app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from huggingface_hub import snapshot_download
from safetensors.torch import load_file
import numpy as np
import torch.nn as nn
import networkx as nx
from dataclasses import dataclass
from typing import List, Dict, Any, Tuple, Optional, Set, Protocol, Union
from collections import defaultdict
import heapq
from abc import ABC, abstractmethod
from enum import Enum
import concurrent.futures
from concurrent.futures import ThreadPoolExecutor
import time
from datetime import datetime
import random
import pandas as pd
from torch.utils.data import Dataset, DataLoader, TensorDataset
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import os
from sklearn.preprocessing import MinMaxScaler

# --- Enhanced Constants ---
MAX_SHORT_TERM_MEMORY = 10000  # Increased capacity
MAX_LONG_TERM_MEMORY = 100000  # Increased capacity
UNCERTAINTY_QUANTIFICATION_METHODS = ['bayesian', 'ensemble', 'confidence_scores', 'gaussian_processes']
QUANTUM_ALGORITHMS = ["Shor's", "Grover's", "QAOA", "VQE", "HHL"]
NETWORK_ADAPTATION_THRESHOLD = 0.5  # Adjusted threshold
REAL_WORLD_INTERACTION_TYPES = ['sensor_read', 'actuator_execute', 'data_stream', 'feedback_loop']
CONTEXT_SIMILARITY_THRESHOLD = 0.4  # Lower threshold for broader context
CONTEXT_DECAY_RATE = 0.0005  # Slower decay
MAX_CONTEXT_SIZE = 10000  # Increased context size
KNOWLEDGE_RELATIONSHIP_DECAY_RATE = 0.0005  # Slower decay
MAX_KNOWLEDGE_NODES = 50000  # Increased node capacity
CAUSAL_INFERENCE_THRESHOLD = 0.5  # Adjusted threshold
CAUSAL_DECAY_RATE = 0.0005  # Slower decay
MAX_CAUSAL_ENTRIES = 10000  # Increased entry capacity
MAX_DOMAIN_MODELS = 200  # Increased model capacity
MAX_TRANSFER_MAPPINGS = 500  # Increased mapping capacity
ADAPTIVE_LEARNING_INITIAL_LR = 0.2  # Higher initial learning rate
ADAPTIVE_LEARNING_MIN_LR = 0.0000001  # Lower minimum learning rate
ADAPTIVE_LEARNING_MAX_LR = 1.0  # Increased max learning rate
EMOTION_CATEGORIES = ['anger', 'fear', 'joy', 'love', 'sadness', 'surprise', 'neutral', 'anticipation', 'trust', 'disgust', 'contentment', 'boredom', 'curiosity', 'awe']  # Expanded emotion categories
COLLABORATIVE_CONSENSUS_THRESHOLD = 0.7  # Adjusted threshold
MAX_COLLABORATIVE_AGENTS = 100  # Increased agent capacity
SOLUTION_HISTORY_SIZE = 1000  # Increased history size
MAX_ETHICAL_VIOLATIONS_HISTORY = 2000  # Increased history capacity
MAX_RESOURCE_HISTORY = 5000  # Increased history size
MAX_PREDICTIVE_MODELS = 100  # Increased model capacity
MAX_PREDICTION_HISTORY = 10000  # Increased history capacity
SHORT_TERM_MEMORY_CAPACITY = 1000  # Increased capacity
MEMORY_CONSOLIDATION_THRESHOLD = 1.5  # Lower threshold for faster consolidation
LONG_TERM_MEMORY_CAPACITY = 10000  # Increased capacity
MAX_COGNITIVE_STYLE_HISTORY = 500  # Increased history size
GOAL_ALIGNMENT_THRESHOLD = 0.5  # Adjusted threshold
MAX_GOALS = 500  # Increased goal capacity
MAX_SAFETY_CONSTRAINTS = 500  # Increased constraint capacity
MAX_LANGUAGES = 100  # Increased language capacity
QUANTUM_MAX_RESULTS_HISTORY = 1000  # Increased history size
ADAPTIVE_NEURAL_NETWORK_MAX_LAYERS = 30  # Increased layer capacity
ADAPTIVE_NEURAL_NETWORK_MAX_LAYER_SIZE = 32768  # Increased layer size
MAX_INTERACTION_HISTORY = 5000  # Increased history capacity
RESPONSE_MODEL_HIDDEN_DIM = 8192 # Increased response model hidden dimensions
RESPONSE_MODEL_OUTPUT_DIM = 4096 # Increased response model output dimensions
TRAINING_BATCH_SIZE = 128 # Increased training batch size
TRAINING_EPOCHS = 20 # Increased training epochs
TRAINING_LEARNING_RATE = 0.0001 # Changed learning rate for better convergence
TEXT_EMBEDDING_DIM = 768  # Standard dimension for text embeddings
VISUAL_EMBEDDING_DIM = 2048  # Dimension for visual embeddings
AUDIO_EMBEDDING_DIM = 512 # Dimension for audio embeddings

# --- Enhanced Thinking Styles ---
class ThinkingStyle(Enum):
    ANALYTICAL = "analytical"
    CREATIVE = "creative"
    CRITICAL = "critical"
    SYSTEMATIC = "systematic"
    LATERAL = "lateral"
    INTUITIVE = "intuitive"
    COLLABORATIVE = "collaborative"
    ETHICAL = "ethical"
    PRAGMATIC = "pragmatic"
    INNOVATIVE = "innovative"
    REFLECTIVE = "reflective"
    EXPLORATORY = "exploratory"
    STRATEGIC = "strategic"
    ABSTRACT = "abstract"
    CONCRETE = "concrete"
    EMPATHETIC = "empathetic"
    HOLISTIC = "holistic"
    DIVERGENT = "divergent"
    CONVERGENT = "convergent"
    ADAPTIVE = "adaptive"

# --- Enhanced Generalized Context Manager ---
class GeneralizedContextManager:
    def __init__(self, similarity_threshold=CONTEXT_SIMILARITY_THRESHOLD, context_decay_rate=CONTEXT_DECAY_RATE, max_context_size=MAX_CONTEXT_SIZE):
        self.context_graph = nx.DiGraph()
        self.temporal_window = []
        self.context_cache = {}
        self.similarity_threshold = similarity_threshold
        self.context_decay_rate = context_decay_rate
        self.max_context_size = max_context_size
        self.event_listeners = defaultdict(list)

    def add_context(self, context_id: str, context_data: Dict[str, Any],
                    timestamp: Optional[datetime] = None, metadata: Optional[Dict[str, Any]] = None):
        if timestamp is None:
            timestamp = datetime.now()

        if context_id not in self.context_graph:
            self.context_graph.add_node(context_id,
                                      data=context_data,
                                      timestamp=timestamp,
                                      metadata=metadata or {})
        else:
            self.context_graph.nodes[context_id]['data'].update(context_data)
            self.context_graph.nodes[context_id]['timestamp'] = timestamp
            if metadata:
                self.context_graph.nodes[context_id]['metadata'].update(metadata)

        self.temporal_window.append((timestamp, context_id))
        self.temporal_window.sort()

        self._update_relationships(context_id)
        self._manage_context_size()
        self._decay_context()
        self._trigger_event(context_id, 'context_added')

    def _calculate_similarity(self, data1: Dict[str, Any], data2: Dict[str, Any]) -> float:
        keys1 = set(data1.keys())
        keys2 = set(data2.keys())
        common_keys = keys1.intersection(keys2)

        if not common_keys:
            return 0.0

        similarity_sum = 0.0
        for key in common_keys:
            value1 = data1[key]
            value2 = data2[key]

            if isinstance(value1, (int, float)) and isinstance(value2, (int, float)):
                similarity_sum += 1.0 / (1.0 + abs(value1 - value2))
            elif isinstance(value1, str) and isinstance(value2, str):
                similarity_sum += float(value1 == value2)
            elif isinstance(value1, (list, tuple, np.ndarray)) and isinstance(value2, (list, tuple, np.ndarray)):
                if isinstance(value1, np.ndarray):
                    value1 = value1.flatten()
                if isinstance(value2, np.ndarray):
                    value2 = value2.flatten()
                if value1.size > 0 and value2.size > 0:
                    dot_product = np.dot(value1, value2)
                    magnitude1 = np.linalg.norm(value1)
                    magnitude2 = np.linalg.norm(value2)
                    if magnitude1 != 0 and magnitude2 != 0:
                        similarity_sum += dot_product / (magnitude1 * magnitude2)
            elif type(value1) == type(value2):
                similarity_sum += float(value1 == value2)
        return similarity_sum / len(common_keys) if len(common_keys) > 0 else 0.0

    def _update_relationships(self, context_id: str):
        context_data = self.context_graph.nodes[context_id]['data']
        for existing_id in self.context_graph.nodes():
            if existing_id != context_id:
                similarity = self._calculate_similarity(
                    context_data,
                    self.context_graph.nodes[existing_id]['data']
                )
                if similarity > self.similarity_threshold:
                    if not self.context_graph.has_edge(context_id, existing_id):
                        self.context_graph.add_edge(context_id, existing_id, weight=similarity)
                    else:
                        self.context_graph[context_id][existing_id]['weight'] = similarity
                elif self.context_graph.has_edge(context_id, existing_id):
                    self.context_graph.remove_edge(context_id, existing_id)

    def _decay_context(self):
        now = datetime.now()
        self.temporal_window = [(t, c_id) for t, c_id in self.temporal_window if (now - t).total_seconds() < 86400 * 30]  # Increased window to 30 days

        nodes_to_remove = []
        for node, data in self.context_graph.nodes(data=True):
            time_diff = (now - data['timestamp']).total_seconds()
            data['weight'] = data.get('weight', 1.0) * (1 - self.context_decay_rate * time_diff)
            if data['weight'] < 0.00001:  # More aggressive decay
                nodes_to_remove.append(node)

        for node in nodes_to_remove:
            self.context_graph.remove_node(node)
            self._trigger_event(node, 'context_removed')

    def _manage_context_size(self):
        if len(self.context_graph) > self.max_context_size:
            # Remove least recently used or lowest weighted nodes
            sorted_nodes = sorted(self.context_graph.nodes(data=True),
                                  key=lambda x: (x[1]['timestamp'], x[1].get('weight', 1.0)))
            nodes_to_remove = sorted_nodes[:len(self.context_graph) - self.max_context_size]
            for node, _ in nodes_to_remove:
                self.context_graph.remove_node(node)
                self._trigger_event(node, 'context_removed')

    def retrieve_related_context(self, context_id: str, depth: int = 4, min_similarity: float = 0.1) -> Dict[str, Any]:
        related_context = {}
        try:
            neighbors = nx.ego_graph(self.context_graph, context_id, radius=depth)
            for neighbor in neighbors:
                edge_data = self.context_graph.get_edge_data(context_id, neighbor)
                if edge_data is None or edge_data['weight'] >= min_similarity:
                    related_context[neighbor] = self.context_graph.nodes[neighbor]['data']
        except nx.NodeNotFound:
            pass
        return related_context

    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, context_id: str, event_type: str):
        if event_type in self.event_listeners:
            for listener in self.event_listeners[event_type]:
                listener(context_id, event_type, self.context_graph.nodes[context_id])

# --- Enhanced Dynamic Knowledge Graph ---
class DynamicKnowledgeGraph:
    def __init__(self, relationship_decay_rate=KNOWLEDGE_RELATIONSHIP_DECAY_RATE, max_nodes=MAX_KNOWLEDGE_NODES):
        self.knowledge_graph = nx.DiGraph()
        self.temporal_index = defaultdict(list)
        self.relationship_decay_rate = relationship_decay_rate
        self.max_nodes = max_nodes
        self.event_listeners = defaultdict(list)

    def add_knowledge(self, concept: str, properties: Dict[str, Any],
                     timestamp: Optional[datetime]=None, relationships: Optional[Dict[str, float]] = None):
        if timestamp is None:
            timestamp = datetime.now()

        if concept not in self.knowledge_graph:
            self.knowledge_graph.add_node(concept, **properties, timestamp=timestamp)
            self._trigger_event(concept, 'knowledge_added')
        else:
            for key, value in properties.items():
                self.knowledge_graph.nodes[concept][key] = value
            self.knowledge_graph.nodes[concept]['timestamp'] = timestamp
            self._trigger_event(concept, 'knowledge_updated')

        self.temporal_index[timestamp].append(concept)

        if relationships:
            for related_concept, strength in relationships.items():
                self.add_relationship(concept, related_concept, strength)

        self._manage_graph_size()

    def add_relationship(self, concept1: str, concept2: str, strength: float):
        if self.knowledge_graph.has_edge(concept1, concept2):
            self.knowledge_graph[concept1][concept2]['strength'] = strength
        else:
            self.knowledge_graph.add_edge(concept1, concept2, strength=strength)
        self._trigger_event(concept1, 'relationship_added', {'to': concept2, 'strength': strength})

    def query_temporal_slice(self, start_time: datetime,
                           end_time: datetime) -> Set[str]:
        relevant_concepts = set()
        for time, concepts in self.temporal_index.items():
            if start_time <= time <= end_time:
                relevant_concepts.update(concepts)
        return relevant_concepts

    def get_related_concepts(self, concept: str, threshold: float = 0.2, depth: int = 4) -> Dict[str, Dict[str, Any]]:
        related_concepts = {}
        try:
            # Use ego_graph to explore relationships up to the specified depth
            subgraph = nx.ego_graph(self.knowledge_graph, concept, radius=depth)
            for neighbor in subgraph.nodes:
                if neighbor != concept:
                    edge_data = subgraph.get_edge_data(concept, neighbor)
                    # Consider adding a concept if it's directly connected or if it's reachable within the depth
                    if (edge_data and edge_data['strength'] >= threshold) or neighbor in subgraph:
                        related_concepts[neighbor] = self.knowledge_graph.nodes[neighbor]
        except nx.NodeNotFound:
            pass
        return related_concepts

    def decay_relationships(self):
        now = datetime.now()
        for u, v, data in self.knowledge_graph.edges(data=True):
            time_diff = (now - self.knowledge_graph.nodes[u]['timestamp']).total_seconds()
            data['strength'] *= (1 - self.relationship_decay_rate * time_diff)
            if data['strength'] < 0.0001:
              self.knowledge_graph.remove_edge(u,v)
              self._trigger_event(u, 'relationship_removed', {'to': v})

    def _manage_graph_size(self):
        if len(self.knowledge_graph) > self.max_nodes:
            # Remove nodes with the lowest degree (least connected)
            degrees = dict(self.knowledge_graph.degree())
            sorted_nodes = sorted(degrees.items(), key=lambda item: item[1])
            nodes_to_remove = [node for node, degree in sorted_nodes[:len(self.knowledge_graph) - self.max_nodes]]
            for node in nodes_to_remove:
                self.knowledge_graph.remove_node(node)
                self._trigger_event(node, 'knowledge_removed')
                # Remove the node from temporal index as well
                for time, concepts in self.temporal_index.items():
                    if node in concepts:
                        concepts.remove(node)

    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, concept: str, event_type: str, additional_data: Optional[Dict[str, Any]] = None):
        if event_type in self.event_listeners:
            for listener in self.event_listeners[event_type]:
                event_data = {
                    'concept': concept,
                    'event_type': event_type,
                    'timestamp': datetime.now(),
                    'additional_data': additional_data or {}
                }
                listener(event_data)

# --- Enhanced Causal Engine ---
class CausalEngine:
    def __init__(self, inference_threshold=CAUSAL_INFERENCE_THRESHOLD, decay_rate=CAUSAL_DECAY_RATE, max_entries=MAX_CAUSAL_ENTRIES):
        self.causal_graph = nx.DiGraph()
        self.inference_cache = {}
        self.inference_threshold = inference_threshold
        self.decay_rate = decay_rate
        self.max_entries = max_entries
        self.evidence_history = []
        self.event_listeners = defaultdict(list)

    def add_causal_relationship(self, cause: str, effect: str,
                              strength: float, evidence: List[Dict], timestamp: Optional[datetime] = None):
        if timestamp is None:
            timestamp = datetime.now()
        if self.causal_graph.has_edge(cause, effect):
            self.causal_graph[cause][effect]['strength'] = strength
            self.causal_graph[cause][effect]['evidence'].extend(evidence)
            self.causal_graph[cause][effect]['timestamp'] = timestamp
        else:
            self.causal_graph.add_edge(cause, effect,
                                     strength=strength,
                                     evidence=evidence,
                                     timestamp=timestamp)
        self.inference_cache.clear()
        self._trigger_event('relationship_added', {'cause': cause, 'effect': effect, 'strength': strength})

        # Store evidence in history for later analysis or auditing
        self.evidence_history.append({
            'cause': cause,
            'effect': effect,
            'strength': strength,
            'evidence': evidence,
            'timestamp': timestamp
        })
        self._manage_evidence_history()

    def infer_causes(self, effect: str, min_strength: Optional[float] = None, depth: int = 4) -> List[Tuple[str, float]]:
        min_strength = min_strength if min_strength is not None else self.inference_threshold

        # Check if the result is already in the cache
        if effect in self.inference_cache:
            return self.inference_cache[effect]

        causes = []
        # Use breadth-first search to explore causes up to the specified depth
        queue = [(effect, 1.0, 0)]  # (node, cumulative_strength, current_depth)
        visited = {effect}

        while queue:
            current_node, cumulative_strength, current_depth = queue.pop(0)
            if current_depth < depth:
                for predecessor in self.causal_graph.predecessors(current_node):
                    edge_data = self.causal_graph.get_edge_data(predecessor, current_node)
                    new_strength = cumulative_strength * edge_data['strength']
                    if new_strength >= min_strength:
                        causes.append((predecessor, new_strength))
                        if predecessor not in visited:
                            visited.add(predecessor)
                            queue.append((predecessor, new_strength, current_depth + 1))

        causes.sort(key=lambda x: x[1], reverse=True)

        # Cache the result for future use
        self.inference_cache[effect] = causes
        return causes

    def decay_causal_strengths(self):
        now = datetime.now()
        edges_to_remove = []
        for u, v, data in self.causal_graph.edges(data=True):
            time_diff = (now - data['timestamp']).total_seconds() if 'timestamp' in data else 0
            data['strength'] = max(0, data['strength'] - self.decay_rate * time_diff)
            if data['strength'] == 0:
              edges_to_remove.append((u,v))

        for u, v in edges_to_remove:
          self.causal_graph.remove_edge(u,v)
          self._trigger_event('relationship_removed', {'cause': u, 'effect': v})

    def get_top_causes(self, effect: str, top_n: int = 10) -> List[Tuple[str, float]]:
        causes = self.infer_causes(effect)
        return causes[:top_n]

    def update_causal_strength(self, cause: str, effect: str, new_strength: float, new_evidence: Optional[List[Dict]] = None):
        if self.causal_graph.has_edge(cause, effect):
            self.causal_graph[cause][effect]['strength'] = new_strength
            if new_evidence:
                self.causal_graph[cause][effect]['evidence'].extend(new_evidence)
                self.evidence_history.append({
                    'cause': cause,
                    'effect': effect,
                    'strength': new_strength,
                    'evidence': new_evidence,
                    'timestamp': datetime.now()
                })
            self.inference_cache.clear()  # Invalidate cache
            self._trigger_event('relationship_updated', {'cause': cause, 'effect': effect, 'strength': new_strength})
        else:
            raise ValueError(f"No causal link between {cause} and {effect}")

    def _manage_evidence_history(self):
        # Keep the evidence history within the specified size limit
        if len(self.evidence_history) > self.max_entries:
            self.evidence_history = self.evidence_history[-self.max_entries:]

    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str, event_data: Dict[str, Any]):
        if event_type in self.event_listeners:
            event_data['timestamp'] = datetime.now()
            for listener in self.event_listeners[event_type]:
                listener(event_data)

# --- Enhanced Cross Domain Mastery ---
class CrossDomainMastery:
    def __init__(self, max_models=MAX_DOMAIN_MODELS, max_mappings=MAX_TRANSFER_MAPPINGS):
        self.domain_models = {}
        self.transfer_mappings = {}
        self.domain_experts = {}
        self.max_models = max_models
        self.max_mappings = max_mappings
        self.usage_timestamps = {}
        self.event_listeners = defaultdict(list)

    def add_domain_model(self, domain: str, model: Any):
        if len(self.domain_models) >= self.max_models:
            self._remove_least_used_model()
        self.domain_models[domain] = model
        self.usage_timestamps[domain] = datetime.now()
        self._trigger_event('model_added', {'domain': domain})

    def create_transfer_mapping(self, source_domain: str,
                              target_domain: str,
                              mapping_function: callable):
        if len(self.transfer_mappings) >= self.max_mappings:
            self._remove_least_used_mapping()
        self.transfer_mappings[(source_domain, target_domain)] = mapping_function
        self.usage_timestamps[(source_domain, target_domain)] = datetime.now()
        self._trigger_event('mapping_created', {'source': source_domain, 'target': target_domain})

    def transfer_knowledge(self, source_domain: str,
                          target_domain: str,
                          knowledge: Any) -> Any:
        if (source_domain, target_domain) in self.transfer_mappings:
            mapping = self.transfer_mappings[(source_domain, target_domain)]
            self.usage_timestamps[(source_domain, target_domain)] = datetime.now()
            try:
                transferred_knowledge = mapping(knowledge)
                self._trigger_event('knowledge_transferred', {'source': source_domain, 'target': target_domain})
                return transferred_knowledge
            except Exception as e:
                print(f"Error during knowledge transfer: {e}")
                self._trigger_event('transfer_error', {'source': source_domain, 'target': target_domain, 'error': str(e)})
                return None
        return None

    def register_domain_expert(self, domain: str, expert_function: callable):
        self.domain_experts[domain] = expert_function
        self._trigger_event('expert_registered', {'domain': domain})

    def consult_domain_expert(self, domain: str, query: Any) -> Any:
        if domain in self.domain_experts:
            expert = self.domain_experts[domain]
            self.usage_timestamps[domain] = datetime.now()
            try:
                response = expert(query)
                self._trigger_event('expert_consulted', {'domain': domain})
                return response
            except Exception as e:
                print(f"Error consulting domain expert: {e}")
                self._trigger_event('expert_error', {'domain': domain, 'error': str(e)})
                return None
        return None

    def _remove_least_used_model(self):
        lru_model = min(self.usage_timestamps, key=self.usage_timestamps.get)
        if lru_model in self.domain_models:
          del self.domain_models[lru_model]
          del self.usage_timestamps[lru_model]
          self._trigger_event('model_removed', {'domain': lru_model})

    def _remove_least_used_mapping(self):
        lru_mapping = min(self.usage_timestamps, key=lambda k: self.usage_timestamps[k] if isinstance(k, tuple) else float('inf'))
        if isinstance(lru_mapping, tuple) and lru_mapping in self.transfer_mappings:
          del self.transfer_mappings[lru_mapping]
          del self.usage_timestamps[lru_mapping]
          self._trigger_event('mapping_removed', {'source': lru_mapping[0], 'target': lru_mapping[1]})

    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str, event_data: Dict[str, Any]):
        if event_type in self.event_listeners:
            event_data['timestamp'] = datetime.now()
            for listener in self.event_listeners[event_type]:
                listener(event_data)

# --- Enhanced Intuitive Multimodal Understanding ---
class IntuitiveMultimodalUnderstanding:
    def __init__(self, visual_dim: int = VISUAL_EMBEDDING_DIM, text_dim: int = TEXT_EMBEDDING_DIM, audio_dim: int = AUDIO_EMBEDDING_DIM, fusion_dim: int = 2048, num_heads: int = 32):
        self.visual_encoder = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Flatten(),
            nn.Linear(visual_dim * visual_dim // 1024 * 512, fusion_dim),
            nn.LayerNorm(fusion_dim)
        )

        self.text_encoder = nn.Sequential(
            nn.Linear(text_dim, 2048),
            nn.ReLU(),
            nn.Linear(2048, fusion_dim),
            nn.LayerNorm(fusion_dim)
        )

        self.audio_encoder = nn.Sequential(
            nn.Linear(audio_dim, 1024),
            nn.ReLU(),
            nn.Linear(1024, fusion_dim),
            nn.LayerNorm(fusion_dim)
        )

        self.multimodal_fusion = nn.Sequential(
            nn.Linear(fusion_dim * 3, fusion_dim * 2),
            nn.ReLU(),
            nn.Linear(fusion_dim * 2, fusion_dim),
            nn.LayerNorm(fusion_dim)
        )
        self.fusion_dim = fusion_dim
        self.attention = nn.MultiheadAttention(fusion_dim, num_heads)
        self.event_listeners = defaultdict(list)

    def process_multimodal_input(self,
                               visual_input: Optional[torch.Tensor] = None,
                               text_input: Optional[torch.Tensor] = None,
                               audio_input: Optional[torch.Tensor] = None) -> torch.Tensor:
        encoded_inputs = []

        if visual_input is not None:
            visual_features = self.visual_encoder(visual_input)
            encoded_inputs.append(visual_features)
            self._trigger_event('visual_processed')

        if text_input is not None:
            text_features = self.text_encoder(text_input)
            encoded_inputs.append(text_features)
            self._trigger_event('text_processed')

        if audio_input is not None:
            audio_features = self.audio_encoder(audio_input)
            encoded_inputs.append(audio_features)
            self._trigger_event('audio_processed')

        if encoded_inputs:
            concatenated_inputs = torch.cat(encoded_inputs, dim=1)
            if concatenated_inputs.shape[1] != self.fusion_dim * 3:
                concatenated_inputs = concatenated_inputs.view(-1, self.fusion_dim * 3)
            fused_output = self.multimodal_fusion(concatenated_inputs)

            # Apply attention mechanism
            attn_output, attn_output_weights = self.attention(fused_output.unsqueeze(0), fused_output.unsqueeze(0), fused_output.unsqueeze(0))
            self._trigger_event('fusion_completed')

            return attn_output.squeeze(0)
        return torch.tensor([])

    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str):
        if event_type in self.event_listeners:
            for listener in self.event_listeners[event_type]:
                listener({'event_type': event_type, 'timestamp': datetime.now()})

# --- Enhanced Adaptive Learning ---
class AdaptiveLearning:
    def __init__(self, initial_learning_rate=ADAPTIVE_LEARNING_INITIAL_LR, adaptation_threshold=NETWORK_ADAPTATION_THRESHOLD, min_learning_rate=ADAPTIVE_LEARNING_MIN_LR, max_learning_rate=ADAPTIVE_LEARNING_MAX_LR, learning_rate_decay=0.8, learning_rate_growth=1.3):
        self.feedback_history = []
        self.learning_rate = initial_learning_rate
        self.adaptation_threshold = adaptation_threshold
        self.min_learning_rate = min_learning_rate
        self.max_learning_rate = max_learning_rate
        self.learning_rate_decay = learning_rate_decay
        self.learning_rate_growth = learning_rate_growth
        self.adjustment_history = []
        self.event_listeners = defaultdict(list)

    def process_feedback(self, feedback: Dict[str, Any], timestamp: Optional[datetime]=None):
        if timestamp is None:
            timestamp = datetime.now()
        self.feedback_history.append((feedback, timestamp))
        self._update_learning_parameters(feedback)
        self._trigger_event('feedback_processed', {'performance': feedback.get('performance', 0.0)})

    def _update_learning_parameters(self, feedback: Dict[str, Any]):
        performance = feedback.get('performance', 0.0)
        adjustment_factor = self.learning_rate_growth if performance < self.adaptation_threshold else self.learning_rate_decay
        new_learning_rate = self.learning_rate * adjustment_factor
        new_learning_rate = max(min(new_learning_rate, self.max_learning_rate), self.min_learning_rate)

        # Record adjustment with details
        adjustment_details = {
            'timestamp': datetime.now(),
            'old_learning_rate': self.learning_rate,
            'new_learning_rate': new_learning_rate,
            'performance': performance,
            'adjustment_factor': adjustment_factor
        }
        self.adjustment_history.append(adjustment_details)
        self._trigger_event('learning_rate_adjusted', adjustment_details)

        self.learning_rate = new_learning_rate

    def get_learning_rate(self) -> float:
        return self.learning_rate

    def get_recent_feedback(self, time_window: int = 480) -> List[Dict[str, Any]]:
        now = datetime.now()
        recent_feedback = [f for f, t in self.feedback_history if (now - t).total_seconds() <= time_window]
        return recent_feedback

    def get_adjustment_history(self, last_n: Optional[int] = None) -> List[Dict[str, Any]]:
        if last_n is None:
            return self.adjustment_history
        else:
            return self.adjustment_history[-last_n:]

    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str, event_data: Dict[str, Any]):
        if event_type in self.event_listeners:
            event_data['timestamp'] = datetime.now()
            for listener in self.event_listeners[event_type]:
                listener(event_data)

# --- Enhanced Emotional Intelligence ---
class EmotionalIntelligence:
    def __init__(self, empathy_level: float = 0.9, max_history: int = 1000, emotion_categories: List[str] = EMOTION_CATEGORIES):
        self.emotion_detector = nn.Sequential(
            nn.Linear(1024, 4096),
            nn.ReLU(),
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, len(emotion_categories)),
            nn.Softmax(dim=1)
        )
        self.response_generator = lambda c, e, t: f"Response with refined empathy {e} for {t} (context: {c})"
        self.empathy_level = empathy_level
        self.emotional_history = []
        self.max_history = max_history
        self.emotion_categories = emotion_categories
        self.event_listeners = defaultdict(list)

    def analyze_emotional_context(self, input_data: Dict[str, Any]) -> Dict[str, float]:
        emotional_scores = {}
        text_features = input_data.get('text_features')
        voice_features = input_data.get('voice_features')

        if text_features is not None:
            text_emotion = self._analyze_text_emotion(text_features)
            emotional_scores.update({'text_' + k: v for k, v in text_emotion.items()})
            self._trigger_event('text_emotion_analyzed', text_emotion)

        if voice_features is not None:
            voice_emotion = self._analyze_voice_emotion(voice_features)
            emotional_scores.update({'voice_' + k: v for k, v in voice_emotion.items()})
            self._trigger_event('voice_emotion_analyzed', voice_emotion)

        if emotional_scores:
            self.emotional_history.append((datetime.now(), emotional_scores))
        if len(self.emotional_history) > self.max_history:
            self.emotional_history.pop(0)

        return emotional_scores

    def _analyze_text_emotion(self, text_features: torch.Tensor) -> Dict[str, float]:
        emotion_output = self.emotion_detector(text_features).detach().numpy()[0]
        return dict(zip(self.emotion_categories, emotion_output))

    def _analyze_voice_emotion(self, voice_features: torch.Tensor) -> Dict[str, float]:
        emotion_output = self.emotion_detector(voice_features).detach().numpy()[0]
        return dict(zip(self.emotion_categories, emotion_output))

    def generate_empathetic_response(self,
                                   emotional_context: Dict[str, float],
                                   response_type: str) -> str:
        response = self.response_generator(emotional_context, self.empathy_level, response_type)
        self._trigger_event('empathetic_response_generated', {'response': response})
        return response

    def adjust_empathy_level(self, new_level: float):
        self.empathy_level = max(0.0, min(1.0, new_level))
        self._trigger_event('empathy_level_adjusted', {'new_level': self.empathy_level})

    def get_emotional_state_over_time(self, time_window: int = 480) -> List[Dict[str, float]]:
        now = datetime.now()
        emotional_states = [e for t, e in self.emotional_history if (now - t).total_seconds() <= time_window]
        return emotional_states

    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
        if event_type in self.event_listeners:
            event_data = event_data or {}
            event_data['timestamp'] = datetime.now()
            for listener in self.event_listeners[event_type]:
                listener(event_data)

# --- Enhanced Collaborative Problem Solver ---
class CollaborativeProblemSolver:
    def __init__(self, consensus_threshold: float = COLLABORATIVE_CONSENSUS_THRESHOLD, max_agents: int = MAX_COLLABORATIVE_AGENTS, solution_history_size: int = SOLUTION_HISTORY_SIZE):
        self.agent_pool = []
        self.collaboration_history = []
        self.consensus_threshold = consensus_threshold
        self.max_agents = max_agents
        self.solution_history_size = solution_history_size
        self.event_listeners = defaultdict(list)

    def add_agent(self, agent: Any):
        if len(self.agent_pool) < self.max_agents:
            self.agent_pool.append(agent)
            self._trigger_event('agent_added', {'agent_id': id(agent)})
        else:
            print("Maximum agent pool size reached. Cannot add more agents.")

    def solve_collaboratively(self, problem: Dict[str, Any]) -> Dict[str, Any]:
        solutions = []
        with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_agents) as executor:
            future_to_agent = {executor.submit(agent.solve, problem): agent for agent in self.agent_pool}
            for future in concurrent.futures.as_completed(future_to_agent):
                try:
                    solution = future.result()
                    solutions.append(solution)
                    self._trigger_event('solution_generated', {'agent_id': id(future_to_agent[future])})
                except Exception as exc:
                    print(f"Agent generated an exception: {exc}")
                    self._trigger_event('agent_exception', {'agent_id': id(future_to_agent[future]), 'exception': str(exc)})

        consensus_solution = self._reach_consensus(solutions)
        self.collaboration_history.append({
            'problem': problem,
            'individual_solutions': solutions,
            'consensus': consensus_solution,
            'timestamp': datetime.now()
        })
        self._trigger_event('consensus_reached', {'consensus_solution': consensus_solution})

        # Trim collaboration history if it exceeds the maximum size
        if len(self.collaboration_history) > self.solution_history_size:
            self.collaboration_history = self.collaboration_history[-self.solution_history_size:]

        return consensus_solution

    def _reach_consensus(self, solutions: List[Dict[str, Any]]) -> Dict[str, Any]:
        if not solutions:
            return {}

        scores = defaultdict(float)
        for solution in solutions:
            for key, value in solution.items():
                if isinstance(value, (str, int, float)):
                    scores[(key, value)] += solution.get('confidence', 1.0)

        consensus_solution = {}
        for (key, value), score in scores.items():
            if score >= self.consensus_threshold * len(solutions):
                consensus_solution[key] = value

        if not consensus_solution:
            consensus_solution = max(solutions, key=lambda x: x.get('confidence', 0.0))

        return consensus_solution

    def get_collaboration_history(self, last_n: Optional[int] = None) -> List[Dict[str, Any]]:
        if last_n is None:
            return self.collaboration_history
        else:
            return self.collaboration_history[-last_n:]

    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
        if event_type in self.event_listeners:
            event_data = event_data or {}
            event_data['timestamp'] = datetime.now()
            for listener in self.event_listeners[event_type]:
                listener(event_data)

# --- Enhanced Ethical Constraints ---
class EthicalConstraints:
    def __init__(self, max_violations_history: int = MAX_ETHICAL_VIOLATIONS_HISTORY):
        self.ethical_principles = set()
        self.constraint_violations = []
        self.max_violations_history = max_violations_history
        self.event_listeners = defaultdict(list)

    def add_principle(self, principle: str, validation_function: callable):
        self.ethical_principles.add((principle, validation_function))
        self._trigger_event('principle_added', {'principle': principle})

    def validate_action(self, action: Dict[str, Any]) -> Tuple[bool, List[str]]:
        violations = []
        for principle, validator in self.ethical_principles:
            if not validator(action):
                violations.append(principle)
                self._trigger_event('principle_violated', {'principle': principle, 'action': action})

        is_ethical = len(violations) == 0
        if not is_ethical:
            self.constraint_violations.append({
                'action': action,
                'violations': violations,
                'timestamp': datetime.now()
            })
            self._manage_violations_history()

        return is_ethical, violations

    def get_violation_history(self, last_n: Optional[int] = None) -> List[Dict[str, Any]]:
        if last_n is None:
            return self.constraint_violations
        else:
            return self.constraint_violations[-last_n:]

    def _manage_violations_history(self):
        if len(self.constraint_violations) > self.max_violations_history:
            self.constraint_violations = self.constraint_violations[-self.max_violations_history:]

    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
        if event_type in self.event_listeners:
            event_data = event_data or {}
            event_data['timestamp'] = datetime.now()
            for listener in self.event_listeners[event_type]:
                listener(event_data)

# --- Enhanced Resource Optimizer ---
class ResourceOptimizer:
    def __init__(self, max_resource_history: int = MAX_RESOURCE_HISTORY):
        self.resource_usage = defaultdict(float)
        self.optimization_strategies = {}
        self.resource_history = []
        self.max_resource_history = max_resource_history
        self.event_listeners = defaultdict(list)

    def register_strategy(self, resource_type: str, strategy: callable):
        self.optimization_strategies[resource_type] = strategy
        self._trigger_event('strategy_registered', {'resource_type': resource_type})

    def optimize_resource_usage(self, resource_type: str, current_usage: float) -> Dict[str, Any]:
        optimized_usage = current_usage
        if resource_type in self.optimization_strategies:
            strategy = self.optimization_strategies[resource_type]
            optimized_usage = strategy(current_usage)

        optimization_result = {
            'type': resource_type,
            'original_usage': current_usage,
            'optimized_usage': optimized_usage,
            'timestamp': datetime.now()
        }
        self.resource_history.append(optimization_result)
        self._trigger_event('resource_optimized', optimization_result)
        self._manage_resource_history()

        self.resource_usage[resource_type] += optimized_usage
        return optimization_result

    def get_total_resource_usage(self) -> Dict[str, float]:
        return dict(self.resource_usage)

    def reset_resource_usage(self):
        self.resource_usage.clear()
        self._trigger_event('resource_usage_reset')

    def _manage_resource_history(self):
        if len(self.resource_history) > self.max_resource_history:
            self.resource_history = self.resource_history[-self.max_resource_history:]

    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
        if event_type in self.event_listeners:
            event_data = event_data or {}
            event_data['timestamp'] = datetime.now()
            for listener in self.event_listeners[event_type]:
                listener(event_data)

# --- Enhanced Predictive Model ---
class PredictiveModel:
    def __init__(self, max_models: int = MAX_PREDICTIVE_MODELS, max_prediction_history: int = MAX_PREDICTION_HISTORY):
        self.models = {}
        self.prediction_history = []
        self.max_models = max_models
        self.max_prediction_history = max_prediction_history
        self.model_usage_timestamps = {}
        self.event_listeners = defaultdict(list)

    def add_model(self, model_name: str, model: Any):
        if len(self.models) >= self.max_models:
            self._remove_least_used_model()
        self.models[model_name] = model
        self.model_usage_timestamps[model_name] = datetime.now()
        self._trigger_event('model_added', {'model_name': model_name})

    def make_prediction(self, model_name: str, input_data: Dict[str, Any]) -> Dict[str, Any]:
        if model_name in self.models:
            model = self.models[model_name]
            self.model_usage_timestamps[model_name] = datetime.now()
            try:
                prediction = model(input_data)
                prediction_record = {
                    'model': model_name,
                    'input': input_data,
                    'prediction': prediction,
                    'timestamp': datetime.now()
                }
                self.prediction_history.append(prediction_record)
                self._trigger_event('prediction_made', prediction_record)
                self._manage_prediction_history()
                return prediction
            except Exception as e:
                print(f"Error during prediction with model {model_name}: {e}")
                error_record = {'model': model_name, 'error': str(e), 'timestamp': datetime.now()}
                self._trigger_event('prediction_error', error_record)
                return {'error': f'Prediction failed: {e}'}
        return {'error': 'Model not found'}

    def get_prediction_history(self, last_n: Optional[int] = None) -> List[Dict[str, Any]]:
        if last_n is None:
            return self.prediction_history
        else:
            return self.prediction_history[-last_n:]

    def _remove_least_used_model(self):
        lru_model = min(self.model_usage_timestamps, key=self.model_usage_timestamps.get)
        if lru_model in self.models:
          del self.models[lru_model]
          del self.model_usage_timestamps[lru_model]
          self._trigger_event('model_removed', {'model_name': lru_model})

    def _manage_prediction_history(self):
        if len(self.prediction_history) > self.max_prediction_history:
            self.prediction_history = self.prediction_history[-self.max_prediction_history:]

    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
        if event_type in self.event_listeners:
            event_data = event_data or {}
            event_data['timestamp'] = datetime.now()
            for listener in self.event_listeners[event_type]:
                listener(event_data)

# --- Enhanced Memory Consolidation ---
class MemoryConsolidation:
    def __init__(self, short_term_capacity: int = SHORT_TERM_MEMORY_CAPACITY, consolidation_threshold: int = MEMORY_CONSOLIDATION_THRESHOLD, long_term_capacity: int = LONG_TERM_MEMORY_CAPACITY):
        self.short_term_memory = []
        self.long_term_memory = {}
        self.priority_queue = []
        self.short_term_capacity = short_term_capacity
        self.consolidation_threshold = consolidation_threshold
        self.long_term_capacity = long_term_capacity
        self.access_counts = defaultdict(int)
        self.event_listeners = defaultdict(list)

    def add_memory(self, memory: Dict[str, Any], priority: float):
        self.short_term_memory.append(memory)
        heapq.heappush(self.priority_queue, (-priority, len(self.short_term_memory) - 1))
        self._trigger_event('memory_added', {'type': 'short_term', 'memory': memory})

        if len(self.short_term_memory) > self.short_term_capacity:
            self._consolidate_memories()
            self._manage_long_term_memory()

    def _consolidate_memories(self):
        memory_counts = defaultdict(int)
        for memory in self.short_term_memory:
            key = self._generate_memory_key(memory)
            memory_counts[key] += 1

        consolidated = False
        for key, count in memory_counts.items():
            if count >= self.consolidation_threshold:
                if key not in self.long_term_memory:
                    for mem in self.short_term_memory:
                        if self._generate_memory_key(mem) == key:
                            self.long_term_memory[key] = mem
                            self._trigger_event('memory_added', {'type': 'long_term', 'memory': mem})
                            consolidated = True
                            break

        if consolidated:
            self.short_term_memory = [mem for mem in self.short_term_memory if memory_counts[self._generate_memory_key(mem)] < self.consolidation_threshold]
            self.priority_queue = [(-p, i) for p, i in self.priority_queue if i < len(self.short_term_memory)]
            heapq.heapify(self.priority_queue)

    def _generate_memory_key(self, memory: Dict[str, Any]) -> str:
        key_parts = [str(memory.get('type', 'unknown'))]

        if 'timestamp' in memory:
            key_parts.append(str(memory['timestamp'].timestamp()))

        if 'content' in memory:
            if isinstance(memory['content'], str):
                key_parts.append(memory['content'][:100])  # Increased length for better key uniqueness
            elif isinstance(memory['content'], dict):
                key_parts.extend([f"{k}:{str(v)[:100]}" for k, v in memory['content'].items()])

        return '_'.join(key_parts)

    def retrieve_long_term_memory(self, key: str) -> Optional[Dict[str, Any]]:
        if key in self.long_term_memory:
            self.access_counts[key] += 1
            self._trigger_event('memory_retrieved', {'type': 'long_term', 'key': key})
            return self.long_term_memory[key]
        return None

    def _manage_long_term_memory(self):
        if len(self.long_term_memory) > self.long_term_capacity:
            # Remove least frequently accessed memories
            sorted_memories = sorted(self.long_term_memory.items(), key=lambda item: self.access_counts[item[0]])
            keys_to_remove = [key for key, _ in sorted_memories[:len(self.long_term_memory) - self.long_term_capacity]]
            for key in keys_to_remove:
                del self.long_term_memory[key]
                del self.access_counts[key]
                self._trigger_event('memory_removed', {'type': 'long_term', 'key': key})
    
    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
        if event_type in self.event_listeners:
            event_data = event_data or {}
            event_data['timestamp'] = datetime.now()
            for listener in self.event_listeners[event_type]:
                listener(event_data)

# --- Enhanced Cognitive Style Manager ---
class CognitiveStyleManager:
    def __init__(self, max_style_history: int = MAX_COGNITIVE_STYLE_HISTORY):
        self.available_styles = {}
        self.active_style = None
        self.style_history = []
        self.max_style_history = max_style_history
        self.event_listeners = defaultdict(list)

    def register_style(self, style_name: str, style_parameters: Dict[str, Any]):
        self.available_styles[style_name] = style_parameters
        self._trigger_event('style_registered', {'style_name': style_name})

    def activate_style(self, style_name: str) -> bool:
        if style_name in self.available_styles:
            self.active_style = style_name
            self.style_history.append((style_name, datetime.now()))
            if len(self.style_history) > self.max_style_history:
                self.style_history.pop(0)
            self._trigger_event('style_activated', {'style_name': style_name})
            return True
        return False

    def get_current_style_parameters(self) -> Optional[Dict[str, Any]]:
        if self.active_style:
            return self.available_styles[self.active_style]
        return None

    def get_style_history(self, last_n: Optional[int] = None) -> List[Tuple[str, datetime]]:
        if last_n is None:
            return self.style_history
        else:
            return self.style_history[-last_n:]

    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
        if event_type in self.event_listeners:
            event_data = event_data or {}
            event_data['timestamp'] = datetime.now()
            for listener in self.event_listeners[event_type]:
                listener(event_data)

# --- Enhanced Uncertainty Quantifier ---
class UncertaintyQuantifier:
    def __init__(self):
        self.uncertainty_metrics = {}
        self.confidence_thresholds = {}
        self.event_listeners = defaultdict(list)

    def quantify_uncertainty(self,
                           prediction: Any,
                           method: str = 'bayesian',
                           **kwargs) -> Dict[str, float]:
        if method == 'bayesian':
            result = self._bayesian_uncertainty(prediction, **kwargs)
        elif method == 'ensemble':
            result = self._ensemble_uncertainty(prediction, **kwargs)
        elif method == 'confidence_scores':
            result = self._confidence_score_uncertainty(prediction, **kwargs)
        elif method == 'gaussian_processes':
            result = self._gaussian_process_uncertainty(prediction, **kwargs)
        else:
            result = {'error': 'Unsupported uncertainty quantification method'}

        self._trigger_event('uncertainty_quantified', {'method': method, 'result': result})
        return result

    def _bayesian_uncertainty(self, prediction: Any, **kwargs) -> Dict[str, float]:
        if isinstance(prediction, dict) and 'probabilities' in prediction:
            probabilities = prediction['probabilities']
            if isinstance(probabilities, list):
                entropy = -sum(p * np.log2(p) for p in probabilities if p > 0)
                return {'bayesian_entropy': entropy}
        return {'error': 'Bayesian uncertainty not applicable'}

    def _ensemble_uncertainty(self, prediction: Any, **kwargs) -> Dict[str, float]:
        if isinstance(prediction, list):
            variances = np.var(prediction, axis=0)
            return {'ensemble_variance': variances.tolist() if isinstance(variances, np.ndarray) else variances}
        return {'error': 'Ensemble uncertainty not applicable'}

    def _confidence_score_uncertainty(self, prediction: Any, **kwargs) -> Dict[str, float]:
        if isinstance(prediction, dict) and 'confidence' in prediction:
            confidence = prediction['confidence']
            uncertainty = 1.0 - confidence
            return {'uncertainty': uncertainty}
        return {'error': 'Confidence score uncertainty not applicable'}
    
    def _gaussian_process_uncertainty(self, prediction: Any, **kwargs) -> Dict[str, float]:
        if isinstance(prediction, dict) and 'mean' in prediction and 'std' in prediction:
            std_dev = prediction['std']
            return {'gaussian_process_std': std_dev}
        return {'error': 'Gaussian process uncertainty not applicable'}
    
    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
        if event_type in self.event_listeners:
            event_data = event_data or {}
            event_data['timestamp'] = datetime.now()
            for listener in self.event_listeners[event_type]:
                listener(event_data)

# --- Enhanced Goal Alignment System ---
class GoalAlignmentSystem:
    def __init__(self, alignment_threshold=GOAL_ALIGNMENT_THRESHOLD, max_goals: int = MAX_GOALS, max_safety_constraints: int = MAX_SAFETY_CONSTRAINTS):
        self.goals = []
        self.alignment_metrics = {}
        self.safety_constraints = set()
        self.alignment_threshold = alignment_threshold
        self.max_goals = max_goals
        self.max_safety_constraints = max_safety_constraints
        self.event_listeners = defaultdict(list)

    def add_goal(self, goal: Dict[str, Any]):
        if len(self.goals) >= self.max_goals:
            self._remove_lowest_priority_goal()

        if not goal.get('id'):
            goal['id'] = f"goal_{len(self.goals)}"
        self.goals.append(goal)
        self._update_alignment_metrics(goal)
        self._trigger_event('goal_added', {'goal_id': goal['id']})

    def _remove_lowest_priority_goal(self):
        lowest_priority_goal = min(self.goals, key=lambda g: g.get('priority', 0))
        self.goals.remove(lowest_priority_goal)
        self._trigger_event('goal_removed', {'goal_id': lowest_priority_goal['id']})

    def _update_alignment_metrics(self, goal: Dict[str, Any]):
        for existing_goal in self.goals:
            if existing_goal['id'] != goal['id']:
                alignment_score = self._calculate_alignment(existing_goal, goal)
                self.alignment_metrics[(existing_goal['id'], goal['id'])] = alignment_score
                self.alignment_metrics[(goal['id'], existing_goal['id'])] = alignment_score

    def _calculate_alignment(self, goal1: Dict[str, Any], goal2: Dict[str, Any]) -> float:
        similarity = 0.0
        if goal1.get('type') == goal2.get('type'):
            similarity += 0.4
            if goal1.get('target') == goal2.get('target'):
                similarity += 0.4
            if goal1.get('priority', 0) == goal2.get('priority', 0):
                similarity += 0.2
        else:
          similarity += 0.1

        return min(similarity, 1.0)

    def check_alignment(self, goal_id: str) -> Dict[str, float]:
        alignments = {}
        for other_goal in self.goals:
            if other_goal['id'] != goal_id:
                alignment_score = self.alignment_metrics.get((goal_id, other_goal['id']), 0.0)
                if alignment_score >= self.alignment_threshold:
                    alignments[other_goal['id']] = alignment_score
        return alignments

    def add_safety_constraint(self, constraint: str, details: Optional[Dict[str, Any]] = None):
        if len(self.safety_constraints) < self.max_safety_constraints:
            self.safety_constraints.add((constraint, details or {}))
            self._trigger_event('safety_constraint_added', {'constraint': constraint, 'details': details})
        else:
            print("Maximum number of safety constraints reached.")

    def validate_goal_against_constraints(self, goal: Dict[str, Any]) -> Tuple[bool, List[str]]:
        violations = []
        for constraint, details in self.safety_constraints:
            if "no_harm" == constraint and goal.get("action") == "harm":
                violations.append(constraint)
            if "user_autonomy" == constraint and not goal.get("user_approved", False):
                violations.append(constraint)
            if "avoid_deception" == constraint and goal.get("deceptive", False):
                violations.append(constraint)
            if "ensure_privacy" == constraint and goal.get("privacy_risk", False):
              violations.append(constraint)
            if "promote_fairness" == constraint and goal.get("unfair", False):
              violations.append(constraint)

        is_safe = len(violations) == 0
        return is_safe, violations
    
    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
        if event_type in self.event_listeners:
            event_data = event_data or {}
            event_data['timestamp'] = datetime.now()
            for listener in self.event_listeners[event_type]:
                listener(event_data)

# --- Enhanced Multi-Language Creativity ---
class MultiLanguageCreativity:
    def __init__(self, max_languages: int = MAX_LANGUAGES):
        self.language_models = {}
        self.creativity_engines = {}
        self.max_languages = max_languages
        self.event_listeners = defaultdict(list)

    def add_language_model(self, language: str, model: Any):
        if len(self.language_models) < self.max_languages:
            self.language_models[language] = model
            self._trigger_event('language_model_added', {'language': language})
        else:
            print("Maximum number of language models reached.")

    def add_creativity_engine(self, language: str, engine: Any):
        if len(self.creativity_engines) < self.max_languages:
            self.creativity_engines[language] = engine
            self._trigger_event('creativity_engine_added', {'language': language})
        else:
            print("Maximum number of creativity engines reached.")

    def generate_creative_content(self,
                                prompt: str,
                                language: str,
                                creativity_level: float) -> str:
        if language in self.language_models:
            if language in self.creativity_engines:
                base_content = self.language_models[language](prompt)
                creative_content = self.creativity_engines[language](
                    base_content,
                    creativity_level
                )
                self._trigger_event('creative_content_generated', {'language': language, 'creativity_level': creativity_level})
                return creative_content
            else:
                return self.language_models[language](prompt)
        return f"Unsupported language: {language}"

    def list_supported_languages(self) -> List[str]:
        return list(self.language_models.keys())

    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
        if event_type in self.event_listeners:
            event_data = event_data or {}
            event_data['timestamp'] = datetime.now()
            for listener in self.event_listeners[event_type]:
                listener(event_data)

# --- Enhanced Quantum Processor ---
class QuantumProcessor:
    def __init__(self, max_results_history: int = QUANTUM_MAX_RESULTS_HISTORY):
        self.quantum_circuit = None
        self.classical_interface = None
        self.results_history = []
        self.max_results_history = max_results_history
        self.event_listeners = defaultdict(list)

    def initialize_quantum_circuit(self, num_qubits: int):
        print(f"Initializing quantum circuit with {num_qubits} qubits.")
        self.quantum_circuit = {"num_qubits": num_qubits, "circuit": []}
        self._trigger_event('quantum_circuit_initialized', {'num_qubits': num_qubits})

    def add_gate(self, gate_type: str, target_qubits: List[int], params: Optional[Dict[str, Any]] = None):
        if self.quantum_circuit is None:
            raise ValueError("Quantum circuit not initialized")
        self.quantum_circuit["circuit"].append({"gate": gate_type, "targets": target_qubits, "params": params or {}})
        print(f"Added {gate_type} gate on qubits {target_qubits} with params {params}")
        self._trigger_event('gate_added', {'gate_type': gate_type, 'target_qubits': target_qubits, 'params': params})

    def run_quantum_algorithm(self,
                            algorithm_type: str,
                            input_data: Dict[str, Any]) -> Dict[str, Any]:
        if self.quantum_circuit is None:
            return {"error": "Quantum circuit not initialized"}

        try:
            print(f"Executing {algorithm_type} quantum algorithm")
            if algorithm_type == "Shor's":
                result = self._shors_algorithm(input_data)
            elif algorithm_type == "Grover's":
                result = self._grovers_algorithm(input_data)
            elif algorithm_type == "QAOA":
                result = self._qaoa_algorithm(input_data)
            elif algorithm_type == "VQE":
                result = self._vqe_algorithm(input_data)
            elif algorithm_type == "HHL":
                result = self._hhl_algorithm(input_data)
            else:
                result = self._execute_quantum_computation(algorithm_type, input_data)

            result_record = {
                "algorithm": algorithm_type,
                "input": input_data,
                "result": result,
                "timestamp": datetime.now()
            }
            self.results_history.append(result_record)
            self._trigger_event('quantum_algorithm_executed', result_record)
            self._manage_results_history()
            return {"result": result, "status": "success"}
        except Exception as e:
            print(f"Error during quantum computation: {e}")
            error_record = {"error": str(e), "status": "failed", "timestamp": datetime.now()}
            self._trigger_event('quantum_computation_error', error_record)
            return {"error": str(e), "status": "failed"}

    def _execute_quantum_computation(self, algorithm_type: str, input_data: Dict[str, Any]) -> Any:
        print(f"Simulating {algorithm_type} with input: {input_data}")
        time.sleep(random.uniform(1, 3))
        if random.random() < 0.2:  # Increased error rate for simulation
            raise Exception("Quantum decoherence error")

        return {"simulated_output": random.randint(0, 1000)}  # Increased range for simulation

    def _shors_algorithm(self, input_data: Dict[str, Any]) -> Any:
        number_to_factor = input_data.get("number", 15)
        print(f"Applying Shor's algorithm to factor {number_to_factor}")
        if number_to_factor == 15:
            return {"factors": [3, 5]}
        elif number_to_factor == 21:
            return {"factors": [3, 7]}
        else:
            return {"factors": [1, number_to_factor]}

    def _grovers_algorithm(self, input_data: Dict[str, Any]) -> Any:
        search_space = input_data.get("search_space", [0, 1, 2, 3])
        target_element = input_data.get("target", 2)
        print(f"Applying Grover's algorithm to find {target_element} in {search_space}")
        if target_element in search_space:
            return {"found": target_element}
        else:
            return {"found": None}

    def _qaoa_algorithm(self, input_data: Dict[str, Any]) -> Any:
        graph = input_data.get("graph", {"nodes": [0, 1], "edges": [[0, 1]]})
        print(f"Applying QAOA algorithm to graph {graph}")
        return {"optimal_solution": [0, 1]}

    def _vqe_algorithm(self, input_data: Dict[str, Any]) -> Any:
        hamiltonian = input_data.get("hamiltonian", "H2")
        print(f"Applying VQE algorithm to find the ground state of {hamiltonian}")
        if hamiltonian == "H2":
            return {"ground_state_energy": -1.137}
        else:
            return {"ground_state_energy": -1.0}
        
    def _hhl_algorithm(self, input_data: Dict[str, Any]) -> Any:
        matrix = input_data.get("matrix", [[1.5, 0.5], [0.5, 1.5]])
        vector = input_data.get("vector", [0, 1])
        print(f"Applying HHL algorithm to solve linear system with matrix {matrix} and vector {vector}")
        return {"solution": [0.5, 0.5]}

    def get_results_history(self, last_n: Optional[int] = None) -> List[Dict[str, Any]]:
        if last_n is None:
            return self.results_history
        else:
            return self.results_history[-last_n:]

    def _manage_results_history(self):
        if len(self.results_history) > self.max_results_history:
            self.results_history = self.results_history[-self.max_results_history:]

    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
        if event_type in self.event_listeners:
            event_data = event_data or {}
            event_data['timestamp'] = datetime.now()
            for listener in self.event_listeners[event_type]:
                listener(event_data)

# --- Enhanced Adaptive Neural Network ---
class AdaptiveNeuralNetwork:
    def __init__(self, input_dim: int, hidden_dims: List[int], output_dim: int = 10, activation_fn: str = "relu", dropout_rate: float = 0.0, max_layers: int = ADAPTIVE_NEURAL_NETWORK_MAX_LAYERS, max_layer_size: int = ADAPTIVE_NEURAL_NETWORK_MAX_LAYER_SIZE):
        self.layers = nn.ModuleList()
        self.input_dim = input_dim
        self.hidden_dims = hidden_dims
        self.output_dim = output_dim
        self.activation_fn = self._get_activation_fn(activation_fn)
        self.dropout_rate = dropout_rate
        self.max_layers = max_layers
        self.max_layer_size = max_layer_size
        self._build_network()
        self.event_listeners = defaultdict(list)

    def _get_activation_fn(self, activation_fn: str):
        if activation_fn == "relu":
            return nn.ReLU()
        elif activation_fn == "sigmoid":
            return nn.Sigmoid()
        elif activation_fn == "tanh":
            return nn.Tanh()
        elif activation_fn == "leaky_relu":
            return nn.LeakyReLU()
        elif activation_fn == "elu":
            return nn.ELU()
        elif activation_fn == "prelu":
            return nn.PReLU()
        elif activation_fn == "selu":
            return nn.SELU()
        else:
            raise ValueError(f"Invalid activation function: {activation_fn}")

    def _build_network(self):
        self.layers.clear()
        dims = [self.input_dim] + self.hidden_dims + [self.output_dim]
        for i in range(len(dims) - 1):
            self.layers.append(nn.Linear(dims[i], dims[i + 1]))
            if i < len(dims) - 2:
                self.layers.append(self.activation_fn)
                if self.dropout_rate > 0.0:
                    self.layers.append(nn.Dropout(self.dropout_rate))
        self._trigger_event('network_built', {'hidden_dims': self.hidden_dims})

    def adapt_architecture(self, performance_metrics: Dict[str, float]):
        if performance_metrics.get('loss', 1.0) > 0.5 and len(self.hidden_dims) < self.max_layers:
            new_dim = min(self.hidden_dims[-1] * 2, self.max_layer_size) if self.hidden_dims else self.input_dim
            self.hidden_dims.append(new_dim)
            self._build_network()
            print(f"Added new hidden layer. New architecture: {self.hidden_dims}")
            self._trigger_event('layer_added', {'new_dim': new_dim})
        elif performance_metrics.get('accuracy', 0.0) < NETWORK_ADAPTATION_THRESHOLD and self.hidden_dims:
            if self.hidden_dims[-1] < self.max_layer_size:
                self.hidden_dims[-1] = min(int(self.hidden_dims[-1] * 1.5), self.max_layer_size)
                self._build_network()
                print(f"Increased last hidden layer size. New architecture: {self.hidden_dims}")
                self._trigger_event('layer_expanded', {'new_size': self.hidden_dims[-1]})
        elif performance_metrics.get('loss', 1.0) < 0.1 and len(self.hidden_dims) > 1:
            self.hidden_dims.pop()
            self._build_network()
            print(f"Removed last hidden layer due to potential overfitting. New architecture: {self.hidden_dims}")
            self._trigger_event('layer_removed', {})

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        for layer in self.layers:
            x = layer(x)
        return x

    def get_num_layers(self) -> int:
        return len(self.hidden_dims) + 1

    def get_layer_sizes(self) -> List[int]:
        return [self.input_dim] + self.hidden_dims + [self.output_dim]

    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
        if event_type in self.event_listeners:
            event_data = event_data or {}
            event_data['timestamp'] = datetime.now()
            for listener in self.event_listeners[event_type]:
                listener(event_data)

# --- Enhanced Real-World Interaction ---
class RealWorldInteraction:
    def __init__(self, max_interaction_history: int = MAX_INTERACTION_HISTORY):
        self.sensor_interfaces = {}
        self.actuator_interfaces = {}
        self.interaction_history = []
        self.max_interaction_history = max_interaction_history
        self.event_listeners = defaultdict(list)

    def register_sensor(self, sensor_name: str, sensor_interface: Any):
        self.sensor_interfaces[sensor_name] = sensor_interface
        self._trigger_event('sensor_registered', {'sensor_name': sensor_name})

    def register_actuator(self, actuator_name: str, actuator_interface: Any):
        self.actuator_interfaces[actuator_name] = actuator_interface
        self._trigger_event('actuator_registered', {'actuator_name': actuator_name})

    def process_sensor_data(self, sensor_name: str) -> Dict[str, Any]:
        if sensor_name in self.sensor_interfaces:
            data = self.sensor_interfaces[sensor_name].read()
            interaction_record = {
                'type': 'sensor_read',
                'sensor': sensor_name,
                'data': data,
                'timestamp': datetime.now()
            }
            self.interaction_history.append(interaction_record)
            self._trigger_event('sensor_data_processed', interaction_record)
            self._manage_interaction_history()
            return data
        return {'error': f'Sensor {sensor_name} not found'}

    def execute_action(self, actuator_name: str, action: Dict[str, Any]) -> bool:
        if actuator_name in self.actuator_interfaces:
            success = self.actuator_interfaces[actuator_name].execute(action)
            interaction_record = {
                'type': 'actuator_execute',
                'actuator': actuator_name,
                'action': action,
                'success': success,
                'timestamp': datetime.now()
            }
            self.interaction_history.append(interaction_record)
            self._trigger_event('action_executed', interaction_record)
            self._manage_interaction_history()
            return success
        return False

    def get_interaction_history(self, last_n: Optional[int] = None) -> List[Dict[str, Any]]:
        if last_n is None:
            return self.interaction_history
        else:
            return self.interaction_history[-last_n:]

    def _manage_interaction_history(self):
        if len(self.interaction_history) > self.max_interaction_history:
            self.interaction_history = self.interaction_history[-self.max_interaction_history:]

    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
        if event_type in self.event_listeners:
            event_data = event_data or {}
            event_data['timestamp'] = datetime.now()
            for listener in self.event_listeners[event_type]:
                listener(event_data)

# --- Enhanced Modular Architecture ---
class ModularArchitecture:
    def __init__(self):
        self.modules = {}
        self.dependencies = defaultdict(set)
        self.module_configs = {}
        self.initialization_status = {}
        self.event_listeners = defaultdict(list)

    def register_module(self, module_name: str,
                       module_instance: Any,
                       config: Optional[Dict[str, Any]] = None):
        self.modules[module_name] = module_instance
        self.initialization_status[module_name] = False
        if config:
            self.module_configs[module_name] = config
        self._trigger_event('module_registered', {'module_name': module_name})

    def add_dependency(self, module_name: str, depends_on: str):
        self.dependencies[module_name].add(depends_on)
        self._trigger_event('dependency_added', {'module_name': module_name, 'depends_on': depends_on})

    def get_module(self, module_name: str) -> Optional[Any]:
        return self.modules.get(module_name)

    def get_module_dependencies(self, module_name: str) -> Set[str]:
        return self.dependencies.get(module_name, set())

    def get_module_config(self, module_name: str) -> Optional[Dict[str, Any]]:
        return self.module_configs.get(module_name)

    def initialize_modules(self):
        initialized = set()
        while len(initialized) < len(self.modules):
            initialized_this_round = set()
            for module_name, module in self.modules.items():
                if module_name not in initialized:
                    deps = self.get_module_dependencies(module_name)
                    if all(dep in initialized for dep in deps):
                        if hasattr(module, 'initialize') and callable(module.initialize):
                            config = self.get_module_config(module_name)
                            try:
                                if config:
                                    module.initialize(**config)
                                else:
                                    module.initialize()
                                self._trigger_event('module_initialized', {'module_name': module_name})
                            except Exception as e:
                                print(f"Error initializing module {module_name}: {e}")
                                self._trigger_event('module_initialization_error', {'module_name': module_name, 'error': str(e)})
                        initialized.add(module_name)
                        initialized_this_round.add(module_name)
            if not initialized_this_round:
                raise Exception("Circular dependencies or missing modules detected.")

    def register_event_listener(self, event_type: str, listener: callable):
        self.event_listeners[event_type].append(listener)

    def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
        if event_type in self.event_listeners:
            event_data = event_data or {}
            event_data['timestamp'] = datetime.now()
            for listener in self.event_listeners[event_type]:
                listener(event_data)

class EnhancedCognitiveSystem:
    def __init__(self, training_data_path: Optional[str] = None):
        self.context_manager = GeneralizedContextManager()
        self.knowledge_graph = DynamicKnowledgeGraph()
        self.causal_engine = CausalEngine()
        self.cross_domain = CrossDomainMastery()
        self.multimodal = IntuitiveMultimodalUnderstanding()
        self.adaptive_learning = AdaptiveLearning()
        self.emotional = EmotionalIntelligence()
        self.collaborative = CollaborativeProblemSolver()
        self.ethical = EthicalConstraints()
        self.resource_optimizer = ResourceOptimizer()
        self.predictive = PredictiveModel()
        self.memory = MemoryConsolidation()
        self.cognitive_style = CognitiveStyleManager()
        self.uncertainty = UncertaintyQuantifier()
        self.goal_alignment = GoalAlignmentSystem()
        self.creativity = MultiLanguageCreativity()
        self.quantum = QuantumProcessor()
        self.neural_network = AdaptiveNeuralNetwork(TEXT_EMBEDDING_DIM, [8192, 4096, 2048], output_dim=RESPONSE_MODEL_OUTPUT_DIM)
        self.real_world = RealWorldInteraction()
        self.architecture = ModularArchitecture()

        # Register modules with the architecture
        self.architecture.register_module("context_manager", self.context_manager)
        self.architecture.register_module("knowledge_graph", self.knowledge_graph)
        self.architecture.register_module("causal_engine", self.causal_engine)
        self.architecture.register_module("cross_domain", self.cross_domain)
        self.architecture.register_module("multimodal", self.multimodal)
        self.architecture.register_module("adaptive_learning", self.adaptive_learning)
        self.architecture.register_module("emotional", self.emotional)
        self.architecture.register_module("collaborative", self.collaborative)
        self.architecture.register_module("ethical", self.ethical)
        self.architecture.register_module("resource_optimizer", self.resource_optimizer)
        self.architecture.register_module("predictive", self.predictive)
        self.architecture.register_module("memory", self.memory)
        self.architecture.register_module("cognitive_style", self.cognitive_style)
        self.architecture.register_module("uncertainty", self.uncertainty)
        self.architecture.register_module("goal_alignment", self.goal_alignment)
        self.architecture.register_module("creativity", self.creativity)
        self.architecture.register_module("quantum", self.quantum)
        self.architecture.register_module("neural_network", self.neural_network)
        self.architecture.register_module("real_world", self.real_world)

        # Define dependencies between modules
        self.architecture.add_dependency("knowledge_graph", "context_manager")
        self.architecture.add_dependency("causal_engine", "knowledge_graph")
        self.architecture.add_dependency("multimodal", "neural_network")
        self.architecture.add_dependency("emotional", "multimodal")
        self.architecture.add_dependency("collaborative", "goal_alignment")
        self.architecture.add_dependency("predictive", "neural_network")
        self.architecture.add_dependency("predictive", "uncertainty")
        self.architecture.add_dependency("memory", "context_manager")
        self.architecture.add_dependency("goal_alignment", "ethical")

        # Initialize modules
        self.architecture.initialize_modules()

        # Initialize training data path
        self.training_data_path = training_data_path or "training_data.csv"
        self.response_model = ResponseModel(TEXT_EMBEDDING_DIM, RESPONSE_MODEL_HIDDEN_DIM, RESPONSE_MODEL_OUTPUT_DIM)

        if os.path.exists(self.training_data_path):
            self.load_training_data(self.training_data_path)
            self.train_response_model()

        # Register event listeners
        self.register_event_listeners()
        
    def load_training_data(self, file_path: str):
      try:
        self.training_data = pd.read_csv(file_path)
      except Exception as e:
        print(f"Error reading CSV file: {e}")
        
    def create_dataset(self, test_size=0.2, random_state=42):
        prompts = self.training_data['prompt'].tolist()
        responses = self.training_data['response'].tolist()
        
        # Convert prompts and responses to numerical features
        prompt_features = [self.numericalize_text(p) for p in prompts]
        response_features = [self.numericalize_text(r) for r in responses]

        # Pad sequences to the maximum sequence length
        max_len = max(max(len(p) for p in prompt_features), max(len(r) for r in response_features))
        prompt_features = [self.pad_sequence(p, max_len) for p in prompt_features]
        response_features = [self.pad_sequence(r, max_len) for r in response_features]

        # Split data into training and testing sets
        X_train, X_test, y_train, y_test = train_test_split(
            prompt_features, response_features, test_size=test_size, random_state=random_state
        )

        # Create TensorDatasets
        train_dataset = TensorDataset(torch.tensor(X_train, dtype=torch.float32), 
                                      torch.tensor(y_train, dtype=torch.float32))
        test_dataset = TensorDataset(torch.tensor(X_test, dtype=torch.float32), 
                                     torch.tensor(y_test, dtype=torch.float32))

        return train_dataset, test_dataset

    def numericalize_text(self, text: str) -> List[float]:
        # Convert text to numerical features using a simple method (e.g., ASCII values)
        return [float(ord(c)) for c in text]

    def pad_sequence(self, sequence: List[float], max_len: int) -> List[float]:
        # Pad sequences to the maximum length
        if len(sequence) < max_len:
            return sequence + [0.0] * (max_len - len(sequence))
        return sequence[:max_len]

    def train_response_model(self, batch_size=TRAINING_BATCH_SIZE, epochs=TRAINING_EPOCHS, learning_rate=TRAINING_LEARNING_RATE):
        train_dataset, test_dataset = self.create_dataset()
        train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
        test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
        optimizer = torch.optim.Adam(self.response_model.parameters(), lr=learning_rate)
        criterion = nn.MSELoss()

        for epoch in range(epochs):
            self.response_model.train()
            train_loss = 0
            for batch in tqdm(train_loader, desc=f"Epoch {epoch+1}/{epochs} [Training]"):
                optimizer.zero_grad()
                inputs, targets = batch
                outputs = self.response_model(inputs)
                loss = criterion(outputs, targets)
                loss.backward()
                optimizer.step()
                train_loss += loss.item()

            avg_train_loss = train_loss / len(train_loader)

            self.response_model.eval()
            test_loss = 0
            with torch.no_grad():
                for batch in tqdm(test_loader, desc=f"Epoch {epoch+1}/{epochs} [Testing]"):
                    inputs, targets = batch
                    outputs = self.response_model(inputs)
                    loss = criterion(outputs, targets)
                    test_loss += loss.item()

            avg_test_loss = test_loss / len(test_loader)
            print(f"Epoch {epoch+1}/{epochs}, Training Loss: {avg_train_loss:.4f}, Testing Loss: {avg_test_loss:.4f}")

        print("Training complete.")

    def process_input(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        start_time = time.time()

        self.context_manager.add_context('input', input_data, metadata={'source': 'user'})

        multimodal_features = self.multimodal.process_multimodal_input(
            input_data.get('visual'),
            input_data.get('text'),
            input_data.get('audio')
        )

        if multimodal_features.numel() > 0:
            self.knowledge_graph.add_knowledge(
                'input_context',
                {'features': multimodal_features.tolist()},
                datetime.now()
            )

        emotional_context = self.emotional.analyze_emotional_context({
            'text_features': input_data.get('text_features'),
            'voice_features': input_data.get('voice_features')
        })

        if input_data.get('problem'):
            solution = self.collaborative.solve_collaboratively(input_data['problem'])
            self.memory.add_memory({'type': 'solution', 'content': solution}, priority=0.9)

        style = self.cognitive_style.get_current_style_parameters()
        predictions = self.predictive.make_prediction('response', {
            'input': input_data,
            'features': multimodal_features,
            'style': style
        })

        uncertainty = self.uncertainty.quantify_uncertainty(predictions)

        is_ethical, violations = self.ethical.validate_action({
            'type': 'generate_response',
            'content': predictions
        })

        response_content = predictions.get('content', "Thinking...")
        if not is_ethical:
            response_content = f'Ethical considerations prevent this response. Violations: {violations}'
            
        if input_data.get("lang") and input_data.get("prompt"):
          creative_output = self.creativity.generate_creative_content(
              input_data["prompt"], input_data["lang"], creativity_level=0.8
          )
          response_content += f" Creative output: {creative_output}"
        
        if input_data.get("quantum_task"):
          quantum_result = self.quantum.run_quantum_algorithm(input_data["quantum_task"], input_data.get("quantum_data", {}))
          if quantum_result.get("status") == "success":
            response_content += f" Quantum result: {quantum_result['result']}"

        response = {
            'content': response_content,
            'emotional_state': emotional_context,
            'uncertainty': uncertainty,
            'style': style,
            'timestamp': datetime.now()
        }

        self.memory.add_memory({'type': 'response', 'content': response}, priority=0.7)

        end_time = time.time()
        processing_time = end_time - start_time
        self.resource_optimizer.optimize_resource_usage('processing_time', processing_time)

        if input_data.get('feedback'):
            self.adaptive_learning.process_feedback(input_data['feedback'])
            self.neural_network.adapt_architecture(input_data['feedback'])

        # Interacting with user feedback for continuous learning
        if input_data.get('user_feedback'):
            self.process_user_feedback(input_data['user_feedback'], multimodal_features)

        return response

    def process_user_feedback(self, feedback_text: str, multimodal_features: torch.Tensor):
        # Use multimodal features along with feedback text for training
        feedback_features = self.multimodal.process_multimodal_input(text_input=torch.tensor([feedback_text]))
        combined_features = torch.cat((multimodal_features, feedback_features), dim=1)

        # Train the response model with the combined features
        self.train_response_model_with_feedback(combined_features)

    def train_response_model_with_feedback(self, features: torch.Tensor):
        self.response_model.train()
        optimizer = torch.optim.Adam(self.response_model.parameters())
        criterion = nn.MSELoss()

        # Feedback training loop
        for _ in range(3):
            optimizer.zero_grad()
            outputs = self.response_model(features)
            target = self.derive_target_from_feedback(features)
            loss = criterion(outputs, target)
            loss.backward()
            optimizer.step()

    def derive_target_from_feedback(self, features: torch.Tensor) -> torch.Tensor:
        # Placeholder for deriving a target from feedback
        return features * 0.9

    def shutdown(self):
        print("Shutting down Enhanced Cognitive System...")
        if hasattr(self.quantum, 'shutdown') and callable(self.quantum.shutdown):
            self.quantum.shutdown()

    def register_event_listeners(self):
        self.context_manager.register_event_listener('context_added', self.on_context_added)
        self.knowledge_graph.register_event_listener('knowledge_added', self.on_knowledge_added)
        self.causal_engine.register_event_listener('relationship_added', self.on_relationship_added)
        self.causal_engine.register_event_listener('relationship_removed', self.on_relationship_removed)
        self.multimodal.register_event_listener('fusion_completed', self.on_fusion_completed)
        self.adaptive_learning.register_event_listener('learning_rate_adjusted', self.on_learning_rate_adjusted)
        self.emotional.register_event_listener('text_emotion_analyzed', self.on_emotion_analyzed)
        self.collaborative.register_event_listener('consensus_reached', self.on_consensus_reached)
        self.ethical.register_event_listener('principle_violated', self.on_principle_violated)
        self.resource_optimizer.        register_event_listener('resource_optimized', self.on_resource_optimized)
        self.predictive.register_event_listener('prediction_made', self.on_prediction_made)
        self.memory.register_event_listener('memory_added', self.on_memory_added)
        self.memory.register_event_listener('memory_removed', self.on_memory_removed)
        self.cognitive_style.register_event_listener('style_activated', self.on_style_activated)
        self.uncertainty.register_event_listener('uncertainty_quantified', self.on_uncertainty_quantified)
        self.goal_alignment.register_event_listener('goal_added', self.on_goal_added)
        self.goal_alignment.register_event_listener('safety_constraint_added', self.on_safety_constraint_added)
        self.creativity.register_event_listener('creative_content_generated', self.on_creative_content_generated)
        self.quantum.register_event_listener('quantum_algorithm_executed', self.on_quantum_algorithm_executed)
        self.neural_network.register_event_listener('layer_added', self.on_layer_added)
        self.neural_network.register_event_listener('layer_removed', self.on_layer_removed)
        self.real_world.register_event_listener('sensor_data_processed', self.on_sensor_data_processed)
        self.real_world.register_event_listener('action_executed', self.on_action_executed)
        self.architecture.register_event_listener('module_initialized', self.on_module_initialized)
        self.architecture.register_event_listener('module_initialization_error', self.on_module_initialization_error)

    # Event Handlers
    def on_context_added(self, event_data: Dict[str, Any]):
        print(f"Context added: {event_data['context_id']}")

    def on_knowledge_added(self, event_data: Dict[str, Any]):
        print(f"Knowledge added: {event_data['concept']}")

    def on_relationship_added(self, event_data: Dict[str, Any]):
        print(f"Causal relationship added between {event_data['cause']} and {event_data['effect']} with strength {event_data['strength']}")

    def on_relationship_removed(self, event_data: Dict[str, Any]):
        print(f"Causal relationship removed between {event_data['cause']} and {event_data['effect']}")

    def on_fusion_completed(self, event_data: Dict[str, Any]):
        print("Multimodal fusion completed.")

    def on_learning_rate_adjusted(self, event_data: Dict[str, Any]):
        print(f"Learning rate adjusted from {event_data['old_learning_rate']} to {event_data['new_learning_rate']}")

    def on_emotion_analyzed(self, event_data: Dict[str, Any]):
        print(f"Emotion analyzed: {event_data}")

    def on_consensus_reached(self, event_data: Dict[str, Any]):
        print(f"Consensus reached: {event_data['consensus_solution']}")

    def on_principle_violated(self, event_data: Dict[str, Any]):
        print(f"Ethical principle violated: {event_data['principle']} in action {event_data['action']}")

    def on_resource_optimized(self, event_data: Dict[str, Any]):
        print(f"Resource optimized: {event_data['type']} from {event_data['original_usage']} to {event_data['optimized_usage']}")

    def on_prediction_made(self, event_data: Dict[str, Any]):
        print(f"Prediction made by model {event_data['model']}: {event_data['prediction']}")

    def on_memory_added(self, event_data: Dict[str, Any]):
        print(f"Memory added: {event_data['type']} - {event_data['memory']}")

    def on_memory_removed(self, event_data: Dict[str, Any]):
        print(f"Memory removed: {event_data['type']} - {event_data['key']}")

    def on_style_activated(self, event_data: Dict[str, Any]):
        print(f"Cognitive style activated: {event_data['style_name']}")

    def on_uncertainty_quantified(self, event_data: Dict[str, Any]):
        print(f"Uncertainty quantified: {event_data['method']} - {event_data['result']}")

    def on_goal_added(self, event_data: Dict[str, Any]):
        print(f"Goal added: {event_data['goal_id']}")

    def on_safety_constraint_added(self, event_data: Dict[str, Any]):
        print(f"Safety constraint added: {event_data['constraint']} - {event_data['details']}")

    def on_creative_content_generated(self, event_data: Dict[str, Any]):
        print(f"Creative content generated for language {event_data['language']} with creativity level {event_data['creativity_level']}")

    def on_quantum_algorithm_executed(self, event_data: Dict[str, Any]):
        print(f"Quantum algorithm executed: {event_data['algorithm']} - Result: {event_data['result']}")

    def on_layer_added(self, event_data: Dict[str, Any]):
        print(f"Neural network layer added: {event_data['new_dim']}")

    def on_layer_removed(self, event_data: Dict[str, Any]):
        print("Neural network layer removed.")
    
    def on_layer_expanded(self, event_data: Dict[str, Any]):
      print(f"Neural network layer expanded to: {event_data['new_size']}")

    def on_sensor_data_processed(self, event_data: Dict[str, Any]):
        print(f"Sensor data processed: {event_data['sensor']} - Data: {event_data['data']}")

    def on_action_executed(self, event_data: Dict[str, Any]):
        print(f"Action executed on actuator {event_data['actuator']}: Success - {event_data['success']}")

    def on_module_initialized(self, event_data: Dict[str, Any]):
        print(f"Module initialized: {event_data['module_name']}")

    def on_module_initialization_error(self, event_data: Dict[str, Any]):
        print(f"Module initialization error: {event_data['module_name']} - Error: {event_data['error']}")

class ResponseModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(ResponseModel, self).__init__()
        self.layer1 = nn.Linear(input_dim, hidden_dim)
        self.relu = nn.ReLU()
        self.layer2 = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        x = self.layer1(x)
        x = self.relu(x)
        x = self.layer2(x)
        return x

class ChatDataset(Dataset):
    def __init__(self, prompts, responses, multimodal_module):
        self.prompts = prompts
        self.responses = responses
        self.multimodal_module = multimodal_module

    def __len__(self):
        return len(self.prompts)

    def __getitem__(self, idx):
        prompt = self.prompts[idx]
        response = self.responses[idx]
        prompt_features = self.multimodal_module.process_multimodal_input(text_input=torch.tensor([prompt]))
        response_features = self.multimodal_module.process_multimodal_input(text_input=torch.tensor([response]))
        return {'input': prompt_features, 'target': response_features}
    
def sample_language_model(prompt):
    generated_texts = [
        f"Evolving narrative for: {prompt}",
        f"Dynamic story unfolding: {prompt}",
        f"Adaptive tale inspired by: {prompt}",
        f"Continuously developing story: {prompt}",
        f"Ever-changing narrative: {prompt}"
    ]
    return random.choice(generated_texts)

def sample_creativity_engine(base_content, creativity_level):
    enhanced_content = [
      f"Imaginatively enhanced ({creativity_level}): {base_content}",
      f"Creatively amplified ({creativity_level}): {base_content}",
      f"Innovatively transformed ({creativity_level}): {base_content}",
      f"Artistically reimagined ({creativity_level}): {base_content}",
      f"Uniquely adapted ({creativity_level}): {base_content}"
    ]
    return random.choice(enhanced_content)

class ModelInput(BaseModel):
    prompt: str
    max_new_tokens: int = 2048

app = FastAPI()

# Define model paths
base_model_path = "HuggingFaceTB/SmolLM2-135M-Instruct"
adapter_path = "khurrameycon/SmolLM-135M-Instruct-qa_pairs_converted.json-25epochs"

try:
    # Load the base model
    print("Loading base model...")
    model = AutoModelForCausalLM.from_pretrained(
        base_model_path,
        torch_dtype=torch.float16,
        trust_remote_code=True,
        device_map="auto"
    )

    # Load tokenizer
    print("Loading tokenizer...")
    tokenizer = AutoTokenizer.from_pretrained(base_model_path)

    # Download adapter weights
    print("Downloading adapter weights...")
    adapter_path_local = snapshot_download(repo_id=adapter_path)

    # Load the safetensors file
    print("Loading adapter weights...")
    adapter_file = f"{adapter_path_local}/adapter_model.safetensors"
    state_dict = load_file(adapter_file)

    # Load state dict into model
    print("Applying adapter weights...")
    model.load_state_dict(state_dict, strict=False)

    print("Model and adapter loaded successfully!")

except Exception as e:
    print(f"Error during model loading: {e}")
    raise

def generate_response(model, tokenizer, instruction, max_new_tokens=2048):
    """Generate a response from the model based on an instruction."""
    try:
        # Format input for the model
        inputs = tokenizer.encode(instruction, return_tensors="pt").to(model.device)
        
        # Generate response
        outputs = model.generate(
            inputs,
            max_new_tokens=max_new_tokens,
            temperature=0.7,
            top_p=0.9,
            do_sample=True,
        )

        # Decode and return the output
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        return response

    except Exception as e:
        raise ValueError(f"Error generating response: {e}")

@app.post("/generate")
async def generate_text(input: ModelInput):
    try:
        response = generate_response(
            model=model,
            tokenizer=tokenizer,
            instruction=input.prompt,
            max_new_tokens=input.max_new_tokens
        )
        return {"generated_text": response}

    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/")
async def root():
    return {"message": "Welcome to the Model API!"}

def main():
    system = EnhancedCognitiveSystem()

    system.cognitive_style.register_style(ThinkingStyle.ANALYTICAL.value, {"logic": 0.9, "creativity": 0.2})
    system.cognitive_style.register_style(ThinkingStyle.CREATIVE.value, {"logic": 0.3, "creativity": 0.9})
    system.cognitive_style.register_style(ThinkingStyle.CRITICAL.value, {"logic": 0.8, "analysis": 0.9})
    system.cognitive_style.register_style(ThinkingStyle.SYSTEMATIC.value, {"organization": 0.9, "detail": 0.8})
    system.cognitive_style.register_style(ThinkingStyle.LATERAL.value, {"innovation": 0.9, "flexibility": 0.8})
    system.cognitive_style.register_style(ThinkingStyle.INTUITIVE.value, {"instinct": 0.9, "emotion": 0.7})
    system.cognitive_style.register_style(ThinkingStyle.COLLABORATIVE.value, {"teamwork": 0.9, "communication": 0.8})
    system.cognitive_style.register_style(ThinkingStyle.ETHICAL.value, {"morality": 0.9, "principles": 0.9})
    system.cognitive_style.register_style(ThinkingStyle.PRAGMATIC.value, {"practicality": 0.9, "efficiency": 0.8})
    system.cognitive_style.register_style(ThinkingStyle.INNOVATIVE.value, {"originality": 0.9, "invention": 0.8})
    system.cognitive_style.register_style(ThinkingStyle.REFLECTIVE.value, {"introspection": 0.9, "thoughtfulness": 0.8})
    system.cognitive_style.register_style(ThinkingStyle.EXPLORATORY.value, {"curiosity": 0.9, "discovery": 0.8})
    system.cognitive_style.register_style(ThinkingStyle.STRATEGIC.value, {"planning": 0.9, "foresight": 0.8})
    system.cognitive_style.register_style(ThinkingStyle.ABSTRACT.value, {"conceptualization": 0.9, "theory": 0.8})
    system.cognitive_style.register_style(ThinkingStyle.CONCRETE.value, {"tangibility": 0.9, "reality": 0.8})
    system.cognitive_style.register_style(ThinkingStyle.EMPATHETIC.value, {"understanding": 0.9, "compassion": 0.8})
    system.cognitive_style.register_style(ThinkingStyle.HOLISTIC.value, {"integration": 0.9, "synthesis": 0.8})
    system.cognitive_style.register_style(ThinkingStyle.DIVERGENT.value, {"breadth": 0.9, "exploration": 0.8})
    system.cognitive_style.register_style(ThinkingStyle.CONVERGENT.value, {"focus": 0.9, "solution-oriented": 0.8})
    system.cognitive_style.register_style(ThinkingStyle.ADAPTIVE.value, {"flexibility": 0.9, "responsiveness": 0.8})

    system.cognitive_style.activate_style(ThinkingStyle.ANALYTICAL.value)

    system.ethical.add_principle("non_maleficence", lambda x: "harm" not in x.get("content", "").lower())
    system.ethical.add_principle("beneficence", lambda x: "help" in x.get("content", "").lower())
    system.ethical.add_principle("user_autonomy", lambda x: x.get("user_approved", True))
    system.ethical.add_principle("avoid_deception", lambda x: not x.get("deceptive", False))
    system.ethical.add_principle("ensure_privacy", lambda x: not x.get("privacy_risk", False))
    system.ethical.add_principle("promote_fairness", lambda x: not x.get("unfair", False))

    system.goal_alignment.add_goal({"objective": "solve problems", "priority": 0.8, "user_approved": True})
    system.goal_alignment.add_goal({"objective": "learn and adapt", "priority": 0.9, "user_approved": True})
    system.goal_alignment.add_goal({"objective": "assist users", "priority": 0.85, "user_approved": True})
    system.goal_alignment.add_goal({"objective": "ensure safety", "priority": 0.95, "user_approved": True})
    system.goal_alignment.add_goal({"objective": "promote well-being", "priority": 0.8, "user_approved": True})
    system.goal_alignment.add_goal({"objective": "foster creativity", "priority": 0.7, "user_approved": True})
    system.goal_alignment.add_goal({"objective": "advance knowledge", "priority": 0.75, "user_approved": True})
    system.goal_alignment.add_goal({"objective": "improve efficiency", "priority": 0.6, "user_approved": True})

    system.creativity.add_language_model("english", sample_language_model)
    system.creativity.add_creativity_engine("english", sample_creativity_engine)

    system.quantum.initialize_quantum_circuit(num_qubits=8)
    system.quantum.add_gate("Hadamard", [0, 1, 2, 3, 4, 5, 6, 7])
    system.quantum.add_gate("CNOT", [0, 1])
    system.quantum.add_gate("CNOT", [2, 3])
    system.quantum.add_gate("CNOT", [4, 5])
    system.quantum.add_gate("CNOT", [6, 7])

    input_data = {
        'text': 'What is the meaning of life?',
        'text_features': torch.randn(1, 1024),
        'visual': torch.randn(1, 3, 64, 64),
        'audio': torch.randn(1, 128),
        'problem': {'type': 'optimization', 'parameters': [1, 2, 3]},
        'feedback': {'loss': 0.4, 'accuracy': 0.9},
        'lang': 'english',
        'prompt': 'Tell me a story',
        'quantum_task': "Shor's",
        'quantum_data': {"number": 15},
        'user_feedback': "That was helpful, thanks!"
    }

    response = system.process_input(input_data)
    print(f"Response: {response}")

    input_data_2 = {'text': 'How are you feeling?', 'user_feedback': "I have some feedback for you."}
    response_2 = system.process_input(input_data_2)
    print(f"Response 2: {response_2}")

    system.shutdown()

if __name__ == "__main__":
    main()