"""
romanai-kernal.py

Copyright (c) 2026 Daniel Harding - RomanAILabs
All Rights Reserved.

Description:
Core Kernel for RomanAI featuring Graph-based Attention, BERT-inspired embeddings,
Hierarchical RL, and a Conceptual 4D-Memory Module.
"""

import os
import torch
import torch.nn as nn
import logging

# Silence TensorFlow/transformers log spam
os.environ.setdefault("TF_CPP_MIN_LOG_LEVEL", "2")
os.environ.setdefault("TRANSFORMERS_NO_ADVISORY_WARNINGS", "1")
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")

from transformers import BertModel, BertTokenizer

# Placeholder for GraphSAGE - assumes you have the library installed or a local module
try:
    from graphsage.graphsage import GraphSAGE
except ImportError:
    # Silent fallback to avoid log spam during module load
    GraphSAGE = object  # Placeholder to prevent immediate crash if just testing logic

# Setup Logging
logging.basicConfig(
    filename='romanai_kernel.log',
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)

class ContextualUnderstandingModule(nn.Module):
    """
    Enhancing Contextual Understanding using Graph-based Attention
    and BERT-inspired embeddings.
    """
    def __init__(self, user_embedding_dim, object_embedding_dim):
        super(ContextualUnderstandingModule, self).__init__()
        # Initialize embeddings (simulated for the kernel structure)
        self.user_embedding_dim = user_embedding_dim
        self.object_embedding_dim = object_embedding_dim
        
        # In a real implementation, these might be loaded from a pre-trained model
        self.user_embeddings = nn.Parameter(torch.randn(1, user_embedding_dim))
        self.object_embeddings = nn.Parameter(torch.randn(1, object_embedding_dim))

    def forward(self, user_attention_weights, object_attention_weights):
        """
        Applies attention weights to embeddings to derive context.
        """
        # Ensure dimensions match for element-wise multiplication
        # Applying weights to the embeddings
        weighted_user = torch.mul(user_attention_weights, self.user_embeddings)
        weighted_object = torch.mul(object_attention_weights, self.object_embeddings)
        
        # Combine context (concatenation or addition depending on architecture)
        context_vector = torch.cat((weighted_user, weighted_object), dim=1)
        return context_vector

class Conceptual4DModule(nn.Module):
    """
    Improving Memory Retention using interaction history and temporal modeling.
    Integrates concepts of M2N (Memory-Augmented Neural Networks).
    """
    def __init__(self, embedding_dim, history_size=100):
        super(Conceptual4DModule, self).__init__()
        self.embedding_dim = embedding_dim
        self.history_size = history_size
        
        # Memory bank to store interaction history (The "4D" aspect)
        self.interaction_history = torch.zeros(history_size, embedding_dim)
        
        # Temporal Convolutional Network (TCN) layer for processing time-series data
        self.tcn_layer = nn.Conv1d(in_channels=embedding_dim, out_channels=embedding_dim, kernel_size=3, padding=1)

    def forward(self, new_interaction_embedding, new_user_attention_weights):
        """
        Integrate new interaction with existing interaction history.
        """
        # Update user embedding based on new attention
        updated_user_embedding = torch.mul(new_user_attention_weights, new_interaction_embedding)
        
        # Slide history window (First-In-First-Out for simple buffer, or use M2N logic)
        # Shift history down
        self.interaction_history = torch.roll(self.interaction_history, shifts=1, dims=0)
        # Insert new interaction at top
        self.interaction_history[0] = updated_user_embedding.detach()

        # Apply Temporal Convolution to learn from the sequence of interactions
        # Reshape for Conv1d: (Batch, Channels, Length)
        history_tensor = self.interaction_history.unsqueeze(0).transpose(1, 2) 
        temporal_context = self.tcn_layer(history_tensor)
        
        return updated_user_embedding, temporal_context

class HierarchicalRLModule(nn.Module):
    """
    Enabling Creative Problem-solving via Contextual Rewards.
    """
    def __init__(self, task_hierarchy_levels):
        super(HierarchicalRLModule, self).__init__()
        self.task_hierarchy_levels = task_hierarchy_levels
        self.exploration_rate = 0.5

    def forward(self, task_reward):
        """
        Hierarchical structure for encouraging exploration of tasks based on reward.
        """
        # Logic: If the reward for the current context is high, explore deeper (creative)
        # If low, stick to known safe paths (exploitation)
        
        if task_reward > self.exploration_rate:
            exploration_mode = True
            # Logic to trigger Generative Models (GANs/VAEs) would go here
        else:
            exploration_mode = False

        return torch.tensor(1.0) if exploration_mode else torch.tensor(0.0)

class RomanAIKernel(nn.Module):
    """
    Main Hybrid Architecture Wrapper.
    Integrates Context, Memory (4D), and RL components.
    """
    def __init__(self):
        super(RomanAIKernel, self).__init__()
        # Configuration
        self.hidden_dim = 768 # Standard BERT size
        
        # Initialize Sub-Modules
        self.context_module = ContextualUnderstandingModule(self.hidden_dim, self.hidden_dim)
        self.memory_4d = Conceptual4DModule(self.hidden_dim)
        self.rl_module = HierarchicalRLModule(task_hierarchy_levels=5)
        
        # BERT for initial embedding (Pre-trained)
        self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
        self.bert = BertModel.from_pretrained('bert-base-uncased')

    def audit_and_log(self, input_text, output_data):
        """
        Auditing and logging for system transparency.
        """
        try:
            with open('romanai_input_logs.txt', 'a') as f:
                f.write(f"Input: {input_text}\n")
            
            with open('romanai_output_logs.txt', 'a') as f:
                f.write(f"Output Logic State: {output_data}\n")
        except Exception as e:
            logging.error(f"Logging failed: {e}")

    def forward(self, input_text, user_attn, obj_attn, reward_signal):
        # 1. Tokenize and Embed Input
        inputs = self.tokenizer(input_text, return_tensors="pt")
        outputs = self.bert(**inputs)
        last_hidden_states = outputs.last_hidden_state
        # Use the CLS token as the sentence embedding
        sentence_embedding = last_hidden_states[:, 0, :]

        # 2. Contextual Understanding
        context = self.context_module(user_attn, obj_attn)
        
        # 3. 4D Memory Processing
        # We pass the BERT embedding as the "new interaction"
        updated_user_state, time_context = self.memory_4d(sentence_embedding, user_attn)
        
        # 4. RL Decision (Explore vs Exploit)
        exploration_decision = self.rl_module(reward_signal)

        # Log the cycle
        self.audit_and_log(input_text, f"Exploration Mode: {exploration_decision.item()}")

        return updated_user_state, exploration_decision

# Example Usage Block (for testing)
if __name__ == "__main__":
    print("Initializing RomanAI Kernel...")
    kernel = RomanAIKernel()
    
    # Dummy data for testing the forward pass
    dummy_input = "Hello Roman, do you remember me?"
    # Dummy attention weights (normally calculated by the Graph Attention layer)
    dummy_user_attn = torch.rand(1, 768) 
    dummy_obj_attn = torch.rand(1, 768)
    dummy_reward = 0.8 # High reward to trigger exploration
    
    try:
        user_state, decision = kernel(dummy_input, dummy_user_attn, dummy_obj_attn, dummy_reward)
        print("Kernel Pass Successful.")
        print(f"Exploration Decision: {decision.item()} (1.0 = True)")
    except Exception as e:
        print(f"Error during kernel pass: {e}")