import time
import torch
import os
import yaml
from types import SimpleNamespace

def dict_to_namespace(d):
    if isinstance(d, dict):
        return SimpleNamespace(**{k: dict_to_namespace(v) for k, v in d.items()})
    elif isinstance(d, list):
        return [dict_to_namespace(i) for i in d]
    else:
        return d

def load_config(path):
    with open(f'/opt/tiger/Abbie/trainer/template/{path}', 'r') as f:
        cfg_dict = yaml.safe_load(f)
        return dict_to_namespace(cfg_dict)


def make_handler(rank, local_dir):
    def handler_fn(p):
        # export trace data when traces ready (schedule cycle ends)
        fname = "profileStep" + str(p.step_num) + "_globalStep" + str(0) + "_rank" + str(rank) + "." + \
                str(int(time.time())) + ".pt.trace.json.gz"
        local_file = os.path.join(local_dir, fname)
        if not os.path.exists(local_dir):
            print("mkdir ", local_dir)
            os.makedirs(local_dir)
        print("Save profile results to {}".format(local_file))
        p.export_chrome_trace(local_file)
        print("Local profile file saved")

    return handler_fn


def collect_scalars_across_data_parallel_group(scalars, dp_group):
    """Reduce a tensor of losses across all GPUs."""
    scalars = torch.cat(
        [loss.clone().detach().view(1) for loss in scalars])
    group_size = torch.distributed.get_world_size(group=dp_group)
    out_scalars = [torch.ones_like(scalars) for i in range(group_size)]
    torch.distributed.all_gather(out_scalars, scalars,
                                 group=dp_group)
    return out_scalars, group_size