model_loader.py

"""Model and artifact loading utilities for the FlowProt Space MVP."""

from __future__ import annotations

import logging
import os
import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional

import torch
from huggingface_hub import snapshot_download
from omegaconf import DictConfig, OmegaConf

LOGGER = logging.getLogger(__name__)
REPO_ROOT = Path(__file__).resolve().parent
MODEL_ROOT = REPO_ROOT / "model"
DEFAULT_APP_CONFIG = REPO_ROOT / "config.yaml"


def ensure_model_pythonpath() -> None:
    """Ensure `model/` package imports resolve from the Space root."""
    model_root_str = str(MODEL_ROOT)
    if model_root_str not in sys.path:
        sys.path.insert(0, model_root_str)


ensure_model_pythonpath()

from models.classifier_wrapper_v2 import ClasfModule  # noqa: E402
from models.proteinflow import ProteinFlow  # noqa: E402


class ArtifactResolutionError(RuntimeError):
    """Raised when checkpoint/config artifacts cannot be resolved."""


class ModelLoadError(RuntimeError):
    """Raised when model instantiation or weights loading fails."""


@dataclass
class ResolvedArtifacts:
    ckpt_path: Path
    config_path: Path
    source: str


@dataclass
class LoadedModelContext:
    model: ProteinFlow
    device: torch.device
    merged_cfg: DictConfig
    artifacts: ResolvedArtifacts


@dataclass
class ResolvedClassifierArtifacts:
    ckpt_path: Path
    source: str


@dataclass
class LoadedClassifierContext:
    classifier: ClasfModule
    device: torch.device
    artifacts: ResolvedClassifierArtifacts


def _as_path(path_value: str) -> Path:
    raw = Path(path_value).expanduser()
    return raw if raw.is_absolute() else (REPO_ROOT / raw).resolve()


def _require_file(path: Path, label: str) -> None:
    if not path.exists() or not path.is_file():
        raise ArtifactResolutionError(f"{label} does not exist: {path}")


def load_runtime_config(config_path: Optional[str] = None) -> DictConfig:
    """Load app/runtime config from file."""
    explicit_path = config_path or os.getenv("FLOWPROT_APP_CONFIG")
    cfg_path = _as_path(explicit_path) if explicit_path else DEFAULT_APP_CONFIG
    if not cfg_path.exists():
        raise ArtifactResolutionError(
            f"App config file is missing: {cfg_path}. "
            "Set FLOWPROT_APP_CONFIG or add config.yaml at repo root."
        )
    cfg = OmegaConf.load(cfg_path)
    LOGGER.info("Loaded runtime config from %s", cfg_path)
    return cfg


def resolve_artifacts(runtime_cfg: Optional[DictConfig] = None) -> ResolvedArtifacts:
    """Resolve checkpoint + checkpoint config.



    Resolution precedence (first match wins):

      1. Env vars (FLOWPROT_CKPT_PATH / FLOWPROT_CKPT_DIR / FLOWPROT_HF_REPO_ID)

         so deployments (e.g. HF Space) can override without editing files.

      2. Runtime config file (inference.ckpt_path + optional inference.ckpt_config_path).

    """
    ckpt_path_env = os.getenv("FLOWPROT_CKPT_PATH")
    ckpt_dir_env = os.getenv("FLOWPROT_CKPT_DIR")
    hf_repo_id = os.getenv("FLOWPROT_HF_REPO_ID")
    config_filename = os.getenv("FLOWPROT_CKPT_CONFIG_FILENAME", "config.yaml")

    cfg_ckpt_path = (
        OmegaConf.select(runtime_cfg, "inference.ckpt_path") if runtime_cfg is not None else None
    )
    cfg_ckpt_config_path = (
        OmegaConf.select(runtime_cfg, "inference.ckpt_config_path")
        if runtime_cfg is not None
        else None
    )

    if ckpt_path_env:
        ckpt_path = _as_path(ckpt_path_env)
        config_path = _as_path(
            os.getenv("FLOWPROT_CKPT_CONFIG_PATH", str(ckpt_path.parent / config_filename))
        )
        source = "local_ckpt_path"
    elif ckpt_dir_env:
        ckpt_dir = _as_path(ckpt_dir_env)
        ckpt_filename = os.getenv("FLOWPROT_CKPT_FILENAME", "epoch.ckpt")
        ckpt_path = ckpt_dir / ckpt_filename
        config_path = _as_path(
            os.getenv("FLOWPROT_CKPT_CONFIG_PATH", str(ckpt_dir / config_filename))
        )
        source = "local_ckpt_dir"
    elif hf_repo_id:
        ckpt_filename = os.getenv("FLOWPROT_CKPT_FILENAME")
        if not ckpt_filename:
            raise ArtifactResolutionError(
                "FLOWPROT_CKPT_FILENAME is required when FLOWPROT_HF_REPO_ID is set."
            )
        revision = os.getenv("FLOWPROT_HF_REVISION")
        token = os.getenv("HF_TOKEN")
        local_dir = snapshot_download(
            repo_id=hf_repo_id,
            revision=revision,
            token=token,
            allow_patterns=[ckpt_filename, config_filename],
        )
        ckpt_path = Path(local_dir) / ckpt_filename
        config_path = Path(local_dir) / config_filename
        source = "hf_hub_snapshot"
    elif cfg_ckpt_path:
        ckpt_path = _as_path(str(cfg_ckpt_path))
        config_path = (
            _as_path(str(cfg_ckpt_config_path))
            if cfg_ckpt_config_path
            else (ckpt_path.parent / config_filename)
        )
        source = "runtime_config"
    else:
        raise ArtifactResolutionError(
            "No model artifact source configured. Set inference.ckpt_path in config.yaml, "
            "or one of the env vars: FLOWPROT_CKPT_PATH, FLOWPROT_CKPT_DIR, or "
            "FLOWPROT_HF_REPO_ID (with FLOWPROT_CKPT_FILENAME)."
        )

    _require_file(ckpt_path, "Checkpoint file")
    _require_file(config_path, "Checkpoint config")
    LOGGER.info("Resolved artifacts from %s", source)
    LOGGER.info("Checkpoint: %s", ckpt_path)
    LOGGER.info("Checkpoint config: %s", config_path)
    return ResolvedArtifacts(ckpt_path=ckpt_path, config_path=config_path, source=source)


def resolve_classifier_artifacts(runtime_cfg: Optional[DictConfig] = None) -> ResolvedClassifierArtifacts:
    """Resolve classifier checkpoint via env var or runtime config."""
    ckpt_path_env = os.getenv("FLOWPROT_CLASSIFIER_CKPT_PATH")
    if ckpt_path_env:
        ckpt_path = _as_path(ckpt_path_env)
        source = "env_classifier_ckpt_path"
    else:
        cfg_path = None
        if runtime_cfg is not None:
            cfg_path = OmegaConf.select(runtime_cfg, "inference.classifier.ckpt_path")
        if cfg_path:
            ckpt_path = _as_path(str(cfg_path))
            source = "runtime_config"
        else:
            ckpt_path = (
                MODEL_ROOT / "ckpt" / "classifier_ckpt" / "epoch=90-step=728000.ckpt"
            ).resolve()
            source = "default_classifier_ckpt"

    _require_file(ckpt_path, "Classifier checkpoint file")
    LOGGER.info("Resolved classifier artifacts from %s", source)
    LOGGER.info("Classifier checkpoint: %s", ckpt_path)
    return ResolvedClassifierArtifacts(ckpt_path=ckpt_path, source=source)


def _resolve_device(merged_cfg: DictConfig) -> torch.device:
    app_cfg = merged_cfg.get("app", {})
    configured = os.getenv("FLOWPROT_DEVICE", str(app_cfg.get("device", "auto"))).strip().lower()
    if configured in {"", "auto"}:
        return torch.device("cuda" if torch.cuda.is_available() else "cpu")
    if configured.startswith("cuda") and not torch.cuda.is_available():
        raise ModelLoadError(
            f"FLOWPROT_DEVICE={configured} requested, but CUDA is not available."
        )
    return torch.device(configured)


def _normalize_state_dict(raw_state_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
    """Normalize Lightning-style checkpoints to raw ProteinFlow module keys."""
    candidates: Dict[str, torch.Tensor] = {}
    for key, value in raw_state_dict.items():
        if key.startswith("model."):
            candidates[key[len("model."):]] = value
    if candidates:
        return candidates

    candidates = {}
    for key, value in raw_state_dict.items():
        if key.startswith("module.model."):
            candidates[key[len("module.model."):]] = value
    if candidates:
        return candidates

    # Fall back to de-DDP or already-normalized keys.
    normalized: Dict[str, torch.Tensor] = {}
    for key, value in raw_state_dict.items():
        normalized[key[len("module."):] if key.startswith("module.") else key] = value
    return normalized


def _merge_runtime_and_checkpoint_cfg(

    runtime_cfg: DictConfig, checkpoint_cfg_path: Path

) -> DictConfig:
    ckpt_cfg = OmegaConf.load(checkpoint_cfg_path)
    OmegaConf.set_struct(runtime_cfg, False)
    OmegaConf.set_struct(ckpt_cfg, False)
    merged = OmegaConf.merge(ckpt_cfg, runtime_cfg)
    if "inference" not in merged:
        merged.inference = OmegaConf.create({})
    if "interpolant" in merged and "interpolant" not in merged.inference:
        merged.inference.interpolant = merged.interpolant
    return merged


class FlowProtModelManager:
    """Lazy model manager with cached loaded context."""

    def __init__(self, config_path: Optional[str] = None):
        self._config_path = config_path
        self._loaded: Optional[LoadedModelContext] = None
        self._last_error: Optional[str] = None

    @property
    def is_loaded(self) -> bool:
        return self._loaded is not None

    @property
    def last_error(self) -> Optional[str]:
        return self._last_error

    def peek_loaded(self) -> Optional[LoadedModelContext]:
        return self._loaded

    def load(self, force_reload: bool = False) -> LoadedModelContext:
        if self._loaded is not None and not force_reload:
            return self._loaded

        try:
            runtime_cfg = load_runtime_config(self._config_path)
            artifacts = resolve_artifacts(runtime_cfg)
            merged_cfg = _merge_runtime_and_checkpoint_cfg(runtime_cfg, artifacts.config_path)
            device = _resolve_device(merged_cfg)

            # PyTorch >=2.6 defaults to weights_only=True, which breaks older
            # Lightning checkpoints that store OmegaConf objects in the payload.
            checkpoint_payload = torch.load(
                artifacts.ckpt_path,
                map_location="cpu",
                weights_only=False,
            )
            state_dict = checkpoint_payload.get("state_dict", checkpoint_payload)
            if not isinstance(state_dict, dict):
                raise ModelLoadError(
                    "Checkpoint payload does not include a valid state_dict dictionary."
                )

            model = ProteinFlow(merged_cfg.model)
            normalized_state_dict = _normalize_state_dict(state_dict)
            missing, unexpected = model.load_state_dict(normalized_state_dict, strict=False)
            if missing:
                LOGGER.warning("Missing checkpoint keys while loading model: %s", missing[:20])
            if unexpected:
                LOGGER.warning(
                    "Unexpected checkpoint keys while loading model: %s", unexpected[:20]
                )

            model.to(device)
            model.eval()

            self._loaded = LoadedModelContext(
                model=model,
                device=device,
                merged_cfg=merged_cfg,
                artifacts=artifacts,
            )
            self._last_error = None
            LOGGER.info("Model loaded successfully on %s", device)
            return self._loaded
        except Exception as exc:
            self._last_error = str(exc)
            LOGGER.exception("Failed to load FlowProt model artifacts.")
            if isinstance(exc, (ArtifactResolutionError, ModelLoadError)):
                raise
            raise ModelLoadError(str(exc)) from exc


class FlowProtClassifierManager:
    """Lazy classifier manager with cached loaded context."""

    def __init__(self, config_path: Optional[str] = None):
        self._config_path = config_path
        self._loaded: Optional[LoadedClassifierContext] = None
        self._last_error: Optional[str] = None

    @property
    def is_loaded(self) -> bool:
        return self._loaded is not None

    @property
    def last_error(self) -> Optional[str]:
        return self._last_error

    def peek_loaded(self) -> Optional[LoadedClassifierContext]:
        return self._loaded

    def load(

        self,

        device: torch.device,

        force_reload: bool = False,

    ) -> LoadedClassifierContext:
        if self._loaded is not None and not force_reload:
            if self._loaded.device == device:
                return self._loaded

        try:
            runtime_cfg = load_runtime_config(self._config_path)
            artifacts = resolve_classifier_artifacts(runtime_cfg)

            # Bypass Lightning's load_from_checkpoint: PyTorch >=2.6 defaults to
            # weights_only=True and Lightning explicitly forwards that flag, which
            # rejects the OmegaConf objects pickled in this checkpoint. We load the
            # payload directly (trusted source) and rebuild the module ourselves.
            checkpoint_payload = torch.load(
                str(artifacts.ckpt_path),
                map_location="cpu",
                weights_only=False,
            )

            classifier_cfg = checkpoint_payload.get("hyper_parameters", {}).get("cfg")
            if classifier_cfg is None:
                sibling_config = artifacts.ckpt_path.parent / "config.yaml"
                _require_file(sibling_config, "Classifier checkpoint config")
                classifier_cfg = OmegaConf.load(sibling_config)

            state_dict = checkpoint_payload.get("state_dict", checkpoint_payload)
            if not isinstance(state_dict, dict):
                raise ModelLoadError(
                    "Classifier checkpoint payload does not include a valid state_dict."
                )

            classifier = ClasfModule(classifier_cfg)
            missing, unexpected = classifier.load_state_dict(state_dict, strict=False)
            if missing:
                LOGGER.warning("Missing classifier checkpoint keys: %s", missing[:20])
            if unexpected:
                LOGGER.warning("Unexpected classifier checkpoint keys: %s", unexpected[:20])

            for param in classifier.parameters():
                param.requires_grad_(True)
            classifier.to(device)
            classifier.eval()

            self._loaded = LoadedClassifierContext(
                classifier=classifier,
                device=device,
                artifacts=artifacts,
            )
            self._last_error = None
            LOGGER.info("Classifier loaded successfully on %s", device)
            return self._loaded
        except Exception as exc:
            self._last_error = str(exc)
            LOGGER.exception("Failed to load FlowProt classifier artifacts.")
            if isinstance(exc, (ArtifactResolutionError, ModelLoadError)):
                raise
            raise ModelLoadError(str(exc)) from exc