"""
Launcher for all experiments. Download pre-training data, normalization statistics, and pre-trained checkpoints if needed.

"""

import os
import sys
import pretty_errors
import logging

import math
import hydra
from omegaconf import OmegaConf
import gdown
from download_url import (
    get_dataset_download_url,
    get_normalization_download_url,
    get_checkpoint_download_url,
)

# allows arbitrary python code execution in configs using the ${eval:''} resolver
OmegaConf.register_new_resolver("eval", eval, replace=True)
OmegaConf.register_new_resolver("round_up", math.ceil)
OmegaConf.register_new_resolver("round_down", math.floor)

# suppress d4rl import error
os.environ["D4RL_SUPPRESS_IMPORT_ERROR"] = "1"

# add logger
log = logging.getLogger(__name__)

# use line-buffering for both stdout and stderr
sys.stdout = open(sys.stdout.fileno(), mode="w", buffering=1)
sys.stderr = open(sys.stderr.fileno(), mode="w", buffering=1)


@hydra.main(
    version_base=None,
    config_path=os.path.join(
        os.getcwd(), "cfg"
    ),  # possibly overwritten by --config-path
)
def main(cfg: OmegaConf):
    # resolve immediately so all the ${now:} resolvers will use the same time.
    OmegaConf.resolve(cfg)

    # For pre-training: download dataset if needed
    if "train_dataset_path" in cfg and not os.path.exists(cfg.train_dataset_path):
        download_url = get_dataset_download_url(cfg)
        download_target = os.path.dirname(cfg.train_dataset_path)
        log.info(f"Downloading dataset from {download_url} to {download_target}")
        gdown.download_folder(url=download_url, output=download_target)

    # For for-tuning: download normalization if needed
    if "normalization_path" in cfg and not os.path.exists(cfg.normalization_path):
        download_url = get_normalization_download_url(cfg)
        download_target = cfg.normalization_path
        dir_name = os.path.dirname(download_target)
        if not os.path.exists(dir_name):
            os.makedirs(dir_name)
        log.info(
            f"Downloading normalization statistics from {download_url} to {download_target}"
        )
        gdown.download(url=download_url, output=download_target, fuzzy=True)

    # For for-tuning: download checkpoint if needed
    if "base_policy_path" in cfg and not os.path.exists(cfg.base_policy_path):
        download_url = get_checkpoint_download_url(cfg)
        if download_url is None:
            raise ValueError(
                f"Unknown checkpoint path. Did you specify the correct path to the policy you trained?"
            )
        download_target = cfg.base_policy_path
        dir_name = os.path.dirname(download_target)
        if not os.path.exists(dir_name):
            os.makedirs(dir_name)
        log.info(f"Downloading checkpoint from {download_url} to {download_target}")
        gdown.download(url=download_url, output=download_target, fuzzy=True)

    # Deal with isaacgym needs to be imported before torch
    if "env" in cfg and "env_type" in cfg.env and cfg.env.env_type == "furniture":
        import furniture_bench

    # run agent
    cls = hydra.utils.get_class(cfg._target_)
    agent = cls(cfg)
    agent.run()


if __name__ == "__main__":
    main()