# coding: UTF-8
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np


class Config(object):
    """配置参数"""

    def __init__(self, dataset, embedding):
        self.model_name = "DPCNN"
        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.vocab_path = dataset + "/data/vocab.pkl"  # 词表
        self.save_path = (
            dataset + "/saved_dict/" + self.model_name + ".ckpt"
        )  # 模型训练结果
        self.log_path = dataset + "/log/" + self.model_name
        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.device = torch.device(
            "cuda" if torch.cuda.is_available() else "cpu"
        )  # 设备

        self.dropout = 0.5  # 随机失活
        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 = 20  # 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
        self.num_filters = 250  # 卷积核数量(channels数)


"""Deep Pyramid Convolutional Neural Networks for Text Categorization"""


class DPCNN(nn.Module):
    def __init__(self, config):
        super(DPCNN, 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.conv_region = nn.Conv2d(1, config.num_filters, (3, config.embed), stride=1)
        self.conv = nn.Conv2d(config.num_filters, config.num_filters, (3, 1), stride=1)
        self.max_pool = nn.MaxPool2d(kernel_size=(3, 1), stride=2)
        self.padding1 = nn.ZeroPad2d((0, 0, 1, 1))  # top bottom
        self.padding2 = nn.ZeroPad2d((0, 0, 0, 1))  # bottom
        self.relu = nn.ReLU()
        self.fc = nn.Linear(config.num_filters, config.num_classes)

    def forward(self, x):
        x = x[0]
        x = self.embedding(x)
        x = x.unsqueeze(1)  # [batch_size, 250, seq_len, 1]
        x = self.conv_region(x)  # [batch_size, 250, seq_len-3+1, 1]

        x = self.padding1(x)  # [batch_size, 250, seq_len, 1]
        x = self.relu(x)
        x = self.conv(x)  # [batch_size, 250, seq_len-3+1, 1]
        x = self.padding1(x)  # [batch_size, 250, seq_len, 1]
        x = self.relu(x)
        x = self.conv(x)  # [batch_size, 250, seq_len-3+1, 1]
        while x.size()[2] > 2:
            x = self._block(x)
        x = x.squeeze()  # [batch_size, num_filters(250)]
        x = self.fc(x)
        return x

    def _block(self, x):
        x = self.padding2(x)
        px = self.max_pool(x)

        x = self.padding1(px)
        x = F.relu(x)
        x = self.conv(x)

        x = self.padding1(x)
        x = F.relu(x)
        x = self.conv(x)

        # Short Cut
        x = x + px
        return x
    
    def feature(self, x):
        """
        提取中间层特征向量，用于可视化
        返回最终squeeze后的特征（全连接层前面的那一层）
        """
        with torch.no_grad():
            x = x[0]
            x = self.embedding(x)
            x = x.unsqueeze(1)  # [batch_size, 1, seq_len, embed]
            x = self.conv_region(x)  # [batch_size, num_filters, seq_len-3+1, 1]

            x = self.padding1(x)  # [batch_size, num_filters, seq_len, 1]
            x = self.relu(x)
            x = self.conv(x)  # [batch_size, num_filters, seq_len-3+1, 1]
            x = self.padding1(x)  # [batch_size, num_filters, seq_len, 1]
            x = self.relu(x)
            x = self.conv(x)  # [batch_size, num_filters, seq_len-3+1, 1]
            while x.size()[2] > 2:
                x = self._block(x)
            features = x.squeeze()  # [batch_size, num_filters(250)]
            return features.cpu().numpy()
    
    def get_prediction(self, x):
        """
        获取模型最终层输出向量（logits）
        """
        with torch.no_grad():
            x = x[0]
            x = self.embedding(x)
            x = x.unsqueeze(1)
            x = self.conv_region(x)

            x = self.padding1(x)
            x = self.relu(x)
            x = self.conv(x)
            x = self.padding1(x)
            x = self.relu(x)
            x = self.conv(x)
            while x.size()[2] > 2:
                x = self._block(x)
            x = x.squeeze()
            predictions = self.fc(x)  # [batch_size, num_classes]
            return predictions.cpu().numpy()
    
    def prediction(self, features):
        """
        根据中间特征向量预测结果
        features: 来自feature()函数的输出 [batch_size, num_filters]
        """
        with torch.no_grad():
            features_tensor = torch.tensor(features, dtype=torch.float32).to(next(self.parameters()).device)
            predictions = self.fc(features_tensor)  # 直接通过最后的分类层
            return predictions.cpu().numpy()