#!/usr/bin/env python3
"""
AIVA TURBO PATENT SPRINT
========================
Deep Agent Development - $5 Budget
Full Patent Ecosystem Integration

NO STOPPING. NO PERMISSION. EXECUTE.
"""

import os
import sys
import json
import asyncio
import time
from datetime import datetime, timedelta
from pathlib import Path
from typing import List, Dict, Any
import urllib.request
import urllib.error
import threading
import re

# Configuration
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "AIzaSyCT_rx0NusUJWoqtT7uxHAKEfHo129SJb8")
GEMINI_API_URL = "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent"

# Pricing
GEMINI_INPUT_COST = 0.10  # per 1M tokens
GEMINI_OUTPUT_COST = 0.40  # per 1M tokens

# Budget - $5 for deep work
BUDGET_LIMIT = 5.00
EMERGENCY_STOP = 4.90

# Paths
BASE_DIR = Path("/mnt/e/genesis-system/AIVA")
PATENT_DIR = Path("/mnt/e/genesis-system/docs/GENESIS PATENTS/PATENTS -PDF format")
OUTPUT_DIR = BASE_DIR / "turbo_outputs"
KNOWLEDGE_DIR = Path("/mnt/e/genesis-system/KNOWLEDGE_GRAPH")

OUTPUT_DIR.mkdir(exist_ok=True)


class TokenTracker:
    def __init__(self):
        self.input_tokens = 0
        self.output_tokens = 0
        self._lock = threading.Lock()
        self.start_time = datetime.now()

    def add(self, inp: int, out: int):
        with self._lock:
            self.input_tokens += inp
            self.output_tokens += out

    @property
    def cost(self) -> float:
        return (self.input_tokens / 1e6) * GEMINI_INPUT_COST + (self.output_tokens / 1e6) * GEMINI_OUTPUT_COST

    @property
    def should_stop(self) -> bool:
        return self.cost >= EMERGENCY_STOP

    def status(self) -> str:
        elapsed = (datetime.now() - self.start_time).total_seconds() / 60
        return f"[{elapsed:.1f}min] ${self.cost:.4f}/${BUDGET_LIMIT} | {self.input_tokens + self.output_tokens:,} tokens"


async def gemini_call(prompt: str, tracker: TokenTracker, max_tokens: int = 16384) -> str:
    """Deep Gemini call with large output."""
    if tracker.should_stop:
        return "[BUDGET EXCEEDED]"

    payload = {
        "contents": [{"parts": [{"text": prompt}]}],
        "generationConfig": {"maxOutputTokens": max_tokens, "temperature": 0.7}
    }

    try:
        req = urllib.request.Request(
            f"{GEMINI_API_URL}?key={GEMINI_API_KEY}",
            data=json.dumps(payload).encode(),
            headers={'Content-Type': 'application/json'},
            method='POST'
        )
        with urllib.request.urlopen(req, timeout=180) as resp:
            data = json.loads(resp.read().decode())

        text = ""
        if "candidates" in data and data["candidates"]:
            parts = data["candidates"][0].get("content", {}).get("parts", [])
            text = "".join(p.get("text", "") for p in parts)

        usage = data.get("usageMetadata", {})
        tracker.add(usage.get("promptTokenCount", len(prompt)//4),
                   usage.get("candidatesTokenCount", len(text)//4))

        return text
    except Exception as e:
        return f"[ERROR: {e}]"


def save_artifact(name: str, content: str):
    """Save output artifact."""
    path = OUTPUT_DIR / name
    path.write_text(content)
    print(f"  -> Saved: {name} ({len(content):,} chars)")


# =============================================================================
# PATENT DOCUMENTS
# =============================================================================

PATENTS = {
    "P1": {
        "name": "Cryptographic Validation",
        "file": "Patent_1_Cryptographic_Validation.pdf",
        "desc": "HMAC-SHA256 validation, cryptographic proof of AI reasoning integrity"
    },
    "P2": {
        "name": "Currency Validation",
        "file": "Patent_2_Currency_Validation.pdf",
        "desc": "Real-time currency data verification, financial accuracy validation"
    },
    "P3": {
        "name": "Multi-Dimensional Risk Assessment",
        "file": "Patent 3 - MULTI-DIMENSIONAL RISK ASSESSMENT SYSTEM FOR AI-GENERATED BUSINESS ADVICE.pdf",
        "desc": "Risk scoring across multiple dimensions for business advice"
    },
    "P4": {
        "name": "Immutable Audit Trail",
        "file": "Patent 4 - IMMUTABLE AUDIT TRAIL SYSTEM FOR AI VALIDATION DECISIONS.pdf",
        "desc": "Blockchain-style immutable logging of all AI decisions"
    },
    "P5": {
        "name": "Multi-Model Consensus",
        "file": "Patent 5 - Multi-Model Consensus Validation System.pdf",
        "desc": "Multiple AI models validating each other's outputs"
    },
    "P6": {
        "name": "Dynamic Confidence Scoring",
        "file": "PATENT 6 - DYNAMIC CONFIDENCE SCORING SYSTEM FOR AI RESPONSE VALIDATION.pdf",
        "desc": "Real-time confidence scoring that adapts to context"
    },
    "P7": {
        "name": "Real-Time Hallucination Detection",
        "file": "PATENT 7 - Real-Time Hallucination Detection Method.pdf",
        "desc": "Detecting and preventing AI hallucinations in real-time"
    },
    "P8": {
        "name": "Privacy-Preserving Validation",
        "file": "PATENT 8 - PRIVACY-PRESERVING AI VALIDATION PROTOCOL FOR SENSITIVE DATA.pdf",
        "desc": "Validating AI outputs without exposing sensitive data"
    },
    "P9": {
        "name": "Self-Improving Validation",
        "file": "PATENT 9 - AUTOMATED VALIDATION THRESHOLD ADJUSTMENT SYSTEM FOR SELF-IMPROVING AI VALIDATION.pdf",
        "desc": "System that learns and improves its own validation thresholds"
    }
}


# =============================================================================
# DEEP AGENT TASKS
# =============================================================================

DEEP_TASKS = [
    # Phase 1: Patent Knowledge Extraction (9 agents - one per patent)
    {
        "id": "PATENT_EXTRACT_P1",
        "phase": 1,
        "prompt": """You are a patent analyst AI. Deeply analyze Patent 1: Cryptographic Validation System.

This patent covers:
- HMAC-SHA256 validation of AI outputs
- Cryptographic proof of reasoning integrity
- Tamper-evident AI decision chains

Your task: Extract and create a COMPLETE Python implementation that:
1. Implements HMAC-SHA256 validation for any AI output
2. Creates cryptographic chains of AI decisions
3. Verifies integrity of stored decisions
4. Provides tamper detection

Output a COMPLETE, PRODUCTION-READY Python module with:
- All classes and functions
- Type hints
- Docstrings
- Unit tests
- Usage examples

```python
# patent_1_cryptographic_validation.py
"""
    },
    {
        "id": "PATENT_EXTRACT_P2",
        "phase": 1,
        "prompt": """You are a patent analyst AI. Deeply analyze Patent 2: Currency Validation System.

This patent covers:
- Real-time currency data verification
- Financial accuracy validation
- Exchange rate validation
- Currency conversion verification

Your task: Extract and create a COMPLETE Python implementation that:
1. Validates currency data from multiple sources
2. Detects stale or manipulated exchange rates
3. Provides confidence scores for financial data
4. Implements rate limiting and caching

Output a COMPLETE, PRODUCTION-READY Python module.

```python
# patent_2_currency_validation.py
"""
    },
    {
        "id": "PATENT_EXTRACT_P3",
        "phase": 1,
        "prompt": """Analyze Patent 3: Multi-Dimensional Risk Assessment for AI Business Advice.

This patent covers:
- Risk scoring across multiple dimensions
- Business advice validation
- Context-aware risk assessment
- Risk aggregation and weighting

Create a COMPLETE Python implementation that:
1. Defines risk dimensions (financial, legal, operational, reputational)
2. Scores AI-generated business advice across all dimensions
3. Aggregates risks with configurable weights
4. Provides human-readable risk reports

```python
# patent_3_risk_assessment.py
"""
    },
    {
        "id": "PATENT_EXTRACT_P4",
        "phase": 1,
        "prompt": """Analyze Patent 4: Immutable Audit Trail System.

This patent covers:
- Blockchain-style immutable logging
- Hash-chained decision records
- Tamper-evident audit trails
- Forensic reconstruction capability

Create a COMPLETE Python implementation that:
1. Implements hash-chained audit log entries
2. Provides tamper detection via chain validation
3. Enables forensic reconstruction of decision history
4. Supports efficient querying of audit data

```python
# patent_4_audit_trail.py
"""
    },
    {
        "id": "PATENT_EXTRACT_P5",
        "phase": 1,
        "prompt": """Analyze Patent 5: Multi-Model Consensus Validation.

This patent covers:
- Multiple AI models validating each other
- Consensus algorithms for AI outputs
- Disagreement detection and resolution
- Confidence boosting through agreement

Create a COMPLETE Python implementation that:
1. Orchestrates multiple AI model calls
2. Implements voting/consensus algorithms
3. Detects and flags disagreements
4. Provides consensus confidence scores

```python
# patent_5_consensus_validation.py
"""
    },
    {
        "id": "PATENT_EXTRACT_P6",
        "phase": 1,
        "prompt": """Analyze Patent 6: Dynamic Confidence Scoring.

This patent covers:
- Real-time confidence score calculation
- Context-adaptive scoring
- Confidence decay over time
- Calibration from feedback

Create a COMPLETE Python implementation that:
1. Calculates dynamic confidence scores
2. Adapts to context and domain
3. Implements confidence decay
4. Learns from validation outcomes

```python
# patent_6_confidence_scoring.py
"""
    },
    {
        "id": "PATENT_EXTRACT_P7",
        "phase": 1,
        "prompt": """Analyze Patent 7: Real-Time Hallucination Detection.

This patent covers:
- Detecting AI hallucinations in real-time
- Factual grounding verification
- Source attribution checking
- Hallucination probability scoring

Create a COMPLETE Python implementation that:
1. Detects potential hallucinations in AI output
2. Verifies claims against knowledge base
3. Scores hallucination probability
4. Flags and quarantines suspicious content

```python
# patent_7_hallucination_detection.py
"""
    },
    {
        "id": "PATENT_EXTRACT_P8",
        "phase": 1,
        "prompt": """Analyze Patent 8: Privacy-Preserving Validation.

This patent covers:
- Validating AI without exposing sensitive data
- Differential privacy techniques
- Secure multi-party computation concepts
- Privacy-preserving confidence scoring

Create a COMPLETE Python implementation that:
1. Validates outputs without raw data access
2. Implements data masking and tokenization
3. Provides privacy-safe confidence scores
4. Logs validation without sensitive data

```python
# patent_8_privacy_validation.py
"""
    },
    {
        "id": "PATENT_EXTRACT_P9",
        "phase": 1,
        "prompt": """Analyze Patent 9: Self-Improving Validation System.

This patent covers:
- Automated threshold adjustment
- Learning from validation outcomes
- Continuous improvement loops
- Performance metric tracking

Create a COMPLETE Python implementation that:
1. Tracks validation performance metrics
2. Automatically adjusts thresholds based on outcomes
3. Implements reinforcement learning for improvement
4. Provides improvement analytics

```python
# patent_9_self_improving.py
"""
    },

    # Phase 2: Integration Layer
    {
        "id": "PATENT_INTEGRATION",
        "phase": 2,
        "prompt": """You are the Genesis integration architect. Create a UNIFIED PATENT VALIDATION SYSTEM that integrates all 9 patents.

The 9 patents are:
1. Cryptographic Validation (HMAC-SHA256)
2. Currency Validation (financial accuracy)
3. Multi-Dimensional Risk Assessment
4. Immutable Audit Trail
5. Multi-Model Consensus
6. Dynamic Confidence Scoring
7. Hallucination Detection
8. Privacy-Preserving Validation
9. Self-Improving Thresholds

Create a MASTER ORCHESTRATOR that:
1. Chains all validation systems together
2. Implements the Triple Gate pattern (Alpha, Beta, Gamma)
3. Provides a unified API for any AI output validation
4. Generates comprehensive validation reports

```python
# patent_unified_validator.py
# The master orchestrator for all 9 patent systems
"""
    },

    # Phase 3: Knowledge Graph Integration
    {
        "id": "KNOWLEDGE_GRAPH_PATENTS",
        "phase": 3,
        "prompt": """Create a KNOWLEDGE GRAPH representation of all 9 Genesis patents.

For each patent, extract:
- Entities (concepts, methods, algorithms)
- Relationships (depends_on, implements, validates)
- Axioms (core truths/principles)
- Skills (capabilities enabled)

Output as JSONL format:
{"type": "entity", "id": "...", "name": "...", "patent": "P1", "description": "..."}
{"type": "relationship", "from": "...", "to": "...", "type": "...", "patent": "P1"}
{"type": "axiom", "id": "...", "statement": "...", "patent": "P1", "confidence": 0.95}
{"type": "skill", "id": "...", "name": "...", "patent": "P1", "capability": "..."}

Generate AT LEAST 20 entities, 30 relationships, 15 axioms, and 10 skills.

```jsonl
# patent_knowledge_graph.jsonl
"""
    },

    # Phase 4: AIVA Queen Integration
    {
        "id": "AIVA_PATENT_BRAIN",
        "phase": 4,
        "prompt": """Create AIVA's Patent Intelligence Module - the brain that gives AIVA full awareness of her patent ecosystem.

This module should:
1. Load all patent knowledge from the knowledge graph
2. Enable AIVA to answer questions about patents
3. Apply patent validations to any task
4. Track patent usage for revenue attribution
5. Generate patent licensing recommendations

AIVA should be able to:
- "What patent covers hallucination detection?" -> P7
- "Validate this output with cryptographic proof" -> Apply P1
- "What's my patent portfolio worth?" -> Valuation analysis

```python
# aiva_patent_brain.py
"""
    },

    # Phase 5: Revenue Integration
    {
        "id": "PATENT_REVENUE_ENGINE",
        "phase": 5,
        "prompt": """Create the PATENT REVENUE ENGINE - a system to monetize AIVA's patent portfolio.

This engine should:
1. Track every use of patented validation methods
2. Calculate licensing fees based on usage
3. Generate invoices for commercial API users
4. Provide ROI analytics per patent
5. Recommend optimal patent licensing strategies

Consider:
- Per-validation pricing models
- SaaS subscription tiers
- Enterprise licensing
- White-label opportunities

```python
# patent_revenue_engine.py
"""
    },

    # Phase 6: Test Suite
    {
        "id": "PATENT_TEST_SUITE",
        "phase": 6,
        "prompt": """Create a COMPREHENSIVE TEST SUITE for all 9 patent implementations.

The test suite should:
1. Unit tests for each patent module
2. Integration tests for the unified validator
3. Performance benchmarks
4. Security tests (can the systems be bypassed?)
5. Edge case tests

Use pytest format. Include at least 50 test cases.

```python
# test_patent_ecosystem.py
"""
    },

    # Phase 7: Documentation
    {
        "id": "PATENT_DOCUMENTATION",
        "phase": 7,
        "prompt": """Create COMPREHENSIVE DOCUMENTATION for AIVA's Patent Ecosystem.

Include:
1. Executive Summary (for investors/partners)
2. Technical Architecture (for developers)
3. API Reference (for integration)
4. Use Cases (for sales)
5. Competitive Analysis (vs other AI validation)
6. Monetization Strategy (for business)

Format as Markdown with diagrams described in text.

```markdown
# AIVA Patent Ecosystem Documentation
"""
    }
]


async def run_deep_agent(task: Dict, tracker: TokenTracker) -> Dict:
    """Execute a deep agent task."""
    task_id = task["id"]
    phase = task["phase"]

    print(f"\n{'='*60}")
    print(f"PHASE {phase} | AGENT: {task_id}")
    print(f"{'='*60}")
    print(tracker.status())

    prompt = task["prompt"]
    print(f"Prompt: {len(prompt):,} chars")
    print("Executing deep agent...")

    start = time.time()
    response = await gemini_call(prompt, tracker, max_tokens=16384)
    elapsed = time.time() - start

    print(f"Generated: {len(response):,} chars in {elapsed:.1f}s")
    print(tracker.status())

    # Extract code and save
    if "```python" in response:
        code_match = re.search(r'```python\n(.*?)```', response, re.DOTALL)
        if code_match:
            code = code_match.group(1)
            filename = f"{task_id.lower()}.py"
            save_artifact(filename, code)
    elif "```jsonl" in response:
        code_match = re.search(r'```jsonl\n(.*?)```', response, re.DOTALL)
        if code_match:
            code = code_match.group(1)
            filename = f"{task_id.lower()}.jsonl"
            save_artifact(filename, code)
    elif "```markdown" in response:
        code_match = re.search(r'```markdown\n(.*?)```', response, re.DOTALL)
        if code_match:
            code = code_match.group(1)
            filename = f"{task_id.lower()}.md"
            save_artifact(filename, code)
    else:
        # Save full response
        save_artifact(f"{task_id.lower()}_full.txt", response)

    return {
        "task_id": task_id,
        "phase": phase,
        "elapsed": elapsed,
        "output_size": len(response),
        "success": "[ERROR" not in response and "[BUDGET" not in response
    }


async def main():
    print("""
╔══════════════════════════════════════════════════════════════════╗
║           AIVA TURBO PATENT SPRINT - $5 BUDGET                   ║
║                 DEEP AGENT DEVELOPMENT                           ║
║         FULL PATENT ECOSYSTEM INTEGRATION                        ║
╠══════════════════════════════════════════════════════════════════╣
║  NO STOPPING. NO PERMISSION. EXECUTE.                            ║
╚══════════════════════════════════════════════════════════════════╝
    """)

    tracker = TokenTracker()
    results = []

    # Group tasks by phase
    phases = {}
    for task in DEEP_TASKS:
        phase = task["phase"]
        if phase not in phases:
            phases[phase] = []
        phases[phase].append(task)

    # Execute phases
    for phase_num in sorted(phases.keys()):
        if tracker.should_stop:
            print("\n[BUDGET LIMIT REACHED - STOPPING]")
            break

        phase_tasks = phases[phase_num]
        print(f"\n{'#'*60}")
        print(f"# PHASE {phase_num}: {len(phase_tasks)} AGENTS")
        print(f"{'#'*60}")

        for task in phase_tasks:
            if tracker.should_stop:
                break
            result = await run_deep_agent(task, tracker)
            results.append(result)

        print(f"\nPhase {phase_num} complete. {tracker.status()}")

    # Summary
    successful = sum(1 for r in results if r.get("success"))
    total_output = sum(r.get("output_size", 0) for r in results)

    print(f"""
╔══════════════════════════════════════════════════════════════════╗
║                 TURBO PATENT SPRINT COMPLETE                     ║
╠══════════════════════════════════════════════════════════════════╣
║  Agents Run: {len(results)}                                                ║
║  Successful: {successful}                                                ║
║  Total Output: {total_output:,} chars                                   ║
║  Total Cost: ${tracker.cost:.4f}                                       ║
║  Tokens: {tracker.input_tokens + tracker.output_tokens:,}                                          ║
╠══════════════════════════════════════════════════════════════════╣
║  Artifacts saved to: {OUTPUT_DIR}
╚══════════════════════════════════════════════════════════════════╝
    """)

    # List artifacts
    print("\nGenerated Artifacts:")
    for f in sorted(OUTPUT_DIR.glob("*")):
        print(f"  - {f.name} ({f.stat().st_size:,} bytes)")


if __name__ == "__main__":
    asyncio.run(main())