from typing import List, Optional, Sequence
from pathlib import Path

import hydra
from omegaconf import OmegaConf, DictConfig
from pytorch_lightning import (
    Callback,
    LightningDataModule,
    LightningModule,
    Trainer,
    seed_everything,
)
from pytorch_lightning.loggers import LightningLoggerBase

from src.utils import utils

log = utils.get_logger(__name__)


def last_modification_time(path):
    """Including files / directory 1-level below the path
    """
    path = Path(path)
    if path.is_file():
        return path.stat().st_mtime
    elif path.is_dir():
        return max(child.stat().st_mtime for child in path.iterdir())
    else:
        return None


def train(config: DictConfig) -> Optional[float]:
    """Contains training pipeline.
    Instantiates all PyTorch Lightning objects from config.

    Args:
        config (DictConfig): Configuration composed by Hydra.

    Returns:
        Optional[float]: Metric score for hyperparameter optimization.
    """

    # Set seed for random number generators in pytorch, numpy and python.random
    if config.get("seed"):
        seed_everything(config.seed, workers=True)

    # We want to add fields to config so need to call OmegaConf.set_struct
    OmegaConf.set_struct(config, False)
    # Init lightning model
    model: LightningModule = hydra.utils.instantiate(config.task, cfg=config, _recursive_=False)
    datamodule: LightningDataModule = model._datamodule

    # Init lightning callbacks
    callbacks: List[Callback] = []
    if "callbacks" in config:
        for _, cb_conf in config.callbacks.items():
            if cb_conf is not None and "_target_" in cb_conf:
                log.info(f"Instantiating callback <{cb_conf._target_}>")
                callbacks.append(hydra.utils.instantiate(cb_conf))

    # Init lightning loggers
    logger: List[LightningLoggerBase] = []
    if "logger" in config:
        for _, lg_conf in config.logger.items():
            if lg_conf is not None and "_target_" in lg_conf:
                log.info(f"Instantiating logger <{lg_conf._target_}>")
                logger.append(hydra.utils.instantiate(lg_conf))

    ckpt_cfg = {}
    if config.get('resume'):
        try:
            checkpoint_path = Path(config.callbacks.model_checkpoint.dirpath)
            if checkpoint_path.is_dir():
                last_ckpt = checkpoint_path / 'last.ckpt'
                autosave_ckpt = checkpoint_path / '.pl_auto_save.ckpt'
                if not (last_ckpt.exists() or autosave_ckpt.exists()):
                    raise FileNotFoundError("Resume requires either last.ckpt or .pl_autosave.ckpt")
                if ((not last_ckpt.exists())
                    or (autosave_ckpt.exists()
                       and last_modification_time(autosave_ckpt) > last_modification_time(last_ckpt))):
                    # autosave_ckpt = autosave_ckpt.replace(autosave_ckpt.with_name('.pl_auto_save_loaded.ckpt'))
                    checkpoint_path = autosave_ckpt
                else:
                    checkpoint_path = last_ckpt
            # DeepSpeed's checkpoint is a directory, not a file
            if checkpoint_path.is_file() or checkpoint_path.is_dir():
                ckpt_cfg = {'ckpt_path': str(checkpoint_path)}
            else:
                log.info(f'Checkpoint file {str(checkpoint_path)} not found. Will start training from scratch')
        except (KeyError, FileNotFoundError):
            pass

    # Configure ddp automatically
    n_devices = config.trainer.get('devices', 1)
    if isinstance(n_devices, Sequence):  # trainer.devices could be [1, 3] for example
        n_devices = len(n_devices)
    if n_devices > 1 and config.trainer.get('strategy', None) is None:
        config.trainer.strategy = dict(
            _target_='pytorch_lightning.strategies.DDPStrategy',
            find_unused_parameters=False,
            gradient_as_bucket_view=True,  # https://pytorch-lightning.readthedocs.io/en/stable/advanced/advanced_gpu.html#ddp-optimizations
        )

    # Init lightning trainer
    log.info(f"Instantiating trainer <{config.trainer._target_}>")
    trainer: Trainer = hydra.utils.instantiate(
        config.trainer, callbacks=callbacks, logger=logger)

    # Train the model
    log.info("Starting training!")
    trainer.fit(model=model, datamodule=datamodule, **ckpt_cfg)

    # Evaluate model on test set, using the best model achieved during training
    if config.get("test_after_training") and not config.trainer.get("fast_dev_run"):
        log.info("Starting testing!")
        trainer.test(model=model, datamodule=datamodule)

    # Make sure everything closed properly
    log.info("Finalizing!")
    utils.finish(
        config=config,
        model=model,
        datamodule=datamodule,
        trainer=trainer,
        callbacks=callbacks,
        logger=logger,
    )

    # Print path to best checkpoint
    if not config.trainer.get("fast_dev_run"):
        log.info(f"Best model ckpt: {trainer.checkpoint_callback.best_model_path}")

    # Return metric score for hyperparameter optimization
    optimized_metric = config.get("optimized_metric")
    if optimized_metric:
        return trainer.callback_metrics[optimized_metric]