#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved


import argparse
import logging
import os
import time

import detectron2.utils.comm as comm
import torch
from d2go.config import (
    CfgNode as CN,
    auto_scale_world_size,
    reroute_config_path,
    temp_defrost,
)
from d2go.distributed import get_local_rank, get_num_processes_per_machine
from d2go.runner import GeneralizedRCNNRunner, create_runner
from d2go.utils.helper import run_once
from d2go.utils.launch_environment import get_launch_environment
from detectron2.utils.collect_env import collect_env_info
from detectron2.utils.file_io import PathManager
from detectron2.utils.logger import setup_logger
from detectron2.utils.serialize import PicklableWrapper
from mobile_cv.common.misc.py import FolderLock, MultiprocessingPdb, post_mortem_if_fail


logger = logging.getLogger(__name__)


def basic_argument_parser(
    distributed=True,
    requires_config_file=True,
    requires_output_dir=True,
):
    """Basic cli tool parser for Detectron2Go binaries"""
    parser = argparse.ArgumentParser(description="PyTorch Object Detection Training")
    parser.add_argument(
        "--runner",
        type=str,
        default="d2go.runner.GeneralizedRCNNRunner",
        help="Full class name, i.e. (package.)module.class",
    )
    parser.add_argument(
        "--config-file",
        help="path to config file",
        default="",
        required=requires_config_file,
        metavar="FILE",
    )
    parser.add_argument(
        "--output-dir",
        help="When given, this will override the OUTPUT_DIR in the config-file",
        required=requires_output_dir,
        default=None,
        type=str,
    )
    parser.add_argument(
        "opts",
        help="Modify config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )

    if distributed:
        parser.add_argument(
            "--num-processes", type=int, default=1, help="number of gpus per machine"
        )
        parser.add_argument("--num-machines", type=int, default=1)
        parser.add_argument(
            "--machine-rank",
            type=int,
            default=0,
            help="the rank of this machine (unique per machine)",
        )
        parser.add_argument(
            "--dist-url", default="file:///tmp/d2go_dist_file_{}".format(time.time())
        )
        parser.add_argument("--dist-backend", type=str, default="NCCL")

    if not requires_config_file:
        # NOTE if not passing yaml file, user should explicitly set the
        # following args, and use `opts` for non-common usecase.
        parser.add_argument(
            "--datasets",
            type=str,
            nargs="+",
            required=True,
            help="cfg.DATASETS.TEST",
        )
        parser.add_argument(
            "--min_size",
            type=int,
            required=True,
            help="cfg.INPUT.MIN_SIZE_TEST",
        )
        parser.add_argument(
            "--max_size",
            type=int,
            required=True,
            help="cfg.INPUT.MAX_SIZE_TEST",
        )
        return parser

    return parser


def create_cfg_from_cli_args(args, default_cfg):
    """
    Instead of loading from defaults.py, this binary only includes necessary
    configs building from scratch, and overrides them from args. There're two
    levels of config:
        _C: the config system used by this binary, which is a sub-set of training
            config, override by configurable_cfg. It can also be override by
            args.opts for convinience.
        configurable_cfg: common configs that user should explicitly specify
            in the args.
    """

    _C = CN()
    _C.INPUT = default_cfg.INPUT
    _C.DATASETS = default_cfg.DATASETS
    _C.DATALOADER = default_cfg.DATALOADER
    _C.TEST = default_cfg.TEST
    if hasattr(default_cfg, "D2GO_DATA"):
        _C.D2GO_DATA = default_cfg.D2GO_DATA
    if hasattr(default_cfg, "TENSORBOARD"):
        _C.TENSORBOARD = default_cfg.TENSORBOARD

    # NOTE configs below might not be necessary, but must add to make code work
    _C.MODEL = CN()
    _C.MODEL.META_ARCHITECTURE = default_cfg.MODEL.META_ARCHITECTURE
    _C.MODEL.MASK_ON = default_cfg.MODEL.MASK_ON
    _C.MODEL.KEYPOINT_ON = default_cfg.MODEL.KEYPOINT_ON
    _C.MODEL.LOAD_PROPOSALS = default_cfg.MODEL.LOAD_PROPOSALS
    assert _C.MODEL.LOAD_PROPOSALS is False, "caffe2 model doesn't support"

    _C.OUTPUT_DIR = args.output_dir

    configurable_cfg = [
        "DATASETS.TEST",
        args.datasets,
        "INPUT.MIN_SIZE_TEST",
        args.min_size,
        "INPUT.MAX_SIZE_TEST",
        args.max_size,
    ]

    cfg = _C.clone()
    cfg.merge_from_list(configurable_cfg)
    cfg.merge_from_list(args.opts)

    return cfg


def prepare_for_launch(args):
    """
    Load config, figure out working directory, create runner.
        - when args.config_file is empty, returned cfg will be the default one
        - returned output_dir will always be non empty, args.output_dir has higher
            priority than cfg.OUTPUT_DIR.
    """
    print(args)
    runner = create_runner(args.runner)

    cfg = runner.get_default_cfg()
    if args.config_file:
        with PathManager.open(reroute_config_path(args.config_file), "r") as f:
            print("Loaded config file {}:\n{}".format(args.config_file, f.read()))
        cfg.merge_from_file(args.config_file)
        cfg.merge_from_list(args.opts)
    else:
        cfg = create_cfg_from_cli_args(args, default_cfg=cfg)
    cfg.freeze()

    assert args.output_dir or args.config_file
    output_dir = args.output_dir or cfg.OUTPUT_DIR
    return cfg, output_dir, runner


def _setup_after_launch(cfg: CN, output_dir: str, runner):
    """
    Set things up after entering DDP, including
        - creating working directory
        - setting up logger
        - logging environment
        - initializing runner
    """
    create_dir_on_global_main_process(output_dir)
    comm.synchronize()
    setup_loggers(output_dir)
    cfg.freeze()
    if cfg.OUTPUT_DIR != output_dir:
        with temp_defrost(cfg):
            logger.warning(
                "Override cfg.OUTPUT_DIR ({}) to be the same as output_dir {}".format(
                    cfg.OUTPUT_DIR, output_dir
                )
            )
            cfg.OUTPUT_DIR = output_dir
    dump_cfg(cfg, os.path.join(output_dir, "config.yaml"))


def setup_after_launch(cfg: CN, output_dir: str, runner):
    _setup_after_launch(cfg, output_dir, runner)
    logger.info("Initializing runner ...")
    runner = initialize_runner(runner, cfg)

    log_info(cfg, runner)

    auto_scale_world_size(cfg, new_world_size=comm.get_world_size())


def setup_after_lightning_launch(cfg: CN, output_dir: str):
    _setup_after_launch(cfg, output_dir, runner=None)
    log_info(cfg, runner=None)


@run_once()
def setup_loggers(output_dir, color=None):
    if not color:
        color = get_launch_environment() == "local"

    d2_logger = setup_logger(
        output_dir,
        distributed_rank=comm.get_rank(),
        color=color,
        name="detectron2",
        abbrev_name="d2",
    )
    fvcore_logger = setup_logger(
        output_dir,
        distributed_rank=comm.get_rank(),
        color=color,
        name="fvcore",
    )
    d2go_logger = setup_logger(
        output_dir,
        distributed_rank=comm.get_rank(),
        color=color,
        name="d2go",
        abbrev_name="d2go",
    )
    mobile_cv_logger = setup_logger(
        output_dir,
        distributed_rank=comm.get_rank(),
        color=color,
        name="mobile_cv",
        abbrev_name="mobile_cv",
    )

    # NOTE: all above loggers have FileHandler pointing to the same file as d2_logger.
    # Those files are opened upon creation, but it seems fine in 'a' mode.

    # NOTE: the root logger might has been configured by other applications,
    # since this already sub-top level, just don't propagate to root.
    d2_logger.propagate = False
    fvcore_logger.propagate = False
    d2go_logger.propagate = False
    mobile_cv_logger.propagate = False


def log_info(cfg, runner):
    num_processes = get_num_processes_per_machine()
    logger.info(
        "Using {} processes per machine. Rank of current process: {}".format(
            num_processes, comm.get_rank()
        )
    )
    wf_id = os.getenv("WORKFLOW_RUN_ID", None)
    if wf_id is not None:
        logger.info("FBLearner Flow Run ID: {}".format(wf_id))
    logger.info("Environment info:\n" + collect_env_info())
    logger.info("Running with full config:\n{}".format(cfg))
    logger.info("Running with runner: {}".format(runner))


def dump_cfg(cfg, path):
    if comm.is_main_process():
        with PathManager.open(path, "w") as f:
            f.write(cfg.dump())
        logger.info("Full config saved to {}".format(path))


def create_dir_on_local_main_process(dir):
    if get_local_rank() == 0 and dir:
        PathManager.mkdirs(dir)


def create_dir_on_global_main_process(dir):
    if comm.get_rank() == 0 and dir:
        PathManager.mkdirs(dir)


def initialize_runner(runner, cfg):
    runner = runner or GeneralizedRCNNRunner()
    runner._initialize(cfg)
    return runner


def caffe2_global_init(logging_print_net_summary=0, num_threads=None):
    if num_threads is None:
        if get_num_processes_per_machine() > 1:
            # by default use single thread when DDP with multiple processes
            num_threads = 1
        else:
            # GlobalInit will clean PyTorch's num_threads and set it to 1,
            # thus keep PyTorch's default value to make it truly default.
            num_threads = torch.get_num_threads()

    if not get_local_rank() == 0:
        logging_print_net_summary = 0  # only enable for local main process

    from caffe2.python import workspace

    workspace.GlobalInit(
        [
            "caffe2",
            "--caffe2_log_level=2",
            "--caffe2_logging_print_net_summary={}".format(logging_print_net_summary),
            "--caffe2_omp_num_threads={}".format(num_threads),
            "--caffe2_mkl_num_threads={}".format(num_threads),
        ]
    )
    logger.info("Using {} threads after GlobalInit".format(torch.get_num_threads()))


def post_mortem_if_fail_for_main(main_func):
    def new_main_func(cfg, output_dir, *args, **kwargs):
        pdb_ = (
            MultiprocessingPdb(FolderLock(output_dir))
            if comm.get_world_size() > 1
            else None  # fallback to use normal pdb for single process
        )
        return post_mortem_if_fail(pdb_)(main_func)(cfg, output_dir, *args, **kwargs)

    return PicklableWrapper(new_main_func)
