import torch
import torch.nn as nn
import torch.nn.functional as F


class DynamicGraphConstructor(nn.Module):
    def __init__(self, node_num, embed_dim):
        super().__init__()
        self.nodevec1 = nn.Parameter(torch.randn(node_num, embed_dim), requires_grad=True)
        self.nodevec2 = nn.Parameter(torch.randn(node_num, embed_dim), requires_grad=True)

    def forward(self):
        adj = torch.matmul(self.nodevec1, self.nodevec2.T)
        adj = F.relu(adj)
        adj = F.softmax(adj, dim=-1)
        return adj


class GraphConvBlock(nn.Module):
    def __init__(self, input_dim, output_dim):
        super().__init__()
        self.theta = nn.Linear(input_dim, output_dim)
        self.residual = (input_dim == output_dim)
        if not self.residual:
            self.res_proj = nn.Linear(input_dim, output_dim)

    def forward(self, x, adj):
        res = x
        x = torch.matmul(adj, x)
        x = self.theta(x)
        x = x + (res if self.residual else self.res_proj(res))
        return F.relu(x)


class TransformerBlock(nn.Module):
    def __init__(self, embed_dim, num_heads=4, dim_feedforward=None):
        super().__init__()
        # feedforward dimension defaults to 2*embed_dim if not provided
        ff_dim = dim_feedforward if dim_feedforward is not None else 2 * embed_dim
        self.layer = nn.TransformerEncoderLayer(
            d_model=embed_dim,
            nhead=num_heads,
            dim_feedforward=ff_dim,
            batch_first=True
        )

    def forward(self, x):
        # x: (batch, seq_len, embed_dim)
        return self.layer(x)


class SandwichBlock(nn.Module):
    def __init__(self, num_nodes, embed_dim, hidden_dim, num_heads=4):
        super().__init__()
        self.transformer1 = TransformerBlock(hidden_dim, num_heads=num_heads, dim_feedforward=hidden_dim * 2)
        self.graph_constructor = DynamicGraphConstructor(num_nodes, embed_dim)
        self.gc = GraphConvBlock(hidden_dim, hidden_dim)
        self.transformer2 = TransformerBlock(hidden_dim, num_heads=num_heads, dim_feedforward=hidden_dim * 2)

    def forward(self, h):
        # h: (batch, num_nodes, hidden_dim)
        h1 = self.transformer1(h)
        adj = self.graph_constructor()
        h2 = self.gc(h1, adj)
        h3 = self.transformer2(h2)
        return h3


class MLP(nn.Module):
    def __init__(self, in_dim, hidden_dims, out_dim, activation=nn.ReLU):
        super().__init__()
        dims = [in_dim] + hidden_dims + [out_dim]
        layers = []
        for i in range(len(dims) - 2):
            layers += [nn.Linear(dims[i], dims[i + 1]), activation()]
        layers += [nn.Linear(dims[-2], dims[-1])]
        self.net = nn.Sequential(*layers)

    def forward(self, x):
        return self.net(x)


class EXP(nn.Module):
    def __init__(self, args):
        super().__init__()
        self.horizon = args['horizon']
        self.output_dim = args['output_dim']
        self.seq_len = args.get('in_len', 12)
        self.hidden_dim = args.get('hidden_dim', 64)
        self.num_nodes = args['num_nodes']
        self.embed_dim = args.get('embed_dim', 16)

        # time embeddings
        self.time_slots = args.get('time_slots', 24 * 60 // args.get('time_slot', 5))
        self.time_embedding = nn.Embedding(self.time_slots, self.hidden_dim)
        self.day_embedding = nn.Embedding(7, self.hidden_dim)

        # input projection
        self.input_proj = MLP(
            in_dim=self.seq_len,
            hidden_dims=[self.hidden_dim],
            out_dim=self.hidden_dim
        )

        # two Sandwich blocks with transformer
        self.sandwich1 = SandwichBlock(self.num_nodes, self.embed_dim, self.hidden_dim)
        self.sandwich2 = SandwichBlock(self.num_nodes, self.embed_dim, self.hidden_dim)

        # output projection
        self.out_proj = MLP(
            in_dim=self.hidden_dim,
            hidden_dims=[2 * self.hidden_dim],
            out_dim=self.horizon * self.output_dim
        )

    def forward(self, x):
        # x: (B, T, N, D_total)
        x_flow = x[..., 0]
        x_time = x[..., 1]
        x_day = x[..., 2]

        B, T, N = x_flow.shape
        assert T == self.seq_len

        # project flow history
        x_flat = x_flow.permute(0, 2, 1).reshape(B * N, T)
        h0 = self.input_proj(x_flat).view(B, N, self.hidden_dim)

        # time embeddings
        t_idx = (x_time[:, -1, :] * (self.time_slots - 1)).long()
        d_idx = x_day[:, -1, :].long()
        time_emb = self.time_embedding(t_idx)
        day_emb = self.day_embedding(d_idx)

        # inject embeddings
        h0 = h0 + time_emb + day_emb

        # Sandwich blocks with residuals
        h1 = self.sandwich1(h0)
        h1 = h1 + h0
        h2 = self.sandwich2(h1)

        # output
        out = self.out_proj(h2)
        out = out.view(B, N, self.horizon, self.output_dim)
        out = out.permute(0, 2, 1, 3)
        return out