"""Custom MONAI transforms for binary coronary artery segmentation."""

import json

import numpy as np
from pathlib import Path
from typing import Dict, Hashable, Mapping, Optional, Any

import torch
from monai import transforms
from monai.config.type_definitions import KeysCollection, NdarrayOrTensor
from monai.utils.enums import TransformBackends
from scipy import ndimage


class ApplyWindowing(transforms.Transform):
    """
    Apply window presets to DICOM images.

    Windowing adapts the greyscale component of a CT image to highlight particular structures
    by reducing the range of Hounsfield units (HU) to be displayed.

    Args:
        window: a string for preset windows (brain, subdural, stroke, temporal bone,
            lungs, abdomen, liver, bone).
        upper: upper threshold for windowing
        lower: lower threshold for windowing
        width: window width
        level: window level (or window center)

    Raises:
        ValueError: if none or multiple of window/lower+upper/width+level are specified.
    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(
        self,
        window: Optional[str] = None,
        upper: Optional[int] = None,
        lower: Optional[int] = None,
        width: Optional[int] = None,
        level: Optional[int] = None,
    ):
        error_message = "Please specifiy either window or upper/lower or width/level."
        if window:
            if upper or lower:
                raise ValueError(error_message)
            if width or level:
                raise ValueError(error_message)
        elif upper and lower:
            if window:
                raise ValueError(error_message)
            if width or level:
                raise ValueError(error_message)
        elif width and level:
            if upper or lower:
                raise ValueError(error_message)
            if window:
                raise ValueError(error_message)
        else:
            raise ValueError(error_message)

        if window:
            if window == "brain":
                width, level = 80, 40
            elif window == "subdural":
                width, level = 130, 50
            elif window == "stroke":
                width, level = 8, 40
            elif window == "temporal bone":
                width, level = 2800, 700
            elif window == "lungs":
                width, level = 150, -600
            elif window == "abdomen":
                width, level = 400, 50
            elif window == "liver":
                width, level = 150, 30
            elif window == "bone":
                width, level = 1800, 400

        if width and level:
            upper = level + width // 2
            lower = level - width // 2

        self.upper = upper
        self.lower = lower

    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
        return img.clip(self.lower, self.upper)


class ApplyWindowingd(transforms.MapTransform):
    "Dictionary-based wrapper of :py:class:`ApplyWindowing`."

    def __init__(
        self,
        keys: KeysCollection,
        window: Optional[str] = None,
        upper: Optional[int] = None,
        lower: Optional[int] = None,
        width: Optional[int] = None,
        level: Optional[int] = None,
        allow_missing_keys: bool = False,
    ):
        super().__init__(keys=keys, allow_missing_keys=allow_missing_keys)
        self.windowing = ApplyWindowing(
            window=window, upper=upper, lower=lower, width=width, level=level
        )

    def __call__(
        self, data: Mapping[Hashable, NdarrayOrTensor]
    ) -> Dict[Hashable, NdarrayOrTensor]:
        d = dict(data)
        for key in self.key_iterator(d):
            d[key] = self.windowing(d[key])
        return d


# =============================================================================
# Normalization
# =============================================================================


def _to_numpy(img: NdarrayOrTensor) -> np.ndarray:
    """Convert tensor to numpy for percentile/statistics computation."""
    if isinstance(img, torch.Tensor):
        return img.cpu().numpy()
    return np.asarray(img)


def _from_numpy(arr: np.ndarray, reference: NdarrayOrTensor) -> NdarrayOrTensor:
    """Convert numpy back to the same type as reference, preserving MetaTensor metadata."""
    if isinstance(reference, torch.Tensor):
        result = torch.from_numpy(arr).to(reference.device)
        if hasattr(reference, 'meta'):
            from monai.data import MetaTensor
            result = MetaTensor(result, meta=reference.meta)
        return result
    return arr


class ZScoreForegroundNormalize(transforms.Transform):
    """
    Z-score normalization using only non-background voxels.

    Applied AFTER windowing. Computes mean and std only from voxels above
    a threshold (excluding background/air), then normalizes the entire image.

    Args:
        background_threshold: Voxels below this value are considered background.
            After windowing to [-100, 900], -50 excludes low-intensity regions.
    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(self, background_threshold: float = -50) -> None:
        self.background_threshold = background_threshold

    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
        arr = _to_numpy(img)
        mask = arr > self.background_threshold
        if mask.sum() > 0:
            mean = arr[mask].mean()
            std = arr[mask].std()
            arr = (arr - mean) / (std + 1e-8)
        else:
            arr = (arr - arr.mean()) / (arr.std() + 1e-8)
        return _from_numpy(arr.astype(np.float32), img)


class ZScoreForegroundNormalized(transforms.MapTransform):
    """Dictionary-based wrapper of :py:class:`ZScoreForegroundNormalize`."""

    def __init__(
        self,
        keys: KeysCollection,
        background_threshold: float = -50,
        allow_missing_keys: bool = False,
    ) -> None:
        super().__init__(keys=keys, allow_missing_keys=allow_missing_keys)
        self.normalizer = ZScoreForegroundNormalize(
            background_threshold=background_threshold
        )

    def __call__(
        self, data: Mapping[Hashable, NdarrayOrTensor]
    ) -> Dict[Hashable, NdarrayOrTensor]:
        d = dict(data)
        for key in self.key_iterator(d):
            d[key] = self.normalizer(d[key])
        return d


# =============================================================================
# Centerline extraction
# =============================================================================


def _get_neighbors(point, skel_arr):
    """Get 26-connected skeleton neighbors of a point."""
    neighbors = []
    for dx in (-1, 0, 1):
        for dy in (-1, 0, 1):
            for dz in (-1, 0, 1):
                if dx == 0 and dy == 0 and dz == 0:
                    continue
                nb = (point[0] + dx, point[1] + dy, point[2] + dz)
                if (0 <= nb[0] < skel_arr.shape[0]
                        and 0 <= nb[1] < skel_arr.shape[1]
                        and 0 <= nb[2] < skel_arr.shape[2]
                        and skel_arr[nb]):
                    neighbors.append(nb)
    return neighbors


def _trace_branch(start, skel_arr, visited, branch_points):
    """Trace a single branch from start until an endpoint or branch point.

    Follows the skeleton greedily through unvisited voxels. Stops when
    hitting a dead end, a branch point, or a previously visited voxel.
    Returns the ordered list of voxel coordinates along the branch.
    """
    path = [start]
    visited.add(start)
    current = start
    while True:
        nbs = [n for n in _get_neighbors(current, skel_arr) if n not in visited]
        if not nbs:
            break
        if len(nbs) == 1:
            current = nbs[0]
            visited.add(current)
            path.append(current)
            if current in branch_points:
                break
        else:
            # Multiple unvisited neighbors — pick closest to current direction
            if len(path) >= 2:
                direction = np.array(path[-1]) - np.array(path[-2])
                dists = [np.dot(np.array(n) - np.array(current), direction) for n in nbs]
                best = nbs[int(np.argmax(dists))]
            else:
                best = nbs[0]
            current = best
            visited.add(current)
            path.append(current)
            if current in branch_points:
                break
    return path


def _smooth_branch(points, affine, smoothing_factor=2.0):
    """Fit a B-spline to branch points and resample at ~1mm intervals.

    Args:
        points: List of (x, y, z) voxel coordinates.
        affine: 4x4 affine matrix mapping voxel to physical (mm).
        smoothing_factor: Spline smoothing (higher = smoother).

    Returns:
        List of [x, y, z] physical coordinates (mm), rounded to 2 decimals.
    """
    from scipy.interpolate import splprep, splev

    pts = np.array(points, dtype=float)

    # Convert to physical coordinates
    ones = np.ones((len(pts), 1))
    homogeneous = np.hstack([pts, ones])  # (N, 4)
    physical = (affine @ homogeneous.T).T[:, :3]  # (N, 3)

    if len(physical) < 4:
        return [[round(float(c), 2) for c in p] for p in physical]

    try:
        k = min(3, len(physical) - 1)
        tck, u = splprep(
            [physical[:, 0], physical[:, 1], physical[:, 2]],
            s=len(physical) * smoothing_factor,
            k=k,
        )
        # Compute arc length and resample at ~1mm
        diffs = np.diff(physical, axis=0)
        total_length = float(np.sum(np.sqrt(np.sum(diffs ** 2, axis=1))))
        n_out = max(int(total_length), 4)
        u_new = np.linspace(0, 1, n_out)
        smooth = np.array(splev(u_new, tck)).T
        return [[round(float(c), 2) for c in p] for p in smooth]
    except Exception:
        return [[round(float(c), 2) for c in p] for p in physical]


def extract_centerlines(binary_mask, affine, min_branch_points=3,
                        min_length_mm=5.0, smoothing_factor=2.0):
    """Extract vessel centerlines from a binary mask.

    Args:
        binary_mask: 3D numpy array (bool or int).
        affine: 4x4 affine matrix (voxel to mm).
        min_branch_points: Discard branches with fewer raw skeleton points.
        min_length_mm: Discard branches shorter than this (mm) after smoothing.
        smoothing_factor: Spline smoothing parameter.

    Returns:
        Dict with 'branches' list, each containing 'id', 'points_mm',
        'length_mm', and 'n_points'.
    """
    from skimage.morphology import skeletonize

    arr = np.asarray(binary_mask).squeeze().astype(bool)
    if not arr.any():
        return {"branches": []}

    skel = skeletonize(arr)

    # Classify skeleton voxels by neighbor count (26-connectivity)
    struct = ndimage.generate_binary_structure(3, 3)
    neighbor_count = ndimage.convolve(
        skel.astype(np.int32), struct.astype(np.int32), mode="constant"
    ) - skel.astype(np.int32)

    endpoints = set(map(tuple, np.argwhere(skel & (neighbor_count == 1))))
    branch_points = set(map(tuple, np.argwhere(skel & (neighbor_count >= 3))))

    # Trace branches starting from endpoints first, then branch points
    visited = set()
    raw_branches = []
    for start in list(endpoints) + list(branch_points):
        if start in visited:
            continue
        path = _trace_branch(start, skel, visited, branch_points)
        if len(path) >= min_branch_points:
            raw_branches.append(path)
        # Also explore unvisited directions from branch points
        if start in branch_points:
            for nb in _get_neighbors(start, skel):
                if nb not in visited:
                    path2 = _trace_branch(nb, skel, visited, branch_points)
                    if len(path2) >= min_branch_points:
                        raw_branches.append([start] + path2)

    # Smooth, convert to physical coordinates, and filter by length
    affine_np = np.array(affine, dtype=float)
    branches = []
    branch_id = 0
    for raw in raw_branches:
        pts_mm = _smooth_branch(raw, affine_np, smoothing_factor)
        if len(pts_mm) < 2:
            continue
        diffs = np.diff(pts_mm, axis=0)
        length = float(np.sum(np.sqrt(np.sum(np.array(diffs) ** 2, axis=1))))
        if length < min_length_mm:
            continue
        branches.append({
            "id": branch_id,
            "points_mm": pts_mm,
            "length_mm": round(length, 2),
            "n_points": len(pts_mm),
        })
        branch_id += 1

    return {"branches": branches}


class ExtractCenterlinesd(transforms.MapTransform):
    """Extract vessel centerlines from binary mask and save as JSON.

    Post-processing transform for inference. Takes the predicted binary mask,
    extracts a spline-smoothed centerline, and writes a JSON file with
    ordered branch points in physical (mm) coordinates.

    Output file: ``{output_dir}/{patient_name}_centerline.json``

    Args:
        keys: Key of the binary mask prediction (typically "pred").
        image_key: Key of the input image (for filename extraction).
        output_dir: Directory to write JSON files.
        min_branch_points: Minimum raw skeleton points per branch.
        min_length_mm: Discard branches shorter than this (mm).
        smoothing_factor: B-spline smoothing (higher = smoother).
    """

    def __init__(
        self,
        keys: KeysCollection,
        image_key: str = "image",
        output_dir: str = "./output",
        min_branch_points: int = 3,
        min_length_mm: float = 5.0,
        smoothing_factor: float = 2.0,
        allow_missing_keys: bool = False,
    ) -> None:
        super().__init__(keys=keys, allow_missing_keys=allow_missing_keys)
        self.image_key = image_key
        self.output_dir = output_dir
        self.min_branch_points = min_branch_points
        self.min_length_mm = min_length_mm
        self.smoothing_factor = smoothing_factor

    def __call__(self, data: Mapping[Hashable, Any]) -> Dict[Hashable, Any]:
        d = dict(data)
        for key in self.key_iterator(d):
            pred = d[key]
            mask_np = _to_numpy(pred)

            # Get affine from prediction metadata
            affine = np.eye(4)
            if hasattr(pred, "meta") and "affine" in pred.meta:
                affine = np.array(pred.meta["affine"], dtype=float)

            centerlines = extract_centerlines(
                mask_np, affine,
                min_branch_points=self.min_branch_points,
                min_length_mm=self.min_length_mm,
                smoothing_factor=self.smoothing_factor,
            )

            # Derive output filename from image metadata
            filename = "unknown"
            img = d.get(self.image_key)
            if img is not None and hasattr(img, "meta"):
                raw = img.meta.get("filename_or_obj", "unknown")
                filename = Path(str(raw)).stem
                for suffix in (".nii", ".nrrd", ".dcm"):
                    filename = filename.replace(suffix, "")

            out_path = Path(self.output_dir) / f"{filename}_centerline.json"
            out_path.parent.mkdir(parents=True, exist_ok=True)
            with open(out_path, "w") as f:
                json.dump(centerlines, f)

        return d