import torch
import torch.nn.functional as F
import torch.nn as nn
from model.STGNCDE import controldiffeq
from model.STGNCDE.vector_fields import *


class NeuralGCDE(nn.Module):
    def __init__(
        self,
        args,
        func_f,
        func_g,
        input_channels,
        hidden_channels,
        output_channels,
        initial,
        device,
        atol,
        rtol,
        solver,
    ):
        super(NeuralGCDE, self).__init__()
        self.num_node = args["num_nodes"]
        self.input_dim = input_channels
        self.hidden_dim = hidden_channels
        self.output_dim = output_channels
        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.func_f = func_f
        self.func_g = func_g
        self.solver = solver
        self.atol = atol
        self.rtol = rtol

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

        self.init_type = "fc"
        if self.init_type == "fc":
            self.initial_h = torch.nn.Linear(self.input_dim, self.hidden_dim)
            self.initial_z = torch.nn.Linear(self.input_dim, self.hidden_dim)
        elif self.init_type == "conv":
            self.start_conv_h = nn.Conv2d(
                in_channels=input_channels,
                out_channels=hidden_channels,
                kernel_size=(1, 1),
            )
            self.start_conv_z = nn.Conv2d(
                in_channels=input_channels,
                out_channels=hidden_channels,
                kernel_size=(1, 1),
            )

    def forward(self, times, coeffs):
        # source: B, T_1, N, D
        # target: B, T_2, N, D

        spline = controldiffeq.NaturalCubicSpline(times, coeffs)
        if self.init_type == "fc":
            h0 = self.initial_h(spline.evaluate(times[0]))
            z0 = self.initial_z(spline.evaluate(times[0]))
        elif self.init_type == "conv":
            h0 = (
                self.start_conv_h(
                    spline.evaluate(times[0]).transpose(1, 2).unsqueeze(-1)
                )
                .transpose(1, 2)
                .squeeze()
            )
            z0 = (
                self.start_conv_z(
                    spline.evaluate(times[0]).transpose(1, 2).unsqueeze(-1)
                )
                .transpose(1, 2)
                .squeeze()
            )

        z_t = controldiffeq.cdeint_gde_dev(
            dX_dt=spline.derivative,  # dh_dt
            h0=h0,
            z0=z0,
            func_f=self.func_f,
            func_g=self.func_g,
            t=times,
            method=self.solver,
            atol=self.atol,
            rtol=self.rtol,
        )

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

        # CNN based predictor
        output = self.end_conv(z_T)  # 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