from typing import Final
import numpy as np

DATA_DIR: Final = '../data'
OUT_DIR: Final = './out'
MODEL_DIR: Final = './models'

NB_THREADS: Final = 1024
NB_THREADS_2D: Final = (32, 32)
NB_THREADS_3D: Final = (16, 16, 4)
M: Final = int(np.log2(NB_THREADS_2D[1]))

# Save state to avoid recalculation on restart
SAVE_STATE: Final = True
# Redo the state even if it's already saved
FORCE_REDO: Final = False
# Use NJIT to greatly accelerate runtime
COMPILE_WITH_C: Final = True
# Use GPU to greatly accelerate runtime (as priority over NJIT)
GPU_BOOSTED: Final = True
# Depending on what you set, the output label will be as follow :
# ┌────────────────┬─────────────┬───────┐
# │ COMPILE_WITH_C │ GPU_BOOSTED │ LABEL │
# ├────────────────┼─────────────┼───────┤
# │ True           │ True        │ GPU   │
# │ True           │ False       │ CPU   │
# │ False          │ True        │ PGPU  │
# │ False          │ False       │ PY    │
# └────────────────┴─────────────┴───────┘

# Number of weak classifiers
# TS: Final = [1]
# TS: Final = [1, 5, 10]
TS: Final = [1, 5, 10, 25, 50]
# TS: Final = [1, 5, 10, 25, 50, 100, 200]
# TS: Final = [1, 5, 10, 25, 50, 100, 200, 300]
# TS: Final = [1, 5, 10, 25, 50, 100, 200, 300, 400, 500, 1000]

# Enable verbose output (for debugging purposes)
__DEBUG: Final = False
# Debugging options
if __DEBUG:
	IDX_INSPECT: Final = 4548
	IDX_INSPECT_OFFSET: Final = 100
	np.seterr(all = 'raise')
	# Debug option (image width * log_10(length) + extra characters)
	np.set_printoptions(linewidth = 19 * 6 + 3)