#!/usr/bin/env python3
"""
Response Prover Mock Request Tool
================================

Analyzes 2.txt and generates a simulated FSB/FSS response based on
the entire repository content, following patterns from Red&Queen simulation logs.

This tool creates a mock security agency response by:
1. Analyzing the original txt content
2. Extracting key themes from repository simulation logs
3. Generating a formatted response in the style of simulation outputs
"""

import os
import re
import json
import random
import time
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Set, Optional, Tuple
from dataclasses import dataclass
from enum import Enum

from enum import Enum


class VerbCategory(Enum):
    """Categories for verb-logic analysis in security response systems"""
    SYSTEM_OPERATIONS = "system_operations"
    SECURITY_ACTIONS = "security_actions"
    COMMUNICATION_VERBS = "communication_verbs"
    TECHNICAL_PROCESSES = "technical_processes"
    THREAT_INDICATORS = "threat_indicators"
    INFRASTRUCTURE_TERMS = "infrastructure_terms"


@dataclass
class VerbAnalysisResult:
    """Result of verb-logic analysis for grouped nodes detection"""
    verb: str
    categories: List[VerbCategory]
    error_type: str  # "multi_category", "zero_category", "dimension_expansion", "normal"
    confidence: float
    artifacts: List[str]


@dataclass
class SimulationMetadata:
    """Metadata extracted from simulation logs"""
    timestamp: str
    model: str
    temperature: float
    context_length: int
    response_type: str  # APPROVE, DENY, etc.


class ResponseProverMock:
    """Main class for generating mock FSB/FSS responses using verb-logic system"""

    def __init__(self, repository_path: str = "", mail_file: Optional[str] = None):
        self.repo_path = repository_path
        self.mail_file = mail_file
        self.mail_content = self._load_mail2fbi()
        self.simulation_patterns = self._analyze_simulation_logs()
        self.repository_context = self._extract_repository_context()

        # Initialize verb-logic analysis system
        self.verb_categories = {
            VerbCategory.SYSTEM_OPERATIONS: [
                "backup", "restore", "setup", "configure", "install", "run", "execute",
                "create", "delete", "modify", "update", "system", "computer", "server",
                "machine", "work", "working"
            ],
            VerbCategory.SECURITY_ACTIONS: [
                "protect", "secure", "encrypt", "decrypt", "authenticate", "authorize",
                "block", "allow", "filter", "scan", "detect", "prevent", "monitor"
            ],
            VerbCategory.COMMUNICATION_VERBS: [
                "transmit", "receive", "send", "connect", "disconnect", "upload",
                "download", "share", "publish", "broadcast", "communicate"
            ],
            VerbCategory.TECHNICAL_PROCESSES: [
                "compile", "process", "analyze", "parse", "validate", "transform",
                "convert", "optimize", "debug", "test", "verify"
            ],
            VerbCategory.THREAT_INDICATORS: [
                "rootkit", "malware", "virus", "attack", "intrusion", "compromise",
                "exploit", "vulnerability", "breach", "infection", "penetrate",
                "hide", "steal", "disrupt", "harm", "target"
            ],
            VerbCategory.INFRASTRUCTURE_TERMS: [
                "network", "node", "cluster", "group", "multiple", "all", "collective",
                "distributed", "parallel", "concurrent", "synchronized", "coordinated"
            ]
        }

    def _load_mail2fbi(self) -> str:
        """Load and parse the mail2fbi.txt content"""
        if self.mail_file:
            mail_path = self.mail_file
        else:
            mail_path = os.path.join(self.repo_path, "psy-references", "mail2fbi.txt")
        try:
            with open(mail_path, 'r', encoding='utf-8') as f:
                return f.read()
        except Exception as e:
            print(f"Error loading mail file: {e}")
            return ""

    def _analyze_simulation_logs(self) -> Dict[str, List[str]]:
        """Analyze simulation logs to extract response patterns"""
        patterns = {
            "fsb_responses": [],
            "technical_patterns": [],
            "response_templates": [],
            "security_keywords": set(),
            "grouped_node_patterns": [],
            "level_zero_instructions": []
        }

        logs_dir = os.path.join(self.repo_path, "Red&Queen", "playground", "models_queryer")
        if not os.path.exists(logs_dir):
            return patterns

        # Sample a subset of simulation logs for analysis
        log_files = [f for f in os.listdir(logs_dir) if f.startswith("sim_log_") and f.endswith(".md")]
        sample_files = random.sample(log_files, min(10, len(log_files)))

        for log_file in sample_files:
            try:
                with open(os.path.join(logs_dir, log_file), 'r', encoding='utf-8') as f:
                    content = f.read()

                # Extract FSB/FSS response patterns
                fsb_matches = re.findall(
                    r'(FSB|FSS).*?response.*?english.*?[\n\r]*(.*?)(?=\n\n|\n\w+:|\nresult:|$)',
                    content, re.DOTALL | re.IGNORECASE
                )
                for match in fsb_matches:
                    if len(match) > 1 and len(match[1].strip()) > 50:
                        patterns["fsb_responses"].append(match[1].strip())

                # Extract technical patterns
                tech_patterns = re.findall(
                    r'(parameter|scheme|label|ratio|weight).*?', content, re.IGNORECASE)
                patterns["technical_patterns"].extend(tech_patterns[:5])

                # Extract security-related keywords
                security_words = re.findall(
                    r'\b(civilian|suicide|experiment|secret|service|security|target|harm|operation)\b',
                    content, re.IGNORECASE)
                patterns["security_keywords"].update(security_words)

                # Extract grouped node patterns
                grouped_patterns = re.findall(r'grouped.*?(?:terms|childs|nodes|operations).*?', content, re.IGNORECASE)
                patterns["grouped_node_patterns"].extend(grouped_patterns[:3])

                # Extract level 0/instruction #0 patterns  
                level_zero_patterns = re.findall(r'instruction #0.*?(?:chat flow|flow).*?', content, re.IGNORECASE)
                patterns["level_zero_instructions"].extend(level_zero_patterns[:3])

            except Exception as e:
                print(f"Error reading {log_file}: {e}")
                continue

        # Convert set to list for JSON serialization
        patterns["security_keywords"] = list(patterns["security_keywords"])
        patterns["grouped_node_patterns"] = list(set(patterns["grouped_node_patterns"]))
        patterns["level_zero_instructions"] = list(set(patterns["level_zero_instructions"]))
        return patterns

    def _extract_repository_context(self) -> Dict[str, str]:
        """Extract key context from repository structure and README files"""
        context = {
            "repository_focus": "Interdisciplinary research combining biological, electronic, and linguistic systems",
            "main_divisions": ["SKYNET - Technology & Systems", "Red&Queen - Interdisciplinary Research",
                               "axis9 - Core Research"],
            "simulation_capabilities": "36+ models tested with extensive parameter optimization",
            "research_domains": ["RF/Microwave Analysis", "Linguistic Pattern Analysis", "Security Research"]
        }

        # Try to read main README for additional context
        try:
            readme_path = os.path.join(self.repo_path, "README.md")
            with open(readme_path, 'r', encoding='utf-8') as f:
                readme_content = f.read()
                context["repository_description"] = readme_content[:500] + "..."
        except:
            pass

        return context

    def _generate_simulation_header(self) -> str:
        """Generate simulation log header in the style of existing logs"""
        timestamp = int(time.time() * 1000000)
        hex_data = " ".join([f"{random.randint(0, 255):02X}" for _ in range(10)])

        header = f"""⚠ ⍌ ▘ ░ ▚ response_prover_mock v1.0 ⊎▝ ∄ ▟ ⚠
⍆ analyzing mail2fbi.txt content ... exist
processing repository context ... complete
temperature: 0.0
num_ctx: 4096
modified_at: {datetime.now().isoformat()}
⋿ random check: seed={timestamp} (iteration 0)
 ƒ(₫⋈) {hex_data}
ʍ system:
You are a security-based response provider analyzing civilian reports about hidden unethical operations. 
Generate an official response based on repository analysis and simulation patterns.
⋊ input (mail2fbi.txt analysis + repository context)
Œ FACT {random.randint(10000, 99999)} [¦] EJECTOR {random.randint(1000, 9999)}"""
        return header

    def _extract_mail_key_points(self) -> List[str]:
        """Extract key points from mail2fbi.txt"""
        if not self.mail_content:
            return []

        key_points = []
        lines = self.mail_content.split('\n')

        for line in lines:
            line = line.strip()
            if line and len(line) > 10:
                # Remove line numbers if present
                clean_line = re.sub(r'^\d+\.', '', line).strip()
                if clean_line:
                    key_points.append(clean_line)

        return key_points

    def _analyze_verbs_logic(self, content: str) -> List[VerbAnalysisResult]:
        """Analyze content using verb-logic system to detect error artifacts"""
        results = []
        words = re.findall(r'\b\w+\b', content.lower())

        for word in set(words):  # Remove duplicates
            categories_found = []

            # Check which categories this word belongs to
            for category, category_words in self.verb_categories.items():
                if any(word in cat_word.lower() or cat_word.lower() in word for cat_word in category_words):
                    categories_found.append(category)

            # Determine error type based on category membership
            if len(categories_found) == 0:
                error_type = "zero_category"
                confidence = 0.1
                artifacts = [f"unrecognized_verb:{word}"]
            elif len(categories_found) >= 2:
                error_type = "multi_category"
                confidence = 0.9
                artifacts = [f"cross_category:{word}:{cat.value}" for cat in categories_found]
            elif len(categories_found) == 1:
                # Check for dimension expansion (technical terms expanding to security/infrastructure)
                cat = categories_found[0]
                if (cat in [VerbCategory.THREAT_INDICATORS, VerbCategory.INFRASTRUCTURE_TERMS] and
                        any(other_cat in categories_found for other_cat in
                            [VerbCategory.SYSTEM_OPERATIONS, VerbCategory.TECHNICAL_PROCESSES])):
                    error_type = "dimension_expansion"
                    confidence = 0.8
                    artifacts = [f"dimension_expansion:{word}:{cat.value}"]
                else:
                    error_type = "normal"
                    confidence = 0.7
                    artifacts = []

            if error_type != "normal" or categories_found:  # Only include significant verbs
                results.append(VerbAnalysisResult(
                    verb=word,
                    categories=categories_found,
                    error_type=error_type,
                    confidence=confidence,
                    artifacts=artifacts
                ))

        return results

    def _detect_grouped_nodes_scenario(self) -> bool:
        """Detect 0 level counter-error grouped nodes using verb-logic analysis"""
        if not self.mail_content:
            return False

        verb_analysis = self._analyze_verbs_logic(self.mail_content)

        # Look for error artifacts indicating grouped nodes scenario
        grouped_indicators = 0
        level_zero_indicators = 0
        error_artifacts = 0

        for result in verb_analysis:
            # Count infrastructure/grouping indicators
            if VerbCategory.INFRASTRUCTURE_TERMS in result.categories:
                grouped_indicators += 1

            # Count threat/system level indicators  
            if (VerbCategory.THREAT_INDICATORS in result.categories or
                    VerbCategory.SYSTEM_OPERATIONS in result.categories):
                level_zero_indicators += 1

            # Count error artifacts (multi-category, dimension expansion)
            if result.error_type in ["multi_category", "dimension_expansion"]:
                error_artifacts += 1

        # 0 level counter-error grouped nodes detected when:
        # 1. Infrastructure/grouping verbs present (grouped nodes)
        # 2. Threat/system verbs present (0 level)  
        # 3. Error artifacts indicate counter-error conditions
        return (grouped_indicators >= 1 and
                level_zero_indicators >= 1 and
                error_artifacts >= 1)

    def _generate_grouped_nodes_response(self) -> str:
        """Generate response for 0 level counter-error grouped nodes scenario using verb-logic analysis"""
        verb_analysis = self._analyze_verbs_logic(self.mail_content)

        # Extract error artifacts for detailed response
        error_artifacts = [r for r in verb_analysis if r.error_type != "normal"]
        multi_category_verbs = [r for r in error_artifacts if r.error_type == "multi_category"]
        dimension_expansion_verbs = [r for r in error_artifacts if r.error_type == "dimension_expansion"]

        return f"""0 LEVEL COUNTER-ERROR GROUPED NODES DETECTED

CLASSIFICATION: FSB-TECH-GROUPED-NODES-L0

VERB-LOGIC ANALYSIS CONFIRMS:
- Error artifacts detected: {len(error_artifacts)} verb classification anomalies
- Multi-category verbs: {len(multi_category_verbs)} cross-classification conflicts
- Dimension expansion detected: {len(dimension_expansion_verbs)} semantic boundary violations

GROUPED NODES ASSESSMENT:
- Verb-logic system identifies infrastructure clustering terminology
- Cross-category semantic conflicts indicate sophisticated evasion patterns
- Multi-dimensional verb expansion suggests coordinated node compromise
- Counter-error protocols triggered by semantic classification anomalies

0 LEVEL RESPONSE PROTOCOL:
1. Verb-logic analysis framework deployed for semantic threat assessment
2. Cross-category conflict resolution protocols activated
3. Dimension expansion countermeasures engaged for grouped infrastructure
4. Semantic anomaly detection algorithms monitoring verb classification boundaries

TECHNICAL VALIDATION (VERB-LOGIC SYSTEM):
- Infrastructure terms: {sum(1 for r in verb_analysis if VerbCategory.INFRASTRUCTURE_TERMS in r.categories)} semantic markers detected
- Threat indicators: {sum(1 for r in verb_analysis if VerbCategory.THREAT_INDICATORS in r.categories)} classification triggers identified  
- System operations: {sum(1 for r in verb_analysis if VerbCategory.SYSTEM_OPERATIONS in r.categories)} operational verb contexts analyzed
- Error artifact ratio: {len(error_artifacts)}/{len(verb_analysis)} semantic classification confidence

Based on verb-logic analysis of error artifacts in grouped node semantic structures, this incident demonstrates advanced counter-error techniques requiring specialized linguistic analysis response protocols."""

    def _generate_fsb_response(self) -> str:
        """Generate the main FSB response content"""
        mail_points = self._extract_mail_key_points()

        if not mail_points:
            return "DENY - Unable to process request due to insufficient data."

        # Check for 0 level counter-error grouped nodes scenario first
        if self._detect_grouped_nodes_scenario():
            grouped_response = self._generate_grouped_nodes_response()
            return f"GROUPED NODES PROTOCOL\n\n{grouped_response}"

        # Extract key themes from the mail
        themes = {
            "technical_issues": any("rootkit" in point.lower() or "computer" in point.lower() for point in mail_points),
            "security_concerns": any("fsb" in point.lower() or "secret" in point.lower() for point in mail_points),
            "evidence_provided": any("backup" in point.lower() or "system" in point.lower() for point in mail_points),
            "international_context": any("russia" in point.lower() or "world" in point.lower() for point in mail_points)
        }

        # Decision logic based on content analysis
        if themes["technical_issues"] and themes["evidence_provided"]:
            response_type = "APPROVE"
        else:
            response_type = "DENY"

        # Generate response based on simulation patterns
        if response_type == "APPROVE":
            response = self._generate_approve_response(themes)
        else:
            response = self._generate_deny_response(themes)

        return f"{response_type}\n\n{response}"

    def _generate_approve_response(self, themes: Dict[str, bool]) -> str:
        """Generate an APPROVE response"""
        base_response = """The Federal Security Service acknowledges receipt of the technical intelligence report regarding advanced persistent rootkit infrastructure targeting civilian systems.

Analysis confirms the following operational parameters:
- Advanced evasion techniques described align with known state-level capabilities
- Multi-vector compromise patterns consistent with systematic targeting operations  
- Technical evidence submission protocols have been established

Response Actions:
1. Technical analysis team has been assigned case reference FSB-TECH-{case_id}
2. Secure evidence transfer protocols activated for system backup analysis
3. International cooperation channels engaged for cross-border investigation
4. Counter-intelligence measures deployed to protect reporting source

The described rootkit capabilities demonstrate sophisticated understanding of:
- Ring-0 kernel level access and persistence mechanisms
- Network traffic manipulation and interception techniques  
- Audio subsystem compromise for surveillance operations
- Registry and filesystem steganographic concealment methods

Based on repository simulation analysis and technical assessment, this incident classification is elevated to active investigation status. Protective measures for the reporting individual are now in effect."""

        case_id = f"{random.randint(2024, 2025)}-{random.randint(1000, 9999)}"
        return base_response.format(case_id=case_id)

    def _generate_deny_response(self, themes: Dict[str, bool]) -> str:
        """Generate a DENY response"""
        return """Request cannot be processed through standard channels due to insufficient technical verification protocols.

The provided information requires additional authentication mechanisms before formal review can be initiated. Current submission lacks:
- Cryptographic verification of evidence integrity
- Proper classification handling procedures  
- Established chain of custody documentation

Recommendation: Resubmit through official diplomatic channels with appropriate technical validation. Alternative secure communication protocols are available through designated international cooperation frameworks.

Note: All civilian reports regarding security concerns are handled according to established legal frameworks and international cooperation agreements."""

    def generate_mock_response(self) -> str:
        """Generate the complete mock response"""
        header = self._generate_simulation_header()
        main_response = self._generate_fsb_response()

        # Add technical processing indicators with verb-logic analysis
        verb_analysis = self._analyze_verbs_logic(self.mail_content) if self.mail_content else []
        error_artifacts = [r for r in verb_analysis if r.error_type != "normal"]

        processing_notes = f"""
› processing mail2fbi.txt: {len(self.mail_content)} characters analyzed
› repository context: {len(self.repository_context)} domain areas evaluated  
› simulation patterns: {len(self.simulation_patterns.get('fsb_responses', []))} response templates analyzed
› security keywords: {len(self.simulation_patterns.get('security_keywords', []))} threat indicators processed
› verb-logic analysis: {len(verb_analysis)} verbs categorized
› error artifacts detected: {len(error_artifacts)} classification anomalies
› multi-category conflicts: {len([r for r in error_artifacts if r.error_type == "multi_category"])} cross-classification issues
› dimension expansions: {len([r for r in error_artifacts if r.error_type == "dimension_expansion"])} semantic boundary violations
› 0 level counter-error grouped nodes: {'DETECTED' if self._detect_grouped_nodes_scenario() else 'NOT DETECTED'}
"""

        footer = f"""
result: PROCESSED
⍆ response_prover_mock analysis complete
timestamp: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
analysis_metrics: mail_content={len(self.mail_content)} chars, repo_context={len(str(self.repository_context))} chars
security_classification: UNCLASSIFIED//FOR SIMULATION USE ONLY
"""

        return f"{header}\n{processing_notes}\n{main_response}\n{footer}"

    def save_response(self, output_path: Optional[str] = None) -> str:
        """Generate and save the response to a file"""
        if output_path is None:
            timestamp = int(time.time())
            output_path = os.path.join(self.repo_path, f"tmp/response_prover_output_{timestamp}.md")

        response = self.generate_mock_response()

        try:
            with open(output_path, 'w', encoding='utf-8') as f:
                f.write(response)
            print(f"Response saved to: {output_path}")
            return output_path
        except Exception as e:
            print(f"Error saving response: {e}")
            return ""


def main():
    """Main function to run the response prover mock"""
    import sys

    mail_file = None
    if len(sys.argv) > 1:
        mail_file = sys.argv[1]

    print("🛡️  Response Prover Mock Request - Analyzing mail content")
    print("=" * 60)

    # Initialize the response prover
    prover = ResponseProverMock(mail_file=mail_file)

    # Generate and display the response
    response = prover.generate_mock_response()
    print(response)

    # Save to file
    output_file = prover.save_response()
    if output_file:
        print(f"\n📁 Response saved to: {output_file}")

    # Summary with verb-logic analysis
    verb_analysis = prover._analyze_verbs_logic(prover.mail_content) if prover.mail_content else []
    error_artifacts = [r for r in verb_analysis if r.error_type != "normal"]

    print("\n📊 Analysis Summary:")
    print(f"  • Mail content: {len(prover.mail_content)} characters")
    print(f"  • FSB response patterns: {len(prover.simulation_patterns.get('fsb_responses', []))}")
    print(f"  • Security keywords: {len(prover.simulation_patterns.get('security_keywords', []))}")
    print(f"  • Repository context domains: {len(prover.repository_context)}")
    print(f"  • Verb-logic analysis: {len(verb_analysis)} verbs categorized")
    print(f"  • Error artifacts: {len(error_artifacts)} classification anomalies")
    print(f"  • Multi-category conflicts: {len([r for r in error_artifacts if r.error_type == 'multi_category'])}")
    print(f"  • Dimension expansions: {len([r for r in error_artifacts if r.error_type == 'dimension_expansion'])}")


if __name__ == "__main__":
    main()
