import numpy as np
import torch
import torch.nn as nn

class ScaledDotProductAttention(nn.Module):
    ''' Scaled Dot-Product Attention '''

    def __init__(self, temperature, attn_dropout=0.1):
        super().__init__()
        self.temperature = temperature
        self.softmax = nn.Softmax(dim=2)

    def forward(self, q, k, v, mask=None):
        attn = torch.bmm(q, k.transpose(1, 2))
        attn = attn / self.temperature
        if mask is not None:
            attn = attn.masked_fill(mask, -np.inf)
        attn = self.softmax(attn)
        output = torch.bmm(attn, v)
        return output, attn



class MultiHeadAttention(nn.Module):
    ''' Multi-Head Attention module '''

    def __init__(self, n_head, d_model, d_k, d_v):
        super().__init__()
        self.n_head = n_head
        self.d_k = d_k
        self.d_v = d_v
        # linear layers
        self.w_qs = nn.Linear(d_model, n_head * d_k)
        self.w_ks = nn.Linear(d_model, n_head * d_k)
        self.w_vs = nn.Linear(d_model, n_head * d_v)
        self.fc = nn.Linear(n_head * d_v, d_model)
        # initialize weights
        nn.init.normal_(self.w_qs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
        nn.init.normal_(self.w_ks.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
        nn.init.normal_(self.w_vs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_v)))
        nn.init.xavier_normal_(self.fc.weight)
        # normalization
        self.layer_norm = nn.LayerNorm(d_model)
        # scaled dot-product
        self.attention = ScaledDotProductAttention(temperature=np.power(d_k, 0.5))


    def forward(self, q, k, v, mask=None):
        # dimensions
        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
        sz_b, len_q, _ = q.size()
        sz_b, len_k, _ = k.size()
        sz_b, len_v, _ = v.size()
        # linear transformations
        residual = q
        q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
        k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
        v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
        # scaled dot-product attention
        q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_q, d_k) # (n*b) x lq x dk
        k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_k, d_k) # (n*b) x lk x dk
        v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_v, d_v) # (n*b) x lv x dv
        if mask:
            mask = mask.repeat(n_head, 1, 1) # (n*b) x .. x ..
        output, attn = self.attention(q, k, v, mask=mask)
        # linear transformation
        output = output.view(n_head, sz_b, len_q, d_v)
        output = output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_q, -1) # b x lq x (n*dv)
        output = self.fc(output)
        # normalization
        output = self.layer_norm(output + residual)
        return output, attn