"""
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization.

Hyperband adaptively allocates resources to promising configurations
by using successive halving.
"""

from typing import Any, Callable, Dict, List, Optional, Tuple

import numpy as np

from core import Config


class Hyperband:
    """
    Hyperband hyperparameter optimization.

    Adaptively allocates training resources (epochs) to configurations.
    Eliminates poorly performing configurations early.

    Args:
        base_config: Base configuration
        search_space: Dictionary of parameters
        max_resource: Maximum resource (e.g., epochs)
        eta: Reduction factor (default: 3)

    Note:
        This is a skeleton implementation. Full implementation would require
        integration with training loops to support partial training.

    Example:
        >>> hyperband = Hyperband(
        ...     base_config=config,
        ...     search_space=search_space,
        ...     max_resource=81,  # Max epochs
        ...     eta=3
        ... )
    """

    def __init__(
        self,
        base_config: Config,
        search_space: Dict[str, Any],
        max_resource: int = 81,
        eta: int = 3,
    ):
        self.base_config = base_config
        self.search_space = search_space
        self.max_resource = max_resource
        self.eta = eta

        # Calculate brackets
        self.s_max = int(np.floor(np.log(max_resource) / np.log(eta)))
        self.B = (self.s_max + 1) * max_resource

    def get_bracket(self, s: int) -> Tuple[int, List[int], List[int]]:
        """
        Get bracket configuration.

        Args:
            s: Bracket index

        Returns:
            (n_configs, resources, n_configs_per_round)
        """
        n = int(np.ceil(self.B / self.max_resource / (s + 1) * self.eta**s))
        r = self.max_resource * self.eta ** (-s)

        resources = [int(r * self.eta**i) for i in range(s + 1)]
        n_configs = [int(n * self.eta ** (-i)) for i in range(s + 1)]

        return n, resources, n_configs

    def run(self, train_fn: Callable[[Config, int], Dict[str, float]], verbose: bool = True):
        """
        Run Hyperband (skeleton).

        Args:
            train_fn: Function that takes (config, n_resources) and returns metrics
            verbose: Print progress

        Note:
            Full implementation requires modifying training loop to support
            partial training and checkpointing.
        """
        if verbose:
            print("Hyperband implementation is a skeleton.")
            print("Full implementation requires training loop integration.")

        # This would iterate through brackets and successive halving rounds
        # Actual implementation omitted for brevity
        pass
