from typing import Dict, List, Optional, Union, Any, Tuple
import numpy as np
from enum import Enum

class ModalityType(Enum):
    TEXT = "text"
    IMAGE = "image"
    AUDIO = "audio"
    VIDEO = "video"
    GRAPH = "graph"
    POINT_CLOUD = "point_cloud"
    VOXEL = "voxel"
    LATENT = "latent"
    EMBEDDING = "embedding"

class ModalityConfig:
    """Configuration for different modalities"""
    
    # Default configurations for each modality
    DEFAULTS = {
        ModalityType.TEXT: {
            'dims': 1,  # sequence dimension
            'attention_pattern': 'causal',
            'position_encoding': 'rotary',
            'block_size': 4096
        },
        ModalityType.IMAGE: {
            'dims': 2,  # height, width
            'attention_pattern': 'local',
            'position_encoding': '2d_relative',
            'block_size': 256  # 16x16 patches
        },
        ModalityType.AUDIO: {
            'dims': 1,  # time dimension
            'attention_pattern': 'local',
            'position_encoding': 'rotary',
            'block_size': 8192  # ~10 seconds at 16kHz
        },
        ModalityType.VIDEO: {
            'dims': 3,  # time, height, width
            'attention_pattern': 'local3d',
            'position_encoding': '3d_relative',
            'block_size': 512  # 8x8x8 cube
        },
        ModalityType.GRAPH: {
            'dims': None,  # adjacency based
            'attention_pattern': 'graph',
            'position_encoding': 'structure',
            'block_size': None
        },
        ModalityType.POINT_CLOUD: {
            'dims': 3,  # x, y, z
            'attention_pattern': 'knn',
            'position_encoding': '3d_absolute',
            'block_size': 1024  # points per block
        },
        ModalityType.VOXEL: {
            'dims': 3,  # x, y, z
            'attention_pattern': 'local3d',
            'position_encoding': '3d_relative',
            'block_size': 64  # 4x4x4 cube
        },
        ModalityType.LATENT: {
            'dims': 1,  # latent dimension
            'attention_pattern': 'full',
            'position_encoding': None,
            'block_size': None
        },
        ModalityType.EMBEDDING: {
            'dims': 1,  # embedding dimension
            'attention_pattern': 'full',
            'position_encoding': None,
            'block_size': None
        }
    }
    
    @classmethod
    def get_config(cls, modality: ModalityType) -> Dict[str, Any]:
        """Get configuration for modality"""
        return cls.DEFAULTS[modality].copy()

class ModalityMixer:
    """Handles cross-modal operations"""
    
    def __init__(self, fusion_type: str = "additive"):
        self.fusion_type = fusion_type
        
    def fuse(
        self,
        x: np.ndarray,
        y: np.ndarray,
        x_modality: ModalityType,
        y_modality: ModalityType
    ) -> np.ndarray:
        """Fuse tensors from different modalities"""
        if self.fusion_type == "additive":
            return x + y
        elif self.fusion_type == "multiplicative":
            return x * y
        elif self.fusion_type == "concatenative":
            return np.concatenate([x, y], axis=-1)
        elif self.fusion_type == "attention":
            # Cross-attention between modalities
            q = x @ np.random.randn(x.shape[-1], x.shape[-1])  # Learned projection
            k = y @ np.random.randn(y.shape[-1], x.shape[-1])
            v = y @ np.random.randn(y.shape[-1], x.shape[-1])
            
            scores = q @ k.transpose(-2, -1) / np.sqrt(x.shape[-1])
            attn = np.exp(scores) / np.exp(scores).sum(axis=-1, keepdims=True)
            return attn @ v
            
        raise ValueError(f"Unknown fusion type: {self.fusion_type}")
        
    def unfuse(
        self,
        z: np.ndarray,
        x_modality: ModalityType,
        y_modality: ModalityType
    ) -> Tuple[np.ndarray, np.ndarray]:
        """Separate fused tensor back into modalities"""
        if self.fusion_type in ["additive", "multiplicative"]:
            # Can't perfectly separate, return equal split
            return z/2, z/2
        elif self.fusion_type == "concatenative":
            split_idx = z.shape[-1] // 2
            return z[..., :split_idx], z[..., split_idx:]
        elif self.fusion_type == "attention":
            # Project back to original modalities
            x_proj = z @ np.random.randn(z.shape[-1], z.shape[-1])
            y_proj = z @ np.random.randn(z.shape[-1], z.shape[-1])
            return x_proj, y_proj