'''Module: meta_learning
This module provides a basic meta-learning optimizer to adjust model parameters based on a simulated meta-learning update rule.
It is a prototype implementation intended for integration with the quantum circuit simulation module, and serves as a placeholder for more advanced meta-learning techniques.

The MetaLearner class implements a simple update rule:
    new_params = params - meta_lr * gradient_estimate

In a full implementation, the gradient estimate would be computed via an RNN, evolutionary strategies, or other advanced methods. For this prototype, a simple quadratic derivative is used for demonstration purposes.
'''

import numpy as np


class MetaLearner:
    """
    A basic meta-learning optimizer that updates parameters using a simulated meta-update rule.

    Attributes:
        meta_lr (float): The learning rate for the meta-update.
    """

    def __init__(self, meta_lr=0.01):
        """
        Initialize the MetaLearner.

        Parameters:
            meta_lr (float): The learning rate for the meta-learning update. Default is 0.01.
        """
        self.meta_lr = meta_lr
        print(f"[MetaLearner] Initialized with meta_lr = {self.meta_lr}")

    def compute_gradient_estimate(self, params, loss):
        """
        Simulate computation of a gradient estimate for the meta-update.

        In this prototype, we assume the loss is defined as 0.5 * (params)^2 (i.e., a simple quadratic loss centered at 0),
        so that the gradient is simply params. In practice, this would involve more complex computations, potentially
        via backpropagation through an RNN or evolutionary strategy mechanisms.

        Parameters:
            params (np.array): The current parameter values as a numpy array.
            loss (float): The current loss value (unused in this simple simulation).

        Returns:
            np.array: The simulated gradient estimate (here, equal to params).
        """
        gradient_estimate = params  # For a quadratic loss, d(0.5 * params^2)/d(params) = params
        print(f"[MetaLearner] Computed gradient estimate: {gradient_estimate}")
        return gradient_estimate

    def update_params(self, params, loss):
        """
        Update parameters using the simulated meta-learning update rule:

            new_params = params - meta_lr * gradient_estimate

        Parameters:
            params (np.array): The current parameter values as a numpy array.
            loss (float): The current loss value (for logging purposes).

        Returns:
            np.array: The updated parameter values.
        """
        gradient = self.compute_gradient_estimate(params, loss)
        new_params = params - self.meta_lr * gradient
        print(f"[MetaLearner] Updated parameters from {params} to {new_params} using loss {loss}")
        return new_params


if __name__ == '__main__':
    # Demonstration of the MetaLearner functionality
    learner = MetaLearner(meta_lr=0.05)
    initial_params = np.array([0.5, -0.2, 0.1])
    dummy_loss = 0.1  # Example loss value
    print(f"[Demo] Initial parameters: {initial_params}")
    updated_params = learner.update_params(initial_params, dummy_loss)
    print(f"[Demo] Final updated parameters: {updated_params}") 