import os

# Configuration for the data
DATA_INDEX_PATH = ''

# Configuration for the model
PATCH_STREAM = True
PATCH_SIZE = 16                                                # Patch Size
PATCH_LENGTH = 1024                                             # Patch Length
CHAR_NUM_LAYERS = 6                                             # Number of layers in the decoder
PATCH_NUM_LAYERS = 20                                           # Number of layers in the encoder
HIDDEN_SIZE = 1280                                               # Hidden Size

# Configuration for the training     
BETA = 0.1                                                      # beta in DPO's objective function
LAMBDA = 10                                                     # lambda in DPOP's objective function
LEARNING_RATE = 1e-6
OPTIMIZATION_STEPS = 10000                                      # Optimization steps for DPO
WANDB_LOGGING = False                                           # Whether to log to wandb
WANDB_KEY = '<your_wandb_key>'

PRETRAINED_PATH = ''
EXP_TAG = ''
NAME =  EXP_TAG + \
        "_beta_" + str(BETA) + \
        "_lambda_" + str(LAMBDA) + \
        "_p_size_" + str(PATCH_SIZE) + \
        "_p_length_" + str(PATCH_LENGTH) + \
        "_p_layers_" + str(PATCH_NUM_LAYERS) + \
        "_c_layers_" + str(CHAR_NUM_LAYERS) + \
        "_h_size_" + str(HIDDEN_SIZE) + \
        "_lr_" + str(LEARNING_RATE)

WEIGHTS_PATH = "weights_notagen_" + NAME + ".pth"                  # Path to save weights
WANDB_NAME = NAME