import copy
import math

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


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


class LayerNorm(nn.Module):
    """Construct a LayerNorm 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 Encoders(nn.Module):
    # Core encoder is a stack of N layers
    def __init__(self, layer, N):
        super(Encoders, 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 SublayerConnection(nn.Module):
    """
    A residual connection followed by a LayerNorm.
    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):
        # Apply residual connection to any sublayer with the same size.
        return x + self.dropout(sublayer(self.norm(x)))


class EncoderLayer(nn.Module):
    """Encoder is made up of self-attn and feed forward"""

    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):
        # Follow Figure 1 (left) for connections.
        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
        return self.sublayer[1](x, self.feed_forward)


def attention(query, key, value, mask=None, dropout=None):
    """Compute 'Scaled Dot Product Attention"""
    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):
        """
        Take in model size and number of heads.
        h和d_model分别表示注意力头的个数，模型input feature的dimension
        """
        super(MultiHeadedAttention, self).__init__()
        assert d_model % h == 0
        # We assume d_v always equals d_k with dimensionality of 512/8=64
        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):
        "Implements Figure 2"
        if mask is not None:
            # Same mask applied to all h heads.
            mask = mask.unsqueeze(1)
        nbatches = query.size(0)

        # 1) Do all the linear projections in batch from d_model => h x d_k
        # after projections, q, k, v of shape (nBatches, h, num of words, d_k)
        # here we compute all eight q,k,v at once then split into 8 heads
        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))]

        # 2) Apply attention on all the projected vectors in batch.
        # x = (B, h, n, d_k)
        x, self.attn = attention(query, key, value, mask=mask,
                                 dropout=self.dropout)

        # 3) "Concat" using a view and apply a final linear.
        x = x.transpose(1, 2).contiguous() \
            .view(nbatches, -1, self.h * self.d_k)
        return self.linears[-1](x)


class PositionwiseFeedForward(nn.Module):
    """Implements FFN equation."""

    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):
        x = F.relu(self.w_1(x))
        x = self.dropout(x)
        x = self.w_2(x)
        return x


class PositionalEncoding(nn.Module):
    """Implement the PE function."""

    def __init__(self, d_model, dropout, max_len=500):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)

        # Compute the positional encodings once in log space.
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2) * -(math.log(10000.0) / d_model))
        # position * div_term 这里生成一个以pos为行坐标，i为列坐标的矩阵
        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)],
                         requires_grad=False)  # x.size(1)就是有多少个pos
        return self.dropout(x)


class TransformerEncoders(nn.Module):
    """combine all encoder layers together"""

    def __init__(self, N=6, d_model=512, d_ff=2048, h=8, dropout=0.1):
        super(TransformerEncoders, self).__init__()
        self.attn = MultiHeadedAttention(h, d_model)
        self.ff = PositionwiseFeedForward(d_model, d_ff, dropout)
        self.position = PositionalEncoding(d_model, dropout)

        # create N layers of encoder
        self.transEncoder = Encoders(EncoderLayer(d_model, self.attn, self.ff, dropout), N)

        # Initialize parameters
        for p in self.transEncoder.parameters():
            if p.dim() > 1:
                nn.init.xavier_uniform_(p)

    def forward(self, x):
        #x = self.position(x)
        x = self.transEncoder(x, None)

        return x


if __name__ == '__main__':
    encoder = TransformerEncoders(d_model=512)
    encoder.to('cuda')

    input = torch.randn(8, 10, 512)
    input = input.to('cuda')
    output = encoder(input)
    print(output.shape, output)
