from __future__ import annotations
from dataclasses import dataclass
from enum import Enum
from typing import List

class Implementation(Enum):
    VECT = "Vectorised"

class MatchSpace(Enum):
    LAB = "Lab (perceptual)"
    RGB = "RGB (euclidean)"

@dataclass
class Config:
    # Core
    grid: int = 32
    out_w: int = 768
    out_h: int = 768
    tile_size: int = 32

    # Hugging Face tile source (always used)
    hf_dataset: str = "Kratos-AI/KAI_car-images"
    hf_split: str = "train"
    hf_limit: int = 200  # Increased for better tile diversity
    hf_cache_dir: str = None  # Optional Hugging Face datasets cache directory

    # Pipeline
    impl: Implementation = Implementation.VECT
    match_space: MatchSpace = MatchSpace.LAB

    # Quantization
    use_uniform_q: bool = False
    q_levels: int = 8
    use_kmeans_q: bool = False
    k_colors: int = 8

    # Creative
    tile_norm_brightness: bool = False
    allow_rotations: bool = False

    # Caching
    tiles_cache_dir: str = None  # Optional on-disk cache for preprocessed tiles

    # Benchmark
    do_bench: bool = False
    bench_grids: List[int] = None