import torch
import torch.nn as nn
from model.AGCRN.AGCRNCell import AGCRNCell


class AVWDCRNN(nn.Module):
    def __init__(self, node_num, dim_in, dim_out, cheb_k, embed_dim, num_layers=1):
        super(AVWDCRNN, self).__init__()
        assert num_layers >= 1, "At least one DCRNN layer in the Encoder."
        self.node_num = node_num
        self.input_dim = dim_in
        self.num_layers = num_layers
        self.dcrnn_cells = nn.ModuleList()
        self.dcrnn_cells.append(AGCRNCell(node_num, dim_in, dim_out, cheb_k, embed_dim))
        for _ in range(1, num_layers):
            self.dcrnn_cells.append(
                AGCRNCell(node_num, dim_out, dim_out, cheb_k, embed_dim)
            )

    def forward(self, x, init_state, node_embeddings):
        # shape of x: (B, T, N, D)
        # shape of init_state: (num_layers, B, N, hidden_dim)
        assert x.shape[2] == self.node_num and x.shape[3] == self.input_dim
        seq_length = x.shape[1]
        current_inputs = x
        output_hidden = []
        for i in range(self.num_layers):
            state = init_state[i]
            inner_states = []
            for t in range(seq_length):
                state = self.dcrnn_cells[i](
                    current_inputs[:, t, :, :], state, node_embeddings
                )
                inner_states.append(state)
            output_hidden.append(state)
            current_inputs = torch.stack(inner_states, dim=1)
        # current_inputs: the outputs of last layer: (B, T, N, hidden_dim)
        # output_hidden: the last state for each layer: (num_layers, B, N, hidden_dim)
        # last_state: (B, N, hidden_dim)
        return current_inputs, output_hidden

    def init_hidden(self, batch_size):
        init_states = []
        for i in range(self.num_layers):
            init_states.append(self.dcrnn_cells[i].init_hidden_state(batch_size))
        return torch.stack(init_states, dim=0)  # (num_layers, B, N, hidden_dim)


class AGCRN(nn.Module):
    def __init__(self, args):
        super(AGCRN, self).__init__()
        self.num_node = args["num_nodes"]
        self.input_dim = args["input_dim"]
        self.hidden_dim = args["rnn_units"]
        self.output_dim = args["output_dim"]
        self.horizon = args["horizon"]
        self.num_layers = args["num_layers"]

        self.default_graph = args["default_graph"]
        self.node_embeddings = nn.Parameter(
            torch.randn(self.num_node, args["embed_dim"]), requires_grad=True
        )

        self.encoder = AVWDCRNN(
            args["num_nodes"],
            args["input_dim"],
            args["rnn_units"],
            args["cheb_k"],
            args["embed_dim"],
            args["num_layers"],
        )

        # predictor
        self.end_conv = nn.Conv2d(
            1,
            args["horizon"] * self.output_dim,
            kernel_size=(1, self.hidden_dim),
            bias=True,
        )

    def forward(self, source):
        # source: B, T_1, N, D
        # target: B, T_2, N, D
        # supports = F.softmax(F.relu(torch.mm(self.nodevec1, self.nodevec1.transpose(0,1))), dim=1)

        init_state = self.encoder.init_hidden(source.shape[0])
        output, _ = self.encoder(
            source[..., :1], init_state, self.node_embeddings
        )  # B, T, N, hidden
        output = output[:, -1:, :, :]  # B, 1, N, hidden

        # CNN based predictor
        output = self.end_conv((output))  # B, T*C, N, 1
        output = output.squeeze(-1).reshape(
            -1, self.horizon, self.output_dim, self.num_node
        )
        output = output.permute(0, 1, 3, 2)  # B, T, N, C

        return output