from typing import Union

import tensorflow as tf

from ..inducing_variables import (InducingPoints, FallbackSharedIndependentInducingVariables,
                                  FallbackSeparateIndependentInducingVariables, SharedIndependentInducingVariables)
from ..kernels import (MultioutputKernel, SeparateIndependent, LinearCoregionalization, SharedIndependent,
                       IndependentLatent)
from .dispatch import Kuu


@Kuu.register(InducingPoints, MultioutputKernel)
def _Kuu(inducing_variable: InducingPoints, kernel: MultioutputKernel, *, jitter=0.0):
    Kmm = kernel(inducing_variable.Z, full=True, full_output_cov=True)  # [M, P, M, P]
    M = tf.shape(Kmm)[0] * tf.shape(Kmm)[1]
    jittermat = jitter * tf.reshape(tf.eye(M, dtype=Kmm.dtype), tf.shape(Kmm))
    return Kmm + jittermat


@Kuu.register(FallbackSharedIndependentInducingVariables, SharedIndependent)
def _Kuu(inducing_variable: FallbackSharedIndependentInducingVariables, kernel: SharedIndependent, *, jitter=0.0):
    Kmm = Kuu(inducing_variable.inducing_variable_shared, kernel.kernel)  # [M, M]
    jittermat = tf.eye(len(inducing_variable), dtype=Kmm.dtype) * jitter
    return Kmm + jittermat


@Kuu.register(FallbackSharedIndependentInducingVariables, (SeparateIndependent, IndependentLatent))
def _Kuu(inducing_variable: FallbackSharedIndependentInducingVariables,
         kernel: Union[SeparateIndependent, IndependentLatent],
         *,
         jitter=0.0):
    Kmm = tf.stack([Kuu(inducing_variable.inducing_variable_shared, k) for k in kernel.kernels], axis=0)  # [L, M, M]
    jittermat = tf.eye(len(inducing_variable), dtype=Kmm.dtype)[None, :, :] * jitter
    return Kmm + jittermat


@Kuu.register(FallbackSeparateIndependentInducingVariables, SharedIndependent)
def _Kuu(inducing_variable: FallbackSeparateIndependentInducingVariables, kernel: SharedIndependent, *, jitter=0.0):
    Kmm = tf.stack([Kuu(f, kernel.kernel) for f in inducing_variable.inducing_variable_list], axis=0)  # [L, M, M]
    jittermat = tf.eye(len(inducing_variable), dtype=Kmm.dtype)[None, :, :] * jitter
    return Kmm + jittermat


@Kuu.register(FallbackSeparateIndependentInducingVariables, (SeparateIndependent, LinearCoregionalization))
def _Kuu(inducing_variable: FallbackSeparateIndependentInducingVariables,
         kernel: Union[SeparateIndependent, LinearCoregionalization],
         *,
         jitter=0.0):
    Kmms = [Kuu(f, k) for f, k in zip(inducing_variable.inducing_variable_list, kernel.kernels)]
    Kmm = tf.stack(Kmms, axis=0)  # [L, M, M]
    jittermat = tf.eye(len(inducing_variable), dtype=Kmm.dtype)[None, :, :] * jitter
    return Kmm + jittermat