("tests_passed", False),
				"risk": experience.get("risk", 0.5),
				"near_miss": experience.get("near_miss", False)
			}
		}
		return processed

	def train_epoch(self, experiences: List[Dict[str, Any]], validation: bool = False) -> Dict[str, float]:
		"""Train or validate for one epoch."""
		metrics = defaultdict(float)
		n_batches = 0

		# Process all experiences in batches
		for i in range(0, len(experiences), self.config.batch_size):
			batch = experiences[i:i + self.config.batch_size]

			# Sample from replay buffer with priorities
			if not validation:
				replay_samples, indices, weights = self.replay_buffer.sample(len(batch))
				batch.extend(replay_samples)

			# Process batch
			processed_batch = [self.process_experience(exp) for exp in batch]

			# Simulate rollouts for each experience
			for exp in processed_batch:
				rollout = self.rollout_api.simulate_plan(
					exp["observation"],
					{"intent": "complete task"},  # Simplified plan
					[exp["action"]]
				)

				# Compute shaped rewards
				rewards = self.reward_shaper.compute_reward(
					action_result={
						"tests_passed": rollout.success_probability > 0.8,
						"files_changed": len(rollout.steps[-1].state_changes.get("files_changed", [])) if rollout.steps else 0,
						"lines_changed": sum(len(c.get("content", "").splitlines())
										  for c in rollout.steps[-1].state_changes.get("files_changed", []))
										  if rollout.steps else 0,
						"modified_files": [c.get("file") for c in rollout.steps[-1].state_changes.get("files_changed", [])]
										if rollout.steps else [],
						"partial_success": 0.5 < rollout.success_probability <= 0.8,
						"risk": rollout.avg_risk
					},
					intent=exp.get("intent", {}),
					world_model_prior={"risk": rollout.avg_risk, "success_prob": rollout.success_probability}
				)

				# Update metrics
				metrics["avg_risk"] += rollout.avg_risk
				metrics["success_prob"] += rollout.success_probability
				metrics["shaped_reward"] += rewards["total"]

				if not validation:
					# Update replay priorities based on shaped rewards
					new_priority = max(1e-6, rewards["total"] + 1.0)  # Shift to positive range
					self.replay_buffer.update_priorities(indices, np.array([new_priority]))

			n_batches += 1

		# Average metrics
		if n_batches > 0:
			for k in metrics:
				metrics[k] /= n_batches

		return dict(metrics)

	def train(self, train_data: List[Dict[str, Any]], val_data: List[Dict[str, Any]]) -> Dict[str, Any]:
		"""Main training loop."""
		best_val_metrics = None
		training_history = []

		# Initialize replay buffer with all experiences
		for exp in train_data:
			priority = self.compute_priority(exp)
			self.replay_buffer.add(exp, priority)

		# Training loop
		for epoch in range(self.config.num_epochs):
			# Training phase
			train_metrics = self.train_epoch(train_data)
			train_metrics["epoch"] = epoch

			# Validation phase
			val_metrics = self.train_epoch(val_data, validation=True)
			val_metrics = {f"val_{k}": v for k, v in val_metrics.items()}

			# Combine metrics
			epoch_metrics = {**train_metrics, **val_metrics}
			training_history.append(epoch_metrics)

			# Update best validation metrics
			if best_val_metrics is None or val_metrics["val_success_prob"] > best_val_metrics["val_success_prob"]:
				best_val_metrics = val_metrics

		return {
			"training_history": training_history,
			"best_val_metrics": best_val_metrics,
			"final_train_metrics": train_metrics,
			"final_val_metrics": val_metrics
		}

def main() -> int:
	ap = argparse.ArgumentParser()
	root = Path(__file__).resolve().parents[1]
	ap.add_argument("--wm-ds", default=str(root / "data" / "skills" / "wm_ds.jsonl"))
	ap.add_argument("--replay", default=str(root / "data" / "replay" / "near_miss.jsonl"))
	ap.add_argument("--devtools", default=str(root / "data" / "devtools" / "dataset.jsonl"))
	ap.add_argument("--out", default=str(root / "models" / "wm_offpolicy"))
	ap.add_argument("--batch-size", type=int, default=32)
	ap.add_argument("--num-epochs", type=int, default=10)
	ap.add_argument("--lr", type=float, default=1e-4)
	ap.add_argument("--replay-size", type=int, default=100000)
	ap.add_argument("--priority-alpha", type=float, default=2.0)
	ap.add_argument("--seed", type=int, default=42)
	args = ap.parse_args()

	# Initialize paths
	wm_path = Path(args.wm_ds)
	rp_path = Path(args.replay)
	dt_path = Path(args.devtools)
	out_dir = Path(args.out)
	out_dir.mkdir(parents=True, exist_ok=True)

	# Load all data
	print("Loading datasets...")
	wm_data = list(iter_jsonl(wm_path))
	replay_data = list(iter_jsonl(rp_path))
	devtools_data = list(iter_jsonl(dt_path))

	# Configure training
	config = TrainingConfig(
		batch_size=args.batch_size,
		num_epochs=args.num_epochs,
		learning_rate=args.lr,
		replay_buffer_size=args.replay_size,
		near_miss_priority_alpha=args.priority_alpha,
		seed=args.seed
	)

	# Initialize rollout API
	rollout_config = RolloutConfig(
		horizon=3,
		max_risk_threshold=0.8,
		min_success_prob=0.2,
		enable_early_stopping=True
	)
	rollout_api = RolloutAPI(config=rollout_config)

	# Initialize reward config
	reward_config = RewardConfig(
		success_reward=1.0,
		failure_penalty=-0.5,
		risk_penalty_factor=0.3,
		file_count_penalty=0.1,
		loc_penalty=0.05,
		max_files_threshold=3,
		max_loc_threshold=200
	)

	# Initialize trainer
	trainer = OffPolicyTrainer(config, rollout_api, reward_config)

	# Combine and shuffle data
	all_data = wm_data + replay_data + devtools_data
	random.shuffle(all_data)

	# Split into train/val
	val_size = int(len(all_data) * config.validation_split)
	train_data = all_data[val_size:]
	val_data = all_data[:val_size]

	print(f"Training on {len(train_data)} examples, validating on {len(val_data)} examples")

	# Train
	results = trainer.train(train_data, val_data)

	# Save metrics
	metrics = {
		"mode": "offpolicy_full",
		"wm_examples": len(wm_data),
		"replay_examples": len(replay_data),
		"devtools_examples": len(devtools_data),
		"train_examples": len(train_data),
		"val_examples": len(val_data),
		"best_val_metrics": results["best_val_metrics"],
		"final_train_metrics": results["final_train_metrics"],
		"final_val_metrics": results["final_val_metrics"],
		"training_history": results["training_history"]
	}

	metrics_path = out_dir / "metrics.json"
	metrics_path.write_text(json.dumps(metrics, ensure_ascii=False, indent=2), encoding="utf-8")
	print(json.dumps({"ok": True, "metrics_path": str(metrics_path)}))
	return 0

if __name__ == "__main__":
	raise SystemExit(main())


TrainingConfig

import logging
import argparse
import json
import random
from collections import defaultdict
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Any, List, Optional, Tuple

import numpy as np
from tqdm import tqdm

from agi_dw.core.world_model.rollout_api import RolloutAPI, RolloutConfig  # type: ignore
from agi_dw.core.world_model.validator_reward import ValidatorRewardShaper, RewardConfig  # type: ignore

@dataclass
class TrainingConfig:
	"""Configuration for off-policy training."""
	batch_size: int = 32
	num_epochs: int = 10
	learning_rate: float = 1e-4
	replay_buffer_size: int = 100000
	near_miss_priority_alpha: float = 2.0
	validation_split: float = 0.1
	checkpoint_every: int = 1000
	seed: int = 42

@dataclass
class ReplayBuffer:
	"""Prioritized replay buffer for off-policy training."""
	max_size: int
	alpha: float  # Priority exponent

	def __post_init__(self):
		self.buffer: List[Dict[str, Any]] = []
		self.priorities: List[float] = []
		self._next_idx = 0
		self._size = 0

	def add(self, experience: Dict[str, Any], priority: float = 1.0):
		if self._size < self.max_size:
			self.buffer.append(experience)
			self.priorities.append(priority)
			self._size += 1
		else:


ReplayBuffer

from tqdm import tqdm

from agi_dw.core.world_model.rollout_api import RolloutAPI, RolloutConfig  # type: ignore
from agi_dw.core.world_model.validator_reward import ValidatorRewardShaper, RewardConfig  # type: ignore

@dataclass
class TrainingConfig:
	"""Configuration for off-policy training."""
	batch_size: int = 32
	num_epochs: int = 10
	learning_rate: float = 1e-4
	replay_buffer_size: int = 100000
	near_miss_priority_alpha: float = 2.0
	validation_split: float = 0.1
	checkpoint_every: int = 1000
	seed: int = 42

@dataclass
class ReplayBuffer:
	"""Prioritized replay buffer for off-policy training."""
	max_size: int
	alpha: float  # Priority exponent

	def __post_init__(self):
		self.buffer: List[Dict[str, Any]] = []
		self.priorities: List[float] = []
		self._next_idx = 0
		self._size = 0

	def add(self, experience: Dict[str, Any], priority: float = 1.0):
		if self._size < self.max_size:
			self.buffer.append(experience)
			self.priorities.append(priority)
			self._size += 1
		else:
			idx = self._next_idx
			self.buffer[idx] = experience
			self.priorities[idx] = priority
			self._next_idx = (idx + 1) % self.max_size

	def sample(self, batch_size: int) -> Tuple[List[Dict[str, Any]], np.ndarray, np.ndarray]:
		if self._size == 0:
			return [], np.array([]), np.array([])

		# Convert priorities to probabilities
		probs = np.array(self.priorities[:self._size]) ** self.alpha
		probs /= probs.sum()

		# Sample indices based on priorities
		indices = np.random.choice(self._size, size=min(batch_size, self._size), p=probs)
		samples = [self.buffer[idx] for idx in indices]
		weights = (self._size * probs[indices]) ** (-1.0)
		weights /= weights.max()  # Normalize weights

		return samples, indices, weights

	def update_priorities(self, indices: np.ndarray, priorities: np.ndarray):
		for idx, priority in zip(indices, priorities):
			if 0 <= idx < self._size:
				self.priorities[idx] = max(1e-6, priority)

def iter_jsonl(path: Path):
	"""Iterator for JSONL files with error handling."""
	if not path.exists():
		return
	with path.open("r", encoding="utf-8") as f:
		for line in f:
			line = line.strip()
			if not line:
				continue
			try:
				yield json.loads(line)
			except Exception:
				continue

class OffPolicyTrainer:
	"""Off-policy trainer combining IL and RL with prioritized replay."""


			return [], np.array([]), np.array([])

		# Convert priorities to probabilities
		probs = np.array(self.priorities[:self._size]) ** self.alpha
		probs /= probs.sum()

		# Sample indices based on priorities
		indices = np.random.choice(self._size, size=min(batch_size, self._size), p=probs)
		samples = [self.buffer[idx] for idx in indices]
		weights = (self._size * probs[indices]) ** (-1.0)
		weights /= weights.max()  # Normalize weights

		return samples, indices, weights

	def update_priorities(self, indices: np.ndarray, priorities: np.ndarray):
		for idx, priority in zip(indices, priorities):
			if 0 <= idx < self._size:
				self.priorities[idx] = max(1e-6, priority)

def iter_jsonl(path: Path):
	"""Iterator for JSONL files with error handling."""
	if not path.exists():
		return
	with path.open("r", encoding="utf-8") as f:
		for line in f:
			line = line.strip()
			if not line:
				continue
			try:
				yield json.loads(line)
			except Exception:
				continue

class OffPolicyTrainer:
	"""Off-policy trainer combining IL and RL with prioritized replay."""

	def __init__(self, config: TrainingConfig, rollout_api: RolloutAPI, reward_config: Optional[RewardConfig] = None):
		self.config = config
		self.rollout_api = rollout_api
		self.reward_shaper = ValidatorRewardShaper(reward_config)
		self.replay_buffer = ReplayBuffer(
			max_size=config.replay_buffer_size,
			alpha=config.near_miss_priority_alpha
		)
		random.seed(config.seed)
		np.random.seed(config.seed)

	def compute_priority(self, experience: Dict[str, Any]) -> float:
		"""Compute priority score for experience."""
		# Higher priority for near-misses and failed attempts
		base_priority = 1.0


OffPolicyTrainer

		for idx, priority in zip(indices, priorities):
			if 0 <= idx < self._size:
				self.priorities[idx] = max(1e-6, priority)

def iter_jsonl(path: Path):
	"""Iterator for JSONL files with error handling."""
	if not path.exists():
		return
	with path.open("r", encoding="utf-8") as f:
		for line in f:
			line = line.strip()
			if not line:
				continue
			try:
				yield json.loads(line)
			except Exception:
				continue

class OffPolicyTrainer:
	"""Off-policy trainer combining IL and RL with prioritized replay."""

	def __init__(self, config: TrainingConfig, rollout_api: RolloutAPI, reward_config: Optional[RewardConfig] = None):
		self.config = config
		self.rollout_api = rollout_api
		self.reward_shaper = ValidatorRewardShaper(reward_config)
		self.replay_buffer = ReplayBuffer(
			max_size=config.replay_buffer_size,
			alpha=config.near_miss_priority_alpha
		)
		random.seed(config.seed)
		np.random.seed(config.seed)

	def compute_priority(self, experience: Dict[str, Any]) -> float:
		"""Compute priority score for experience."""
		# Higher priority for near-misses and failed attempts
		base_priority = 1.0

		# Check if this was a near-miss
		if experience.get("near_miss", False):
			base_priority *= 2.0

		# Check test results
		if not experience.get("tests_passed", True):
			base_priority *= 1.5

		# Check risk level
		risk = float(experience.get("risk", 0.5))
		if risk > 0.7:  # High-risk experiences are valuable for learning
			base_priority *= 1.3

		return base_priority

	def process_experience(self, experience: Dict[str, Any]) -> Dict[str, Any]:
		"""Process raw experience into training format."""
		processed = {
			"observation": experience.get("observation", {}),
			"action": experience.get("action", {}),
			"reward": experience.get("reward", 0.0),
			"next_observation": experience.get("next_observation", {}),
			"done": experience.get("done", False),
			"info": {
				"tests_passed": experience.get("tests_passed", False),
				"risk": experience.get("risk", 0.5),
				"near_miss": experience.get("near_miss", False)
			}
		}
		return processed

	def train_epoch(self, experiences: List[Dict[str, Any]], validation: bool = False) -> Dict[str, float]:
		"""Train or validate for one epoch."""
		metrics = defaultdict(float)
		n_batches = 0

		# Process all experiences in batches
		for i in range(0, len(experiences), self.config.batch_size):
			batch = experiences[i:i + self.config.batch_size]

			# Sample from replay buffer with priorities
			if not validation:
				replay_samples, indices, weights = self.replay_buffer.sample(len(batch))
				batch.extend(replay_samples)

			# Process batch
			processed_batch = [self.process_experience(exp) for exp in batch]

			# Simulate rollouts for each experience
			for exp in processed_batch:
				rollout = self.rollout_api.simulate_plan(
					exp["observation"],
					{"intent": "complete task"},  # Simplified plan
					[exp["action"]]
				)

				# Compute shaped rewards
				rewards = self.reward_shaper.compute_reward(
					action_result={
						"tests_passed": rollout.success_probability > 0.8,
						"files_changed": len(rollout.steps[-1].state_changes.get("files_changed", [])) if rollout.steps else 0,
						"lines_changed": sum(len(c.get("content", "").splitlines())
										  for c in rollout.steps[-1].state_changes.get("files_changed", []))
										  if rollout.steps else 0,
						"modified_files": [c.get("file") for c in rollout.steps[-1].state_changes.get("files_changed", [])]
										if rollout.steps else [],
						"partial_success": 0.5 < rollout.success_probability <= 0.8,
						"risk": rollout.avg_risk
					},
					intent=exp.get("intent", {}),
					world_model_prior={"risk": rollout.avg_risk, "success_prob": rollout.success_probability}
				)

				# Update metrics
				metrics["avg_risk"] += rollout.avg_risk
				metrics["success_prob"] += rollout.success_probability
				metrics["shaped_reward"] += rewards["total"]

				if not validation:
					# Update replay priorities based on shaped rewards
					new_priority = max(1e-6, rewards["total"] + 1.0)  # Shift to positive range
					self.replay_buffer.update_priorities(indices, np.array([new_priority]))

			n_batches += 1

		# Average metrics
		if n_batches > 0:
			for k in metrics:
				metrics[k] /= n_batches

		return dict(metrics)

	def train(self, train_data: List[Dict[str, Any]], val_data: List[Dict[str, Any]]) -> Dict[str, Any]:
		"""Main training loop."""
		best_val_metrics = None
		training_history = []

		# Initialize replay buffer with all experiences
		for exp in train_data:
			priority = self.compute_priority(exp)
			self.replay_buffer.add(exp, priority)

		# Training loop
		for epoch in range(self.config.num_epochs):
			# Training phase
			train_metrics = self.train_epoch(train_data)
			train_metrics["epoch"] = epoch

			# Validation phase
			val_metrics = self.train_epoch(val_data, validation=True)
			val_metrics = {f"val_{k}": v for k, v in val_metrics.items()}

			# Combine metrics
			epoch_metrics = {**train_metrics, **val_metrics}
			training_history.append(epoch_metrics)

			# Update best validation metrics
			if best_val_metrics is None or val_metrics["val_success_prob"] > best_val_metrics["val_success_prob"]:
				best_val_metrics = val_metrics

		return {
			"training_history": training_history,
			"best_val_metrics": best_val_metrics,
			"final_train_metrics": train_metrics,
			"final_val_metrics": val_metrics
		}

def main() -> int:
	ap = argparse.ArgumentParser()
	root = Path(__file__).resolve().parents[1]
	ap.add_argument("--wm-ds", default=str(root / "data" / "skills" / "wm_ds.jsonl"))
	ap.add_argument("--replay", default=str(root / "data" / "replay" / "near_miss.jsonl"))
	ap.add_argument("--devtools", default=str(root / "data" / "devtools" / "dataset.jsonl"))
	ap.add_argument("--out", default=str(root / "models" / "wm_offpolicy"))
	ap.add_argument("--batch-size", type=int, default=32)
	ap.add_argument("--num-epochs", type=int, default=10)
	ap.add_argument("--lr", type=float, default=1e-4)
	ap.add_argument("--replay-size", type=int, default=100000)
	ap.add_argument("--priority-alpha", type=float, default=2.0)
	ap.add_argument("--seed", type=int, default=42)
	args = ap.parse_args()

	# Initialize paths
	wm_path = Path(args.wm_ds)
	rp_path = Path(args.replay)


main

			# Validation phase
			val_metrics = self.train_epoch(val_data, validation=True)
			val_metrics = {f"val_{k}": v for k, v in val_metrics.items()}

			# Combine metrics
			epoch_metrics = {**train_metrics, **val_metrics}
			training_history.append(epoch_metrics)

			# Update best validation metrics
			if best_val_metrics is None or val_metrics["val_success_prob"] > best_val_metrics["val_success_prob"]:
				best_val_metrics = val_metrics

		return {
			"training_history": training_history,
			"best_val_metrics": best_val_metrics,
			"final_train_metrics": train_metrics,
			"final_val_metrics": val_metrics
		}

def main() -> int:
	ap = argparse.ArgumentParser()
	root = Path(__file__).resolve().parents[1]
	ap.add_argument("--wm-ds", default=str(root / "data" / "skills" / "wm_ds.jsonl"))
	ap.add_argument("--replay", default=str(root / "data" / "replay" / "near_miss.jsonl"))
	ap.add_argument("--devtools", default=str(root / "data" / "devtools" / "dataset.jsonl"))
	ap.add_argument("--out", default=str(root / "models" / "wm_offpolicy"))
	ap.add_argument("--batch-size", type=int, default=32)
	ap.add_argument("--num-epochs", type=int, default=10)
	ap.add_argument("--lr", type=float, default=1e-4)
	ap.add_argument("--replay-size", type=int, default=100000)
	ap.add_argument("--priority-alpha", type=float, default=2.0)
	ap.add_argument("--seed", type=int, default=42)
	args = ap.parse_args()

	# Initialize paths
	wm_path = Path(args.wm_ds)
	rp_path = Path(args.replay)
	dt_path = Path(args.devtools)
	out_dir = Path(args.out)
	out_dir.mkdir(parents=True, exist_ok=True)

	# Load all data
	print("Loading datasets...")
	wm_data = list(iter_jsonl(wm_path))
	replay_data = list(iter_jsonl(rp_path))
	devtools_data = list(iter_jsonl(dt_path))

	# Configure training
	config = TrainingConfig(
		batch_size=args.batch_size,
		num_epochs=args.num_epochs,
		learning_rate=args.lr,
		replay_buffer_size=args.replay_size,
		near_miss_priority_alpha=args.priority_alpha,
		seed=args.seed
	)

	# Initialize rollout API
	rollout_config = RolloutConfig(
		horizon=3,
		max_risk_threshold=0.8,
		min_success_prob=0.2,
		enable_early_stopping=True
	)
	rollout_api = RolloutAPI(config=rollout_config)

	# Initialize reward config
	reward_config = RewardConfig(
		success_reward=1.0,
		failure_penalty=-0.5,
		risk_penalty_factor=0.3,
		file_count_penalty=0.1,
		loc_penalty=0.05,
		max_files_threshold=3,
		max_loc_threshold=200
	)

	# Initialize trainer
	trainer = OffPolicyTrainer(config, rollout_api, reward_config)

	# Combine and shuffle data
	all_data = wm_data + replay_data + devtools_data
	random.shuffle(all_data)

	# Split into train/val
	val_size = int(len(all_data) * config.validation_split)
	train_data = all_data[val_size:]
	val_data = all_data[:val_size]

	print(f"Training on {len(train_data)} examples, validating on {len(val_data)} examples")

	# Train
	results = trainer.train(train_data, val_data)

	# Save metrics
	metrics = {
		"mode": "offpolicy_full",
		"wm_examples": len(wm_data),
		"replay_examples": len(replay_data),
		"devtools_examples": len(devtools_data),
		"train_examples": len(train_data),
		"val_examples": len(val_data),
		"best_val_metrics": results["best_val_metrics"],
		"final_train_metrics": results["final_train_metrics"],
		"final_val_metrics": results["final_val_metrics"],
		"training_history": results["training_history"]
	}

	metrics_path = out_dir / "metrics.json"
	metrics_path.write_text(json.dumps(metrics, ensure_ascii=False, indent=2), encoding="utf-8")
	print(json.dumps({"ok": True, "metrics_path": str(metrics_path)}))
	return 0

if __name__ == "__main__":
	raise SystemExit(main())


@dataclass
class TrainingConfig:
	"""Configuration for off-policy training."""
	batch_size: int = 32
	num_epochs: int = 10
	learning_rate: float = 1e-4
	replay_buffer_size: int = 100000
	near_miss_priority_alpha: float = 2.0
	validation_split: float = 0.1
	checkpoint_every: int = 1000
	seed: int = 42

@dataclass
class ReplayBuffer:
	"""Prioritized replay buffer for off-policy training."""
	max_size: int
	alpha: float  # Priority exponent

	def __post_init__(self):
		self.buffer: List[Dict[str, Any]] = []
		self.priorities: List[float] = []
		self._next_idx = 0
		self._size = 0

	def add(self, experience: Dict[str, Any], priority: float = 1.0):
		if self._size < self.max_size:
			self.buffer.append(experience)
			self.priorities.append(priority)
			self._size += 1
		else:
			idx = self._next_idx
			self.buffer[idx] = experience
			self.priorities[idx] = priority
			self._next_idx = (idx + 1) % self.max_size

	def sample(self, batch_size: int) -> Tuple[List[Dict[str, Any]], np.ndarray, np.ndarray]:
		if self._size == 0:
			return [], np.array([]), np.array([])

		# Convert priorities to probabilities
		probs = np.array(self.priorities[:self._size]) ** self.alpha
		probs /= probs.sum()


add

	learning_rate: float = 1e-4
	replay_buffer_size: int = 100000
	near_miss_priority_alpha: float = 2.0
	validation_split: float = 0.1
	checkpoint_every: int = 1000
	seed: int = 42

@dataclass
class ReplayBuffer:
	"""Prioritized replay buffer for off-policy training."""
	max_size: int
	alpha: float  # Priority exponent

	def __post_init__(self):
		self.buffer: List[Dict[str, Any]] = []
		self.priorities: List[float] = []
		self._next_idx = 0
		self._size = 0

	def add(self, experience: Dict[str, Any], priority: float = 1.0):
		if self._size < self.max_size:
			self.buffer.append(experience)
			self.priorities.append(priority)
			self._size += 1
		else:
			idx = self._next_idx
			self.buffer[idx] = experience
			self.priorities[idx] = priority
			self._next_idx = (idx + 1) % self.max_size

	def sample(self, batch_size: int) -> Tuple[List[Dict[str, Any]], np.ndarray, np.ndarray]:
		if self._size == 0:
			return [], np.array([]), np.array([])

		# Convert priorities to probabilities
		probs = np.array(self.priorities[:self._size]) ** self.alpha
		probs /= probs.sum()

		# Sample indices based on priorities
		indices = np.random.choice(self._size, size=min(batch_size, self._size), p=probs)
		samples = [self.buffer[idx] for idx in indices]
		weights = (self._size * probs[indices]) ** (-1.0)
		weights /= weights.max()  # Normalize weights

		return samples, indices, weights

	def update_priorities(self, indices: np.ndarray, priorities: np.ndarray):
		for idx, priority in zip(indices, priorities):
			if 0 <= idx < self._size:


sample

	max_size: int
	alpha: float  # Priority exponent

	def __post_init__(self):
		self.buffer: List[Dict[str, Any]] = []
		self.priorities: List[float] = []
		self._next_idx = 0
		self._size = 0

	def add(self, experience: Dict[str, Any], priority: float = 1.0):
		if self._size < self.max_size:
			self.buffer.append(experience)
			self.priorities.append(priority)
			self._size += 1
		else:
			idx = self._next_idx
			self.buffer[idx] = experience
			self.priorities[idx] = priority
			self._next_idx = (idx + 1) % self.max_size

	def sample(self, batch_size: int) -> Tuple[List[Dict[str, Any]], np.ndarray, np.ndarray]:
		if self._size == 0:
			return [], np.array([]), np.array([])

		# Convert priorities to probabilities
		probs = np.array(self.priorities[:self._size]) ** self.alpha
		probs /= probs.sum()

		# Sample indices based on priorities
		indices = np.random.choice(self._size, size=min(batch_size, self._size), p=probs)
		samples = [self.buffer[idx] for idx in indices]
		weights = (self._size * probs[indices]) ** (-1.0)
		weights /= weights.max()  # Normalize weights

		return samples, indices, weights

	def update_priorities(self, indices: np.ndarray, priorities: np.ndarray):
		for idx, priority in zip(indices, priorities):
			if 0 <= idx < self._size:
				self.priorities[idx] = max(1e-6, priority)

def iter_jsonl(path: Path):
	"""Iterator for JSONL files with error handling."""
	if not path.exists():
		return
	with path.open("r", encoding="utf-8") as f:
		for line in f:
			line = line.strip()
			if not line:
				continue
			try:
				yield json.loads(line)
			except Exception:
				continue


			self.buffer[idx] = experience
			self.priorities[idx] = priority
			self._next_idx = (idx + 1) % self.max_size

	def sample(self, batch_size: int) -> Tuple[List[Dict[str, Any]], np.ndarray, np.ndarray]:
		if self._size == 0:
			return [], np.array([]), np.array([])

		# Convert priorities to probabilities
		probs = np.array(self.priorities[:self._size]) ** self.alpha
		probs /= probs.sum()

		# Sample indices based on priorities
		indices = np.random.choice(self._size, size=min(batch_size, self._size), p=probs)
		samples = [self.buffer[idx] for idx in indices]
		weights = (self._size * probs[indices]) ** (-1.0)
		weights /= weights.max()  # Normalize weights

		return samples, indices, weights

	def update_priorities(self, indices: np.ndarray, priorities: np.ndarray):
		for idx, priority in zip(indices, priorities):
			if 0 <= idx < self._size:
				self.priorities[idx] = max(1e-6, priority)

def iter_jsonl(path: Path):
	"""Iterator for JSONL files with error handling."""
	if not path.exists():
		return
	with path.open("r", encoding="utf-8") as f:
		for line in f:
			line = line.strip()
			if not line:
				continue
			try:
				yield json.loads(line)
			except Exception:
				continue

class OffPolicyTrainer:
	"""Off-policy trainer combining IL and RL with prioritized replay."""


def iter_jsonl(path: Path):
	"""Iterator for JSONL files with error handling."""
	if not path.exists():
		return
	with path.open("r", encoding="utf-8") as f:
		for line in f:
			line = line.strip()
			if not line:
				continue
			try:
				yield json.loads(line)
			except Exception:
				continue

class OffPolicyTrainer:
	"""Off-policy trainer combining IL and RL with prioritized replay."""

	def __init__(self, config: TrainingConfig, rollout_api: RolloutAPI, reward_config: Optional[RewardConfig] = None):
		self.config = config
		self.rollout_api = rollout_api
		self.reward_shaper = ValidatorRewardShaper(reward_config)
		self.replay_buffer = ReplayBuffer(
			max_size=config.replay_buffer_size,
			alpha=config.near_miss_priority_alpha
		)
		random.seed(config.seed)
		np.random.seed(config.seed)

	def compute_priority(self, experience: Dict[str, Any]) -> float:
		"""Compute priority score for experience."""
		# Higher priority for near-misses and failed attempts
		base_priority = 1.0

		# Check if this was a near-miss
		if experience.get("near_miss", False):
			base_priority *= 2.0

		# Check test results
		if not experience.get("tests_passed", True):
			base_priority *= 1.5

		# Check risk level
		risk = float(experience.get("risk", 0.5))
		if risk > 0.7:  # High-risk experiences are valuable for learning
			base_priority *= 1.3

		return base_priority


			try:
				yield json.loads(line)
			except Exception:
				continue

class OffPolicyTrainer:
	"""Off-policy trainer combining IL and RL with prioritized replay."""

	def __init__(self, config: TrainingConfig, rollout_api: RolloutAPI, reward_config: Optional[RewardConfig] = None):
		self.config = config
		self.rollout_api = rollout_api
		self.reward_shaper = ValidatorRewardShaper(reward_config)
		self.replay_buffer = ReplayBuffer(
			max_size=config.replay_buffer_size,
			alpha=config.near_miss_priority_alpha
		)
		random.seed(config.seed)
		np.random.seed(config.seed)

	def compute_priority(self, experience: Dict[str, Any]) -> float:
		"""Compute priority score for experience."""
		# Higher priority for near-misses and failed attempts
		base_priority = 1.0

		# Check if this was a near-miss
		if experience.get("near_miss", False):
			base_priority *= 2.0

		# Check test results
		if not experience.get("tests_passed", True):
			base_priority *= 1.5

		# Check risk level
		risk = float(experience.get("risk", 0.5))
		if risk > 0.7:  # High-risk experiences are valuable for learning
			base_priority *= 1.3

		return base_priority

	def process_experience(self, experience: Dict[str, Any]) -> Dict[str, Any]:
		"""Process raw experience into training format."""
		processed = {
			"observation": experience.get("observation", {}),
			"action": experience.get("action", {}),
			"reward": experience.get("reward", 0.0),
			"next_observation": experience.get("next_observation", {}),
			"done": experience.get("done", False),
			"info": {
				"tests_passed": experience.get("tests_passed", False),
				"risk": experience.get("risk", 0.5),
				"near_miss": experience.get("near_miss", False)
			}
		}
		return processed

	def train_epoch(self, experiences: List[Dict[str, Any]], validation: bool = False) -> Dict[str, float]:
		"""Train or validate for one epoch."""
		metrics = defaultdict(float)


	def compute_priority(self, experience: Dict[str, Any]) -> float:
		"""Compute priority score for experience."""
		# Higher priority for near-misses and failed attempts
		base_priority = 1.0

		# Check if this was a near-miss
		if experience.get("near_miss", False):
			base_priority *= 2.0

		# Check test results
		if not experience.get("tests_passed", True):
			base_priority *= 1.5

		# Check risk level
		risk = float(experience.get("risk", 0.5))
		if risk > 0.7:  # High-risk experiences are valuable for learning
			base_priority *= 1.3

		return base_priority

	def process_experience(self, experience: Dict[str, Any]) -> Dict[str, Any]:
		"""Process raw experience into training format."""
		processed = {
			"observation": experience.get("observation", {}),
			"action": experience.get("action", {}),
			"reward": experience.get("reward", 0.0),
			"next_observation": experience.get("next_observation", {}),
			"done": experience.get("done", False),
			"info": {
				"tests_passed": experience.get("tests_passed", False),
				"risk": experience.get("risk", 0.5),
				"near_miss": experience.get("near_miss", False)
			}
		}
		return processed

	def train_epoch(self, experiences: List[Dict[str, Any]], validation: bool = False) -> Dict[str, float]:
		"""Train or validate for one epoch."""
		metrics = defaultdict(float)
		n_batches = 0

		# Process all experiences in batches
		for i in range(0, len(experiences), self.config.batch_size):
			batch = experiences[i:i + self.config.batch_size]

			# Sample from replay buffer with priorities
			if not validation:
				replay_samples, indices, weights = self.replay_buffer.sample(len(batch))
				batch.extend(replay_samples)

			# Process batch
			processed_batch = [self.process_experience(exp) for exp in batch]

			# Simulate rollouts for each experience
			for exp in processed_batch: