import time

import numpy as np
import torch

import dgl
import dgl.function as fn

from .. import utils


@utils.benchmark("time", timeout=600)
@utils.parametrize("graph_name", ["ogbn-arxiv", "reddit", "ogbn-proteins"])
@utils.parametrize("format", ["csc"])
@utils.parametrize("feat_size", [4, 32, 256])
@utils.parametrize("msg_type", ["copy_u", "u_mul_e"])
@utils.parametrize("reduce_type", ["sum", "mean", "max"])
def track_time(graph_name, format, feat_size, msg_type, reduce_type):
    device = utils.get_bench_device()
    graph = utils.get_graph(graph_name, format)
    graph = graph.to(device)
    graph.ndata["h"] = torch.randn(
        (graph.num_nodes(), feat_size), device=device
    )
    graph.edata["e"] = torch.randn((graph.num_edges(), 1), device=device)

    msg_builtin_dict = {
        "copy_u": fn.copy_u("h", "x"),
        "u_mul_e": fn.u_mul_e("h", "e", "x"),
    }

    reduce_builtin_dict = {
        "sum": fn.sum("x", "h_new"),
        "mean": fn.mean("x", "h_new"),
        "max": fn.max("x", "h_new"),
    }

    # dry run

    for i in range(3):
        graph.update_all(
            msg_builtin_dict[msg_type], reduce_builtin_dict[reduce_type]
        )

    # timing

    with utils.Timer() as t:
        for i in range(10):
            graph.update_all(
                msg_builtin_dict[msg_type], reduce_builtin_dict[reduce_type]
            )

    return t.elapsed_secs / 10
