from typing import Dict, Any
import torch
import torch.nn as nn
import numpy as np
from .states import AwarenessState, AwarenessLevel

class AwarenessEngine:
    def __init__(self):
        self.attention_network = nn.Sequential(
            nn.Linear(768, 512),
            nn.ReLU(),
            nn.Linear(512, 256)
        )

    async def process(self, input_state: Dict[str, Any]) -> AwarenessState:
        attention_vector = self._compute_attention(input_state)
        awareness_level = self._calculate_awareness(attention_vector)
        level = self._determine_awareness_level(awareness_level)

        cognitive_state = {
            "attention_focus": self._compute_attention_focus(attention_vector),
            "processing_depth": awareness_level,
            "cognitive_load": self._estimate_cognitive_load(input_state)
        }

        return AwarenessState(
            attention_vector=attention_vector.detach().numpy(),
            awareness_level=awareness_level,
            cognitive_state=cognitive_state,
            emotional_valence=self._compute_emotional_valence(input_state),
            consciousness_level=0.8,
            level=level
        )

    def _compute_attention_focus(self, attention_vector: torch.Tensor) -> float:
        return float(torch.mean(attention_vector))

    def _estimate_cognitive_load(self, input_state: Dict[str, Any]) -> float:
        return 0.5  # Default mid-range load

    def _determine_awareness_level(self, awareness_level: float) -> AwarenessLevel:
        if awareness_level > 0.8:
            return AwarenessLevel.TRANSCENDENT
        elif awareness_level > 0.6:
            return AwarenessLevel.INTEGRATED
        elif awareness_level > 0.4:
            return AwarenessLevel.REFLECTIVE
        elif awareness_level > 0.2:
            return AwarenessLevel.PERCEPTUAL
        return AwarenessLevel.BASIC

    def _compute_attention(self, input_state: Dict[str, Any]) -> torch.Tensor:
        return torch.ones(256)

    def _calculate_awareness(self, attention_vector: torch.Tensor) -> float:
        return 0.8

    def _process_cognitive_state(self, input_state: Dict[str, Any]) -> Dict[str, Any]:
        return {"state": "active", "focus_level": 0.9}

    def _compute_emotional_valence(self, input_state: Dict[str, Any]) -> float:
        return 0.5