"""
Main entry point for Helium multimodal framework.
Provides high-level interface for model inference with modality support.
"""

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

class HeliumMultiModal:
    """High-level interface for multimodal deep learning"""
    
    def __init__(
        self,
        num_tensor_cores: int = 8000,
        bits: int = 2,
        memory_size: Optional[int] = None,
        bandwidth_tbps: float = 10000
    ):
        # Initialize virtual GPU
        self.gpu = ModalityAwareVirtualGPU(
            num_tensor_cores=num_tensor_cores,
            bits=bits,
            memory_size=memory_size,
            bandwidth_tbps=bandwidth_tbps
        )
        
        # Assign tensor cores to modalities
        self._assign_cores_to_modalities()
        
        # Initialize ops
        self.ops = TensorOps()
        
    def _assign_cores_to_modalities(self) -> None:
        """Assign tensor cores to different modalities"""
        core_assignments = [
            (ModalityType.TEXT, 0),
            (ModalityType.IMAGE, 0),
            (ModalityType.AUDIO, 0),
            (ModalityType.VIDEO, 0)
        ]
        
        for modality, core_idx in core_assignments:
            self.gpu.assign_modality_to_core(modality, core_idx)
            
    def process_text(
        self,
        text_inputs: np.ndarray,
        **kwargs
    ) -> np.ndarray:
        """Process text inputs"""
        return self.gpu.matmul(
            text_inputs,
            np.eye(text_inputs.shape[-1]),
            x_modality=ModalityType.TEXT
        )
        
    def process_image(
        self,
        image_inputs: np.ndarray,
        **kwargs
    ) -> np.ndarray:
        """Process image inputs"""
        return self.gpu.conv(
            image_inputs,
            np.random.randn(3, 3, 3, 64),
            stride=1,
            padding=1,
            modality=ModalityType.IMAGE
        )
        
    def process_audio(
        self,
        audio_inputs: np.ndarray,
        **kwargs
    ) -> np.ndarray:
        """Process audio inputs"""
        return self.gpu.conv(
            audio_inputs,
            np.random.randn(16, 1, 64),
            stride=2,
            padding=7,
            modality=ModalityType.AUDIO
        )
        
    def process_video(
        self,
        video_inputs: np.ndarray,
        **kwargs
    ) -> np.ndarray:
        """Process video inputs"""
        return self.gpu.conv(
            video_inputs,
            np.random.randn(3, 3, 3, 3, 64),
            stride=(1, 2, 2),
            padding=(1, 1, 1),
            modality=ModalityType.VIDEO
        )
        
    def fuse_modalities(
        self,
        outputs: Dict[ModalityType, np.ndarray]
    ) -> np.ndarray:
        """Fuse outputs from different modalities"""
        fused = None
        modalities = list(outputs.keys())
        
        for i in range(len(modalities)):
            for j in range(i + 1, len(modalities)):
                mod_i, mod_j = modalities[i], modalities[j]
                x, y = outputs[mod_i], outputs[mod_j]
                
                if fused is None:
                    fused = self.gpu.fuse_modalities(x, y, mod_i, mod_j)
                else:
                    fused = self.gpu.fuse_modalities(fused, y, mod_i, mod_j)
                    
        return fused if fused is not None else next(iter(outputs.values()))
        
    def process_batch(
        self,
        batch: Dict[ModalityType, np.ndarray],
        **kwargs
    ) -> np.ndarray:
        """Process a batch of multimodal inputs"""
        outputs = {}
        
        # Process each modality
        for modality, inputs in batch.items():
            if modality == ModalityType.TEXT:
                outputs[modality] = self.process_text(inputs, **kwargs)
            elif modality == ModalityType.IMAGE:
                outputs[modality] = self.process_image(inputs, **kwargs)
            elif modality == ModalityType.AUDIO:
                outputs[modality] = self.process_audio(inputs, **kwargs)
            elif modality == ModalityType.VIDEO:
                outputs[modality] = self.process_video(inputs, **kwargs)
                
        # Fuse modality outputs
        return self.fuse_modalities(outputs)
        
    def cleanup(self) -> None:
        """Clean up resources"""
        self.gpu.clear_memory()