import os
import random

import numpy as np
import torch
from torch.utils.data import Dataset

# taken from https://github.com/optas/latent_3d_points/blob/8e8f29f8124ed5fc59439e8551ba7ef7567c9a37/src/in_out.py
synsetid_to_cate = {
    "02691156": "airplane",
    "02773838": "bag",
    "02801938": "basket",
    "02808440": "bathtub",
    "02818832": "bed",
    "02828884": "bench",
    "02876657": "bottle",
    "02880940": "bowl",
    "02924116": "bus",
    "02933112": "cabinet",
    "02747177": "can",
    "02942699": "camera",
    "02954340": "cap",
    "02958343": "car",
    "03001627": "chair",
    "03046257": "clock",
    "03207941": "dishwasher",
    "03211117": "monitor",
    "04379243": "table",
    "04401088": "telephone",
    "02946921": "tin_can",
    "04460130": "tower",
    "04468005": "train",
    "03085013": "keyboard",
    "03261776": "earphone",
    "03325088": "faucet",
    "03337140": "file",
    "03467517": "guitar",
    "03513137": "helmet",
    "03593526": "jar",
    "03624134": "knife",
    "03636649": "lamp",
    "03642806": "laptop",
    "03691459": "speaker",
    "03710193": "mailbox",
    "03759954": "microphone",
    "03761084": "microwave",
    "03790512": "motorcycle",
    "03797390": "mug",
    "03928116": "piano",
    "03938244": "pillow",
    "03948459": "pistol",
    "03991062": "pot",
    "04004475": "printer",
    "04074963": "remote_control",
    "04090263": "rifle",
    "04099429": "rocket",
    "04225987": "skateboard",
    "04256520": "sofa",
    "04330267": "stove",
    "04530566": "vessel",
    "04554684": "washer",
    "02992529": "cellphone",
    "02843684": "birdhouse",
    "02871439": "bookshelf",
    # '02858304': 'boat', no boat in our dataset, merged into vessels
    # '02834778': 'bicycle', not in our taxonomy
}
cate_to_synsetid = {v: k for k, v in synsetid_to_cate.items()}


class Uniform15KPC(Dataset):
    def __init__(
        self,
        root_dir,
        subdirs,
        tr_sample_size=10000,
        te_sample_size=10000,
        split="train",
        scale=1.0,
        normalize_per_shape=False,
        box_per_shape=False,
        random_subsample=False,
        normalize_std_per_axis=False,
        all_points_mean=None,
        all_points_std=None,
        input_dim=3,
        use_mask=False,
    ):
        self.root_dir = root_dir
        self.split = split
        self.in_tr_sample_size = tr_sample_size
        self.in_te_sample_size = te_sample_size
        self.subdirs = subdirs
        self.scale = scale
        self.random_subsample = random_subsample
        self.input_dim = input_dim
        self.use_mask = use_mask
        self.box_per_shape = box_per_shape
        if use_mask:
            self.mask_transform = PointCloudMasks(radius=5, elev=5, azim=90)

        self.all_cate_mids = []
        self.cate_idx_lst = []
        self.all_points = []

        for cate_idx, subd in enumerate(self.subdirs):
            # NOTE: [subd] here is synset id
            sub_path = os.path.join(root_dir, subd, self.split)
            if not os.path.isdir(sub_path):
                print("Directory missing : %s" % sub_path)
                continue

            all_mids = []
            for x in os.listdir(sub_path):
                if not x.endswith(".npy"):
                    continue
                all_mids.append(os.path.join(self.split, x[: -len(".npy")]))

            # NOTE: [mid] contains the split: i.e. "train/<mid>" or "val/<mid>" or "test/<mid>"
            for mid in all_mids:
                # obj_fname = os.path.join(sub_path, x)
                obj_fname = os.path.join(root_dir, subd, mid + ".npy")
                try:
                    point_cloud = np.load(obj_fname)  # (15k, 3)

                except:
                    continue

                assert point_cloud.shape[0] == 15000
                self.all_points.append(point_cloud[np.newaxis, ...])
                self.cate_idx_lst.append(cate_idx)
                self.all_cate_mids.append((subd, mid))

        # Shuffle the index deterministically (based on the number of examples)
        self.shuffle_idx = list(range(len(self.all_points)))
        random.Random(38383).shuffle(self.shuffle_idx)
        self.cate_idx_lst = [self.cate_idx_lst[i] for i in self.shuffle_idx]
        self.all_points = [self.all_points[i] for i in self.shuffle_idx]
        self.all_cate_mids = [self.all_cate_mids[i] for i in self.shuffle_idx]

        # Normalization
        self.all_points = np.concatenate(self.all_points)  # (N, 15000, 3)
        self.normalize_per_shape = normalize_per_shape
        self.normalize_std_per_axis = normalize_std_per_axis
        if all_points_mean is not None and all_points_std is not None:  # using loaded dataset stats
            self.all_points_mean = all_points_mean
            self.all_points_std = all_points_std
        elif self.normalize_per_shape:  # per shape normalization
            B, N = self.all_points.shape[:2]
            self.all_points_mean = self.all_points.mean(axis=1).reshape(B, 1, input_dim)
            if normalize_std_per_axis:
                self.all_points_std = self.all_points.reshape(B, N, -1).std(axis=1).reshape(B, 1, input_dim)
            else:
                self.all_points_std = self.all_points.reshape(B, -1).std(axis=1).reshape(B, 1, 1)
        elif self.box_per_shape:
            B, N = self.all_points.shape[:2]
            self.all_points_mean = self.all_points.min(axis=1).reshape(B, 1, input_dim)

            self.all_points_std = self.all_points.max(axis=1).reshape(B, 1, input_dim) - self.all_points.min(
                axis=1
            ).reshape(B, 1, input_dim)
        else:  # normalize across the dataset
            self.all_points_mean = self.all_points.reshape(-1, input_dim).mean(axis=0).reshape(1, 1, input_dim)
            if normalize_std_per_axis:
                self.all_points_std = self.all_points.reshape(-1, input_dim).std(axis=0).reshape(1, 1, input_dim)
            else:
                self.all_points_std = self.all_points.reshape(-1).std(axis=0).reshape(1, 1, 1)

        self.all_points = (self.all_points - self.all_points_mean) / self.all_points_std
        if self.box_per_shape:
            self.all_points = self.all_points - 0.5
        self.train_points = self.all_points[:, :10000]
        self.test_points = self.all_points[:, 10000:]

        self.tr_sample_size = min(10000, tr_sample_size)
        self.te_sample_size = min(5000, te_sample_size)
        print("Total number of data:%d" % len(self.train_points))
        print("Min number of points: (train)%d (test)%d" % (self.tr_sample_size, self.te_sample_size))
        assert self.scale == 1, "Scale (!= 1) is deprecated"

    def get_pc_stats(self, idx):
        if self.normalize_per_shape or self.box_per_shape:
            m = self.all_points_mean[idx].reshape(1, self.input_dim)
            s = self.all_points_std[idx].reshape(1, -1)
            return m, s

        return self.all_points_mean.reshape(1, -1), self.all_points_std.reshape(1, -1)

    def renormalize(self, mean, std):
        self.all_points = self.all_points * self.all_points_std + self.all_points_mean
        self.all_points_mean = mean
        self.all_points_std = std
        self.all_points = (self.all_points - self.all_points_mean) / self.all_points_std
        self.train_points = self.all_points[:, :10000]
        self.test_points = self.all_points[:, 10000:]

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

    def __getitem__(self, idx):
        tr_out = self.train_points[idx]
        if self.random_subsample:
            tr_idxs = np.random.choice(tr_out.shape[0], self.tr_sample_size)
        else:
            tr_idxs = np.arange(self.tr_sample_size)
        tr_out = torch.from_numpy(tr_out[tr_idxs, :]).float()

        te_out = self.test_points[idx]
        if self.random_subsample:
            te_idxs = np.random.choice(te_out.shape[0], self.te_sample_size)
        else:
            te_idxs = np.arange(self.te_sample_size)
        te_out = torch.from_numpy(te_out[te_idxs, :]).float()

        m, s = self.get_pc_stats(idx)
        cate_idx = self.cate_idx_lst[idx]
        sid, mid = self.all_cate_mids[idx]

        out = {
            "idx": idx,
            "train_points": tr_out,
            "test_points": te_out,
            "mean": m,
            "std": s,
            "cate_idx": cate_idx,
            "sid": sid,
            "mid": mid,
        }

        if self.use_mask:
            # masked = torch.from_numpy(self.mask_transform(self.all_points[idx]))
            # ss = min(masked.shape[0], self.in_tr_sample_size//2)
            # masked = masked[:ss]
            #
            # tr_mask = torch.ones_like(masked)
            # masked = torch.cat([masked, torch.zeros(self.in_tr_sample_size - ss, 3)],dim=0)#F.pad(masked, (self.in_tr_sample_size-masked.shape[0], 0), "constant", 0)
            #
            # tr_mask =  torch.cat([tr_mask, torch.zeros(self.in_tr_sample_size- ss, 3)],dim=0)#F.pad(tr_mask, (self.in_tr_sample_size-tr_mask.shape[0], 0), "constant", 0)
            # out['train_points_masked'] = masked
            # out['train_masks'] = tr_mask
            tr_mask = self.mask_transform(tr_out)
            out["train_masks"] = tr_mask

        return out


class ShapeNet15kPointClouds(Uniform15KPC):
    def __init__(
        self,
        root_dir="data/ShapeNetCore.v2.PC15k",
        categories=["airplane"],
        tr_sample_size=10000,
        te_sample_size=2048,
        split="train",
        scale=1.0,
        normalize_per_shape=False,
        normalize_std_per_axis=False,
        box_per_shape=False,
        random_subsample=False,
        all_points_mean=None,
        all_points_std=None,
        use_mask=False,
    ):
        self.root_dir = root_dir
        self.split = split
        assert self.split in ["train", "test", "val"]
        self.tr_sample_size = tr_sample_size
        self.te_sample_size = te_sample_size
        self.cates = categories
        if "all" in categories:
            self.synset_ids = list(cate_to_synsetid.values())
        else:
            self.synset_ids = [cate_to_synsetid[c] for c in self.cates]

        # assert 'v2' in root_dir, "Only supporting v2 right now."
        self.gravity_axis = 1
        self.display_axis_order = [0, 2, 1]

        super(ShapeNet15kPointClouds, self).__init__(
            root_dir,
            self.synset_ids,
            tr_sample_size=tr_sample_size,
            te_sample_size=te_sample_size,
            split=split,
            scale=scale,
            normalize_per_shape=normalize_per_shape,
            box_per_shape=box_per_shape,
            normalize_std_per_axis=normalize_std_per_axis,
            random_subsample=random_subsample,
            all_points_mean=all_points_mean,
            all_points_std=all_points_std,
            input_dim=3,
            use_mask=use_mask,
        )