import torch
import torch.nn as nn

class GodelaiAgent(nn.Module):
    def __init__(self, base_model, propagation_gamma=2.0, min_surplus_energy=0.1):
        super().__init__()
        self.compression_layer = base_model # The "Body" (Standard Transformer)
        self.state_memory = []              # The "History" (State traces)
        
        # Hyperparameters for Wisdom
        self.gamma = propagation_gamma      # Penalty severity for losing adaptability
        self.epsilon = 0.05                 # "Death line" for adaptability
        self.surplus_reservation = min_surplus_energy # "有余力": Reserved capacity
        
        # Metrics
        self.last_T_score = 1.0             # Initial Propagation Potential (T)

    def measure_propagation_potential(self, current_weights, gradients):
        """
        Calculates 'T': The Transmission Fidelity.
        This measures the 'meta-modifiability' or 'generalizability' of the state.
        
        如果梯度指向极其狭窄的山谷（过拟合），T 会下降。
        如果权重分布保持了广义的连接性（高熵），T 保持高位。
        """
        # (Simplified implementation: using Hessian spectrum or Gradient Diversity)
        # Here we conceptually measure: "How hard is it to change my mind later?"
        rigidity = torch.norm(gradients) / (torch.std(current_weights) + 1e-6)
        T_score = 1.0 / (1.0 + rigidity) 
        return T_score

    def forward_step(self, data, target):
        # 1. Standard Compression Step (Solving the Task)
        # -------------------------------------------------------
        prediction = self.compression_layer(data)
        task_loss = nn.MSELoss()(prediction, target)
        
        # 2. The Propagation Check (The "Wisdom" Check)
        # -------------------------------------------------------
        # Before updating, we simulate the gradient step to see effect on T
        gradients = torch.autograd.grad(task_loss, self.compression_layer.parameters(), create_graph=True)
        
        current_T = self.measure_propagation_potential(self.compression_layer.parameters(), gradients)
        
        # 3. Calculate Propagation Layer Loss (L_prop)
        # Formulated from your "Propagation Layer Conservation" principle
        # -------------------------------------------------------
        if current_T < self.last_T_score:
            # PENALTY: You are destroying your future adaptability!
            # Non-linear penalty ensures the model "feels pain" when losing wisdom.
            l_prop = (self.last_T_score - current_T) ** self.gamma
        else:
            l_prop = 0.0
            
        # 4. The "Surplus Energy" Constraint (有余力)
        # -------------------------------------------------------
        # Ensure that the update magnitude does not exhaust the "Surplus Energy" buffer.
        # We clamp the task_loss influence if it threatens the reserve.
        total_loss = task_loss + (10.0 * l_prop) # Wisdom is weighted heavily

        return total_loss, current_T

    def optimizer_step(self, optimizer, total_loss, current_T):
        """
        The Evolution Step.
        Crucial: Triggers the "Fail-Safe" if wisdom drops too low.
        """
        # Fail-Safe Protocol (Trigger from your notes)
        if current_T < self.epsilon:
            print("[ALERT] Propagation Potential Critical! Triggering Forced Reflection.")
            # FREEZE non-propagation layers. 
            # Only allow updates that restore T (Architectural adjustments).
            self.trigger_reflection_mode()
            return # Skip standard update

        # Standard Update
        optimizer.zero_grad()
        total_loss.backward()
        
        # Update State History
        self.state_memory.append(self.last_T_score)
        self.last_T_score = current_T.item() # Update the benchmark
        
        optimizer.step()

    def trigger_reflection_mode(self):
        """
        The 'Sleep' or 'Meditation' Phase.
        Re-organizes weights without ingesting new data to restore structure.
        """
        # Implementation of "Sleep" logic to restore T
        pass