# This code is part of Qiskit.
#
# (C) Copyright IBM 2019, 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.

"""Bound Optimization BY Quadratic Approximation (BOBYQA) optimizer."""

from __future__ import annotations

from collections.abc import Callable
from typing import Any

import numpy as np
from qiskit.utils import optionals as _optionals
from .optimizer import Optimizer, OptimizerSupportLevel, OptimizerResult, POINT


@_optionals.HAS_SKQUANT.require_in_instance
class BOBYQA(Optimizer):
    """Bound Optimization BY Quadratic Approximation algorithm.

    BOBYQA finds local solutions to nonlinear, non-convex minimization problems with optional
    bound constraints, without requirement of derivatives of the objective function.

    Uses skquant.opt installed with pip install scikit-quant.
    For further detail, please refer to
    https://github.com/scikit-quant/scikit-quant and https://qat4chem.lbl.gov/software.
    """

    def __init__(
        self,
        maxiter: int = 1000,
    ) -> None:
        """
        Args:
            maxiter: Maximum number of function evaluations.

        Raises:
            MissingOptionalLibraryError: scikit-quant not installed
        """
        super().__init__()
        self._maxiter = maxiter

    def get_support_level(self):
        """Returns support level dictionary."""
        return {
            "gradient": OptimizerSupportLevel.ignored,
            "bounds": OptimizerSupportLevel.required,
            "initial_point": OptimizerSupportLevel.required,
        }

    @property
    def settings(self) -> dict[str, Any]:
        return {"maxiter": self._maxiter}

    def minimize(
        self,
        fun: Callable[[POINT], float],
        x0: POINT,
        jac: Callable[[POINT], POINT] | None = None,
        bounds: list[tuple[float, float]] | None = None,
    ) -> OptimizerResult:
        from skquant import opt as skq

        res, history = skq.minimize(
            func=fun,
            x0=np.asarray(x0),
            bounds=np.array(bounds),
            budget=self._maxiter,
            method="bobyqa",
        )

        optimizer_result = OptimizerResult()
        optimizer_result.x = res.optpar
        optimizer_result.fun = res.optval
        optimizer_result.nfev = len(history)
        return optimizer_result