# @author lucasmiranda42
# encoding: utf-8
# module deepof

"""

Functions and general utilities for the deepof tensorflow models. See documentation for details

"""

from itertools import combinations
from tensorflow.keras import backend as K
from tensorflow.keras.constraints import Constraint
from tensorflow.keras.layers import Layer
import tensorflow as tf
import tensorflow_probability as tfp

tfd = tfp.distributions
tfpl = tfp.layers


# Helper functions
@tf.function
def far_away_uniform_initialiser(
    shape: tuple, minval: int = 0, maxval: int = 15, iters: int = 100000
) -> tf.Tensor:
    """
    Returns a uniformly initialised matrix in which the columns are as far as possible

        Parameters:
            - shape (tuple): shape of the object to generate.
            - minval (int): Minimum value of the uniform distribution from which to sample
            - maxval (int): Maximum value of the uniform distribution from which to sample
            - iters (int): the algorithm generates values at random and keeps those runs that
            are the farthest apart. Increasing this parameter will lead to more accurate,
            results while making the function run slowlier.

        Returns:
            - init (tf.Tensor): tensor of the specified shape in which the column vectors
             are as far as possible

    """

    init = tf.random.uniform(shape, minval, maxval)
    init_dist = tf.abs(tf.norm(tf.math.subtract(init[1:], init[:1])))
    i = 0

    while tf.less(i, iters):
        temp = tf.random.uniform(shape, minval, maxval)
        dist = tf.abs(tf.norm(tf.math.subtract(temp[1:], temp[:1])))

        if dist > init_dist:
            init_dist = dist
            init = temp

        i += 1

    return init


def compute_kernel(x: tf.Tensor, y: tf.Tensor) -> tf.Tensor:
    """

    Computes the MMD between the two specified vectors using a gaussian kernel.

        Parameters:
            - x (tf.Tensor): left tensor
            - y (tf.Tensor): right tensor

        Returns
            - kernel (tf.Tensor): returns the result of applying the kernel, for
            each training instance

    """

    x_size = tf.shape(x)[0]
    y_size = tf.shape(y)[0]
    dim = tf.shape(x)[1]
    tiled_x = tf.tile(
        tf.reshape(x, tf.stack([x_size, 1, dim])), tf.stack([1, y_size, 1])
    )
    tiled_y = tf.tile(
        tf.reshape(y, tf.stack([1, y_size, dim])), tf.stack([x_size, 1, 1])
    )
    kernel = tf.exp(
        -tf.reduce_mean(tf.square(tiled_x - tiled_y), axis=2) / tf.cast(dim, tf.float32)
    )
    return kernel


@tf.function
def compute_mmd(tensors: tuple) -> tf.Tensor:
    """

        Computes the MMD between the two specified vectors using a gaussian kernel.

            Parameters:
                - tensors (tuple): tuple containing two tf.Tensor objects

            Returns
                - mmd (tf.Tensor): returns the maximum mean discrepancy for each
                training instance

        """

    x = tensors[0]
    y = tensors[1]

    x_kernel = compute_kernel(x, x)
    y_kernel = compute_kernel(y, y)
    xy_kernel = compute_kernel(x, y)
    mmd = (
        tf.reduce_mean(x_kernel)
        + tf.reduce_mean(y_kernel)
        - 2 * tf.reduce_mean(xy_kernel)
    )
    return mmd


# Custom auxiliary classes
class OneCycleScheduler(tf.keras.callbacks.Callback):
    def __init__(
        self,
        iterations,
        max_rate,
        start_rate=None,
        last_iterations=None,
        last_rate=None,
    ):
        self.iterations = iterations
        self.max_rate = max_rate
        self.start_rate = start_rate or max_rate / 10
        self.last_iterations = last_iterations or iterations // 10 + 1
        self.half_iteration = (iterations - self.last_iterations) // 2
        self.last_rate = last_rate or self.start_rate / 1000
        self.iteration = 0

    def _interpolate(self, iter1, iter2, rate1, rate2):
        return (rate2 - rate1) * (self.iteration - iter1) / (iter2 - iter1) + rate1

    def on_batch_begin(self, batch, logs):
        if self.iteration < self.half_iteration:
            rate = self._interpolate(
                0, self.half_iteration, self.start_rate, self.max_rate
            )
        elif self.iteration < 2 * self.half_iteration:
            rate = self._interpolate(
                self.half_iteration,
                2 * self.half_iteration,
                self.max_rate,
                self.start_rate,
            )
        else:
            rate = self._interpolate(
                2 * self.half_iteration,
                self.iterations,
                self.start_rate,
                self.last_rate,
            )
            rate = max(rate, self.last_rate)
        self.iteration += 1
        K.set_value(self.model.optimizer.lr, rate)


class UncorrelatedFeaturesConstraint(Constraint):
    def __init__(self, encoding_dim, weightage=1.0):
        self.encoding_dim = encoding_dim
        self.weightage = weightage

    def get_config(self):

        config = super().get_config().copy()
        config.update(
            {"encoding_dim": self.encoding_dim, "weightage": self.weightage,}
        )
        return config

    def get_covariance(self, x):
        x_centered_list = []

        for i in range(self.encoding_dim):
            x_centered_list.append(x[:, i] - K.mean(x[:, i]))

        x_centered = tf.stack(x_centered_list)
        covariance = K.dot(x_centered, K.transpose(x_centered)) / tf.cast(
            x_centered.get_shape()[0], tf.float32
        )

        return covariance

    # Constraint penalty
    def uncorrelated_feature(self, x):
        if self.encoding_dim <= 1:
            return 0.0
        else:
            output = K.sum(
                K.square(
                    self.covariance
                    - tf.math.multiply(self.covariance, tf.eye(self.encoding_dim))
                )
            )
            return output

    def __call__(self, x):
        self.covariance = self.get_covariance(x)
        return self.weightage * self.uncorrelated_feature(x)


# Custom Layers
class MCDropout(tf.keras.layers.Dropout):
    def call(self, inputs, **kwargs):
        return super().call(inputs, training=True)


class KLDivergenceLayer(tfpl.KLDivergenceAddLoss):
    def __init__(self, *args, **kwargs):
        self.is_placeholder = True
        super(KLDivergenceLayer, self).__init__(*args, **kwargs)

    def get_config(self):
        config = super().get_config().copy()
        config.update(
            {"is_placeholder": self.is_placeholder,}
        )
        return config

    def call(self, distribution_a):
        kl_batch = self._regularizer(distribution_a)
        self.add_loss(kl_batch, inputs=[distribution_a])
        self.add_metric(
            kl_batch, aggregation="mean", name="kl_divergence",
        )
        self.add_metric(self._regularizer._weight, aggregation="mean", name="kl_rate")

        return distribution_a


class DenseTranspose(Layer):
    def __init__(self, dense, output_dim, activation=None, **kwargs):
        self.dense = dense
        self.output_dim = output_dim
        self.activation = tf.keras.activations.get(activation)
        super().__init__(**kwargs)

    def get_config(self):
        config = super().get_config().copy()
        config.update(
            {
                "dense": self.dense,
                "output_dim": self.output_dim,
                "activation": self.activation,
            }
        )
        return config

    def build(self, batch_input_shape):
        self.biases = self.add_weight(
            name="bias", shape=[self.dense.input_shape[-1]], initializer="zeros"
        )
        super().build(batch_input_shape)

    def call(self, inputs, **kwargs):
        z = tf.matmul(inputs, self.dense.weights[0], transpose_b=True)
        return self.activation(z + self.biases)

    def compute_output_shape(self, input_shape):
        return input_shape[0], self.output_dim


class MMDiscrepancyLayer(Layer):
    """
    Identity transform layer that adds MM Discrepancy
    to the final model loss.
    """

    def __init__(self, batch_size, prior, beta=1.0, *args, **kwargs):
        self.is_placeholder = True
        self.batch_size = batch_size
        self.beta = beta
        self.prior = prior
        super(MMDiscrepancyLayer, self).__init__(*args, **kwargs)

    def get_config(self):
        config = super().get_config().copy()
        config.update({"batch_size": self.batch_size})
        config.update({"beta": self.beta})
        config.update({"prior": self.prior})
        return config

    def call(self, z, **kwargs):
        true_samples = self.prior.sample(self.batch_size)
        mmd_batch = self.beta * compute_mmd([true_samples, z])
        self.add_loss(K.mean(mmd_batch), inputs=z)
        self.add_metric(mmd_batch, aggregation="mean", name="mmd")
        self.add_metric(self.beta, aggregation="mean", name="mmd_rate")

        return z


class Gaussian_mixture_overlap(Layer):
    """
    Identity layer that measures the overlap between the components of the latent Gaussian Mixture
    using a specified metric (MMD, Wasserstein, Fischer-Rao)
    """

    def __init__(self, lat_dims, n_components, loss=False, samples=10, *args, **kwargs):
        self.lat_dims = lat_dims
        self.n_components = n_components
        self.loss = loss
        self.samples = samples
        super(Gaussian_mixture_overlap, self).__init__(*args, **kwargs)

    def get_config(self):
        config = super().get_config().copy()
        config.update({"lat_dims": self.lat_dims})
        config.update({"n_components": self.n_components})
        config.update({"loss": self.loss})
        config.update({"samples": self.samples})
        return config

    def call(self, target, loss=False):

        dists = []
        for k in range(self.n_components):
            locs = (target[..., : self.lat_dims, k],)
            scales = tf.keras.activations.softplus(target[..., self.lat_dims :, k])

            dists.append(
                tfd.BatchReshape(tfd.MultivariateNormalDiag(locs, scales), [-1])
            )

        dists = [tf.transpose(gauss.sample(self.samples), [1, 0, 2]) for gauss in dists]

        ### MMD-based overlap ###
        intercomponent_mmd = K.mean(
            tf.convert_to_tensor(
                [
                    tf.vectorized_map(compute_mmd, [dists[c[0]], dists[c[1]]])
                    for c in combinations(range(len(dists)), 2)
                ],
                dtype=tf.float32,
            )
        )

        self.add_metric(
            -intercomponent_mmd, aggregation="mean", name="intercomponent_mmd"
        )

        if self.loss:
            self.add_loss(-intercomponent_mmd, inputs=[target])

        return target


class Dead_neuron_control(Layer):
    """
    Identity layer that adds latent space and clustering stats
    to the metrics compiled by the model
    """

    def __init__(self, *args, **kwargs):
        super(Dead_neuron_control, self).__init__(*args, **kwargs)

    def call(self, z, z_gauss, z_cat, **kwargs):

        # Adds metric that monitors dead neurons in the latent space
        self.add_metric(
            tf.math.zero_fraction(z_gauss), aggregation="mean", name="dead_neurons"
        )

        return z


class Entropy_regulariser(Layer):
    """
    Identity layer that adds cluster weight entropy to the loss function
    """

    def __init__(self, weight=1.0, *args, **kwargs):
        self.weight = weight
        super(Entropy_regulariser, self).__init__(*args, **kwargs)

    def get_config(self):
        config = super().get_config().copy()
        config.update({"weight": self.weight})

    def call(self, z, **kwargs):

        # axis=1 increases the entropy of a cluster across instances
        # axis=0 increases the entropy of the assignment for a given instance
        entropy = K.sum(tf.multiply(z + 1e-5, tf.math.log(z) + 1e-5), axis=1)

        # Adds metric that monitors dead neurons in the latent space
        self.add_metric(entropy, aggregation="mean", name="-weight_entropy")

        self.add_loss(self.weight * K.sum(entropy), inputs=[z])

        return z