# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#

import argparse
import logging
import os
import pprint
import sys
import yaml

import submitit

from src.eval_wilds import main as app_main

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logger = logging.getLogger()


parser = argparse.ArgumentParser()
parser.add_argument("--folder", type=str, help="location to save submitit logs")
parser.add_argument(
    "--batch-launch",
    action="store_true",
    help="whether fname points to a file to batch-lauch several config files",
)
parser.add_argument(
    "--fname",
    type=str,
    help="yaml file containing config file names to launch",
    default="configs.yaml",
)
parser.add_argument("--partition", type=str, help="cluster partition to submit jobs on")
parser.add_argument(
    "--nodes", type=int, default=1, help="num. nodes to request for job"
)
parser.add_argument(
    "--tasks-per-node", type=int, default=1, help="num. procs to per node"
)
parser.add_argument("--time", type=int, default=4300, help="time in minutes to run job")


class Trainer:
    def __init__(self, fname="configs.yaml"):
        self.fname = fname

    def __call__(self):
        fname = self.fname
        logger.info(f"called-params {fname}")

        params = None
        with open(fname, "r") as y_file:
            params = yaml.load(y_file, Loader=yaml.FullLoader)
            logger.info("loaded params...")
            pp = pprint.PrettyPrinter(indent=4)
            pp.pprint(params)

        app_main(args=params)

    def checkpoint(self):
        fb_trainer = Trainer(self.fname)
        return submitit.helpers.DelayedSubmission(
            fb_trainer,
        )


def launch():
    executor = submitit.SlurmExecutor(
        folder=os.path.join(args.folder, "job_%j"), max_num_timeout=20
    )
    executor.update_parameters(
        partition=args.partition,
        mem_per_gpu="55G",
        time=args.time,
        nodes=args.nodes,
        ntasks_per_node=args.tasks_per_node,
        cpus_per_task=10,
        gpus_per_node=args.tasks_per_node,
    )

    config_fnames = [args.fname]

    jobs, trainers = [], []
    with executor.batch():
        for cf in config_fnames:
            fb_trainer = Trainer(cf)
            job = executor.submit(
                fb_trainer,
            )
            trainers.append(fb_trainer)
            jobs.append(job)

    for job in jobs:
        print(job.job_id)


if __name__ == "__main__":
    args = parser.parse_args()
    launch()