"""
Advanced tensor core with modality support for virtual GPU simulation.
Extends TensorCore with modality-aware operations and distributed computing.
"""

import numpy as np
from typing import Optional, Dict, Any, Tuple, List, Union
from .modality import ModalityType, ModalityConfig, ModalityMixer
from .tensor_ops import TensorOps

class ModalityAwareTensorCore(TensorOps):
    """TensorCore extension with modality support"""
    
    def __init__(
        self,
        bits: int = 2,
        memory_size: Optional[int] = None,
        bandwidth_tbps: float = 10000,
        sm=None,
        storage=None,
        device: Optional[str] = None
    ):
        super().__init__(device)
        
        # Initialize base TensorCore components
        self.bits = bits
        self.bandwidth_tbps = bandwidth_tbps
        self.sm = sm
        self.storage = storage
        
        # Initialize modality components
        self.modality_mixer = ModalityMixer()
        self._active_modality: Optional[ModalityType] = None
        self._modality_configs: Dict[ModalityType, Dict[str, Any]] = {}
        
        # Virtual memory space for modalities
        self.modality_memory: Dict[ModalityType, Dict[str, np.ndarray]] = {
            modality: {} for modality in ModalityType
        }
        
    def set_modality(self, modality: ModalityType) -> None:
        """Set active modality for subsequent operations"""
        self._active_modality = modality
        if modality not in self._modality_configs:
            self._modality_configs[modality] = ModalityConfig.get_config(modality)
            
    def get_active_modality(self) -> Optional[ModalityType]:
        """Get currently active modality"""
        return self._active_modality
        
    def matmul(
        self,
        x: np.ndarray,
        y: np.ndarray,
        x_modality: Optional[ModalityType] = None,
        y_modality: Optional[ModalityType] = None
    ) -> np.ndarray:
        """Matrix multiplication with modality handling"""
        x_mod = x_modality or self._active_modality
        y_mod = y_modality or self._active_modality
        
        return super().matmul(x, y, x_mod, y_mod)
        
    def conv(
        self,
        x: np.ndarray,
        weight: np.ndarray,
        stride: Union[int, Tuple[int, ...]] = 1,
        padding: Union[int, Tuple[int, ...]] = 0,
        modality: Optional[ModalityType] = None
    ) -> np.ndarray:
        """Convolution with modality handling"""
        mod = modality or self._active_modality
        return super().conv(x, weight, stride, padding, mod)
        
    def attention(
        self,
        q: np.ndarray,
        k: np.ndarray,
        v: np.ndarray,
        mask: Optional[np.ndarray] = None,
        modality: Optional[ModalityType] = None
    ) -> np.ndarray:
        """Attention with modality handling"""
        mod = modality or self._active_modality
        return super().attention(q, k, v, mod, mask)
        
    def pool(
        self,
        x: np.ndarray,
        kernel_size: Union[int, Tuple[int, ...]],
        stride: Optional[Union[int, Tuple[int, ...]]] = None,
        mode: str = 'max',
        modality: Optional[ModalityType] = None
    ) -> np.ndarray:
        """Pooling with modality handling"""
        mod = modality or self._active_modality
        return super().pool(x, kernel_size, stride, mode, mod)
        
    def normalize(
        self,
        x: np.ndarray,
        modality: Optional[ModalityType] = None,
        eps: float = 1e-5
    ) -> np.ndarray:
        """Normalization with modality handling"""
        mod = modality or self._active_modality
        return super().normalize(x, mod, eps)
        
    def fuse_modalities(
        self,
        x: np.ndarray,
        y: np.ndarray,
        x_modality: ModalityType,
        y_modality: ModalityType
    ) -> np.ndarray:
        """Fuse tensors from different modalities"""
        return self.modality_mixer.fuse(x, y, x_modality, y_modality)
        
    def unfuse_modalities(
        self,
        z: np.ndarray,
        x_modality: ModalityType,
        y_modality: ModalityType
    ) -> Tuple[np.ndarray, np.ndarray]:
        """Separate fused tensor back into modalities"""
        return self.modality_mixer.unfuse(z, x_modality, y_modality)
        
    def store_modality_tensor(
        self,
        data: np.ndarray,
        modality: ModalityType,
        key: str
    ) -> None:
        """Store tensor in modality-specific memory space"""
        self.modality_memory[modality][key] = data
        
    def load_modality_tensor(
        self,
        modality: ModalityType,
        key: str
    ) -> Optional[np.ndarray]:
        """Load tensor from modality-specific memory space"""
        return self.modality_memory[modality].get(key)
        
    def clear_modality_memory(
        self,
        modality: Optional[ModalityType] = None
    ) -> None:
        """Clear modality-specific memory space"""
        if modality:
            self.modality_memory[modality].clear()
        else:
            for mod in self.modality_memory:
                self.modality_memory[mod].clear()