import tensorflow as tf
from keras import backend
from keras.utils import losses_utils, tf_utils


def inv_kl_divergence(y_true, y_pred):
    y_pred = tf.convert_to_tensor(y_pred)
    y_true = tf.cast(y_true, y_pred.dtype)
    y_true = backend.clip(y_true, backend.epsilon(), 1)
    y_pred = backend.clip(y_pred, backend.epsilon(), 1)
    return tf.reduce_sum(y_pred * tf.math.log(y_pred / y_true), axis=-1)


def masked_bce(y_true, y_pred):
    y_true = tf.cast(y_true, dtype=tf.float32)
    mask = y_true != -1

    return tf.keras.metrics.binary_crossentropy(
        tf.boolean_mask(y_true, mask), tf.boolean_mask(y_pred, mask)
    )


class LossFunctionWrapper(tf.keras.losses.Loss):
    def __init__(
        self, fn, reduction=losses_utils.ReductionV2.AUTO, name=None, **kwargs
    ):
        super().__init__(reduction=reduction, name=name)
        self.fn = fn
        self._fn_kwargs = kwargs

    def call(self, y_true, y_pred):
        if tf.is_tensor(y_pred) and tf.is_tensor(y_true):
            y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(y_pred, y_true)

        ag_fn = tf.__internal__.autograph.tf_convert(
            self.fn, tf.__internal__.autograph.control_status_ctx()
        )
        return ag_fn(y_true, y_pred, **self._fn_kwargs)

    def get_config(self):
        config = {}
        for k, v in self._fn_kwargs.items():
            config[k] = backend.eval(v) if tf_utils.is_tensor_or_variable(v) else v
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))


class InvKLD(LossFunctionWrapper):
    def __init__(
        self, reduction=losses_utils.ReductionV2.AUTO, name="inv_kl_divergence"
    ):
        super().__init__(inv_kl_divergence, name=name, reduction=reduction)


class MaskedBCE(LossFunctionWrapper):
    def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name="masked_bce"):
        super().__init__(masked_bce, name=name, reduction=reduction)