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import numpy as np
import tensorflow as tf


def loss_ops_from_difference(target_op, output_ops):
    """
    Loss operation which directly compares the target with the output over all nodes and edges
    Args:
        target_op: The target of the model
        output_ops: A list of the outputs of the model, one for each message-passing step

    Returns: The loss for each message-passing step

    """
    loss_ops = [
        tf.losses.softmax_cross_entropy(target_op.nodes, output_op.nodes)
        for output_op in output_ops
    ]
    return loss_ops


def loss_ops_preexisting_no_penalty(target_op, output_ops):
    """
    Loss operation which doesn't penalise the output values for pre-existing nodes and edges, treating them as slack
    variables

    Args:
        target_op: The target of the model
        output_ops: A list of the outputs of the model, one for each message-passing step

    Returns: The loss for each message-passing step

    """
    loss_ops = []
    node_mask_op = tf.math.reduce_any(
        tf.math.not_equal(target_op.nodes, tf.constant(np.array([1., 0., 0.]), dtype=tf.float32)), axis=1)
    target_nodes = tf.boolean_mask(target_op.nodes, node_mask_op)
    output_nodes = tf.boolean_mask(output_op.nodes, node_mask_op)

    for output_op in output_ops:

        loss_op = tf.losses.softmax_cross_entropy(target_nodes, output_nodes)

        loss_ops.append(loss_op)

    return loss_ops
numpy.array