#!/usr/bin/env python3
"""
Nova PyTorch Runner (V0)

This script implements the core Soul+Mask+Fast-Weights architecture for the Nova LLM.
It serves as a standalone PyTorch service for inference, masked updates, and guards.
"""

import torch
import torch.nn as nn
from transformers import AutoModelForCausalLM, AutoTokenizer
import mlflow
import mlflow.pytorch

# --- Configuration Placeholders ---
MODEL_NAME = "Qwen/Qwen3-8B-Instruct" # Placeholder: Will be replaced with Qwen3-32B or similar
SOUL_DIM = 512 # Dimension of the soul vector
MASK_PCT = 0.05 # Percentage of parameters in the plasticity mask
FAST_WEIGHT_HALF_LIFE_SEC = 120 # Half-life for fast-weights decay

# --- Nova Core Model (Conceptual) ---
class NovaCore(nn.Module):
    def __init__(self, vocab_size, d_model):
        super().__init__()
        # Placeholder for actual Transformer blocks
        self.transformer = AutoModelForCausalLM.from_pretrained(MODEL_NAME)

        # 1. Soul Injection (Conceptual)
        self.soul_vector = nn.Parameter(torch.randn(SOUL_DIM))
        # Conceptual soul gates: a simple linear layer that would process soul_vector
        # and apply it to some part of the transformer's internal representation.
        # In a real model, this would be integrated within transformer blocks.
        self.soul_gates = nn.Linear(SOUL_DIM, d_model) # d_model is conceptual output dim

        # 2. Plasticity Mask (Conceptual)
        self.plastic_params = []
        # Conceptual selection of plastic parameters:
        # In a real scenario, this would involve careful selection, e.g.,
        # for name, param in self.transformer.named_parameters():
        #     if "bias" in name or "layer_norm" in name: # Example criteria
        #         self.plastic_params.append(param)
        # For this conceptual scaffold, we'll just add a dummy parameter to illustrate.
        self.dummy_plastic_param = nn.Parameter(torch.randn(10))
        self.plastic_params.append(self.dummy_plastic_param)

        # 3. Fast-Weights (Conceptual)
        self.fast_weights_cache = {}

    def forward(self, input_ids):
        # Conceptual application of soul_vector
        # In a real model, soul_vector would modulate internal activations.
        # Here, we just show it's processed.
        soul_modulation = self.soul_gates(self.soul_vector)
        # This modulation would then be applied within the transformer's layers.

        output = self.transformer(input_ids)
        return output

# --- Turn Loop (Conceptual) ---
def nova_turn_loop(model: NovaCore, tokenizer: AutoTokenizer, input_text: str):
    # 1) Infer
    input_ids = tokenizer.encode(input_text, return_tensors="pt")
    model_output = model(input_ids)

    # 2) Score (identity/utility/self-sup) - Placeholder
    identity_score = 0.0
    utility_score = 0.0

    # 3) Imprint fast-weights - Placeholder
    # Update model.fast_weights_cache based on current turn

    # 4) Masked SGD step (clip, EMA, EWC, orth-grad, ΔW caps) - Conceptual
    # In a real scenario, this would involve a tiny optimizer step on model.plastic_params
    # For conceptual illustration:
    if model.plastic_params:
        # Simulate a conceptual loss dependent on a plastic parameter
        # This ensures gradients are computed for plastic_params
        conceptual_loss = (model.dummy_plastic_param ** 2).sum() # Example: simple squared sum loss
        conceptual_loss.backward() # Simulate backward pass

        # Simulate a conceptual optimizer step on plastic_params
        # In reality, this would be a proper optimizer (e.g., Adam, SGD)
        with torch.no_grad():
            for param in model.plastic_params:
                if param.grad is not None:
                    param.data -= 1e-5 * param.grad # Conceptual tiny update
                    param.grad.zero_() # Zero out gradients after update (like a real optimizer)

    # 5) Guards (rollback on 2σ drop) - Placeholder
    # Check identity_score and potentially rollback

    # 6) Log - Placeholder
    print(f"Turn processed. Identity Score: {identity_score}, Utility Score: {utility_score}")

    # MLflow: Log metrics per turn (conceptual)
    mlflow.log_metric("identity_score", identity_score)
    mlflow.log_metric("utility_score", utility_score)


# --- Main Execution (Conceptual) ---
if __name__ == "__main__":
    # MLflow: Start an MLflow run
    with mlflow.start_run():
        # Placeholder for actual model loading and setup
        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
        model = NovaCore(tokenizer.vocab_size, d_model=768) # d_model is a placeholder

        print("Nova PyTorch Runner (V0) initialized.")
        print("This is a conceptual scaffold. Actual implementation will follow.")

        # MLflow: Log parameters (conceptual)
        mlflow.log_param("model_name", MODEL_NAME)
        mlflow.log_param("soul_dim", SOUL_DIM)
        mlflow.log_param("mask_pct", MASK_PCT)
        mlflow.log_param("fast_weight_half_life_sec", FAST_WEIGHT_HALF_LIFE_SEC)

        # Example turn loop execution
        # nova_turn_loop(model, tokenizer, "Hello, Nova. How are you today?")

        # MLflow: Log the model (conceptual)
        # mlflow.pytorch.log_model(model, "nova_model")