import os
from typing import List, Optional, Callable
from functools import partial

import numpy as np
import torch
from scipy.interpolate import UnivariateSpline


def smooth_3d_array(points, num=None, **kwargs):
    x, y, z = points[:, 0], points[:, 1], points[:, 2]
    points = np.zeros((num, 3))
    if num is None:
        num = len(x)
    w = np.arange(0, len(x), 1)
    sx = UnivariateSpline(w, x, **kwargs)
    sy = UnivariateSpline(w, y, **kwargs)
    sz = UnivariateSpline(w, z, **kwargs)
    wnew = np.linspace(0, len(x), num)
    points[:, 0] = sx(wnew)
    points[:, 1] = sy(wnew)
    points[:, 2] = sz(wnew)
    return points


def calculate_tnb_frame(curve, epsilon=1e-8):
    curve = np.asarray(curve)

    # Calculate T (tangent)
    T = np.gradient(curve, axis=0)
    T_norms = np.linalg.norm(T, axis=1)
    T = T / T_norms[:, np.newaxis]

    # Identify straight segments
    is_straight = T_norms < epsilon

    # Calculate N (normal) for non-straight parts
    dT = np.gradient(T, axis=0)
    N = dT - np.sum(dT * T, axis=1)[:, np.newaxis] * T
    N_norms = np.linalg.norm(N, axis=1)

    # Handle points where the normal is undefined or in straight segments
    undefined_N = (N_norms < epsilon) | is_straight

    if np.all(undefined_N):
        # print("the entire curve is straight")
        # If the entire curve is straight, choose an arbitrary normal
        N = np.zeros_like(T)
        N[:, 0] = T[:, 1]
        N[:, 1] = -T[:, 0]
        N = N / np.linalg.norm(N, axis=1)[:, np.newaxis]
    elif np.any(undefined_N):
        # print("handling straight parts")
        # Only proceed with interpolation if there are any straight parts
        # Find segments of curved and straight parts
        segment_changes = np.where(np.diff(undefined_N))[0] + 1
        segments = np.split(np.arange(len(curve)), segment_changes)

        for segment in segments:
            if undefined_N[segment[0]]:
                # This is a straight segment
                left_curved = np.where(~undefined_N[: segment[0]])[0]
                right_curved = (
                    np.where(~undefined_N[segment[-1] + 1 :])[0] + segment[-1] + 1
                )

                if len(left_curved) > 0 and len(right_curved) > 0:
                    # Interpolate between left and right curved parts
                    left_N = N[left_curved[-1]]
                    right_N = N[right_curved[0]]
                    t = np.linspace(0, 1, len(segment))
                    N[segment] = (1 - t[:, np.newaxis]) * left_N + t[
                        :, np.newaxis
                    ] * right_N
                elif len(left_curved) > 0:
                    # Use normal from left curved part
                    N[segment] = N[left_curved[-1]]
                elif len(right_curved) > 0:
                    # Use normal from right curved part
                    N[segment] = N[right_curved[0]]
                else:
                    # No curved parts found, use arbitrary normal
                    N[segment] = np.array([T[segment[0]][1], -T[segment[0]][0], 0])

                # Ensure N is perpendicular to T
                N[segment] = (
                    N[segment]
                    - np.sum(N[segment] * T[segment], axis=1)[:, np.newaxis]
                    * T[segment]
                )
                N[segment] = (
                    N[segment] / np.linalg.norm(N[segment], axis=1)[:, np.newaxis]
                )
    else:
        # print("no straight parts")
        pass

    # If there are no straight parts, N is already calculated correctly for all points

    # Calculate B (binormal) ensuring orthogonality
    B = np.cross(T, N)

    # Ensure perfect orthogonality through Gram-Schmidt
    N = N - np.sum(N * T, axis=1)[:, np.newaxis] * T
    N = N / np.linalg.norm(N, axis=1)[:, np.newaxis]

    B = B - np.sum(B * T, axis=1)[:, np.newaxis] * T
    B = B - np.sum(B * N, axis=1)[:, np.newaxis] * N
    B = B / np.linalg.norm(B, axis=1)[:, np.newaxis]

    return T, N, B


def get_closest(pc_a, pc_b):
    """
    For each point in pc_a, find the closest point in pc_b
    Returns the distance and index of the closest point in pc_b for each point in pc_a
    Parameters
    ----------
    pc_a : [Mx3]
    pc_b : [Nx3]
    """
    tree = KDTree(pc_b)
    dist, idx = tree.query(pc_a, workers=-1)

    if np.max(idx) >= pc_b.shape[0]:
        raise ValueError("idx is out of range")

    return dist, idx


def straighten_using_frenet(helix, points):
    """
    Straighten the structure based on the helix (skeleton) using the Frenet frame.

    Args:
    - helix (numpy array): Points forming the helix (skeleton).
    - points (numpy array): Points surrounding the helix.

    Returns:
    - straightened_helix (numpy array): Straightened version of the helix.
    - straightened_points (numpy array): Transformed surrounding points.
    """
    # Compute the Frenet frame for the helix
    T, N, B = calculate_tnb_frame(helix)

    # Parameterize the helix based on cumulative distance (arclength)
    deltas = np.diff(helix, axis=0)
    distances = np.linalg.norm(deltas, axis=1)
    cumulative_distances = np.insert(np.cumsum(distances), 0, 0)

    # Map helix to a straight line along Z-axis
    straightened_helix = np.column_stack(
        (
            np.zeros_like(cumulative_distances),
            np.zeros_like(cumulative_distances),
            cumulative_distances,
        )
    )

    distances_to_helix, closest_idxs = get_closest(points, helix)
    vectors = points - helix[closest_idxs]
    r = distances_to_helix
    T_closest = T[closest_idxs]
    N_closest = N[closest_idxs]
    B_closest = B[closest_idxs]
    theta = np.arctan2(
        np.einsum("ij,ij->i", vectors, N_closest),
        np.einsum("ij,ij->i", vectors, B_closest),
    )
    phi = np.arccos(np.einsum("ij,ij->i", vectors, T_closest) / r)
    x = r * np.sin(phi) * np.cos(theta)
    y = r * np.sin(phi) * np.sin(theta)
    z = cumulative_distances[closest_idxs] + r * np.cos(phi)
    straightened_points = np.column_stack((x, y, z))

    return straightened_helix, np.array(straightened_points)


def frenet_transformation(pc, skel, lb):
    skel_smooth = smooth_3d_array(skel, num=skel.shape[0] * 100, s=200000)
    skel_trans, pc_trans = straighten_using_frenet(skel_smooth, pc)
    return pc_trans, skel_trans, lb


def transformation(trunk_id, pc, trunk_pc, label, frenet: bool):
    """
    Normalize the point cloud to unit sphere
    do frenet transformation

    Parameters
    ----------
    trunk_id : int
    pc
    trunk_pc
    label
    frenet : whether not to do FreNet transformation
    """

    unmodified_pc = pc.copy()
    if frenet:
        pc, trunk_pc, label = frenet_transformation(pc, trunk_pc, label)

    # NOTE: trunk_pc has variable length and cannot be collated using default_collate
    # normalize [N, 3] to unit sphere
    pc = pc - np.mean(pc, axis=0)
    m = np.max(np.sqrt(np.sum(pc**2, axis=1)))
    pc = pc / m

    # cast to int
    label = label.astype(int)

    return trunk_id, pc, label, unmodified_pc


class CachedDataset:
    def __init__(
        self,
        output_path: str,
        num_points: int,
        folds: List[List[int]],
        fold: int,
        is_train: bool,
        transform: Optional[Callable] = None,
    ):
        self.num_points = num_points
        self.transform = transform
        self.spanning_paths = np.load(
            os.path.join(output_path, "spanning_paths.npz"), allow_pickle=True
        )["spanning_paths"].item()

        if fold == -1:
            print("Loading all folds, ignoring is_train")
            trunk_ids = self.spanning_paths.keys()
        else:
            if is_train:
                trunk_ids = [
                    item
                    for idx, sublist in enumerate(folds)
                    if idx != fold
                    for item in sublist
                ]
            else:
                trunk_ids = folds[fold]
        self.trunk_ids = sorted(trunk_ids)

        files = []
        i = 0
        for id in sorted(self.spanning_paths.keys()):
            for path in self.spanning_paths[id]:
                if id in self.trunk_ids:
                    files.append(os.path.join(output_path, f"{i}.npz"))
                    assert os.path.exists(files[-1])
                i += 1
        self.files = files

    def __len__(self):
        return len(self.files)

    def __getitem__(self, idx):
        data = np.load(self.files[idx])
        trunk_id, pc, trunk_pc, label = (
            data["trunk_id"],
            data["pc"],
            data["trunk_pc"],
            data["label"],
        )
        assert trunk_id in self.trunk_ids

        # PC is [N, 3], downsample to [num_points, 3]
        random_permutation = np.random.permutation(pc.shape[0])
        pc = pc[random_permutation[: self.num_points]]
        label = label[random_permutation[: self.num_points]]

        if self.transform is None:
            return trunk_id, pc, trunk_pc, label
        else:
            return self.transform(trunk_id, pc, trunk_pc, label)


def get_dataloader(
    species: str,
    num_points: int,
    fold: int,
    is_train: bool,
    batch_size: int,
    num_workers: int,
    frenet: bool,
    distributed: bool = False,
    collate_fn: Optional[Callable] = None,
    path_length=10000,
):
    """
    Returns FreSeg dataloader for the given species and fold

    Parameters
    ----------
    species: one of ["seg_den", "mouse", "human"]
    num_points: number of points to sample from the point cloud
    fold: -1 to fetch all folds, 0-4 for seg_den
    is_train: bool
    batch_size
    num_workers
    frenet: whether to do FreNet transformation
    distributed: bool
    collate_fn
    path_length : fixed length of skeleton path (not configurable)
    """

    assert species in ["seg_den", "mouse", "human"]
    seg_den_folds = [
        [3, 5, 11, 12, 23, 28, 29, 32, 39, 42],
        [8, 15, 19, 27, 30, 34, 35, 36, 46, 49],
        [9, 14, 16, 17, 21, 26, 31, 33, 43, 44],
        [2, 6, 7, 13, 18, 24, 25, 38, 41, 50],
        [1, 4, 10, 20, 22, 37, 40, 45, 47, 48],
    ]

    if species != "seg_den":
        assert (
            fold == -1
        ), "Fold must be -1 for mouse and human datasets, since no splits"

    dataset = CachedDataset(
        f"{species}_1000000_{path_length}",
        num_points=num_points,
        folds=seg_den_folds if species == "seg_den" else [],
        fold=fold,
        is_train=is_train,
        transform=partial(transformation, frenet=frenet),
    )

    dataloader = torch.utils.data.DataLoader(
        dataset,
        batch_size=batch_size,
        shuffle=is_train and not distributed,
        num_workers=num_workers,
        pin_memory=True,
        drop_last=is_train,
        sampler=torch.utils.data.DistributedSampler(dataset) if distributed else None,
        collate_fn=collate_fn,
    )

    return dataloader, dataset.files


if __name__ == "__main__":
    human_loader, _ = get_dataloader(
        species="human",
        num_points=1024,
        fold=-1,
        is_train=True,
        batch_size=32,
        num_workers=8,
        frenet=False,
    )
    for i, data in enumerate(human_loader):
        trunk_id, pc, label, original_pc = data
        """
        trunk_id: array of trunk ids of length batch_size
        pc: point cloud in isotropic coordinates, modified using transformation(), shape [batch, num_points, 3]
        label: corresponding value of seg volume at that point, shape [batch, num_points]
            will be 0 if part of trunk, unique spine segment id otherwise
        original_pc: point cloud in isotropic coordinates, unmodified, shape [batch, num_points, 3]
        """
        pass