from dataclasses import dataclass
from typing import Dict, Any, List
import torch
import torch.nn as nn

@dataclass
class PerceptionState:
    visual_data: torch.Tensor
    audio_data: torch.Tensor
    text_data: torch.Tensor
    context_vector: torch.Tensor
    attention_weights: Dict[str, float]

class VisualProcessor(nn.Module):
    def __init__(self):
        super().__init__()
        # Visual processing layers would be defined here

    def forward(self, visual_input):
        # Process visual input
        return visual_input if visual_input is not None else torch.zeros(1)

class AudioProcessor(nn.Module):
    def __init__(self):
        super().__init__()
        # Audio processing layers would be defined here

    def forward(self, audio_input):
        # Process audio input
        return audio_input if audio_input is not None else torch.zeros(1)

class TextProcessor(nn.Module):
    def __init__(self):
        super().__init__()
        # Text processing layers would be defined here

    def forward(self, text_input):
        # Process text input
        return text_input if text_input is not None else torch.zeros(1)

class ModalityFusion(nn.Module):
    def __init__(self):
        super().__init__()
        # Fusion layers would be defined here

    def forward(self, visual, audio, text):
        # Fusion logic
        return torch.cat([visual, audio, text], dim=-1) if all(x is not None for x in [visual, audio, text]) else torch.zeros(1)

class MultiModalEncoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.visual_encoder = VisualProcessor()
        self.audio_encoder = AudioProcessor()
        self.text_encoder = TextProcessor()
        self.fusion_layer = ModalityFusion()

    def forward(self, inputs: Dict[str, torch.Tensor]) -> PerceptionState:
        visual_features = self.visual_encoder(inputs.get('visual'))
        audio_features = self.audio_encoder(inputs.get('audio'))
        text_features = self.text_encoder(inputs.get('text'))

        fused_representation = self.fusion_layer(
            visual_features,
            audio_features,
            text_features
        )

        return self._create_perception_state(visual_features, audio_features, text_features, fused_representation)

    def _create_perception_state(self, visual_features, audio_features, text_features, fused_representation):
        # Create an attention weights dictionary
        attention_weights = {
            'visual': 0.33,
            'audio': 0.33,
            'text': 0.34
        }

        return PerceptionState(
            visual_data=visual_features,
            audio_data=audio_features,
            text_data=text_features,
            context_vector=fused_representation,
            attention_weights=attention_weights
        )