"""Configuration constants for the Card Model."""

# --- Card encoding ---
NUM_CARDS = 52          # Standard 52-card deck (0-51)
PAD_TOKEN = 52          # Padding token for missing board cards
VOCAB_SIZE = 53         # 52 cards + 1 pad token

# --- Card ID mapping (OpenSpiel universal_poker) ---
# ID = rank * 4 + suit, where:
#   rank: 0=2, 1=3, ..., 8=T, 9=J, 10=Q, 11=K, 12=A
#   suit: 0=c, 1=d, 2=h, 3=s
RANK_NAMES = ['2', '3', '4', '5', '6', '7', '8', '9', 'T', 'J', 'Q', 'K', 'A']
SUIT_NAMES = ['c', 'd', 'h', 's']

# --- Game configuration ---
# Botzone 比赛规则对齐:
#   - 2人 HUNL, 初始筹码 20000, SB=50, BB=100
#   - OpenSpiel blind 参数顺序: [player0_blind, player1_blind]
#   - blind=50 100 → player0=SB(50), player1=BB(100)
#   - numBoardCards=0 3 1 1 → Preflop/Flop/Turn/River 发牌数
HUNL_GAME_STRING = (
    "universal_poker("
    "betting=nolimit,"
    "numPlayers=2,"
    "numRanks=13,"
    "numSuits=4,"
    "numHoleCards=2,"
    "numRounds=4,"
    "numBoardCards=0 3 1 1,"
    "stack=20000 20000,"
    "blind=50 100,"
    "bettingAbstraction=fullgame)"
)

# --- Data generation ---
NUM_ROLLOUTS = 1000     # Monte Carlo rollouts per sample
NUM_BINS = 50           # Equity histogram bins
HOLE_SIZE = 2           # Number of hole cards
BOARD_SIZE = 5          # Max number of board cards (3 flop + 1 turn + 1 river)

# --- Model ---
EMBEDDING_DIM = 64
MLP_HIDDEN = [512, 512, 256]

# --- Paths ---
import os
_PACKAGE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(_PACKAGE_DIR, "data")
CHECKPOINT_DIR = os.path.join(_PACKAGE_DIR, "checkpoints")

# --- Training ---
#BATCH_SIZE = 256
#LEARNING_RATE = 1e-3
WEIGHT_DECAY = 1e-4
#NUM_EPOCHS = 50
LAMBDA_MSE = 0.1        # Weight for MSE loss relative to EMD loss
#NUM_TRAIN_SAMPLES = 20000   # Adjust up to 50000+ for production quality
#NUM_VAL_SAMPLES = 2000      # Adjust up to 5000+ for production quality
#NUM_WORKERS = 4          # DataLoader workers

#NUM_ROLLOUTS = 1000         # 保持不变，保证直方图精度
#NUM_BINS = 50               # 保持不变

# 扩大百倍的数据量，让它背下所有极端牌型（如天皇家同花顺）
NUM_TRAIN_SAMPLES = 10_000_000   # 200 万局样本 (大概需要几个小时生成)
NUM_VAL_SAMPLES = 100_000        # 5 万局验证
NUM_WORKERS = 22                # 填入你 CPU 的核心数 (例如 32 或 64)

# 增大 Batch Size 榨取显存
BATCH_SIZE = 16384           
LEARNING_RATE = 5e-4        # 调小一点，使其收敛更平滑
NUM_EPOCHS = 64           # 多跑几轮