# Path: weakly_supervised_control/disentanglement/tensorsketch/normalization/base.py
# class Norm(Module):
#     """Abstract class for normalization modules."""
# 
#     NAME = "norm"
# 
#     @staticmethod
#     def normalize(*inputs):
#         raise NotImplementedError("Implement normalize.")
# 
#     @staticmethod
#     def add(module, norm, use_out_hook=True):
#         hooks = module.select_hooks_dict(use_out_hook)
#         assert not hasattr(module, norm.NAME), (
#             "{} already in module attributes".format(norm.NAME))
#         assert norm.NAME not in hooks, (
#             "{} already in module hooks".format(norm.NAME))
# 
#         def hook(self, *inputs):
#             return norm(*inputs)
# 
#         setattr(module, norm.NAME, norm)
#         hooks.update({norm.NAME: hook})
# 
#     @staticmethod
#     def remove(module, norm_name, use_out_hook):
#         norm = getattr(module, norm_name)
#         hooks = module.select_hooks_dict(use_out_hook)
# 
#         delattr(module, norm.NAME)
#         del hooks[norm.NAME]
# 
# Path: weakly_supervised_control/disentanglement/tensorsketch/modules/base.py
# def build_with_name_scope(build_parameters):
#     @tf.Module.with_name_scope
#     def build_params_once_with_ns(self, *args):
#         assert not self.built, "{}.built already True".format(self.name)
#         build_parameters(self, *args)
#         self.built = True
#     return build_params_once_with_ns
# 
# Path: weakly_supervised_control/disentanglement/tensorsketch/utils.py
# def assign_moving_average(target, value, momentum):
#     target.assign(momentum * target + (1 - momentum) * value)


import tensorflow as tf
from weakly_supervised_control.disentanglement.tensorsketch.normalization.base import Norm
from weakly_supervised_control.disentanglement.tensorsketch.modules.base import build_with_name_scope
from weakly_supervised_control.disentanglement.tensorsketch.utils import assign_moving_average
        if self.affine:
            self.scale.assign(tf.ones(self.scale.shape))
            self.bias.assign(tf.zeros(self.bias.shape))

        if self.track_statistics:
            self.running_mean.assign(tf.zeros(self.running_mean.shape))
            self.running_variance.assign(tf.ones(self.running_variance.shape))

    def forward(self, x):
        return self.normalize(x, self.scale, self.bias,
                              self.momentum, self.epsilon, self.training,
                              self.running_mean, self.running_variance, self.fuse)

    @staticmethod
    def normalize(x, scale, bias, momentum, epsilon, training=True,
                  running_mean=None, running_variance=None, fuse=False):
        if training:
            if fuse:
                x, mean, variance = tf.nn.fused_batch_norm(x, scale, bias,
                                                           epsilon=epsilon,
                                                           is_training=True)
            else:
                mean, variance = tf.nn.moments(
                    x, list(range(len(x.shape) - 1)))
                x = tf.nn.batch_normalization(
                    x, mean, variance, bias, scale, epsilon)

            if running_mean is not None:
                size = x.shape[0]
                variance = variance * size / (size - 1)

# Path: weakly_supervised_control/disentanglement/tensorsketch/normalization/base.py
# class Norm(Module):
#     """Abstract class for normalization modules."""
# 
#     NAME = "norm"
# 
#     @staticmethod
#     def normalize(*inputs):
#         raise NotImplementedError("Implement normalize.")
# 
#     @staticmethod
#     def add(module, norm, use_out_hook=True):
#         hooks = module.select_hooks_dict(use_out_hook)
#         assert not hasattr(module, norm.NAME), (
#             "{} already in module attributes".format(norm.NAME))
#         assert norm.NAME not in hooks, (
#             "{} already in module hooks".format(norm.NAME))
# 
#         def hook(self, *inputs):
#             return norm(*inputs)
# 
#         setattr(module, norm.NAME, norm)
#         hooks.update({norm.NAME: hook})
# 
#     @staticmethod
#     def remove(module, norm_name, use_out_hook):
#         norm = getattr(module, norm_name)
#         hooks = module.select_hooks_dict(use_out_hook)
# 
#         delattr(module, norm.NAME)
#         del hooks[norm.NAME]
# 
# Path: weakly_supervised_control/disentanglement/tensorsketch/modules/base.py
# def build_with_name_scope(build_parameters):
#     @tf.Module.with_name_scope
#     def build_params_once_with_ns(self, *args):
#         assert not self.built, "{}.built already True".format(self.name)
#         build_parameters(self, *args)
#         self.built = True
#     return build_params_once_with_ns
# 
# Path: weakly_supervised_control/disentanglement/tensorsketch/utils.py
# def assign_moving_average(target, value, momentum):
#     target.assign(momentum * target + (1 - momentum) * value)


# Path: weakly_supervised_control/disentanglement/tensorsketch/normalization/batch_normalization.py
import tensorflow as tf
from weakly_supervised_control.disentanglement.tensorsketch.normalization.base import Norm
from weakly_supervised_control.disentanglement.tensorsketch.modules.base import build_with_name_scope
from weakly_supervised_control.disentanglement.tensorsketch.utils import assign_moving_average

        if self.affine:
            self.scale.assign(tf.ones(self.scale.shape))
            self.bias.assign(tf.zeros(self.bias.shape))

        if self.track_statistics:
            self.running_mean.assign(tf.zeros(self.running_mean.shape))
            self.running_variance.assign(tf.ones(self.running_variance.shape))

    def forward(self, x):
        return self.normalize(x, self.scale, self.bias,
                              self.momentum, self.epsilon, self.training,
                              self.running_mean, self.running_variance, self.fuse)

    @staticmethod
    def normalize(x, scale, bias, momentum, epsilon, training=True,
                  running_mean=None, running_variance=None, fuse=False):
        if training:
            if fuse:
                x, mean, variance = tf.nn.fused_batch_norm(x, scale, bias,
                                                           epsilon=epsilon,
                                                           is_training=True)
            else:
                mean, variance = tf.nn.moments(
                    x, list(range(len(x.shape) - 1)))
                x = tf.nn.batch_normalization(
                    x, mean, variance, bias, scale, epsilon)

            if running_mean is not None:
                size = x.shape[0]
                variance = variance * size / (size - 1)

                assign_moving_average(running_mean, mean, momentum)