# Path: libml/data.py
# DATA_DIR = None
# _DATA_CACHE = None
# SAMPLES_PER_CLASS = [1, 2, 3, 4, 5, 10, 25, 100, 400]
# FLAGS = flags.FLAGS
#     DATA_DIR = FLAGS.data_dir or os.environ['ML_DATA']
# DATASETS = functools.partial(create_datasets, augment_module.augment_function)
# PAIR_DATASETS = functools.partial(create_datasets, augment_module.pair_augment_function)
# MANY_DATASETS = functools.partial(create_datasets, augment_module.many_augment_function)
# def _data_setup():
# def record_parse_mnist(serialized_example, image_shape=None):
# def record_parse(serialized_example, image_shape=None):
# def compute_mean_std(data: tf.data.Dataset):
#         def iterator():
#     def __init__(self, data: tf.data.Dataset, augment_fn: AugmentPair, parse_fn=record_parse, image_shape=None):
#     def from_files(cls, filenames: list, augment_fn: AugmentPair, parse_fn=record_parse, image_shape=None):
#             def fetch_dataset(filename):
#     def empty_data(cls, image_shape, augment_fn: AugmentPair = None):
#         def _get_null_input(_):
#     def __getattr__(self, item):
#         def call_and_update(*args, **kwargs):
#     def parse(self):
#     def numpy_augment(self, *args, **kwargs):
#     def augment(self):
#     def memoize(self):
#         def tf_get(index, image_shape):
#             def get(index):
#     def __init__(self, name, train_labeled: DataSet, train_unlabeled: DataSet, test: DataSet, valid: DataSet,
#                  height=32, width=32, colors=3, nclass=10, mean=0, std=1, p_labeled=None, p_unlabeled=None):
#     def creator(cls, name, seed, label, valid, augment, parse_fn=record_parse, do_memoize=False,
#                 nclass=10, colors=3, height=32, width=32):
#         def create():
# def create_datasets(augment_fn):
# class DataSet:
# class DataSets:
# 
# Path: libml/utils.py
# class EasyDict(dict):
#     def __init__(self, *args, **kwargs):
#         super(EasyDict, self).__init__(*args, **kwargs)
#         self.__dict__ = self


import collections
import gzip
import os
import tarfile
import tempfile
import numpy as np
import scipy.io
import tensorflow as tf
from urllib import request
from absl import app
from tqdm import trange
from libml import data as libml_data
from libml.utils import EasyDict
    with tempfile.NamedTemporaryFile() as f:
        if tf.gfile.Exists('stl10/stl10_binary.tar.gz'):
            f = tf.gfile.Open('stl10/stl10_binary.tar.gz', 'rb')
        else:
            request.urlretrieve(URLS['stl10'], f.name)
        tar = tarfile.open(fileobj=f)
        train_X = tar.extractfile('stl10_binary/train_X.bin')
        train_y = tar.extractfile('stl10_binary/train_y.bin')

        test_X = tar.extractfile('stl10_binary/test_X.bin')
        test_y = tar.extractfile('stl10_binary/test_y.bin')

        unlabeled_X = tar.extractfile('stl10_binary/unlabeled_X.bin')

        train_set = {'images': np.frombuffer(train_X.read(), dtype=np.uint8),
                     'labels': np.frombuffer(train_y.read(), dtype=np.uint8) - 1}

        test_set = {'images': np.frombuffer(test_X.read(), dtype=np.uint8),
                    'labels': np.frombuffer(test_y.read(), dtype=np.uint8) - 1}

        _imgs = np.frombuffer(unlabeled_X.read(), dtype=np.uint8)
        unlabeled_set = {'images': _imgs,
                         'labels': np.zeros(100000, dtype=np.uint8)}

        fold_indices = tar.extractfile('stl10_binary/fold_indices.txt').read()

    train_set['images'] = _encode_png(unflatten(train_set['images']))
    test_set['images'] = _encode_png(unflatten(test_set['images']))
    unlabeled_set['images'] = _encode_png(unflatten(unlabeled_set['images']))
    return dict(train=train_set, test=test_set, unlabeled=unlabeled_set,

# Path: libml/data.py
# DATA_DIR = None
# _DATA_CACHE = None
# SAMPLES_PER_CLASS = [1, 2, 3, 4, 5, 10, 25, 100, 400]
# FLAGS = flags.FLAGS
#     DATA_DIR = FLAGS.data_dir or os.environ['ML_DATA']
# DATASETS = functools.partial(create_datasets, augment_module.augment_function)
# PAIR_DATASETS = functools.partial(create_datasets, augment_module.pair_augment_function)
# MANY_DATASETS = functools.partial(create_datasets, augment_module.many_augment_function)
# def _data_setup():
# def record_parse_mnist(serialized_example, image_shape=None):
# def record_parse(serialized_example, image_shape=None):
# def compute_mean_std(data: tf.data.Dataset):
#         def iterator():
#     def __init__(self, data: tf.data.Dataset, augment_fn: AugmentPair, parse_fn=record_parse, image_shape=None):
#     def from_files(cls, filenames: list, augment_fn: AugmentPair, parse_fn=record_parse, image_shape=None):
#             def fetch_dataset(filename):
#     def empty_data(cls, image_shape, augment_fn: AugmentPair = None):
#         def _get_null_input(_):
#     def __getattr__(self, item):
#         def call_and_update(*args, **kwargs):
#     def parse(self):
#     def numpy_augment(self, *args, **kwargs):
#     def augment(self):
#     def memoize(self):
#         def tf_get(index, image_shape):
#             def get(index):
#     def __init__(self, name, train_labeled: DataSet, train_unlabeled: DataSet, test: DataSet, valid: DataSet,
#                  height=32, width=32, colors=3, nclass=10, mean=0, std=1, p_labeled=None, p_unlabeled=None):
#     def creator(cls, name, seed, label, valid, augment, parse_fn=record_parse, do_memoize=False,
#                 nclass=10, colors=3, height=32, width=32):
#         def create():
# def create_datasets(augment_fn):
# class DataSet:
# class DataSets:
# 
# Path: libml/utils.py
# class EasyDict(dict):
#     def __init__(self, *args, **kwargs):
#         super(EasyDict, self).__init__(*args, **kwargs)
#         self.__dict__ = self


# Path: scripts/create_datasets.py
import collections
import gzip
import os
import tarfile
import tempfile
import numpy as np
import scipy.io
import tensorflow as tf
from urllib import request
from absl import app
from tqdm import trange
from libml import data as libml_data
from libml.utils import EasyDict

    with tempfile.NamedTemporaryFile() as f:
        if tf.gfile.Exists('stl10/stl10_binary.tar.gz'):
            f = tf.gfile.Open('stl10/stl10_binary.tar.gz', 'rb')
        else:
            request.urlretrieve(URLS['stl10'], f.name)
        tar = tarfile.open(fileobj=f)
        train_X = tar.extractfile('stl10_binary/train_X.bin')
        train_y = tar.extractfile('stl10_binary/train_y.bin')

        test_X = tar.extractfile('stl10_binary/test_X.bin')
        test_y = tar.extractfile('stl10_binary/test_y.bin')

        unlabeled_X = tar.extractfile('stl10_binary/unlabeled_X.bin')

        train_set = {'images': np.frombuffer(train_X.read(), dtype=np.uint8),
                     'labels': np.frombuffer(train_y.read(), dtype=np.uint8) - 1}

        test_set = {'images': np.frombuffer(test_X.read(), dtype=np.uint8),
                    'labels': np.frombuffer(test_y.read(), dtype=np.uint8) - 1}

        _imgs = np.frombuffer(unlabeled_X.read(), dtype=np.uint8)
        unlabeled_set = {'images': _imgs,
                         'labels': np.zeros(100000, dtype=np.uint8)}

        fold_indices = tar.extractfile('stl10_binary/fold_indices.txt').read()

    train_set['images'] = _encode_png(unflatten(train_set['images']))
    test_set['images'] = _encode_png(unflatten(test_set['images']))
    unlabeled_set['images'] = _encode_png(unflatten(unlabeled_set['images']))
    return dict(train=train_set, test=test_set, unlabeled=unlabeled_set,

                files=[EasyDict(filename="stl10_fold_indices.txt", data=fold_indices)])