# 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_path, embedding='random'):
        self.model_name = 'TextCNN'
        self.train_path = dataset_path + '/train.txt'
        self.dev_path = dataset_path + '/dev.txt'
        self.test_path = dataset_path + '/test.txt'
        self.class_list = [x.strip() for x in open(
            dataset_path + '/class.txt', encoding='utf-8').readlines()]
        self.vocab_path = dataset_path + '/vocab.pkl'
        
        # 预训练词向量
        if embedding != 'random':
            self.embedding_pretrained = torch.tensor(
                np.load(dataset_path + '/' + embedding)["embeddings"].astype('float32'))
        else:
            self.embedding_pretrained = None
            
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

        # 模型参数
        self.dropout = 0.5
        self.require_improvement = 1000
        self.num_classes = len(self.class_list)
        self.n_vocab = 0  # 在运行时赋值
        self.num_epochs = 20
        self.batch_size = 128
        self.pad_size = 32
        self.learning_rate = 1e-3
        self.embed = self.embedding_pretrained.size(1) if self.embedding_pretrained is not None else 300
        self.filter_sizes = (2, 3, 4)
        self.num_filters = 256


class TextCNN(nn.Module):
    """TextCNN模型"""
    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[0])
        out = out.unsqueeze(1)
        out = torch.cat([self.conv_and_pool(out, conv) for conv in self.convs], 1)
        features = self.dropout(out)  # 保存特征层输出
        out = self.fc(features)
        return out, features

    def feature(self, x):
        """
        提取中间层特征向量，用于可视化
        返回dropout层的输出（最终层前面的那一层）
        """
        with torch.no_grad():
            out = self.embedding(x[0])
            out = out.unsqueeze(1)
            out = torch.cat([self.conv_and_pool(out, conv) for conv in self.convs], 1)
            features = self.dropout(out)
            return features.cpu().numpy()

    def get_prediction(self, x):
        """
        获取模型最终层的输出向量（logits）
        返回未经softmax的原始输出
        """
        with torch.no_grad():
            out = self.embedding(x[0])
            out = out.unsqueeze(1)
            out = torch.cat([self.conv_and_pool(out, conv) for conv in self.convs], 1)
            features = self.dropout(out)
            predictions = self.fc(features)
            return predictions.cpu().numpy()

    def prediction(self, features_vector):
        """
        根据中间特征向量预测结果
        features_vector: 与feature函数输出形状一致的向量
        返回预测类别
        """
        with torch.no_grad():
            if isinstance(features_vector, np.ndarray):
                features_vector = torch.tensor(features_vector, dtype=torch.float32).to(self.fc.weight.device)
            
            # 确保输入维度正确
            if len(features_vector.shape) == 1:
                features_vector = features_vector.unsqueeze(0)
                
            predictions = self.fc(features_vector)
            predicted_classes = torch.argmax(predictions, dim=1)
            return predicted_classes.cpu().numpy()