import argparse
import datetime
import glob
import json
import os
import sys
import time
import random

import numpy as np
import torch
import torchvision
import copy


try:
    import lightning.pytorch as pl
except:
    import pytorch_lightning as pl

from functools import partial

from omegaconf import OmegaConf
from prefetch_generator import BackgroundGenerator
from torch.utils.data import DataLoader, Dataset

try:
    from lightning.pytorch import seed_everything
    from lightning.pytorch.callbacks import Callback
    from lightning.pytorch.trainer import Trainer
    from lightning.pytorch.utilities import rank_zero_info
    LIGHTNING_PACK_NAME = "lightning.pytorch."
except:
    from pytorch_lightning import seed_everything
    from pytorch_lightning.callbacks import Callback
    from pytorch_lightning.trainer import Trainer
    from pytorch_lightning.utilities import rank_zero_info
    LIGHTNING_PACK_NAME = "pytorch_lightning."

from ldm.data.base import Txt2ImgIterableBaseDataset
from ldm.util import instantiate_from_config

import mlperf_logging_utils
import mlperf_logging.mllog.constants as mllog_constants
from mlperf_logging_utils import mllogger


def get_parser(**parser_kwargs):
    # A function to create an ArgumentParser object and add arguments to it

    def str2bool(v):
        # A helper function to parse boolean values from command line arguments
        if isinstance(v, bool):
            return v
        if v.lower() in ("yes", "true", "t", "y", "1"):
            return True
        elif v.lower() in ("no", "false", "f", "n", "0"):
            return False
        else:
            raise argparse.ArgumentTypeError("Boolean value expected.")
    # Create an ArgumentParser object with specifies kwargs
    parser = argparse.ArgumentParser(**parser_kwargs)

    # Add vairous command line arguments with their default balues and descriptions
    parser.add_argument(
        "-n",
        "--name",
        type=str,
        const=True,
        default="",
        nargs="?",
        help="postfix for logdir",
    )
    parser.add_argument(
        "-r",
        "--resume",
        type=str,
        const=True,
        default="",
        nargs="?",
        help="resume from logdir or checkpoint in logdir",
    )
    parser.add_argument(
        "-b",
        "--base",
        nargs="*",
        metavar="base_config.yaml",
        help="paths to base configs. Loaded from left-to-right. "
        "Parameters can be overwritten or added with command-line options of the form `--key value`.",
        default=list(),
    )
    parser.add_argument(
        "-m",
        "--mode",
        type=str,
        default="train",
        choices=["train", "validate"],
        help="run mode, train or validation",
    )
    parser.add_argument(
        "-v",
        "--validation",
        type=str2bool,
        const=True,
        default=False,
        nargs="?",
        help="validation",
    )
    parser.add_argument(
        "-p",
        "--project",
        help="name of new or path to existing project",
    )
    parser.add_argument(
        "-c",
        "--ckpt",
        type=str,
        const=True,
        default="",
        nargs="?",
        help="load pretrained checkpoint from stable AI",
    )
    parser.add_argument(
        "-d",
        "--debug",
        type=str2bool,
        nargs="?",
        const=True,
        default=False,
        help="enable post-mortem debugging",
    )
    parser.add_argument(
        "-s",
        "--seed",
        type=int,
        default=random.SystemRandom().randint(0, 2**32 - 1),
        help="seed for seed_everything",
    )
    parser.add_argument(
        "--fid_threshold",
        type=int,
        default=90,
        help="halt training once this FID validation score or a smaller one is achieved."
             "if used with --clip_threshold, both metrics need to reach their targets.",
    )
    parser.add_argument(
        "--clip_threshold",
        type=int,
        default=0.15,
        help="halt training once this CLIP validation score or a higher one is achieved."
             "if used with --fid_threshold, both metrics need to reach their targets.",
    )
    parser.add_argument(
        "-f",
        "--postfix",
        type=str,
        default="",
        help="post-postfix for default name",
    )
    parser.add_argument(
        "-l",
        "--logdir",
        type=str,
        default="/results",
        help="directory for logging dat shit",
    )
    parser.add_argument(
        "--scale_lr",
        type=str2bool,
        nargs="?",
        const=True,
        default=True,
        help="scale base-lr by ngpu * batch_size * n_accumulate",
    )
    parser.add_argument(
        "--train_log_interval",
        type=int,
        default=100,
        help="Training logging interval"
    )
    parser.add_argument(
        "--validation_log_interval",
        type=int,
        default=10,
        help="Validation logging interval"
    )
    parser.add_argument(
        "--hpus",
        type=int,
        default=0,
        help="number of hpu devices to run",
    )
    parser.add_argument(
        "--use_lazy_mode",
        type=lambda x: x.lower() == 'true',
        default=True,
        help="Run Lazy or Eager Mode on HPU, default: lazy",
    )
    parser.add_argument(
        "--batch_size",
        type=int,
        default=0,
        help="batch size for training",
    )
    parser.add_argument(
        "--log_freq",
        type=int,
        default=50,
        help="frequency of logging loss.",
    )
    parser.add_argument(
        "--autocast", dest="use_autocast",
        action="store_true",
        help="Use PyTorch autocast on Gaudi"
    )
    parser.add_argument(
        "--warmup", dest="warmup_path",
        type=str, default=None,  # Set a default value if needed
        help="Path to the warmup dataset file"
    )
    parser.add_argument(
        "--async_checkpoint", dest='async_checkpoint',
        action='store_true',
        help="Enables usage of asynchronous checkpoint saving during training."
    )
    parser.add_argument(
        "--current_validation_iter", dest="current_validation_iter",
        type=int, default=1,
        help="Id of validation run"
    )
    parser.add_argument(
        "--validation_iters", dest="validation_iters",
        type=int, default=5,
        help="Number of all validation runs"
    )
    return parser

# A function that returns the non-default arguments between two objects
def nondefault_trainer_args(opt):
    # create an argument parsser
    parser = argparse.ArgumentParser()
    # parse the empty arguments to obtain the default values
    args = parser.parse_args([])
    # return all non-default arguments
    return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k))

class ListEarlyStopping(Callback):
    # Early stopping class that accepts a list of metrics and all stopping_thresholds
    # must be met before stopping
    def __init__(self, monitor_metrics: list = ["validation/fid", "validation/clip"],
                 mode_metrics: dict = {'validation/fid': 'min', 'validation/clip': 'max'},
                 stopping_thresholds: dict = {"validation/fid": None, "validation/clip": None},
                 check_finite: bool = False):
        super(ListEarlyStopping, self).__init__()

        self.monitor_metrics = monitor_metrics
        self.mode_metrics = mode_metrics
        self.stopping_thresholds = stopping_thresholds
        self.check_finite = check_finite

    def check_metrics(self, current_metrics):
        should_stop = []
        for metric in self.monitor_metrics:
            if metric in current_metrics:
                current_value = current_metrics[metric]

                if self.check_finite and not torch.isfinite(torch.as_tensor(current_value)):
                    raise ValueError(f"The monitored metric {metric} has become non-finite.")

                # Skip metrics without a stopping thresholds
                if self.stopping_thresholds[metric] is None:
                    continue

                if self.mode_metrics[metric] == 'min':
                    should_stop.append(current_value <= self.stopping_thresholds[metric])

                if self.mode_metrics[metric] == 'max':
                    should_stop.append(current_value >= self.stopping_thresholds[metric])

        # A minimum of one metric should have been reviewed.
        return False if not should_stop else all(should_stop)

    def on_validation_end(self, trainer, pl_module):
        logs = trainer.callback_metrics
        should_stop = self.check_metrics(logs)
        if should_stop:
            rank_zero_info('Early stopping conditioned have been met. Stopping training.')
            trainer.should_stop = True

class SetupCallback(Callback):
    # I nitialize the callback with the necessary parameters

    def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config):
        super().__init__()
        self.resume = resume
        self.now = now
        self.logdir = logdir
        self.ckptdir = ckptdir
        self.cfgdir = cfgdir
        self.config = config
        self.lightning_config = lightning_config

    # Save a checkpoint if training is interrupted with keyboard interrupt
    def on_keyboard_interrupt(self, trainer, pl_module):
        if trainer.global_rank == 0:
            print("Summoning checkpoint.")
            ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
            # trainer.save_checkpoint(ckpt_path)

    # Create necessary directories and save configuration files before training starts
    # def on_pretrain_routine_start(self, trainer, pl_module):
    def on_fit_start(self, trainer, pl_module):
        if trainer.global_rank == 0:
            # Create logdirs and save configs
            os.makedirs(self.logdir, exist_ok=True)
            os.makedirs(self.ckptdir, exist_ok=True)
            os.makedirs(self.cfgdir, exist_ok=True)

            print("Project config")
            print(OmegaConf.to_yaml(self.config))
            OmegaConf.save(self.config, os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)))

            # Save project config and lightning config as YAML files
            print("Lightning config")
            print(OmegaConf.to_yaml(self.lightning_config))
            OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}),
                           os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now)))

        # Remove log directory if resuming training and directory already exists
        else:
            # ModelCheckpoint callback created log directory --- remove it
            if not self.resume and os.path.exists(self.logdir):
                dst, name = os.path.split(self.logdir)
                dst = os.path.join(dst, "child_runs", name)
                os.makedirs(os.path.split(dst)[0], exist_ok=True)
                try:
                    os.rename(self.logdir, dst)
                except FileNotFoundError:
                    pass

    # def on_fit_end(self, trainer, pl_module):
    #     if trainer.global_rank == 0:
    #         ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
    #         rank_zero_info(f"Saving final checkpoint in {ckpt_path}.")
    #         trainer.save_checkpoint(ckpt_path)


class CUDACallback(Callback):
    # see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py

    def on_train_start(self, trainer, pl_module):
        rank_zero_info("Training is starting")

    # the method is called at the end of each training epoch
    def on_train_end(self, trainer, pl_module):
        rank_zero_info("Training is ending")

    def on_train_epoch_start(self, trainer, pl_module):
        # Reset the memory use counter
        torch.cuda.reset_peak_memory_stats(trainer.strategy.root_device.index)
        torch.cuda.synchronize(trainer.strategy.root_device.index)
        self.start_time = time.time()

    def on_train_epoch_end(self, trainer, pl_module):
        torch.cuda.synchronize(trainer.strategy.root_device.index)
        max_memory = torch.cuda.max_memory_allocated(trainer.strategy.root_device.index) / 2**20
        epoch_time = time.time() - self.start_time

        try:
            max_memory = trainer.strategy.reduce(max_memory)
            epoch_time = trainer.strategy.reduce(epoch_time)

            rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
            rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB")
        except AttributeError:
            pass

class HPUCallback(Callback):
    def on_train_start(self, trainer, pl_module):
        rank_zero_info("Training is starting on HPU")

    # the method is called at the end of each training epoch
    def on_train_end(self, trainer, pl_module):
        rank_zero_info("Training is ending")

    def on_train_epoch_start(self, trainer, pl_module):
        # Reset the memory use counter
        torch.hpu.memory.reset_peak_memory_stats(trainer.strategy.root_device.index)
        torch.hpu.synchronize()
        self.start_time = time.time()

    def on_train_epoch_end(self, trainer, pl_module):
        torch.hpu.synchronize()
        max_memory = torch.hpu.memory.max_memory_allocated(trainer.strategy.root_device.index) / 2 ** 20
        epoch_time = time.time() - self.start_time

        try:
            max_memory = trainer.strategy.reduce(max_memory)
            epoch_time = trainer.strategy.reduce(epoch_time)

            rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
            rank_zero_info(f"Average Peak memory {max_memory:.2f} MiB")
        except AttributeError:
            pass


if __name__ == "__main__":
    # custom parser to specify config files, train, test and debug mode,
    # postfix, resume.
    # `--key value` arguments are interpreted as arguments to the trainer.
    # `nested.key=value` arguments are interpreted as config parameters.
    # configs are merged from left-to-right followed by command line parameters.

    # model:
    #   base_learning_rate: float
    #   target: path to lightning module
    #   params:
    #       key: value
    # data:
    #   target: main.DataModuleFromConfig
    #   params:
    #      batch_size: int
    #      wrap: bool
    #      train:
    #          target: path to train dataset
    #          params:
    #              key: value
    #      validation:
    #          target: path to validation dataset
    #          params:
    #              key: value
    #      test:
    #          target: path to test dataset
    #          params:
    #              key: value
    # lightning: (optional, has sane defaults and can be specified on cmdline)
    #   trainer:
    #       additional arguments to trainer
    #   logger:
    #       logger to instantiate
    #   modelcheckpoint:
    #       modelcheckpoint to instantiate
    #   callbacks:
    #       callback1:
    #           target: importpath
    #           params:
    #               key: value

    parser = get_parser()

    opt, unknown = parser.parse_known_args()

    # get the current time to create a new logging directory
    now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")

    # add cwd for convenience and to make classes in this file available when
    # running as `python main.py`
    # (in particular `main.DataModuleFromConfig`)
    sys.path.append(os.getcwd())

    # Veirfy the arguments are both specified
    if opt.name and opt.resume:
        raise ValueError("-n/--name and -r/--resume cannot be specified both."
                         "If you want to resume training in a new log folder, "
                         "use -n/--name in combination with --resume_from_checkpoint")

    if opt.log_freq >= opt.train_log_interval:
        raise ValueError("--log_freq should be lesser than --train_log_interval")

    # Check if the "resume" option is specified, resume training from the checkpoint if it is true
    ckpt = None
    if opt.resume:
        rank_zero_info("Resuming from {}".format(opt.resume))
        if not os.path.exists(opt.resume):
            raise ValueError("Cannot find {}".format(opt.resume))
        if os.path.isfile(opt.resume):
            paths = opt.resume.split("/")
            # idx = len(paths)-paths[::-1].index("logs")+1
            # logdir = "/".join(paths[:idx])
            logdir = "/".join(paths[:-2])
            rank_zero_info("logdir: {}".format(logdir))
            ckpt = opt.resume
        else:
            assert os.path.isdir(opt.resume), opt.resume
            logdir = opt.resume.rstrip("/")
            ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")

        # Finds all ".yaml" configuration files in the log directory and adds them to the list of base configurations
        base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))
        opt.base = base_configs + opt.base
        # Gets the name of the current log directory by splitting the path and taking the last element.
        _tmp = logdir.split("/")
        nowname = _tmp[-1]
    else:
        if opt.name:
            name = "_" + opt.name
        elif opt.base:
            rank_zero_info("Using base config {}".format(opt.base))
            cfg_fname = os.path.split(opt.base[0])[-1]
            cfg_name = os.path.splitext(cfg_fname)[0]
            name = "_" + cfg_name
        else:
            name = ""
        nowname = now + name + opt.postfix
        logdir = os.path.join(opt.logdir, nowname)

        # Sets the checkpoint path of the 'ckpt' option is specified
        if opt.ckpt:
            ckpt = opt.ckpt

    # Create the checkpoint and configuration directories within the log directory.
    ckptdir = os.path.join(logdir, "checkpoints")
    cfgdir = os.path.join(logdir, "configs")
    # Sets the seed for the random number generator to ensure reproducibility
    seed_everything(opt.seed)

    is_success = False

    # Read timestamp for current checkpoint
    validation_timestamp = None
    if opt.mode == "validate":
        if os.path.exists(mllogger.filename):
            with open(mllogger.filename, 'r') as mllog_file:
                mllogs = mllog_file.readlines()

            step = opt.current_validation_iter * 1000
            for mllog in mllogs:
                mllog = json.loads(mllog.replace(":::MLLOG ", ""))
                if mllog["key"] == "checkpoint_saved" and mllog["metadata"]["step_num"] == step:
                    validation_timestamp = mllog["time_ms"]
                    break

    # Intinalize and save configuratioon using the OmegaConf library.
    try:
        # init and save configs
        configs = [OmegaConf.load(cfg) for cfg in opt.base]
        cli = OmegaConf.from_dotlist(unknown)
        config = OmegaConf.merge(*configs, cli)
        lightning_config = config.pop("lightning", OmegaConf.create())
        # merge trainer cli with config
        trainer_config = lightning_config.get("trainer", OmegaConf.create())

        for k in nondefault_trainer_args(opt):
            trainer_config[k] = getattr(opt, k)

        if not opt.use_lazy_mode:
            os.environ["PT_HPU_LAZY_MODE"] = "2"
        if opt.hpus:
            trainer_config["devices"] = opt.hpus
        if opt.batch_size:
            config.data.params.train.params.batch_size = opt.batch_size

        if trainer_config["accelerator"] == "hpu":
            os.environ["PT_HPU_ENABLE_REFINE_DYNAMIC_SHAPES"] = "0"
            os.environ["PT_HPU_POOL_MEM_ACQUIRE_PERC"] = "99"
            os.environ["PT_ENABLE_INT64_SUPPORT"] = "true"

        # Check whether the accelerator is gpu or hpu
        if not trainer_config["accelerator"] == "gpu" and not trainer_config["accelerator"] == "hpu":
            del trainer_config["accelerator"]
            cpu = True
        else:
            cpu = False
        trainer_opt = argparse.Namespace(**trainer_config)
        lightning_config.trainer = trainer_config

        # model
        use_fp16 = trainer_config.get("precision", 32) == 16
        if use_fp16:
            config.model["params"].update({"use_fp16": True})
        else:
            config.model["params"].update({"use_fp16": False})

        if ckpt is not None:
            # If a checkpoint path is specified in the ckpt variable, the code updates the "ckpt" key in the "params" dictionary of the config.model configuration with the value of ckpt
            config.model["params"].update({"ckpt": ckpt})
            rank_zero_info("Using ckpt_path = {}".format(config.model["params"]["ckpt"]))
        if opt.use_autocast:
            config.model.params.use_autocast = opt.use_autocast

        config.model.params.hpu = trainer_config["accelerator"] == "hpu"
        model = instantiate_from_config(config.model)

        if opt.log_freq > 0:
            model.log_freq = opt.log_freq

        # Configure gradient accumulation
        if 'accumulate_grad_batches' in lightning_config.trainer:
            accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches
        else:
            accumulate_grad_batches = 1
        lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches

        # Configure number of GPUs
        if not cpu:
            ngpu = trainer_config["devices"] * trainer_config["num_nodes"]
        else:
            ngpu = 1

        # Configure batch size
        local_batch_size = config.data.params.train.params.batch_size
        global_batch_size = local_batch_size*ngpu

        # trainer and callbacks
        trainer_kwargs = dict()

        # config the logger
        # Default logger configs to  log training metrics during the training process.
        # These loggers are specified as targets in the dictionary, along with the configuration settings specific to each logger.
        default_logger_cfgs = {
            "wandb": {
                "target": LIGHTNING_PACK_NAME + "loggers.WandbLogger",
                "params": {
                    "name": nowname,
                    "save_dir": logdir,
                    "offline": opt.debug,
                    "id": nowname,
                }
            },
            "tensorboard": {
                "target": LIGHTNING_PACK_NAME + "loggers.TensorBoardLogger",
                "params": {
                    "save_dir": logdir,
                    "name": "diff_tb",
                    "log_graph": True
                }
            }
        }

        # Set up the logger for TensorBoard
        default_logger_cfg = default_logger_cfgs["tensorboard"]
        if "logger" in lightning_config:
            logger_cfg = lightning_config.logger
        else:
            logger_cfg = default_logger_cfg
        logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
        trainer_kwargs["logger"] = instantiate_from_config(logger_cfg)

        # config the strategy, defualt is ddp
        if "strategy" in trainer_config:
            strategy_cfg = trainer_config["strategy"]
            strategy_cfg["target"] = LIGHTNING_PACK_NAME + strategy_cfg["target"]
        else:
            strategy_cfg = {
                "target": LIGHTNING_PACK_NAME + "strategies.DDPStrategy",
                "params": {
                    "find_unused_parameters": False
                }
            }

        if trainer_config["accelerator"] == "hpu":
            if trainer_config["devices"] == 1:
                from lightning_habana.pytorch.strategies import SingleHPUStrategy
                from lightning_habana.pytorch.accelerator import HPUAccelerator
                trainer_kwargs["strategy"] = SingleHPUStrategy()
                trainer_kwargs["accelerator"] = HPUAccelerator()
            elif trainer_config["devices"] > 1:
                from lightning_habana.pytorch.strategies import HPUParallelStrategy
                from lightning_habana.pytorch.accelerator import HPUAccelerator
                parallel_hpus = [torch.device("hpu")] * trainer_config["devices"]
                trainer_kwargs["strategy"] = HPUParallelStrategy(parallel_devices=parallel_hpus,
                        find_unused_parameters=False, gradient_as_bucket_view=True)
                trainer_kwargs["accelerator"] = HPUAccelerator()
        else:
            trainer_kwargs["strategy"] = instantiate_from_config(strategy_cfg)

        # Set up various callbacks, including logging, learning rate monitoring, and CUDA management
        # add callback which sets up log directory
        default_callbacks_cfg = {
            "setup_callback": {                           # callback to set up the training
                "target": "main.SetupCallback",
                "params": {
                    "resume": opt.resume,                 # resume training if applicable
                    "now": now,
                    "logdir": logdir,                     # directory to save the log file
                    "ckptdir": ckptdir,                   # directory to save the checkpoint file
                    "cfgdir": cfgdir,                     # directory to save the configuration file
                    "config": config,                     # configuration dictionary
                    "lightning_config": lightning_config, # LightningModule configuration
                }
            },
            "learning_rate_logger": {                     # callback to log learning rate
                "target": "lightning.pytorch.callbacks.LearningRateMonitor",
                "params": {
                    "logging_interval": "step",           # logging frequency (either 'step' or 'epoch')
                }
            },
            "fid_clip_early_stop_callback" : {
                "target": "main.ListEarlyStopping",
                "params": {
                    "stopping_thresholds": {"validation/fid": opt.fid_threshold, "validation/clip": opt.clip_threshold},
                    "check_finite": True
                }
            },
        }
        if trainer_config["accelerator"] == "hpu":
            default_callbacks_cfg["hpu_callback"] = {"target": "main.HPUCallback"}
        elif trainer_config["accelerator"] == "gpu":
            default_callbacks_cfg["cuda_callback"] = {"target": "main.CUDACallback"}
        # If the LightningModule configuration has specified callbacks, use those
        # Otherwise, create an empty OmegaConf configuration object
        if "callbacks" in lightning_config:
            callbacks_cfg = lightning_config.callbacks
        else:
            callbacks_cfg = OmegaConf.create()


        # Merge the default callbacks configuration with the specified callbacks configuration, and instantiate the callbacks
        callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)

        trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
        trainer_kwargs["callbacks"].append(mlperf_logging_utils.MLPerfLoggingCallback(logger=mllogger,
                                                                                      train_log_interval=opt.train_log_interval,
                                                                                      validation_log_interval=opt.validation_log_interval,
                                                                                      validation_iter=opt.current_validation_iter,
                                                                                      validation_timestamp=validation_timestamp,
                                                                                      seed=opt.seed,
                                                                                      gradient_accumulation_steps=accumulate_grad_batches,
                                                                                      global_batch_size=global_batch_size))

        # Set up ModelCheckpoint callback to save best models
        # modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
        # specify which metric is used to determine best models
        default_modelckpt_cfg = {
            "target": LIGHTNING_PACK_NAME + "callbacks.ModelCheckpoint",
            "params": {
                "dirpath": ckptdir,
                "filename": "{epoch:06}-{step:09}",
                "verbose": True,
                "save_last": True,
            }
        }

        if "modelcheckpoint" in lightning_config:
            modelckpt_cfg = lightning_config.modelcheckpoint
        else:
            modelckpt_cfg = OmegaConf.create()
        modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
        trainer_kwargs["callbacks"].append(instantiate_from_config(modelckpt_cfg))

        if opt.async_checkpoint:
            from lightning.fabric.plugins import TorchCheckpointIO
            from lightning.fabric.utilities import move_data_to_device
            from concurrent.futures import ThreadPoolExecutor

            class CustomCheckpointIO(TorchCheckpointIO):
                def save_checkpoint(self, checkpoint, path, storage_options=None):
                    if getattr(self, '_executor', None) is None:
                        self._executor = ThreadPoolExecutor(max_workers=1)
                    def _save_checkpoint(checkpoint, filepath) -> None:
                        torch.save(checkpoint, filepath)

                    checkpoint = move_data_to_device(checkpoint, torch.device("cpu"))

                    # log timestamp of current checkpoint
                    if "step" in path:
                        file_name = os.path.basename(path)
                        step_num = int(file_name.split("step=")[1].split('.')[0])
                        mllogger.event(key="checkpoint_saved", metadata={mllog_constants.STEP_NUM: step_num})

                    self._executor.submit(_save_checkpoint, checkpoint, path)

            trainer_kwargs["plugins"] = [CustomCheckpointIO()]

        # Create a Trainer object with the specified command-line arguments and keyword arguments, and set the log directory
        if opt.warmup_path is not None:
            trainer_opt_copy = copy.deepcopy(trainer_opt)
            trainer_opt_copy.max_steps=100
            trainer_opt_copy.max_epochs=1
            if "strategy" in trainer_opt_copy:
                delattr(trainer_opt_copy, 'strategy')
            trainer_warmup = Trainer(**vars(trainer_opt_copy), strategy=copy.deepcopy(trainer_kwargs["strategy"]))
        if "logger" in trainer_kwargs:
            delattr(trainer_opt, 'logger')
        if "strategy" in trainer_kwargs:
            delattr(trainer_opt, 'strategy')
        if "accelerator" in trainer_kwargs:
            delattr(trainer_opt, 'accelerator')
        trainer = Trainer(**vars(trainer_opt), **trainer_kwargs)
        trainer.logdir = logdir
        # Create a data module based on the configuration file
        data = instantiate_from_config(config.data)

        # Configure learning rate based on the batch size, base learning rate and number of GPUs
        # If scale_lr is true, calculate the learning rate based on additional factors
        base_lr = config.model.base_learning_rate
        if opt.scale_lr:
            model.learning_rate = accumulate_grad_batches * ngpu * local_batch_size * base_lr
            rank_zero_info(
                "Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (local_batch_size) * {:.2e} (base_lr)"
                .format(model.learning_rate, accumulate_grad_batches, ngpu, local_batch_size, base_lr))
        else:
            model.learning_rate = base_lr
            rank_zero_info("++++ NOT USING LR SCALING ++++")
            rank_zero_info(f"Setting learning rate to {model.learning_rate:.2e}")

        # Allow checkpointing via USR1
        def melk(*args, **kwargs):
            # run all checkpoint hooks
            if trainer.global_rank == 0:
                print("Summoning checkpoint.")
                ckpt_path = os.path.join(ckptdir, "last.ckpt")
                trainer.save_checkpoint(ckpt_path)

        def divein(*args, **kwargs):
            if trainer.global_rank == 0:
                import pudb
                pudb.set_trace()

        import signal
        # Assign melk to SIGUSR1 signal and divein to SIGUSR2 signal
        signal.signal(signal.SIGUSR1, melk)
        signal.signal(signal.SIGUSR2, divein)

        # Run the training and validation
        if opt.mode=="train":
            try:
                if opt.warmup_path is not None:
                    training_url = config.data.params.train.params.urls
                    config.data.params.train.params.urls = opt.warmup_path
                    # Warmup
                    trainer_warmup.fit(model, data)
                    # Actual training
                    config.data.params.train.params.urls = training_url
                trainer.fit(model, data)
            except Exception:
                melk()
                raise
        elif opt.mode=="validate":
            trainer.validate(model, data)
        else:
            raise ValueError(f"Unknown mode {opt.mode}")

        if opt.mode == "validate":
            # Default is True in case thresholds are not defined
            fid_success = True
            clip_success = True
            if opt.fid_threshold is not None:
                fid_success =  "validation/fid" in trainer.callback_metrics and opt.fid_threshold >= trainer.callback_metrics["validation/fid"].item()
            if opt.clip_threshold is not None:
                clip_success = "validation/clip" in trainer.callback_metrics and opt.clip_threshold <= trainer.callback_metrics["validation/clip"].item()
            is_success = fid_success and clip_success

    except Exception:
        # If there's an exception, debug it if opt.debug is true and the trainer's global rank is 0
        if opt.debug and trainer.global_rank == 0:
            try:
                import pudb as debugger
            except ImportError:
                import pdb as debugger
            debugger.post_mortem()
        raise
    finally:
        #  Move the log directory to debug_runs if opt.debug is true and the trainer's global
        if opt.debug and not opt.resume and trainer.global_rank == 0:
            dst, name = os.path.split(logdir)
            dst = os.path.join(dst, "debug_runs", name)
            os.makedirs(os.path.split(dst)[0], exist_ok=True)
            os.rename(logdir, dst)
        if trainer.global_rank == 0:
            print(trainer.profiler.summary())

        if opt.mode == "validate":
            is_last_run = opt.current_validation_iter == opt.validation_iters
            if is_success or is_last_run:
                mllogger.end(
                    mllog_constants.RUN_STOP, time_ms=validation_timestamp,
                    metadata={mllog_constants.STATUS: mllog_constants.SUCCESS if is_success else mllog_constants.ABORTED}
                )