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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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# BOHB: Robust and Efficient Hyperparameter Optimization at Scale #
# required to install hpbandster ##################################
# pip install hpbandster         ##################################
###################################################################
# bash ./scripts-search/algos/BOHB.sh -1         ##################
###################################################################
import os, sys, time, random, argparse
from copy import deepcopy
from pathlib import Path
import torch

from xautodl.config_utils import load_config
from xautodl.datasets import get_datasets, SearchDataset
from xautodl.procedures import prepare_seed, prepare_logger
from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
from xautodl.models import CellStructure, get_search_spaces
from nas_201_api import NASBench201API as API

# BOHB: Robust and Efficient Hyperparameter Optimization at Scale, ICML 2018
import ConfigSpace
from hpbandster.optimizers.bohb import BOHB
import hpbandster.core.nameserver as hpns
from hpbandster.core.worker import Worker


def get_configuration_space(max_nodes, search_space):
    cs = ConfigSpace.ConfigurationSpace()
    # edge2index   = {}
    for i in range(1, max_nodes):
        for j in range(i):
            node_str = "{:}<-{:}".format(i, j)
            cs.add_hyperparameter(
                ConfigSpace.CategoricalHyperparameter(node_str, search_space)
            )
    return cs


def config2structure_func(max_nodes):
    def config2structure(config):
        genotypes = []
        for i in range(1, max_nodes):
            xlist = []
            for j in range(i):
                node_str = "{:}<-{:}".format(i, j)
                op_name = config[node_str]
                xlist.append((op_name, j))
            genotypes.append(tuple(xlist))
        return CellStructure(genotypes)

    return config2structure


class MyWorker(Worker):
    def __init__(
        self,
        *args,
        convert_func=None,
        dataname=None,
        nas_bench=None,
        time_budget=None,
        **kwargs
    ):
        super().__init__(*args, **kwargs)
        self.convert_func = convert_func
        self._dataname = dataname
        self._nas_bench = nas_bench
        self.time_budget = time_budget
        self.seen_archs = []
        self.sim_cost_time = 0
        self.real_cost_time = 0
        self.is_end = False

    def get_the_best(self):
        assert len(self.seen_archs) > 0
        best_index, best_acc = -1, None
        for arch_index in self.seen_archs:
            info = self._nas_bench.get_more_info(
                arch_index, self._dataname, None, hp="200", is_random=True
            )
            vacc = info["valid-accuracy"]
            if best_acc is None or best_acc < vacc:
                best_acc = vacc
                best_index = arch_index
        assert best_index != -1
        return best_index

    def compute(self, config, budget, **kwargs):
        start_time = time.time()
        structure = self.convert_func(config)
        arch_index = self._nas_bench.query_index_by_arch(structure)
        info = self._nas_bench.get_more_info(
            arch_index, self._dataname, None, hp="200", is_random=True
        )
        cur_time = info["train-all-time"] + info["valid-per-time"]
        cur_vacc = info["valid-accuracy"]
        self.real_cost_time += time.time() - start_time
        if self.sim_cost_time + cur_time <= self.time_budget and not self.is_end:
            self.sim_cost_time += cur_time
            self.seen_archs.append(arch_index)
            return {
                "loss": 100 - float(cur_vacc),
                "info": {
                    "seen-arch": len(self.seen_archs),
                    "sim-test-time": self.sim_cost_time,
                    "current-arch": arch_index,
                },
            }
        else:
            self.is_end = True
            return {
                "loss": 100,
                "info": {
                    "seen-arch": len(self.seen_archs),
                    "sim-test-time": self.sim_cost_time,
                    "current-arch": None,
                },
            }


def main(xargs, nas_bench):
    assert torch.cuda.is_available(), "CUDA is not available."
    torch.backends.cudnn.enabled = True
    torch.backends.cudnn.benchmark = False
    torch.backends.cudnn.deterministic = True
    torch.set_num_threads(xargs.workers)
    prepare_seed(xargs.rand_seed)
    logger = prepare_logger(args)

    if xargs.dataset == "cifar10":
        dataname = "cifar10-valid"
    else:
        dataname = xargs.dataset
    if xargs.data_path is not None:
        train_data, valid_data, xshape, class_num = get_datasets(
            xargs.dataset, xargs.data_path, -1
        )
        split_Fpath = "configs/nas-benchmark/cifar-split.txt"
        cifar_split = load_config(split_Fpath, None, None)
        train_split, valid_split = cifar_split.train, cifar_split.valid
        logger.log("Load split file from {:}".format(split_Fpath))
        config_path = "configs/nas-benchmark/algos/R-EA.config"
        config = load_config(
            config_path, {"class_num": class_num, "xshape": xshape}, logger
        )
        # To split data
        train_data_v2 = deepcopy(train_data)
        train_data_v2.transform = valid_data.transform
        valid_data = train_data_v2
        search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
        # data loader
        train_loader = torch.utils.data.DataLoader(
            train_data,
            batch_size=config.batch_size,
            sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
            num_workers=xargs.workers,
            pin_memory=True,
        )
        valid_loader = torch.utils.data.DataLoader(
            valid_data,
            batch_size=config.batch_size,
            sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
            num_workers=xargs.workers,
            pin_memory=True,
        )
        logger.log(
            "||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
                xargs.dataset, len(train_loader), len(valid_loader), config.batch_size
            )
        )
        logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
        extra_info = {
            "config": config,
            "train_loader": train_loader,
            "valid_loader": valid_loader,
        }
    else:
        config_path = "configs/nas-benchmark/algos/R-EA.config"
        config = load_config(config_path, None, logger)
        logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
        extra_info = {"config": config, "train_loader": None, "valid_loader": None}

    # nas dataset load
    assert xargs.arch_nas_dataset is not None and os.path.isfile(xargs.arch_nas_dataset)
    search_space = get_search_spaces("cell", xargs.search_space_name)
    cs = get_configuration_space(xargs.max_nodes, search_space)

    config2structure = config2structure_func(xargs.max_nodes)
    hb_run_id = "0"

    NS = hpns.NameServer(run_id=hb_run_id, host="localhost", port=0)
    ns_host, ns_port = NS.start()
    num_workers = 1

    # nas_bench = AANASBenchAPI(xargs.arch_nas_dataset)
    # logger.log('{:} Create NAS-BENCH-API DONE'.format(time_string()))
    workers = []
    for i in range(num_workers):
        w = MyWorker(
            nameserver=ns_host,
            nameserver_port=ns_port,
            convert_func=config2structure,
            dataname=dataname,
            nas_bench=nas_bench,
            time_budget=xargs.time_budget,
            run_id=hb_run_id,
            id=i,
        )
        w.run(background=True)
        workers.append(w)

    start_time = time.time()
    bohb = BOHB(
        configspace=cs,
        run_id=hb_run_id,
        eta=3,
        min_budget=12,
        max_budget=200,
        nameserver=ns_host,
        nameserver_port=ns_port,
        num_samples=xargs.num_samples,
        random_fraction=xargs.random_fraction,
        bandwidth_factor=xargs.bandwidth_factor,
        ping_interval=10,
        min_bandwidth=xargs.min_bandwidth,
    )

    results = bohb.run(xargs.n_iters, min_n_workers=num_workers)

    bohb.shutdown(shutdown_workers=True)
    NS.shutdown()

    real_cost_time = time.time() - start_time

    id2config = results.get_id2config_mapping()
    incumbent = results.get_incumbent_id()
    logger.log(
        "Best found configuration: {:} within {:.3f} s".format(
            id2config[incumbent]["config"], real_cost_time
        )
    )
    best_arch = config2structure(id2config[incumbent]["config"])

    info = nas_bench.query_by_arch(best_arch, "200")
    if info is None:
        logger.log("Did not find this architecture : {:}.".format(best_arch))
    else:
        logger.log("{:}".format(info))
    logger.log("-" * 100)

    logger.log(
        "workers : {:.1f}s with {:} archs".format(
            workers[0].time_budget, len(workers[0].seen_archs)
        )
    )
    logger.close()
    return logger.log_dir, nas_bench.query_index_by_arch(best_arch), real_cost_time


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        "BOHB: Robust and Efficient Hyperparameter Optimization at Scale"
    )
    parser.add_argument("--data_path", type=str, help="Path to dataset")
    parser.add_argument(
        "--dataset",
        type=str,
        choices=["cifar10", "cifar100", "ImageNet16-120"],
        help="Choose between Cifar10/100 and ImageNet-16.",
    )
    # channels and number-of-cells
    parser.add_argument("--search_space_name", type=str, help="The search space name.")
    parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
    parser.add_argument("--channel", type=int, help="The number of channels.")
    parser.add_argument(
        "--num_cells", type=int, help="The number of cells in one stage."
    )
    parser.add_argument(
        "--time_budget",
        type=int,
        help="The total time cost budge for searching (in seconds).",
    )
    # BOHB
    parser.add_argument(
        "--strategy",
        default="sampling",
        type=str,
        nargs="?",
        help="optimization strategy for the acquisition function",
    )
    parser.add_argument(
        "--min_bandwidth",
        default=0.3,
        type=float,
        nargs="?",
        help="minimum bandwidth for KDE",
    )
    parser.add_argument(
        "--num_samples",
        default=64,
        type=int,
        nargs="?",
        help="number of samples for the acquisition function",
    )
    parser.add_argument(
        "--random_fraction",
        default=0.33,
        type=float,
        nargs="?",
        help="fraction of random configurations",
    )
    parser.add_argument(
        "--bandwidth_factor",
        default=3,
        type=int,
        nargs="?",
        help="factor multiplied to the bandwidth",
    )
    parser.add_argument(
        "--n_iters",
        default=100,
        type=int,
        nargs="?",
        help="number of iterations for optimization method",
    )
    # log
    parser.add_argument(
        "--workers",
        type=int,
        default=2,
        help="number of data loading workers (default: 2)",
    )
    parser.add_argument(
        "--save_dir", type=str, help="Folder to save checkpoints and log."
    )
    parser.add_argument(
        "--arch_nas_dataset",
        type=str,
        help="The path to load the architecture dataset (tiny-nas-benchmark).",
    )
    parser.add_argument("--print_freq", type=int, help="print frequency (default: 200)")
    parser.add_argument("--rand_seed", type=int, help="manual seed")
    args = parser.parse_args()
    # if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
    if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset):
        nas_bench = None
    else:
        print(
            "{:} build NAS-Benchmark-API from {:}".format(
                time_string(), args.arch_nas_dataset
            )
        )
        nas_bench = API(args.arch_nas_dataset)
    if args.rand_seed < 0:
        save_dir, all_indexes, num, all_times = None, [], 500, []
        for i in range(num):
            print("{:} : {:03d}/{:03d}".format(time_string(), i, num))
            args.rand_seed = random.randint(1, 100000)
            save_dir, index, ctime = main(args, nas_bench)
            all_indexes.append(index)
            all_times.append(ctime)
        print("\n average time : {:.3f} s".format(sum(all_times) / len(all_times)))
        torch.save(all_indexes, save_dir / "results.pth")
    else:
        main(args, nas_bench)