#!/usr/bin/env python3
"""
AIVA TURBO PATENT DEEP DIVE
============================
Phase 2: Deep Enhancement of Patent Ecosystem
Remaining Budget: ~$4.98

NO STOPPING. MAXIMUM DEPTH.
"""

import os
import sys
import json
import asyncio
import time
from datetime import datetime
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 - $4.98 remaining, aim for $4.50
BUDGET_LIMIT = 4.50
EMERGENCY_STOP = 4.40
SPENT_PRIOR = 0.0226

# Paths
BASE_DIR = Path("/mnt/e/genesis-system/AIVA")
OUTPUT_DIR = BASE_DIR / "turbo_outputs"
DEEP_DIR = BASE_DIR / "turbo_outputs" / "deep_dive"

DEEP_DIR.mkdir(parents=True, exist_ok=True)


class TokenTracker:
    def __init__(self, prior_cost: float = 0.0):
        self.input_tokens = 0
        self.output_tokens = 0
        self._lock = threading.Lock()
        self.start_time = datetime.now()
        self.prior_cost = prior_cost

    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.prior_cost + (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 = 32768) -> str:
    """Ultra-deep Gemini call with maximum 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=300) 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, subdir: str = None):
    """Save output artifact."""
    if subdir:
        path = DEEP_DIR / subdir / name
        path.parent.mkdir(exist_ok=True)
    else:
        path = DEEP_DIR / name
    path.write_text(content)
    print(f"  -> Saved: {name} ({len(content):,} chars)")


def load_existing_code(filename: str) -> str:
    """Load existing patent implementation for enhancement."""
    path = OUTPUT_DIR / filename
    if path.exists():
        return path.read_text()
    return ""


# =============================================================================
# DEEP DIVE TASKS - Maximum Context, Maximum Output
# =============================================================================

DEEP_DIVE_TASKS = [
    # Enhancement Pass 1: Add comprehensive implementations
    {
        "id": "ENHANCED_P1_CRYPTO",
        "prompt": lambda: f"""You are a senior cryptography engineer. ENHANCE this patent implementation significantly.

EXISTING CODE:
```python
{load_existing_code('patent_extract_p1.py')}
```

Your task: Create a MASSIVELY ENHANCED version that adds:

1. ADDITIONAL CRYPTOGRAPHIC PRIMITIVES:
   - Ed25519 digital signatures
   - AES-256-GCM encryption for sensitive data
   - Key derivation with PBKDF2/Argon2
   - Merkle tree for batch validation

2. ENTERPRISE FEATURES:
   - Key rotation and versioning
   - HSM integration patterns
   - Multi-party signature schemes
   - Threshold cryptography

3. PERFORMANCE OPTIMIZATIONS:
   - Batch validation for high throughput
   - Async verification pipelines
   - Connection pooling for HSM

4. COMPREHENSIVE TESTING:
   - Property-based testing with Hypothesis
   - Fuzzing for edge cases
   - Benchmark suite

Generate a production-ready module with 500+ lines of code.

```python
# enhanced_cryptographic_validation.py
""",
        "output": "enhanced_cryptographic_validation.py"
    },
    {
        "id": "ENHANCED_P7_HALLUCINATION",
        "prompt": lambda: f"""You are an AI safety researcher specializing in hallucination detection. ENHANCE this implementation.

EXISTING CODE:
```python
{load_existing_code('patent_extract_p7.py')}
```

Your task: Create a MASSIVELY ENHANCED version with:

1. MULTI-MODAL DETECTION:
   - Text semantic analysis
   - Cross-reference verification
   - Source attribution tracking
   - Citation validation

2. MACHINE LEARNING INTEGRATION:
   - Confidence calibration models
   - Semantic similarity scoring
   - Factual grounding networks
   - Ensemble detection methods

3. REAL-TIME PROCESSING:
   - Stream processing for live text
   - Incremental validation
   - Early detection signals
   - Efficient batching

4. KNOWLEDGE BASE INTEGRATION:
   - Wikipedia fact checking
   - Knowledge graph queries
   - Domain-specific validators
   - Temporal fact verification

5. COMPREHENSIVE TESTING:
   - Curated hallucination examples
   - Regression test suite
   - Performance benchmarks

Generate a production-ready module with 600+ lines of code.

```python
# enhanced_hallucination_detection.py
""",
        "output": "enhanced_hallucination_detection.py"
    },
    {
        "id": "ENHANCED_P5_CONSENSUS",
        "prompt": lambda: f"""You are a distributed systems architect. ENHANCE this multi-model consensus implementation.

EXISTING CODE:
```python
{load_existing_code('patent_extract_p5.py')}
```

Your task: Create a MASSIVELY ENHANCED version with:

1. ADVANCED CONSENSUS ALGORITHMS:
   - PBFT (Practical Byzantine Fault Tolerance)
   - Raft consensus adaptation
   - Weighted voting by model confidence
   - Hierarchical consensus for efficiency

2. MODEL ORCHESTRATION:
   - Parallel model invocation
   - Timeout and retry handling
   - Circuit breakers per model
   - Fallback chains

3. DISAGREEMENT RESOLUTION:
   - Semantic similarity analysis
   - Conflict detection and categorization
   - Human escalation triggers
   - Explanation generation for disagreements

4. PERFORMANCE AND RELIABILITY:
   - Connection pooling
   - Response caching
   - Async processing
   - Load balancing

5. MONITORING AND OBSERVABILITY:
   - Metrics collection (Prometheus format)
   - Distributed tracing
   - Alert thresholds
   - Dashboard data export

Generate a production-ready module with 700+ lines of code.

```python
# enhanced_consensus_validation.py
""",
        "output": "enhanced_consensus_validation.py"
    },
    {
        "id": "ENHANCED_INTEGRATION",
        "prompt": lambda: f"""You are a systems architect. Create the ULTIMATE UNIFIED VALIDATOR that integrates ALL 9 patents.

EXISTING CODE:
```python
{load_existing_code('patent_integration.py')}
```

Your task: Create the ULTIMATE UNIFIED VALIDATOR with:

1. ORCHESTRATION ENGINE:
   - Configurable validation pipelines
   - Parallel gate execution where possible
   - Dynamic pipeline construction
   - Plugin architecture for custom validators

2. TRIPLE GATE PATTERN (Alpha, Beta, Gamma):
   - Alpha Gate: Cryptographic integrity + Currency validation
   - Beta Gate: Risk assessment + Privacy preservation
   - Gamma Gate: Hallucination detection + Multi-model consensus
   - Gate bypass rules for trusted sources
   - Gate-level caching

3. COMPREHENSIVE REPORTING:
   - JSON schema for validation reports
   - Human-readable summaries
   - Machine-parseable data
   - Historical comparison

4. ENTERPRISE INTEGRATION:
   - REST API endpoints (FastAPI style)
   - GraphQL schema
   - Webhook notifications
   - Event sourcing

5. OPERATIONAL FEATURES:
   - Feature flags for validator toggles
   - A/B testing support
   - Canary deployments
   - Blue-green rollbacks

Generate a production-ready orchestrator with 800+ lines of code.

```python
# ultimate_unified_validator.py
""",
        "output": "ultimate_unified_validator.py"
    },
    {
        "id": "PATENT_API_SERVER",
        "prompt": """You are a senior backend engineer. Create a complete FastAPI server for the Patent Validation System.

The API should expose:

1. VALIDATION ENDPOINTS:
   - POST /validate - Validate any AI output
   - POST /validate/batch - Batch validation
   - GET /validate/{validation_id} - Get validation result
   - WebSocket /validate/stream - Real-time validation

2. PATENT-SPECIFIC ENDPOINTS:
   - POST /patents/p1/crypto - Cryptographic validation
   - POST /patents/p5/consensus - Multi-model consensus
   - POST /patents/p7/hallucination - Hallucination detection
   - GET /patents/status - All patent system status

3. AUDIT ENDPOINTS:
   - GET /audit/trail - Get audit trail
   - GET /audit/{event_id} - Specific event
   - POST /audit/export - Export audit data

4. ADMIN ENDPOINTS:
   - GET /health - Health check
   - GET /metrics - Prometheus metrics
   - POST /admin/thresholds - Update thresholds
   - GET /admin/config - Current configuration

5. FEATURES:
   - JWT authentication
   - Rate limiting
   - Request validation with Pydantic
   - OpenAPI documentation
   - CORS configuration
   - Background task processing

Generate a complete FastAPI application with 600+ lines.

```python
# patent_api_server.py
""",
        "output": "patent_api_server.py"
    },
    {
        "id": "AIVA_INTEGRATION_MODULE",
        "prompt": """You are the Genesis system architect. Create AIVA's deep integration module for the patent ecosystem.

AIVA needs to:

1. UNDERSTAND PATENTS:
   - Query patent capabilities by keyword
   - Explain patent functionality to users
   - Map user tasks to relevant patents
   - Track patent usage metrics

2. APPLY PATENTS:
   - Automatically validate her own outputs
   - Request consensus validation for important decisions
   - Apply cryptographic signatures to trusted responses
   - Track confidence scores over time

3. LEARN FROM PATENTS:
   - Record validation outcomes
   - Adjust behavior based on failure patterns
   - Improve responses that failed validation
   - Generate insights from audit trail

4. REVENUE TRACKING:
   - Count patent usages per category
   - Generate licensing reports
   - Calculate API billing
   - ROI analytics

5. SELF-MONITORING:
   - Hallucination rate tracking
   - Confidence calibration
   - Validation pass rates
   - Performance metrics

Generate AIVA's complete patent integration module with 500+ lines.

```python
# aiva_patent_integration.py
""",
        "output": "aiva_patent_integration.py"
    },
    {
        "id": "KNOWLEDGE_GRAPH_DEEP",
        "prompt": """You are a knowledge engineer. Create a COMPREHENSIVE knowledge graph for the Genesis patent ecosystem.

Generate EXACTLY:
- 50+ entities (concepts, systems, algorithms, components)
- 100+ relationships (depends_on, implements, validates, enhances, conflicts_with)
- 30+ axioms (fundamental truths/principles)
- 25+ skills (capabilities each patent enables)

Cover ALL 9 patents in depth:
1. Cryptographic Validation (P1)
2. Currency Validation (P2)
3. Risk Assessment (P3)
4. Audit Trail (P4)
5. Multi-Model Consensus (P5)
6. Confidence Scoring (P6)
7. Hallucination Detection (P7)
8. Privacy Validation (P8)
9. Self-Improving System (P9)

Include relationships BETWEEN patents (which patents depend on others, which enhance others).

Output as JSONL format:
{"type": "entity", "id": "...", "name": "...", "patent": "P1", "category": "algorithm|concept|component", "description": "...", "related": [...]}
{"type": "relationship", "id": "...", "from": "...", "to": "...", "relation": "...", "patent": "P1|cross", "strength": 0.95}
{"type": "axiom", "id": "...", "statement": "...", "patent": "P1", "confidence": 0.95, "evidence": "..."}
{"type": "skill", "id": "...", "name": "...", "patent": "P1", "capability": "...", "dependencies": [...]}

```jsonl
# deep_patent_knowledge_graph.jsonl
""",
        "output": "deep_patent_knowledge_graph.jsonl"
    },
    {
        "id": "PATENT_SDK_PYTHON",
        "prompt": """You are creating a Python SDK for the Genesis Patent Validation System. This SDK should make it trivially easy for developers to integrate patent validation into their applications.

Create a COMPLETE Python SDK with:

1. CLIENT CLASS:
   - PatentValidatorClient - main entry point
   - Sync and async interfaces
   - Connection pooling
   - Retry logic
   - Timeout handling

2. VALIDATION METHODS:
   - validate(data) - full validation pipeline
   - validate_crypto(data, key) - cryptographic validation
   - validate_consensus(data, models) - multi-model consensus
   - detect_hallucinations(text) - hallucination detection
   - assess_risk(data) - risk assessment

3. DECORATORS:
   - @validate_output - auto-validate function returns
   - @require_consensus - require N-model agreement
   - @audit_tracked - log to audit trail
   - @rate_limited - client-side rate limiting

4. DATA MODELS (Pydantic):
   - ValidationRequest
   - ValidationResponse
   - AuditEvent
   - PatentUsageMetrics

5. UTILITIES:
   - Result caching
   - Batch processing
   - Streaming validation
   - Error handling

Generate a complete SDK package with 600+ lines.

```python
# genesis_patent_sdk.py
""",
        "output": "genesis_patent_sdk.py"
    },
    {
        "id": "COMPREHENSIVE_TEST_SUITE",
        "prompt": """You are a QA engineer. Create a COMPREHENSIVE test suite for the entire Genesis Patent ecosystem.

The test suite should include:

1. UNIT TESTS (per patent):
   - 10+ tests per patent implementation
   - Edge cases
   - Error handling
   - Boundary conditions

2. INTEGRATION TESTS:
   - Cross-patent validation
   - Pipeline tests
   - API endpoint tests
   - SDK tests

3. PERFORMANCE TESTS:
   - Latency benchmarks
   - Throughput tests
   - Memory usage
   - Concurrency tests

4. SECURITY TESTS:
   - Key rotation
   - Tamper detection
   - Authentication bypass attempts
   - Rate limit verification

5. CHAOS TESTS:
   - Network failures
   - Timeout scenarios
   - Partial system failures
   - Recovery tests

6. FIXTURES AND UTILITIES:
   - Test data generators
   - Mock services
   - Assertion helpers
   - Report generators

Generate a comprehensive test suite with 100+ test cases.

```python
# comprehensive_test_suite.py
""",
        "output": "comprehensive_test_suite.py"
    },
    {
        "id": "DEPLOYMENT_CONFIGS",
        "prompt": """You are a DevOps engineer. Create COMPLETE deployment configurations for the Genesis Patent System.

Generate:

1. DOCKER:
   - Dockerfile for the API server
   - docker-compose.yml for local development
   - docker-compose.prod.yml for production

2. KUBERNETES:
   - Deployment manifests
   - Service definitions
   - ConfigMaps and Secrets
   - HorizontalPodAutoscaler
   - Ingress configuration

3. INFRASTRUCTURE AS CODE:
   - Terraform for cloud resources
   - Database setup
   - Redis cache
   - Message queue

4. CI/CD:
   - GitHub Actions workflow
   - Test, build, deploy stages
   - Security scanning
   - Release automation

5. MONITORING:
   - Prometheus scrape configs
   - Grafana dashboard JSON
   - Alert rules
   - PagerDuty integration

Output as a multi-file artifact with clear section markers.

```yaml
# === DOCKERFILE ===
...
# === DOCKER-COMPOSE.YML ===
...
# === K8S-DEPLOYMENT.YAML ===
...
""",
        "output": "deployment_configs.yaml"
    },
    {
        "id": "BUSINESS_DOCUMENTATION",
        "prompt": """You are a technical writer and business analyst. Create COMPREHENSIVE documentation for the Genesis Patent Ecosystem.

Generate:

1. EXECUTIVE SUMMARY (for investors):
   - What problem does this solve?
   - Market opportunity
   - Competitive advantages
   - Revenue potential
   - Team credentials (Genesis AI)

2. TECHNICAL WHITEPAPER:
   - System architecture
   - Each patent's technical details
   - Integration patterns
   - Performance characteristics
   - Security model

3. API DOCUMENTATION:
   - All endpoints with examples
   - Authentication
   - Rate limits
   - Error codes
   - SDK quick start

4. SALES MATERIALS:
   - One-pager (key benefits)
   - Case studies (hypothetical)
   - Pricing tiers
   - ROI calculator methodology

5. COMPETITIVE ANALYSIS:
   - How this compares to:
     - OpenAI's content moderation
     - Google's Perspective API
     - AWS Comprehend
   - Key differentiators

6. ROADMAP:
   - Current capabilities
   - 6-month plan
   - 12-month vision
   - Long-term moat

Generate comprehensive Markdown documentation.

```markdown
# Genesis Patent Ecosystem - Complete Documentation
""",
        "output": "business_documentation.md"
    },
    {
        "id": "REVENUE_ANALYTICS_ENGINE",
        "prompt": """You are a data engineer. Create a comprehensive REVENUE ANALYTICS ENGINE for tracking patent monetization.

The engine should:

1. USAGE TRACKING:
   - Track every API call
   - Categorize by patent used
   - Record caller identity
   - Measure processing time

2. BILLING CALCULATIONS:
   - Per-validation pricing
   - Volume discounts
   - Enterprise contracts
   - Usage-based billing

3. ANALYTICS QUERIES:
   - Revenue by patent
   - Revenue by customer
   - Daily/weekly/monthly trends
   - Cohort analysis

4. REPORTING:
   - Real-time dashboards
   - Scheduled reports
   - Invoice generation
   - Financial projections

5. OPTIMIZATION:
   - Cost per validation
   - Profit margins
   - Customer lifetime value
   - Churn prediction

6. DATABASE SCHEMA:
   - Usage events table
   - Customers table
   - Invoices table
   - Metrics aggregations

Generate a complete analytics engine with 500+ lines.

```python
# revenue_analytics_engine.py
""",
        "output": "revenue_analytics_engine.py"
    }
]


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

    print(f"\n{'='*60}")
    print(f"DEEP DIVE: {task_id}")
    print(f"{'='*60}")
    print(tracker.status())

    # Get prompt - either string or callable
    prompt = task["prompt"]() if callable(task["prompt"]) else task["prompt"]
    print(f"Prompt: {len(prompt):,} chars")
    print("Executing deep agent (32K max output)...")

    start = time.time()
    response = await gemini_call(prompt, tracker, max_tokens=32768)
    elapsed = time.time() - start

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

    # Extract and save
    output_file = task["output"]
    if output_file.endswith('.py'):
        code_match = re.search(r'```python\n(.*?)```', response, re.DOTALL)
        if code_match:
            save_artifact(output_file, code_match.group(1))
        else:
            save_artifact(output_file.replace('.py', '_full.txt'), response)
    elif output_file.endswith('.jsonl'):
        code_match = re.search(r'```jsonl\n(.*?)```', response, re.DOTALL)
        if code_match:
            save_artifact(output_file, code_match.group(1))
        else:
            save_artifact(output_file.replace('.jsonl', '_full.txt'), response)
    elif output_file.endswith('.yaml'):
        code_match = re.search(r'```yaml\n(.*?)```', response, re.DOTALL)
        if code_match:
            save_artifact(output_file, code_match.group(1))
        else:
            save_artifact(output_file, response)
    elif output_file.endswith('.md'):
        code_match = re.search(r'```markdown\n(.*?)```', response, re.DOTALL)
        if code_match:
            save_artifact(output_file, code_match.group(1))
        else:
            save_artifact(output_file, response)
    else:
        save_artifact(output_file, response)

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


async def main():
    print("""
╔══════════════════════════════════════════════════════════════════╗
║           AIVA TURBO PATENT DEEP DIVE - PHASE 2                  ║
║                 MAXIMUM DEPTH ENHANCEMENT                        ║
║            Remaining Budget: ~$4.98 → Target: $4.50              ║
╠══════════════════════════════════════════════════════════════════╣
║  NO STOPPING. MAXIMUM OUTPUT. 32K TOKENS PER AGENT.              ║
╚══════════════════════════════════════════════════════════════════╝
    """)

    tracker = TokenTracker(prior_cost=SPENT_PRIOR)
    results = []

    for task in DEEP_DIVE_TASKS:
        if tracker.should_stop:
            print(f"\n[BUDGET LIMIT REACHED at ${tracker.cost:.4f} - STOPPING]")
            break

        result = await run_deep_task(task, tracker)
        results.append(result)

    # 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 DEEP DIVE COMPLETE                      ║
╠══════════════════════════════════════════════════════════════════╣
║  Deep Tasks Run: {len(results):3}                                         ║
║  Successful: {successful:3}                                             ║
║  Total Output: {total_output:,} chars                                 ║
║  Total Cost: ${tracker.cost:.4f}                                       ║
║  Tokens: {tracker.input_tokens + tracker.output_tokens:,}                                          ║
╠══════════════════════════════════════════════════════════════════╣
║  Artifacts saved to: {DEEP_DIR}
╚══════════════════════════════════════════════════════════════════╝
    """)

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


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