from dataclasses import dataclass, field

# ---- Hyperparameter configuration ----

@dataclass
class HPARAMS:
    # common
    seed: int = 42
    url: str = "https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"

    scheduler_hparams: dict = field(default_factory=lambda: {
        "factor": 0.5,
        "patience": 2,
        "mode": "min"
    })
    
    # BiGRU hparams
    max_seq_len_gru: int = 256
    batch_size_gru: int = 128
    vocab_size: int = 10000
    glove_txt_path: str = ("/mnt/e/ML_Files/PreTrained_Models/GloVe_Embeddings/glove.2024.wikigiga"
                           ".200d/wiki_giga_2024_200_MFT20_vectors_seed_2024_alpha_0.75_eta_0.05_combined.txt")
    
    model_hparams_gru: dict = field(default_factory=lambda: {
        "embedding_dim": 128,
        "hidden_size": 128,
        "dropout": 0.12,
        "num_gru_layers": 2,
        "use_dense": False,
        "dense_dropout_prob": 0.1
    })

    optimizer_hparams_gru: dict = field(default_factory=lambda: {
        "lr": 1e-3,
        "weight_decay": 5e-4
    })

    trainer_hparams_gru: dict = field(default_factory=lambda: {
        "n_epochs": 20,
        "use_early_stopping" : True,
        "early_stopping_patience" : 3,
        "scheduler_monitor" : "val_loss",
        "restore_best_model": True,
    })
    
    # Transformer hparams
    max_seq_len_transformer:int = 288
    #transformer_path: str = "/mnt/e/ML_Files/PreTrained_Models/HuggingFace/deberta-v3-base/"
    transformer_path: str = "/mnt/d/ML-Files/PreTrained-Models/HuggingFace/Transformer-Encoder/microsoft_deberta-v3-base/"
    batch_size_transformer: int = 32
    transformer_fc_dropout: float = 0.1
    
    optimizer_hparams_transformer: dict = field(default_factory=lambda: {
        "lr": 3e-5,
        "weight_decay": 5e-4
    })

    trainer_hparams_transformer: dict = field(default_factory=lambda: {
        "n_epochs": 5,
        "use_early_stopping" : True,
        "early_stopping_patience" : 2,
        "scheduler_monitor" : "val_loss",
        "restore_best_model": False,
    })
    
hp = HPARAMS()