# This code is part of Qiskit.
#
# (C) Copyright IBM 2019, 2021.
#
# 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."""


import logging
import numpy as np
from qiskit.aqua import MissingOptionalLibraryError
from .optimizer import Optimizer, OptimizerSupportLevel

logger = logging.getLogger(__name__)

try:
    import skquant.opt as skq
    _HAS_SKQUANT = True
except ImportError:
    _HAS_SKQUANT = False


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.
    """

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

        Raises:
            MissingOptionalLibraryError: scikit-quant not installed
        """
        if not _HAS_SKQUANT:
            raise MissingOptionalLibraryError(
                libname='scikit-quant',
                name='BOBYQA',
                pip_install="pip install 'qiskit-aqua[skquant]'")
        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
        }

    def optimize(self, num_vars, objective_function, gradient_function=None,
                 variable_bounds=None, initial_point=None):
        """ Runs the optimization. """
        super().optimize(num_vars, objective_function, gradient_function,
                         variable_bounds, initial_point)
        res, history = skq.minimize(objective_function, np.array(initial_point),
                                    bounds=np.array(variable_bounds), budget=self._maxiter,
                                    method="bobyqa")
        return res.optpar, res.optval, len(history)
