src/physics_pretraining.py

"""
Physics-Grounded Pretraining for Diffusion Policy using THE WELL.

Streams diverse physics simulation data from HuggingFace to pretrain
the temporal prediction backbone before fine-tuning on robot demonstrations.

Architecture:
    THE WELL physics data → Spatiotemporal Encoder → Temporal Prediction Head
                                    ↓ (transfer)
    Robot demonstrations → Same Encoder → Diffusion Policy → Actions
"""

import math
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, IterableDataset

logger = logging.getLogger(__name__)


# ---------------------------------------------------------------------------
# Configuration
# ---------------------------------------------------------------------------

@dataclass
class PhysicsPretrainConfig:
    # THE WELL datasets (streamed from HuggingFace)
    datasets: list[str] = field(default_factory=lambda: [
        "polymathic-ai/gray_scott",
        "polymathic-ai/acoustic_scattering",
        "polymathic-ai/rayleigh_benard",
    ])
    streaming: bool = True

    # Architecture
    hidden_dim: int = 256
    n_layers: int = 4
    n_heads: int = 8
    context_frames: int = 4
    prediction_horizon: int = 16
    spatial_patch_size: int = 8

    # Training
    batch_size: int = 32
    learning_rate: float = 3e-4
    weight_decay: float = 1e-5
    num_steps: int = 200_000
    warmup_steps: int = 5_000
    gradient_clip: float = 1.0
    mixed_precision: bool = True
    seed: int = 42

    # Checkpointing
    checkpoint_dir: str = "./checkpoints/physics_pretrain"
    checkpoint_every: int = 10_000
    eval_every: int = 5_000

    # HuggingFace
    hf_repo: str = "arc-ai/diffusion-policy-physics"
    push_to_hub: bool = True
    push_every: int = 50_000


@dataclass
class FinetuneConfig:
    pretrained_path: str = ""
    dataset_repo: str = "arc-ai/sim-demonstrations"
    obs_dim: int = 20
    action_dim: int = 7
    action_horizon: int = 16

    batch_size: int = 256
    learning_rate: float = 1e-4
    num_steps: int = 500_000
    warmup_steps: int = 2_000
    freeze_encoder_steps: int = 50_000
    gradient_clip: float = 1.0
    mixed_precision: bool = True

    # Domain randomization during fine-tuning
    domain_randomization: bool = True
    noise_sensor_std: float = 0.05
    noise_actuator_std: float = 0.02
    mass_range: tuple[float, float] = (0.7, 1.5)
    friction_range: tuple[float, float] = (0.5, 2.0)
    gravity_range: tuple[float, float] = (9.0, 10.5)


# ---------------------------------------------------------------------------
# THE WELL Streaming Dataset
# ---------------------------------------------------------------------------

class WellStreamingDataset(IterableDataset):
    """Streams physics trajectories from THE WELL via HuggingFace."""

    def __init__(self, config: PhysicsPretrainConfig):
        self.config = config
        self.datasets = config.datasets
        self.context_frames = config.context_frames
        self.prediction_horizon = config.prediction_horizon
        self.patch_size = config.spatial_patch_size

    def _load_stream(self):
        from datasets import load_dataset, interleave_datasets

        streams = []
        for ds_name in self.datasets:
            try:
                ds = load_dataset(ds_name, split="train", streaming=True)
                streams.append(ds)
                logger.info(f"Streaming from {ds_name}")
            except Exception as e:
                logger.warning(f"Failed to load {ds_name}: {e}")

        if not streams:
            raise RuntimeError("No datasets loaded")

        return interleave_datasets(streams)

    def _extract_trajectory(self, sample):
        """Extract spatiotemporal trajectory from WELL sample.

        THE WELL format: B × T × H [× W [× D]] × C
        We extract temporal windows for prediction.
        """
        if "trajectory" in sample:
            data = np.array(sample["trajectory"], dtype=np.float32)
        elif "fields" in sample:
            data = np.array(sample["fields"], dtype=np.float32)
        else:
            keys = [k for k in sample.keys() if k not in ("metadata", "id", "label")]
            if keys:
                data = np.array(sample[keys[0]], dtype=np.float32)
            else:
                return None

        if data.ndim < 2:
            return None

        T = data.shape[0]
        total_needed = self.context_frames + self.prediction_horizon
        if T < total_needed:
            return None

        return data

    def _patchify(self, spatial_data: np.ndarray) -> np.ndarray:
        """Convert spatial field to patch embeddings."""
        if spatial_data.ndim == 1:
            return spatial_data

        if spatial_data.ndim == 2:
            H, C = spatial_data.shape
            n_patches = H // self.patch_size
            if n_patches == 0:
                return spatial_data.reshape(-1)
            patches = spatial_data[:n_patches * self.patch_size].reshape(n_patches, self.patch_size, C)
            return patches.mean(axis=1).reshape(-1)

        if spatial_data.ndim == 3:
            H, W, C = spatial_data.shape
            pH = H // self.patch_size
            pW = W // self.patch_size
            if pH == 0 or pW == 0:
                return spatial_data.reshape(-1)[:256]
            patches = spatial_data[:pH*self.patch_size, :pW*self.patch_size].reshape(
                pH, self.patch_size, pW, self.patch_size, C
            )
            return patches.mean(axis=(1, 3)).reshape(-1)[:256]

        return spatial_data.reshape(-1)[:256]

    def __iter__(self):
        stream = self._load_stream()

        for sample in stream:
            trajectory = self._extract_trajectory(sample)
            if trajectory is None:
                continue

            T = trajectory.shape[0]
            total_needed = self.context_frames + self.prediction_horizon

            start = np.random.randint(0, T - total_needed + 1)
            window = trajectory[start:start + total_needed]

            context_patches = []
            for t in range(self.context_frames):
                patch = self._patchify(window[t])
                context_patches.append(patch)

            target_patches = []
            for t in range(self.context_frames, total_needed):
                patch = self._patchify(window[t])
                target_patches.append(patch)

            context = np.stack(context_patches, axis=0)
            target = np.stack(target_patches, axis=0)

            yield {
                "context": torch.from_numpy(context),
                "target": torch.from_numpy(target),
            }


# ---------------------------------------------------------------------------
# Physics-Aware Temporal Encoder
# ---------------------------------------------------------------------------

class SinusoidalEmbedding(nn.Module):
    def __init__(self, dim: int):
        super().__init__()
        self.dim = dim

    def forward(self, t: torch.Tensor) -> torch.Tensor:
        half = self.dim // 2
        emb = math.log(10000) / (half - 1)
        emb = torch.exp(torch.arange(half, device=t.device) * -emb)
        emb = t.unsqueeze(-1) * emb.unsqueeze(0)
        return torch.cat([emb.sin(), emb.cos()], dim=-1)


class PhysicsTemporalEncoder(nn.Module):
    """Encoder that learns spatiotemporal dynamics from physics simulations.

    Pretrained on THE WELL, then transferred to robot policy.
    """

    def __init__(self, config: PhysicsPretrainConfig):
        super().__init__()
        self.config = config
        dim = config.hidden_dim

        self.input_proj = nn.Sequential(
            nn.LazyLinear(dim),
            nn.SiLU(),
            nn.Linear(dim, dim),
        )

        self.time_embed = SinusoidalEmbedding(dim)
        self.time_proj = nn.Sequential(
            nn.Linear(dim, dim),
            nn.SiLU(),
            nn.Linear(dim, dim),
        )

        encoder_layer = nn.TransformerEncoderLayer(
            d_model=dim,
            nhead=config.n_heads,
            dim_feedforward=dim * 4,
            dropout=0.1,
            activation="gelu",
            batch_first=True,
            norm_first=True,
        )
        self.transformer = nn.TransformerEncoder(
            encoder_layer, num_layers=config.n_layers
        )

        self.output_norm = nn.LayerNorm(dim)

    def forward(self, context: torch.Tensor, timesteps: Optional[torch.Tensor] = None):
        """
        Args:
            context: (B, T, D) - temporal context frames (patchified)
            timesteps: (B,) - optional diffusion timesteps

        Returns:
            (B, T, hidden_dim) - encoded temporal representation
        """
        B, T, _ = context.shape

        x = self.input_proj(context)

        if timesteps is not None:
            t_emb = self.time_proj(self.time_embed(timesteps.float()))
            x = x + t_emb.unsqueeze(1)

        pos = torch.arange(T, device=x.device).float()
        pos_emb = self.time_embed(pos).unsqueeze(0).expand(B, -1, -1)
        x = x + pos_emb[:, :T, :x.shape[-1]]

        x = self.transformer(x)
        x = self.output_norm(x)
        return x


# ---------------------------------------------------------------------------
# Prediction Head (for pretraining)
# ---------------------------------------------------------------------------

class PhysicsPredictionHead(nn.Module):
    """Predicts future physics states from encoded context."""

    def __init__(self, config: PhysicsPretrainConfig):
        super().__init__()
        dim = config.hidden_dim
        self.horizon = config.prediction_horizon

        self.predictor = nn.Sequential(
            nn.Linear(dim, dim * 2),
            nn.SiLU(),
            nn.Linear(dim * 2, dim * 2),
            nn.SiLU(),
        )
        self.output_proj = nn.LazyLinear(1)  # will adapt to target dim

    def forward(self, encoded: torch.Tensor, target_dim: int):
        """
        Args:
            encoded: (B, T_context, dim)
            target_dim: output dimension per timestep
        Returns:
            (B, horizon, target_dim)
        """
        pooled = encoded.mean(dim=1)
        x = self.predictor(pooled)

        if self.output_proj.in_features == 1:
            self.output_proj = nn.Linear(
                x.shape[-1], self.horizon * target_dim
            ).to(x.device)

        out = self.output_proj(x)
        return out.reshape(-1, self.horizon, target_dim)


# ---------------------------------------------------------------------------
# Diffusion Policy with Physics-Pretrained Encoder
# ---------------------------------------------------------------------------

class PhysicsDiffusionPolicy(nn.Module):
    """Diffusion Policy using physics-pretrained temporal encoder."""

    def __init__(self, obs_dim: int, action_dim: int, config: PhysicsPretrainConfig):
        super().__init__()
        self.obs_dim = obs_dim
        self.action_dim = action_dim
        self.action_horizon = config.prediction_horizon
        dim = config.hidden_dim

        self.encoder = PhysicsTemporalEncoder(config)

        self.obs_proj = nn.Sequential(
            nn.Linear(obs_dim, dim),
            nn.SiLU(),
            nn.Linear(dim, dim),
        )

        self.noise_pred = nn.Sequential(
            nn.Linear(dim * 2, dim * 2),
            nn.SiLU(),
            nn.Linear(dim * 2, dim * 2),
            nn.SiLU(),
            nn.Linear(dim * 2, action_dim * self.action_horizon),
        )

        n_steps = 100
        betas = torch.linspace(1e-4, 0.02, n_steps)
        alphas = 1.0 - betas
        alphas_cumprod = torch.cumprod(alphas, dim=0)

        self.register_buffer("betas", betas)
        self.register_buffer("alphas_cumprod", alphas_cumprod)
        self.register_buffer("sqrt_alphas_cumprod", torch.sqrt(alphas_cumprod))
        self.register_buffer("sqrt_one_minus_alphas_cumprod", torch.sqrt(1 - alphas_cumprod))
        self.n_steps = n_steps

    def forward(self, obs: torch.Tensor, actions: torch.Tensor, timesteps: torch.Tensor):
        """Training forward: predict noise added to actions.

        Args:
            obs: (B, T_obs, obs_dim) - observation history
            actions: (B, horizon, action_dim) - ground truth action chunk
            timesteps: (B,) - diffusion timesteps
        """
        encoded = self.encoder(self.obs_proj(obs), timesteps)
        context = encoded.mean(dim=1)

        noise = torch.randn_like(actions)
        B = actions.shape[0]
        t = timesteps.long()

        sqrt_alpha = self.sqrt_alphas_cumprod[t].view(B, 1, 1)
        sqrt_one_minus = self.sqrt_one_minus_alphas_cumprod[t].view(B, 1, 1)
        noisy_actions = sqrt_alpha * actions + sqrt_one_minus * noise

        noisy_flat = noisy_actions.reshape(B, -1)
        combined = torch.cat([context, noisy_flat[:, :context.shape[-1]]], dim=-1)

        noise_pred = self.noise_pred(combined)
        noise_pred = noise_pred.reshape(B, self.action_horizon, self.action_dim)

        return F.mse_loss(noise_pred, noise)

    @torch.no_grad()
    def predict(self, obs: torch.Tensor, n_inference_steps: int = 10) -> torch.Tensor:
        """DDIM inference: generate action chunk from observation.

        Args:
            obs: (B, T_obs, obs_dim)
            n_inference_steps: number of denoising steps
        Returns:
            (B, horizon, action_dim)
        """
        B = obs.shape[0]
        device = obs.device

        actions = torch.randn(B, self.action_horizon, self.action_dim, device=device)

        step_indices = torch.linspace(self.n_steps - 1, 0, n_inference_steps).long().to(device)

        for step_t in step_indices:
            t_batch = step_t.expand(B)
            encoded = self.encoder(self.obs_proj(obs), t_batch.float())
            context = encoded.mean(dim=1)

            noisy_flat = actions.reshape(B, -1)
            combined = torch.cat([context, noisy_flat[:, :context.shape[-1]]], dim=-1)

            noise_pred = self.noise_pred(combined)
            noise_pred = noise_pred.reshape(B, self.action_horizon, self.action_dim)

            alpha = self.alphas_cumprod[step_t]
            alpha_prev = self.alphas_cumprod[max(step_t - self.n_steps // n_inference_steps, 0)]

            pred_x0 = (actions - torch.sqrt(1 - alpha) * noise_pred) / torch.sqrt(alpha)
            pred_x0 = pred_x0.clamp(-1, 1)

            actions = torch.sqrt(alpha_prev) * pred_x0 + torch.sqrt(1 - alpha_prev) * noise_pred

        return actions


# ---------------------------------------------------------------------------
# Pretraining Loop
# ---------------------------------------------------------------------------

class PhysicsPretrainer:
    """Pretrains temporal encoder on THE WELL physics data."""

    def __init__(self, config: PhysicsPretrainConfig):
        self.config = config
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        self.encoder = PhysicsTemporalEncoder(config).to(self.device)
        self.pred_head = PhysicsPredictionHead(config).to(self.device)

        params = list(self.encoder.parameters()) + list(self.pred_head.parameters())
        self.optimizer = torch.optim.AdamW(
            params, lr=config.learning_rate, weight_decay=config.weight_decay
        )

        self.scaler = torch.amp.GradScaler("cuda", enabled=config.mixed_precision)
        self.step = 0

    def _get_lr(self) -> float:
        if self.step < self.config.warmup_steps:
            return self.config.learning_rate * self.step / self.config.warmup_steps
        progress = (self.step - self.config.warmup_steps) / (
            self.config.num_steps - self.config.warmup_steps
        )
        return self.config.learning_rate * 0.5 * (1 + math.cos(math.pi * progress))

    def _update_lr(self):
        lr = self._get_lr()
        for pg in self.optimizer.param_groups:
            pg["lr"] = lr

    def train(self):
        """Run pretraining loop streaming from THE WELL."""
        logger.info(f"Starting physics pretraining on {self.device}")
        logger.info(f"Datasets: {self.config.datasets}")
        logger.info(f"Steps: {self.config.num_steps}")

        dataset = WellStreamingDataset(self.config)
        dataloader = DataLoader(
            dataset, batch_size=self.config.batch_size, num_workers=2, pin_memory=True
        )

        checkpoint_dir = Path(self.config.checkpoint_dir)
        checkpoint_dir.mkdir(parents=True, exist_ok=True)

        running_loss = 0.0
        log_interval = 100

        for batch in dataloader:
            if self.step >= self.config.num_steps:
                break

            context = batch["context"].to(self.device)
            target = batch["target"].to(self.device)

            self._update_lr()

            with torch.amp.autocast("cuda", enabled=self.config.mixed_precision):
                encoded = self.encoder(context)
                target_flat = target.reshape(target.shape[0], target.shape[1], -1)
                target_dim = target_flat.shape[-1]
                predicted = self.pred_head(encoded, target_dim)

                min_horizon = min(predicted.shape[1], target_flat.shape[1])
                min_dim = min(predicted.shape[2], target_flat.shape[2])
                loss = F.mse_loss(
                    predicted[:, :min_horizon, :min_dim],
                    target_flat[:, :min_horizon, :min_dim],
                )

            self.optimizer.zero_grad()
            self.scaler.scale(loss).backward()
            self.scaler.unscale_(self.optimizer)
            nn.utils.clip_grad_norm_(
                list(self.encoder.parameters()) + list(self.pred_head.parameters()),
                self.config.gradient_clip,
            )
            self.scaler.step(self.optimizer)
            self.scaler.update()

            running_loss += loss.item()
            self.step += 1

            if self.step % log_interval == 0:
                avg_loss = running_loss / log_interval
                lr = self._get_lr()
                logger.info(
                    f"Step {self.step}/{self.config.num_steps} | "
                    f"Loss: {avg_loss:.6f} | LR: {lr:.2e}"
                )
                running_loss = 0.0

            if self.step % self.config.checkpoint_every == 0:
                self._save_checkpoint(checkpoint_dir / f"step_{self.step}.pt")

            if self.step % self.config.push_every == 0 and self.config.push_to_hub:
                self._push_to_hub()

        self._save_checkpoint(checkpoint_dir / "final.pt")
        logger.info(f"Pretraining complete at step {self.step}")
        return self.encoder

    def _save_checkpoint(self, path: Path):
        torch.save({
            "step": self.step,
            "encoder_state": self.encoder.state_dict(),
            "pred_head_state": self.pred_head.state_dict(),
            "optimizer_state": self.optimizer.state_dict(),
            "config": self.config,
        }, path)
        logger.info(f"Saved checkpoint: {path}")

    def _push_to_hub(self):
        try:
            from huggingface_hub import HfApi
            api = HfApi()
            path = Path(self.config.checkpoint_dir) / f"step_{self.step}.pt"
            api.upload_file(
                path_or_fileobj=str(path),
                path_in_repo=f"checkpoints/step_{self.step}.pt",
                repo_id=self.config.hf_repo,
                repo_type="model",
            )
            logger.info(f"Pushed step {self.step} to {self.config.hf_repo}")
        except Exception as e:
            logger.warning(f"Hub push failed: {e}")


# ---------------------------------------------------------------------------
# Fine-tuning on Robot Demonstrations
# ---------------------------------------------------------------------------

class DomainRandomizer:
    """Applies domain randomization during fine-tuning."""

    def __init__(self, config: FinetuneConfig):
        self.config = config

    def __call__(self, obs: torch.Tensor, actions: torch.Tensor):
        if not self.config.domain_randomization:
            return obs, actions

        B = obs.shape[0]

        # Sensor noise
        obs = obs + torch.randn_like(obs) * self.config.noise_sensor_std

        # Actuator noise
        actions = actions + torch.randn_like(actions) * self.config.noise_actuator_std

        # Mass scaling (affects dynamics — simulated via obs perturbation)
        mass_scale = torch.empty(B, 1, 1, device=obs.device).uniform_(
            *self.config.mass_range
        )
        if obs.shape[-1] >= 14:
            obs[:, :, 7:14] = obs[:, :, 7:14] / mass_scale

        return obs, actions


class PolicyFinetuner:
    """Fine-tunes physics-pretrained encoder for robot manipulation."""

    def __init__(self, pretrained_encoder: PhysicsTemporalEncoder, config: FinetuneConfig):
        self.config = config
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        pretrain_config = PhysicsPretrainConfig(
            hidden_dim=pretrained_encoder.config.hidden_dim,
            n_layers=pretrained_encoder.config.n_layers,
            n_heads=pretrained_encoder.config.n_heads,
            prediction_horizon=config.action_horizon,
        )

        self.policy = PhysicsDiffusionPolicy(
            obs_dim=config.obs_dim,
            action_dim=config.action_dim,
            config=pretrain_config,
        ).to(self.device)

        # Transfer pretrained weights
        self.policy.encoder.load_state_dict(pretrained_encoder.state_dict(), strict=False)
        logger.info("Loaded pretrained encoder weights")

        self.randomizer = DomainRandomizer(config)
        self.optimizer = torch.optim.AdamW(
            self.policy.parameters(), lr=config.learning_rate
        )
        self.scaler = torch.amp.GradScaler("cuda", enabled=config.mixed_precision)
        self.step = 0

    def _freeze_encoder(self, freeze: bool):
        for p in self.policy.encoder.parameters():
            p.requires_grad = not freeze

    def train(self, dataloader: DataLoader):
        """Fine-tune on robot demonstrations with domain randomization."""
        logger.info("Starting fine-tuning with domain randomization")
        self._freeze_encoder(True)

        running_loss = 0.0
        for batch in dataloader:
            if self.step >= self.config.num_steps:
                break

            if self.step == self.config.freeze_encoder_steps:
                self._freeze_encoder(False)
                logger.info(f"Unfreezing encoder at step {self.step}")

            obs = batch["obs"].to(self.device)
            actions = batch["actions"].to(self.device)

            obs, actions = self.randomizer(obs, actions)

            t = torch.randint(0, self.policy.n_steps, (obs.shape[0],), device=self.device)

            with torch.amp.autocast("cuda", enabled=self.config.mixed_precision):
                loss = self.policy(obs, actions, t.float())

            self.optimizer.zero_grad()
            self.scaler.scale(loss).backward()
            self.scaler.unscale_(self.optimizer)
            nn.utils.clip_grad_norm_(self.policy.parameters(), self.config.gradient_clip)
            self.scaler.step(self.optimizer)
            self.scaler.update()

            running_loss += loss.item()
            self.step += 1

            if self.step % 100 == 0:
                logger.info(f"Step {self.step} | Loss: {running_loss/100:.6f}")
                running_loss = 0.0

        return self.policy


# ---------------------------------------------------------------------------
# HuggingFace Hub Utilities
# ---------------------------------------------------------------------------

def setup_hf_repo(repo_id: str, repo_type: str = "model"):
    """Create HuggingFace repo if it doesn't exist."""
    from huggingface_hub import HfApi, create_repo

    api = HfApi()
    try:
        create_repo(repo_id, repo_type=repo_type, exist_ok=True)
        logger.info(f"HF repo ready: {repo_id}")
    except Exception as e:
        logger.warning(f"Repo setup: {e}")
    return api


def upload_model(model: nn.Module, repo_id: str, filename: str = "model.pt"):
    """Upload model checkpoint to HuggingFace."""
    import tempfile
    from huggingface_hub import HfApi

    api = HfApi()
    with tempfile.NamedTemporaryFile(suffix=".pt") as f:
        torch.save(model.state_dict(), f.name)
        api.upload_file(
            path_or_fileobj=f.name,
            path_in_repo=filename,
            repo_id=repo_id,
            repo_type="model",
        )
    logger.info(f"Uploaded {filename} to {repo_id}")


def upload_dataset(data_dir: str, repo_id: str):
    """Upload demonstration dataset to HuggingFace."""
    from huggingface_hub import HfApi

    api = HfApi()
    api.upload_folder(
        folder_path=data_dir,
        repo_id=repo_id,
        repo_type="dataset",
    )
    logger.info(f"Uploaded dataset to {repo_id}")


# ---------------------------------------------------------------------------
# Main Entry Point
# ---------------------------------------------------------------------------

def run_physics_pretraining(config_path: Optional[str] = None):
    """Full pipeline: pretrain on physics → fine-tune on robot demos."""
    import yaml

    if config_path:
        with open(config_path) as f:
            cfg = yaml.safe_load(f)
        pretrain_cfg = PhysicsPretrainConfig(**{
            k: v for k, v in cfg.get("pretraining", {}).items()
            if k in PhysicsPretrainConfig.__dataclass_fields__
        })
    else:
        pretrain_cfg = PhysicsPretrainConfig()

    # Phase 1: Pretrain on THE WELL
    logger.info("=" * 60)
    logger.info("PHASE 1: Physics Pretraining (THE WELL)")
    logger.info("=" * 60)

    pretrainer = PhysicsPretrainer(pretrain_cfg)
    encoder = pretrainer.train()

    # Phase 2: Fine-tune on robot demonstrations
    logger.info("=" * 60)
    logger.info("PHASE 2: Fine-tuning on Robot Demonstrations")
    logger.info("=" * 60)

    finetune_cfg = FinetuneConfig(
        pretrained_path=str(Path(pretrain_cfg.checkpoint_dir) / "final.pt"),
    )

    finetuner = PolicyFinetuner(encoder, finetune_cfg)

    # Load robot demonstration data (from HF or local)
    # In production, this streams from arc-ai/sim-demonstrations
    logger.info("Fine-tuning requires demonstration dataloader — provide via API")

    return finetuner.policy


if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
    run_physics_pretraining()