import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math, copy, time
import re
from torch.autograd import Variable


class EncoderDecoder(nn.Module):
    def __init__(self, encoder, decoder, src_embed, tgt_embed, generator):
        super(EncoderDecoder, self).__init__()
        self.encoder = encoder
        self.decoder = decoder
        self.src_embed = src_embed
        self.tgt_embed = tgt_embed
        self.generator = generator

    def forward(self, src, tgt, src_mask, tgt_mask):
        return self.decode(self.encode(src, src_mask), src_mask, tgt, tgt_mask)

    def encode(self, src, src_mask):
        return self.encoder(self.src_embed(src), src_mask)

    def decode(self, memory, src_mask, tgt, tgt_mask):
        return self.decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask)

    def save(self, path):
        #只保存模型参数
        params = {
            'state_dict': self.state_dict()
        }
        torch.save(params, path)

class Generator(nn.Module):
    def __init__(self, d_model, vocab):
        super(Generator, self).__init__()
        self.proj = nn.Linear(d_model, vocab)

    def forward(self, x):
        return F.log_softmax(self.proj(x), dim=-1)


def clones(module, N):
    return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])


class Encoder(nn.Module):
    def __init__(self, layer, N):
        super(Encoder, self).__init__()
        self.layers = clones(layer, N)
        self.norm = LayerNorm(layer.size)

    def forward(self, x, mask):
        "Pass the input (and mask) through each layer in turn."
        for layer in self.layers:
            x = layer(x, mask)
        return self.norm(x)


class EncoderLayer(nn.Module):
    def __init__(self, size, self_attn, feed_forward, dropout):
        super(EncoderLayer, self).__init__()
        self.self_attn = self_attn
        self.feed_forward = feed_forward
        self.sublayer = clones(SublayerConnection(size, dropout), 2)
        self.size = size

    def forward(self, x, mask):
        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
        return self.sublayer[1](x, self.feed_forward)


class Decoder(nn.Module):
    def __init__(self, layer, N):
        super(Decoder, self).__init__()
        self.layers = clones(layer, N)
        self.norm = LayerNorm(layer.size)

    def forward(self, x, memory, src_mask, tgt_mask):
        for layer in self.layers:
            x = layer(x, memory, src_mask, tgt_mask)
        return self.norm(x)


class DecoderLayer(nn.Module):
    def __init__(self, size, self_attn, src_attn, feed_forward, dropout):
        super(DecoderLayer, self).__init__()
        self.size = size
        self.self_attn = self_attn
        self.src_attn = src_attn
        self.feed_forward = feed_forward
        self.sublayer = clones(SublayerConnection(size, dropout), 3)

    def forward(self, x, memory, src_mask, tgt_mask):
        m = memory
        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask))
        x = self.sublayer[1](x, lambda x: self.src_attn(x, m, m, src_mask))
        return self.sublayer[2](x, self.feed_forward)


class LayerNorm(nn.Module):
    def __init__(self, features, eps=1e-6):
        super(LayerNorm, self).__init__()
        self.a_2 = nn.Parameter(torch.ones(features))
        self.b_2 = nn.Parameter(torch.zeros(features))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2


class SublayerConnection(nn.Module):
    """
    A residual connection followed by a layer norm.
    Note for code simplicity the norm is first as opposed to last.
    """
    def __init__(self, size, dropout):
        super(SublayerConnection, self).__init__()
        self.norm = LayerNorm(size)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, sublayer):
        return x + self.dropout(sublayer(self.norm(x)))

# def subsequent_mask(size):
#     attn_shape = (1, size, size)
#     subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')
#     #return的时候上三角转下三角
#     return torch.from_numpy(subsequent_mask) == 0

def subsequent_mask(size):
    #取下三角矩阵
    attn_shape = (1, size, size)
    subsequent_mask = np.tril(np.ones(attn_shape)).astype('uint8')
    return torch.from_numpy(subsequent_mask)


def attention(query, key, value, mask=None, dropout=None):
    d_k = query.size(-1)
    scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
    if mask is not None:
        scores = scores.masked_fill(mask == 0, -1e9)
    p_attn = F.softmax(scores, dim=-1)
    if dropout is not None:
        p_attn = dropout(p_attn)
    return torch.matmul(p_attn, value), p_attn


class MultiHeadedAttention(nn.Module):
    def __init__(self, h, d_model, dropout=0.1):
        super(MultiHeadedAttention, self).__init__()
        assert d_model % h == 0
        self.d_k = d_model // h
        self.h = h
        self.linears = clones(nn.Linear(d_model, d_model), 4)
        self.attn = None
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, query, key, value, mask=None):
        if mask is not None:
            mask = mask.unsqueeze(1)
        nbatches = query.size(0)
        query, key, value = [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2) for l, x in zip(self.linears, (query, key, value))]
        x, self.attn = attention(query, key, value, mask=mask, dropout=self.dropout)
        x = x.transpose(1, 2).contiguous().view(nbatches, -1, self.h * self.d_k)
        return self.linears[-1](x)


class PositionwiseFeedForward(nn.Module):
    def __init__(self, d_model, d_ff, dropout=0.1):
        super(PositionwiseFeedForward, self).__init__()
        self.w_1 = nn.Linear(d_model, d_ff)
        self.w_2 = nn.Linear(d_ff, d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        return self.w_2(self.dropout(F.relu(self.w_1(x))))


class Embeddings(nn.Module):
    def __init__(self, d_model, vocab):
        super(Embeddings, self).__init__()
        self.lut = nn.Embedding(len(vocab), d_model)
        self.d_model = d_model
    def forward(self, x):
        return self.lut(x) * math.sqrt(self.d_model)


class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float32).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2, dtype=torch.float32) * -(math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0)
        self.register_buffer('pe', pe)

    def forward(self, x):
        x = x + Variable(self.pe[:, :x.size(1)])
        return self.dropout(x)

def id2string(ids, id2w_vocab):
    text = [id2w_vocab[id] for id in ids]
    text = "".join(text)
    return text

def greedy_decode(model, data_iter, max_len, start_symbol, SRC, TGT):
    a = re.compile(r'\<.*?\>')
    trans_output = []
    bos_idx = TGT.vocab.stoi["<s>"]
    eos_idx = TGT.vocab.stoi["</s>"]
    blank_idx = TGT.vocab.stoi[" "]
    for step, batch in enumerate(data_iter):
        encoder_out = model.encode(batch.src,batch.src_mask)
        #size [batch_size,seq_len,model_dim]
        ys = torch.ones(encoder_out.size(0), 1).fill_(start_symbol).type_as(batch.src.data)                                                                         
        for i in range(max_len - 1):
            out = model.decode(encoder_out, batch.src_mask,
                               Variable(ys),
                               Variable(subsequent_mask(ys.size(1))
                                        .type_as(batch.src.data)))
            #out.size()[batch_size,1,d_model]
            # prob = model.generator(out[:,-1])
            prob = model.generator(out)
            #prob.size()[batch_size,tgt_vocab]
            _,next_word = prob.max(-1)
            next_word = next_word.data
            if i!=0:
                next_word =next_word.data[:,-1].unsqueeze(1)
            #next_word.size()[batch_size,1,d_model]
            ys = torch.cat([ys,next_word],dim=1)

        clean_out = []
        for line in ys:
            sent = []
            for word in line:
                if word == bos_idx or word == blank_idx:
                    continue
                elif word == eos_idx:
                    break
                else:
                    sent.append(word)
            clean_out.append(' '.join(id2string(sent, TGT.vocab.itos)))

        if step % 500 == 0:
            src = [a.sub('',''.join(id2string(line, SRC.vocab.itos))) for line in batch.src]
            tgt = [a.sub('', ''.join(id2string(line, TGT.vocab.itos))) for line in batch.trg]
            for i in range(5):
                print('*' * 50)
                print('Source: ', src[i])
                print('Target: ', tgt[i])
                print('Top Hypothesis: ',clean_out[i])
        trans_output.extend(clean_out)
    return trans_output

def line_greedy_decode(max_len,model, src, src_mask, start_symbol, TGT):
    memory = model.encode(src, src_mask)
    ys = torch.ones(1, 1).fill_(start_symbol).type_as(src.data)
    output = []
    for i in range(max_len-1):
        out = model.decode(memory, src_mask, 
                           Variable(ys), 
                           Variable(subsequent_mask(ys.size(1))
                                    .type_as(src.data)))
        prob = model.generator(out[:, -1])
        _, next_word = torch.max(prob, dim = 1)
        next_word = next_word.data[0]
        output_word = TGT.itos[next_word]
        ys = torch.cat([ys, torch.ones(1, 1).type_as(src.data).fill_(next_word)], dim=1)    
        if output_word == "</s>": 
            break
        else:
            output.append(output_word)
        
    return "".join(output)
