import hydra

from omegaconf import DictConfig
from pytorch_lightning import Callback
from pytorch_lightning.loggers import Logger
from typing import List

from amelia_tf.utils import pylogger

log = pylogger.get_pylogger(__name__)

def instantiate_callbacks(callbacks_cfg: DictConfig) -> List[Callback]:
    """Instantiates callbacks from config.

    Input
    -----
        callbacks_cfg[DictConfig]: configuration parameters for the callbacks to instantiate.

    Output
    ------
        callbacks[List[Callback]]: list of instantiated callbacks.
    """
    callbacks: List[Callback] = []

    if not callbacks_cfg:
        log.warning("No callback configs found! Skipping..")
        return callbacks

    if not isinstance(callbacks_cfg, DictConfig):
        raise TypeError("Callbacks config must be a DictConfig!")

    for _, cb_conf in callbacks_cfg.items():
        if isinstance(cb_conf, DictConfig) and "_target_" in cb_conf:
            log.info(f"Instantiating callback <{cb_conf._target_}>")
            callbacks.append(hydra.utils.instantiate(cb_conf))

    return callbacks

def instantiate_loggers(logger_cfg: DictConfig) -> List[Logger]:
    """Instantiates loggers from config.

    Input
    -----
        logger_cfg[DictConfig]: configuration parameters for the loggers to instantiate.

    Output
    ------
        logger[List[Logger]]: list of instantiated loggers.
    """
    logger: List[Logger] = []

    if not logger_cfg:
        log.warning("No logger configs found! Skipping...")
        return logger

    if not isinstance(logger_cfg, DictConfig):
        raise TypeError("Logger config must be a DictConfig!")

    for _, lg_conf in logger_cfg.items():
        if isinstance(lg_conf, DictConfig) and "_target_" in lg_conf:
            log.info(f"Instantiating logger <{lg_conf._target_}>")
            logger.append(hydra.utils.instantiate(lg_conf))

    return logger