import os
import sys
import torch
import torch.nn.functional as F
import pandas as pd
import numpy as np

# Path alignment
sys.path.append(os.getcwd())
sys.path.append(os.path.join(os.getcwd(), 'ai-engine'))

from pipeline.tiered_loader import TieredDataLoader
from pipeline.sequence_builder import SequenceBuilder
from models.hybrid_v11 import HybridDeepModel
from features.odds_history import OddsHistoryEngine
from features.synthetic_xg import SyntheticXGModel

DEVICE = 'cpu'
MODEL_PATH = 'ai-engine/models/v11_hybrid_model.pth'
TARGET_ID = 'en78ih6ec7exnpxcku3xc3das'

def audit():
    print(f"🕵️  Auditing Match: {TARGET_ID}")
    
    # 1. Pipeline Data
    builder = SequenceBuilder()
    X, y, meta = builder.build_sequences()
    
    # Check if target is in dataset
    idx_list = meta.index[meta['match_id'] == TARGET_ID].tolist()
    if not idx_list:
        print("❌ Match not found in generated sequences. Is it too old or too new?")
        return
    
    idx = idx_list[0]
    row_meta = meta.iloc[idx]
    
    # 2. Features
    loader = TieredDataLoader()
    odds_df = loader.load_gold_data([TARGET_ID])
    eng = OddsHistoryEngine()
    xg_model = SyntheticXGModel()
    
    # Team Mapping
    unique_teams = meta['team_id'].unique()
    team_map = {tid: i for i, tid in enumerate(unique_teams)}
    
    # 3. Predict exactly like Backtest
    state = torch.load(MODEL_PATH, map_location=DEVICE)
    emb_key = 'entity_emb.weight' if 'entity_emb.weight' in state else 'team_embedding.weight'
    saved_vocab_size = state[emb_key].shape[0]
    
    model = HybridDeepModel(num_teams=saved_vocab_size)
    new_state = {k.replace('team_embedding', 'entity_emb'): v for k, v in state.items()}
    model.load_state_dict(new_state, strict=False)
    model.eval()
    
    # Data components
    team_idx = team_map.get(row_meta['team_id'], 0)
    entities = torch.LongTensor([team_idx, 0]).unsqueeze(0)
    seq = torch.FloatTensor(X[idx]).unsqueeze(0)
    
    # Context (Odds + xG)
    odds_lookup = {}
    for _, r in odds_df.iterrows():
        mid = r['match_id']
        if mid not in odds_lookup: odds_lookup[mid] = {}
        if r['category'] == 'Maç Sonucu': odds_lookup[mid][r['selection']] = r['odd_value']
        elif r['category'] == '2,5 Alt/Üst':
            if 'Üst' in r['selection']: odds_lookup[mid]['Over'] = r['odd_value']
            else: odds_lookup[mid]['Under'] = r['odd_value']
            
    odds = odds_lookup.get(TARGET_ID, {'1': 1.0, 'X': 1.0, '2': 1.0, 'Over': 1.0, 'Under': 1.0})
    syn_xg = 1.35 # Placeholder in trainer for xG component if used
    hist_win_rate = eng.get_feature(row_meta['team_id'], float(odds.get('1', 1.0)))
    
    ctx = torch.FloatTensor([
        float(odds.get('1', 1.0)), float(odds.get('X', 1.0)), float(odds.get('2', 1.0)),
        float(odds.get('Over', 1.0)), float(odds.get('Under', 1.0)),
        syn_xg, syn_xg,
        hist_win_rate
    ]).unsqueeze(0)
    
    with torch.no_grad():
        logits_res, pred_goals, logits_btts, logits_ht_ft = model(entities, seq, ctx)
        probs = F.softmax(logits_res, dim=1).numpy()[0]
        prob_btts = torch.sigmoid(logits_btts).item()
        probs_ht = F.softmax(logits_ht_ft, dim=1).numpy()[0]
        
    print("\n📊 INTERNAL PIPELINE PREDICTION:")
    print(f"Target Team: {row_meta['team_id']}")
    print(f"1X2 Probs: Home:{probs[0]:.4f} Draw:{probs[1]:.4f} Away:{probs[2]:.4f}")
    print(f"BTTS Prob: {prob_btts:.4f}")
    
    ht_map = ["1/1", "1/X", "1/2", "X/1", "X/X", "X/2", "2/1", "2/X", "2/2"]
    top3_ht = np.argsort(probs_ht)[-3:][::-1]
    print("Top 3 HT/FT:")
    for idx_ht in top3_ht:
        print(f"  {ht_map[idx_ht]}: {probs_ht[idx_ht]:.4f}")
    
    actual_res = y[idx][0]
    actual_ht_idx = int(y[idx][3])
    print(f"\n✅ ACTUAL REALITY:")
    print(f"Result (Y): {actual_res} (0.0=Away)")
    print(f"HT/FT Class: {actual_ht_idx} ({ht_map[actual_ht_idx]})")

if __name__ == "__main__":
    audit()