import numpy as np
import tensorflow as tf

from .. import kernels
from .. import mean_functions as mfn
from ..covariances import Kuf
from ..inducing_variables import InducingVariables
from ..probability_distributions import DiagonalGaussian, Gaussian, MarkovGaussian
from ..quadrature import mvnquad
from . import dispatch
from .expectations import quadrature_expectation

register = dispatch.quadrature_expectation.register


NoneType = type(None)


def get_eval_func(obj, inducing_variable, slice=None):
    """
    Return the function of interest (kernel or mean) for the expectation
    depending on the type of :obj: and whether any inducing are given
    """

    slice = ... if slice is None else slice
    if inducing_variable is not None:
        # kernel + inducing_variable combination
        if not isinstance(inducing_variable, InducingVariables) or not isinstance(
            obj, kernels.Kernel
        ):
            raise TypeError("If `inducing_variable` is supplied, `obj` must be a kernel.")
        return lambda x: tf.transpose(Kuf(inducing_variable, obj, x))[slice]
    elif isinstance(obj, mfn.MeanFunction):
        return lambda x: obj(x)[slice]
    elif isinstance(obj, kernels.Kernel):
        return lambda x: obj(x, full_cov=False)

    raise NotImplementedError()


@dispatch.quadrature_expectation.register(
    (Gaussian, DiagonalGaussian),
    object,
    (InducingVariables, NoneType),
    object,
    (InducingVariables, NoneType),
)
def _quadrature_expectation(p, obj1, inducing_variable1, obj2, inducing_variable2, nghp=None):
    """
    General handling of quadrature expectations for Gaussians and DiagonalGaussians
    Fallback method for missing analytic expectations
    """
    nghp = 100 if nghp is None else nghp

    # logger.warning(
    #     "Quadrature is used to calculate the expectation. This means that "
    #     "an analytical implementations is not available for the given combination."
    # )

    if obj1 is None:
        raise NotImplementedError("First object cannot be None.")

    if not isinstance(p, DiagonalGaussian):
        cov = p.cov
    else:
        iskern1 = isinstance(obj1, kernels.Kernel)
        iskern2 = isinstance(obj2, kernels.Kernel)
        if iskern1 and iskern2 and obj1.on_separate_dims(obj2):  # no joint expectations required
            eKxz1 = quadrature_expectation(p, (obj1, inducing_variable1), nghp=nghp)
            eKxz2 = quadrature_expectation(p, (obj2, inducing_variable2), nghp=nghp)
            return eKxz1[:, :, None] * eKxz2[:, None, :]
        cov = tf.linalg.diag(p.cov)

    if obj2 is None:

        def eval_func(x):
            fn = get_eval_func(obj1, inducing_variable1)
            return fn(x)

    else:

        def eval_func(x):
            fn1 = get_eval_func(obj1, inducing_variable1, np.s_[:, :, None])
            fn2 = get_eval_func(obj2, inducing_variable2, np.s_[:, None, :])
            return fn1(x) * fn2(x)

    return mvnquad(eval_func, p.mu, cov, nghp)


@dispatch.quadrature_expectation.register(
    MarkovGaussian, object, (InducingVariables, NoneType), object, (InducingVariables, NoneType)
)
def _quadrature_expectation(p, obj1, inducing_variable1, obj2, inducing_variable2, nghp=None):
    """
    Handling of quadrature expectations for Markov Gaussians (useful for time series)
    Fallback method for missing analytic expectations wrt Markov Gaussians
    Nota Bene: obj1 is always associated with x_n, whereas obj2 always with x_{n+1}
               if one requires e.g. <x_{n+1} K_{x_n, Z}>_p(x_{n:n+1}), compute the
               transpose and then transpose the result of the expectation
    """
    nghp = 40 if nghp is None else nghp

    # logger.warning(
    #     "Quadrature is used to calculate the expectation. This means that "
    #     "an analytical implementations is not available for the given combination."
    # )

    if obj2 is None:

        def eval_func(x):
            return get_eval_func(obj1, inducing_variable1)(x)

        mu, cov = p.mu[:-1], p.cov[0, :-1]  # cross covariances are not needed
    elif obj1 is None:

        def eval_func(x):
            return get_eval_func(obj2, inducing_variable2)(x)

        mu, cov = p.mu[1:], p.cov[0, 1:]  # cross covariances are not needed
    else:

        def eval_func(x):
            x1 = tf.split(x, 2, 1)[0]
            x2 = tf.split(x, 2, 1)[1]
            res1 = get_eval_func(obj1, inducing_variable1, np.s_[:, :, None])(x1)
            res2 = get_eval_func(obj2, inducing_variable2, np.s_[:, None, :])(x2)
            return res1 * res2

        mu = tf.concat((p.mu[:-1, :], p.mu[1:, :]), 1)  # Nx2D
        cov_top = tf.concat((p.cov[0, :-1, :, :], p.cov[1, :-1, :, :]), 2)  # NxDx2D
        cov_bottom = tf.concat((tf.linalg.adjoint(p.cov[1, :-1, :, :]), p.cov[0, 1:, :, :]), 2)
        cov = tf.concat((cov_top, cov_bottom), 1)  # Nx2Dx2D

    return mvnquad(eval_func, mu, cov, nghp)