"""Inference orchestration for the FlowProt Hugging Face Space MVP."""

from __future__ import annotations

import logging
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Optional

import numpy as np
import torch
from omegaconf import OmegaConf

from model_loader import (
    REPO_ROOT,
    ArtifactResolutionError,
    FlowProtClassifierManager,
    FlowProtModelManager,
    ModelLoadError,
    ensure_model_pythonpath,
    load_runtime_config,
)

ensure_model_pythonpath()

from utils.experiments import save_traj  # noqa: E402
from utils.flows import Interpolant  # noqa: E402
from utils.modelUtils import to_numpy  # noqa: E402
from utils.pdbUtils import parse_pdb_feats  # noqa: E402

LOGGER = logging.getLogger(__name__)


class InferenceError(RuntimeError):
    """Raised when runtime inference fails."""


@dataclass
class InferenceResult:
    mode: str
    run_dir: str
    sample_files: List[str]
    trajectory_files: List[str]
    x0_trajectory_files: List[str]
    seed: int
    artifacts_source: str
    guidance_scale: Optional[float] = None
    target_class: Optional[int] = None
    fixed_residue_count: Optional[int] = None
    num_timesteps: Optional[int] = None


def _cfg_get(cfg, key: str, default):
    value = OmegaConf.select(cfg, key)
    return default if value is None else value


class FlowProtInferenceService:
    """Service layer for Gradio UI and smoke checks."""

    def __init__(self, config_path: Optional[str] = None):
        self._config_path = config_path
        self._runtime_cfg = load_runtime_config(config_path=config_path)
        self._model_manager = FlowProtModelManager(config_path=config_path)
        self._classifier_manager = FlowProtClassifierManager(config_path=config_path)
        self._mvp_mode = str(_cfg_get(self._runtime_cfg, "app.mvp_mode", "unconditional")).lower()
        self._conditional_enabled = bool(
            _cfg_get(self._runtime_cfg, "app.enable_conditional", False)
        )
        self._classifier_enabled = bool(
            _cfg_get(self._runtime_cfg, "app.enable_classifier", True)
        )
        self._last_inference_error: Optional[str] = None

        if bool(_cfg_get(self._runtime_cfg, "app.load_on_startup", False)):
            try:
                self.preload_model()
            except Exception:  # pragma: no cover - surfaced in health/status.
                LOGGER.exception("Startup model preload failed.")

    @property
    def mvp_mode(self) -> str:
        return self._mvp_mode

    @property
    def conditional_enabled(self) -> bool:
        return self._conditional_enabled

    @property
    def classifier_enabled(self) -> bool:
        return self._classifier_enabled

    def health_check(self) -> Dict[str, object]:
        loaded_ctx = self._model_manager.peek_loaded()
        loaded_clf = self._classifier_manager.peek_loaded()
        return {
            "status": "ok",
            "mvp_mode": self._mvp_mode,
            "conditional_enabled": self._conditional_enabled,
            "classifier_enabled": self._classifier_enabled,
            "model_loaded": self._model_manager.is_loaded,
            "classifier_loaded": self._classifier_manager.is_loaded,
            "device": str(loaded_ctx.device) if loaded_ctx else None,
            "artifacts_source": loaded_ctx.artifacts.source if loaded_ctx else None,
            "checkpoint_path": str(loaded_ctx.artifacts.ckpt_path) if loaded_ctx else None,
            "classifier_checkpoint_path": (
                str(loaded_clf.artifacts.ckpt_path) if loaded_clf else None
            ),
            "classifier_artifacts_source": (
                loaded_clf.artifacts.source if loaded_clf else None
            ),
            "loader_error": self._model_manager.last_error,
            "classifier_loader_error": self._classifier_manager.last_error,
            "inference_error": self._last_inference_error,
        }

    def preload_model(self) -> None:
        loaded = self._model_manager.load()
        if self._classifier_enabled:
            self._classifier_manager.load(device=loaded.device)

    def generate(
        self,
        length: int,
        num_samples: int,
        mode: Optional[str] = None,
        seed: Optional[int] = None,
        guidance_scale: Optional[float] = None,
        target_class: Optional[int] = None,
        num_timesteps: Optional[int] = None,
        reference_pdb_path: Optional[str] = None,
        fixed_residues: Optional[List[int]] = None,
        use_classifier_guidance: bool = False,
    ) -> InferenceResult:
        selected_mode = (mode or self._mvp_mode).lower()
        if selected_mode == "conditional":
            if not self._conditional_enabled:
                raise InferenceError(
                    "Conditional mode is disabled for MVP. "
                    "Set app.enable_conditional=true in config.yaml to enable the UI toggle."
                )
            return self.generate_conditional(
                num_samples=num_samples,
                reference_pdb_path=reference_pdb_path,
                fixed_residues=fixed_residues,
                seed=seed,
                num_timesteps=num_timesteps,
                use_classifier_guidance=use_classifier_guidance,
                guidance_scale=guidance_scale,
                target_class=target_class,
            )
        if selected_mode == "classifier":
            if not self._classifier_enabled:
                raise InferenceError(
                    "Classifier-guided mode is disabled. "
                    "Set app.enable_classifier=true in config.yaml to enable the UI toggle."
                )
            return self.generate_classifier_guided(
                length=length,
                num_samples=num_samples,
                seed=seed,
                guidance_scale=guidance_scale,
                target_class=target_class,
                num_timesteps=num_timesteps,
            )
        if selected_mode != "unconditional":
            raise InferenceError(f"Unsupported mode: {selected_mode}")
        return self.generate_unconditional(
            length=length,
            num_samples=num_samples,
            seed=seed,
            num_timesteps=num_timesteps,
        )

    @staticmethod
    def _resolve_interpolant_cfg(merged_cfg, num_timesteps: Optional[int]):
        interpolant_cfg = OmegaConf.select(merged_cfg, "inference.interpolant")
        if interpolant_cfg is None:
            raise InferenceError(
                "Missing interpolant config in merged runtime config (inference.interpolant)."
            )
        if num_timesteps is not None:
            steps = int(num_timesteps)
            if steps < 1 or steps > 1000:
                raise InferenceError("num_timesteps must be in [1, 1000].")
            interpolant_cfg = OmegaConf.merge(
                interpolant_cfg, OmegaConf.create({"sampling": {"num_timesteps": steps}})
            )
        return interpolant_cfg

    def generate_unconditional(
        self,
        length: int,
        num_samples: int,
        seed: Optional[int] = None,
        num_timesteps: Optional[int] = None,
    ) -> InferenceResult:
        min_length = int(_cfg_get(self._runtime_cfg, "app.min_length", 32))
        max_length = int(_cfg_get(self._runtime_cfg, "app.max_length", 1024))
        max_samples = int(_cfg_get(self._runtime_cfg, "app.max_samples_per_request", 4))
        if length < min_length or length > max_length:
            raise InferenceError(
                f"Length must be in [{min_length}, {max_length}] for this Space deployment."
            )
        if num_samples < 1 or num_samples > max_samples:
            raise InferenceError(
                f"num_samples must be in [1, {max_samples}] for this Space deployment."
            )

        try:
            loaded = self._model_manager.load()
            self._last_inference_error = None

            effective_seed = (
                int(seed)
                if seed is not None
                else int(_cfg_get(loaded.merged_cfg, "inference.seed", 123))
            )
            np.random.seed(effective_seed)
            torch.manual_seed(effective_seed)
            if loaded.device.type == "cuda":
                torch.cuda.manual_seed_all(effective_seed)

            interpolant_cfg = self._resolve_interpolant_cfg(loaded.merged_cfg, num_timesteps)
            effective_timesteps = int(
                _cfg_get(interpolant_cfg, "sampling.num_timesteps", 100)
            )

            output_root = Path(str(_cfg_get(loaded.merged_cfg, "inference.output_dir", "space_outputs")))
            if not output_root.is_absolute():
                output_root = (REPO_ROOT / output_root).resolve()
            run_id = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
            run_dir = output_root / f"space_unconditional_{run_id}"
            run_dir.mkdir(parents=True, exist_ok=True)

            interpolant = Interpolant(interpolant_cfg)
            interpolant.set_device(loaded.device)

            sample_files: List[str] = []
            trajectory_files: List[str] = []
            x0_trajectory_files: List[str] = []

            for sample_id in range(num_samples):
                sample_dir = run_dir / f"length_{length}" / f"sample_{sample_id}"
                sample_dir.mkdir(parents=True, exist_ok=True)

                atom37_traj, model_traj, _ = interpolant.sample(
                    num_batch=1,
                    num_res=length,
                    model=loaded.model,
                )
                bb_traj = to_numpy(torch.concat(atom37_traj, dim=0))
                model_x0_traj = np.flip(to_numpy(torch.concat(model_traj, dim=0)), axis=0)
                saved = save_traj(
                    sample=bb_traj[-1],
                    bb_prot_traj=bb_traj,
                    x0_traj=model_x0_traj,
                    diffuse_mask=np.ones(length, dtype=np.float32),
                    output_dir=str(sample_dir),
                )
                sample_files.append(saved["sample_path"])
                trajectory_files.append(saved["traj_path"])
                x0_trajectory_files.append(saved["x0_traj_path"])

            return InferenceResult(
                mode="unconditional",
                run_dir=str(run_dir),
                sample_files=sample_files,
                trajectory_files=trajectory_files,
                x0_trajectory_files=x0_trajectory_files,
                seed=effective_seed,
                artifacts_source=loaded.artifacts.source,
                num_timesteps=effective_timesteps,
            )
        except (ArtifactResolutionError, ModelLoadError, InferenceError) as exc:
            self._last_inference_error = str(exc)
            raise
        except Exception as exc:
            self._last_inference_error = str(exc)
            LOGGER.exception("Unexpected failure during unconditional inference.")
            raise InferenceError(str(exc)) from exc

    def generate_classifier_guided(
        self,
        length: int,
        num_samples: int,
        seed: Optional[int] = None,
        guidance_scale: Optional[float] = None,
        target_class: Optional[int] = None,
        num_timesteps: Optional[int] = None,
    ) -> InferenceResult:
        min_length = int(_cfg_get(self._runtime_cfg, "app.min_length", 32))
        max_length = int(_cfg_get(self._runtime_cfg, "app.max_length", 1024))
        max_samples = int(_cfg_get(self._runtime_cfg, "app.max_samples_per_request", 4))
        if length < min_length or length > max_length:
            raise InferenceError(
                f"Length must be in [{min_length}, {max_length}] for this Space deployment."
            )
        if num_samples < 1 or num_samples > max_samples:
            raise InferenceError(
                f"num_samples must be in [1, {max_samples}] for this Space deployment."
            )

        try:
            loaded = self._model_manager.load()
            classifier_ctx = self._classifier_manager.load(device=loaded.device)
            self._last_inference_error = None

            effective_seed = (
                int(seed)
                if seed is not None
                else int(_cfg_get(loaded.merged_cfg, "inference.seed", 123))
            )
            effective_guidance_scale = (
                float(guidance_scale)
                if guidance_scale is not None
                else float(
                    _cfg_get(loaded.merged_cfg, "inference.classifier.guidance_scale", 0.2)
                )
            )
            effective_target_class = (
                int(target_class)
                if target_class is not None
                else int(_cfg_get(loaded.merged_cfg, "inference.classifier.target_class", 1))
            )
            if effective_target_class not in (0, 1):
                raise InferenceError("target_class must be 0 or 1 for the binary classifier.")
            if effective_guidance_scale < 0:
                raise InferenceError("guidance_scale must be non-negative.")

            np.random.seed(effective_seed)
            torch.manual_seed(effective_seed)
            if loaded.device.type == "cuda":
                torch.cuda.manual_seed_all(effective_seed)

            interpolant_cfg = self._resolve_interpolant_cfg(loaded.merged_cfg, num_timesteps)
            effective_timesteps = int(
                _cfg_get(interpolant_cfg, "sampling.num_timesteps", 100)
            )

            output_root = Path(str(_cfg_get(loaded.merged_cfg, "inference.output_dir", "space_outputs")))
            if not output_root.is_absolute():
                output_root = (REPO_ROOT / output_root).resolve()
            run_id = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
            run_dir = output_root / f"space_classifier_{run_id}"
            run_dir.mkdir(parents=True, exist_ok=True)

            interpolant = Interpolant(interpolant_cfg)
            interpolant.set_device(loaded.device)

            sample_files: List[str] = []
            trajectory_files: List[str] = []
            x0_trajectory_files: List[str] = []

            for sample_id in range(num_samples):
                sample_dir = run_dir / f"length_{length}" / f"sample_{sample_id}"
                sample_dir.mkdir(parents=True, exist_ok=True)

                atom37_traj, model_traj, _ = interpolant.sample_clf(
                    num_batch=1,
                    num_res=length,
                    model=loaded.model,
                    clf_model=classifier_ctx.classifier,
                    guidance_scale=effective_guidance_scale,
                    target_class=effective_target_class,
                )
                bb_traj = to_numpy(torch.concat(atom37_traj, dim=0))
                model_x0_traj = np.flip(to_numpy(torch.concat(model_traj, dim=0)), axis=0)
                saved = save_traj(
                    sample=bb_traj[-1],
                    bb_prot_traj=bb_traj,
                    x0_traj=model_x0_traj,
                    diffuse_mask=np.ones(length, dtype=np.float32),
                    output_dir=str(sample_dir),
                )
                sample_files.append(saved["sample_path"])
                trajectory_files.append(saved["traj_path"])
                x0_trajectory_files.append(saved["x0_traj_path"])

            return InferenceResult(
                mode="classifier",
                run_dir=str(run_dir),
                sample_files=sample_files,
                trajectory_files=trajectory_files,
                x0_trajectory_files=x0_trajectory_files,
                seed=effective_seed,
                artifacts_source=loaded.artifacts.source,
                guidance_scale=effective_guidance_scale,
                target_class=effective_target_class,
                num_timesteps=effective_timesteps,
            )
        except (ArtifactResolutionError, ModelLoadError, InferenceError) as exc:
            self._last_inference_error = str(exc)
            raise
        except Exception as exc:
            self._last_inference_error = str(exc)
            LOGGER.exception("Unexpected failure during classifier-guided inference.")
            raise InferenceError(str(exc)) from exc

    def generate_conditional(
        self,
        num_samples: int,
        reference_pdb_path: Optional[str],
        fixed_residues: Optional[List[int]] = None,
        seed: Optional[int] = None,
        num_timesteps: Optional[int] = None,
        use_classifier_guidance: bool = False,
        guidance_scale: Optional[float] = None,
        target_class: Optional[int] = None,
        chain_id: str = "A",
        temperature: float = 1.0,
    ) -> InferenceResult:
        max_samples = int(_cfg_get(self._runtime_cfg, "app.max_samples_per_request", 4))
        if num_samples < 1 or num_samples > max_samples:
            raise InferenceError(
                f"num_samples must be in [1, {max_samples}] for this Space deployment."
            )
        if not reference_pdb_path:
            raise InferenceError(
                "Conditional mode requires a reference PDB upload to define fixed positions."
            )
        reference_path = Path(reference_pdb_path)
        if not reference_path.exists():
            raise InferenceError(f"Reference PDB not found: {reference_path}")

        try:
            loaded = self._model_manager.load()
            classifier_ctx = None
            if use_classifier_guidance:
                if not self._classifier_enabled:
                    raise InferenceError(
                        "Classifier guidance requested but classifier is disabled."
                    )
                classifier_ctx = self._classifier_manager.load(device=loaded.device)
            self._last_inference_error = None

            effective_seed = (
                int(seed)
                if seed is not None
                else int(_cfg_get(loaded.merged_cfg, "inference.seed", 123))
            )
            np.random.seed(effective_seed)
            torch.manual_seed(effective_seed)
            if loaded.device.type == "cuda":
                torch.cuda.manual_seed_all(effective_seed)

            pdb_feats = parse_pdb_feats(
                "reference", str(reference_path), chain_id=chain_id, exclude_hetatm=True
            )
            if "bb_positions" not in pdb_feats:
                raise InferenceError("Reference PDB has no backbone positions to fix.")
            num_res = int(pdb_feats["aatype"].shape[0])
            min_length = int(_cfg_get(self._runtime_cfg, "app.min_length", 32))
            max_length = int(_cfg_get(self._runtime_cfg, "app.max_length", 1024))
            if num_res < min_length or num_res > max_length:
                raise InferenceError(
                    f"Reference length {num_res} is outside [{min_length}, {max_length}]."
                )

            residue_indices = np.asarray(pdb_feats["residue_index"]).reshape(-1)
            fixed_positions = torch.tensor(
                np.asarray(pdb_feats["bb_positions"]), dtype=torch.float32, device=loaded.device
            )
            if fixed_positions.ndim != 2 or fixed_positions.shape[1] != 3:
                raise InferenceError(
                    f"Expected fixed_positions of shape [N, 3], got {tuple(fixed_positions.shape)}."
                )

            if fixed_residues:
                fixed_mask = torch.zeros(num_res, dtype=torch.bool, device=loaded.device)
                missing: List[int] = []
                for resnum in fixed_residues:
                    matches = np.where(residue_indices == int(resnum))[0]
                    if len(matches) == 0:
                        missing.append(int(resnum))
                        continue
                    fixed_mask[int(matches[0])] = True
                if missing:
                    raise InferenceError(
                        f"Fixed residue number(s) not found in reference PDB: {missing}."
                    )
                if not bool(fixed_mask.any()):
                    raise InferenceError("No valid fixed residues resolved from the request.")
            else:
                fixed_mask = torch.ones(num_res, dtype=torch.bool, device=loaded.device)

            fixed_residue_count = int(fixed_mask.sum().item())

            interpolant_cfg = self._resolve_interpolant_cfg(loaded.merged_cfg, num_timesteps)
            effective_timesteps = int(
                _cfg_get(interpolant_cfg, "sampling.num_timesteps", 100)
            )

            effective_guidance_scale = None
            effective_target_class = None
            clf_model = None
            if classifier_ctx is not None:
                clf_model = classifier_ctx.classifier
                effective_guidance_scale = (
                    float(guidance_scale)
                    if guidance_scale is not None
                    else float(_cfg_get(loaded.merged_cfg, "inference.classifier.guidance_scale", 0.2))
                )
                effective_target_class = (
                    int(target_class)
                    if target_class is not None
                    else int(_cfg_get(loaded.merged_cfg, "inference.classifier.target_class", 1))
                )

            output_root = Path(str(_cfg_get(loaded.merged_cfg, "inference.output_dir", "space_outputs")))
            if not output_root.is_absolute():
                output_root = (REPO_ROOT / output_root).resolve()
            run_id = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
            run_dir = output_root / f"space_conditional_{run_id}"
            run_dir.mkdir(parents=True, exist_ok=True)

            interpolant = Interpolant(interpolant_cfg)
            interpolant.set_device(loaded.device)

            flow_mask_np = to_numpy((~fixed_mask).float())

            sample_files: List[str] = []
            trajectory_files: List[str] = []
            x0_trajectory_files: List[str] = []

            for sample_id in range(num_samples):
                sample_dir = run_dir / f"length_{num_res}" / f"sample_{sample_id}"
                sample_dir.mkdir(parents=True, exist_ok=True)

                fixed_positions_b = fixed_positions.unsqueeze(0)
                atom37_traj, clean_atom37_traj, _ = interpolant.sample_conditional(
                    num_batch=1,
                    num_res=num_res,
                    model=loaded.model,
                    fixed_positions=fixed_positions_b,
                    fixed_mask=fixed_mask,
                    clf_model=clf_model,
                    guidance_scale=effective_guidance_scale or 0.2,
                    target_class=effective_target_class or 1,
                    temperature=temperature,
                )
                bb_traj = to_numpy(torch.concat(atom37_traj, dim=0))
                model_x0_traj = np.flip(to_numpy(torch.concat(clean_atom37_traj, dim=0)), axis=0)
                saved = save_traj(
                    sample=bb_traj[-1],
                    bb_prot_traj=bb_traj,
                    x0_traj=model_x0_traj,
                    diffuse_mask=flow_mask_np,
                    output_dir=str(sample_dir),
                )
                sample_files.append(saved["sample_path"])
                trajectory_files.append(saved["traj_path"])
                x0_trajectory_files.append(saved["x0_traj_path"])

            return InferenceResult(
                mode="conditional",
                run_dir=str(run_dir),
                sample_files=sample_files,
                trajectory_files=trajectory_files,
                x0_trajectory_files=x0_trajectory_files,
                seed=effective_seed,
                artifacts_source=loaded.artifacts.source,
                guidance_scale=effective_guidance_scale,
                target_class=effective_target_class,
                fixed_residue_count=fixed_residue_count,
                num_timesteps=effective_timesteps,
            )
        except (ArtifactResolutionError, ModelLoadError, InferenceError) as exc:
            self._last_inference_error = str(exc)
            raise
        except Exception as exc:
            self._last_inference_error = str(exc)
            LOGGER.exception("Unexpected failure during conditional inference.")
            raise InferenceError(str(exc)) from exc