# CognoSphere Unified Multimodal Language Model (CSUMLM)

import tensorflow as tf
import numpy as np
import os
import random

# Data Processing
class DataProcessor:
    def __init__(self, data_dir):
        self.data_dir = data_dir
        self.text_data = []
        self.image_data = []
        self.audio_data = []
        self.load_data()

    def load_data(self):
        # Load text data
        text_files = os.listdir(os.path.join(self.data_dir, 'text'))
        for file in text_files:
            with open(os.path.join(self.data_dir, 'text', file), 'r') as f:
                self.text_data.extend(f.readlines())

        # Load image data
        image_files = os.listdir(os.path.join(self.data_dir, 'images'))
        for file in image_files:
            self.image_data.append(os.path.join(self.data_dir, 'images', file))

        # Load audio data
        audio_files = os.listdir(os.path.join(self.data_dir, 'audio'))
        for file in audio_files:
            self.audio_data.append(os.path.join(self.data_dir, 'audio', file))

    def get_batch(self, batch_size):
        # Randomly sample data from each modality
        text_batch = random.sample(self.text_data, batch_size)
        image_batch = random.sample(self.image_data, batch_size)
        audio_batch = random.sample(self.audio_data, batch_size)

        return text_batch, image_batch, audio_batch

# Hybrid Learning Engine
class HybridLearningEngine:
    def __init__(self, data_processor):
        self.data_processor = data_processor
        self.model = self.build_model()

    def build_model(self):
        # Define the model architecture
        # Combine transfer learning, deep learning, self-supervised learning, meta-learning,
        # deep meta-learning, reinforcement learning, and cross-domain analogy extraction
        # ...

        return model

    def train(self, epochs, batch_size):
        for epoch in range(epochs):
            text_batch, image_batch, audio_batch = self.data_processor.get_batch(batch_size)

            # Train the model on the batch
            # ...

# Advanced Attention Mechanism
class AttentionMechanism:
    def __init__(self):
        self.traditional_attention = TraditionalAttention()
        self.self_attention = SelfAttention()
        self.linear_attention = LinearAttention()

    def apply_attention(self, inputs):
        # Combine traditional attention, self-attention, and linear attention
        # ...

        return attended_inputs

# Hierarchical Belief Desire Intent Tree/Chain of Thought Structure
class BeliefDesireIntentTree:
    def __init__(self):
        self.root = None

    def build_tree(self, inputs):
        # Construct the Belief Desire Intent Tree/Chain of Thought Structure
        # ...

        return self.root

# Modular Python Architecture
class CSUMLM:
    def __init__(self, data_dir):
        self.data_processor = DataProcessor(data_dir)
        self.learning_engine = HybridLearningEngine(self.data_processor)
        self.attention_mechanism = AttentionMechanism()
        self.belief_desire_intent_tree = BeliefDesireIntentTree()

    def train(self, epochs, batch_size):
        self.learning_engine.train(epochs, batch_size)

    def process_input(self, inputs):
        # Preprocess inputs
        # ...

        # Apply attention mechanism
        attended_inputs = self.attention_mechanism.apply_attention(inputs)

        # Build Belief Desire Intent Tree/Chain of Thought Structure
        belief_desire_intent_tree = self.belief_desire_intent_tree.build_tree(attended_inputs)

        # Generate output based on the tree
        # ...

        return output

# Real-time Learning Mechanisms
class RealtimeLearningMechanism:
    def __init__(self, model):
        self.model = model

    def update_model(self, new_data):
        # Update the model with new data
        # ...

# Dynamic Knowledge Base
class DynamicKnowledgeBase:
    def __init__(self):
        self.knowledge_base = {}

    def update_knowledge_base(self, new_knowledge):
        # Update the knowledge base with new linguistic and multimodal patterns
        # ...

# Explainability and Transparency
class Explainer:
    def __init__(self, model):
        self.model = model

    def explain_prediction(self, inputs):
        # Generate explanations for model predictions and responses
        # ...

        return explanation

# Internal Retrieval Augmented Generation Enhanced Logic (I-RAGEL)
class IRAGEL:
    def __init__(self, model, knowledge_base):
        self.model = model
        self.knowledge_base = knowledge_base

    def retrieve_or_generate(self, inputs):
        # Retrieve or generate additional linguistic and multimodal data
        # ...

        return augmented_inputs

    def reflect_and_improve(self, inputs, outputs):
        # Reflect on generated logic and improve decision-making processes
        # ...

        return improved_outputs

    def self_train(self, inputs, outputs):
        # Implement self-training for continuous performance enhancement
        # ...

# Main CSUMLM Class
class CSUMLM:
    def __init__(self, data_dir):
        self.data_processor = DataProcessor(data_dir)
        self.learning_engine = HybridLearningEngine(self.data_processor)
        self.attention_mechanism = AttentionMechanism()
        self.belief_desire_intent_tree = BeliefDesireIntentTree()
        self.realtime_learning_mechanism = RealtimeLearningMechanism(self.learning_engine.model)
        self.knowledge_base = DynamicKnowledgeBase()
        self.explainer = Explainer(self.learning_engine.model)
        self.iragel = IRAGEL(self.learning_engine.model, self.knowledge_base)

    def train(self, epochs, batch_size):
        self.learning_engine.train(epochs, batch_size)

    def process_input(self, inputs):
        # Preprocess inputs
        # ...

        # Apply attention mechanism
        attended_inputs = self.attention_mechanism.apply_attention(inputs)

        # Build Belief Desire Intent Tree/Chain of Thought Structure
        belief_desire_intent_tree = self.belief_desire_intent_tree.build_tree(attended_inputs)

        # Retrieve or generate additional data
        augmented_inputs = self.iragel.retrieve_or_generate(attended_inputs)

        # Generate output based on the tree and augmented inputs
        outputs = self.learning_engine.model(augmented_inputs, belief_desire_intent_tree)

        # Reflect and improve outputs
        improved_outputs = self.iragel.reflect_and_improve(augmented_inputs, outputs)

        # Explain predictions
        explanation = self.explainer.explain_prediction(improved_outputs)

        # Update knowledge base and model
        self.knowledge_base.update_knowledge_base(new_knowledge)
        self.realtime_learning_mechanism.update_model(new_data)

        # Self-train the model
        self.iragel.self_train(augmented_inputs, improved_outputs)

        return improved_outputs, explanation