import torch

from torch import nn

import torch.nn.functional as F

import math

import copy

import numpy as np

from torch.nn.utils.rnn import pack_padded_sequence

class Sparsemax(nn.Module):

    """Sparsemax function."""

    def __init__(self, dim=None):

        super(Sparsemax, self).__init__()

        self.dim = -1 if dim is None else dim

    def forward(self, input, device='cuda'):

        original_size = input.size()

        input = input.view(-1, input.size(self.dim))

        dim = 1

        number_of_logits = input.size(dim)

        input = input - torch.max(input, dim=dim, keepdim=True)[0].expand_as(input)

        zs = torch.sort(input=input, dim=dim, descending=True)[0]

        range = torch.arange(start=1, end=number_of_logits+1, device=device, dtype=torch.float32).view(1, -1)

        range = range.expand_as(zs)

        bound = 1 + range * zs

        cumulative_sum_zs = torch.cumsum(zs, dim)

        is_gt = torch.gt(bound, cumulative_sum_zs).type(input.type())

        k = torch.max(is_gt * range, dim, keepdim=True)[0]

        zs_sparse = is_gt * zs

        taus = (torch.sum(zs_sparse, dim, keepdim=True) - 1) / k

        taus = taus.expand_as(input)

        self.output = torch.max(torch.zeros_like(input), input - taus)

        output = self.output.view(original_size)

        return output

    def backward(self, grad_output):

        dim = 1

        nonzeros = torch.ne(self.output, 0)

        sum = torch.sum(grad_output * nonzeros, dim=dim) / torch.sum(nonzeros, dim=dim)

        self.grad_input = nonzeros * (grad_output - sum.expand_as(grad_output))

        return self.grad_input

class SingleAttention(nn.Module):

    def __init__(self, attention_input_dim, attention_hidden_dim, attention_type='add', demographic_dim=12, time_aware=False, use_demographic=False):

        super(SingleAttention, self).__init__()

        self.attention_type = attention_type

        self.attention_hidden_dim = attention_hidden_dim

        self.attention_input_dim = attention_input_dim

        self.use_demographic = use_demographic

        self.demographic_dim = demographic_dim

        self.time_aware = time_aware

        # batch_time = torch.arange(0, batch_mask.size()[1], dtype=torch.float32).reshape(1, batch_mask.size()[1], 1)

        # batch_time = batch_time.repeat(batch_mask.size()[0], 1, 1)

        if attention_type == 'add':

            if self.time_aware == True:

                # self.Wx = nn.Parameter(torch.randn(attention_input_dim+1, attention_hidden_dim))

                self.Wx = nn.Parameter(torch.randn(attention_input_dim, attention_hidden_dim))

                self.Wtime_aware = nn.Parameter(torch.randn(1, attention_hidden_dim))

                nn.init.kaiming_uniform_(self.Wtime_aware, a=math.sqrt(5))

            else:

                self.Wx = nn.Parameter(torch.randn(attention_input_dim, attention_hidden_dim))

            self.Wt = nn.Parameter(torch.randn(attention_input_dim, attention_hidden_dim))

            self.Wd = nn.Parameter(torch.randn(demographic_dim, attention_hidden_dim))

            self.bh = nn.Parameter(torch.zeros(attention_hidden_dim,))

            self.Wa = nn.Parameter(torch.randn(attention_hidden_dim, 1))

            self.ba = nn.Parameter(torch.zeros(1,))

            nn.init.kaiming_uniform_(self.Wd, a=math.sqrt(5))

            nn.init.kaiming_uniform_(self.Wx, a=math.sqrt(5))

            nn.init.kaiming_uniform_(self.Wt, a=math.sqrt(5))

            nn.init.kaiming_uniform_(self.Wa, a=math.sqrt(5))

        elif attention_type == 'mul':

            self.Wa = nn.Parameter(torch.randn(attention_input_dim, attention_input_dim))

            self.ba = nn.Parameter(torch.zeros(1,))

            nn.init.kaiming_uniform_(self.Wa, a=math.sqrt(5))

        elif attention_type == 'concat':

            if self.time_aware == True:

                self.Wh = nn.Parameter(torch.randn(2*attention_input_dim+1, attention_hidden_dim))

            else:

                self.Wh = nn.Parameter(torch.randn(2*attention_input_dim, attention_hidden_dim))

            self.Wa = nn.Parameter(torch.randn(attention_hidden_dim, 1))

            self.ba = nn.Parameter(torch.zeros(1,))

            nn.init.kaiming_uniform_(self.Wh, a=math.sqrt(5))

            nn.init.kaiming_uniform_(self.Wa, a=math.sqrt(5))

        else:

            raise RuntimeError('Wrong attention type.')

        self.tanh = nn.Tanh()

        self.softmax = nn.Softmax()

    def forward(self, input, demo=None):

        batch_size, time_step, input_dim = input.size() # batch_size * time_step * hidden_dim(i)

        #assert(input_dim == self.input_dim)

        # time_decays = torch.zeros((time_step,time_step)).to(device)# t*t

        # for this_time in range(time_step):

        #     for pre_time in range(time_step):

        #         if pre_time > this_time:

        #             break

        #         time_decays[this_time][pre_time] = torch.tensor(this_time - pre_time, dtype=torch.float32).to(device)

        # b_time_decays = tile(time_decays, 0, batch_size).view(batch_size,time_step,time_step).unsqueeze(-1).to(device)# b t t 1

        time_decays = torch.tensor(range(47,-1,-1), dtype=torch.float32).unsqueeze(-1).unsqueeze(0).to(self.device)# 1*t*1

        b_time_decays = time_decays.repeat(batch_size,1,1)# b t 1

        if self.attention_type == 'add': #B*T*I  @ H*I

            q = torch.matmul(input[:,-1,:], self.Wt)# b h

            q = torch.reshape(q, (batch_size, 1, self.attention_hidden_dim)) #B*1*H

            if self.time_aware == True:

                # k_input = torch.cat((input, time), dim=-1)

                k = torch.matmul(input, self.Wx)#b t h

                # k = torch.reshape(k, (batch_size, 1, time_step, self.attention_hidden_dim)) #B*1*T*H

                time_hidden = torch.matmul(b_time_decays, self.Wtime_aware)#  b t h

            else:

                k = torch.matmul(input, self.Wx)# b t h

                # k = torch.reshape(k, (batch_size, 1, time_step, self.attention_hidden_dim)) #B*1*T*H

            if self.use_demographic == True:

                d = torch.matmul(demo, self.Wd) #B*H

                d = torch.reshape(d, (batch_size, 1, self.attention_hidden_dim)) # b 1 h

            h = q + k + self.bh # b t h

            if self.time_aware == True:

                h += time_hidden

            h = self.tanh(h) #B*T*H

            e = torch.matmul(h, self.Wa) + self.ba #B*T*1

            e = torch.reshape(e, (batch_size, time_step))# b t

        elif self.attention_type == 'mul':

            e = torch.matmul(input[:,-1,:], self.Wa)#b i

            e = torch.matmul(e.unsqueeze(1), input.permute(0,2,1)).squeeze() + self.ba #b t

        elif self.attention_type == 'concat':

            q = input[:,-1,:].unsqueeze(1).repeat(1,time_step,1)# b t i

            k = input

            c = torch.cat((q, k), dim=-1) #B*T*2I

            if self.time_aware == True:

                c = torch.cat((c, b_time_decays), dim=-1) #B*T*2I+1

            h = torch.matmul(c, self.Wh)

            h = self.tanh(h)

            e = torch.matmul(h, self.Wa) + self.ba #B*T*1

            e = torch.reshape(e, (batch_size, time_step)) # b t 

        a = self.softmax(e) #B*T

        v = torch.matmul(a.unsqueeze(1), input).squeeze() #B*I

        return v, a

class FinalAttentionQKV(nn.Module):

    def __init__(self, attention_input_dim, attention_hidden_dim, attention_type='add', dropout=None):

        super(FinalAttentionQKV, self).__init__()

        self.attention_type = attention_type

        self.attention_hidden_dim = attention_hidden_dim

        self.attention_input_dim = attention_input_dim

        self.W_q = nn.Linear(attention_input_dim, attention_hidden_dim)

        self.W_k = nn.Linear(attention_input_dim, attention_hidden_dim)

        self.W_v = nn.Linear(attention_input_dim, attention_hidden_dim)

        self.W_out = nn.Linear(attention_hidden_dim, 1)

        self.b_in = nn.Parameter(torch.zeros(1,))

        self.b_out = nn.Parameter(torch.zeros(1,))

        nn.init.kaiming_uniform_(self.W_q.weight, a=math.sqrt(5))

        nn.init.kaiming_uniform_(self.W_k.weight, a=math.sqrt(5))

        nn.init.kaiming_uniform_(self.W_v.weight, a=math.sqrt(5))

        nn.init.kaiming_uniform_(self.W_out.weight, a=math.sqrt(5))

        self.Wh = nn.Parameter(torch.randn(2*attention_input_dim, attention_hidden_dim))

        self.Wa = nn.Parameter(torch.randn(attention_hidden_dim, 1))

        self.ba = nn.Parameter(torch.zeros(1,))

        nn.init.kaiming_uniform_(self.Wh, a=math.sqrt(5))

        nn.init.kaiming_uniform_(self.Wa, a=math.sqrt(5))

        self.dropout = nn.Dropout(p=dropout)

        self.tanh = nn.Tanh()

        self.softmax = nn.Softmax(dim=1)

        self.sigmoid = nn.Sigmoid()

        self.sparsemax = Sparsemax()

    def forward(self, input):

        batch_size, time_step, input_dim = input.size() # batch_size * input_dim + 1 * hidden_dim(i)

        input_q = self.W_q(torch.mean(input,1)) # b h

        input_k = self.W_k(input[:,:-1,:])# b t h

        input_v = self.W_v(input[:,:-1,:])# b t h

        if self.attention_type == 'add': #B*T*I  @ H*I

            q = torch.reshape(input_q, (batch_size, 1, self.attention_hidden_dim)) #B*1*H

            h = q + input_k + self.b_in # b t h

            h = self.tanh(h) #B*T*H

            e = self.W_out(h) # b t 1

            e = torch.reshape(e, (batch_size, time_step))# b t

        elif self.attention_type == 'mul':

            q = torch.reshape(input_q, (batch_size, self.attention_hidden_dim, 1)) #B*h 1

            e = torch.matmul(input_k, q).squeeze()#b t

        elif self.attention_type == 'concat':

            q = input_q.unsqueeze(1).repeat(1,time_step,1)# b t h

            k = input_k

            c = torch.cat((q, k), dim=-1) #B*T*2I

            h = torch.matmul(c, self.Wh)

            h = self.tanh(h)

            e = torch.matmul(h, self.Wa) + self.ba #B*T*1

            e = torch.reshape(e, (batch_size, time_step)) # b t 

        a = self.softmax(e) #B*T

        if self.dropout is not None:

            a = self.dropout(a)

        v = torch.matmul(a.unsqueeze(1), input_v).squeeze() #B*I

        return v, a

def clones(module, N):

    "Produce N identical layers."

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

# def tile(a, dim, n_tile):

#     init_dim = a.size(dim)

#     repeat_idx = [1] * a.dim()

#     repeat_idx[dim] = n_tile

#     a = a.repeat(*(repeat_idx))

#     order_index = torch.LongTensor(np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)])).to(self.device)

#     return torch.index_select(a, dim, order_index).to(self.device)

class PositionwiseFeedForward(nn.Module): # new added

    "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):

        return self.w_2(self.dropout(F.relu(self.w_1(x)))), None

class PositionalEncoding(nn.Module): # new added / not use anymore

    "Implement the PE function."

    def __init__(self, d_model, dropout, max_len=400):

        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))

        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 + torch.autograd.Variable(self.pe[:, :x.size(1)], 

                         requires_grad=False)

        return self.dropout(x)

def subsequent_mask(size):

    "Mask out subsequent positions."

    attn_shape = (1, size, size)

    subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')

    return torch.from_numpy(subsequent_mask) == 0 # 下三角矩阵

def attention(query, key, value, mask=None, dropout=None):

    "Compute 'Scaled Dot Product Attention'"

    d_k = query.size(-1)# b h t d_k

    scores = torch.matmul(query, key.transpose(-2, -1)) \

             / math.sqrt(d_k) # b h t t

    if mask is not None:# 1 1 t t

        scores = scores.masked_fill(mask == 0, -1e9)# b h t t 下三角

    p_attn = F.softmax(scores, dim = -1)# b h t t

    if dropout is not None:

        p_attn = dropout(p_attn)

    return torch.matmul(p_attn, value), p_attn # b h t v (d_k) 

class MultiHeadedAttention(nn.Module):

    def __init__(self, h, d_model, dropout=0):

        "Take in model size and number of heads."

        super(MultiHeadedAttention, self).__init__()

        assert d_model % h == 0

        # We assume d_v always equals d_k

        self.d_k = d_model // h

        self.h = h

        self.linears = clones(nn.Linear(d_model, self.d_k * self.h), 3)

        self.final_linear = nn.Linear(d_model, d_model)

        self.attn = None

        self.dropout = nn.Dropout(p=dropout)

    def forward(self, query, key, value, mask=None):

        if mask is not None:

            # Same mask applied to all h heads.

            mask = mask.unsqueeze(1) # 1 1 t t

        nbatches = query.size(0)# b

        input_dim = query.size(1)# i+1

        feature_dim = query.size(-1)# i+1

        #input size -> # batch_size * d_input * hidden_dim

        # d_model => h * d_k 

        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))] # b num_head d_input d_k

        x, self.attn = attention(query, key, value, mask=mask, 

                                 dropout=self.dropout)# b num_head d_input d_v (d_k) 

        x = x.transpose(1, 2).contiguous() \

             .view(nbatches, -1, self.h * self.d_k)# batch_size * d_input * hidden_dim

        #DeCov 

        DeCov_contexts = x.transpose(0, 1).transpose(1, 2) # I+1 H B

#         print(DeCov_contexts.shape)

        Covs = cov(DeCov_contexts[0,:,:])

        DeCov_loss = 0.5 * (torch.norm(Covs, p = 'fro')**2 - torch.norm(torch.diag(Covs))**2 ) 

        for i in range(17+1 -1):

            Covs = cov(DeCov_contexts[i+1,:,:])

            DeCov_loss += 0.5 * (torch.norm(Covs, p = 'fro')**2 - torch.norm(torch.diag(Covs))**2 ) 

        return self.final_linear(x), DeCov_loss

class LayerNorm(nn.Module):

    def __init__(self, size, eps=1e-7):

        super(LayerNorm, self).__init__()

        self.a_2 = nn.Parameter(torch.ones(size))

        self.b_2 = nn.Parameter(torch.zeros(size))

        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

def cov(m, y=None):

    if y is not None:

        m = torch.cat((m, y), dim=0)

    m_exp = torch.mean(m, dim=1)

    x = m - m_exp[:, None]

    cov = 1 / (x.size(1) - 1) * x.mm(x.t())

    return cov

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):

        "Apply residual connection to any sublayer with the same size."

        returned_value = sublayer(self.norm(x))

        return x + self.dropout(returned_value[0]) , returned_value[1]

class AICare(nn.Module):

    def __init__(self, lab_dim=17, demo_dim=4, hidden_dim=32, d_model=32,  MHD_num_head=4, d_ff=64, device='cuda', keep_prob=0.5, **kwargs):

        super(AICare, self).__init__()

        # hyperparameters

        self.lab_dim = lab_dim  

        self.hidden_dim = hidden_dim  # d_model

        self.d_model = d_model

        self.MHD_num_head = MHD_num_head

        self.device = device

        self.d_ff = d_ff

        self.keep_prob = keep_prob

        # layers

        self.PositionalEncoding = PositionalEncoding(self.d_model, dropout = 0, max_len = 400)

#         self.GRUs = clones(nn.GRU(1, self.hidden_dim, batch_first = True), self.lab_dim)

        self.GRUs = clones(nn.RNN(1, self.hidden_dim, bidirectional = True, batch_first = True), self.lab_dim)

        self.LastStepAttentions = clones(SingleAttention(self.hidden_dim, 8, attention_type='concat', demographic_dim=12, time_aware=True, use_demographic=False),self.lab_dim)

        self.FinalAttentionQKV = FinalAttentionQKV(self.hidden_dim, self.hidden_dim, attention_type='mul',dropout = 1 - self.keep_prob)

        self.MultiHeadedAttention = MultiHeadedAttention(self.MHD_num_head, self.d_model,dropout = 1 - self.keep_prob)

        self.SublayerConnection = SublayerConnection(self.d_model, dropout = 1 - self.keep_prob)

        self.PositionwiseFeedForward = PositionwiseFeedForward(self.d_model, self.d_ff, dropout=0.1)

        self.demo_proj_main = nn.Linear(demo_dim, self.hidden_dim)

        self.demo_proj = nn.Linear(demo_dim, self.hidden_dim)

        self.output_proj = nn.Linear(self.hidden_dim*2, self.hidden_dim)

        self.dropout = nn.Dropout(p = 1 - self.keep_prob)

        self.tanh=nn.Tanh()

        self.softmax = nn.Softmax()

        self.sigmoid = nn.Sigmoid()

        self.relu=nn.ReLU()

    def forward(self, input, demo_input, mask, **kwargs):

        # input shape [batch_size, timestep, feature_dim]

        demo_main = self.tanh(self.demo_proj_main(demo_input)).unsqueeze(1)# b hidden_dim

        batch_size = input.size(0)

        time_step = input.size(1)

        feature_dim = input.size(2)

        assert(feature_dim == self.lab_dim)# input Tensor : 256 * 48 * 76

        assert(self.d_model % self.MHD_num_head == 0)

        lens = mask.sum(dim=1)

        GRU_embeded_input = torch.sum(self.GRUs[0](pack_padded_sequence(input[:,:,0].unsqueeze(-1), lens.cpu(), batch_first=True, enforce_sorted=False))[1], 0).squeeze().unsqueeze(1) # b 1 h

#         print(GRU_embeded_input.shape)

        for i in range(feature_dim-1):

            embeded_input = torch.sum(self.GRUs[i+1](pack_padded_sequence(input[:,:,i+1].unsqueeze(-1), lens.cpu(), batch_first=True, enforce_sorted=False))[1], 0).squeeze().unsqueeze(1)  # b 1 h

            GRU_embeded_input = torch.cat((GRU_embeded_input, embeded_input), 1)

#         print(demo_main.shape)

        GRU_embeded_input = torch.cat((GRU_embeded_input, demo_main), 1)# b i+1 h

        posi_input = self.dropout(GRU_embeded_input) # batch_size * d_input * hidden_dim

        weighted_contexts = self.FinalAttentionQKV(posi_input)[0]

        combined_hidden = torch.cat((weighted_contexts, \

                                     demo_main.squeeze(1)),-1)#b n h

        out = self.output_proj(combined_hidden)

        # out = self.dropout(out)

        # output = self.output(self.dropout(combined_hidden))# b 1

        # output = self.sigmoid(output)

        # return output

        return out