#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2020/10/26 13:38
# @Author  : Scheaven
# @File    :  defaults.py
# @description:

"""
    This file contains components with some default boilerplate logic user may need
    in training / testing. They will not work for everyone, but many users may find them useful.
    The behavior of functions/classes in this file is subject to change,
    since they are meant to represent the "common default behavior" people need in their projects.
"""
import argparse
import logging
import os
import sys
from utils import comm
from utils.env import seed_all_rng
from utils.file_io import PathManager
from utils.logger import setup_logger
from utils.collect_env import collect_env_info
from collections import OrderedDict

import torch
# import torch.nn.functional as F
# from torch.nn.parallel import DistributedDataParallel

def default_argument_parser():
    """
    Create a parser with some common arguments used by fastreid users.
    Returns:
        argparse.ArgumentParser:
    """
    parser = argparse.ArgumentParser(description="fastreid Training")
    parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file")
    parser.add_argument(
        "--finetune",
        action="store_true",
        help="whether to attempt to finetune from the trained model",
    )
    parser.add_argument(
        "--resume",
        action="store_true",
        help="whether to attempt to resume from the checkpoint directory",
    )
    parser.add_argument("--eval-only", action="store_true", help="perform evaluation only")
    parser.add_argument("--num-gpus", type=int, default=1, help="number of gpus *per machine*")
    parser.add_argument("--num-machines", type=int, default=1, help="total number of machines")
    parser.add_argument("--img_a1", default="1.jpg", help="input image")
    parser.add_argument("--img_a2", default="2.jpg", help="input image2")
    parser.add_argument("--img_b1", default="1.jpg", help="input image")
    parser.add_argument("--img_b2", default="2.jpg", help="input image2")
    parser.add_argument(
        "--machine-rank", type=int, default=0, help="the rank of this machine (unique per machine)"
    )

    # PyTorch still may leave orphan processes in multi-gpu training.
    # Therefore we use a deterministic way to obtain port,
    # so that users are aware of orphan processes by seeing the port occupied.
    port = 2 ** 15 + 2 ** 14 + hash(os.getuid() if sys.platform != "win32" else 1) % 2 ** 14
    parser.add_argument("--dist-url", default="tcp://127.0.0.1:{}".format(port))
    parser.add_argument(
        "opts",
        help="Modify config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )
    return parser

def default_setup(cfg, args):
    """
    Perform some basic common setups at the beginning of a job, including:
    1. Set up the detectron2 logger
    2. Log basic information about environment, cmdline arguments, and config
    3. Backup the config to the output directory
    Args:
        cfg (CfgNode): the full config to be used
        args (argparse.NameSpace): the command line arguments to be logged
    """
    output_dir = cfg.OUTPUT_DIR
    if comm.is_main_process() and output_dir:
        PathManager.mkdirs(output_dir)

    rank = comm.get_rank()
    setup_logger(output_dir, distributed_rank=rank, name="fvcore")
    logger = setup_logger(output_dir, distributed_rank=rank)

    logger.info("Rank of current process: {}. World size: {}".format(rank, comm.get_world_size()))
    logger.info("Environment info:\n" + collect_env_info())

    logger.info("Command line arguments: " + str(args))
    if hasattr(args, "config_file") and args.config_file != "":
        logger.info(
            "Contents of args.config_file={}:\n{}".format(
                args.config_file, PathManager.open(args.config_file, "r").read()
            )
        )

    logger.info("Running with full config:\n{}".format(cfg))
    if comm.is_main_process() and output_dir:
        # Note: some of our scripts may expect the existence of
        # config.yaml in output directory
        path = os.path.join(output_dir, "config.yaml")
        with PathManager.open(path, "w") as f:
            f.write(cfg.dump())
        logger.info("Full config saved to {}".format(os.path.abspath(path)))

    # make sure each worker has a different, yet deterministic seed if specified
    seed_all_rng()

    # cudnn benchmark has large overhead. It shouldn't be used considering the small size of
    # typical validation set.
    if not (hasattr(args, "eval_only") and args.eval_only):
        torch.backends.cudnn.benchmark = cfg.CUDNN_BENCHMARK