"""
EMBODIOS Inference Engine - Core AI execution for hardware control
"""

import numpy as np
import struct
from pathlib import Path
from typing import Dict, List, Tuple, Optional, Any
import mmap
import ctypes

class EMBODIOSInferenceEngine:
    """Minimal inference engine for running AI models on bare metal"""
    
    def __init__(self):
        self.model_loaded = False
        self.weights = None
        self.config = None
        self.hardware_tokens = self._init_hardware_tokens()
        self.memory_map = None
        self.weights_mmap: Optional[mmap.mmap] = None
        self.weights_data: Optional[bytes] = None
        self.weights_offset_diff: int = 0
        self.architecture: Optional[Dict[str, Any]] = None
        
    def _init_hardware_tokens(self) -> Dict[str, int]:
        """Initialize special tokens for hardware operations"""
        # Special tokens that map to hardware operations
        return {
            # GPIO tokens
            "<GPIO_READ>": 32000,
            "<GPIO_WRITE>": 32001,
            "<GPIO_HIGH>": 32002,
            "<GPIO_LOW>": 32003,
            
            # Memory operations
            "<MEM_READ>": 32010,
            "<MEM_WRITE>": 32011,
            "<MEM_ALLOC>": 32012,
            
            # Device control
            "<I2C_READ>": 32020,
            "<I2C_WRITE>": 32021,
            "<SPI_XFER>": 32022,
            "<UART_TX>": 32023,
            "<UART_RX>": 32024,
            
            # System control
            "<INT_ENABLE>": 32030,
            "<INT_DISABLE>": 32031,
            "<HALT>": 32032,
        }
    
    def load_model(self, model_path: str):
        """Load EMBODIOS format model"""
        with open(model_path, 'rb') as f:
            # Read header
            magic = f.read(4)
            if magic != b'AIOS':
                raise ValueError("Invalid EMBODIOS model format")
            
            version = struct.unpack('I', f.read(4))[0]
            
            # Read metadata sizes
            metadata_size = struct.unpack('I', f.read(4))[0]
            arch_size = struct.unpack('I', f.read(4))[0]
            weights_offset = struct.unpack('I', f.read(4))[0]
            weights_size = struct.unpack('I', f.read(4))[0]
            
            # Load metadata
            import json
            metadata_json = f.read(metadata_size).decode('utf-8')
            self.config = json.loads(metadata_json)
            
            # Load architecture
            arch_json = f.read(arch_size).decode('utf-8')
            self.architecture = json.loads(arch_json)
            
            # Memory-map weights for efficient access
            try:
                # mmap requires page-aligned offset
                page_size = mmap.PAGESIZE
                aligned_offset = (weights_offset // page_size) * page_size
                offset_diff = weights_offset - aligned_offset
                
                self.weights_mmap = mmap.mmap(f.fileno(), weights_size + offset_diff, 
                                             access=mmap.ACCESS_READ,
                                             offset=aligned_offset)
                # Store the difference to adjust reads
                self.weights_offset_diff = offset_diff
            except (OSError, ValueError):
                # Fallback: read weights into memory for small models or testing
                f.seek(weights_offset)
                self.weights_data = f.read(weights_size)
                self.weights_mmap = None
                self.weights_offset_diff = 0
            
            self.model_loaded = True
    
    def inference(self, input_tokens: List[int]) -> Tuple[List[int], Optional[Dict]]:
        """Run inference and return both tokens and hardware commands"""
        if not self.model_loaded:
            raise RuntimeError("No model loaded")
        
        # Check for hardware control tokens in input
        hardware_commands = self._extract_hardware_commands(input_tokens)
        
        # Simple transformer inference (simplified for demonstration)
        output_tokens = self._forward_pass(input_tokens)
        
        # Process output for hardware operations
        hw_operations = self._process_hardware_tokens(output_tokens)
        
        return output_tokens, hw_operations
    
    def _extract_hardware_commands(self, tokens: List[int]) -> List[Dict]:
        """Extract hardware commands from token stream"""
        commands = []
        i = 0
        
        while i < len(tokens):
            token = tokens[i]
            
            # GPIO operations
            if token == self.hardware_tokens["<GPIO_WRITE>"]:
                if i + 2 < len(tokens):
                    pin = tokens[i + 1]
                    value = tokens[i + 2]
                    commands.append({
                        'type': 'gpio_write',
                        'pin': pin,
                        'value': value == self.hardware_tokens["<GPIO_HIGH>"]
                    })
                    i += 3
                    continue
            
            # Memory operations
            elif token == self.hardware_tokens["<MEM_READ>"]:
                if i + 1 < len(tokens):
                    addr = tokens[i + 1]
                    commands.append({
                        'type': 'mem_read',
                        'address': addr
                    })
                    i += 2
                    continue
            
            # I2C operations
            elif token == self.hardware_tokens["<I2C_READ>"]:
                if i + 2 < len(tokens):
                    device = tokens[i + 1]
                    register = tokens[i + 2]
                    commands.append({
                        'type': 'i2c_read',
                        'device': device,
                        'register': register
                    })
                    i += 3
                    continue
            
            i += 1
        
        return commands
    
    def _forward_pass(self, input_tokens: List[int]) -> List[int]:
        """Simplified transformer forward pass"""
        # This is a highly simplified version
        # Real implementation would need proper transformer layers
        
        # Convert tokens to embeddings
        embeddings = self._embed_tokens(input_tokens)
        
        # Apply self-attention (simplified)
        attended = self._self_attention(embeddings)
        
        # Feed-forward network
        output = self._feed_forward(attended)
        
        # Convert back to tokens
        output_tokens = self._decode_embeddings(output)
        
        return output_tokens
    
    def _embed_tokens(self, tokens: List[int]) -> np.ndarray:
        """Convert tokens to embeddings"""
        if not self.architecture:
            raise RuntimeError("Model not loaded")
        
        vocab_size = self.architecture['vocab_size']
        hidden_size = self.architecture['hidden_size']
        
        # Get embedding weights from memory map
        embedding_offset = 0
        embedding_size = vocab_size * hidden_size * 4  # float32
        
        if self.weights_mmap is not None:
            # Use memory-mapped weights
            offset = embedding_offset + self.weights_offset_diff
            embeddings = np.frombuffer(
                self.weights_mmap[offset:offset + embedding_size],
                dtype=np.float32
            ).reshape(vocab_size, hidden_size)
        else:
            # Use in-memory weights (for small models/testing)
            if self.weights_data is None:
                raise RuntimeError("No weights loaded")
            embeddings = np.frombuffer(
                self.weights_data[embedding_offset:embedding_offset + embedding_size],
                dtype=np.float32
            ).reshape(vocab_size, hidden_size)
        
        # Look up embeddings
        return embeddings[tokens]
    
    def _self_attention(self, embeddings: np.ndarray) -> np.ndarray:
        """Simplified self-attention mechanism"""
        seq_len, hidden_size = embeddings.shape
        
        # Simplified attention without actual weights for demo
        # Real implementation would use Q, K, V projections
        scores = np.dot(embeddings, embeddings.T) / np.sqrt(hidden_size)
        attention_weights = self._softmax(scores)
        
        return np.dot(attention_weights, embeddings)
    
    def _feed_forward(self, x: np.ndarray) -> np.ndarray:
        """Feed-forward network"""
        # Simplified - real implementation would use actual FFN weights
        return x * 1.1  # Placeholder
    
    def _decode_embeddings(self, embeddings: np.ndarray) -> List[int]:
        """Convert embeddings back to tokens"""
        # Simplified - would need proper decoding
        # For now, return some hardware control tokens as example
        return [
            self.hardware_tokens["<GPIO_WRITE>"],
            17,  # Pin number
            self.hardware_tokens["<GPIO_HIGH>"]
        ]
    
    def _softmax(self, x: np.ndarray) -> np.ndarray:
        """Compute softmax"""
        exp_x = np.exp(x - np.max(x, axis=-1, keepdims=True))
        return exp_x / np.sum(exp_x, axis=-1, keepdims=True)
    
    def _process_hardware_tokens(self, tokens: List[int]) -> Dict[str, List]:
        """Process output tokens for hardware operations"""
        operations: Dict[str, List] = {
            'gpio': [],
            'memory': [],
            'i2c': [],
            'interrupts': []
        }
        
        # Extract hardware operations from output
        hw_commands = self._extract_hardware_commands(tokens)
        
        for cmd in hw_commands:
            if cmd['type'].startswith('gpio'):
                operations['gpio'].append(cmd)
            elif cmd['type'].startswith('mem'):
                operations['memory'].append(cmd)
            elif cmd['type'].startswith('i2c'):
                operations['i2c'].append(cmd)
        
        return operations


class BareMetalInference:
    """Bare metal inference without OS dependencies"""
    
    def __init__(self, phys_addr: int, model_size: int):
        self.model_base = phys_addr
        self.model_size = model_size
        self.scratch_memory = phys_addr + model_size
        
    def run_inference_loop(self):
        """Main inference loop for bare metal execution"""
        while True:
            # Check for hardware interrupts
            interrupts = self.check_interrupts()
            
            if interrupts:
                # Convert interrupt to tokens
                input_tokens = self.interrupt_to_tokens(interrupts)
                
                # Run inference
                output_tokens = self.bare_metal_forward(input_tokens)
                
                # Execute hardware commands
                self.execute_hardware_commands(output_tokens)
            
            # Small delay to prevent burning CPU
            self.cpu_pause()
    
    def check_interrupts(self) -> Optional[int]:
        """Check hardware interrupt status"""
        # Read interrupt controller (simplified)
        INT_STATUS_REG = 0xFEE00000
        return self.read_mmio(INT_STATUS_REG)
    
    def interrupt_to_tokens(self, interrupt: int) -> List[int]:
        """Convert hardware interrupt to token sequence"""
        # Map interrupt numbers to token sequences
        interrupt_map = {
            0x10: [32000, 22],  # GPIO interrupt on pin 22
            0x20: [32023],      # UART RX interrupt
            0x30: [32020, 0x48] # I2C interrupt from device 0x48
        }
        
        return interrupt_map.get(interrupt, [])
    
    def bare_metal_forward(self, tokens: List[int]) -> List[int]:
        """Forward pass using only physical memory"""
        # This would implement matrix operations using only
        # physical memory addresses and basic CPU instructions
        
        # Placeholder - real implementation would do actual inference
        return [32001, 17, 32002]  # GPIO write pin 17 high
    
    def execute_hardware_commands(self, tokens: List[int]):
        """Execute hardware commands from tokens"""
        i = 0
        while i < len(tokens):
            if tokens[i] == 32001:  # GPIO_WRITE
                pin = tokens[i + 1]
                value = tokens[i + 2]
                self.gpio_write(pin, value == 32002)
                i += 3
            else:
                i += 1
    
    def gpio_write(self, pin: int, value: bool):
        """Direct GPIO control"""
        GPIO_BASE = 0xFE200000  # BCM2835 GPIO base for RPi
        
        # Calculate register and bit
        reg_offset = (pin // 32) * 4
        bit = pin % 32
        
        if value:
            # Set pin high
            self.write_mmio(GPIO_BASE + 0x1C + reg_offset, 1 << bit)
        else:
            # Set pin low
            self.write_mmio(GPIO_BASE + 0x28 + reg_offset, 1 << bit)
    
    def read_mmio(self, addr: int) -> int:
        """Read from memory-mapped I/O"""
        # In bare metal, this would be direct memory access
        ptr = ctypes.cast(addr, ctypes.POINTER(ctypes.c_uint32))
        return ptr.contents.value
    
    def write_mmio(self, addr: int, value: int):
        """Write to memory-mapped I/O"""
        # In bare metal, this would be direct memory access
        ptr = ctypes.cast(addr, ctypes.POINTER(ctypes.c_uint32))
        ptr.contents = ctypes.c_uint32(value)
    
    def cpu_pause(self):
        """Pause CPU briefly"""
        # In bare metal, this would be x86 PAUSE or ARM WFE instruction
        pass


# Integration with EMBODIOS
def create_inference_engine_for_model(model_path: str) -> EMBODIOSInferenceEngine:
    """Create and initialize inference engine"""
    engine = EMBODIOSInferenceEngine()
    engine.load_model(model_path)
    return engine