# import packages

import copy

# import packages

import math

import torch

import torch.nn.functional as F

from torch import nn

from torch.autograd import Variable

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:

                # 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:

                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))

        elif attention_type == "new":

            self.Wt = nn.Parameter(

                torch.randn(attention_input_dim, attention_hidden_dim)

            )

            self.Wx = nn.Parameter(

                torch.randn(attention_input_dim, attention_hidden_dim)

            )

            self.rate = nn.Parameter(torch.zeros(1) + 0.8)

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

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

        else:

            raise RuntimeError("Wrong attention type.")

        self.tanh = nn.Tanh()

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

        self.sigmoid = nn.Sigmoid()

        self.relu = nn.ReLU()

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

        (

            batch_size,

            time_step,

            input_dim,

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

        time_decays = (

            torch.tensor(range(time_step - 1, -1, -1), dtype=torch.float32)

            .unsqueeze(-1).unsqueeze(0).to(device=device)

        )  # 1*t*1

        b_time_decays = time_decays.repeat(batch_size, 1, 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 = torch.matmul(input, self.Wx)  # b 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

            if self.use_demographic:

                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:

                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:

                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

        elif self.attention_type == "new":

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

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

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

            dot_product = torch.matmul(q, k.transpose(1, 2)).squeeze()  # b t

            denominator = self.sigmoid(self.rate) * (

                torch.log(2.72 + (1 - self.sigmoid(dot_product)))

                * (b_time_decays.squeeze())

            )

            e = self.relu(self.sigmoid(dot_product) / (denominator))  # 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()

    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(input[:, -1, :])  # b h

        input_k = self.W_k(input)  # b t h

        input_v = self.W_v(input)  # 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

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(0.0, max_len).unsqueeze(1)

        div_term = torch.exp(

            torch.arange(0.0, 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 + Variable(self.pe[:, : x.size(1)], requires_grad=False)

        return self.dropout(x)

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 = nn.ModuleList(

            [nn.Linear(d_model, self.d_k * self.h) for _ in range(3)]

        )

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

        self.attn = None

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

    def attention(self, 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)

    def cov(self, 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

    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 = self.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

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

        DeCov_loss = 0.5 * (

            torch.norm(Covs, p="fro") ** 2 - torch.norm(torch.diag(Covs)) ** 2

        )

        for i in range(feature_dim - 1):

            Covs = self.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, features, eps=1e-7):

        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):

        "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 ConCare(nn.Module):

    def __init__(

        self,

        lab_dim,  # lab_dim

        hidden_dim,

        demo_dim,

        d_model,

        MHD_num_head,

        d_ff,

        # output_dim,

        # device,

        drop=0.5,

    ):

        super(ConCare, 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.d_ff = d_ff

        # self.output_dim = output_dim

        self.drop = drop

        self.demo_dim = demo_dim

        # layers

        self.PositionalEncoding = PositionalEncoding(

            self.d_model, dropout=0, max_len=400

        )

        self.GRUs = nn.ModuleList(

            [

                copy.deepcopy(nn.GRU(1, self.hidden_dim, batch_first=True))

                for _ in range(self.lab_dim)

            ]

        )

        self.LastStepAttentions = nn.ModuleList(

            [

                copy.deepcopy(

                    SingleAttention(

                        self.hidden_dim,

                        8,

                        attention_type="new",

                        demographic_dim=12,

                        time_aware=True,

                        use_demographic=False,

                    )

                )

                for _ in range(self.lab_dim)

            ]

        )

        self.FinalAttentionQKV = FinalAttentionQKV(

            self.hidden_dim,

            self.hidden_dim,

            attention_type="mul",

            dropout=self.drop,

        )

        self.MultiHeadedAttention = MultiHeadedAttention(

            self.MHD_num_head, self.d_model, dropout=self.drop

        )

        self.SublayerConnection = SublayerConnection(self.d_model, dropout=self.drop)

        self.PositionwiseFeedForward = PositionwiseFeedForward(

            self.d_model, self.d_ff, dropout=0.1

        )

        self.demo_lab_proj = nn.Linear(self.demo_dim + self.lab_dim, self.hidden_dim)

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

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

        self.output0 = nn.Linear(self.hidden_dim, self.hidden_dim)

        # self.output1 = nn.Linear(self.hidden_dim, self.output_dim)

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

        self.tanh = nn.Tanh()

        self.softmax = nn.Softmax()

        self.sigmoid = nn.Sigmoid()

        self.relu = nn.ReLU()

    def concare_encoder(self, input, demo_input, device):

        # 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

        # forward

        GRU_embeded_input = self.GRUs[0](

            input[:, :, 0].unsqueeze(-1).to(device=device),

            Variable(

                torch.zeros(batch_size, self.hidden_dim)

                .to(device=device)

                .unsqueeze(0)

            ),

        )[

            0

        ]  # b t h

        Attention_embeded_input = self.LastStepAttentions[0](GRU_embeded_input, device)[

            0

        ].unsqueeze(

            1

        )  # b 1 h

        for i in range(feature_dim - 1):

            embeded_input = self.GRUs[i + 1](

                input[:, :, i + 1].unsqueeze(-1),

                Variable(

                    torch.zeros(batch_size, self.hidden_dim)

                    .to(device=device)

                    .unsqueeze(0)

                ),

            )[

                0

            ]  # b 1 h

            embeded_input = self.LastStepAttentions[i + 1](embeded_input, device)[0].unsqueeze(

                1

            )  # b 1 h

            Attention_embeded_input = torch.cat(

                (Attention_embeded_input, embeded_input), 1

            )  # b i h

        Attention_embeded_input = torch.cat(

            (Attention_embeded_input, demo_main), 1

        )  # b i+1 h

        posi_input = self.dropout(

            Attention_embeded_input

        )  # batch_size * d_input+1 * hidden_dim

        contexts = self.SublayerConnection(

            posi_input,

            lambda x: self.MultiHeadedAttention(

                posi_input, posi_input, posi_input, None

            ),

        )  # # batch_size * d_input * hidden_dim

        DeCov_loss = contexts[1]

        contexts = contexts[0]

        contexts = self.SublayerConnection(

            contexts, lambda x: self.PositionwiseFeedForward(contexts)

        )[0]

        weighted_contexts = self.FinalAttentionQKV(contexts)[0]

        return weighted_contexts

    def forward(self, x, device, info=None):

        """extra info is not used here"""

        batch_size, time_steps, _ = x.size()

        demo_input = x[:, 0, : self.demo_dim]

        lab_input = x[:, :, self.demo_dim :]

        out = torch.zeros((batch_size, time_steps, self.hidden_dim))

        for cur_time in range(time_steps):

            # print(cur_time, end=" ")

            cur_lab = lab_input[:, : cur_time + 1, :]

            # print("cur_lab", cur_lab.shape)

            if cur_time == 0:

                out[:, cur_time, :] = self.demo_lab_proj(x[:, 0, :])

            else:

                out[:, cur_time, :] = self.concare_encoder(cur_lab, demo_input, device)

        # print()

        return out