from enum import Enum
from dataclasses import dataclass
from typing import Dict, List, Optional, Any
import numpy as np
import torch
import torch.nn as nn

class SignLevel(Enum):
    SYNTACTIC = "syntactic"
    SEMANTIC = "semantic"
    PRAGMATIC = "pragmatic"

@dataclass
class SemioticState:
    sign_level: SignLevel
    meaning_vector: np.ndarray
    context_relations: Dict[str, float]
    interpretation_confidence: float
    sign_vector: np.ndarray
    context_embedding: np.ndarray
    semantic_relations: Dict[str, float]

class SemioticProcessor:
    def __init__(self):
        self.sign_encoder = nn.Sequential(
            nn.Linear(768, 256),
            nn.ReLU(),
            nn.Linear(256, 128)
        )
        self.network_builder = SemioticNetworkBuilder()
        self.interpreter = SignInterpreter()
        self.generator = SignGenerator()
        self.meaning_network = {}
        
    def process_signs(self, input_data: Dict[str, Any]) -> SemioticState:
        network = self.network_builder.construct(input_data)
        interpretation = self.interpreter.interpret(network)
        
        if self._requires_generation(interpretation):
            generated_signs = self.generator.create_signs(interpretation)
            return self._integrate_semiotic_state(interpretation, generated_signs)
        
        return self._create_semiotic_state(interpretation)