import tensorflow as tf

from tensorflow.python.eager.context import graph_mode

from unification import unify, reify, var

from symbolic_pymc.tensorflow.meta import mt


with graph_mode(), tf.Graph().as_default() as test_graph:

    # Divide two constant integers
    first_div_mt = mt(1) / mt(2)

    # Create a TF graph for that division
    first_div_tf = first_div_mt.reify()

    # Create a division "pattern" with variable/unknown dividends and name
    # parameter
    div_lv = mt.realdiv(var('b'), var('c'), name=var('name'))

    # Unify the "pattern" with the TF graph and get the logic variable
    # replacements map/dict
    s = unify(first_div_tf, div_lv)

    # Change the dividends in the substitution map so that the following
    # reification will produce a new new graph
    s[var('b')] = 1
    s[var('b')] = 3

    div_mt = reify(div_lv, s)

    # Attempting to create this TF graph within the same parent graph
    # (i.e. `test_graph`) as `first_div_tf` will throw a
    # `MetaReificationError`, because it would create two graphs with the same
    # name (not possible/expected in TF).
    div_mt.reify()

with graph_mode(), test_graph.as_default():
    # Reify an equivalent "pattern" that uses the same logic variables, save
    # the name, e.g.
    div_mt = reify(mt.realdiv(var('b'), var('c')), s)

    # No problem here
    div_mt.reify()


with graph_mode(), test_graph.as_default():
    # Manually change the name value in the replacements map, e.g.
    s[var('name')] = "new_name"
    div_mt = reify(div_lv, s)

    # No problem here
    div_mt.reify()