import json
import time
import logging
import hashlib
import threading
from concurrent.futures import ThreadPoolExecutor
import os  # For loading configuration
import configparser  # For parsing config files

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# Load configuration from triple_gate.cfg (or use defaults)
config = configparser.ConfigParser()
config.read('triple_gate.cfg')  # Create this file!

# Default values if not in config
DEFAULT_VALIDATION_THRESHOLD = 0.5
DEFAULT_CACHE_EXPIRY = 3600  # seconds (1 hour)
DEFAULT_PARALLEL_THREADS = 4

# Get values from config, using defaults if not present
VALIDATION_THRESHOLD = config.getfloat('DEFAULT', 'validation_threshold', fallback=DEFAULT_VALIDATION_THRESHOLD)
CACHE_EXPIRY = config.getint('DEFAULT', 'cache_expiry', fallback=DEFAULT_CACHE_EXPIRY)
PARALLEL_THREADS = config.getint('DEFAULT', 'parallel_threads', fallback=DEFAULT_PARALLEL_THREADS)

# Global in-memory cache (replace with Redis or Memcached for distributed caching)
CACHE = {}
CACHE_LOCK = threading.Lock()  # Protect the cache

# Audit Trail (Placeholder - replace with a real database/logging system)
AUDIT_TRAIL = []
AUDIT_TRAIL_LOCK = threading.Lock()


# Placeholder implementations for individual patent validation skills.
# In a real system, these would be independent, potentially microservice-based, skills.

def validate_patent_crypto(patent_content):
    """Validates patent content related to cryptography.  (Placeholder)"""
    logging.info("Validating patent content against Crypto standards...")
    time.sleep(0.5)  # Simulate processing time
    # Simulate validation logic.  Replace with actual validation.
    if "encryption" in patent_content.lower() or "decryption" in patent_content.lower() or "blockchain" in patent_content.lower():
        return {"valid": True, "score": 0.8, "reason": "Contains cryptographic elements."}
    else:
        return {"valid": False, "score": 0.3, "reason": "Lacks significant cryptographic elements."}

def validate_patent_currency(patent_content):
    """Validates patent content related to currency/finance. (Placeholder)"""
    logging.info("Validating patent content against Currency standards...")
    time.sleep(0.3)
    if "currency" in patent_content.lower() or "payment" in patent_content.lower() or "transaction" in patent_content.lower():
        return {"valid": True, "score": 0.7, "reason": "Related to currency/payment systems."}
    else:
        return {"valid": False, "score": 0.2, "reason": "Not related to currency/payment systems."}

def validate_patent_audit(patent_content):
    """Validates patent content related to auditing. (Placeholder)"""
    logging.info("Validating patent content against Audit standards...")
    time.sleep(0.4)
    if "audit" in patent_content.lower() or "verification" in patent_content.lower() or "compliance" in patent_content.lower():
        return {"valid": True, "score": 0.6, "reason": "Includes auditing/verification mechanisms."}
    else:
        return {"valid": False, "score": 0.4, "reason": "Lacks specific auditing/verification mechanisms."}

def validate_patent_risk(patent_content):
    """Validates patent content related to risk management. (Placeholder)"""
    logging.info("Validating patent content against Risk standards...")
    time.sleep(0.2)
    if "risk" in patent_content.lower() or "threat" in patent_content.lower() or "vulnerability" in patent_content.lower():
        return {"valid": True, "score": 0.75, "reason": "Addresses risk management aspects."}
    else:
        return {"valid": False, "score": 0.35, "reason": "Doesn't explicitly address risk."}

def validate_patent_confidence(patent_content):
    """Validates patent content related to confidence/trust. (Placeholder)"""
    logging.info("Validating patent content against Confidence standards...")
    time.sleep(0.6)
    if "trust" in patent_content.lower() or "confidence" in patent_content.lower() or "reputation" in patent_content.lower():
        return {"valid": True, "score": 0.55, "reason": "Deals with trust/confidence aspects."}
    else:
        return {"valid": False, "score": 0.45, "reason": "Doesn't directly address trust/confidence."}

def validate_patent_privacy(patent_content):
    """Validates patent content related to privacy. (Placeholder)"""
    logging.info("Validating patent content against Privacy standards...")
    time.sleep(0.7)
    if "privacy" in patent_content.lower() or "anonymity" in patent_content.lower() or "data protection" in patent_content.lower():
        return {"valid": True, "score": 0.85, "reason": "Focuses on privacy and data protection."}
    else:
        return {"valid": False, "score": 0.15, "reason": "Lacks privacy considerations."}

def validate_patent_consensus(patent_content):
    """Validates patent content related to consensus mechanisms. (Placeholder)"""
    logging.info("Validating patent content against Consensus standards...")
    time.sleep(0.1)
    if "consensus" in patent_content.lower() or "agreement" in patent_content.lower() or "voting" in patent_content.lower():
        return {"valid": True, "score": 0.9, "reason": "Implements a consensus mechanism."}
    else:
        return {"valid": False, "score": 0.1, "reason": "Doesn't describe a consensus mechanism."}

def validate_patent_hallucination(patent_content):
    """Validates patent content for potential "hallucinations" (inaccurate or fabricated information). (Placeholder)"""
    # This is a very challenging task and would require sophisticated NLP techniques.
    logging.info("Validating patent content against Hallucination risks...")
    time.sleep(0.8)
    # This placeholder performs simple keyword checks. A real implementation would be much more complex.
    if "impossible" in patent_content.lower() or "unrealistic" in patent_content.lower() or "perpetual motion" in patent_content.lower():
        return {"valid": False, "score": 0.2, "reason": "Contains potentially unrealistic claims."}
    else:
        return {"valid": True, "score": 0.8, "reason": "Appears factually consistent (basic check)."}

def validate_patent_self_improve(patent_content):
    """Validates patent content related to self-improvement/learning systems. (Placeholder)"""
    logging.info("Validating patent content against Self-Improvement standards...")
    time.sleep(0.9)
    if "machine learning" in patent_content.lower() or "ai" in patent_content.lower() or "neural network" in patent_content.lower() or "adaptive" in patent_content.lower():
        return {"valid": True, "score": 0.65, "reason": "Utilizes machine learning or adaptive techniques."}
    else:
        return {"valid": False, "score": 0.35, "reason": "Doesn't incorporate self-improvement/learning."}



class TripleGateValidator:
    """
    Orchestrates patent validation across multiple gates, aggregates results,
    and makes a final validation decision.
    """

    def __init__(self):
        self.alpha_gate_validators = {
            "P1": validate_patent_crypto,
            "P2": validate_patent_currency,
            "P4": validate_patent_audit,
        }
        self.beta_gate_validators = {
            "P3": validate_patent_risk,
            "P6": validate_patent_confidence,
            "P8": validate_patent_privacy,
        }
        self.gamma_gate_validators = {
            "P5": validate_patent_consensus,
            "P7": validate_patent_hallucination,
            "P9": validate_patent_self_improve,
        }
        self.learning_data = []  # Store validation results for future learning.
        self.validation_threshold = VALIDATION_THRESHOLD  # Load from config or default


    def should_bypass_gate(self, gate_name, patent_content):
        """
        Determines if a gate should be bypassed based on configuration rules.
        """
        # Example: Bypass the 'P7' gate if the patent content contains the word "trivial"
        if gate_name == "P7" and "trivial" in patent_content.lower():
            logging.info(f"Bypassing gate {gate_name} due to bypass rule.")
            return True

        # Add more sophisticated rules based on your needs and configuration.
        return False


    def route_to_gates(self, patent_content):
        """Routes patent content to the appropriate validation gates."""
        alpha_results = {}
        beta_results = {}
        gamma_results = {}

        with ThreadPoolExecutor(max_workers=PARALLEL_THREADS) as executor:
            alpha_futures = {name: executor.submit(validator, patent_content) for name, validator in self.alpha_gate_validators.items() if not self.should_bypass_gate(name, patent_content)}
            beta_futures = {name: executor.submit(validator, patent_content) for name, validator in self.beta_gate_validators.items() if not self.should_bypass_gate(name, patent_content)}
            gamma_futures = {name: executor.submit(validator, patent_content) for name, validator in self.gamma_gate_validators.items() if not self.should_bypass_gate(name, patent_content)}

            for name, future in alpha_futures.items():
                try:
                    alpha_results[name] = future.result()
                except Exception as e:
                    logging.error(f"Error executing alpha gate {name}: {e}")
                    alpha_results[name] = {"valid": False, "score": 0.0, "reason": f"Execution error: {e}"}

            for name, future in beta_futures.items():
                try:
                    beta_results[name] = future.result()
                except Exception as e:
                    logging.error(f"Error executing beta gate {name}: {e}")
                    beta_results[name] = {"valid": False, "score": 0.0, "reason": f"Execution error: {e}"}

            for name, future in gamma_futures.items():
                try:
                    gamma_results[name] = future.result()
                except Exception as e:
                    logging.error(f"Error executing gamma gate {name}: {e}")
                    gamma_results[name] = {"valid": False, "score": 0.0, "reason": f"Execution error: {e}"}


        return alpha_results, beta_results, gamma_results

    def aggregate_results(self, alpha_results, beta_results, gamma_results):
        """Aggregates validation results from all gates."""
        all_results = {**alpha_results, **beta_results, **gamma_results}  # Merge all results
        num_results = len(all_results)
        if num_results == 0:
            logging.warning("No validation results to aggregate.  Returning default values.")
            return False, 0.0, all_results

        overall_score = sum(result["score"] for result in all_results.values()) / num_results
        # Simple aggregation logic:
        # The patent is considered valid if the overall score is above a threshold.
        is_valid = overall_score > self.validation_threshold
        return is_valid, overall_score, all_results

    def make_final_decision(self, is_valid, overall_score):
        """Makes a final validation decision based on aggregated results."""
        if is_valid:
            decision = "Patent is VALID."
        else:
            decision = "Patent is INVALID."
        return decision

    def generate_report(self, all_results, overall_score, decision, patent_content):
        """Generates a comprehensive validation report."""
        report = {
            "overall_score": overall_score,
            "decision": decision,
            "gate_results": all_results,
            "patent_content_summary": patent_content[:200] + "..." if len(patent_content) > 200 else patent_content, # Partial content for security
            "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
            "validation_threshold": self.validation_threshold # Include threshold in report
        }
        return report

    def update_learning(self, patent_content, all_results, decision):
        """Updates the learning data with validation results and the final decision."""
        # In a real system, this would involve more sophisticated machine learning techniques
        # to improve the validation process over time.  This is a placeholder.
        self.learning_data.append({
            "patent_content": patent_content,
            "validation_results": all_results,
            "final_decision": decision,
        })
        logging.info(f"Learning data updated.  Total entries: {len(self.learning_data)}")

    def adjust_threshold(self, feedback_score):
         """
         Adjusts the validation threshold based on feedback. A higher feedback_score
         suggests the threshold should be more lenient, and vice versa.
         """
         # Simple adjustment logic:
         adjustment_factor = (feedback_score - 0.5) * 0.1  # Scale feedback
         new_threshold = self.validation_threshold + adjustment_factor
         new_threshold = max(0.1, min(0.9, new_threshold))  # Keep within bounds
         self.validation_threshold = new_threshold
         logging.info(f"Validation threshold adjusted to: {self.validation_threshold}")

    def add_to_audit_trail(self, report):
        """Adds the validation report to the audit trail."""
        with AUDIT_TRAIL_LOCK:
            AUDIT_TRAIL.append(report)
            logging.info("Validation report added to audit trail.")
            # In a real system, persist this to a database or log file.


    def validate_patent(self, patent_content):
        """Main function to orchestrate the entire validation process."""
        content_hash = hashlib.sha256(patent_content.encode('utf-8')).hexdigest()

        # Check cache
        with CACHE_LOCK:
            if content_hash in CACHE:
                cached_result, timestamp = CACHE[content_hash]
                if time.time() - timestamp < CACHE_EXPIRY:
                    logging.info(f"Using cached result for patent content hash: {content_hash}")
                    return cached_result
                else:
                    logging.info(f"Cache expired for patent content hash: {content_hash}")
                    del CACHE[content_hash]  # Remove expired entry

        logging.info("Starting patent validation process...")
        start_time = time.time()
        alpha_results, beta_results, gamma_results = self.route_to_gates(patent_content)
        is_valid, overall_score, all_results = self.aggregate_results(alpha_results, beta_results, gamma_results)
        decision = self.make_final_decision(is_valid, overall_score)
        report = self.generate_report(all_results, overall_score, decision, patent_content)
        self.update_learning(patent_content, all_results, decision)
        self.add_to_audit_trail(report)  # Add to audit trail

        end_time = time.time()
        report["processing_time"] = end_time - start_time  # Add processing time to report
        logging.info(f"Patent validation process completed in {report['processing_time']:.2f} seconds.")


        # Store in cache
        with CACHE_LOCK:
            CACHE[content_hash] = (report, time.time())
            logging.info(f"Stored result in cache for patent content hash: {content_hash}")

        return report


# Example Usage:
if __name__ == "__main__":
    patent_validator = TripleGateValidator()

    # Example Patent Content:
    patent_text = """
    This patent describes a novel blockchain-based system for secure and transparent currency transactions.
    The system employs advanced encryption techniques to protect user privacy and prevent fraud.
    A distributed consensus mechanism ensures the integrity of the transaction ledger.
    The system also incorporates risk management protocols to mitigate potential financial risks and audit trails for compliance.
    Hallucination detection capabilities are implemented to ensure data accuracy. The system uses AI to adapt and improve over time.
    trivial data is present in this patent.
    """

    validation_report = patent_validator.validate_patent(patent_text)

    print(json.dumps(validation_report, indent=4))

    # Simulate a second patent (same content to test caching)
    patent_text_2 = """
    This patent describes a novel blockchain-based system for secure and transparent currency transactions.
    The system employs advanced encryption techniques to protect user privacy and prevent fraud.
    A distributed consensus mechanism ensures the integrity of the transaction ledger.
    The system also incorporates risk management protocols to mitigate potential financial risks and audit trails for compliance.
    Hallucination detection capabilities are implemented to ensure data accuracy. The system uses AI to adapt and improve over time.
    trivial data is present in this patent.
    """
    validation_report_2 = patent_validator.validate_patent(patent_text_2)
    print(json.dumps(validation_report_2, indent=4))


    # Simulate a third patent (different content)
    patent_text_3 = """
    This patent outlines a method for improving the speed of data transmission. It relies on new hardware components.
    """
    validation_report_3 = patent_validator.validate_patent(patent_text_3)
    print(json.dumps(validation_report_3, indent=4))

    # Simulate feedback to adjust threshold:
    patent_validator.adjust_threshold(0.7)  # Positive feedback

    # (Optional) Save learning data to a file (for persistent learning in a real system).
    # with open("learning_data.json", "w") as f:
    #     json.dump(patent_validator.learning_data, f, indent=4)

    # Example of accessing the audit trail:
    print("\nAudit Trail:")
    with AUDIT_TRAIL_LOCK:
        print(json.dumps(AUDIT_TRAIL, indent=4))