import os
import json
import csv
import math
import hashlib
from collections import deque, defaultdict, Counter
import numpy as np
from scipy import stats, signal
from typing import Optional, Dict, List
import warnings
warnings.filterwarnings('ignore')

try:
    from sklearn.neural_network import MLPRegressor
    from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, IsolationForest
    from sklearn.preprocessing import StandardScaler, RobustScaler
    import pywt
    ADVANCED_ML = True
except ImportError:
    ADVANCED_ML = False

try:
    import torch
    import torch.nn as nn
    import torch.optim as optim
    CUDA_AVAILABLE = torch.cuda.is_available()
    DEVICE = torch.device("cuda" if CUDA_AVAILABLE else "cpu")
except ImportError:
    CUDA_AVAILABLE = False
    DEVICE = None
    class MockNN:
        class Module: pass
    nn = MockNN

FEATURE_DIMENSION = 185
ML_MIN_TRAINING_SIZE = 50

class SimpleKalmanFilter:
    def __init__(self, process_variance=1e-5, measurement_variance=0.1):
        self.process_variance = process_variance
        self.measurement_variance = measurement_variance
        self.posteri_estimate = 50.0
        self.posteri_error_estimate = 1.0

    def update(self, measurement):
        priori_estimate = self.posteri_estimate
        priori_error_estimate = self.posteri_error_estimate + self.process_variance
        blending_factor = priori_error_estimate / (priori_error_estimate + self.measurement_variance)
        self.posteri_estimate = priori_estimate + blending_factor * (measurement - priori_estimate)
        self.posteri_error_estimate = (1 - blending_factor) * priori_error_estimate
        return self.posteri_estimate
    
    def get_noise_level(self):
        return self.posteri_error_estimate

class RegimeDetector:
    def __init__(self, window_size=50):
        self.window_size = window_size
        self.regime_history = deque(maxlen=100)
        
    def calculate_hurst(self, ts):
        if len(ts) < 20: return 0.5
        lags = range(2, min(20, len(ts)//2))
        tau = [np.std(np.subtract(ts[lag:], ts[:-lag])) for lag in lags]
        if len(tau) > 0:
            try: return np.polyfit(np.log(lags), np.log(tau), 1)[0] * 2.0
            except: return 0.5
        return 0.5

    def detect_micro_regime(self, data):
        if len(data) < 10: return "NORMAL", 0.0
        recent = np.array(data)[-10:]
        volatility = np.std(recent)
        if volatility > 40: return "CHAOS", 1.0
        slope, _, _, _, _ = stats.linregress(np.arange(len(recent)), recent)
        if abs(slope) > 5.0: return "FLASH_TREND", abs(slope) / 10.0
        return "NORMAL", 0.0

    def detect(self, data):
        if len(data) < self.window_size:
            return "RANDOM", 0.5
        recent_data = np.array(data)[-self.window_size:]
        hurst = self.calculate_hurst(recent_data)
        acf_1 = np.corrcoef(recent_data[:-1], recent_data[1:])[0, 1]
        volatility = np.std(recent_data)
        
        regime = "RANDOM"
        strength = 0.5
        if hurst > 0.6 and acf_1 > 0.1:
            regime, strength = "TRENDING", min(1.0, (hurst - 0.5) * 2 + acf_1)
        elif hurst < 0.4 and acf_1 < -0.1:
            regime, strength = "MEAN_REVERTING", min(1.0, (0.5 - hurst) * 2 + abs(acf_1))
        elif volatility < 15:
            regime, strength = "LOW_VOLATILITY", 1.0 - (volatility / 15.0)
        elif volatility > 35:
            regime, strength = "HIGH_VOLATILITY", (volatility - 35.0) / 15.0
            
        self.regime_history.append(regime)
        return regime, strength

class AdvancedFeatureEngineer:
    def __init__(self):
        pass

    def extract_wavelet_features(self, data, wavelet='db4', level=3):
        if len(data) < 2**level: return np.zeros(level * 4)
        coeffs = pywt.wavedec(data, wavelet, level=level)
        features = []
        for coeff in coeffs:
            features.extend([np.mean(coeff), np.std(coeff), np.max(np.abs(coeff)), stats.skew(coeff) if len(coeff) > 3 else 0])
        return np.array(features)

    def extract_statistical_moments(self, data, windows=[5, 10, 20, 50]):
        features = []
        for window in windows:
            if len(data) >= window:
                wd = data[-window:]
                features.extend([np.mean(wd), np.std(wd), stats.skew(wd), stats.kurtosis(wd), np.median(wd), np.percentile(wd, 25), np.percentile(wd, 75)])
            else:
                features.extend([50, 29, 0, 0, 50, 37.5, 62.5])
        return np.array(features)

    def extract_identifier_features(self, identifier, nonce=0):
        if not identifier: return np.zeros(15)
        ords = [ord(c) for c in identifier]
        features = [
            np.mean(ords), np.std(ords), np.sum(ords), ords[0], ords[-1],
            sum(1 for c in identifier if c.isdigit()), sum(1 for c in identifier if c.isupper()),
            int(hashlib.md5(identifier.encode()).hexdigest(), 16) % 100, len(set(identifier)),
            1 if 'x' in identifier else 0, nonce, nonce % 10, nonce % 100, np.log1p(nonce), 1 if nonce == 0 else 0
        ]
        return np.array(features)
        
    def extract_all_features(self, data, current_identifier=None, current_nonce=0):
        if len(data) < 20: return np.zeros(FEATURE_DIMENSION)
        features = []
        features.extend([np.mean(data), np.std(data), np.min(data), np.max(data), np.median(data), np.percentile(data, 25), np.percentile(data, 75), stats.skew(data), stats.kurtosis(data)])
        features.extend(data[-5:].tolist())
        diffs = np.diff(data)
        features.extend([np.mean(diffs), np.std(diffs), np.min(diffs), np.max(diffs)])
        features.extend(self.extract_statistical_moments(data))
        
        # Simplified feature extraction for brevity
        if current_identifier:
            features.extend(self.extract_identifier_features(current_identifier, current_nonce))
        
        while len(features) < FEATURE_DIMENSION: features.append(0.0)
        return np.array(features[:FEATURE_DIMENSION])

from .online_learning import OnlineLearningTracker
from .meta_learning import MetaLearningOptimizer
from .advanced_dl import TransformerPredictor, HAS_TORCH
from .anomaly import RNGAnomalyDetector

class GroundbreakingPredictor:
    def __init__(self):
        self.history = deque(maxlen=1000)
        self.identifier_history = deque(maxlen=1000)
        self.nonce_history = deque(maxlen=1000)
        self.feature_engineer = AdvancedFeatureEngineer()
        self.regime_detector = RegimeDetector()
        self.kalman_filter = SimpleKalmanFilter()
        
        # Anomaly Detection
        self.anomaly_detector = RNGAnomalyDetector()
        
        # Deep Learning Transformer
        self.transformer = TransformerPredictor() if HAS_TORCH else None
        
        # Meta Learning (Self Evolving)
        self.meta_learner = MetaLearningOptimizer()
        
        # True Online Learning (River)
        self.online_tracker = OnlineLearningTracker(FEATURE_DIMENSION)
        
        self.models = {}
        self.model_scores = defaultdict(lambda: 50.0)
        self.model_confidences = defaultdict(lambda: 0.5)
        self.accuracy_history = deque(maxlen=100)
        
        self.shadow_model = None
        self.shadow_trained = False
        
        if ADVANCED_ML:
            self._init_ml_models()

        self.predictions = deque(maxlen=100)
        self.total_preds = 0
        self.correct_preds = 0
        self.denoised_history = deque(maxlen=1000)
        
        if ADVANCED_ML:
            self._init_ml_models()

        self.predictions = deque(maxlen=100)
        self.total_preds = 0
        self.correct_preds = 0
        self.denoised_history = deque(maxlen=1000)

    def _init_ml_models(self):
        try:
            self.models['deep_nn'] = MLPRegressor(hidden_layer_sizes=(128, 64), max_iter=100, random_state=42)
            self.models['random_forest'] = RandomForestRegressor(n_estimators=50, max_depth=5, random_state=42)
            self.shadow_model = GradientBoostingRegressor(n_estimators=30, learning_rate=0.05, max_depth=3, random_state=42)
            self.scalers = {name: StandardScaler() for name in self.models.keys()}
            self.shadow_scaler = StandardScaler()
            self.models_trained = {name: False for name in self.models.keys()}
        except Exception:
            pass

    def train_ml_models(self):
        if not ADVANCED_ML or len(self.history) < ML_MIN_TRAINING_SIZE: return
        data = np.array(self.history)
        X, y = [], []
        for i in range(50, len(data)):
            current_id = self.identifier_history[i] if i < len(self.identifier_history) else None
            current_nonce = self.nonce_history[i] if i < len(self.nonce_history) else 0
            X.append(self.feature_engineer.extract_all_features(data[:i], current_id, current_nonce))
            y.append(data[i])
            
        if len(X) < 50: return
        X, y = np.array(X), np.array(y)
        
        # Train Deep Learning Transformer First
        if self.transformer and not self.transformer.is_trained:
             self.transformer.train(data.tolist())
        
        for name, model in self.models.items():
            try:
                X_scaled = self.scalers[name].fit_transform(X)
                model.fit(X_scaled, y)
                self.models_trained[name] = True
                split = int(len(X) * 0.8)
                if split > 0:
                    y_pred = model.predict(X_scaled[split:])
                    mae = np.mean(np.abs(y_pred - y[split:]))
                    self.model_scores[name] = max(0, min(100, 100 * (1 - mae / 50)))
            except:
                pass

    def add(self, result, identifier=None, nonce=0):
        # Update streaming models instantly
        if len(self.history) >= 20:
             current_id = self.identifier_history[-1] if self.identifier_history else None
             current_nonce = self.nonce_history[-1] if self.nonce_history else 0
             features = self.feature_engineer.extract_all_features(np.array(self.history), current_id, current_nonce)
             self.online_tracker.update(features, result)
             
        self.denoised_history.append(self.kalman_filter.update(result))
        if len(self.predictions) > 0:
            last_pred = self.predictions[-1]
            won = (last_pred >= 50 and result > 50.5) or (last_pred < 50 and result < 49.5)
            self.total_preds += 1
            if won: self.correct_preds += 1
            
        # Update Anomaly Detector
        self.anomaly_detector.add(result)
        
        # Evolution processing
        if len(self.predictions) > 0:
            last_pred = self.predictions[-1]
            won = (last_pred >= 50 and result > 50.5) or (last_pred < 50 and result < 49.5)
            self.total_preds += 1
            if won: self.correct_preds += 1
            
            acc = self.correct_preds / self.total_preds
            self.accuracy_history.append(acc)
            if len(self.accuracy_history) > 10 and len(self.accuracy_history) % 10 == 0:
                 self.meta_learner.update_weights([acc] * len(self.meta_learner.strategies))
                 self.meta_learner.evolve()
            
        self.history.append(result)
        self.identifier_history.append(identifier if identifier else "")
        self.nonce_history.append(nonce)
        
        if len(self.history) >= ML_MIN_TRAINING_SIZE and len(self.history) % 50 == 0:
            self.train_ml_models()

    def predict(self, current_identifier=None, current_nonce=0):
        if len(self.history) < 20: return None
        data = np.array(self.history)
        all_predictions = {}
        
        regime, regime_strength = self.regime_detector.detect(data)
        
        # Simple Mean Reversion Fallback
        mean_dev = np.mean(data[-20:]) - 50
        all_predictions['mean_rev_short'] = 50 - (mean_dev * 1.2)
        
        features = self.feature_engineer.extract_all_features(data, current_identifier, current_nonce).reshape(1, -1)
        features_1d = features[0]
        
        # 1. Fetch Online River Predictors
        online_preds = self.online_tracker.predict(features_1d)
        for p_name, p_data in online_preds.items():
             all_predictions[p_name] = p_data["prediction"]
             self.model_scores[p_name] = p_data["score"]
        
        # 2. Fetch Deep Batch Predictors
        if ADVANCED_ML:
            for name, model in self.models.items():
                if self.models_trained.get(name, False):
                    try:
                        X_scaled = self.scalers[name].transform(features)
                        all_predictions[f'ml_{name}'] = np.clip(model.predict(X_scaled)[0], 0, 100)
                    except: pass
                    
        # 3. Fetch Deep Sequence Transformer
        if self.transformer and self.transformer.is_trained:
             t_pred = self.transformer.predict(data.tolist())
             if t_pred:
                 all_predictions['transformer_dl'] = t_pred
                 self.model_scores['transformer_dl'] = 75.0 # High base trust for Transformer
                    
        if not all_predictions: return None
        
        # Anomaly Check 
        anomaly_state, anomaly_score = self.anomaly_detector.check_anomaly()
        
        weights, values = [], []
        best_strategy = self.meta_learner.get_best_strategy()
        
        for name, pred in all_predictions.items():
            base_score = self.model_scores.get(name, 50.0)
            base_score *= best_strategy['momentum_weight'] # Apply meta-learned momentum
            weights.append(np.exp(base_score / 20.0))
            values.append(pred)
            
        if best_strategy['ensemble_method'] == 'voting':
             # Majority voting logic simple translation
             high_votes = sum(1 for v in values if v > 50)
             ensemble_pred = 80 if high_votes > len(values)/2 else 20
        else: # weighted or softmax default
             ensemble_pred = np.average(values, weights=weights)
             
        # Apply threshold adjustment from meta-learner
        ensemble_pred += best_strategy['threshold_adj'] * 10 
             
        confidence = 1.0 / (1.0 + np.average((np.array(values) - ensemble_pred)**2, weights=weights) / 100.0)
        
        # Penalize confidence if anomalous state detected
        if anomaly_state == -1:
             confidence *= 0.5 
        
        self.predictions.append(ensemble_pred)
        return {
            'prediction': float(ensemble_pred),
            'confidence': float(np.clip(confidence, 0.3, 0.95)),
            'accuracy': self.correct_preds / self.total_preds if self.total_preds > 0 else 0.0,
            'micro_regime': self.regime_detector.detect_micro_regime(data)[0],
            'rng_integrity': 'ANOMALY' if anomaly_state == -1 else 'NORMAL'
        }
