# ─────────────────────────────────────────────
#  game_config.py  –  all balance numbers live here
# ─────────────────────────────────────────────

STARTING_MONEY: float = 100.0

# ── Upgrades ────────────────────────────────
# Each upgrade has a list of levels.
# level 0  = not purchased (baseline)
# level 1+ = purchased tiers
#
# quality_score  : additive contribution to total_quality  (0–100 scale)
# cost           : price to buy that tier (cumulative spend per tier, not total)

UPGRADES: dict = {
    "model_size": {
        "label": "Model Size",
        "icon": "⬡",
        "description": "Number of parameters in your model",
        "levels": [
            {"name": "Nano  (7M)", "cost": 0, "quality_score": 0},
            {"name": "Small (70M)", "cost": 20, "quality_score": 8},
            {"name": "Medium (500M)", "cost": 60, "quality_score": 18},
            {"name": "Large (3B)", "cost": 150, "quality_score": 32},
            {"name": "Giant (30B)", "cost": 400, "quality_score": 50},
        ],
    },
    "dataset_size": {
        "label": "Dataset Size",
        "icon": "◈",
        "description": "Tokens of training data",
        "levels": [
            {"name": "Scraps  (1B)", "cost": 0, "quality_score": 0},
            {"name": "Common (10B)", "cost": 15, "quality_score": 6},
            {"name": "Large  (100B)", "cost": 45, "quality_score": 14},
            {"name": "Massive (1T)", "cost": 120, "quality_score": 26},
            {"name": "Web-scale (10T)", "cost": 300, "quality_score": 40},
        ],
    },
    "dataset_quality": {
        "label": "Data Quality",
        "icon": "◉",
        "description": "Curation and filtering pipeline",
        "levels": [
            {"name": "Raw dump", "cost": 0, "quality_score": 0},
            {"name": "Deduplicated", "cost": 10, "quality_score": 5},
            {"name": "Filtered", "cost": 30, "quality_score": 12},
            {"name": "Curated", "cost": 80, "quality_score": 22},
            {"name": "RLHF-aligned", "cost": 200, "quality_score": 35},
        ],
    },
    "architecture": {
        "label": "Architecture",
        "icon": "◬",
        "description": "Model architecture design",
        "levels": [
            {"name": "Vanilla LSTM", "cost": 0, "quality_score": 0},
            {"name": "GPT-style", "cost": 25, "quality_score": 10},
            {"name": "Flash-Attn", "cost": 70, "quality_score": 20},
            {"name": "MoE layers", "cost": 180, "quality_score": 33},
            {"name": "Custom SSM", "cost": 450, "quality_score": 48},
        ],
    },
    "hardware": {
        "label": "Hardware",
        "icon": "▣",
        "description": "Training compute",
        "levels": [
            {"name": "CPU cluster", "cost": 0, "quality_score": 0},
            {"name": "GTX 1080 ×4", "cost": 30, "quality_score": 7},
            {"name": "A100 ×8", "cost": 90, "quality_score": 17},
            {"name": "H100 pod", "cost": 250, "quality_score": 30},
            {"name": "Custom silicon", "cost": 600, "quality_score": 45},
        ],
    },
}

# Max quality score achievable (sum of all top-tier quality_scores)
# Used to normalise quality to 0–1.  Recomputed at runtime, but shown here for reference.
# = 50 + 40 + 35 + 48 + 45 = 218

# ── Training economics ───────────────────────
BASE_TRAIN_COST: float = 10.0  # $ cost at level-0 everything
TRAIN_COST_QUALITY_SCALE: float = 0.15  # extra cost per quality point

BASE_REWARD: float = 25.0  # $ reward at level-0 everything
QUALITY_EXPONENT: float = 2.2  # reward grows super-linearly with quality
MAX_QUALITY_REWARD_MULT: float = 40.0  # cap multiplier at max quality

REWARD_NOISE_PCT: float = 0.12  # ±12 % random variance per run

# ── Cloud Inference ──────────────────────────
CLOUD_UNLOCK_COST: float = 150.0  # one-time cost to unlock
CLOUD_BASE_REVENUE: float = 8.0  # $ per tick (every 8 s) at quality 0
CLOUD_QUALITY_MULT: float = 0.06  # additional revenue per quality point
CLOUD_TICK_SECONDS: int = 8  # JS poll interval hint (stored here for reference)

# ── Chat temperature mapping ─────────────────
# quality_ratio = total_quality / max_quality   (0.0 – 1.0)
# temperature   = lerp between TEMP_MAX and TEMP_MIN based on quality_ratio
CHAT_TEMP_MAX: float = 2.0  # chaotic gibberish at quality 0
CHAT_TEMP_MIN: float = 0.3  # coherent at max quality

# ── HuggingFace model ────────────────────────
HF_MODEL_ID: str = "FlameF0X/Qwen3-4B-Distilled-Claude-4.6"
HF_MAX_NEW_TOKENS: int = 400