import os
import sys
import torch
import numpy as np
import pickle
from tqdm import tqdm
from torch.utils.data import TensorDataset, DataLoader
from torch import nn
import logging4


# %% models

class Config(object):
    def __init__(self, model_name, dataset, embedding):
        self.model_name = model_name  # 'TextRNN', 'TextCNN'
        self.train_path = dataset + '/data/train.txt'  # 训练集
        self.dev_path = dataset + '/data/dev.txt'  # 验证集
        self.test_path = dataset + '/data/test.txt'  # 测试集
        self.class_list = [x.strip() for x in open(
            dataset + '/data/class.txt', encoding='utf-8').readlines()]  # 类别名单
        self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'  # 模型训练结果
        self.vocab_path = dataset + '/data/vocab.pkl'  # 词表
        self.embedding_pretrained = torch.tensor(
            np.load(dataset + '/data/' + embedding)["embeddings"].astype('float32')) \
            if embedding != 'random' else None  # 预训练词向量
        self.dropout = 0.5
        self.require_improvement = 1000  # 若超过1000batch效果还没提升，则提前结束训练
        self.num_classes = len(self.class_list)  # 类别数
        self.n_vocab = 0  # 词表大小，在运行时赋值
        self.num_epochs = 10  # epoch数
        self.batch_size = 128  # mini-batch大小
        self.pad_size = 32  # 每句话处理成的长度(短填长切)
        self.learning_rate = 1e-3  # 学习率
        self.embed = self.embedding_pretrained.size(1) \
            if self.embedding_pretrained is not None else 300  # 字向量维度, 若使用了预训练词向量，则维度统一

        if self.model_name == 'TextRNN':
            self.hidden_size = 128  # lstm隐藏层
            self.num_layers = 2  # lstm层数
        elif self.model_name == 'TextCNN':
            self.filter_sizes = (2, 3, 4)  # 卷积核尺寸
            self.num_filters = 256  # 卷积核数量(channels数)

        self.logger = None
        self.set_logger('log_' + model_name + '.txt')

        self.device = None
        self.set_device()

    def set_logger(self, logfile):
        logger = logging4.Logger(name=self.model_name)
        logger.add_channel(filename=logfile, level=logging4.INFO)
        logger.add_channel(filename=sys.stdout, level=logging4.INFO)
        self.logger = logger

    def set_device(self):
        self.logger.info('可用GPU的数量：', torch.cuda.device_count())
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # 设备


class TextRNN(nn.Module):
    def __init__(self, config):
        super(TextRNN, self).__init__()
        if config.embedding_pretrained is not None:
            self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
        else:
            self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)
        self.lstm = nn.LSTM(config.embed, config.hidden_size, config.num_layers,
                            bidirectional=True, batch_first=True, dropout=config.dropout)
        self.fc = nn.Linear(config.hidden_size * 2, config.num_classes)

    def forward(self, x):
        out = self.embedding(x)  # [batch_size, seq_len, embeding]=[128, 32, 300]
        out, _ = self.lstm(out)
        out = self.fc(out[:, -1, :])  # 句子最后时刻的 hidden state
        return out


class TextCNN(nn.Module):
    def __init__(self, config):
        super(TextCNN, self).__init__()
        if config.embedding_pretrained is not None:
            self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
        else:
            self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)
        self.convs = nn.ModuleList(
            [nn.Conv2d(1, config.num_filters, (k, config.embed)) for k in config.filter_sizes])
        self.dropout = nn.Dropout(config.dropout)
        self.fc = nn.Linear(config.num_filters * len(config.filter_sizes), config.num_classes)

    def conv_and_pool(self, x, conv):
        x = F.relu(conv(x)).squeeze(3)
        x = F.max_pool1d(x, x.size(2)).squeeze(2)
        return x

    def forward(self, x):
        out = self.embedding(x)
        out = out.unsqueeze(1)
        out = torch.cat([self.conv_and_pool(out, conv) for conv in self.convs], 1)
        out = self.dropout(out)
        out = self.fc(out)
        return out


# %%Data

MAX_VOCAB_SIZE = 10000  # 词表长度限制
UNK, PAD = '<UNK>', '<PAD>'  # 未知字，padding符号


def get_vocab_from_file(file_path, tokenizer, max_size, min_freq):
    vocab_dic = {}
    with open(file_path, 'r', encoding='UTF-8') as f:
        for line in tqdm(f):
            lin = line.strip()
            if not lin:
                continue
            content = lin.split('\t')[0]
            for word in tokenizer(content):
                vocab_dic[word] = vocab_dic.get(word, 0) + 1
        vocab_list = sorted([_ for _ in vocab_dic.items() if _[1] >= min_freq], key=lambda x: x[1], reverse=True)[
                     :max_size]
        vocab_dic = {word_count[0]: idx for idx, word_count in enumerate(vocab_list)}
        vocab_dic.update({UNK: len(vocab_dic), PAD: len(vocab_dic) + 1})
    return vocab_dic


def sentence2idx(sentence, tokenizer, vocab, pad_size=32):
    words_line = []
    token = tokenizer(sentence)
    if pad_size:
        if len(token) < pad_size:
            token.extend([PAD] * (pad_size - len(token)))
        else:
            token = token[:pad_size]
    for word in token:
        words_line.append(vocab.get(word, vocab.get(UNK)))
    return words_line


def build_dataset(config, ues_word=False):
    if ues_word:
        tokenizer = lambda x: x.split(' ')  # word-level
    else:
        tokenizer = lambda x: [y for y in x]  # char-level

    # 如果有vocab，就使用vocab，否则就从train生成
    # vocab 的内容： {" ": 0, "字": 1, "中": 2, ...}
    if os.path.exists(config.vocab_path):
        vocab = pickle.load(open(config.vocab_path, 'rb'))
    else:
        vocab = get_vocab_from_file(config.train_path, tokenizer=tokenizer, max_size=MAX_VOCAB_SIZE, min_freq=1)
        pickle.dump(vocab, open(config.vocab_path, 'wb'))
        config.logger.info(f'Build a new vocab to {config.vocab_path}')

    config.logger.info(f"Vocab size: {len(vocab)}")

    def load_dataset(path, config, pad_size=32):
        data_list, label_list, seq_len_list = [], [], []
        with open(path, 'r', encoding='UTF-8') as f:
            for line in tqdm(f):
                lin = line.strip()
                if not lin:
                    continue
                sentence, label = lin.split('\t')

                words_line = sentence2idx(sentence, tokenizer, vocab, pad_size=pad_size)

                data_list.append(words_line)
                label_list.append(int(label))

        return TensorDataset(torch.tensor(data_list).type(torch.int64).to(config.device)
                             , torch.tensor(label_list).type(torch.int64).to(config.device))

    train = load_dataset(config.train_path, config, config.pad_size)
    dev = load_dataset(config.dev_path, config, config.pad_size)
    test = load_dataset(config.test_path, config, config.pad_size)
    return vocab, train, dev, test


# %%train
import torch.nn as nn
import torch.nn.functional as F
from sklearn import metrics


# 权重初始化，默认xavier
def init_network(model, method='xavier', exclude='embedding', seed=123):
    for name, w in model.named_parameters():
        if exclude not in name:
            if 'weight' in name:
                if method == 'xavier':
                    nn.init.xavier_normal_(w)
                elif method == 'kaiming':
                    nn.init.kaiming_normal_(w)
                else:
                    nn.init.normal_(w)
            elif 'bias' in name:
                nn.init.constant_(w, 0)
            else:
                pass


def train(config, model, train_iter, dev_iter, test_iter):
    model.train()
    optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)

    # 学习率指数衰减，每次epoch：学习率 = gamma * 学习率
    # scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
    total_batch = 0  # 记录进行到多少batch
    dev_best_loss = float('inf')
    last_improve = 0  # 记录上次验证集loss下降的batch数
    flag = False  # 记录是否很久没有效果提升

    for epoch in range(config.num_epochs):
        config.logger.info('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs))
        # scheduler.step() # 学习率衰减
        for data, labels in train_iter:
            outputs = model(data)
            model.zero_grad()
            loss = F.cross_entropy(outputs, labels)
            loss.backward()
            optimizer.step()
            if total_batch % 100 == 0:
                # 每多少轮输出在训练集和验证集上的效果
                true = labels.data.cpu()
                predic = torch.max(outputs.data, 1)[1].cpu()
                train_acc = metrics.accuracy_score(true, predic)
                dev_acc, dev_loss = evaluate(config, model, dev_iter)
                if dev_loss < dev_best_loss:
                    dev_best_loss = dev_loss
                    torch.save(model.state_dict(), config.save_path)
                    improve = '*'
                    last_improve = total_batch
                else:
                    improve = ''
                msg = 'Iter: {0:>6}, Train Loss: {1:>5.2}, Train Acc: {2:>6.2%}, Val Loss: {3:>5.2}, Val Acc: {4:>6.2%} {5}'
                config.logger.info(msg.format(total_batch, loss.item(), train_acc, dev_loss, dev_acc, improve))

                model.train()
            total_batch += 1
            if total_batch - last_improve > config.require_improvement:
                # 验证集loss超过1000batch没下降，结束训练
                config.logger.info("No optimization for a long time, auto-stopping...")
                flag = True
                break
        if flag:
            break

    get_profamance_on_tests(config, model, test_iter)


def get_profamance_on_tests(config, model, test_iter):
    model.load_state_dict(torch.load(config.save_path))
    model.eval()
    test_acc, test_loss, test_report, test_confusion = evaluate(config, model, test_iter, test=True)
    msg = 'Test Loss: {0:>5.2},  Test Acc: {1:>6.2%}'
    config.logger.info(msg.format(test_loss, test_acc))
    config.logger.info("Precision, Recall and F1-Score...")
    config.logger.info(test_report)
    config.logger.info("Confusion Matrix...")
    config.logger.info(test_confusion)


def evaluate(config, model, data_iter, test=False):
    model.eval()
    loss_total = 0
    predict_all = np.array([], dtype=int)
    labels_all = np.array([], dtype=int)
    with torch.no_grad():
        for data, labels in data_iter:
            outputs = model(data)
            loss = F.cross_entropy(outputs, labels)
            loss_total += loss
            labels = labels.data.cpu().numpy()
            predic = torch.max(outputs.data, 1)[1].cpu().numpy()
            labels_all = np.append(labels_all, labels)
            predict_all = np.append(predict_all, predic)

    acc = metrics.accuracy_score(labels_all, predict_all)
    if test:
        report = metrics.classification_report(labels_all, predict_all, target_names=config.class_list, digits=4)
        confusion = metrics.confusion_matrix(labels_all, predict_all)
        return acc, loss_total / len(data_iter), report, confusion
    return acc, loss_total / len(data_iter)


# %%run


# model_name = 'TextRNN'  # 'TextRCNN'  # TextCNN, TextRNN, FastText, TextRCNN, TextRNN_Att, DPCNN, Transformer
dataset = 'THUCNews'  # 数据集

embedding_type = 'embedding_SougouNews.npz'  # 搜狗新闻:embedding_SougouNews.npz, 腾讯:embedding_Tencent.npz, 随机初始化:random

word = False  # True for word, False for char，如果为 True，用空格来分割；如果为 False，每个字符是个基本单位。

# config = Config('TextRNN', dataset, embedding_type)
# model = TextRNN(config).to(config.device)

config = Config('TextCNN', dataset, embedding_type)
config.logger.info("Loading data...")

model = TextCNN(config).to(config.device)

batch_size = 128

vocab, train_data, dev_data, test_data = build_dataset(config, word)

train_iter = DataLoader(dataset=train_data, batch_size=batch_size, shuffle=True)
dev_iter = DataLoader(dataset=dev_data, batch_size=batch_size, shuffle=True)
test_iter = DataLoader(dataset=test_data, batch_size=batch_size, shuffle=True, )

config.n_vocab = len(vocab)

# %%train
config.logger.info(model)
init_network(model)
train(config, model, train_iter, dev_iter, test_iter)

# %%加载已经保存好的模型
model.load_state_dict(torch.load(config.save_path))

sentence = '状元心经：考前一周重点是回顾和整理'
model.eval()

sentence_idx = torch.tensor(
    sentence2idx(sentence, list, vocab, pad_size=32), dtype=torch.int64, device=config.device
).reshape(1, -1)

outputs = model(sentence_idx)
predict = torch.argmax(outputs.data, 1).cpu()
config.logger.info(predict)