# 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: 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/data_pair.py
# DATASETS = {}


import functools
import os
import tensorflow as tf
from absl import app
from absl import flags
from easydict import EasyDict
from libml import models, utils
from libml.data_pair import DATASETS
        loss = tf.reduce_mean(loss)
        tf.summary.scalar('losses/xe', loss)
        tf.summary.scalar('losses/pm', loss_pm)

        ema = tf.train.ExponentialMovingAverage(decay=ema)
        ema_op = ema.apply(utils.model_vars())
        ema_getter = functools.partial(utils.getter_ema, ema)
        post_ops.append(ema_op)
        post_ops.extend([tf.assign(v, v * (1 - wd)) for v in utils.model_vars('classify') if 'kernel' in v.name])

        train_op = tf.train.AdamOptimizer(lr).minimize(loss + loss_pm * warmup * consistency_weight,
                                                       colocate_gradients_with_ops=True)
        with tf.control_dependencies([train_op]):
            train_op = tf.group(*post_ops)

        # Tuning op: only retrain batch norm.
        skip_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
        classifier(x_in, training=True)
        train_bn = tf.group(*[v for v in tf.get_collection(tf.GraphKeys.UPDATE_OPS)
                              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 = DATASETS[FLAGS.dataset]()

# 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: 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/data_pair.py
# DATASETS = {}


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

        loss = tf.reduce_mean(loss)
        tf.summary.scalar('losses/xe', loss)
        tf.summary.scalar('losses/pm', loss_pm)

        ema = tf.train.ExponentialMovingAverage(decay=ema)
        ema_op = ema.apply(utils.model_vars())
        ema_getter = functools.partial(utils.getter_ema, ema)
        post_ops.append(ema_op)
        post_ops.extend([tf.assign(v, v * (1 - wd)) for v in utils.model_vars('classify') if 'kernel' in v.name])

        train_op = tf.train.AdamOptimizer(lr).minimize(loss + loss_pm * warmup * consistency_weight,
                                                       colocate_gradients_with_ops=True)
        with tf.control_dependencies([train_op]):
            train_op = tf.group(*post_ops)

        # Tuning op: only retrain batch norm.
        skip_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
        classifier(x_in, training=True)
        train_bn = tf.group(*[v for v in tf.get_collection(tf.GraphKeys.UPDATE_OPS)
                              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 = DATASETS[FLAGS.dataset]()

    log_width = utils.ilog2(dataset.width)