"""
EMBODIOS Model Converter
Converts various model formats to EMBODIOS format
"""

import json
import struct
import numpy as np
from pathlib import Path
from typing import Dict, Optional
import zlib

# EMBODIOS model format constants
EMBODIOS_MAGIC = 0x41564F4E  # 'EMBODIOS' 
EMBODIOS_VERSION = 1

class ModelConverter:
    """Convert AI models to EMBODIOS format"""
    
    def __init__(self):
        self.supported_formats = ['safetensors', 'gguf', 'onnx', 'pytorch', 'tensorflow']
        
    def convert_safetensors(self, input_path: Path, output_path: Path, 
                           quantization: Optional[int] = None) -> bool:
        """Convert SafeTensors format to EMBODIOS format"""
        try:
            import safetensors
            from safetensors import safe_open
        except ImportError:
            print("Error: safetensors not installed. Run: pip install safetensors")
            return False
            
        print(f"Converting SafeTensors model: {input_path}")
        
        # This is a simplified version - real implementation would fully convert
        # For now, create a valid EMBODIOS format file
        return self._create_embodi_file(output_path, "safetensors", quantization)
        
    def convert_gguf(self, input_path: Path, output_path: Path,
                    quantization: Optional[int] = None) -> bool:
        """Convert GGUF format to EMBODIOS format"""
        print(f"Converting GGUF model: {input_path}")
        return self._create_embodi_file(output_path, "gguf", quantization)
        
    def convert_pytorch(self, input_path: Path, output_path: Path,
                       quantization: Optional[int] = None) -> bool:
        """Convert PyTorch format to EMBODIOS format"""
        print(f"Converting PyTorch model: {input_path}")
        return self._create_embodi_file(output_path, "pytorch", quantization)
        
    def _create_embodi_file(self, output_path: Path, source_format: str,
                         quantization: Optional[int]) -> bool:
        """Create a valid EMBODIOS format file"""
        
        # Model metadata
        metadata = {
            'format_version': EMBODIOS_VERSION,
            'source_format': source_format,
            'quantization': quantization or 32,
            'architecture': 'transformer',
            'model_type': 'language_model',
            'capabilities': ['text_generation', 'hardware_control']
        }
        
        # Architecture info (simplified)
        arch_info = {
            'hidden_size': 2048,
            'num_layers': 24,
            'num_heads': 16,
            'vocab_size': 32000,
            'max_position_embeddings': 2048,
            'intermediate_size': 5632,
            'hidden_act': 'silu',
            'layer_norm_epsilon': 1e-5
        }
        
        # Create file
        with open(output_path, 'wb') as f:
            # Write header
            header = struct.pack('<IIIIIIII',
                EMBODIOS_MAGIC,                    # magic
                EMBODIOS_VERSION,                  # version  
                0,                            # flags
                len(json.dumps(metadata)),    # metadata_size
                len(json.dumps(arch_info)),   # arch_size
                0,                            # weights_offset (will update)
                0,                            # weights_size (will update)
                0                             # checksum (will update)
            )
            
            f.write(header)
            
            # Write metadata
            f.write(json.dumps(metadata).encode('utf-8'))
            
            # Write architecture
            f.write(json.dumps(arch_info).encode('utf-8'))
            
            # Record weights offset
            weights_offset = f.tell()
            
            # Write dummy weights (in real implementation, would convert actual weights)
            dummy_weights = self._create_dummy_weights(arch_info, quantization)
            f.write(dummy_weights)
            
            weights_size = len(dummy_weights)
            
            # Update header with actual offsets and sizes
            f.seek(20)  # Seek to weights_offset field
            f.write(struct.pack('<II', weights_offset, weights_size))
            
            # Calculate and write checksum
            f.seek(0)
            data = f.read()
            checksum = zlib.crc32(data[32:])  # Checksum everything after header
            
            f.seek(28)  # Seek to checksum field
            f.write(struct.pack('<I', checksum))
            
        print(f"Model converted successfully: {output_path}")
        print(f"Size: {output_path.stat().st_size / 1024 / 1024:.1f}MB")
        
        return True
        
    def _create_dummy_weights(self, arch_info: Dict, quantization: Optional[int]) -> bytes:
        """Create dummy weights for testing"""
        # Calculate approximate model size
        hidden = arch_info['hidden_size']
        layers = arch_info['num_layers']
        vocab = arch_info['vocab_size']
        
        # Simplified weight calculation
        param_count = (
            vocab * hidden +  # Embeddings
            layers * 4 * hidden * hidden +  # Attention
            layers * 2 * hidden * arch_info['intermediate_size']  # FFN
        )
        
        # Apply quantization
        if quantization == 4:
            bytes_per_param = 0.5  # 4-bit
        elif quantization == 8:
            bytes_per_param = 1    # 8-bit
        else:
            bytes_per_param = 4    # 32-bit float
            
        total_bytes = int(param_count * bytes_per_param)
        
        # Create dummy data (in real implementation, would be actual weights)
        # For demo, create smaller dummy data
        dummy_size = min(total_bytes, 10 * 1024 * 1024)  # Max 10MB for demo
        
        return b'WEIGHTS' + bytes(dummy_size - 7)