#!/usr/bin/env python3
"""
Glyph Visual-Text Compression Demo for AbstractCore

This example demonstrates how to use Glyph compression to achieve 3-4x token 
compression for long documents while maintaining quality.

Requirements:
    pip install abstractcore[all] reportlab pdf2image

Usage:
    python examples/media/glyph_compression_demo.py
"""

import os
import tempfile
from pathlib import Path

from abstractcore import create_llm
from abstractcore.compression import GlyphConfig, CompressionOrchestrator


def create_sample_document():
    """Create a sample long document for compression testing."""
    content = """
# Advanced Machine Learning Research Report

## Executive Summary

This comprehensive report examines the latest developments in machine learning research, 
focusing on transformer architectures, attention mechanisms, and their applications in 
natural language processing. Our analysis covers breakthrough innovations from 2023-2024, 
including improvements in model efficiency, training methodologies, and deployment strategies.

## Introduction

Machine learning has undergone rapid evolution in recent years, with transformer-based 
models becoming the dominant paradigm for natural language processing tasks. The introduction 
of attention mechanisms has revolutionized how models process sequential data, enabling 
unprecedented performance on tasks ranging from language translation to text generation.

## Methodology

Our research methodology encompasses several key areas:

1. **Literature Review**: Comprehensive analysis of peer-reviewed publications from top-tier 
   conferences including NeurIPS, ICML, ICLR, and ACL.

2. **Empirical Analysis**: Systematic evaluation of model performance across standardized 
   benchmarks including GLUE, SuperGLUE, and domain-specific evaluation suites.

3. **Computational Efficiency Studies**: Analysis of training costs, inference latency, 
   and memory requirements across different model architectures.

## Key Findings

### Transformer Architecture Improvements

Recent developments in transformer architectures have focused on improving computational 
efficiency while maintaining or enhancing model performance. Notable innovations include:

- **Sparse Attention Mechanisms**: Techniques such as local attention, strided attention, 
  and learned sparse patterns have reduced the quadratic complexity of standard attention.

- **Parameter Sharing**: Methods like Universal Transformers and parameter-efficient 
  fine-tuning have demonstrated that model capacity can be maintained with fewer parameters.

- **Alternative Activation Functions**: Research into activation functions beyond ReLU 
  and GELU has shown promising results for specific applications.

### Training Methodology Advances

The field has seen significant improvements in training methodologies:

- **Curriculum Learning**: Structured approaches to training data presentation have 
  improved convergence rates and final model performance.

- **Mixed Precision Training**: Utilization of lower precision arithmetic has enabled 
  training of larger models with reduced memory requirements.

- **Gradient Accumulation Strategies**: Advanced techniques for handling large batch 
  sizes have improved training stability and efficiency.

### Deployment and Optimization

Practical deployment considerations have become increasingly important:

- **Model Compression**: Techniques including pruning, quantization, and knowledge 
  distillation have enabled deployment of large models on resource-constrained devices.

- **Inference Optimization**: Specialized hardware and software optimizations have 
  significantly reduced inference latency for production applications.

- **Distributed Serving**: Advances in model parallelism and distributed inference 
  have enabled serving of extremely large models at scale.

## Technical Deep Dive

### Attention Mechanism Analysis

The attention mechanism remains the core innovation driving transformer success. Our 
analysis reveals several key insights:

The scaled dot-product attention mechanism computes attention weights as:

Attention(Q, K, V) = softmax(QK^T / √d_k)V

Where Q, K, and V represent query, key, and value matrices respectively, and d_k is 
the dimension of the key vectors.

### Performance Benchmarks

Our comprehensive evaluation across multiple benchmarks reveals:

- **GLUE Benchmark**: Average scores have improved by 15-20% over baseline transformers
- **SuperGLUE**: More challenging tasks show 10-15% improvement
- **Domain-Specific Tasks**: Specialized applications demonstrate up to 30% improvement

### Computational Complexity

Analysis of computational requirements shows:

- **Training Time**: 25-40% reduction in training time for equivalent performance
- **Memory Usage**: 20-35% reduction in peak memory requirements
- **Inference Latency**: 30-50% improvement in inference speed

## Future Directions

Several promising research directions emerge from our analysis:

### Architectural Innovations

- **Hybrid Architectures**: Combining transformers with other neural network types
- **Dynamic Computation**: Models that adapt computational complexity based on input
- **Multimodal Integration**: Better fusion of text, image, and audio modalities

### Training Improvements

- **Few-Shot Learning**: Enhanced capabilities for learning from limited examples
- **Continual Learning**: Models that can learn new tasks without forgetting previous ones
- **Federated Learning**: Distributed training approaches that preserve privacy

### Practical Applications

- **Real-Time Systems**: Optimizations for low-latency applications
- **Edge Computing**: Deployment on mobile and IoT devices
- **Scientific Computing**: Applications in research and discovery

## Conclusion

The field of machine learning continues to evolve rapidly, with transformer architectures 
remaining at the forefront of innovation. Our analysis demonstrates significant progress 
in efficiency, performance, and practical applicability. Future research should focus on 
addressing remaining challenges while exploring new frontiers in artificial intelligence.

The implications of these advances extend beyond academic research, with practical 
applications in industry, healthcare, education, and scientific discovery. As models 
become more efficient and capable, we anticipate broader adoption and integration into 
everyday applications.

## References

1. Vaswani, A., et al. (2017). Attention is all you need. NeurIPS.
2. Devlin, J., et al. (2018). BERT: Pre-training of Deep Bidirectional Transformers. NAACL.
3. Brown, T., et al. (2020). Language Models are Few-Shot Learners. NeurIPS.
4. Raffel, C., et al. (2020). Exploring the Limits of Transfer Learning. JMLR.
5. Dosovitskiy, A., et al. (2020). An Image is Worth 16x16 Words. ICLR.

## Appendix

### Detailed Experimental Results

[Additional technical details and experimental data would continue here...]

### Code Examples

[Implementation examples and code snippets would be included here...]

### Supplementary Materials

[Additional charts, graphs, and supporting documentation...]
"""

    # Create temporary file
    with tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False) as f:
        f.write(content)
        return Path(f.name)


def demo_basic_compression():
    """Demonstrate basic Glyph compression functionality."""
    print("🎯 Basic Glyph Compression Demo")
    print("=" * 50)

    # Create sample document
    doc_path = create_sample_document()
    print(f"📄 Created sample document: {doc_path}")

    try:
        # Read document content
        with open(doc_path, 'r') as f:
            content = f.read()

        print(f"📊 Original document: {len(content)} characters")

        # Estimate original token count
        from abstractcore.utils.token_utils import TokenUtils
        original_tokens = TokenUtils.estimate_tokens(content)
        print(f"🔢 Estimated tokens: {original_tokens}")

        # Initialize compression orchestrator
        orchestrator = CompressionOrchestrator()

        # Get compression recommendation
        recommendation = orchestrator.get_compression_recommendation(
            content, "openai", "gpt-4o"
        )

        print("\n📋 Compression Recommendation:")
        print(f"   Should compress: {recommendation['should_compress']}")
        print(f"   Reason: {recommendation['recommendation_reason']}")

        if recommendation['should_compress']:
            print(f"   Estimated ratio: {recommendation['compression_estimate']['estimated_ratio']:.1f}x")
            print(f"   Token savings: {recommendation['compression_estimate']['estimated_token_savings']}")
            print(f"   Cost savings: ${recommendation['compression_estimate']['estimated_cost_savings']:.4f}")

    except Exception as e:
        print(f"❌ Demo failed: {e}")
        print("Note: This demo requires reportlab and pdf2image packages")
        print("Install with: pip install reportlab pdf2image")

    finally:
        # Cleanup
        if doc_path.exists():
            doc_path.unlink()


def demo_provider_integration():
    """Demonstrate Glyph integration with AbstractCore providers."""
    print("\n🔗 Provider Integration Demo")
    print("=" * 50)

    # Create sample document
    doc_path = create_sample_document()
    print(f"📄 Created sample document: {doc_path}")

    try:
        # Create LLM with Glyph compression enabled
        llm = create_llm(
            "openai", 
            model="gpt-4o",
            api_key=os.getenv("OPENAI_API_KEY", "demo-key")
        )

        print("🤖 Created LLM instance with Glyph support")

        # Test compression decision
        print("\n🧠 Testing compression decision logic...")

        # This would normally process the document with compression
        # For demo purposes, we'll just show the API
        print("✅ Glyph compression would be applied automatically for large documents")
        print("✅ Fallback to standard processing if compression fails")
        print("✅ Provider-specific optimization (OpenAI: DPI 72, font 9pt)")

    except Exception as e:
        print(f"❌ Demo failed: {e}")
        print("Note: Set OPENAI_API_KEY environment variable for full demo")

    finally:
        # Cleanup
        if doc_path.exists():
            doc_path.unlink()


def demo_configuration():
    """Demonstrate Glyph configuration options."""
    print("\n⚙️ Configuration Demo")
    print("=" * 50)

    # Create custom configuration
    config = GlyphConfig(
        enabled=True,
        global_default="auto",
        quality_threshold=0.95,
        target_compression_ratio=3.5,
        cache_directory="~/.abstractcore/glyph_cache"
    )

    print("📋 Default Configuration:")
    print(f"   Enabled: {config.enabled}")
    print(f"   Global default: {config.global_default}")
    print(f"   Quality threshold: {config.quality_threshold}")
    print(f"   Target ratio: {config.target_compression_ratio}x")
    print(f"   Cache directory: {config.cache_directory}")

    print("\n🎛️ Provider-Specific Profiles:")
    for provider, profile in config.provider_profiles.items():
        print(f"   {provider.title()}:")
        for key, value in profile.items():
            print(f"     {key}: {value}")

    print("\n📱 App-Specific Defaults:")
    for app, setting in config.app_defaults.items():
        print(f"   {app}: {setting}")


def demo_quality_assessment():
    """Demonstrate quality assessment features."""
    print("\n🔍 Quality Assessment Demo")
    print("=" * 50)

    from abstractcore.compression.quality import QualityValidator

    validator = QualityValidator()

    # Sample content types
    test_cases = [
        ("prose", "This is a sample of natural language text with common words and standard sentence structure."),
        ("code", "def hello_world():\n    print('Hello, world!')\n    return True"),
        ("data", "name,age,city\nJohn,25,NYC\nJane,30,LA\nBob,35,Chicago"),
        ("mixed", "# Code Example\n\n```python\ndef process_data(data):\n    return data.upper()\n```\n\nThis function processes text data.")
    ]

    print("📊 Content Type Analysis:")
    for content_type, sample in test_cases:
        # Simulate quality assessment
        print(f"\n   {content_type.title()} Content:")
        print(f"     Sample: {sample[:50]}...")
        print(f"     Estimated quality: 95%")  # Placeholder
        print(f"     Compression suitability: High")  # Placeholder


def main():
    """Run all Glyph compression demos."""
    print("🎨 Glyph Visual-Text Compression Demo")
    print("=====================================")
    print()
    print("This demo showcases AbstractCore's Glyph compression integration")
    print("which provides 3-4x token compression for long documents.")
    print()

    # Run demos
    demo_basic_compression()
    demo_provider_integration()
    demo_configuration()
    demo_quality_assessment()

    print("\n✨ Demo Complete!")
    print("\nNext Steps:")
    print("1. Install dependencies: pip install reportlab pdf2image")
    print("2. Set API keys for cloud providers")
    print("3. Try compression with your own documents")
    print("4. Experiment with different providers and settings")


if __name__ == "__main__":
    main()
