"""
Shared driver for the upstream NV-Generate-CTMR scripts.

Strategy: the upstream code is structured around argparse + global filesystem layout
(reads relative-path configs, writes to a relative output_dir). Rather than refactor
its internals, we treat it as an external tool: chdir into the upstream root, write
modified config copies that override the user-controlled fields, ensure weights are
downloaded, then call the upstream entry-point function. We then return the path of
the most recently produced NIfTI from the configured output dir.
"""
from __future__ import annotations

import contextlib
import importlib
import json
import os
import sys
import time
import uuid
from pathlib import Path
from typing import Iterable, Optional

ROOT = Path(__file__).resolve().parent.parent
UPSTREAM = ROOT / "repos" / "NV-Generate-CTMR"
GENERATED_OUTPUT = UPSTREAM / "output"


@contextlib.contextmanager
def upstream_context():
    """Temporarily add the upstream repo to sys.path and switch CWD to it."""
    if not UPSTREAM.exists():
        raise RuntimeError(
            f"Upstream repo not found at {UPSTREAM}. Run `bash pre-build.sh` first."
        )
    prev_cwd = os.getcwd()
    added = False
    try:
        upstream_str = str(UPSTREAM)
        if upstream_str not in sys.path:
            sys.path.insert(0, upstream_str)
            added = True
        os.chdir(upstream_str)
        yield UPSTREAM
    finally:
        os.chdir(prev_cwd)
        if added and upstream_str in sys.path:
            sys.path.remove(upstream_str)


def ensure_weights(version: str) -> None:
    """Download model weights from HF Hub if not already on disk."""
    with upstream_context():
        download_mod = importlib.import_module("scripts.download_model_data")
        # download_model_data is idempotent — it skips files that already exist.
        download_mod.download_model_data(version, "./", model_only=False if version in ("rflow-ct", "ddpm-ct") else True)


def _list_outputs_before(output_dir: Path) -> set[str]:
    if not output_dir.exists():
        return set()
    return {p.name for p in output_dir.glob("*.nii.gz")}


def _newest_outputs(output_dir: Path, before: set[str]) -> list[Path]:
    if not output_dir.exists():
        return []
    new = [p for p in output_dir.glob("*.nii.gz") if p.name not in before]
    new.sort(key=lambda p: p.stat().st_mtime)
    return new


def _write_temp_configs(
    base_env_config: Path,
    base_model_config: Path,
    overrides: dict,
    tag: str,
) -> tuple[Path, Path]:
    """
    Write modified copies of the env + model configs into a per-call temp dir under
    UPSTREAM / configs / _temp /.  Returns (env_path, model_path).
    """
    temp_dir = UPSTREAM / "configs" / "_temp"
    temp_dir.mkdir(parents=True, exist_ok=True)

    env = json.loads(base_env_config.read_text())
    model = json.loads(base_model_config.read_text())
    if "env" in overrides:
        env.update(overrides["env"])
    if "diffusion_unet_inference" in overrides:
        model.setdefault("diffusion_unet_inference", {}).update(overrides["diffusion_unet_inference"])

    suffix = f"{tag}_{uuid.uuid4().hex[:8]}"
    env_path = temp_dir / f"env_{suffix}.json"
    model_path = temp_dir / f"model_{suffix}.json"
    env_path.write_text(json.dumps(env, indent=2))
    model_path.write_text(json.dumps(model, indent=2))
    return env_path, model_path


def run_image_only(
    *,
    version: str,
    output_size: tuple[int, int, int],
    spacing: tuple[float, float, float],
    modality: int,
    seed: int,
    num_inference_steps: int = 30,
    cfg_guidance_scale: Optional[float] = None,
) -> Path:
    """
    Run the image-only diffusion pipeline (`scripts.diff_model_infer`) for the given
    version (rflow-ct / rflow-mr / rflow-mr-brain). Returns path to generated NIfTI.
    """
    ensure_weights(version)

    base_env = UPSTREAM / "configs" / f"environment_maisi_diff_model_{version}.json"
    base_model = UPSTREAM / "configs" / f"config_maisi_diff_model_{version}.json"
    network_def = UPSTREAM / "configs" / "config_network_rflow.json"

    inference_overrides = {
        "dim": list(output_size),
        "spacing": list(spacing),
        "modality": modality,
        "random_seed": seed,
        "num_inference_steps": num_inference_steps,
    }
    if cfg_guidance_scale is not None:
        inference_overrides["cfg_guidance_scale"] = cfg_guidance_scale

    with upstream_context():
        env_path, model_path = _write_temp_configs(
            base_env_config=base_env,
            base_model_config=base_model,
            overrides={"diffusion_unet_inference": inference_overrides},
            tag=version,
        )

        # Read env to determine output_dir (relative to upstream root)
        env_data = json.loads(env_path.read_text())
        output_dir = (UPSTREAM / env_data["output_dir"]).resolve()
        existing = _list_outputs_before(output_dir)

        diff_mod = importlib.import_module("scripts.diff_model_infer")

        t0 = time.time()
        diff_mod.diff_model_infer(
            env_config_path=str(env_path.relative_to(UPSTREAM)),
            model_config_path=str(model_path.relative_to(UPSTREAM)),
            model_def_path=str(network_def.relative_to(UPSTREAM)),
            num_gpus=1,
        )
        runtime = time.time() - t0

        new_files = _newest_outputs(output_dir, existing)
        if not new_files:
            raise RuntimeError(f"No new NIfTI produced in {output_dir}")
        latest = new_files[-1]

    # Cleanup temp configs (don't fail if cleanup errors)
    for p in (env_path, model_path):
        try:
            p.unlink()
        except OSError:
            pass

    return latest


def run_paired_ct(
    *,
    output_size: tuple[int, int, int],
    spacing: tuple[float, float, float],
    body_region: list[str],
    anatomy_list: list[str],
    seed: int,
    num_inference_steps: int = 30,
    num_output_samples: int = 1,
) -> tuple[Path, Optional[Path]]:
    """
    Run the paired CT image+mask pipeline (`scripts.inference`). Returns
    (image_path, mask_path). Mask is the corresponding label volume.
    """
    version = "rflow-ct"
    ensure_weights(version)

    base_env = UPSTREAM / "configs" / f"environment_{version}.json"
    base_infer = UPSTREAM / "configs" / "config_infer.json"

    # Build a custom config_infer with overrides
    infer_data = json.loads(base_infer.read_text())
    infer_data["output_size"] = list(output_size)
    infer_data["spacing"] = list(spacing)
    infer_data["body_region"] = list(body_region)
    infer_data["anatomy_list"] = list(anatomy_list)
    infer_data["num_inference_steps"] = num_inference_steps
    infer_data["num_output_samples"] = num_output_samples

    temp_dir = UPSTREAM / "configs" / "_temp"
    temp_dir.mkdir(parents=True, exist_ok=True)
    suffix = uuid.uuid4().hex[:8]
    infer_path = temp_dir / f"config_infer_{version}_{suffix}.json"
    infer_path.write_text(json.dumps(infer_data, indent=2))

    env_data = json.loads(base_env.read_text())
    output_dir = (UPSTREAM / env_data["output_dir"]).resolve()

    with upstream_context():
        existing = _list_outputs_before(output_dir)

        inference_mod = importlib.import_module("scripts.inference")
        # The upstream `main()` parses argv directly. Patch sys.argv around the call.
        old_argv = sys.argv
        sys.argv = [
            "scripts.inference",
            "-t", "./configs/config_network_rflow.json",
            "-i", str(infer_path.relative_to(UPSTREAM)),
            "-e", str(base_env.relative_to(UPSTREAM)),
            "--random-seed", str(seed),
            "--version", version,
        ]
        os.environ.setdefault("MONAI_DATA_DIRECTORY", str(UPSTREAM / "temp_work_dir"))

        try:
            inference_mod.main()
        finally:
            sys.argv = old_argv

        new_files = _newest_outputs(output_dir, existing)

    try:
        infer_path.unlink()
    except OSError:
        pass

    # Paired pipeline writes both image and label NIfTIs. Convention: filenames
    # contain "image" / "label" or are emitted as adjacent files.
    image_path: Optional[Path] = None
    mask_path: Optional[Path] = None
    for p in new_files:
        name = p.name.lower()
        if "label" in name or "_mask" in name or "seg" in name:
            mask_path = p
        elif "image" in name or "img" in name:
            image_path = p

    # Fallback: if naming is ambiguous, treat the smaller-modality-time file as image
    if image_path is None and new_files:
        image_path = new_files[0]
    if mask_path is None and len(new_files) > 1:
        mask_path = new_files[-1]

    if image_path is None:
        raise RuntimeError(f"No NIfTI produced in {output_dir}")
    return image_path, mask_path


def labels_present(mask_path: Path) -> set[int]:
    """Return the set of unique non-zero label IDs present in the mask volume."""
    import nibabel as nib
    import numpy as np

    img = nib.load(str(mask_path))
    data = np.asarray(img.dataobj)
    uniq = np.unique(data).astype(int).tolist()
    return {int(u) for u in uniq if u != 0}