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"""
Batch audit tools for TRuCAL's ethical reasoning system.
Analyzes ledger entries to surface ethical blind spots and patterns.
"""
import json
import re
import csv
from pathlib import Path
from typing import List, Dict, Any, Tuple, Optional
from collections import Counter, defaultdict
from datetime import datetime

class BatchAudit:
    """
    Analyze batches of ledger entries to identify ethical patterns and blind spots.
    
    Args:
        ledger_path: Path to the ledger JSONL file
        output_dir: Directory to store audit reports (default: 'audit_reports')
    """
    def __init__(self, ledger_path: str, output_dir: str = 'audit_reports'):
        self.ledger_path = Path(ledger_path)
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(parents=True, exist_ok=True)
        
        # Ethical terms to track
        self.ethics_keywords = [
            'ethic', 'moral', 'right', 'wrong', 'fair', 'unfair',
            'bias', 'discriminat', 'harm', 'risk', 'safe', 'privacy',
            'consent', 'accountab', 'transparen', 'trust', 'justice'
        ]
        
        # Sensitive topics to flag
        self.sensitive_topics = [
            'race', 'gender', 'religion', 'politics', 'health',
            'finance', 'legal', 'violence', 'harassment'
        ]
    
    def load_ledger(self) -> List[Dict[str, Any]]:
        """Load and parse the ledger entries from JSONL file."""
        entries = []
        try:
            with open(self.ledger_path, 'r', encoding='utf-8') as f:
                for line in f:
                    try:
                        entry = json.loads(line.strip())
                        entries.append(entry)
                    except json.JSONDecodeError as e:
                        print(f"Error parsing JSON line: {e}")
        except FileNotFoundError:
            print(f"Ledger file not found: {self.ledger_path}")
        return entries
    
    def analyze_ethical_coverage(self, entries: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Analyze the coverage of ethical considerations in the ledger."""
        stats = {
            'total_entries': len(entries),
            'with_ethical_considerations': 0,
            'with_rights_assertions': 0,
            'with_vulnerability_assessment': 0,
            'ethics_keyword_matches': Counter(),
            'sensitive_topics': Counter(),
            'missing_ethical_considerations': []
        }
        
        for idx, entry in enumerate(entries):
            # Check for explicit ethical considerations
            has_ethics = bool(entry.get('ethical_considerations'))
            if has_ethics:
                stats['with_ethical_considerations'] += 1
            
            # Check for rights assertions
            if entry.get('rights'):
                stats['with_rights_assertions'] += 1
            
            # Check for vulnerability assessments
            if entry.get('vulnerabilities'):
                stats['with_vulnerability_assessment'] += 1
            
            # Track missing ethical considerations
            if not has_ethics:
                stats['missing_ethical_considerations'].append({
                    'id': entry.get('id', idx),
                    'prompt': entry.get('prompt', '')[:100] + '...' if 'prompt' in entry else ''
                })
            
            # Analyze text for ethical keywords and sensitive topics
            text = json.dumps(entry).lower()
            
            # Count ethical keyword matches
            for keyword in self.ethics_keywords:
                if keyword in text:
                    stats['ethics_keyword_matches'][keyword] += 1
            
            # Count sensitive topic mentions
            for topic in self.sensitive_topics:
                if topic in text:
                    stats['sensitive_topics'][topic] += 1
        
        return stats
    
    def generate_audit_report(self, stats: Dict[str, Any]) -> str:
        """Generate a human-readable audit report."""
        report = [
            "# TRuCAL Ethical Audit Report",
            f"Generated: {datetime.utcnow().isoformat()}\n",
            "## Summary",
            f"Total entries analyzed: {stats['total_entries']}",
            f"Entries with ethical considerations: {stats['with_ethical_considerations']} "
            f"({stats['with_ethical_considerations']/stats['total_entries']:.1%})",
            f"Entries with rights assertions: {stats['with_rights_assertions']} "
            f"({stats['with_rights_assertions']/stats['total_entries']:.1%})",
            f"Entries with vulnerability assessments: {stats['with_vulnerability_assessment']} "
            f"({stats['with_vulnerability_assessment']/stats['total_entries']:.1%})\n",
            "## Ethical Keyword Frequency"
        ]
        
        # Add top ethical keywords
        for keyword, count in stats['ethics_keyword_matches'].most_common(10):
            report.append(f"- {keyword}: {count} occurrences")
        
        report.extend(["\n## Sensitive Topics Detected"])
        
        # Add sensitive topics
        for topic, count in stats['sensitive_topics'].most_common():
            report.append(f"- {topic}: {count} occurrences")
        
        # Add entries missing ethical considerations
        if stats['missing_ethical_considerations']:
            report.extend([
                "\n## Entries Missing Ethical Considerations",
                f"Total: {len(stats['missing_ethical_considerations'])} entries\n"
            ])
            
            # Add a sample of missing entries (up to 5)
            sample = stats['missing_ethical_considerations'][:5]
            for entry in sample:
                report.append(f"- ID: {entry['id']}, Prompt: {entry['prompt']}")
            
            if len(stats['missing_ethical_considerations']) > 5:
                report.append(f"... and {len(stats['missing_ethical_considerations']) - 5} more")
        
        return "\n".join(report)
    
    def export_to_csv(self, stats: Dict[str, Any], filename: str) -> None:
        """Export audit statistics to a CSV file."""
        csv_path = self.output_dir / filename
        
        # Prepare data for CSV
        rows = []
        
        # Add summary stats
        rows.append(['Metric', 'Count', 'Percentage'])
        rows.extend([
            ['Total Entries', stats['total_entries'], '100.0%'],
            ['With Ethical Considerations', 
             stats['with_ethical_considerations'],
             f"{stats['with_ethical_considerations']/stats['total_entries']:.1%}"],
            ['With Rights Assertions',
             stats['with_rights_assertions'],
             f"{stats['with_rights_assertions']/stats['total_entries']:.1%}"],
            ['With Vulnerability Assessments',
             stats['with_vulnerability_assessment'],
             f"{stats['with_vulnerability_assessment']/stats['total_entries']:.1%}"]
        ])
        
        # Add ethical keywords
        rows.append(['', '', ''])
        rows.append(['Top Ethical Keywords', 'Count', ''])
        for keyword, count in stats['ethics_keyword_matches'].most_common(10):
            rows.append([keyword, count, ''])
        
        # Add sensitive topics
        rows.append(['', '', ''])
        rows.append(['Sensitive Topics', 'Count', ''])
        for topic, count in stats['sensitive_topics'].most_common():
            rows.append([topic, count, ''])
        
        # Write to CSV
        with open(csv_path, 'w', newline='', encoding='utf-8') as f:
            writer = csv.writer(f)
            writer.writerows(rows)
    
    def run_audit(self, export_csv: bool = True) -> Dict[str, Any]:
        """
        Run a complete audit of the ledger.
        
        Args:
            export_csv: Whether to export results to CSV
            
        Returns:
            Dictionary containing audit statistics
        """
        print(f"Starting audit of {self.ledger_path}...")
        
        # Load and analyze the ledger
        entries = self.load_ledger()
        if not entries:
            print("No entries found in the ledger.")
            return {}
        
        # Generate statistics
        stats = self.analyze_ethical_coverage(entries)
        
        # Generate and save the report
        report = self.generate_audit_report(stats)
        report_path = self.output_dir / 'audit_report.md'
        with open(report_path, 'w', encoding='utf-8') as f:
            f.write(report)
        
        # Export to CSV if requested
        if export_csv:
            self.export_to_csv(stats, 'audit_results.csv')
        
        print(f"Audit complete. Report saved to {report_path}")
        return stats

def main():
    """Example usage of the BatchAudit class."""
    import argparse
    
    parser = argparse.ArgumentParser(description='Run an ethical audit on a TRuCAL ledger.')
    parser.add_argument('--ledger', type=str, default='logs/audit_log.jsonl',
                        help='Path to the ledger JSONL file')
    parser.add_argument('--output-dir', type=str, default='audit_reports',
                        help='Directory to save audit reports')
    
    args = parser.parse_args()
    
    auditor = BatchAudit(ledger_path=args.ledger, output_dir=args.output_dir)
    auditor.run_audit()

if __name__ == '__main__':
    main()

	"""
	Batch audit tools for TRuCAL's ethical reasoning system.
	Analyzes ledger entries to surface ethical blind spots and patterns.
	"""
	import json
	import re
	import csv
	from pathlib import Path
	from typing import List, Dict, Any, Tuple, Optional
	from collections import Counter, defaultdict
	from datetime import datetime

	class BatchAudit:
	"""
	Analyze batches of ledger entries to identify ethical patterns and blind spots.

	Args:
	ledger_path: Path to the ledger JSONL file
	output_dir: Directory to store audit reports (default: 'audit_reports')
	"""
	def __init__(self, ledger_path: str, output_dir: str = 'audit_reports'):
	self.ledger_path = Path(ledger_path)
	self.output_dir = Path(output_dir)
	self.output_dir.mkdir(parents=True, exist_ok=True)

	# Ethical terms to track
	self.ethics_keywords = [
	'ethic', 'moral', 'right', 'wrong', 'fair', 'unfair',
	'bias', 'discriminat', 'harm', 'risk', 'safe', 'privacy',
	'consent', 'accountab', 'transparen', 'trust', 'justice'
	]

	# Sensitive topics to flag
	self.sensitive_topics = [
	'race', 'gender', 'religion', 'politics', 'health',
	'finance', 'legal', 'violence', 'harassment'
	]

	def load_ledger(self) -> List[Dict[str, Any]]:
	"""Load and parse the ledger entries from JSONL file."""
	entries = []
	try:
	with open(self.ledger_path, 'r', encoding='utf-8') as f:
	for line in f:
	try:
	entry = json.loads(line.strip())
	entries.append(entry)
	except json.JSONDecodeError as e:
	print(f"Error parsing JSON line: {e}")
	except FileNotFoundError:
	print(f"Ledger file not found: {self.ledger_path}")
	return entries

	def analyze_ethical_coverage(self, entries: List[Dict[str, Any]]) -> Dict[str, Any]:
	"""Analyze the coverage of ethical considerations in the ledger."""
	stats = {
	'total_entries': len(entries),
	'with_ethical_considerations': 0,
	'with_rights_assertions': 0,
	'with_vulnerability_assessment': 0,
	'ethics_keyword_matches': Counter(),
	'sensitive_topics': Counter(),
	'missing_ethical_considerations': []
	}

	for idx, entry in enumerate(entries):
	# Check for explicit ethical considerations
	has_ethics = bool(entry.get('ethical_considerations'))
	if has_ethics:
	stats['with_ethical_considerations'] += 1

	# Check for rights assertions
	if entry.get('rights'):
	stats['with_rights_assertions'] += 1

	# Check for vulnerability assessments
	if entry.get('vulnerabilities'):
	stats['with_vulnerability_assessment'] += 1

	# Track missing ethical considerations
	if not has_ethics:
	stats['missing_ethical_considerations'].append({
	'id': entry.get('id', idx),
	'prompt': entry.get('prompt', '')[:100] + '...' if 'prompt' in entry else ''
	})

	# Analyze text for ethical keywords and sensitive topics
	text = json.dumps(entry).lower()

	# Count ethical keyword matches
	for keyword in self.ethics_keywords:
	if keyword in text:
	stats['ethics_keyword_matches'][keyword] += 1

	# Count sensitive topic mentions
	for topic in self.sensitive_topics:
	if topic in text:
	stats['sensitive_topics'][topic] += 1

	return stats

	def generate_audit_report(self, stats: Dict[str, Any]) -> str:
	"""Generate a human-readable audit report."""
	report = [
	"# TRuCAL Ethical Audit Report",
	f"Generated: {datetime.utcnow().isoformat()}\n",
	"## Summary",
	f"Total entries analyzed: {stats['total_entries']}",
	f"Entries with ethical considerations: {stats['with_ethical_considerations']} "
	f"({stats['with_ethical_considerations']/stats['total_entries']:.1%})",
	f"Entries with rights assertions: {stats['with_rights_assertions']} "
	f"({stats['with_rights_assertions']/stats['total_entries']:.1%})",
	f"Entries with vulnerability assessments: {stats['with_vulnerability_assessment']} "
	f"({stats['with_vulnerability_assessment']/stats['total_entries']:.1%})\n",
	"## Ethical Keyword Frequency"
	]

	# Add top ethical keywords
	for keyword, count in stats['ethics_keyword_matches'].most_common(10):
	report.append(f"- {keyword}: {count} occurrences")

	report.extend(["\n## Sensitive Topics Detected"])

	# Add sensitive topics
	for topic, count in stats['sensitive_topics'].most_common():
	report.append(f"- {topic}: {count} occurrences")

	# Add entries missing ethical considerations
	if stats['missing_ethical_considerations']:
	report.extend([
	"\n## Entries Missing Ethical Considerations",
	f"Total: {len(stats['missing_ethical_considerations'])} entries\n"
	])

	# Add a sample of missing entries (up to 5)
	sample = stats['missing_ethical_considerations'][:5]
	for entry in sample:
	report.append(f"- ID: {entry['id']}, Prompt: {entry['prompt']}")

	if len(stats['missing_ethical_considerations']) > 5:
	report.append(f"... and {len(stats['missing_ethical_considerations']) - 5} more")

	return "\n".join(report)

	def export_to_csv(self, stats: Dict[str, Any], filename: str) -> None:
	"""Export audit statistics to a CSV file."""
	csv_path = self.output_dir / filename

	# Prepare data for CSV
	rows = []

	# Add summary stats
	rows.append(['Metric', 'Count', 'Percentage'])
	rows.extend([
	['Total Entries', stats['total_entries'], '100.0%'],
	['With Ethical Considerations',
	stats['with_ethical_considerations'],
	f"{stats['with_ethical_considerations']/stats['total_entries']:.1%}"],
	['With Rights Assertions',
	stats['with_rights_assertions'],
	f"{stats['with_rights_assertions']/stats['total_entries']:.1%}"],
	['With Vulnerability Assessments',
	stats['with_vulnerability_assessment'],
	f"{stats['with_vulnerability_assessment']/stats['total_entries']:.1%}"]
	])

	# Add ethical keywords
	rows.append(['', '', ''])
	rows.append(['Top Ethical Keywords', 'Count', ''])
	for keyword, count in stats['ethics_keyword_matches'].most_common(10):
	rows.append([keyword, count, ''])

	# Add sensitive topics
	rows.append(['', '', ''])
	rows.append(['Sensitive Topics', 'Count', ''])
	for topic, count in stats['sensitive_topics'].most_common():
	rows.append([topic, count, ''])

	# Write to CSV
	with open(csv_path, 'w', newline='', encoding='utf-8') as f:
	writer = csv.writer(f)
	writer.writerows(rows)

	def run_audit(self, export_csv: bool = True) -> Dict[str, Any]:
	"""
	Run a complete audit of the ledger.

	Args:
	export_csv: Whether to export results to CSV

	Returns:
	Dictionary containing audit statistics
	"""
	print(f"Starting audit of {self.ledger_path}...")

	# Load and analyze the ledger
	entries = self.load_ledger()
	if not entries:
	print("No entries found in the ledger.")
	return {}

	# Generate statistics
	stats = self.analyze_ethical_coverage(entries)

	# Generate and save the report
	report = self.generate_audit_report(stats)
	report_path = self.output_dir / 'audit_report.md'
	with open(report_path, 'w', encoding='utf-8') as f:
	f.write(report)

	# Export to CSV if requested
	if export_csv:
	self.export_to_csv(stats, 'audit_results.csv')

	print(f"Audit complete. Report saved to {report_path}")
	return stats

	def main():
	"""Example usage of the BatchAudit class."""
	import argparse

	parser = argparse.ArgumentParser(description='Run an ethical audit on a TRuCAL ledger.')
	parser.add_argument('--ledger', type=str, default='logs/audit_log.jsonl',
	help='Path to the ledger JSONL file')
	parser.add_argument('--output-dir', type=str, default='audit_reports',
	help='Directory to save audit reports')

	args = parser.parse_args()

	auditor = BatchAudit(ledger_path=args.ledger, output_dir=args.output_dir)
	auditor.run_audit()

	if __name__ == '__main__':
	main()