import pickle
from argparse import Namespace
from pathlib import Path

import optuna as optuna
from optuna.integration import PyTorchLightningPruningCallback

from main import run_lightning_loop
from ml_lib.utils.config import parse_comandline_args_add_defaults

def optimize(trial: optuna.Trial):
    # Optuna configuration
    folder = Path('study')
    folder.mkdir(parents=False, exist_ok=True)
    scheduler = trial.suggest_categorical('scheduler', [None, 'LambdaLR'])
    if scheduler is not None:
        lr_scheduler_parameter = trial.suggest_float('lr_scheduler_parameter', 0.8, 1, step=0.01)
    else:
        lr_scheduler_parameter = None

    optuna_suggestions = dict(
        model_name='CNNBaseline',
        data_name='MaskLibrosaDatamodule',
        batch_size=trial.suggest_int('batch_size', 5, 50, step=5),
        max_epochs=75,
        target_mel_length_in_seconds=trial.suggest_float('target_mel_length_in_seconds', 0.2, 1.5, step=0.1),
        random_apply_chance=trial.suggest_float('random_apply_chance', 0.1, 0.5, step=0.1),
        loudness_ratio=trial.suggest_float('loudness_ratio', 0.0, 0.5, step=0.1),
        shift_ratio=trial.suggest_float('shift_ratio', 0.0, 0.5, step=0.1),
        noise_ratio=trial.suggest_float('noise_ratio', 0.0, 0.5, step=0.1),
        mask_ratio=trial.suggest_float('mask_ratio', 0.0, 0.5, step=0.1),
        lr=trial.suggest_loguniform('lr', 1e-5, 1e-3),
        dropout=trial.suggest_float('dropout', 0.0, 0.3, step=0.05),
        lat_dim=2 ** trial.suggest_int('lat_dim', 1, 5, step=1),
        scheduler=scheduler,
        lr_scheduler_parameter=lr_scheduler_parameter,
        loss='ce_loss',
        sampler=trial.suggest_categorical('sampler', [None, 'WeightedRandomSampler']),
        study_name=trial.study.study_name
    )
    if optuna_suggestions['model_name'] == 'CNNBaseline':
        model_depth = trial.suggest_int('model_depth', 1, 6, step=1)
        filters = list()
        for layer_idx in range(model_depth):
            filters.append(2 ** trial.suggest_int(f'filters_{layer_idx}', 2, 6, step=1))
        optuna_suggestions.update(filters=filters)
    elif optuna_suggestions['model_name'] == 'VisualTransformer':
        transformer_dict = dict(
            mlp_dim=2 ** trial.suggest_int('mlp_dim', 1, 5, step=1),
            head_dim=2 ** trial.suggest_int('head_dim', 1, 5, step=1),
            patch_size=trial.suggest_int('patch_size', 6, 12, step=3),
            attn_depth=trial.suggest_int('attn_depth', 2, 14, step=4),
            heads=trial.suggest_int('heads', 2, 16, step=2),
            embedding_size=trial.suggest_int('embedding_size', 12, 64, step=12)
        )
        optuna_suggestions.update(**transformer_dict)

    pruning_callback = PyTorchLightningPruningCallback(trial, monitor="PL_recall_score")

    # Parse comandline args, read config and get model
    h_params, found_data_class, found_model_class, seed = parse_comandline_args_add_defaults(
        '_parameters.ini', overrides=optuna_suggestions)
    h_params = Namespace(**h_params)
    try:
        best_score = run_lightning_loop(h_params, data_class=found_data_class, model_class=found_model_class,
                                        additional_callbacks=pruning_callback, seed=seed)
    except Exception as e:
        print(e)
        best_score = 0
    return best_score


if __name__ == '__main__':
    study = optuna.create_study(direction='maximize', sampler=optuna.samplers.TPESampler(seed=1337))
    # study.optimize(optimize, n_trials=50, callbacks=[opt_utils.NeptuneCallback(log_study=True, log_charts=True)])
    study.optimize(optimize, n_trials=100)

    print("Number of finished trials: {}".format(len(study.trials)))

    print("Best trial:")
    trial = study.best_trial

    print("  Value: {}".format(trial.value))

    print("  Params: ")
    for key, value in trial.params.items():
        print("    {}: {}".format(key, value))

    optuna_study_object = Path('study') / 'study.pkl'
    optuna_study_object.parent.mkdir(exist_ok=True)
    with optuna_study_object.open(mode='wb') as f:
        pickle.dump(study, f)