#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
#####################################################
import os, sys, time, argparse, collections
from copy import deepcopy
import torch
from pathlib import Path
from collections import defaultdict

lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
if str(lib_dir) not in sys.path:
    sys.path.insert(0, str(lib_dir))
from log_utils import AverageMeter, time_string, convert_secs2time
from config_utils import load_config, dict2config
from datasets import get_datasets

# NAS-Bench-201 related module or function
from models import CellStructure, get_cell_based_tiny_net
from nas_201_api import ArchResults, ResultsCount
from procedures import bench_pure_evaluate as pure_evaluate


def create_result_count(used_seed, dataset, arch_config, results, dataloader_dict):
    xresult = ResultsCount(
        dataset,
        results["net_state_dict"],
        results["train_acc1es"],
        results["train_losses"],
        results["param"],
        results["flop"],
        arch_config,
        used_seed,
        results["total_epoch"],
        None,
    )

    net_config = dict2config(
        {
            "name": "infer.tiny",
            "C": arch_config["channel"],
            "N": arch_config["num_cells"],
            "genotype": CellStructure.str2structure(arch_config["arch_str"]),
            "num_classes": arch_config["class_num"],
        },
        None,
    )
    network = get_cell_based_tiny_net(net_config)
    network.load_state_dict(xresult.get_net_param())
    if "train_times" in results:  # new version
        xresult.update_train_info(
            results["train_acc1es"], results["train_acc5es"], results["train_losses"], results["train_times"]
        )
        xresult.update_eval(results["valid_acc1es"], results["valid_losses"], results["valid_times"])
    else:
        if dataset == "cifar10-valid":
            xresult.update_OLD_eval("x-valid", results["valid_acc1es"], results["valid_losses"])
            loss, top1, top5, latencies = pure_evaluate(
                dataloader_dict["{:}@{:}".format("cifar10", "test")], network.cuda()
            )
            xresult.update_OLD_eval("ori-test", {results["total_epoch"] - 1: top1}, {results["total_epoch"] - 1: loss})
            xresult.update_latency(latencies)
        elif dataset == "cifar10":
            xresult.update_OLD_eval("ori-test", results["valid_acc1es"], results["valid_losses"])
            loss, top1, top5, latencies = pure_evaluate(
                dataloader_dict["{:}@{:}".format(dataset, "test")], network.cuda()
            )
            xresult.update_latency(latencies)
        elif dataset == "cifar100" or dataset == "ImageNet16-120":
            xresult.update_OLD_eval("ori-test", results["valid_acc1es"], results["valid_losses"])
            loss, top1, top5, latencies = pure_evaluate(
                dataloader_dict["{:}@{:}".format(dataset, "valid")], network.cuda()
            )
            xresult.update_OLD_eval("x-valid", {results["total_epoch"] - 1: top1}, {results["total_epoch"] - 1: loss})
            loss, top1, top5, latencies = pure_evaluate(
                dataloader_dict["{:}@{:}".format(dataset, "test")], network.cuda()
            )
            xresult.update_OLD_eval("x-test", {results["total_epoch"] - 1: top1}, {results["total_epoch"] - 1: loss})
            xresult.update_latency(latencies)
        else:
            raise ValueError("invalid dataset name : {:}".format(dataset))
    return xresult


def account_one_arch(arch_index, arch_str, checkpoints, datasets, dataloader_dict):
    information = ArchResults(arch_index, arch_str)

    for checkpoint_path in checkpoints:
        checkpoint = torch.load(checkpoint_path, map_location="cpu")
        used_seed = checkpoint_path.name.split("-")[-1].split(".")[0]
        for dataset in datasets:
            assert dataset in checkpoint, "Can not find {:} in arch-{:} from {:}".format(
                dataset, arch_index, checkpoint_path
            )
            results = checkpoint[dataset]
            assert results["finish-train"], "This {:} arch seed={:} does not finish train on {:} ::: {:}".format(
                arch_index, used_seed, dataset, checkpoint_path
            )
            arch_config = {
                "channel": results["channel"],
                "num_cells": results["num_cells"],
                "arch_str": arch_str,
                "class_num": results["config"]["class_num"],
            }

            xresult = create_result_count(used_seed, dataset, arch_config, results, dataloader_dict)
            information.update(dataset, int(used_seed), xresult)
    return information


def GET_DataLoaders(workers):

    torch.set_num_threads(workers)

    root_dir = (Path(__file__).parent / ".." / "..").resolve()
    torch_dir = Path(os.environ["TORCH_HOME"])
    # cifar
    cifar_config_path = root_dir / "configs" / "nas-benchmark" / "CIFAR.config"
    cifar_config = load_config(cifar_config_path, None, None)
    print("{:} Create data-loader for all datasets".format(time_string()))
    print("-" * 200)
    TRAIN_CIFAR10, VALID_CIFAR10, xshape, class_num = get_datasets("cifar10", str(torch_dir / "cifar.python"), -1)
    print(
        "original CIFAR-10 : {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
            len(TRAIN_CIFAR10), len(VALID_CIFAR10), xshape, class_num
        )
    )
    cifar10_splits = load_config(root_dir / "configs" / "nas-benchmark" / "cifar-split.txt", None, None)
    assert cifar10_splits.train[:10] == [0, 5, 7, 11, 13, 15, 16, 17, 20, 24] and cifar10_splits.valid[:10] == [
        1,
        2,
        3,
        4,
        6,
        8,
        9,
        10,
        12,
        14,
    ]
    temp_dataset = deepcopy(TRAIN_CIFAR10)
    temp_dataset.transform = VALID_CIFAR10.transform
    # data loader
    trainval_cifar10_loader = torch.utils.data.DataLoader(
        TRAIN_CIFAR10, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True
    )
    train_cifar10_loader = torch.utils.data.DataLoader(
        TRAIN_CIFAR10,
        batch_size=cifar_config.batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.train),
        num_workers=workers,
        pin_memory=True,
    )
    valid_cifar10_loader = torch.utils.data.DataLoader(
        temp_dataset,
        batch_size=cifar_config.batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.valid),
        num_workers=workers,
        pin_memory=True,
    )
    test__cifar10_loader = torch.utils.data.DataLoader(
        VALID_CIFAR10, batch_size=cifar_config.batch_size, shuffle=False, num_workers=workers, pin_memory=True
    )
    print(
        "CIFAR-10  : trval-loader has {:3d} batch with {:} per batch".format(
            len(trainval_cifar10_loader), cifar_config.batch_size
        )
    )
    print(
        "CIFAR-10  : train-loader has {:3d} batch with {:} per batch".format(
            len(train_cifar10_loader), cifar_config.batch_size
        )
    )
    print(
        "CIFAR-10  : valid-loader has {:3d} batch with {:} per batch".format(
            len(valid_cifar10_loader), cifar_config.batch_size
        )
    )
    print(
        "CIFAR-10  : test--loader has {:3d} batch with {:} per batch".format(
            len(test__cifar10_loader), cifar_config.batch_size
        )
    )
    print("-" * 200)
    # CIFAR-100
    TRAIN_CIFAR100, VALID_CIFAR100, xshape, class_num = get_datasets("cifar100", str(torch_dir / "cifar.python"), -1)
    print(
        "original CIFAR-100: {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
            len(TRAIN_CIFAR100), len(VALID_CIFAR100), xshape, class_num
        )
    )
    cifar100_splits = load_config(root_dir / "configs" / "nas-benchmark" / "cifar100-test-split.txt", None, None)
    assert cifar100_splits.xvalid[:10] == [1, 3, 4, 5, 8, 10, 13, 14, 15, 16] and cifar100_splits.xtest[:10] == [
        0,
        2,
        6,
        7,
        9,
        11,
        12,
        17,
        20,
        24,
    ]
    train_cifar100_loader = torch.utils.data.DataLoader(
        TRAIN_CIFAR100, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True
    )
    valid_cifar100_loader = torch.utils.data.DataLoader(
        VALID_CIFAR100,
        batch_size=cifar_config.batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xvalid),
        num_workers=workers,
        pin_memory=True,
    )
    test__cifar100_loader = torch.utils.data.DataLoader(
        VALID_CIFAR100,
        batch_size=cifar_config.batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xtest),
        num_workers=workers,
        pin_memory=True,
    )
    print("CIFAR-100  : train-loader has {:3d} batch".format(len(train_cifar100_loader)))
    print("CIFAR-100  : valid-loader has {:3d} batch".format(len(valid_cifar100_loader)))
    print("CIFAR-100  : test--loader has {:3d} batch".format(len(test__cifar100_loader)))
    print("-" * 200)

    imagenet16_config_path = "configs/nas-benchmark/ImageNet-16.config"
    imagenet16_config = load_config(imagenet16_config_path, None, None)
    TRAIN_ImageNet16_120, VALID_ImageNet16_120, xshape, class_num = get_datasets(
        "ImageNet16-120", str(torch_dir / "cifar.python" / "ImageNet16"), -1
    )
    print(
        "original TRAIN_ImageNet16_120: {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
            len(TRAIN_ImageNet16_120), len(VALID_ImageNet16_120), xshape, class_num
        )
    )
    imagenet_splits = load_config(root_dir / "configs" / "nas-benchmark" / "imagenet-16-120-test-split.txt", None, None)
    assert imagenet_splits.xvalid[:10] == [1, 2, 3, 6, 7, 8, 9, 12, 16, 18] and imagenet_splits.xtest[:10] == [
        0,
        4,
        5,
        10,
        11,
        13,
        14,
        15,
        17,
        20,
    ]
    train_imagenet_loader = torch.utils.data.DataLoader(
        TRAIN_ImageNet16_120,
        batch_size=imagenet16_config.batch_size,
        shuffle=True,
        num_workers=workers,
        pin_memory=True,
    )
    valid_imagenet_loader = torch.utils.data.DataLoader(
        VALID_ImageNet16_120,
        batch_size=imagenet16_config.batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xvalid),
        num_workers=workers,
        pin_memory=True,
    )
    test__imagenet_loader = torch.utils.data.DataLoader(
        VALID_ImageNet16_120,
        batch_size=imagenet16_config.batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xtest),
        num_workers=workers,
        pin_memory=True,
    )
    print(
        "ImageNet-16-120  : train-loader has {:3d} batch with {:} per batch".format(
            len(train_imagenet_loader), imagenet16_config.batch_size
        )
    )
    print(
        "ImageNet-16-120  : valid-loader has {:3d} batch with {:} per batch".format(
            len(valid_imagenet_loader), imagenet16_config.batch_size
        )
    )
    print(
        "ImageNet-16-120  : test--loader has {:3d} batch with {:} per batch".format(
            len(test__imagenet_loader), imagenet16_config.batch_size
        )
    )

    # 'cifar10', 'cifar100', 'ImageNet16-120'
    loaders = {
        "cifar10@trainval": trainval_cifar10_loader,
        "cifar10@train": train_cifar10_loader,
        "cifar10@valid": valid_cifar10_loader,
        "cifar10@test": test__cifar10_loader,
        "cifar100@train": train_cifar100_loader,
        "cifar100@valid": valid_cifar100_loader,
        "cifar100@test": test__cifar100_loader,
        "ImageNet16-120@train": train_imagenet_loader,
        "ImageNet16-120@valid": valid_imagenet_loader,
        "ImageNet16-120@test": test__imagenet_loader,
    }
    return loaders


def simplify(save_dir, meta_file, basestr, target_dir):
    meta_infos = torch.load(meta_file, map_location="cpu")
    meta_archs = meta_infos["archs"]  # a list of architecture strings
    meta_num_archs = meta_infos["total"]
    meta_max_node = meta_infos["max_node"]
    assert meta_num_archs == len(meta_archs), "invalid number of archs : {:} vs {:}".format(
        meta_num_archs, len(meta_archs)
    )

    sub_model_dirs = sorted(list(save_dir.glob("*-*-{:}".format(basestr))))
    print("{:} find {:} directories used to save checkpoints".format(time_string(), len(sub_model_dirs)))

    subdir2archs, num_evaluated_arch = collections.OrderedDict(), 0
    num_seeds = defaultdict(lambda: 0)
    for index, sub_dir in enumerate(sub_model_dirs):
        xcheckpoints = list(sub_dir.glob("arch-*-seed-*.pth"))
        arch_indexes = set()
        for checkpoint in xcheckpoints:
            temp_names = checkpoint.name.split("-")
            assert (
                len(temp_names) == 4 and temp_names[0] == "arch" and temp_names[2] == "seed"
            ), "invalid checkpoint name : {:}".format(checkpoint.name)
            arch_indexes.add(temp_names[1])
        subdir2archs[sub_dir] = sorted(list(arch_indexes))
        num_evaluated_arch += len(arch_indexes)
        # count number of seeds for each architecture
        for arch_index in arch_indexes:
            num_seeds[len(list(sub_dir.glob("arch-{:}-seed-*.pth".format(arch_index))))] += 1
    print(
        "{:} There are {:5d} architectures that have been evaluated ({:} in total).".format(
            time_string(), num_evaluated_arch, meta_num_archs
        )
    )
    for key in sorted(list(num_seeds.keys())):
        print(
            "{:} There are {:5d} architectures that are evaluated {:} times.".format(time_string(), num_seeds[key], key)
        )

    dataloader_dict = GET_DataLoaders(6)

    to_save_simply = save_dir / "simplifies"
    to_save_allarc = save_dir / "simplifies" / "architectures"
    if not to_save_simply.exists():
        to_save_simply.mkdir(parents=True, exist_ok=True)
    if not to_save_allarc.exists():
        to_save_allarc.mkdir(parents=True, exist_ok=True)

    assert (save_dir / target_dir) in subdir2archs, "can not find {:}".format(target_dir)
    arch2infos, datasets = {}, ("cifar10-valid", "cifar10", "cifar100", "ImageNet16-120")
    evaluated_indexes = set()
    target_directory = save_dir / target_dir
    target_less_dir = save_dir / "{:}-LESS".format(target_dir)
    arch_indexes = subdir2archs[target_directory]
    num_seeds = defaultdict(lambda: 0)
    end_time = time.time()
    arch_time = AverageMeter()
    for idx, arch_index in enumerate(arch_indexes):
        checkpoints = list(target_directory.glob("arch-{:}-seed-*.pth".format(arch_index)))
        ckps_less = list(target_less_dir.glob("arch-{:}-seed-*.pth".format(arch_index)))
        # create the arch info for each architecture
        try:
            arch_info_full = account_one_arch(
                arch_index, meta_archs[int(arch_index)], checkpoints, datasets, dataloader_dict
            )
            arch_info_less = account_one_arch(
                arch_index, meta_archs[int(arch_index)], ckps_less, ["cifar10-valid"], dataloader_dict
            )
            num_seeds[len(checkpoints)] += 1
        except:
            print("Loading {:} failed, : {:}".format(arch_index, checkpoints))
            continue
        assert int(arch_index) not in evaluated_indexes, "conflict arch-index : {:}".format(arch_index)
        assert 0 <= int(arch_index) < len(meta_archs), "invalid arch-index {:} (not found in meta_archs)".format(
            arch_index
        )
        arch_info = {"full": arch_info_full, "less": arch_info_less}
        evaluated_indexes.add(int(arch_index))
        arch2infos[int(arch_index)] = arch_info
        torch.save(
            {"full": arch_info_full.state_dict(), "less": arch_info_less.state_dict()},
            to_save_allarc / "{:}-FULL.pth".format(arch_index),
        )
        arch_info["full"].clear_params()
        arch_info["less"].clear_params()
        torch.save(
            {"full": arch_info_full.state_dict(), "less": arch_info_less.state_dict()},
            to_save_allarc / "{:}-SIMPLE.pth".format(arch_index),
        )
        # measure elapsed time
        arch_time.update(time.time() - end_time)
        end_time = time.time()
        need_time = "{:}".format(convert_secs2time(arch_time.avg * (len(arch_indexes) - idx - 1), True))
        print(
            "{:} {:} [{:03d}/{:03d}] : {:} still need {:}".format(
                time_string(), target_dir, idx, len(arch_indexes), arch_index, need_time
            )
        )
    # measure time
    xstrs = ["{:}:{:03d}".format(key, num_seeds[key]) for key in sorted(list(num_seeds.keys()))]
    print("{:} {:} done : {:}".format(time_string(), target_dir, xstrs))
    final_infos = {
        "meta_archs": meta_archs,
        "total_archs": meta_num_archs,
        "basestr": basestr,
        "arch2infos": arch2infos,
        "evaluated_indexes": evaluated_indexes,
    }
    save_file_name = to_save_simply / "{:}.pth".format(target_dir)
    torch.save(final_infos, save_file_name)
    print(
        "Save {:} / {:} architecture results into {:}.".format(len(evaluated_indexes), meta_num_archs, save_file_name)
    )


def merge_all(save_dir, meta_file, basestr):
    meta_infos = torch.load(meta_file, map_location="cpu")
    meta_archs = meta_infos["archs"]
    meta_num_archs = meta_infos["total"]
    meta_max_node = meta_infos["max_node"]
    assert meta_num_archs == len(meta_archs), "invalid number of archs : {:} vs {:}".format(
        meta_num_archs, len(meta_archs)
    )

    sub_model_dirs = sorted(list(save_dir.glob("*-*-{:}".format(basestr))))
    print("{:} find {:} directories used to save checkpoints".format(time_string(), len(sub_model_dirs)))
    for index, sub_dir in enumerate(sub_model_dirs):
        arch_info_files = sorted(list(sub_dir.glob("arch-*-seed-*.pth")))
        print(
            "The {:02d}/{:02d}-th directory : {:} : {:} runs.".format(
                index, len(sub_model_dirs), sub_dir, len(arch_info_files)
            )
        )

    arch2infos, evaluated_indexes = dict(), set()
    for IDX, sub_dir in enumerate(sub_model_dirs):
        ckp_path = sub_dir.parent / "simplifies" / "{:}.pth".format(sub_dir.name)
        if ckp_path.exists():
            sub_ckps = torch.load(ckp_path, map_location="cpu")
            assert sub_ckps["total_archs"] == meta_num_archs and sub_ckps["basestr"] == basestr
            xarch2infos = sub_ckps["arch2infos"]
            xevalindexs = sub_ckps["evaluated_indexes"]
            for eval_index in xevalindexs:
                assert eval_index not in evaluated_indexes and eval_index not in arch2infos
                # arch2infos[eval_index] = xarch2infos[eval_index].state_dict()
                arch2infos[eval_index] = {
                    "full": xarch2infos[eval_index]["full"].state_dict(),
                    "less": xarch2infos[eval_index]["less"].state_dict(),
                }
                evaluated_indexes.add(eval_index)
            print(
                "{:} [{:03d}/{:03d}] merge data from {:} with {:} models.".format(
                    time_string(), IDX, len(sub_model_dirs), ckp_path, len(xevalindexs)
                )
            )
        else:
            raise ValueError("Can not find {:}".format(ckp_path))
            # print ('{:} [{:03d}/{:03d}] can not find {:}, skip.'.format(time_string(), IDX, len(subdir2archs), ckp_path))

    evaluated_indexes = sorted(list(evaluated_indexes))
    print("Finally, there are {:} architectures that have been trained and evaluated.".format(len(evaluated_indexes)))

    to_save_simply = save_dir / "simplifies"
    if not to_save_simply.exists():
        to_save_simply.mkdir(parents=True, exist_ok=True)
    final_infos = {
        "meta_archs": meta_archs,
        "total_archs": meta_num_archs,
        "arch2infos": arch2infos,
        "evaluated_indexes": evaluated_indexes,
    }
    save_file_name = to_save_simply / "{:}-final-infos.pth".format(basestr)
    torch.save(final_infos, save_file_name)
    print(
        "Save {:} / {:} architecture results into {:}.".format(len(evaluated_indexes), meta_num_archs, save_file_name)
    )


if __name__ == "__main__":

    parser = argparse.ArgumentParser(
        description="NAS-BENCH-201", formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    parser.add_argument("--mode", type=str, choices=["cal", "merge"], help="The running mode for this script.")
    parser.add_argument(
        "--base_save_dir",
        type=str,
        default="./output/NAS-BENCH-201-4",
        help="The base-name of folder to save checkpoints and log.",
    )
    parser.add_argument("--target_dir", type=str, help="The target directory.")
    parser.add_argument("--max_node", type=int, default=4, help="The maximum node in a cell.")
    parser.add_argument("--channel", type=int, default=16, help="The number of channels.")
    parser.add_argument("--num_cells", type=int, default=5, help="The number of cells in one stage.")
    args = parser.parse_args()

    save_dir = Path(args.base_save_dir)
    meta_path = save_dir / "meta-node-{:}.pth".format(args.max_node)
    assert save_dir.exists(), "invalid save dir path : {:}".format(save_dir)
    assert meta_path.exists(), "invalid saved meta path : {:}".format(meta_path)
    print("start the statistics of our nas-benchmark from {:} using {:}.".format(save_dir, args.target_dir))
    basestr = "C{:}-N{:}".format(args.channel, args.num_cells)

    if args.mode == "cal":
        simplify(save_dir, meta_path, basestr, args.target_dir)
    elif args.mode == "merge":
        merge_all(save_dir, meta_path, basestr)
    else:
        raise ValueError("invalid mode : {:}".format(args.mode))