"""
VGPU-optimized ArgMax Implementation
Handles maximum value computation with hardware acceleration
"""
from typing import Tuple, List, Optional
import array
from ..hardware.hal import HardwareAbstractionLayer, HardwareType

class VGPUArgMax:
    """
    Hardware-accelerated argmax implementation
    Uses VGPU compute units and parallel reduction
    """
    def __init__(self, hal: HardwareAbstractionLayer):
        self.hal = hal
        self.chunk_size = 1024  # Process in chunks for better cache utilization
        
    def argmax(self, tensor_addr: int, shape: Tuple[int, ...], axis: int = -1) -> int:
        """
        Find index of maximum value along specified axis
        Uses hardware-level parallel reduction
        
        Args:
            tensor_addr: Memory address of input tensor
            shape: Shape of input tensor
            axis: Axis along which to find maximum
            
        Returns:
            Index of maximum value
        """
        # Queue hardware instruction for argmax
        instruction = {
            'op': 'argmax',
            'src': tensor_addr,
            'shape': shape,
            'axis': axis
        }
        self.hal.queue_instruction(HardwareType.COMPUTE_UNIT, instruction)
        
        # Execute and get result
        self.hal.flush_queues()
        return self._get_result(tensor_addr)
        
    def batch_argmax(self, tensor_addr: int, shape: Tuple[int, ...],
                    axis: int = -1, batch_size: int = 1) -> List[int]:
        """
        Compute argmax for batched input
        Processes multiple sequences in parallel
        
        Args:
            tensor_addr: Memory address of input tensor
            shape: Shape of input tensor
            axis: Axis along which to find maximum
            batch_size: Number of sequences in batch
            
        Returns:
            List of indices, one per batch item
        """
        # Queue batched hardware instruction
        instruction = {
            'op': 'batch_argmax',
            'src': tensor_addr,
            'shape': shape,
            'axis': axis,
            'batch_size': batch_size
        }
        self.hal.queue_instruction(HardwareType.COMPUTE_UNIT, instruction)
        
        # Execute and get results
        self.hal.flush_queues()
        return self._get_batch_result(tensor_addr, batch_size)
        
    def stream_argmax(self, tensor_addr: int, shape: Tuple[int, ...],
                     axis: int = -1, stream_id: Optional[int] = None) -> int:
        """
        Stream-based argmax computation
        Allows asynchronous execution
        
        Args:
            tensor_addr: Memory address of input tensor
            shape: Shape of input tensor
            axis: Axis along which to find maximum
            stream_id: Optional stream for async execution
            
        Returns:
            Index of maximum value
        """
        # Queue stream instruction
        instruction = {
            'op': 'stream_argmax',
            'src': tensor_addr,
            'shape': shape,
            'axis': axis,
            'stream_id': stream_id
        }
        self.hal.queue_instruction(HardwareType.COMPUTE_UNIT, instruction)
        
        if stream_id is None:
            # Synchronous execution
            self.hal.flush_queues()
            return self._get_result(tensor_addr)
        else:
            # Async execution - return immediately
            return None
            
    def masked_argmax(self, tensor_addr: int, mask_addr: int,
                     shape: Tuple[int, ...], axis: int = -1) -> int:
        """
        Compute argmax with attention mask
        Ignores masked-out values
        
        Args:
            tensor_addr: Memory address of input tensor
            mask_addr: Memory address of attention mask
            shape: Shape of input tensor
            axis: Axis along which to find maximum
            
        Returns:
            Index of maximum unmasked value
        """
        # Queue masked operation
        instruction = {
            'op': 'masked_argmax',
            'src': tensor_addr,
            'mask': mask_addr,
            'shape': shape,
            'axis': axis
        }
        self.hal.queue_instruction(HardwareType.COMPUTE_UNIT, instruction)
        
        # Execute and get result
        self.hal.flush_queues()
        return self._get_result(tensor_addr)
        
    def _get_result(self, tensor_addr: int) -> int:
        """Get result from hardware computation"""
        # Read result from device memory
        result_addr = tensor_addr + 0x1000  # Offset for result storage
        return self.hal.read_memory(result_addr, 4)[0]  # Read 4 bytes for int32
        
    def _get_batch_result(self, tensor_addr: int, batch_size: int) -> List[int]:
        """Get batch results from hardware computation"""
        # Read batch results
        result_addr = tensor_addr + 0x1000
        result_bytes = self.hal.read_memory(result_addr, 4 * batch_size)
        
        # Convert to integers
        results = []
        for i in range(0, len(result_bytes), 4):
            val = int.from_bytes(result_bytes[i:i+4], byteorder='little')
            results.append(val)
            
        return results

class ArgMaxKernel:
    """
    VGPU Kernel for argmax computation
    Implements the low-level hardware operations
    """
    @staticmethod
    def parallel_reduction(values: array.array, start_idx: int, end_idx: int) -> Tuple[int, float]:
        """
        Parallel reduction to find maximum value and index
        
        Args:
            values: Input array
            start_idx: Start index for this reduction
            end_idx: End index for this reduction
            
        Returns:
            Tuple of (max_index, max_value)
        """
        max_idx = start_idx
        max_val = values[start_idx]
        
        # Parallel scan in hardware
        for i in range(start_idx + 1, end_idx):
            val = values[i]
            if val > max_val:
                max_idx = i
                max_val = val
                
        return max_idx, max_val
        
    @staticmethod
    def chunked_reduction(values: array.array, chunk_size: int) -> int:
        """
        Process large arrays in chunks for better cache utilization
        
        Args:
            values: Input array
            chunk_size: Size of chunks to process
            
        Returns:
            Index of global maximum
        """
        n = len(values)
        chunk_results = []
        
        # Process chunks
        for start in range(0, n, chunk_size):
            end = min(start + chunk_size, n)
            max_idx, max_val = ArgMaxKernel.parallel_reduction(values, start, end)
            chunk_results.append((max_idx, max_val))
            
        # Find global maximum
        global_max_idx = chunk_results[0][0]
        global_max_val = chunk_results[0][1]
        
        for idx, val in chunk_results[1:]:
            if val > global_max_val:
                global_max_idx = idx
                global_max_val = val
                
        return global_max_idx
        
    @staticmethod
    def masked_reduction(values: array.array, mask: array.array,
                        start_idx: int, end_idx: int) -> Tuple[int, float]:
        """
        Reduction with attention mask
        
        Args:
            values: Input array
            mask: Attention mask (0 = masked)
            start_idx: Start index
            end_idx: End index
            
        Returns:
            Tuple of (max_index, max_value)
        """
        max_idx = -1
        max_val = float('-inf')
        
        # Parallel scan with mask
        for i in range(start_idx, end_idx):
            if mask[i] != 0:  # Check mask
                val = values[i]
                if val > max_val:
                    max_idx = i
                    max_val = val
                    
        return max_idx, max_val
