import numpy

import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F

class HAN(nn.Module):
    def __init__(self, input_size, output_size, 
                word_hidden_size, word_num_layers, word_context_size, 
                sent_hidden_size, sent_num_layers, sent_context_size):
        super(HAN, self).__init__()

        self.word_layer = AttentionNet(input_size, 
                                    word_hidden_size, 
                                    word_num_layers, 
                                    word_context_size)
        self.sent_layer = AttentionNet(2* word_hidden_size, 
                                    sent_hidden_size, 
                                    sent_num_layers, 
                                    sent_context_size)
        self.output_layer = nn.Linear(2* sent_hidden_size, output_size)
        self.softmax = nn.Softmax()

    def forward(self, x, level='w'):
        # input is a sequence of vector
        # if level == w, a seq of words (a sent); level == s, a seq of sents (a doc)
        if level == 's':
            v = self.sent_layer(x)
            output = self.softmax(self.output_layer(v))
            return output
        elif level == 'w':
            s = self.word_layer(x)
            return s
        else:
            print('unknow level in Parameter!')


class AttentionNet(nn.Module):
    def __init__(self, input_size, gru_hidden_size, gru_num_layers, context_vec_size):
        super(AttentionNet, self).__init__()
        
        self.input_size = input_size
        self.gru_hidden_size = gru_hidden_size
        self.gru_num_layers = gru_num_layers
        self.context_vec_size = context_vec_size

        # Encoder
        self.gru = nn.GRU(input_size=input_size, 
                        hidden_size=gru_hidden_size, 
                        num_layers=gru_num_layers, 
                        batch_first=False, 
                        bidirectional=True)
        # Attention
        self.fc = nn.Linear(2* gru_hidden_size, context_vec_size)
        self.tanh = nn.Tanh()
        self.softmax = nn.Softmax()
        # context vector
        self.context_vec = nn.Parameter(torch.Tensor(context_vec_size, 1))
        self.context_vec.data.uniform_(-0.1, 0.1)

    def forward(self, inputs):
        # inputs's dim seq_len*word_dim
        inputs = torch.unsqueeze(inputs, 1)
        h_t, hidden = self.gru(inputs)
        h_t = torch.squeeze(h_t, 1)
        u = self.tanh(self.fc(h_t))
        alpha = self.softmax(torch.mm(u, self.context_vec))
        output = torch.mm(h_t.t(), alpha)
        return output