###########################################################################################
# Training script for MACE
# Authors: Ilyes Batatia, Gregor Simm, David Kovacs
# This program is distributed under the MIT License (see MIT.md)
###########################################################################################

import ast
import glob
import json
import logging
import os
from copy import deepcopy
from pathlib import Path
from typing import List, Optional

import torch.distributed
from e3nn.util import jit
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.optim import LBFGS
from torch.utils.data import ConcatDataset
from torch_ema import ExponentialMovingAverage

import mace
from mace import data, tools
from mace.calculators.foundations_models import mace_mp, mace_off
from mace.cli.convert_cueq_e3nn import run as run_cueq_to_e3nn
from mace.cli.convert_e3nn_cueq import run as run_e3nn_to_cueq
from mace.cli.visualise_train import TrainingPlotter
from mace.data import KeySpecification, update_keyspec_from_kwargs
from mace.tools import torch_geometric
from mace.tools.distributed_tools import init_distributed
from mace.tools.model_script_utils import configure_model
from mace.tools.multihead_tools import (
    HeadConfig,
    assemble_mp_data,
    dict_head_to_dataclass,
    prepare_default_head,
    prepare_pt_head,
)
from mace.tools.run_train_utils import (
    combine_datasets,
    load_dataset_for_path,
    normalize_file_paths,
)
from mace.tools.scripts_utils import (
    LRScheduler,
    SubsetCollection,
    check_path_ase_read,
    convert_to_json_format,
    dict_to_array,
    extract_config_mace_model,
    get_atomic_energies,
    get_avg_num_neighbors,
    get_config_type_weights,
    get_dataset_from_xyz,
    get_files_with_suffix,
    get_loss_fn,
    get_optimizer,
    get_params_options,
    get_swa,
    print_git_commit,
    remove_pt_head,
    setup_wandb,
)
from mace.tools.tables_utils import create_error_table
from mace.tools.utils import AtomicNumberTable


def main() -> None:
    """
    This script runs the training/fine tuning for mace
    """
    args = tools.build_default_arg_parser().parse_args()
    run(args)


def run(args) -> None:
    """
    This script runs the training/fine tuning for mace
    """
    tag = tools.get_tag(name=args.name, seed=args.seed)
    args, input_log_messages = tools.check_args(args)

    # default keyspec to update using heads dictionary
    args.key_specification = KeySpecification()
    update_keyspec_from_kwargs(args.key_specification, vars(args))

    if args.device == "xpu":
        try:
            import intel_extension_for_pytorch as ipex
        except ImportError as e:
            raise ImportError(
                "Error: Intel extension for PyTorch not found, but XPU device was specified"
            ) from e
    rank, local_rank, world_size = init_distributed(args)

    # Setup
    tools.set_seeds(args.seed)
    tools.setup_logger(level=args.log_level, tag=tag, directory=args.log_dir, rank=rank)
    logging.info("===========VERIFYING SETTINGS===========")
    for message, loglevel in input_log_messages:
        logging.log(level=loglevel, msg=message)

    if args.distributed:
        torch.cuda.set_device(local_rank)
        logging.info(f"Process group initialized: {torch.distributed.is_initialized()}")
        logging.info(f"Processes: {world_size}")

    try:
        logging.info(f"MACE version: {mace.__version__}")
    except AttributeError:
        logging.info("Cannot find MACE version, please install MACE via pip")
    logging.debug(f"Configuration: {args}")

    tools.set_default_dtype(args.default_dtype)
    device = tools.init_device(args.device)
    commit = print_git_commit()
    model_foundation: Optional[torch.nn.Module] = None
    foundation_model_avg_num_neighbors = 0
    if args.foundation_model is not None:
        if args.foundation_model in ["small", "medium", "large"]:
            logging.info(
                f"Using foundation model mace-mp-0 {args.foundation_model} as initial checkpoint."
            )
            calc = mace_mp(
                model=args.foundation_model,
                device=args.device,
                default_dtype=args.default_dtype,
            )
            model_foundation = calc.models[0]
        elif args.foundation_model in ["small_off", "medium_off", "large_off"]:
            model_type = args.foundation_model.split("_")[0]
            logging.info(
                f"Using foundation model mace-off-2023 {model_type} as initial checkpoint. ASL license."
            )
            calc = mace_off(
                model=model_type,
                device=args.device,
                default_dtype=args.default_dtype,
            )
            model_foundation = calc.models[0]
        else:
            model_foundation = torch.load(
                args.foundation_model, map_location=args.device
            )
            logging.info(
                f"Using foundation model {args.foundation_model} as initial checkpoint."
            )
        args.r_max = model_foundation.r_max.item()
        foundation_model_avg_num_neighbors = model_foundation.interactions[
            0
        ].avg_num_neighbors
        if (
            args.foundation_model not in ["small", "medium", "large"]
            and args.pt_train_file is None
        ):
            logging.warning(
                "Using multiheads finetuning with a foundation model that is not a Materials Project model, need to provied a path to a pretraining file with --pt_train_file."
            )
            args.multiheads_finetuning = False
        if args.multiheads_finetuning:
            assert (
                args.E0s != "average"
            ), "average atomic energies cannot be used for multiheads finetuning"
            # check that the foundation model has a single head, if not, use the first head
            if not args.force_mh_ft_lr:
                logging.info(
                    "Multihead finetuning mode, setting learning rate to 0.0001 and EMA to True. To use a different learning rate, set --force_mh_ft_lr=True."
                )
                args.lr = 0.0001
                args.ema = True
                args.ema_decay = 0.99999
            logging.info(
                "Using multiheads finetuning mode, setting learning rate to 0.0001 and EMA to True"
            )
            if hasattr(model_foundation, "heads"):
                if len(model_foundation.heads) > 1:
                    logging.warning(
                        "Mutlihead finetuning with models with more than one head is not supported, using the first head as foundation head."
                    )
                    model_foundation = remove_pt_head(
                        model_foundation, args.foundation_head
                    )
    else:
        args.multiheads_finetuning = False

    if args.heads is not None:
        args.heads = ast.literal_eval(args.heads)
        for _, head_dict in args.heads.items():
            # priority is global args < head property_key values < head info_keys+arrays_keys
            head_keyspec = deepcopy(args.key_specification)
            update_keyspec_from_kwargs(head_keyspec, head_dict)
            head_keyspec.update(
                info_keys=head_dict.get("info_keys", {}),
                arrays_keys=head_dict.get("arrays_keys", {}),
            )
            head_dict["key_specification"] = head_keyspec
    else:
        args.heads = prepare_default_head(args)
    if args.multiheads_finetuning:
        pt_keyspec = (
            args.heads["pt_head"]["key_specification"]
            if "pt_head" in args.heads
            else deepcopy(args.key_specification)
        )
        args.heads["pt_head"] = prepare_pt_head(
            args, pt_keyspec, foundation_model_avg_num_neighbors
        )

    logging.info("===========LOADING INPUT DATA===========")
    heads = list(args.heads.keys())
    logging.info(f"Using heads: {heads}")
    logging.info("Using the key specifications to parse data:")
    for name, head_dict in args.heads.items():
        head_keyspec = head_dict["key_specification"]
        logging.info(f"{name}: {head_keyspec}")

    head_configs: List[HeadConfig] = []
    for head, head_args in args.heads.items():
        logging.info(f"=============    Processing head {head}     ===========")
        head_config = dict_head_to_dataclass(head_args, head, args)

        # Handle train_file and valid_file - normalize to lists
        if hasattr(head_config, "train_file") and head_config.train_file is not None:
            head_config.train_file = normalize_file_paths(head_config.train_file)
        if hasattr(head_config, "valid_file") and head_config.valid_file is not None:
            head_config.valid_file = normalize_file_paths(head_config.valid_file)
        if hasattr(head_config, "test_file") and head_config.test_file is not None:
            head_config.test_file = normalize_file_paths(head_config.test_file)

        if (
            head_config.statistics_file is not None
            and head_config.head_name != "pt_head"
        ):
            with open(head_config.statistics_file, "r") as f:  # pylint: disable=W1514
                statistics = json.load(f)
            logging.info("Using statistics json file")
            head_config.atomic_numbers = statistics["atomic_numbers"]
            head_config.mean = statistics["mean"]
            head_config.std = statistics["std"]
            head_config.avg_num_neighbors = statistics["avg_num_neighbors"]
            head_config.compute_avg_num_neighbors = False
            if isinstance(statistics["atomic_energies"], str) and statistics[
                "atomic_energies"
            ].endswith(".json"):
                with open(statistics["atomic_energies"], "r", encoding="utf-8") as f:
                    atomic_energies = json.load(f)
                head_config.E0s = atomic_energies
                head_config.atomic_energies_dict = ast.literal_eval(atomic_energies)
            else:
                head_config.E0s = statistics["atomic_energies"]
                head_config.atomic_energies_dict = ast.literal_eval(
                    statistics["atomic_energies"]
                )
        if head_config.train_file == ["mp"]:
            assert (
                head_config.head_name == "pt_head"
            ), "Only pt_head should use mp as train_file"
            logging.info(
                "Using the full Materials Project data for replay. You can construct a different subset using `fine_tuning_select.py` script."
            )
            collections = assemble_mp_data(args, head_config, tag)
            head_config.collections = collections
        elif any(check_path_ase_read(f) for f in head_config.train_file):
            train_files_ase_list = [
                f for f in head_config.train_file if check_path_ase_read(f)
            ]
            valid_files_ase_list = None
            test_files_ase_list = None
            if head_config.valid_file:
                valid_files_ase_list = [
                    f for f in head_config.valid_file if check_path_ase_read(f)
                ]
            if head_config.test_file:
                test_files_ase_list = [
                    f for f in head_config.test_file if check_path_ase_read(f)
                ]
            config_type_weights = get_config_type_weights(
                head_config.config_type_weights
            )
            collections, atomic_energies_dict = get_dataset_from_xyz(
                work_dir=args.work_dir,
                train_path=train_files_ase_list,
                valid_path=valid_files_ase_list,
                valid_fraction=head_config.valid_fraction,
                config_type_weights=config_type_weights,
                test_path=test_files_ase_list,
                seed=args.seed,
                key_specification=head_config.key_specification,
                head_name=head_config.head_name,
                keep_isolated_atoms=head_config.keep_isolated_atoms,
            )
            head_config.collections = SubsetCollection(
                train=collections.train,
                valid=collections.valid,
                tests=collections.tests,
            )
            head_config.atomic_energies_dict = atomic_energies_dict
            logging.info(
                f"Total number of configurations: train={len(collections.train)}, valid={len(collections.valid)}, "
                f"tests=[{', '.join([name + ': ' + str(len(test_configs)) for name, test_configs in collections.tests])}],"
            )
        head_configs.append(head_config)

    if all(
        check_path_ase_read(head_config.train_file[0]) for head_config in head_configs
    ):
        size_collections_train = sum(
            len(head_config.collections.train) for head_config in head_configs
        )
        size_collections_valid = sum(
            len(head_config.collections.valid) for head_config in head_configs
        )
        if size_collections_train < args.batch_size:
            logging.error(
                f"Batch size ({args.batch_size}) is larger than the number of training data ({size_collections_train})"
            )
        if size_collections_valid < args.valid_batch_size:
            logging.warning(
                f"Validation batch size ({args.valid_batch_size}) is larger than the number of validation data ({size_collections_valid})"
            )

    if args.multiheads_finetuning:
        logging.info(
            "==================Using multiheads finetuning mode=================="
        )
        args.loss = "universal"

        all_ase_readable = all(
            all(check_path_ase_read(f) for f in head_config.train_file)
            for head_config in head_configs
        )
        head_config_pt = filter(lambda x: x.head_name == "pt_head", head_configs)
        head_config_pt = next(head_config_pt, None)
        assert head_config_pt is not None, "Pretraining head not found"
        if all_ase_readable:
            ratio_pt_ft = size_collections_train / len(head_config_pt.collections.train)
            if ratio_pt_ft < 0.1:
                logging.warning(
                    f"Ratio of the number of configurations in the training set and the in the pt_train_file is {ratio_pt_ft}, "
                    f"increasing the number of configurations in the fine-tuning heads by {int(0.1 / ratio_pt_ft)}"
                )
                for head_config in head_configs:
                    if head_config.head_name == "pt_head":
                        continue
                    head_config.collections.train += (
                        head_config.collections.train * int(0.1 / ratio_pt_ft)
                    )
            logging.info(
                f"Total number of configurations in pretraining: train={len(head_config_pt.collections.train)}, valid={len(head_config_pt.collections.valid)}"
            )
        else:
            logging.debug(
                "Using LMDB/HDF5 datasets for pretraining or fine-tuning - skipping ratio check"
            )

    # Atomic number table
    # yapf: disable
    for head_config in head_configs:
        if head_config.atomic_numbers is None:
            assert all(check_path_ase_read(f) for f in head_config.train_file), "Must specify atomic_numbers when using .h5 or .aselmdb train_file input"
            z_table_head = tools.get_atomic_number_table_from_zs(
                z
                for configs in (head_config.collections.train, head_config.collections.valid)
                for config in configs
                for z in config.atomic_numbers
            )
            head_config.atomic_numbers = z_table_head.zs
            head_config.z_table = z_table_head
        else:
            if head_config.statistics_file is None:
                logging.info("Using atomic numbers from command line argument")
            else:
                logging.info("Using atomic numbers from statistics file")
            zs_list = ast.literal_eval(head_config.atomic_numbers)
            assert isinstance(zs_list, list)
            z_table_head = tools.AtomicNumberTable(zs_list)
            head_config.atomic_numbers = zs_list
            head_config.z_table = z_table_head
        # yapf: enable
    all_atomic_numbers = set()
    for head_config in head_configs:
        all_atomic_numbers.update(head_config.atomic_numbers)
    z_table = AtomicNumberTable(sorted(list(all_atomic_numbers)))
    if args.foundation_model_elements and model_foundation:
        z_table = AtomicNumberTable(sorted(model_foundation.atomic_numbers.tolist()))
    logging.info(f"Atomic Numbers used: {z_table.zs}")

    # Atomic energies
    print('*****************', head_configs[0].E0s)
    print('************', head_configs[0].E0s.lower())
    atomic_energies_dict = {}
    for head_config in head_configs:
        if head_config.atomic_energies_dict is None or len(head_config.atomic_energies_dict) == 0:
            assert head_config.E0s is not None, "Atomic energies must be provided"
            if all(check_path_ase_read(f) for f in head_config.train_file) and head_config.E0s.lower() != "foundation":
                atomic_energies_dict[head_config.head_name] = get_atomic_energies(
                    head_config.E0s, head_config.collections.train, head_config.z_table
                )
            elif head_config.E0s.lower() == "foundation":
                assert args.foundation_model is not None
                z_table_foundation = AtomicNumberTable(
                    [int(z) for z in model_foundation.atomic_numbers]
                )
                foundation_atomic_energies = model_foundation.atomic_energies_fn.atomic_energies
                if foundation_atomic_energies.ndim > 1:
                    foundation_atomic_energies = foundation_atomic_energies.squeeze()
                    if foundation_atomic_energies.ndim == 2:
                        foundation_atomic_energies = foundation_atomic_energies[0]
                        logging.info("Foundation model has multiple heads, using the first head as foundation E0s.")
                atomic_energies_dict[head_config.head_name] = {
                    z: foundation_atomic_energies[
                        z_table_foundation.z_to_index(z)
                    ].item()
                    for z in z_table.zs
                }
            else:
                atomic_energies_dict[head_config.head_name] = get_atomic_energies(head_config.E0s, None, head_config.z_table)
        else:
            atomic_energies_dict[head_config.head_name] = head_config.atomic_energies_dict

    # Atomic energies for multiheads finetuning
    if args.multiheads_finetuning:
        assert (
            model_foundation is not None
        ), "Model foundation must be provided for multiheads finetuning"
        z_table_foundation = AtomicNumberTable(
            [int(z) for z in model_foundation.atomic_numbers]
        )
        foundation_atomic_energies = model_foundation.atomic_energies_fn.atomic_energies
        if foundation_atomic_energies.ndim > 1:
            foundation_atomic_energies = foundation_atomic_energies.squeeze()
            if foundation_atomic_energies.ndim == 2:
                foundation_atomic_energies = foundation_atomic_energies[0]
                logging.info("Foundation model has multiple heads, using the first head as foundation E0s.")
        atomic_energies_dict["pt_head"] = {
            z: foundation_atomic_energies[
                z_table_foundation.z_to_index(z)
            ].item()
            for z in z_table.zs
        }
    heads = sorted(heads, key=lambda x: -1000 if x == "pt_head" else 0)
    # Padding atomic energies if keeping all elements of the foundation model
    if args.foundation_model_elements and model_foundation:
        atomic_energies_dict_padded = {}
        for head_name, head_energies in atomic_energies_dict.items():
            energy_head_padded = {}
            for z in z_table.zs:
                energy_head_padded[z] = head_energies.get(z, 0.0)
            atomic_energies_dict_padded[head_name] = energy_head_padded
        atomic_energies_dict = atomic_energies_dict_padded

    if args.model == "AtomicDipolesMACE":
        atomic_energies = None
        dipole_only = True
        args.compute_dipole = True
        args.compute_energy = False
        args.compute_forces = False
        args.compute_virials = False
        args.compute_stress = False
    else:
        dipole_only = False
        if args.model == "EnergyDipolesMACE":
            args.compute_dipole = True
            args.compute_energy = True
            args.compute_forces = True
            args.compute_virials = False
            args.compute_stress = False
        else:
            args.compute_energy = True
            args.compute_dipole = False
        # atomic_energies: np.ndarray = np.array(
        #     [atomic_energies_dict[z] for z in z_table.zs]
        # )
        atomic_energies = dict_to_array(atomic_energies_dict, heads)
        for head_config in head_configs:
            try:
                logging.info(f"Atomic Energies used (z: eV) for head {head_config.head_name}: " + "{" + ", ".join([f"{z}: {atomic_energies_dict[head_config.head_name][z]}" for z in head_config.z_table.zs]) + "}")
            except KeyError as e:
                raise KeyError(f"Atomic number {e} not found in atomic_energies_dict for head {head_config.head_name}, add E0s for this atomic number") from e

    # Load datasets for each head, supporting multiple files per head
    valid_sets = {head: [] for head in heads}
    train_sets = {head: [] for head in heads}

    for head_config in head_configs:
        train_datasets = []

        logging.info(f"Processing datasets for head '{head_config.head_name}'")
        ase_files = [f for f in head_config.train_file if check_path_ase_read(f)]
        non_ase_files = [f for f in head_config.train_file if not check_path_ase_read(f)]

        if ase_files:
            dataset = load_dataset_for_path(
            file_path=ase_files,
            r_max=args.r_max,
            z_table=z_table,
            head_config=head_config,
            heads=heads,
            collection=head_config.collections.train,
            )
            train_datasets.append(dataset)
            logging.debug(f"Successfully loaded dataset from ASE files: {ase_files}")

        for file in non_ase_files:
            dataset = load_dataset_for_path(
            file_path=file,
            r_max=args.r_max,
            z_table=z_table,
            head_config=head_config,
            heads=heads,
            )
            train_datasets.append(dataset)
            logging.debug(f"Successfully loaded dataset from non-ASE file: {file}")

        if not train_datasets:
            raise ValueError(f"No valid training datasets found for head {head_config.head_name}")

        train_sets[head_config.head_name] = combine_datasets(train_datasets, head_config.head_name)

        if head_config.valid_file:
            valid_datasets = []

            valid_ase_files = [f for f in head_config.valid_file if check_path_ase_read(f)]
            valid_non_ase_files = [f for f in head_config.valid_file if not check_path_ase_read(f)]

            if valid_ase_files:
                valid_dataset = load_dataset_for_path(
                    file_path=valid_ase_files,
                    r_max=args.r_max,
                    z_table=z_table,
                    head_config=head_config,
                    heads=heads,
                    collection=head_config.collections.valid,
                )
                valid_datasets.append(valid_dataset)
                logging.debug(f"Successfully loaded validation dataset from ASE files: {valid_ase_files}")
            for valid_file in valid_non_ase_files:
                valid_dataset = load_dataset_for_path(
                file_path=valid_file,
                r_max=args.r_max,
                z_table=z_table,
                head_config=head_config,
                heads=heads,
            )
                valid_datasets.append(valid_dataset)
                logging.debug(f"Successfully loaded validation dataset from {valid_file}")

            # Combine validation datasets
            if valid_datasets:
                valid_sets[head_config.head_name] = combine_datasets(valid_datasets, f"{head_config.head_name}_valid")
                logging.info(f"Combined validation datasets for {head_config.head_name}")

        # If no valid file is provided but collection exist, use the validation set from the collection
        if head_config.valid_file is None and head_config.collections.valid:
            valid_sets[head_config.head_name] = [
                data.AtomicData.from_config(
                    config, z_table=z_table, cutoff=args.r_max, heads=heads
                )
                for config in head_config.collections.valid
            ]
        if not valid_sets[head_config.head_name]:
            raise ValueError(f"No valid datasets found for head {head_config.head_name}, please provide a valid_file or a valid_fraction")

        # Create data loader for this head
        if isinstance(train_sets[head_config.head_name], list):
            dataset_size = len(train_sets[head_config.head_name])
        else:
            dataset_size = len(train_sets[head_config.head_name])
        logging.info(f"Head '{head_config.head_name}' training dataset size: {dataset_size}")

        train_loader_head = torch_geometric.dataloader.DataLoader(
            dataset=train_sets[head_config.head_name],
            batch_size=args.batch_size,
            shuffle=True,
            drop_last=(not args.lbfgs),
            pin_memory=args.pin_memory,
            num_workers=args.num_workers,
            generator=torch.Generator().manual_seed(args.seed),
        )
        head_config.train_loader = train_loader_head

    # concatenate all the trainsets
    train_set = ConcatDataset([train_sets[head] for head in heads])
    train_sampler, valid_sampler = None, None
    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(
            train_set,
            num_replicas=world_size,
            rank=rank,
            shuffle=True,
            drop_last=(not args.lbfgs),
            seed=args.seed,
        )
        valid_samplers = {}
        for head, valid_set in valid_sets.items():
            valid_sampler = torch.utils.data.distributed.DistributedSampler(
                valid_set,
                num_replicas=world_size,
                rank=rank,
                shuffle=True,
                drop_last=True,
                seed=args.seed,
            )
            valid_samplers[head] = valid_sampler
    train_loader = torch_geometric.dataloader.DataLoader(
        dataset=train_set,
        batch_size=args.batch_size,
        sampler=train_sampler,
        shuffle=(train_sampler is None),
        drop_last=(train_sampler is None and not args.lbfgs),
        pin_memory=args.pin_memory,
        num_workers=args.num_workers,
        generator=torch.Generator().manual_seed(args.seed),
    )
    valid_loaders = {heads[i]: None for i in range(len(heads))}
    if not isinstance(valid_sets, dict):
        valid_sets = {"Default": valid_sets}
    for head, valid_set in valid_sets.items():
        valid_loaders[head] = torch_geometric.dataloader.DataLoader(
            dataset=valid_set,
            batch_size=args.valid_batch_size,
            sampler=valid_samplers[head] if args.distributed else None,
            shuffle=False,
            drop_last=False,
            pin_memory=args.pin_memory,
            num_workers=args.num_workers,
            generator=torch.Generator().manual_seed(args.seed),
        )

    loss_fn = get_loss_fn(args, dipole_only, args.compute_dipole)
    args.avg_num_neighbors = get_avg_num_neighbors(head_configs, args, train_loader, device)

    # Model
    model, output_args = configure_model(args, train_loader, atomic_energies, model_foundation, heads, z_table, head_configs)
    model.to(device)

    logging.debug(model)
    logging.info(f"Total number of parameters: {tools.count_parameters(model)}")
    logging.info("")
    logging.info("===========OPTIMIZER INFORMATION===========")
    logging.info(f"Using {args.optimizer.upper()} as parameter optimizer")
    logging.info(f"Batch size: {args.batch_size}")
    if args.ema:
        logging.info(f"Using Exponential Moving Average with decay: {args.ema_decay}")
    logging.info(
        f"Number of gradient updates: {int(args.max_num_epochs*len(train_set)/args.batch_size)}"
    )
    logging.info(f"Learning rate: {args.lr}, weight decay: {args.weight_decay}")
    logging.info(loss_fn)

    # Cueq
    if args.enable_cueq and not args.only_cueq:
        logging.info("Converting model to CUEQ for accelerated training")
        assert model.__class__.__name__ in ["MACE", "ScaleShiftMACE"]
        model = run_e3nn_to_cueq(deepcopy(model), device=device)
    # Optimizer
    param_options = get_params_options(args, model)
    optimizer: torch.optim.Optimizer
    optimizer = get_optimizer(args, param_options)
    if args.device == "xpu":
        logging.info("Optimzing model and optimzier for XPU")
        model, optimizer = ipex.optimize(model, optimizer=optimizer)
    logger = tools.MetricsLogger(
        directory=args.results_dir, tag=tag + "_train"
    )  # pylint: disable=E1123

    lr_scheduler = LRScheduler(optimizer, args)

    swa: Optional[tools.SWAContainer] = None
    swas = [False]
    if args.swa:
        swa, swas = get_swa(args, model, optimizer, swas, dipole_only)

    checkpoint_handler = tools.CheckpointHandler(
        directory=args.checkpoints_dir,
        tag=tag,
        keep=args.keep_checkpoints,
        swa_start=args.start_swa,
    )

    start_epoch = 0
    restart_lbfgs = False
    opt_start_epoch = None
    if args.restart_latest:
        try:
            opt_start_epoch = checkpoint_handler.load_latest(
                state=tools.CheckpointState(model, optimizer, lr_scheduler),
                swa=True,
                device=device,
            )
        except Exception:  # pylint: disable=W0703
            try:
                opt_start_epoch = checkpoint_handler.load_latest(
                    state=tools.CheckpointState(model, optimizer, lr_scheduler),
                    swa=False,
                    device=device,
                )
            except Exception: # pylint: disable=W0703
                restart_lbfgs = True
        if opt_start_epoch is not None:
            start_epoch = opt_start_epoch

    ema: Optional[ExponentialMovingAverage] = None
    if args.ema:
        ema = ExponentialMovingAverage(model.parameters(), decay=args.ema_decay)
    else:
        for group in optimizer.param_groups:
            group["lr"] = args.lr

    if args.lbfgs:
        logging.info("Switching optimizer to LBFGS")
        optimizer = LBFGS(model.parameters(),
                          history_size=200,
                          max_iter=20,
                          line_search_fn="strong_wolfe")
        if restart_lbfgs:
            opt_start_epoch = checkpoint_handler.load_latest(
                state=tools.CheckpointState(model, optimizer, lr_scheduler),
                swa=False,
                device=device,
            )
            if opt_start_epoch is not None:
                start_epoch = opt_start_epoch

    if args.wandb:
        setup_wandb(args)
    if args.distributed:
        distributed_model = DDP(model, device_ids=[local_rank])
    else:
        distributed_model = None


    train_valid_data_loader = {}
    for head_config in head_configs:
        data_loader_name = "train_" + head_config.head_name
        train_valid_data_loader[data_loader_name] = head_config.train_loader
    for head, valid_loader in valid_loaders.items():
        data_load_name = "valid_" + head
        train_valid_data_loader[data_load_name] = valid_loader

    if args.plot and args.plot_frequency > 0:
        try:
            plotter = TrainingPlotter(
                results_dir=logger.path,
                heads=heads,
                table_type=args.error_table,
                train_valid_data=train_valid_data_loader,
                test_data={},
                output_args=output_args,
                device=device,
                plot_frequency=args.plot_frequency,
                distributed=args.distributed,
                swa_start=swa.start if swa else None
                )
        except Exception as e:  # pylint: disable=W0718
            logging.debug(f"Creating Plotter failed: {e}")
    else:
        plotter = None

    if args.dry_run:
        logging.info("DRY RUN mode enabled. Stopping now.")
        return


    tools.train(
        model=model,
        loss_fn=loss_fn,
        train_loader=train_loader,
        valid_loaders=valid_loaders,
        optimizer=optimizer,
        lr_scheduler=lr_scheduler,
        checkpoint_handler=checkpoint_handler,
        eval_interval=args.eval_interval,
        start_epoch=start_epoch,
        max_num_epochs=args.max_num_epochs,
        logger=logger,
        patience=args.patience,
        save_all_checkpoints=args.save_all_checkpoints,
        output_args=output_args,
        device=device,
        swa=swa,
        ema=ema,
        max_grad_norm=args.clip_grad,
        log_errors=args.error_table,
        log_wandb=args.wandb,
        distributed=args.distributed,
        distributed_model=distributed_model,
        plotter=plotter,
        train_sampler=train_sampler,
        rank=rank,
    )

    logging.info("")
    logging.info("===========RESULTS===========")

    train_valid_data_loader = {}
    for head_config in head_configs:
        data_loader_name = "train_" + head_config.head_name
        train_valid_data_loader[data_loader_name] = head_config.train_loader
    for head, valid_loader in valid_loaders.items():
        data_load_name = "valid_" + head
        train_valid_data_loader[data_load_name] = valid_loader
    test_sets = {}
    stop_first_test = False
    test_data_loader = {}
    if all(
        head_config.test_file == head_configs[0].test_file
        for head_config in head_configs
    ) and head_configs[0].test_file is not None:
        stop_first_test = True
    if all(
        head_config.test_dir == head_configs[0].test_dir
        for head_config in head_configs
    ) and head_configs[0].test_dir is not None:
        stop_first_test = True
    for head_config in head_configs:
        if all(check_path_ase_read(f) for f in head_config.train_file):
            for name, subset in head_config.collections.tests:
                test_sets[name] = [
                    data.AtomicData.from_config(
                        config, z_table=z_table, cutoff=args.r_max, heads=heads
                    )
                    for config in subset
                ]
        if head_config.test_dir is not None:
            if not args.multi_processed_test:
                test_files = get_files_with_suffix(head_config.test_dir, "_test.h5")
                for test_file in test_files:
                    name = os.path.splitext(os.path.basename(test_file))[0]
                    test_sets[name] = data.HDF5Dataset(
                        test_file, r_max=args.r_max, z_table=z_table, heads=heads, head=head_config.head_name
                    )
            else:
                test_folders = glob(head_config.test_dir + "/*")
                for folder in test_folders:
                    name = os.path.splitext(os.path.basename(test_file))[0]
                    test_sets[name] = data.dataset_from_sharded_hdf5(
                        folder, r_max=args.r_max, z_table=z_table, heads=heads, head=head_config.head_name
                    )
        for test_name, test_set in test_sets.items():
            test_sampler = None
            if args.distributed:
                test_sampler = torch.utils.data.distributed.DistributedSampler(
                    test_set,
                    num_replicas=world_size,
                    rank=rank,
                    shuffle=True,
                    drop_last=True,
                    seed=args.seed,
                )
            try:
                drop_last = test_set.drop_last
            except AttributeError as e:  # pylint: disable=W0612
                drop_last = False
            test_loader = torch_geometric.dataloader.DataLoader(
                test_set,
                batch_size=args.valid_batch_size,
                shuffle=(test_sampler is None),
                drop_last=drop_last,
                num_workers=args.num_workers,
                pin_memory=args.pin_memory,
            )
            test_data_loader[test_name] = test_loader
        if stop_first_test:
            break

    for swa_eval in swas:
        epoch = checkpoint_handler.load_latest(
            state=tools.CheckpointState(model, optimizer, lr_scheduler),
            swa=swa_eval,
            device=device,
        )
        model.to(device)
        if args.distributed:
            distributed_model = DDP(model, device_ids=[local_rank])
        model_to_evaluate = model if not args.distributed else distributed_model
        if swa_eval:
            logging.info(f"Loaded Stage two model from epoch {epoch} for evaluation")
        else:
            logging.info(f"Loaded Stage one model from epoch {epoch} for evaluation")

        if rank == 0:
            # Save entire model
            if swa_eval:
                model_path = Path(args.checkpoints_dir) / (tag + "_stagetwo.model")
            else:
                model_path = Path(args.checkpoints_dir) / (tag + ".model")
            logging.info(f"Saving model to {model_path}")
            model_to_save = deepcopy(model)
            if args.enable_cueq and not args.only_cueq:
                print("RUNING CUEQ TO E3NN")
                print("swa_eval", swa_eval)
                model_to_save = run_cueq_to_e3nn(deepcopy(model), device=device)
            if args.save_cpu:
                model_to_save = model_to_save.to("cpu")
            torch.save(model_to_save, model_path)
            extra_files = {
                "commit.txt": commit.encode("utf-8") if commit is not None else b"",
                "config.yaml": json.dumps(
                    convert_to_json_format(extract_config_mace_model(model))
                ),
            }
            if swa_eval:
                torch.save(
                    model_to_save, Path(args.model_dir) / (args.name + "_stagetwo.model")
                )
                try:
                    path_complied = Path(args.model_dir) / (
                        args.name + "_stagetwo_compiled.model"
                    )
                    logging.info(f"Compiling model, saving metadata {path_complied}")
                    model_compiled = jit.compile(deepcopy(model_to_save))
                    torch.jit.save(
                        model_compiled,
                        path_complied,
                        _extra_files=extra_files,
                    )
                except Exception as e:  # pylint: disable=W0718
                    pass
            else:
                torch.save(model_to_save, Path(args.model_dir) / (args.name + ".model"))
                try:
                    path_complied = Path(args.model_dir) / (
                        args.name + "_compiled.model"
                    )
                    logging.info(f"Compiling model, saving metadata to {path_complied}")
                    model_compiled = jit.compile(deepcopy(model_to_save))
                    torch.jit.save(
                        model_compiled,
                        path_complied,
                        _extra_files=extra_files,
                    )
                except Exception as e:  # pylint: disable=W0718
                    pass

        logging.info("Computing metrics for training, validation, and test sets")
        for param in model.parameters():
            param.requires_grad = False
        skip_heads = args.skip_evaluate_heads.split(",") if args.skip_evaluate_heads else []
        if skip_heads:
            logging.info(f"Skipping evaluation for heads: {skip_heads}")
        table_train_valid = create_error_table(
            table_type=args.error_table,
            all_data_loaders=train_valid_data_loader,
            model=model_to_evaluate,
            loss_fn=loss_fn,
            output_args=output_args,
            log_wandb=args.wandb,
            device=device,
            distributed=args.distributed,
            skip_heads=skip_heads,
        )
        logging.info("Error-table on TRAIN and VALID:\n" + str(table_train_valid))

        if test_data_loader:
            table_test = create_error_table(
                table_type=args.error_table,
                all_data_loaders=test_data_loader,
                model=model_to_evaluate,
                loss_fn=loss_fn,
                output_args=output_args,
                log_wandb=args.wandb,
                device=device,
                distributed=args.distributed,
            )
            logging.info("Error-table on TEST:\n" + str(table_test))
        if args.plot:
            try:
                plotter = TrainingPlotter(
                    results_dir=logger.path,
                    heads=heads,
                    table_type=args.error_table,
                    train_valid_data=train_valid_data_loader,
                    test_data=test_data_loader,
                    output_args=output_args,
                    device=device,
                    plot_frequency=args.plot_frequency,
                    distributed=args.distributed,
                    swa_start=swa.start if swa else None
                )
                plotter.plot(epoch, model_to_evaluate, rank)
            except Exception as e:  # pylint: disable=W0718
                logging.debug(f"Plotting failed: {e}")

        if args.distributed:
            torch.distributed.barrier()

    logging.info("Done")
    if args.distributed:
        torch.distributed.destroy_process_group()


if __name__ == "__main__":
    main()
