# 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/utils.py
# _GPUS = None
# FLAGS = flags.FLAGS
#         _GPUS = tuple([x.name for x in local_device_protos if x.device_type == 'GPU'])
# def get_config():
# def setup_tf():
# def smart_shape(x):
# def ilog2(x):
# def find_latest_checkpoint(dir, glob_term='model.ckpt-*.meta'):
# def get_latest_global_step(dir):
# def get_latest_global_step_in_subdir(dir):
# def getter_ema(ema, getter, name, *args, **kwargs):
# def model_vars(scope=None):
# def gpu(x):
# def get_available_gpus():
# def average_gradients(tower_grads):
# def para_list(fn, *args):
# def para_mean(fn, *args):
# def para_cat(fn, *args):
# 
# Path: libml/models.py
# class CNN13(ClassifySemi):
# class ConvNet(ClassifySemi):
# class ResNet(ClassifySemi):
# class ShakeNet(ClassifySemi):
# class MultiModel(CNN13, ConvNet, ResNet, ShakeNet):
#     def classifier(self, x, scales, filters, training, getter=None, **kwargs):
#     def classifier(self, x, scales, filters, getter=None, **kwargs):
#     def classifier(self, x, scales, filters, repeat, training, getter=None, **kwargs):
#         def conv_args(k, f):
#         def residual(x0, filters, stride=1, activate_before_residual=False):
#     def classifier(self, x, scales, filters, repeat, training, getter=None, **kwargs):
#         def conv_args(k, f):
#         def residual(x0, filters, stride=1):
#             def branch():
#     def augment(self, x, l, smoothing, **kwargs):
#     def classifier(self, x, arch, **kwargs):
#     MODEL_CNN13, MODEL_CONVNET, MODEL_RESNET, MODEL_SHAKE = 'cnn13 convnet resnet shake'.split()
#     MODELS = MODEL_CNN13, MODEL_CONVNET, MODEL_RESNET, MODEL_SHAKE


import functools
import os
import tensorflow as tf
from absl import app
from absl import flags
from easydict import EasyDict
from libml import data, utils, models
                              if v not in skip_ops])

        return EasyDict(
            x=x_in, y=y_in, label=l_in, train_op=train_op, tune_op=train_bn,
            classify_raw=tf.nn.softmax(classifier(x_in, training=False)),  # No EMA, for debugging.
            classify_op=tf.nn.softmax(classifier(x_in, getter=ema_getter, training=False)))


def main(argv):
    del argv  # Unused.
    dataset = data.DATASETS[FLAGS.dataset]()
    log_width = utils.ilog2(dataset.width)
    model = Mixup(
        os.path.join(FLAGS.train_dir, dataset.name),
        dataset,
        lr=FLAGS.lr,
        wd=FLAGS.wd,
        arch=FLAGS.arch,
        batch=FLAGS.batch,
        nclass=dataset.nclass,
        ema=FLAGS.ema,
        beta=FLAGS.beta,

        scales=FLAGS.scales or (log_width - 2),
        filters=FLAGS.filters,
        repeat=FLAGS.repeat)
    model.train(FLAGS.train_kimg << 10, FLAGS.report_kimg << 10)


if __name__ == '__main__':

# 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/utils.py
# _GPUS = None
# FLAGS = flags.FLAGS
#         _GPUS = tuple([x.name for x in local_device_protos if x.device_type == 'GPU'])
# def get_config():
# def setup_tf():
# def smart_shape(x):
# def ilog2(x):
# def find_latest_checkpoint(dir, glob_term='model.ckpt-*.meta'):
# def get_latest_global_step(dir):
# def get_latest_global_step_in_subdir(dir):
# def getter_ema(ema, getter, name, *args, **kwargs):
# def model_vars(scope=None):
# def gpu(x):
# def get_available_gpus():
# def average_gradients(tower_grads):
# def para_list(fn, *args):
# def para_mean(fn, *args):
# def para_cat(fn, *args):
# 
# Path: libml/models.py
# class CNN13(ClassifySemi):
# class ConvNet(ClassifySemi):
# class ResNet(ClassifySemi):
# class ShakeNet(ClassifySemi):
# class MultiModel(CNN13, ConvNet, ResNet, ShakeNet):
#     def classifier(self, x, scales, filters, training, getter=None, **kwargs):
#     def classifier(self, x, scales, filters, getter=None, **kwargs):
#     def classifier(self, x, scales, filters, repeat, training, getter=None, **kwargs):
#         def conv_args(k, f):
#         def residual(x0, filters, stride=1, activate_before_residual=False):
#     def classifier(self, x, scales, filters, repeat, training, getter=None, **kwargs):
#         def conv_args(k, f):
#         def residual(x0, filters, stride=1):
#             def branch():
#     def augment(self, x, l, smoothing, **kwargs):
#     def classifier(self, x, arch, **kwargs):
#     MODEL_CNN13, MODEL_CONVNET, MODEL_RESNET, MODEL_SHAKE = 'cnn13 convnet resnet shake'.split()
#     MODELS = MODEL_CNN13, MODEL_CONVNET, MODEL_RESNET, MODEL_SHAKE


# Path: mixup.py
import functools
import os
import tensorflow as tf
from absl import app
from absl import flags
from easydict import EasyDict
from libml import data, utils, models

                              if v not in skip_ops])

        return EasyDict(
            x=x_in, y=y_in, label=l_in, train_op=train_op, tune_op=train_bn,
            classify_raw=tf.nn.softmax(classifier(x_in, training=False)),  # No EMA, for debugging.
            classify_op=tf.nn.softmax(classifier(x_in, getter=ema_getter, training=False)))


def main(argv):
    del argv  # Unused.
    dataset = data.DATASETS[FLAGS.dataset]()
    log_width = utils.ilog2(dataset.width)
    model = Mixup(
        os.path.join(FLAGS.train_dir, dataset.name),
        dataset,
        lr=FLAGS.lr,
        wd=FLAGS.wd,
        arch=FLAGS.arch,
        batch=FLAGS.batch,
        nclass=dataset.nclass,
        ema=FLAGS.ema,
        beta=FLAGS.beta,

        scales=FLAGS.scales or (log_width - 2),
        filters=FLAGS.filters,
        repeat=FLAGS.repeat)
    model.train(FLAGS.train_kimg << 10, FLAGS.report_kimg << 10)


if __name__ == '__main__':

    utils.setup_tf()