import os
import torch, datasets
import math, gc, time, random, copy
from itertools import chain
from typing import Dict, Tuple
from torch.utils.data import ConcatDataset, DataLoader, Dataset, random_split
import numpy as np
from torch.utils.data import BatchSampler
from dataset.node_tree import NodeTree

# import warnings
# warnings.filterwarnings("ignore", ".*does not have many workers.*")


class MeaningMap:
    def __init__(self, size=1048576, vocab_size=4096, max_subitem=10, min_subitem=1, use_cache=True, seed=42):
        assert size > 0 and vocab_size > 0 and max_subitem > 0 and min_subitem > 0, "Invalid input"
        assert min_subitem <= max_subitem, "Invalid input"
        np.random.seed(seed)
        self.size = size
        self.vocab_size = vocab_size
        self.max_subitem = max_subitem
        self.min_subitem = min_subitem
        datastep = 0x8000000

        path = "./data/"
        file = "structured_language_" + str(size) + "_" + str(vocab_size)
        file += "_" + str(max_subitem) + "_" + str(min_subitem)
        file_prop = path + file + "_prop.npy"
        file_data = path + file + "_data.npy"
        file_level = path + file + "_level.npy"
        file_rank_idx = path + file + "_rank_idx.npy"
        file_rank_all = path + file + "_rank_all.npy"

        start_time = time.time()
        if not os.path.exists(path):
            os.mkdir(path)
        if (
            os.path.exists(file_prop)
            and os.path.exists(file_data)
            and os.path.exists(file_level)
            and os.path.exists(file_rank_idx)
            and os.path.exists(file_rank_all)
            and use_cache
        ):
            print("Mapping Load from disk cache: " + file)
            slhwm = np.load(file_prop)
            self.ms_map = slhwm[:, 4:]
            self.ms_data = np.load(file_data)
            self.ms_start = slhwm[:, 0]
            self.ms_len = slhwm[:, 1]
            self.ms_level = np.load(file_level)
            self.ms_rank_idx = np.load(file_rank_idx)
            self.ms_rank_all = np.load(file_rank_all)
            self.ms_height = slhwm[:, 2]
            self.ms_weight = slhwm[:, 3]
            print("Mapping Load end, elapsed:" + str(time.time() - start_time) + "s")
        else:
            print("Mapping Disk cache miss, build new one. size:" + str(size))

            map = np.empty((size, max_subitem), dtype=np.int32)

            index = np.arange(0, size)
            map = np.random.random((size, max_subitem))

            mask_zero = map.copy()
            mask_zero[:, 0:min_subitem] = 0.0
            mask_zero.sort(axis=1)
            thre = np.random.random((size)).reshape(-1, 1).repeat(max_subitem, axis=1)
            mask_zero = mask_zero > thre

            item_sum = map.sum(axis=1)
            scale = (index / item_sum).reshape(-1, 1).repeat(max_subitem, axis=1)
            map = (map * scale).astype(np.int32)

            map[mask_zero] = -1

            map[:vocab_size, 0] = np.arange(0, vocab_size)
            map[:vocab_size, 1:] = -1

            ms_level = []  # meaning level, vocab's level is 0
            ms_rank_idx = []  # meaning index of all level
            ms_rank_all = []  # meaning all of all level
            ms_start = np.zeros((size), dtype=np.int32)  # meaning sequence start
            ms_end = np.zeros((size), dtype=np.int32)  # meaning sequence end
            ms_len = np.zeros((size), dtype=np.int32)  # meaning sequence len
            ms_height = np.zeros((size), dtype=np.int32)  # meaning tree height
            ms_weight = np.zeros((size), dtype=np.int32)  # meaning tree weight
            ms_data = np.zeros((datastep), dtype=np.int32)  # meaning sequence
            ms_level = np.zeros((datastep), dtype=np.uint32)  # meaning level, vocab's level is 0
            ms_rank_idx = np.zeros((datastep), dtype=np.uint32)  # meaning index of all level
            ms_rank_all = np.zeros((datastep), dtype=np.uint32)  # meaning all of all level

            index = 0
            for i in range(self.vocab_size):
                ms_data[i] = i
                ms_start[i] = index
                ms_end[i] = index + 1
                ms_len[i] = 1
                ms_level[i] = 0
                ms_rank_idx[i] = 0xFFFFFFF
                ms_rank_all[i] = 0xFFFFFFF
                ms_height[i] = 0
                ms_weight[i] = 1
                index = index + 1

            for i in range(self.vocab_size, size):
                m = map[i]  # 当前meaning的拆分的分支
                m = m[m >= 0]  # donot cut off the map such as [0]
                m_len = len(m)  # 当前meaning的拆分的分支个数
                m_list = m.tolist()
                assert m_list, "map list can not be empty list"

                # 获取每个子meaning的start和end，并且生成序列组合成当前meaning完整的叶index（<vocab_size)
                idx = np.concatenate([np.arange(ms_start[m], ms_end[m]) for m in m_list])
                idxidx = np.concatenate(
                    [np.ones(l, dtype=np.uint32) * i for i, l in enumerate(ms_end[m_list] - ms_start[m_list])]
                )
                len_ma = len(idx)

                end = index + len_ma
                if ms_data.size < end:  # 超过存储数据结构的大小，扩展一个datastep容量
                    ms_data = np.concatenate([ms_data, np.zeros((datastep), dtype=np.int32)])
                    ms_level = np.concatenate([ms_level, np.zeros((datastep), dtype=np.uint32)])
                    ms_rank_idx = np.concatenate([ms_rank_idx, np.zeros((datastep), dtype=np.uint32)])
                    ms_rank_all = np.concatenate([ms_rank_all, np.zeros((datastep), dtype=np.uint32)])

                ms_data[index:end] = ms_data[idx]  # 拼接当前meaning的所有token到data数据结构里面
                ms_level[index:end] = ms_level[idx] + 1  # 处理level
                ms_rank_idx[index:end] = (ms_rank_idx[idx] * 16 + idxidx).astype(np.uint32)  # 处理rank_idx
                ms_rank_all[index:end] = (ms_rank_all[idx] * 16 + m_len).astype(np.uint32)  # 处理rank_all

                ms_start[i] = index
                ms_end[i] = end
                ms_len[i] = len_ma
                ms_height[i] = max(ms_height[m_list]) + 1
                ms_weight[i] = sum(ms_weight[m_list])
                index = index + len_ma
                if i % 10000 == 0:
                    print(i)

            print("Mapping end, elapsed:" + str(time.time() - start_time) + "s")

            np.save(file_data, ms_data)
            np.save(file_level, ms_level)
            np.save(file_rank_idx, ms_rank_idx)
            np.save(file_rank_all, ms_rank_all)

            ms_start = np.array(ms_start).astype(np.int32)
            ms_height = np.array(ms_height).astype(np.int32)
            ms_weight = np.array(ms_weight).astype(np.int32)
            ms_len = np.array(ms_len).astype(np.int32)
            slhwm = np.concatenate(
                (
                    ms_start.reshape((-1, 1)),
                    ms_len.reshape((-1, 1)),
                    ms_height.reshape((-1, 1)),
                    ms_weight.reshape((-1, 1)),
                    map,
                ),
                axis=1,
            )
            np.save(file_prop, slhwm)

            self.ms_data = ms_data  # map[i]=ms_data[ms_start[i]:ms_start[i]+ms_len[i]]
            self.ms_level = ms_level
            self.ms_rank_idx = ms_rank_idx
            self.ms_rank_all = ms_rank_all

            self.ms_map = map  # ms_map[i] = [sub(i),sub(i),sub(i),sub(i)...sub(i)]
            self.ms_start = ms_start
            self.ms_len = ms_len
            self.ms_height = ms_height
            self.ms_weight = ms_weight
            print("Mapping Disk cache build end, elapsed:" + str(time.time() - start_time) + "s")

    def get_sequence(self, meaning):  # return sequence[meaning]
        start = self.ms_start[meaning]
        len = self.ms_len[meaning]
        return (
            self.ms_data[start : start + len],
            self.ms_level[start : start + len],
            self.ms_rank_idx[start : start + len],
            self.ms_rank_all[start : start + len],
        )

    def get_nodetree(self, meaning):  # return meaning all sub items
        def get_tree_node(ms_map, meaning, vocab_size, parent, seqlist):
            ms = ms_map[meaning]
            for m in ms[ms >= 0].tolist():
                if m >= self.vocab_size:
                    pn = NodeTree(str(m), parent)
                    get_tree_node(ms_map, m, vocab_size, pn, seqlist)
                else:
                    pn = NodeTree("<" + str(m) + ">", parent)
                    seqlist.append(pn)

        root = NodeTree(str(meaning))
        seqlist = []
        get_tree_node(self.ms_map, meaning, self.vocab_size, root, seqlist)
        root.seq_node = seqlist
        return root

    # 返回每个token相对于上一个token的level变化
    # 返回两个list，分别表示 common -> current -> common 两个变化的level距离
    def get_level_change(self, meaning):
        def level_change(ms_map, meaning, current_to_common, common_to_current):
            ms = ms_map[meaning]
            for m in ms[ms >= 0].tolist():
                if m >= self.vocab_size:
                    common_to_current[-1] = common_to_current[-1] + 1
                    level_change(ms_map, m, current_to_common, common_to_current)
                else:
                    current_to_common.append(0)
                    common_to_current.append(0)
            current_to_common[-2] = current_to_common[-2] + 1

        common_to_current = []
        common_to_current.append(1)
        current_to_common = []
        current_to_common.append(0)
        level_change(self.ms_map, meaning, current_to_common, common_to_current)
        current_to_common = current_to_common[:-1]
        common_to_current = common_to_current[:-1]
        return current_to_common, common_to_current

    # 根据meaning的层级结构范围一个二位的数组，表示所有token跟前面token是否有关系
    def get_relation_table(self, meaning):
        current_to_common, common_to_current = self.get_level_change(meaning)
        width = len(current_to_common)
        relation = np.zeros((width, width), dtype=int)
        relation[0, 0] = 1
        for i in range(1, width, 1):
            if i == width - 2:
                print(1)
            ori = current_to_common[i] - common_to_current[i]
            start_index = width
            for s in range(i - 1, -1, -1):
                if ori < 0:
                    break
                ori = ori - common_to_current[s] + current_to_common[s]
                start_index = s
            relation[i, start_index : i + 1] = 1.0
        return relation

    def max_length(self):
        return max(self.ms_len)


class MeaningDataset(Dataset):

    def __init__(
        self,
        start,
        end,
        vocab_size,
        size=None,
        max_subitem=10,
        min_subitem=1,
        min_seq_len=2,
        seed=42,
        use_cache=True,
    ):
        np.random.seed(seed)
        self.start = start
        self.end = end
        self.vocab_size = vocab_size
        self.max_subitem = max_subitem
        self.min_subitem = min_subitem
        self.use_cache = use_cache
        self.min_seq_len = min_seq_len
        print("Build MeaningDataset from MeaningMap.")
        self.val_mask_level = None
        self.val_mask_idx = None
        self.seq = []
        self.level = []
        self.rank_idx = []
        self.rank_all = []
        self.seq_meaning = []
        map = self.get_meaning_map()
        self.m_height = map.ms_height
        self.m_weight = map.ms_weight
        if size:
            meanings = np.random.randint(start, end, size=(size))
        else:
            meanings = np.arange(start, end)

        seq_len = []
        for m in meanings:
            d, l, i, a = map.get_sequence(m)
            if len(d) >= min_seq_len:
                self.seq.append(d)
                self.level.append(l)
                self.seq_meaning.append(m)
                seq_len.append(len(d))

                dm = np.ones(i.shape, dtype=np.uint32)
                dm = ((dm * 0xFFFFFFFF) << (l * 4)).astype(np.uint32)
                shift = (8 - l) * 4
                rank_idx = (i & 0xF) << 28
                rank_idx = rank_idx + ((i & 0xF0) << 20)
                rank_idx = rank_idx + ((i & 0xF00) << 12)
                rank_idx = rank_idx + ((i & 0xF000) << 4)
                rank_idx = rank_idx + ((i & 0xF0000) >> 4)
                rank_idx = rank_idx + ((i & 0xF00000) >> 12)
                rank_idx = rank_idx + ((i & 0xF000000) >> 20)
                rank_idx = rank_idx + ((i & 0xF0000000) >> 28)
                rank_idx = ((rank_idx >> shift) + dm).astype(np.uint32)
                rank_all = (a & 0xF) << 28
                rank_all = rank_all + ((a & 0xF0) << 20)
                rank_all = rank_all + ((a & 0xF00) << 12)
                rank_all = rank_all + ((a & 0xF000) << 4)
                rank_all = rank_all + ((a & 0xF0000) >> 4)
                rank_all = rank_all + ((a & 0xF00000) >> 12)
                rank_all = rank_all + ((a & 0xF000000) >> 20)
                rank_all = rank_all + ((a & 0xF0000000) >> 28)
                rank_all = ((rank_all >> shift) + dm).astype(np.uint32)

                self.rank_idx.append(rank_idx)
                self.rank_all.append(rank_all)

        unique, counts = np.unique(seq_len, return_counts=True)
        print("Build MeaningDataset end.")
        print("----------------------------------------------------------------")
        print("MeaningDataset start:" + str(start) + " end:" + str(end) + " space:" + str(end - start))
        print("MeaningDataset size:" + str(len(seq_len)))
        print("MeaningDataset max sequence length:" + str(max(unique)))
        print("MeaningDataset most popular sequence length:" + str(unique[np.argmax(counts)]))
        print("----------------------------------------------------------------")

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

    def len(self):
        return len(self.seq)

    def get_meaning_map(self):
        return MeaningMap(self.end, self.vocab_size, self.max_subitem, self.min_subitem, self.use_cache)

    def set_mask(self, level=None, idx=None):
        if self.val_mask_level is not None and self.val_mask_idx is not None:
            assert len(self.val_mask_level) > 0, "len must > 0"
            assert len(self.val_mask_level) == len(self.val_mask_idx), "mask level and mask index must be same length"
            assert isinstance(self.val_mask_level, list), "mask level must be list"
            assert isinstance(self.val_mask_idx, list), "mask index must be list"
        self.val_mask_level = level
        self.val_mask_idx = idx

    def __getitem__(self, idx):
        return self.get_batch([idx])

    def get_batch(self, idx_list):  # must equal sequence length
        data = [self.seq[i] for i in idx_list]
        output = {}
        data = torch.tensor(np.stack(data, axis=0)).long()
        output["input_ids"] = data
        output["labels"] = data.clone()
        output["token_type_ids"] = torch.zeros(data.shape)
        output["val_mask"] = self.get_seq_mask_tensor(idx_list)
        output["meaning"] = [self.seq_meaning[i] for i in idx_list]
        return output

    def get_token(self, idx):  # must equal sequence length
        return self.seq[idx]

    def get_meaning(self, idx):
        return self.seq_meaning[idx]

    def copy(self, start, end):
        new = copy.deepcopy(self)
        new.seq = new.seq[start:end]
        new.level = new.level[start:end]
        new.rank_idx = new.rank_idx[start:end]
        new.rank_all = new.rank_all[start:end]
        new.seq_meaning = new.seq_meaning[start:end]
        new.val_mask_level = self.val_mask_level
        new.val_mask_idx = self.val_mask_idx
        return new

    def split(self, ratio):
        l = self.len()
        middle = int(l * ratio)
        return self.copy(0, middle), self.copy(middle, l)

    def get_seq_mask(self, idx, level, index):
        # assert index < 15, "index must < 15"
        # assert level < 8, "level must < 8"
        rank_idx = (self.rank_idx[idx] >> (4 * level)).astype(np.int32) & 0xF
        rank_all = (self.rank_all[idx] >> (4 * level)).astype(np.int32) & 0xF
        return rank_idx == (rank_all + index if index < 0 else index)

    def get_seq_mask_tensor(self, idx_list):
        if self.val_mask_level is not None and self.val_mask_idx is not None:
            mask = torch.tensor(
                np.stack(
                    [self.get_seq_mask(idx, self.val_mask_level[0], self.val_mask_idx[0]) for idx in idx_list], axis=0
                )
            )
            for i, l in enumerate(self.val_mask_level[1:]):
                mask = mask & torch.tensor(
                    np.stack([self.get_seq_mask(idx, l, self.val_mask_idx[i + 1]) for idx in idx_list], axis=0)
                )
            return mask
        else:
            return None


class BatchGroupMeaningDataloader(Dataset):
    def __init__(self, meaning_dataset: MeaningDataset, batch_size, shuffle=True, drop_last=True):
        self.meaning_dataset = meaning_dataset
        self.batch_size = batch_size
        self.drop_last = drop_last

        seq_len = [len(s) for s in meaning_dataset.seq]
        unique, counts = np.unique(seq_len, return_counts=True)
        gl = {}
        for u in unique:
            gl[u] = np.where(seq_len == u)[0]

        lens = list(gl.keys())
        gs = {}
        if shuffle:
            for k in gl.keys():
                sl = gl[k].copy()
                np.random.shuffle(sl)
                gs[k] = sl
        else:
            for k in gl.keys():
                sl = gl[k].copy()
                gs[k] = sl

        index = np.zeros((0, batch_size), dtype=np.int64)
        for l in lens:
            batch = len(gs[l]) // batch_size
            new = gs[l][0 : batch * batch_size].reshape(batch, batch_size)
            index = np.concatenate((index, new), axis=0)

        if shuffle:
            index_shuffle = np.arange(0, index.shape[0])
            np.random.shuffle(index_shuffle)
            index = index[index_shuffle]
        self.indexBatch = index
        print("Dataloader batch size:" + str(batch_size) + " count:" + str(len(index)))
        print("Dataloader total:" + str(len(seq_len)) + " drop:" + str(len(seq_len) - len(index) * batch_size))

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

    def __getitem__(self, idx):
        return self.meaning_dataset.get_batch(self.indexBatch[idx])

    def detection_collate(batch):
        return batch[0]

    def dataloader(self, num_workers=1):
        return DataLoader(
            self, batch_size=1, num_workers=num_workers, collate_fn=BatchGroupMeaningDataloader.detection_collate
        )


if __name__ == "__main__":

    md = MeaningDataset(100000, 115200, vocab_size=1024, size=1024, use_cache=False)
    md.set_mask([1], [-1])
    train, val = md.split(0.95)
    fdaf = md.__getitem__(920)
    print(md.print_tree(920))
    print(md.rank_idx[920])
    print(md.rank_all[920])
    mask = md.get_seq_mask(920, 0, -1)
    print(mask)
    mask = md.get_seq_mask(920, 1, 0)
    print(mask)
    mask = md.get_seq_mask(920, 1, -1)
    print(mask)
    mask = md.get_seq_mask(920, 1, 1)
    print(mask)
    assert all(
        np.equal(
            mask[0:57],
            np.array(
                [
                    False,
                    False,
                    False,
                    False,
                    False,
                    False,
                    False,
                    False,
                    False,
                    False,
                    False,
                    True,
                    True,
                    True,
                    True,
                    True,
                    True,
                    True,
                    True,
                    True,
                    False,
                    False,
                    False,
                    False,
                    False,
                    False,
                    True,
                    False,
                    False,
                    False,
                    False,
                    False,
                    False,
                    False,
                    True,
                    True,
                    True,
                    True,
                    False,
                    False,
                    False,
                    False,
                    True,
                    True,
                    True,
                    True,
                    True,
                    True,
                    True,
                    True,
                    True,
                    False,
                    False,
                    False,
                    False,
                    False,
                    False,
                ]
            ),
        )
    ), "False"

    md = MeaningDataset(100000, 115200, vocab_size=1024, size=1024, min_subitem=2, use_cache=False)
    md.set_mask([0, 1], [0, -1])
    dl = BatchGroupMeaningDataloader(md, 1)
    length = len(dl)
    it = iter(dl)
    ne1 = next(it)
    tree = ne1["tree"]
    mask = ne1["mask"].cpu().numpy()
    t = MeaningMap.print_tree(tree)
    print(t)
    m, l = MeaningMap.get_tree_indexed_str(tree, mask, "")
    print(m)
    ne2 = next(it)
    ne3 = next(it)