{"id":1476,"date":"2025-02-20T12:14:10","date_gmt":"2025-02-20T10:14:10","guid":{"rendered":"https:\/\/www.technology-asgard.com\/?p=1476"},"modified":"2025-02-26T21:07:00","modified_gmt":"2025-02-26T19:07:00","slug":"project-sd-code-for-image-processing","status":"publish","type":"post","link":"https:\/\/www.technology-asgard.com\/en\/project-sd-code-for-image-processing\/","title":{"rendered":"Project SD – Code for image processing"},"content":{"rendered":"\n

Project SD – Code for image processing

This is a work, and the author’s right to a work under international law comes into force from the moment the work is created.
<\/p>\n\n\n\n

Author – Sukhachev Denis Pavlovich


Let’s analyze the main components and features of this advanced code:<\/p>\n\n\n\n

1. **Modes of thinking** (CognitionMode):<\/p>\n\n\n\n

   – ANALYTICAL – for logical analysis<\/p>\n\n\n\n

   – CREATIVE – for creative thinking<\/p>\n\n\n\n

   – EMOTIONAL – for emotional thinking<\/p>\n\n\n\n

   – INSTINCTIVE – for instinctive reactions<\/p>\n\n\n\n

   – LEARNING – for active learning<\/p>\n\n\n\n

2. **Memory System**:<\/p>\n\n\n\n

   – Stores different types of information: visual, semantic, emotional, spatial<\/p>\n\n\n\n

   – Everyone has a memory:<\/p>\n\n\n\n

     * Importance<\/p>\n\n\n\n

     * Emotional context<\/p>\n\n\n\n

     * Associations<\/p>\n\n\n\n

     * Time stamp<\/p>\n\n\n\n

     * Reinforcement counter<\/p>\n\n\n\n

3. **Emotional System** (EmotionalState):<\/p>\n\n\n\n

   – Primary emotions (joy, sadness, anger, fear)<\/p>\n\n\n\n

   – Secondary emotions (love, hate, anxiety)<\/p>\n\n\n\n

   – Mood (long-term emotional state)<\/p>\n\n\n\n

   – Social context (empathy, trust)<\/p>\n\n\n\n

4) **Spatial Memory** (SpatialMemory):<\/p>\n\n\n\n

   – 3D representation of space<\/p>\n\n\n\n

   – Navigation system between objects<\/p>\n\n\n\n

   – Quick spatial search<\/p>\n\n\n\n

   – Graph of relationships between objects<\/p>\n\n\n\n

5) **Language Processor** (LanguageProcessor):<\/p>\n\n\n\n

   – Natural language processing<\/p>\n\n\n\n

   – Understanding the emotional coloring of the text<\/p>\n\n\n\n

   – Generate responses based on context<\/p>\n\n\n\n

   – Storing the history of the dialog<\/p>\n\n\n\n

6. **ReinforcementLearner**:<\/p>\n\n\n\n

   – Reinforcement learning<\/p>\n\n\n\n

   – Experience buffer<\/p>\n\n\n\n

   – Adaptive learning<\/p>\n\n\n\n

   – Optimization of actions<\/p>\n\n\n\n

7. **Enhanced Consciousness**:<\/p>\n\n\n\n

   – Integration of all components<\/p>\n\n\n\n

   – Dynamic selection of thinking mode<\/p>\n\n\n\n

   – Processing of complex inputs<\/p>\n\n\n\n

   – Generating responses<\/p>\n\n\n\n

Key features:<\/p>\n\n\n\n

1. adaptability**:<\/p>\n\n\n\n

   – Automatically adjusts to the type of input data<\/p>\n\n\n\n

   – Changes the mode of thinking depending on the situation<\/p>\n\n\n\n

   – Learning from experience<\/p>\n\n\n\n

2. integration**:<\/p>\n\n\n\n

   – Combines visual, textual and emotional thinking<\/p>\n\n\n\n

   – Creates complex associative connections<\/p>\n\n\n\n

   – Preserves the context of interaction<\/p>\n\n\n\n

3. **Emotional Intelligence**:<\/p>\n\n\n\n

   – Understands and generates emotional reactions<\/p>\n\n\n\n

   – Takes into account the social context<\/p>\n\n\n\n

   – Develops empathy<\/p>\n\n\n\n

4:<\/p>\n\n\n\n

   – Constantly improving<\/p>\n\n\n\n

   – Keeps useful experience<\/p>\n\n\n\n

   – Optimize your reactions<\/p>\n\n\n\n

Possible applications:<\/p>\n\n\n\n

1. Creating complex dialog systems<\/p>\n\n\n\n

2. Analyze and understand the context<\/p>\n\n\n\n

3. Generation of creative content<\/p>\n\n\n\n

4. Emotional interaction with the user<\/p>\n\n\n\n

5. Spatial planning and navigation<\/p>\n\n\n\n

6. Training and adaptation to new conditions<\/p>\n\n\n\n

Limitations and potential improvements:<\/p>\n\n\n\n

1. High computational complexity<\/p>\n\n\n\n

2. The need for a large amount of memory<\/p>\n\n\n\n

3. Ability to add more modalities<\/p>\n\n\n\n

4. Expanding the emotional spectrum<\/p>\n\n\n\n

5. Improvement of training mechanisms

<\/p>\n\n\n\n

import numpy as np<\/p>\n\n\n\n

import torch<\/p>\n\n\n\n

import torch.nn as nn<\/p>\n\n\n\n

import torch.nn.functional as F<\/p>\n\n\n\n

from transformers import GPT2LMHeadModel, GPT2Tokenizer<\/p>\n\n\n\n

from dataclasses import dataclass<\/p>\n\n\n\n

from typing import Dict, List, Tuple, Optional, Union<\/p>\n\n\n\n

import networkx as nx<\/p>\n\n\n\n

from scipy.spatial import KDTree<\/p>\n\n\n\n

import cv2<\/p>\n\n\n\n

from enum import Enum, auto<\/p>\n\n\n\n

import pickle<\/p>\n\n\n\n

from collections import defaultdict<\/p>\n\n\n\n

import gymnasium as gym<\/p>\n\n\n\n

from stable_baselines3 import PPO<\/p>\n\n\n\n

class CognitionMode(Enum):<\/p>\n\n\n\n

    ANALYTICAL = auto() # Logical thinking<\/p>\n\n\n\n

    CREATIVE = auto() # Creative thinking<\/p>\n\n\n\n

    EMOTIONAL = auto() # Emotional thinking<\/p>\n\n\n\n

    INSTINCTIVE = auto() # Instinctive thinking<\/p>\n\n\n\n

    LEARNING = auto() # Learning mode<\/p>\n\n\n\n

@dataclass<\/p>\n\n\n\n

class Memory:<\/p>\n\n\n\n

    “””Memory structure with emotional and contextual coloring”””<\/p>\n\n\n\n

    content: Union[np.ndarray, str]<\/p>\n\n\n\n

    type: str # ‘visual’, ‘semantic’, ’emotional’, ‘spatial’<\/p>\n\n\n\n

    importance: float<\/p>\n\n\n\n

    emotional_context: Dict[str, float]<\/p>\n\n\n\n

    associations: List[str]<\/p>\n\n\n\n

    timestamp: float<\/p>\n\n\n\n

    spatial_context: Optional[np.ndarray] = None<\/p>\n\n\n\n

    reinforcement_count: int = 0<\/p>\n\n\n\n

@dataclass<\/p>\n\n\n\n

class EmotionalState:<\/p>\n\n\n\n

    “””Extended emotional state”””<\/p>\n\n\n\n

    primary: Dict[str, float] # Basic emotions<\/p>\n\n\n\n

    secondary: Dict[str, float] # Complex emotions<\/p>\n\n\n\n

    mood: Dict[str, float] # Long-term mood<\/p>\n\n\n\n

    social_context: Dict[str, float] # Social context<\/p>\n\n\n\n

    def evolve(self, stimulus: Dict[str, float], learning_rate: float = 0.1):<\/p>\n\n\n\n

        “””The evolution of an emotional state under the influence of a stimulus”””<\/p>\n\n\n\n

        for category in [self.primary, self.secondary, self.mood]:<\/p>\n\n\n\n

            for emotion in category:<\/p>\n\n\n\n

                if emotion in stimulus:<\/p>\n\n\n\n

                    category[emotion] = (1 – learning_rate) * category[emotion] + \\<\/p>\n\n\n\n

                                     learning_rate * stimulus[emotion]<\/p>\n\n\n\n

class SpatialMemory:<\/p>\n\n\n\n

    “””Spatial memory with 3D representation”””<\/p>\n\n\n\n

    def __init__(self, dimensions: Tuple[int, int, int]):<\/p>\n\n\n\n

        self.space =  np.zeros(dimensions)<\/p>\n\n\n\n

        self.objects = {}<\/p>\n\n\n\n

        self.spatial_tree =  None<\/p>\n\n\n\n

        self.navigation_graph =  nx.Graph()<\/p>\n\n\n\n

    def add_object(self, position: np.ndarray, object_data: Dict):<\/p>\n\n\n\n

        “””Adding an object to spatial memory”””<\/p>\n\n\n\n

        obj_id =  len(self.objects)<\/p>\n\n\n\n

        self.objects[obj_id] = {<\/p>\n\n\n\n

            ‘position’: position,<\/p>\n\n\n\n

            ‘data’: object_data,<\/p>\n\n\n\n

            ‘connections’: []<\/p>\n\n\n\n

        }<\/p>\n\n\n\n

        self._update_spatial_tree()<\/p>\n\n\n\n

        self._update_navigation_graph(obj_id)<\/p>\n\n\n\n

    def _update_spatial_tree(self):<\/p>\n\n\n\n

        “””KD-tree update for fast spatial search”””<\/p>\n\n\n\n

        positions = [obj[‘position’] for obj in self.objects.values()]<\/p>\n\n\n\n

        self.spatial_tree =  KDTree(positions)<\/p>\n\n\n\n

    def _update_navigation_graph(self, new_obj_id):<\/p>\n\n\n\n

        “””Update navigation graph”””<\/p>\n\n\n\n

        new_pos =  self.objects[new_obj_id][‘position’]<\/p>\n\n\n\n

        # Adding links to nearby objects<\/p>\n\n\n\n

        if len(self.objects) > 1:<\/p>\n\n\n\n

            distances, indices =  self.spatial_tree.query(new_pos, k=4)<\/p>\n\n\n\n

            for dist, idx in zip(distances, indices):<\/p>\n\n\n\n

                if dist < 10.0: # Distance threshold for communication<\/p>\n\n\n\n

                    self.navigation_graph.add_edge(new_obj_id, idx, weight= dist)<\/p>\n\n\n\n

class LanguageProcessor:<\/p>\n\n\n\n

    “””Natural language processor with emotional understanding”””<\/p>\n\n\n\n

    def __init__(self):<\/p>\n\n\n\n

        self.model = GPT2LMHeadModel.from_pretrained(‘gpt2’)<\/p>\n\n\n\n

        self.tokenizer = GPT2Tokenizer.from_pretrained(‘gpt2’)<\/p>\n\n\n\n

        self.emotional_patterns =  self._load_emotional_patterns()<\/p>\n\n\n\n

        self.context_history = []<\/p>\n\n\n\n

    def _load_emotional_patterns(self) -> Dict[str, List[str]]:<\/p>\n\n\n\n

        “””Loading patterns of emotional coloring of text”””<\/p>\n\n\n\n

        # You can expand this dictionary<\/p>\n\n\n\n

        return {<\/p>\n\n\n\n

            ‘joy’: [‘happy’, ‘exciting’, ‘wonderful’],<\/p>\n\n\n\n

            ‘sadness’: [‘sad’, ‘depressing’, ‘unfortunate’],<\/p>\n\n\n\n

            ‘anger’: [‘angry’, ‘furious’, ‘annoying’],<\/p>\n\n\n\n

            ‘fear’: [‘scary’, ‘frightening’, ‘terrifying’],<\/p>\n\n\n\n

            ‘surprise’: [‘surprising’, ‘unexpected’, ‘amazing’]<\/p>\n\n\n\n

        }<\/p>\n\n\n\n

    def process_text(self, text: str) -> Dict:<\/p>\n\n\n\n

        “””Text processing with emotion and context detection”””<\/p>\n\n\n\n

        tokens =  self.tokenizer.encode(text, return_tensors=’pt’)<\/p>\n\n\n\n

        output =  self.model(tokens)<\/p>\n\n\n\n

        # Analysis of emotional coloring<\/p>\n\n\n\n

        emotions =  self._analyze_emotions(text)<\/p>\n\n\n\n

        # Highlighting key concepts<\/p>\n\n\n\n

        concepts =  self._extract_concepts(text)<\/p>\n\n\n\n

        # Context history update<\/p>\n\n\n\n

        self.context_history.append({<\/p>\n\n\n\n

            ‘text’: text,<\/p>\n\n\n\n

            ’emotions’: ’emotions’,<\/p>\n\n\n\n

            ‘concepts’: concepts<\/p>\n\n\n\n

        })<\/p>\n\n\n\n

        return {<\/p>\n\n\n\n

            ’emotions’: ’emotions’,<\/p>\n\n\n\n

            ‘concepts’: concepts,<\/p>\n\n\n\n

            ‘logits’: output.logits<\/p>\n\n\n\n

        }<\/p>\n\n\n\n

    def generate_response(self,<\/p>\n\n\n\n

                         prompt: str,<\/p>\n\n\n\n

                         emotional_context: Dict[str, float]) -> str:<\/p>\n\n\n\n

        “””Response generation based on emotional context”””<\/p>\n\n\n\n

        # Adding emotional markers to the promo<\/p>\n\n\n\n

        enhanced_prompt =  self._enhance_prompt_with_emotions(prompt, emotional_context)<\/p>\n\n\n\n

        # Text generation<\/p>\n\n\n\n

        tokens =  self.tokenizer.encode(enhanced_prompt, return_tensors=’pt’)<\/p>\n\n\n\n

        output =  self.model.generate(<\/p>\n\n\n\n

            tokens,<\/p>\n\n\n\n

            max_length=100,<\/p>\n\n\n\n

            num_return_sequences=1,<\/p>\n\n\n\n

            no_repeat_ngram_size=2<\/p>\n\n\n\n

        )<\/p>\n\n\n\n

        return self.tokenizer.decode(output[0], skip_special_tokens= True)<\/p>\n\n\n\n

class ReinforcementLearner:<\/p>\n\n\n\n

    “””Reinforcement learning system”””<\/p>\n\n\n\n

    def __init__(self, state_dim: int, action_dim: int):<\/p>\n\n\n\n

        self.env =  self._create_custom_env(state_dim, action_dim)<\/p>\n\n\n\n

        self.model = PPO(“MlpPolicy”, self.env)<\/p>\n\n\n\n

        self.experience_buffer = []<\/p>\n\n\n\n

        self.learning_rate = 0.001<\/p>\n\n\n\n

    def _create_custom_env(self, state_dim: int, action_dim: int) -> gym.Env:<\/p>\n\n\n\n

        “””Creating an environment for learning”””<\/p>\n\n\n\n

        # Can be extended for more complex environments<\/p>\n\n\n\n

        class CustomEnv(gym.Env):<\/p>\n\n\n\n

            def __init__(self):<\/p>\n\n\n\n

                self.observation_space =  gym.spaces.Box(<\/p>\n\n\n\n

                    low=-np.inf, high=np.inf, shape=(state_dim,))<\/p>\n\n\n\n

                self.action_space =  gym.spaces.Box(<\/p>\n\n\n\n

                    low=-1, high=1, shape=(action_dim,))<\/p>\n\n\n\n

            def step(self, action):<\/p>\n\n\n\n

                # Logic of interaction with the environment<\/p>\n\n\n\n

                next_state =  self.state +  action<\/p>\n\n\n\n

                reward =  self._calculate_reward(next_state)<\/p>\n\n\n\n

                done False =<\/p>\n\n\n\n

                return next_state, reward, done, {}<\/p>\n\n\n\n

            def reset(self):<\/p>\n\n\n\n

                self.state =  np.random.randomn(state_dim)<\/p>\n\n\n\n

                return self.state<\/p>\n\n\n\n

            def _calculate_reward(self, state):<\/p>\n\n\n\n

                # You can expand the logic of calculating rewards<\/p>\n\n\n\n

                return -np.sum(np.square(state))<\/p>\n\n\n\n

        return CustomEnv()<\/p>\n\n\n\n

    def learn_from_experience(self, state, action, reward, next_state):<\/p>\n\n\n\n

        “””Experience-based learning”””<\/p>\n\n\n\n

        self.experience_buffer.append((state, action, reward, next_state))<\/p>\n\n\n\n

        if len(self.experience_buffer) >= 1000: # Batch size<\/p>\n\n\n\n

            self.model.learn(total_timesteps=1000)<\/p>\n\n\n\n

            self.experience_buffer = []<\/p>\n\n\n\n

    def get_action(self, state: np.ndarray) -> np.ndarray:<\/p>\n\n\n\n

        “””Getting an action based on the current state”””<\/p>\n\n\n\n

        return self.model.predict(state)[0]<\/p>\n\n\n\n

class EnhancedConsciousness(AdvancedConsciousness):<\/p>\n\n\n\n

    “””Expanded version of consciousness”””<\/p>\n\n\n\n

    def __init__(self, num_params=9, num_harmonics=4, visual_dim=256):<\/p>\n\n\n\n

        super().__init__(num_params, num_harmonics, visual_dim)<\/p>\n\n\n\n

        # New components<\/p>\n\n\n\n

        self.language_processor =  LanguageProcessor()<\/p>\n\n\n\n

        self.spatial_memory =  SpatialMemory((100, 100, 100))<\/p>\n\n\n\n

        self.emotional_state =  EmotionalState(<\/p>\n\n\n\n

            primary={‘joy’: 0.5, ‘sadness’: 0.1, ‘anger’: 0.1,<\/p>\n\n\n\n

                    ‘fear’: 0.1, ‘surprise’: 0.2},<\/p>\n\n\n\n

            secondary={‘love’: 0.3, ‘hate’: 0.1, ‘anxiety’: 0.2},<\/p>\n\n\n\n

            mood={‘positive’: 0.6, ‘negative’: 0.4},<\/p>\n\n\n\n

            social_context={’empathy’: 0.5, ‘trust’: 0.7}<\/p>\n\n\n\n

        )<\/p>\n\n\n\n

        # Training system<\/p>\n\n\n\n

        self.reinforcement_learner =  ReinforcementLearner(<\/p>\n\n\n\n

            state_dim= num_params,<\/p>\n\n\n\n

            action_dim num_params=<\/p>\n\n\n\n

        )<\/p>\n\n\n\n

        # Mode of thinking<\/p>\n\n\n\n

        self.cognition_mode =  CognitionMode.ANALYTICAL<\/p>\n\n\n\n

        # Long-term memory<\/p>\n\n\n\n

        self.long_term_memory = []<\/p>\n\n\n\n

        self.memory_graph =  nx.Graph()<\/p>\n\n\n\n

    def think(self, input_data: Dict) -> Dict:<\/p>\n\n\n\n

        “””The main method of thinking”””<\/p>\n\n\n\n

        # Defining the mode of thinking<\/p>\n\n\n\n

        self.cognition_mode =  self._define_cognition_mode(input_data)<\/p>\n\n\n\n

        # Processing of input data depending on the mode<\/p>\n\n\n\n

        if ‘text’ in input_data:<\/p>\n\n\n\n

            language_result =  self.language_processor.process_text(input_data[‘text’])<\/p>\n\n\n\n

            self._update_emotional_state(language_result[’emotions’])<\/p>\n\n\n\n

        if ‘visual’ in input_data:<\/p>\n\n\n\n

            visual_result =  self.process_visual_thought(input_data[‘visual’])<\/p>\n\n\n\n

            self._store_in_spatial_memory(visual_result)<\/p>\n\n\n\n

        # Integration of all inputs<\/p>\n\n\n\n

        integrated_state =  self._integrate_inputs(input_data)<\/p>\n\n\n\n

        # Learning from experience<\/p>\n\n\n\n

        if self.cognition_mode ==  CognitionMode.LEARNING:<\/p>\n\n\n\n

            self._learn_from_current_state(integrated_state)<\/p>\n\n\n\n

        # Generating a response<\/p>\n\n\n\n

        response =  self._generate_response(integrated_state)<\/p>\n\n\n\n

        return response<\/p>\n\n\n\n

    def _define_cognition_mode(self, input_data: Dict) -> CognitionMode:<\/p>\n\n\n\n

        “””Determining the mode of thinking based on input”””<\/p>\n\n\n\n

        if ‘force_mode’ in input_data:<\/p>\n\n\n\n

            return input_data[‘force_mode’]<\/p>\n\n\n\n

        # Analysis of login complexity<\/p>\n\n\n\n

        complexity =  self._calculate_input_complexity(input_data)<\/p>\n\n\n\n

        # Analysis of emotional stress<\/p>\n\n\n\n

        emotional_intensity =  self._calculate_emotional_intensity(input_data)<\/p>\n\n\n\n

        # Selecting a mode<\/p>\n\n\n\n

        if complexity > 0.8:<\/p>\n\n\n\n

            return CognitionMode.ANALYTICAL<\/p>\n\n\n\n

        elif emotional_intensity > 0.7:<\/p>\n\n\n\n

            return CognitionMode.EMOTIONAL<\/p>\n\n\n\n

        elif ‘learning_required’ in input_data:<\/p>\n\n\n\n

            return CognitionMode.LEARNING<\/p>\n\n\n\n

        else:<\/p>\n\n\n\n

            return CognitionMode.CREATIVE<\/p>\n\n\n\n

    def _calculate_input_complexity(self, input_data: Dict) -> float:<\/p>\n\n\n\n

        “””Calculating the complexity of input data”””<\/p>\n\n\n\n

        complexity = 0.0<\/p>\n\n\n\n

        if ‘text’ in input_data:<\/p>\n\n\n\n

            # Text complexity<\/p>\n\n\n\n

            text_length =  len(input_data[‘text’].split())<\/p>\n\n\n\n

            complexity +=  min(text_length \/ 1000, 1.0) * 0.4<\/p>\n\n\n\n

        if ‘visual’ in input_data:<\/p>\n\n\n\n

            # Image complexity<\/p>\n\n\n\n

            visual_complexity =  np.std(input_data[‘visual’]) \/ 128.0<\/p>\n\n\n\n

            complexity +=  visual_complexity * 0.3<\/p>\n\n\n\n

        return min(complexity, 1.0)<\/p>\n\n\n\n

    def _calculate_emotional_intensity(self, input_data: Dict) -> float:<\/p>\n\n\n\n

        “””Calculation of the emotional load of the input”””<\/p>\n\n\n\n

        intensity = 0.0<\/p>\n\n\n\n

        if ‘text’ in input_data:<\/p>\n\n\n\n

            # Emotional load of the text<\/p>\n\n\n\n

            emotions = self.language_processor.process_text(input_data[‘text’])[’emotions’]<\/p>\n\n\n\n

            intensity +=  max(emotions.values()) * 0.6<\/p>\n\n\n\n

        if ‘visual’ in input_data:<\/p>\n\n\n\n

            # Emotional load of the image<\/p>\n\n\n\n

            visual_thought =  self.process_visual_thought(input_data[‘visual’])<\/p>\n\n\n\n

            intensity +=  max(visual_thought.emotional_context.values()) * 0.4<\/p>\n\n\n\n

        return intensity<\/p>\n\n\n\n

    def _learn_from_current_state(self, state: Dict):<\/p>\n\n\n\n

        “””Learning from the current state”””<\/p>\n\n\n\n

        # Converting a state to a vector<\/p>\n\n\n\n

        state_vector =  self._state_to_vector(state)<\/p>\n\n\n\n

        # Getting action from the learning system<\/p>\n\n\n\n

        action =  self.reinforcement_learner.get_action(state_vector)<\/p>\n\n\n\n

        # Performing an action and receiving a reward<\/p>\n\n\n\n

        next_state =  self._apply_action(state, action)<\/p>\n\n\n\n

        reward =  self._calculate_reward(state, next_state)<\/p>\n\n\n\n

        # Training<\/p>\n\n\n\n

        self.reinforcement_learner.learn_from_experience(<\/p>\n\n\n\n

            state_vector, action, reward, self._state_to_vector(next_state))<\/p>\n\n\n\n

    def _state_to_vector(self, state: Dict) -> np.ndarray:<\/p>\n\n\n\n

        “””Transforming a state into a vector for training”””<\/p>\n\n\n\n

        vector_components = []<\/p>\n\n\n\n

        # Adding different state components<\/p>\n\n\n\n

        if ‘quantum_state’ in state:<\/p>\n\n\n\n

            vector_components.append(state[‘quantum_state’].flatten())<\/p>\n\n\n\n

        if ’emotional_state’ in state:<\/p>\n\n\n\n

            emotions = [state[’emotional_state’][e] for e in<\/p>\n","protected":false},"excerpt":{"rendered":"

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