#!/usr/bin/env python3
"""
Hutter Prize Compression Shim

Python shim for file I/O and benchmarking of the Lean Hutter Maximum Compression module.

Per AGENTS.md rules:
- This shim only handles file I/O, JSON serialization, and benchmarking
- Core compression logic lives in Lean (Semantics.HutterMaximumCompression)
- No compression decisions or cost calculations in Python

Usage:
    python 5-Applications/scripts/hutter_compression_shim.py input.txt output.bin
"""

import sys
import json
import time
import subprocess
from pathlib import Path


def read_input_file(filepath: str) -> bytes:
    """Read input file (enwik8, enwik9, or test data)."""
    with open(filepath, 'rb') as f:
        return f.read()


def write_output_file(filepath: str, data: bytes) -> None:
    """Write compressed output file."""
    with open(filepath, 'wb') as f:
        f.write(data)


def call_lean_bindserver(metrics: dict) -> dict:
    """
    Call Lean BindServer to get compression bind result.

    This is a placeholder - actual implementation would call:
    lake exe bindserver with appropriate JSON-L payload.

    For now, returns a mock response to demonstrate the interface.
    """
    # TODO: Implement actual BindServer call
    # cmd = ["lake", "exe", "bindserver"]
    # result = subprocess.run(cmd, input=json.dumps(metrics), capture_output=True, text=True)
    # return json.loads(result.stdout)
    
    # Mock response for testing
    return {
        "left": metrics,
        "right": "hutter_compressed_output",
        "metric": {
            "cost": 0x00010000,  # 1.0 in Q16.16
            "tensor": "informational",
            "torsion": 0x00000000,
            "reference": "hutter_maximum_compression",
            "history_len": 0
        },
        "cost": metrics.get("totalCost", 0),
        "witness": {
            "left_invariant": metrics.get("invariant", "unknown"),
            "right_invariant": "hutter_compression_verified",
            "conserved": True,
            "trace_hash": "mock_hash"
        },
        "lawful": metrics.get("compressionRatio", 0) > 0.618
    }


def simulate_lean_compression(input_data: bytes) -> dict:
    """
    Simulate Lean compression pipeline for benchmarking.

    This is a placeholder that demonstrates the expected interface.
    Real implementation would call the actual Lean functions via BindServer.
    
    Returns:
        Dictionary with compression metrics
    """
    total_positions = len(input_data)
    
    # Mock compression: emit symbols at square positions (demonstration)
    emitted_symbols = 0
    lawful_symbols = 0
    total_cost = 0
    
    for pos in range(total_positions):
        k = int(pos ** 0.5)
        a = pos - k*k
        is_square = (a == 0)
        
        # Simplified emission rule: emit at squares
        if is_square:
            emitted_symbols += 1
            lawful_symbols += 1
            total_cost += 0x00000100  # Minimal cost
    
    compression_ratio = emitted_symbols / total_positions if total_positions > 0 else 0
    
    metrics = {
        "totalPositions": total_positions,
        "emittedSymbols": emitted_symbols,
        "lawfulSymbols": lawful_symbols,
        "totalCost": total_cost,
        "avgCost": total_cost / emitted_symbols if emitted_symbols > 0 else 0,
        "compressionRatio": compression_ratio,
        "invariant": "lawful_hutter_compression" if compression_ratio > 0.618 else "unlawful_hutter_drift"
    }
    
    return metrics


def benchmark_compression(input_file: str, output_file: str) -> dict:
    """
    Run compression benchmark.
    
    Args:
        input_file: Path to input file (enwik8, enwik9, etc.)
        output_file: Path for compressed output
        
    Returns:
        Benchmark results dictionary
    """
    print(f"Reading input: {input_file}")
    input_data = read_input_file(input_file)
    input_size = len(input_data)
    
    print("Running Lean compression pipeline...")
    start_time = time.time()
    
    # Get compression metrics from Lean (simulated for now)
    metrics = simulate_lean_compression(input_data)
    
    # Call Lean BindServer for formal verification
    bind_result = call_lean_bindserver(metrics)
    
    compression_time = time.time() - start_time
    
    # Write compressed output (placeholder)
    output_data = json.dumps({
        "metrics": metrics,
        "bind": bind_result
    }).encode('utf-8')
    write_output_file(output_file, output_data)
    output_size = len(output_data)
    
    # Calculate benchmark results
    results = {
        "input_file": input_file,
        "output_file": output_file,
        "input_size": input_size,
        "output_size": output_size,
        "compression_ratio": metrics["compressionRatio"],
        "compression_time": compression_time,
        "throughput_mbps": (input_size / (1024 * 1024)) / compression_time if compression_time > 0 else 0,
        "metrics": metrics,
        "bind_result": bind_result,
        "lawful": bind_result["lawful"]
    }
    
    return results


def main():
    if len(sys.argv) != 3:
        print("Usage: python hutter_compression_shim.py <input_file> <output_file>")
        sys.exit(1)
    
    input_file = sys.argv[1]
    output_file = sys.argv[2]
    
    if not Path(input_file).exists():
        print(f"Error: Input file not found: {input_file}")
        sys.exit(1)
    
    print("=== Hutter Prize Maximum Compression Benchmark ===")
    print()
    
    results = benchmark_compression(input_file, output_file)
    
    print()
    print("=== Benchmark Results ===")
    print(f"Input:  {results['input_file']} ({results['input_size']:,} bytes)")
    print(f"Output: {results['output_file']} ({results['output_size']:,} bytes)")
    print(f"Compression Ratio: {results['compression_ratio']:.6f}")
    print(f"Compression Time: {results['compression_time']:.3f}s")
    print(f"Throughput: {results['throughput_mbps']:.2f} MB/s")
    print(f"Lawful: {results['lawful']}")
    print()
    print(f"Emitted Symbols: {results['metrics']['emittedSymbols']:,} / {results['metrics']['totalPositions']:,}")
    print(f"Lawful Symbols: {results['metrics']['lawfulSymbols']:,}")
    print(f"Total Cost: {results['metrics']['totalCost']:,}")
    print(f"Invariant: {results['metrics']['invariant']}")
    
    # Save results to JSON
    results_file = output_file + ".json"
    with open(results_file, 'w') as f:
        json.dump(results, f, indent=2)
    print()
    print(f"Results saved to: {results_file}")


if __name__ == "__main__":
    main()