# 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
        l = tf.one_hot(l_in, self.nclass)
        wd *= lr
        warmup = tf.clip_by_value(tf.to_float(self.step) / (warmup_pos * (FLAGS.train_kimg << 10)), 0, 1)

        y = tf.reshape(tf.transpose(y_in, [1, 0, 2, 3, 4]), [-1] + hwc)
        y_1, y_2 = tf.split(y, 2)

        mix = tf.distributions.Beta(beta, beta).sample([tf.shape(x_in)[0], 1, 1, 1])
        mix = tf.maximum(mix, 1 - mix)

        classifier = functools.partial(self.classifier, **kwargs)
        logits_x = classifier(x_in, training=True)
        post_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)  # Take only first call to update batch norm.

        ema = tf.train.ExponentialMovingAverage(decay=ema)
        ema_op = ema.apply(utils.model_vars())
        ema_getter = functools.partial(utils.getter_ema, ema)
        logits_teacher = classifier(y_1, training=True, getter=ema_getter)
        labels_teacher = tf.stop_gradient(tf.nn.softmax(logits_teacher))
        labels_teacher = labels_teacher * mix[:, :, 0, 0] + labels_teacher[::-1] * (1 - mix[:, :, 0, 0])
        logits_student = classifier(y_1 * mix + y_1[::-1] * (1 - mix), training=True)
        loss_mt = tf.reduce_mean((labels_teacher - tf.nn.softmax(logits_student)) ** 2, -1)
        loss_mt = tf.reduce_mean(loss_mt)

        loss = tf.nn.softmax_cross_entropy_with_logits_v2(labels=l, logits=logits_x)
        loss = tf.reduce_mean(loss)
        tf.summary.scalar('losses/xe', loss)
        tf.summary.scalar('losses/mt', loss_mt)

        post_ops.append(ema_op)

# 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: ict.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

        l = tf.one_hot(l_in, self.nclass)
        wd *= lr
        warmup = tf.clip_by_value(tf.to_float(self.step) / (warmup_pos * (FLAGS.train_kimg << 10)), 0, 1)

        y = tf.reshape(tf.transpose(y_in, [1, 0, 2, 3, 4]), [-1] + hwc)
        y_1, y_2 = tf.split(y, 2)

        mix = tf.distributions.Beta(beta, beta).sample([tf.shape(x_in)[0], 1, 1, 1])
        mix = tf.maximum(mix, 1 - mix)

        classifier = functools.partial(self.classifier, **kwargs)
        logits_x = classifier(x_in, training=True)
        post_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)  # Take only first call to update batch norm.

        ema = tf.train.ExponentialMovingAverage(decay=ema)
        ema_op = ema.apply(utils.model_vars())
        ema_getter = functools.partial(utils.getter_ema, ema)
        logits_teacher = classifier(y_1, training=True, getter=ema_getter)
        labels_teacher = tf.stop_gradient(tf.nn.softmax(logits_teacher))
        labels_teacher = labels_teacher * mix[:, :, 0, 0] + labels_teacher[::-1] * (1 - mix[:, :, 0, 0])
        logits_student = classifier(y_1 * mix + y_1[::-1] * (1 - mix), training=True)
        loss_mt = tf.reduce_mean((labels_teacher - tf.nn.softmax(logits_student)) ** 2, -1)
        loss_mt = tf.reduce_mean(loss_mt)

        loss = tf.nn.softmax_cross_entropy_with_logits_v2(labels=l, logits=logits_x)
        loss = tf.reduce_mean(loss)
        tf.summary.scalar('losses/xe', loss)
        tf.summary.scalar('losses/mt', loss_mt)

        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])