from math import sqrt

import numpy as np
from sklearn.gaussian_process.kernels import Kernel as sk_Kernel
from sklearn.gaussian_process.kernels import ConstantKernel as sk_ConstantKernel
from sklearn.gaussian_process.kernels import DotProduct as sk_DotProduct
from sklearn.gaussian_process.kernels import Exponentiation as sk_Exponentiation
from sklearn.gaussian_process.kernels import ExpSineSquared as sk_ExpSineSquared
from sklearn.gaussian_process.kernels import Hyperparameter
from sklearn.gaussian_process.kernels import Matern as sk_Matern
from sklearn.gaussian_process.kernels import (
    NormalizedKernelMixin as sk_NormalizedKernelMixin,
)
from sklearn.gaussian_process.kernels import Product as sk_Product
from sklearn.gaussian_process.kernels import RationalQuadratic as sk_RationalQuadratic
from sklearn.gaussian_process.kernels import RBF as sk_RBF
from sklearn.gaussian_process.kernels import (
    StationaryKernelMixin as sk_StationaryKernelMixin,
)
from sklearn.gaussian_process.kernels import Sum as sk_Sum
from sklearn.gaussian_process.kernels import WhiteKernel as sk_WhiteKernel


class Kernel(sk_Kernel):
    """
    Base class for deephyper.skopt.gaussian_process kernels.
    Supports computation of the gradient of the kernel with respect to X
    """

    def __add__(self, b):
        if not isinstance(b, Kernel):
            return Sum(self, ConstantKernel(b))
        return Sum(self, b)

    def __radd__(self, b):
        if not isinstance(b, Kernel):
            return Sum(ConstantKernel(b), self)
        return Sum(b, self)

    def __mul__(self, b):
        if not isinstance(b, Kernel):
            return Product(self, ConstantKernel(b))
        return Product(self, b)

    def __rmul__(self, b):
        if not isinstance(b, Kernel):
            return Product(ConstantKernel(b), self)
        return Product(b, self)

    def __pow__(self, b):
        return Exponentiation(self, b)

    def gradient_x(self, x, X_train):
        """
        Computes gradient of K(x, X_train) with respect to x

        Parameters
        ----------
        x: array-like, shape=(n_features,)
            A single test point.

        X_train: array-like, shape=(n_samples, n_features)
            Training data used to fit the gaussian process.

        Returns
        -------
        gradient_x: array-like, shape=(n_samples, n_features)
            Gradient of K(x, X_train) with respect to x.
        """
        raise NotImplementedError


class RBF(Kernel, sk_RBF):
    def gradient_x(self, x, X_train):
        # diff = (x - X) / length_scale
        # size = (n_train_samples, n_dimensions)
        x = np.asarray(x)
        X_train = np.asarray(X_train)

        length_scale = np.asarray(self.length_scale)
        diff = x - X_train
        diff /= length_scale

        # e = -exp(0.5 * \sum_{i=1}^d (diff ** 2))
        # size = (n_train_samples, 1)
        exp_diff_squared = np.sum(diff**2, axis=1)
        exp_diff_squared *= -0.5
        exp_diff_squared = np.exp(exp_diff_squared, exp_diff_squared)
        exp_diff_squared = np.expand_dims(exp_diff_squared, axis=1)
        exp_diff_squared *= -1

        # gradient = (e * diff) / length_scale
        gradient = exp_diff_squared * diff
        gradient /= length_scale
        return gradient


class Matern(Kernel, sk_Matern):
    def gradient_x(self, x, X_train):
        x = np.asarray(x)
        X_train = np.asarray(X_train)
        length_scale = np.asarray(self.length_scale)

        # diff = (x - X_train) / length_scale
        # size = (n_train_samples, n_dimensions)
        diff = x - X_train
        diff /= length_scale

        # dist_sq = \sum_{i=1}^d (diff ^ 2)
        # dist = sqrt(dist_sq)
        # size = (n_train_samples,)
        dist_sq = np.sum(diff**2, axis=1)
        dist = np.sqrt(dist_sq)

        if self.nu == 0.5:
            # e = -np.exp(-dist) / dist
            # size = (n_train_samples, 1)
            scaled_exp_dist = -dist
            scaled_exp_dist = np.exp(scaled_exp_dist, scaled_exp_dist)
            scaled_exp_dist *= -1

            # grad = (e * diff) / length_scale
            # For all i in [0, D) if x_i equals y_i.
            # 1. e -> -1
            # 2. (x_i - y_i) / \sum_{j=1}^D (x_i - y_i)**2 approaches 1.
            # Hence the gradient when for all i in [0, D),
            # x_i equals y_i is -1 / length_scale[i].
            gradient = -np.ones((X_train.shape[0], x.shape[0]))
            mask = dist != 0.0
            scaled_exp_dist[mask] /= dist[mask]
            scaled_exp_dist = np.expand_dims(scaled_exp_dist, axis=1)
            gradient[mask] = scaled_exp_dist[mask] * diff[mask]
            gradient /= length_scale
            return gradient

        elif self.nu == 1.5:
            # grad(fg) = f'g + fg'
            # where f = 1 + sqrt(3) * euclidean((X - Y) / length_scale)
            # where g = exp(-sqrt(3) * euclidean((X - Y) / length_scale))
            sqrt_3_dist = sqrt(3) * dist
            f = np.expand_dims(1 + sqrt_3_dist, axis=1)

            # When all of x_i equals y_i, f equals 1.0, (1 - f) equals
            # zero, hence from below
            # f * g_grad + g * f_grad (where g_grad = -g * f_grad)
            # -f * g * f_grad + g * f_grad
            # g * f_grad * (1 - f) equals zero.
            # sqrt_3_by_dist can be set to any value since diff equals
            # zero for this corner case.
            sqrt_3_by_dist = np.zeros_like(dist)
            nzd = dist != 0.0
            sqrt_3_by_dist[nzd] = sqrt(3) / dist[nzd]
            dist_expand = np.expand_dims(sqrt_3_by_dist, axis=1)

            f_grad = diff / length_scale
            f_grad *= dist_expand

            sqrt_3_dist *= -1
            exp_sqrt_3_dist = np.exp(sqrt_3_dist, sqrt_3_dist)
            g = np.expand_dims(exp_sqrt_3_dist, axis=1)
            g_grad = -g * f_grad

            # f * g_grad + g * f_grad (where g_grad = -g * f_grad)
            f *= -1
            f += 1
            return g * f_grad * f

        elif self.nu == 2.5:
            # grad(fg) = f'g + fg'
            # where f = (1 + sqrt(5) * euclidean((X - Y) / length_scale) +
            #            5 / 3 * sqeuclidean((X - Y) / length_scale))
            # where g = exp(-sqrt(5) * euclidean((X - Y) / length_scale))
            sqrt_5_dist = sqrt(5) * dist
            f2 = (5.0 / 3.0) * dist_sq
            f2 += sqrt_5_dist
            f2 += 1
            f = np.expand_dims(f2, axis=1)

            # For i in [0, D) if x_i equals y_i
            # f = 1 and g = 1
            # Grad = f'g + fg' = f' + g'
            # f' = f_1' + f_2'
            # Also g' = -g * f1'
            # Grad = f'g - g * f1' * f
            # Grad = g * (f' - f1' * f)
            # Grad = f' - f1'
            # Grad = f2' which equals zero when x = y
            # Since for this corner case, diff equals zero,
            # dist can be set to anything.
            nzd_mask = dist != 0.0
            nzd = dist[nzd_mask]
            dist[nzd_mask] = np.reciprocal(nzd, nzd)

            dist *= sqrt(5)
            dist = np.expand_dims(dist, axis=1)
            diff /= length_scale
            f1_grad = dist * diff
            f2_grad = (10.0 / 3.0) * diff
            f_grad = f1_grad + f2_grad

            sqrt_5_dist *= -1
            g = np.exp(sqrt_5_dist, sqrt_5_dist)
            g = np.expand_dims(g, axis=1)
            g_grad = -g * f1_grad
            return f * g_grad + g * f_grad


class RationalQuadratic(Kernel, sk_RationalQuadratic):
    def gradient_x(self, x, X_train):
        x = np.asarray(x)
        X_train = np.asarray(X_train)
        alpha = self.alpha
        length_scale = self.length_scale

        # diff = (x - X_train) / length_scale
        # size = (n_train_samples, n_dimensions)
        diff = x - X_train
        diff /= length_scale

        # dist = -(1 + (\sum_{i=1}^d (diff^2) / (2 * alpha)))** (-alpha - 1)
        # size = (n_train_samples,)
        scaled_dist = np.sum(diff**2, axis=1)
        scaled_dist /= 2 * self.alpha
        scaled_dist += 1
        scaled_dist **= -alpha - 1
        scaled_dist *= -1

        scaled_dist = np.expand_dims(scaled_dist, axis=1)
        diff_by_ls = diff / length_scale
        return scaled_dist * diff_by_ls


class ExpSineSquared(Kernel, sk_ExpSineSquared):
    def gradient_x(self, x, X_train):
        x = np.asarray(x)
        X_train = np.asarray(X_train)
        length_scale = self.length_scale
        periodicity = self.periodicity

        diff = x - X_train
        sq_dist = np.sum(diff**2, axis=1)
        dist = np.sqrt(sq_dist)

        pi_by_period = dist * (np.pi / periodicity)
        sine = np.sin(pi_by_period) / length_scale
        sine_squared = -2 * sine**2
        exp_sine_squared = np.exp(sine_squared)

        grad_wrt_exp = -2 * np.sin(2 * pi_by_period) / length_scale**2

        # When x_i -> y_i for all i in [0, D), the gradient becomes
        # zero. See https://github.com/MechCoder/Notebooks/blob/master/ExpSineSquared%20Kernel%20gradient%20computation.ipynb
        # for a detailed math explanation
        # grad_wrt_theta can be anything since diff is zero
        # for this corner case, hence we set to zero.
        grad_wrt_theta = np.zeros_like(dist)
        nzd = dist != 0.0
        grad_wrt_theta[nzd] = np.pi / (periodicity * dist[nzd])
        return (
            np.expand_dims(grad_wrt_theta * exp_sine_squared * grad_wrt_exp, axis=1)
            * diff
        )


class ConstantKernel(Kernel, sk_ConstantKernel):
    def gradient_x(self, x, X_train):
        return np.zeros_like(X_train)


class WhiteKernel(Kernel, sk_WhiteKernel):
    def gradient_x(self, x, X_train):
        return np.zeros_like(X_train)


class Exponentiation(Kernel, sk_Exponentiation):
    def gradient_x(self, x, X_train):
        x = np.asarray(x)
        X_train = np.asarray(X_train)
        expo = self.exponent
        kernel = self.kernel

        K = np.expand_dims(kernel(np.expand_dims(x, axis=0), X_train)[0], axis=1)
        return expo * K ** (expo - 1) * kernel.gradient_x(x, X_train)


class Sum(Kernel, sk_Sum):
    def gradient_x(self, x, X_train):
        return self.k1.gradient_x(x, X_train) + self.k2.gradient_x(x, X_train)


class Product(Kernel, sk_Product):
    def gradient_x(self, x, X_train):
        x = np.asarray(x)
        x = np.expand_dims(x, axis=0)
        X_train = np.asarray(X_train)
        f_ggrad = np.expand_dims(self.k1(x, X_train)[0], axis=1) * self.k2.gradient_x(
            x, X_train
        )
        fgrad_g = np.expand_dims(self.k2(x, X_train)[0], axis=1) * self.k1.gradient_x(
            x, X_train
        )
        return f_ggrad + fgrad_g


class DotProduct(Kernel, sk_DotProduct):
    def gradient_x(self, x, X_train):
        return np.asarray(X_train)


class HammingKernel(sk_StationaryKernelMixin, sk_NormalizedKernelMixin, Kernel):
    r"""
    The HammingKernel is used to handle categorical inputs.

    ``K(x_1, x_2) = exp(\sum_{j=1}^{d} -ls_j * (I(x_1j != x_2j)))``

    Parameters
    -----------
    * `length_scale` [float, array-like, shape=[n_features,], 1.0 (default)]
        The length scale of the kernel. If a float, an isotropic kernel is
        used. If an array, an anisotropic kernel is used where each dimension
        of l defines the length-scale of the respective feature dimension.

    * `length_scale_bounds` [array-like, [1e-5, 1e5] (default)]
        The lower and upper bound on length_scale
    """

    def __init__(self, length_scale=1.0, length_scale_bounds=(1e-5, 1e5)):
        self.length_scale = length_scale
        self.length_scale_bounds = length_scale_bounds

    @property
    def hyperparameter_length_scale(self):
        length_scale = self.length_scale
        anisotropic = np.iterable(length_scale) and len(length_scale) > 1
        if anisotropic:
            return Hyperparameter(
                "length_scale", "numeric", self.length_scale_bounds, len(length_scale)
            )
        return Hyperparameter("length_scale", "numeric", self.length_scale_bounds)

    def __call__(self, X, Y=None, eval_gradient=False):
        """Return the kernel k(X, Y) and optionally its gradient.

        Parameters
        ----------
        * `X` [array-like, shape=(n_samples_X, n_features)]
            Left argument of the returned kernel k(X, Y)

        * `Y` [array-like, shape=(n_samples_Y, n_features) or None(default)]
            Right argument of the returned kernel k(X, Y). If None, k(X, X)
            if evaluated instead.

        * `eval_gradient` [bool, False(default)]
            Determines whether the gradient with respect to the kernel
            hyperparameter is determined. Only supported when Y is None.

        Returns
        -------
        * `K` [array-like, shape=(n_samples_X, n_samples_Y)]
            Kernel k(X, Y)

        * `K_gradient` [array-like, shape=(n_samples_X, n_samples_X, n_dims)]
            The gradient of the kernel k(X, X) with respect to the
            hyperparameter of the kernel. Only returned when eval_gradient
            is True.
        """
        length_scale = self.length_scale
        anisotropic = np.iterable(length_scale) and len(length_scale) > 1

        if np.iterable(length_scale):
            if len(length_scale) > 1:
                length_scale = np.asarray(length_scale, dtype=float)
            else:
                length_scale = float(length_scale[0])
        else:
            length_scale = float(length_scale)

        X = np.atleast_2d(X)
        if anisotropic and X.shape[1] != len(length_scale):
            raise ValueError(
                "Expected X to have %d features, got %d"
                % (len(length_scale), X.shape[1])
            )

        n_samples, n_dim = X.shape

        Y_is_None = Y is None
        if Y_is_None:
            Y = X
        elif eval_gradient:
            raise ValueError("gradient can be evaluated only when Y != X")
        else:
            Y = np.atleast_2d(Y)

        indicator = np.expand_dims(X, axis=1) != Y
        kernel_prod = np.exp(-np.sum(length_scale * indicator, axis=2))

        # dK / d theta = (dK / dl) * (dl / d theta)
        # theta = log(l) => dl / d (theta) = e^theta = l
        # dK / d theta = l * dK / dl

        # dK / dL computation
        if anisotropic:
            grad = -np.expand_dims(kernel_prod, axis=-1) * np.array(
                indicator, dtype=np.float32
            )
        else:
            grad = -np.expand_dims(kernel_prod * np.sum(indicator, axis=2), axis=-1)

        grad *= length_scale
        if eval_gradient:
            return kernel_prod, grad
        return kernel_prod
