<commit_before>import unittest

import numpy
import six

import chainer
from chainer import cuda
from chainer import functions
from chainer.functions.normalization import batch_normalization
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer.testing import condition


def _batch_normalization(expander, gamma, beta, x, mean, var):
    mean = mean[expander]
    std = numpy.sqrt(var)[expander]
    y_expect = (gamma[expander] * (x - mean) / std + beta[expander])
    return y_expect



@testing.parameterize(*testing.product({
    'ndim': [0, 1, 2, 3],
}))
class TestBatchNormalization(unittest.TestCase):
    def setUp(self):
        self.expander = (None, Ellipsis) + (None,) * (self.ndim)
        self.aggr_axes = (0,) + tuple(six.moves.range(2, self.ndim + 2))
        self.eps = 1e-5

        self.gamma = numpy.random.uniform(.5, 1, (3,)).astype(numpy.float32)
        self.beta = numpy.random.uniform(-1, 1, (3,)).astype(numpy.float32)

        shape = (7, 3) + (2,) * self.ndim
        self.x = numpy.random.uniform(-1, 1, shape).astype(numpy.float32)<commit_msg>Flake8<commit_after>import unittest

import numpy
import six

import chainer
from chainer import cuda
from chainer import functions
from chainer.functions.normalization import batch_normalization
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer.testing import condition


def _batch_normalization(expander, gamma, beta, x, mean, var):
    mean = mean[expander]
    std = numpy.sqrt(var)[expander]
    y_expect = (gamma[expander] * (x - mean) / std + beta[expander])
    return y_expect


@testing.parameterize(*testing.product({
    'ndim': [0, 1, 2, 3],
}))
class TestBatchNormalization(unittest.TestCase):
    def setUp(self):
        self.expander = (None, Ellipsis) + (None,) * (self.ndim)
        self.aggr_axes = (0,) + tuple(six.moves.range(2, self.ndim + 2))
        self.eps = 1e-5

        self.gamma = numpy.random.uniform(.5, 1, (3,)).astype(numpy.float32)
        self.beta = numpy.random.uniform(-1, 1, (3,)).astype(numpy.float32)

        shape = (7, 3) + (2,) * self.ndim
        self.x = numpy.random.uniform(-1, 1, shape).astype(numpy.float32)
{'fraction_non_alphanumeric': 0.13244444444444445, 'fraction_numerical': 0.023555555555555555, 'mean_word_length': 3.689583333333333, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '2501340', 'n_tokens_mistral': 796, 'n_tokens_neox': 749, 'n_words': 187}