"""
"""
from SALib.sample.morris.strategy import Strategy
from scipy.special import comb as nchoosek  # type: ignore
from itertools import combinations, islice
import sys
import numpy as np  # type: ignore
from typing import List


class BruteForce(Strategy):
    """Implements the brute force optimisation strategy
    """

    def _sample(self, input_sample, num_samples,
                num_params, k_choices, num_groups=None):
        return self.brute_force_most_distant(input_sample, num_samples,
                                             num_params, k_choices, num_groups)

    def brute_force_most_distant(self, input_sample: np.ndarray, num_samples: int,
                                 num_params: int, k_choices: int,
                                 num_groups: int=None) -> List:
        """Use brute force method to find most distant trajectories

        Arguments
        ---------
        input_sample : numpy.ndarray
        num_samples : int
            The number of samples to generate
        num_params : int
            The number of parameters
        k_choices : int
            The number of optimal trajectories
        num_groups : int, default=None
            The number of groups

        Returns
        -------
        list
        """
        scores = self.find_most_distant(input_sample,
                                        num_samples,
                                        num_params,
                                        k_choices,
                                        num_groups)

        maximum_combo = self.find_maximum(scores, num_samples, k_choices)

        return maximum_combo

    def find_most_distant(self, input_sample: np.ndarray, num_samples: int,
                          num_params: int, k_choices:int, num_groups: int=None) -> np.ndarray:
        """
        Finds the 'k_choices' most distant choices from the
        'num_samples' trajectories contained in 'input_sample'

        Arguments
        ---------
        input_sample : numpy.ndarray
        num_samples : int
            The number of samples to generate
        num_params : int
            The number of parameters
        k_choices : int
            The number of optimal trajectories
        num_groups : int, default=None
            The number of groups

        Returns
        -------
        numpy.ndarray
        """
        # Now evaluate the (N choose k_choices) possible combinations
        if nchoosek(num_samples, k_choices) >= sys.maxsize:
            raise ValueError("Number of combinations is too large")
        number_of_combinations = int(nchoosek(num_samples, k_choices))

        # First compute the distance matrix for each possible pairing
        # of trajectories and store in a shared-memory array
        distance_matrix = self.compute_distance_matrix(input_sample,
                                                       num_samples,
                                                       num_params,
                                                       num_groups)

        # Initialise the output array
        chunk = int(1e6)
        if chunk > number_of_combinations:
            chunk = number_of_combinations

        counter = 0
        # Generate a list of all the possible combinations
        combo_gen = combinations(range(num_samples), k_choices)
        scores = np.zeros(number_of_combinations, dtype=np.float32)
        # Generate the pairwise indices once
        pairwise = np.array(
            [y for y in combinations(range(k_choices), 2)])

        mappable = self.mappable
        for combos in self.grouper(chunk, combo_gen):
            scores[(counter * chunk):((counter + 1) * chunk)] \
                = mappable(combos, pairwise, distance_matrix)
            counter += 1
        return scores

    @staticmethod
    def grouper(n, iterable):
        it = iter(iterable)
        while True:
            chunk = tuple(islice(it, n))
            if not chunk:
                return
            yield chunk

    @staticmethod
    def mappable(combos, pairwise, distance_matrix):
        '''
        Obtains scores from the distance_matrix for each pairwise combination
        held in the combos array

        Arguments
        ----------
        combos : numpy.ndarray
        pairwise : numpy.ndarray
        distance_matrix : numpy.ndarray
        '''
        combos = np.array(combos)
        # Create a list of all pairwise combination for each combo in combos
        combo_list = combos[:, pairwise[:, ]]

        addresses = (combo_list[:, :, 1], combo_list[:, :, 0])

        all_distances = distance_matrix[addresses]
        new_scores = np.sqrt(
            np.einsum('ij,ij->i', all_distances, all_distances))
        return new_scores

    def find_maximum(self, scores, N, k_choices):
        """Finds the `k_choices` maximum scores from `scores`

        Arguments
        ---------
        scores : numpy.ndarray
        N : int
        k_choices : int

        Returns
        -------
        list
        """
        if not isinstance(scores, np.ndarray):
            raise TypeError("Scores input is not a numpy array")

        index_of_maximum = int(scores.argmax())
        maximum_combo = self.nth(combinations(
            range(N), k_choices), index_of_maximum, None)
        return sorted(maximum_combo)

    @staticmethod
    def nth(iterable, n, default=None):
        """Returns the nth item or a default value

        Arguments
        ---------
        iterable : iterable
        n : int
        default : default=None
            The default value to return
        """

        if type(n) != int:
            raise TypeError("n is not an integer")

        return next(islice(iterable, n, None), default)