# Path: libml/data.py
# _DATA_CACHE = None
# DATA_DIR = os.environ['ML_DATA']
# FLAGS = flags.FLAGS
# DATASETS = {}
# def record_parse(serialized_example):
# def default_parse(dataset: tf.data.Dataset, parse_fn=record_parse) -> tf.data.Dataset:
# def dataset(filenames: list) -> tf.data.Dataset:
# def memoize(dataset: tf.data.Dataset) -> tf.data.Dataset:
#     def tf_get(index):
#         def get(index):
# def augment_mirror(x):
# def augment_shift(x, w):
# def augment_noise(x, std):
# def compute_mean_std(data: tf.data.Dataset):
#         def iterator():
#     def __init__(self, name, train_labeled, train_unlabeled, test, valid, eval_labeled, eval_unlabeled,
#                  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=default_parse, do_memoize=True, colors=3,
#                 nclass=10, height=32, width=32, name_suffix=''):
#         def create():
# class DataSet:
# 
# Path: libml/data_pair.py
# def stack_augment(augment):
#     def func(x):
#         xl = [augment(x) for _ in range(FLAGS.nu)]
# 
#         return dict(image=tf.stack([x['image'] for x in xl]),
#                     label=tf.stack([x['label'] for x in xl]))
# 
#     return func


import itertools
from libml.data import DataSet, augment_svhn
from libml.data_pair import stack_augment
# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.



DATASETS = {}
DATASETS.update(
    [DataSet.creator('svhn500', 0, label, valid, [augment_svhn, stack_augment(augment_svhn)], do_memoize=False)
     for label, valid in itertools.product([27, 38, 77, 156, 355, 671, 867], [1, 5000])])
DATASETS.update(

# Path: libml/data.py
# _DATA_CACHE = None
# DATA_DIR = os.environ['ML_DATA']
# FLAGS = flags.FLAGS
# DATASETS = {}
# def record_parse(serialized_example):
# def default_parse(dataset: tf.data.Dataset, parse_fn=record_parse) -> tf.data.Dataset:
# def dataset(filenames: list) -> tf.data.Dataset:
# def memoize(dataset: tf.data.Dataset) -> tf.data.Dataset:
#     def tf_get(index):
#         def get(index):
# def augment_mirror(x):
# def augment_shift(x, w):
# def augment_noise(x, std):
# def compute_mean_std(data: tf.data.Dataset):
#         def iterator():
#     def __init__(self, name, train_labeled, train_unlabeled, test, valid, eval_labeled, eval_unlabeled,
#                  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=default_parse, do_memoize=True, colors=3,
#                 nclass=10, height=32, width=32, name_suffix=''):
#         def create():
# class DataSet:
# 
# Path: libml/data_pair.py
# def stack_augment(augment):
#     def func(x):
#         xl = [augment(x) for _ in range(FLAGS.nu)]
# 
#         return dict(image=tf.stack([x['image'] for x in xl]),
#                     label=tf.stack([x['label'] for x in xl]))
# 
#     return func


# Path: privacy/lib/data_pair.py
import itertools
from libml.data import DataSet, augment_svhn
from libml.data_pair import stack_augment

# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.



DATASETS = {}
DATASETS.update(
    [DataSet.creator('svhn500', 0, label, valid, [augment_svhn, stack_augment(augment_svhn)], do_memoize=False)
     for label, valid in itertools.product([27, 38, 77, 156, 355, 671, 867], [1, 5000])])
DATASETS.update(

    [DataSet.creator('svhn300', 0, label, valid, [augment_svhn, stack_augment(augment_svhn)], do_memoize=False)