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# -*- coding: utf-8 -*- 
"""
a bunch of lasagne code implementing gumbel softmax
https://arxiv.org/abs/1611.01144
"""
import numpy as np
import theano
import theano.tensor as T
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
from lasagne.random import get_rng

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"""symbolic helper functions"""

import theano
import theano.tensor as T
import lasagne
from lasagne.layers import *
import numpy as np

def get_mask(tokens,pad_ix=-1):
    assert tokens.ndim==2

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@lru_cache()
def infer_batch_axes(model,
                     get_dummy_input=lambda bsize: {'inp': tf.ones([bsize, 3], dtype='int32'),
                                                    'inp_len': tf.constant([3]*bsize, dtype='int32')},
                     check_at=(3, 5, 7),
                     sess=None, **flags):
    """
    This function attempts to figure out batch dimensions by seeing what axes change on different batch sizes.
    It stands as a monument of hatred to a person who thought that time-major axes order in tensorflow RNN is cool.
    :param model: TranslateModel instance to figure out batch dimensions for.