from dspy.teleprompt.mipro_optimizer import MIPRO

"""
===============================================================
DEPRECATED!!!
PLEASE USE MIPRO INSTEAD.
===============================================================

USAGE SUGGESTIONS:

The following code can be used to compile a optimized signature teleprompter using the BayesianSignatureOptimizer, and evaluate it on an end task:

from dspy.teleprompt import BayesianSignatureOptimizer

teleprompter = BayesianSignatureOptimizer(prompt_model=prompt_model, task_model=task_model, metric=metric, n=10, init_temperature=1.0)
kwargs = dict(num_threads=NUM_THREADS, display_progress=True, display_table=0)
compiled_prompt_opt = teleprompter.compile(program, devset=devset[:DEV_NUM], optuna_trials_num=100, max_bootstrapped_demos=3, max_labeled_demos=5, eval_kwargs=kwargs)
eval_score = evaluate(compiled_prompt_opt, devset=evalset[:EVAL_NUM], **kwargs)

Note that this teleprompter takes in the following parameters:

* prompt_model: The model used for prompt generation. When unspecified, defaults to the model set in settings (ie. dspy.settings.configure(lm=task_model)).
* task_model: The model used for prompt generation. When unspecified, defaults to the model set in settings (ie. dspy.settings.configure(lm=task_model)).
* metric: The task metric used for optimization.
* n: The number of new prompts and sets of fewshot examples to generate and evaluate. Default=10.
* init_temperature: The temperature used to generate new prompts. Higher roughly equals more creative. Default=1.0.
* verbose: Tells the method whether or not to print intermediate steps.
* track_stats: Tells the method whether or not to track statistics about the optimization process.
                If True, the method will track a dictionary with a key corresponding to the trial number, 
                and a value containing a dict with the following keys:
                    * program: the program being evaluated at a given trial
                    * score: the last average evaluated score for the program
                    * pruned: whether or not this program was pruned
                This information will be returned as attributes of the best program.
"""


class BayesianSignatureOptimizer(MIPRO):
    def __init__(
        self,
        prompt_model=None,
        task_model=None,
        teacher_settings={},
        n=10,
        metric=None,
        init_temperature=1.0,
        verbose=False,
        track_stats=True,
        view_data_batch_size=10,
    ):
        print(
            "\u001b[31m[WARNING] BayesianSignatureOptimizer has been deprecated and replaced with MIPRO.  BayesianSignatureOptimizer will be removed in a future release. \u001b[31m",
        )

        super().__init__(
            metric=metric,
            prompt_model=prompt_model,
            task_model=task_model,
            teacher_settings=teacher_settings,
            num_candidates=n,
            init_temperature=init_temperature,
            verbose=verbose,
            track_stats=track_stats,
            view_data_batch_size=view_data_batch_size,
        )

    def compile(
        self,
        student,
        *,
        devset,
        max_bootstrapped_demos,
        max_labeled_demos,
        eval_kwargs,
        seed=42,
        optuna_trials_num,
        view_data=True,
        view_examples=True,
        requires_permission_to_run=False,
        num_trials=None,
    ):
        return super().compile(
            student,
            trainset=devset,
            max_bootstrapped_demos=max_bootstrapped_demos,
            max_labeled_demos=max_labeled_demos,
            eval_kwargs=eval_kwargs,
            seed=seed,
            view_data=view_data,
            view_examples=view_examples,
            requires_permission_to_run=requires_permission_to_run,
            num_trials=optuna_trials_num,
        )