# # src/config.py

# import torch

# DATA_PATH = "../data/train-00000-of-00001-20a6e56929a59ecf.parquet"
# VOCAB_PATH = "../saved/vocab.json"
# MODEL_PATH = "../saved/best_model.pt"

# DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# # Training
# MAX_SRC_LEN = 30   # max Teluguish chars per word
# MAX_TGT_LEN = 30   # max Telugu chars per word (incl <sos>, <eos>)
# BATCH_SIZE = 256
# NUM_EPOCHS = 15
# LR = 1e-3
# TEACHER_FORCING = 0.5

# # Model sizes
# SRC_CHAR_EMB = 64
# ENC_HIDDEN = 128   # GNN output dim
# TGT_CHAR_EMB = 128
# DEC_HIDDEN = 256   # LSTM hidden size
# GNN_LAYERS = 2
# DROPOUT = 0.2


# src/config.py
from pathlib import Path
import torch

# Base directory = project root (one level above src/)
BASE_DIR = Path(__file__).resolve().parent.parent

# Paths
DATA_PATH = BASE_DIR / "data" / "train-00000-of-00001-20a6e56929a59ecf.parquet"
VOCAB_PATH = BASE_DIR / "saved" / "vocab.json"
MODEL_PATH = BASE_DIR / "saved" / "model_30_epoch.pt"

# Device
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# Training / model hyperparams (whatever you already had)
MAX_SRC_LEN = 30
MAX_TGT_LEN = 30
BATCH_SIZE = 256
NUM_EPOCHS = 10
LR = 3e-2
TEACHER_FORCING = 0.5

SRC_CHAR_EMB = 64
ENC_HIDDEN = 128
TGT_CHAR_EMB = 128
DEC_HIDDEN = 256
GNN_LAYERS = 2
DROPOUT = 0.2