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import os
import sys
import numpy as np
import torch
# Add project root to path
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
import engine_rust
from ai.models.training_config import POLICY_SIZE
from ai.training.train import AlphaNet
def verify_parity():
# 1. Setup Models
model_path_pt = "ai/models/alphanet_best.pt"
model_path_onnx = "ai/models/alphanet.onnx"
device = torch.device("cpu")
checkpoint = torch.load(model_path_pt, map_location=device)
state_dict = checkpoint["model_state"] if "model_state" in checkpoint else checkpoint
model_pt = AlphaNet(policy_size=POLICY_SIZE)
model_pt.load_state_dict(state_dict)
model_pt.eval()
nmcts_rust = engine_rust.PyNeuralMCTS(model_path_onnx)
# 2. Setup Game
with open("engine/data/cards_compiled.json", "r", encoding="utf-8") as f:
db_content = f.read()
db = engine_rust.PyCardDatabase(db_content)
game = engine_rust.PyGameState(db)
# Simple init
p0_deck = [124, 127] * 20
p1_deck = [124, 127] * 20
p0_lives = [1024, 1025, 1027]
p1_lives = [1024, 1025, 1027]
game.initialize_game(p0_deck, p1_deck, [20000] * 10, [20000] * 10, p0_lives, p1_lives)
# 3. Compare Encoding
obs_rust = np.array(game.get_observation(), dtype=np.float32)
# 4. Compare Inference
with torch.no_grad():
logits_pt, val_pt = model_pt(torch.from_numpy(obs_rust).unsqueeze(0))
logits_pt = logits_pt.numpy()[0]
val_pt = val_pt.numpy()[0][0]
print(f"Observation Size: {len(obs_rust)}")
print(f"Value (Python): {val_pt:.6f}")
# Note: We need a way to get raw evaluate results from Rust for deep parity
# But for now, we'll check suggestions as a proxy
num_sims = 100
suggestions = nmcts_rust.get_suggestions(game, num_sims)
print("\nTop Python Actions (Logits):")
top_pt = np.argsort(logits_pt)[::-1][:5]
for i in top_pt:
print(f" Action {i:4d}: {logits_pt[i]:.4f}")
print("\nRust Suggestions (1 simulation):")
for action, score, visits in suggestions[:5]:
print(f" Action {action:4d}: Score {score:.4f}, Visits {visits}")
if __name__ == "__main__":
verify_parity()
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