<commit_before>from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import array
import functools
import gzip
import operator
import os
import struct
import tempfile

import numpy as np

from cleverhans import utils


def maybe_download_mnist_file(file_name, datadir=None, force=False):
    try:
        from urllib.parse import urljoin
        from urllib.request import urlretrieve
    except ImportError:
        from urlparse import urljoin
        from urllib import urlretrieve

    if not datadir:
        datadir = tempfile.gettempdir()
    dest_file = os.path.join(datadir, file_name)

    if force or not os.path.isfile(file_name):
        url = urljoin('http://yann.lecun.com/exdb/mnist/', file_name)
        urlretrieve(url, dest_file)
    return dest_file


def download_and_parse_mnist_file(file_name, datadir=None, force=False):
    file_name = maybe_download_mnist_file(file_name, datadir=datadir,
                                          force=force)

    # Open the file and unzip it if necessary
    if os.path.splitext(file_name)[1] == '.gz':
        open_fn = gzip.open
    else:
        open_fn = open

    # Parse the file
    with open_fn(file_name, 'rb') as file_descriptor:
        header = file_descriptor.read(4)
        assert len(header) == 4

        zeros, data_type, n_dims = struct.unpack('>HBB', header)
        assert zeros == 0

        hex_to_data_type = {
            0x08: 'B',
            0x09: 'b',
            0x0b: 'h',
            0x0c: 'i',
            0x0d: 'f',
            0x0e: 'd'}
        data_type = hex_to_data_type[data_type]

        # data_type unicode to ascii conversion (Python2 fix)
        data_type = data_type.encode('ascii', 'ignore')

        dim_sizes = struct.unpack(
            '>' + 'I' * n_dims,
            file_descriptor.read(4 * n_dims))

        data = array.array(data_type, file_descriptor.read())
        data.byteswap()

        desired_items = functools.reduce(operator.mul, dim_sizes)
        assert len(data) == desired_items
        return np.array(data).reshape(dim_sizes)


def data_mnist(datadir='/tmp/', train_start=0, train_end=60000, test_start=0,
               test_end=10000):
    """
    Load and preprocess MNIST dataset<commit_msg>Fix the fix to work with Python3 again<commit_after>from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import array
import functools
import gzip
import operator
import os
import struct
import tempfile

import numpy as np

from cleverhans import utils


def maybe_download_mnist_file(file_name, datadir=None, force=False):
    try:
        from urllib.parse import urljoin
        from urllib.request import urlretrieve
    except ImportError:
        from urlparse import urljoin
        from urllib import urlretrieve

    if not datadir:
        datadir = tempfile.gettempdir()
    dest_file = os.path.join(datadir, file_name)

    if force or not os.path.isfile(file_name):
        url = urljoin('http://yann.lecun.com/exdb/mnist/', file_name)
        urlretrieve(url, dest_file)
    return dest_file


def download_and_parse_mnist_file(file_name, datadir=None, force=False):
    file_name = maybe_download_mnist_file(file_name, datadir=datadir,
                                          force=force)

    # Open the file and unzip it if necessary
    if os.path.splitext(file_name)[1] == '.gz':
        open_fn = gzip.open
    else:
        open_fn = open

    # Parse the file
    with open_fn(file_name, 'rb') as file_descriptor:
        header = file_descriptor.read(4)
        assert len(header) == 4

        zeros, data_type, n_dims = struct.unpack('>HBB', header)
        assert zeros == 0

        hex_to_data_type = {
            0x08: 'B',
            0x09: 'b',
            0x0b: 'h',
            0x0c: 'i',
            0x0d: 'f',
            0x0e: 'd'}
        data_type = hex_to_data_type[data_type]

        # data_type unicode to ascii conversion (Python2 fix)
        if sys.version_info[0] < 3:
            data_type = data_type.encode('ascii', 'ignore')

        dim_sizes = struct.unpack(
            '>' + 'I' * n_dims,
            file_descriptor.read(4 * n_dims))

        data = array.array(data_type, file_descriptor.read())
        data.byteswap()

        desired_items = functools.reduce(operator.mul, dim_sizes)
        assert len(data) == desired_items
        return np.array(data).reshape(dim_sizes)


def data_mnist(datadir='/tmp/', train_start=0, train_end=60000, test_start=0,
               test_end=10000):
    """
    Load and preprocess MNIST dataset
{'fraction_non_alphanumeric': 0.08925164299342803, 'fraction_numerical': 0.014203943184227264, 'mean_word_length': 2.560754716981132, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 7, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 10, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '2940676', 'n_tokens_mistral': 1572, 'n_tokens_neox': 1518, 'n_words': 369}