# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# 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
#
#     http://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.
# ==============================================================================
"""The V2 implementation of Normalization layers."""

import tensorflow.compat.v2 as tf
from keras import backend
from keras import constraints
from keras import initializers
from keras import regularizers
from keras.engine.base_layer import Layer
from keras.engine.input_spec import InputSpec
from keras.utils import control_flow_util
from tensorflow.python.ops.control_flow_ops import get_enclosing_xla_context
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util.tf_export import keras_export


class BatchNormalizationBase(Layer):
  r"""Layer that normalizes its inputs.

  Batch normalization applies a transformation that maintains the mean output
  close to 0 and the output standard deviation close to 1.

  Importantly, batch normalization works differently during training and
  during inference.

  **During training** (i.e. when using `fit()` or when calling the layer/model
  with the argument `training=True`), the layer normalizes its output using
  the mean and standard deviation of the current batch of inputs. That is to
  say, for each channel being normalized, the layer returns
  `gamma * (batch - mean(batch)) / sqrt(var(batch) + epsilon) + beta`, where:

  - `epsilon` is small constant (configurable as part of the constructor
  arguments)
  - `gamma` is a learned scaling factor (initialized as 1), which
  can be disabled by passing `scale=False` to the constructor.
  - `beta` is a learned offset factor (initialized as 0), which
  can be disabled by passing `center=False` to the constructor.

  **During inference** (i.e. when using `evaluate()` or `predict()`) or when
  calling the layer/model with the argument `training=False` (which is the
  default), the layer normalizes its output using a moving average of the
  mean and standard deviation of the batches it has seen during training. That
  is to say, it returns
  `gamma * (batch - self.moving_mean) / sqrt(self.moving_var + epsilon) + beta`.

  `self.moving_mean` and `self.moving_var` are non-trainable variables that
  are updated each time the layer in called in training mode, as such:

  - `moving_mean = moving_mean * momentum + mean(batch) * (1 - momentum)`
  - `moving_var = moving_var * momentum + var(batch) * (1 - momentum)`

  As such, the layer will only normalize its inputs during inference
  *after having been trained on data that has similar statistics as the
  inference data*.

  Args:
    axis: Integer or a list of integers, the axis that should be normalized
      (typically the features axis). For instance, after a `Conv2D` layer with
      `data_format="channels_first"`, set `axis=1` in `BatchNormalization`.
    momentum: Momentum for the moving average.
    epsilon: Small float added to variance to avoid dividing by zero.
    center: If True, add offset of `beta` to normalized tensor. If False, `beta`
      is ignored.
    scale: If True, multiply by `gamma`. If False, `gamma` is not used. When the
      next layer is linear (also e.g. `nn.relu`), this can be disabled since the
      scaling will be done by the next layer.
    beta_initializer: Initializer for the beta weight.
    gamma_initializer: Initializer for the gamma weight.
    moving_mean_initializer: Initializer for the moving mean.
    moving_variance_initializer: Initializer for the moving variance.
    beta_regularizer: Optional regularizer for the beta weight.
    gamma_regularizer: Optional regularizer for the gamma weight.
    beta_constraint: Optional constraint for the beta weight.
    gamma_constraint: Optional constraint for the gamma weight.
    renorm: Whether to use [Batch Renormalization](
      https://arxiv.org/abs/1702.03275). This adds extra variables during
        training. The inference is the same for either value of this parameter.
    renorm_clipping: A dictionary that may map keys 'rmax', 'rmin', 'dmax' to
      scalar `Tensors` used to clip the renorm correction. The correction `(r,
      d)` is used as `corrected_value = normalized_value * r + d`, with `r`
      clipped to [rmin, rmax], and `d` to [-dmax, dmax]. Missing rmax, rmin,
      dmax are set to inf, 0, inf, respectively.
    renorm_momentum: Momentum used to update the moving means and standard
      deviations with renorm. Unlike `momentum`, this affects training and
      should be neither too small (which would add noise) nor too large (which
      would give stale estimates). Note that `momentum` is still applied to get
      the means and variances for inference.
    fused: if `True`, use a faster, fused implementation, or raise a ValueError
      if the fused implementation cannot be used. If `None`, use the faster
      implementation if possible. If False, do not used the fused
      implementation.
      Note that in TensorFlow 1.x, the meaning of `fused=True` is different: if
        `False`, the layer uses the system-recommended implementation.
    trainable: Boolean, if `True` the variables will be marked as trainable.
    virtual_batch_size: An `int`. By default, `virtual_batch_size` is `None`,
      which means batch normalization is performed across the whole batch. When
      `virtual_batch_size` is not `None`, instead perform "Ghost Batch
      Normalization", which creates virtual sub-batches which are each
      normalized separately (with shared gamma, beta, and moving statistics).
      Must divide the actual batch size during execution.
    adjustment: A function taking the `Tensor` containing the (dynamic) shape of
      the input tensor and returning a pair (scale, bias) to apply to the
      normalized values (before gamma and beta), only during training. For
      example, if `axis=-1`,
        `adjustment = lambda shape: (
          tf.random.uniform(shape[-1:], 0.93, 1.07),
          tf.random.uniform(shape[-1:], -0.1, 0.1))` will scale the normalized
            value by up to 7% up or down, then shift the result by up to 0.1
            (with independent scaling and bias for each feature but shared
            across all examples), and finally apply gamma and/or beta. If
            `None`, no adjustment is applied. Cannot be specified if
            virtual_batch_size is specified.

  Call arguments:
    inputs: Input tensor (of any rank).
    training: Python boolean indicating whether the layer should behave in
      training mode or in inference mode.
      - `training=True`: The layer will normalize its inputs using the mean and
        variance of the current batch of inputs.
      - `training=False`: The layer will normalize its inputs using the mean and
        variance of its moving statistics, learned during training.

  Input shape: Arbitrary. Use the keyword argument `input_shape` (tuple of
    integers, does not include the samples axis) when using this layer as the
    first layer in a model.

  Output shape: Same shape as input.

  Reference:
    - [Ioffe and Szegedy, 2015](https://arxiv.org/abs/1502.03167).
  """

  # By default, the base class uses V2 behavior. The BatchNormalization V1
  # subclass sets this to False to use the V1 behavior.
  _USE_V2_BEHAVIOR = True

  def __init__(self,
               axis=-1,
               momentum=0.99,
               epsilon=1e-3,
               center=True,
               scale=True,
               beta_initializer='zeros',
               gamma_initializer='ones',
               moving_mean_initializer='zeros',
               moving_variance_initializer='ones',
               beta_regularizer=None,
               gamma_regularizer=None,
               beta_constraint=None,
               gamma_constraint=None,
               renorm=False,
               renorm_clipping=None,
               renorm_momentum=0.99,
               fused=None,
               trainable=True,
               virtual_batch_size=None,
               adjustment=None,
               name=None,
               **kwargs):
    super(BatchNormalizationBase, self).__init__(name=name, **kwargs)
    if isinstance(axis, (list, tuple)):
      self.axis = axis[:]
    elif isinstance(axis, int):
      self.axis = axis
    else:
      raise TypeError('Expected an int or a list/tuple of ints for the '
                      'argument \'axis\', but received: %r' % axis)
    self.momentum = momentum
    self.epsilon = epsilon
    self.center = center
    self.scale = scale
    self.beta_initializer = initializers.get(beta_initializer)
    self.gamma_initializer = initializers.get(gamma_initializer)
    self.moving_mean_initializer = initializers.get(moving_mean_initializer)
    self.moving_variance_initializer = initializers.get(
        moving_variance_initializer)
    self.beta_regularizer = regularizers.get(beta_regularizer)
    self.gamma_regularizer = regularizers.get(gamma_regularizer)
    self.beta_constraint = constraints.get(beta_constraint)
    self.gamma_constraint = constraints.get(gamma_constraint)
    self.renorm = renorm
    self.virtual_batch_size = virtual_batch_size
    self.adjustment = adjustment
    if self._USE_V2_BEHAVIOR:
      if fused:
        self._raise_if_fused_cannot_be_used()
      # We leave fused as None if self._fused_can_be_used()==True, since we
      # still may set it to False in self.build() if the input rank is not 4.
      elif fused is None and not self._fused_can_be_used():
        fused = False
    elif fused is None:
      fused = True
    self.supports_masking = True

    self.fused = fused
    self._bessels_correction_test_only = True
    self.trainable = trainable

    if renorm:
      renorm_clipping = renorm_clipping or {}
      keys = ['rmax', 'rmin', 'dmax']
      if set(renorm_clipping) - set(keys):
        raise ValueError('renorm_clipping %s contains keys not in %s' %
                         (renorm_clipping, keys))
      self.renorm_clipping = renorm_clipping
      self.renorm_momentum = renorm_momentum

  def _raise_if_fused_cannot_be_used(self):
    """Raises a ValueError if fused implementation cannot be used.

    In addition to the checks done in this function, the input tensors rank must
    be 4 or 5. The input rank check can only be done once the input shape is
    known.
    """
    # Note the ValueErrors in this function are caught and not reraised in
    # _fused_can_be_used(). No other exception besides ValueError should be
    # raised here.

    # Currently fused batch norm doesn't support renorm. It also only supports a
    # channel dimension on axis 1 or 3 (rank=4) / 1 or 4 (rank5), when no
    # virtual batch size or adjustment is used.
    if self.renorm:
      raise ValueError('Passing both `fused=True` and `renorm=True` is '
                       'not supported')
    axis = [self.axis] if isinstance(self.axis, int) else self.axis
    # Axis -3 is equivalent to 1, and axis -1 is equivalent to 3, when the
    # input rank is 4. Similarly, the valid axis is -4, -1, 1, 4 when the rank
    # is 5. The combination of ranks and axes will be checked later.
    if len(axis) > 1 or axis[0] not in (-4, -3, -1, 1, 3, 4):
      raise ValueError('Passing `fused=True` is only supported when axis is 1 '
                       'or 3 for input rank = 4 or 1 or 4 for input rank = 5. '
                       'Got axis %s' % (axis,))
    if self.virtual_batch_size is not None:
      raise ValueError('Passing `fused=True` is not supported when '
                       '`virtual_batch_size` is specified.')
    if self.adjustment is not None:
      raise ValueError('Passing `fused=True` is not supported when '
                       '`adjustment` is specified.')
    # TODO(reedwm): Support fp64 in FusedBatchNorm then remove this check.
    if self._compute_dtype not in ('float16', 'bfloat16', 'float32', None):
      raise ValueError(
          'Passing `fused=True` is only supported when the compute '
          'dtype is float16, bfloat16, or float32. Got dtype: %s' %
          (self._compute_dtype,))

  def _fused_can_be_used(self):
    try:
      self._raise_if_fused_cannot_be_used()
      return True
    except ValueError:
      return False

  @property
  def trainable(self):
    return self._trainable

  @trainable.setter
  def trainable(self, value):
    self._trainable = value

  @property
  def _param_dtype(self):
    # Raise parameters of fp16 batch norm to fp32
    if self.dtype == tf.float16 or self.dtype == tf.bfloat16:
      return tf.float32
    else:
      return self.dtype or tf.float32

  def _support_zero_size_input(self):
    if not tf.distribute.has_strategy():
      return False
    strategy = tf.distribute.get_strategy()
    # TODO(b/195085185): remove experimental_enable_get_next_as_optional after
    # migrating all users.
    return getattr(
        strategy.extended, 'enable_partial_batch_handling',
        getattr(strategy.extended, 'experimental_enable_get_next_as_optional',
                False))

  def build(self, input_shape):
    input_shape = tf.TensorShape(input_shape)
    if not input_shape.ndims:
      raise ValueError('Input has undefined rank.')
    ndims = len(input_shape)

    # Convert axis to list and resolve negatives
    if isinstance(self.axis, int):
      self.axis = [self.axis]

    for idx, x in enumerate(self.axis):
      if x < 0:
        self.axis[idx] = ndims + x

    # Validate axes
    for x in self.axis:
      if x < 0 or x >= ndims:
        raise ValueError('Invalid axis: %s' % (self.axis,))
    if len(self.axis) != len(set(self.axis)):
      raise ValueError('Duplicate axis: %s' % (self.axis,))

    if self.virtual_batch_size is not None:
      if self.virtual_batch_size <= 0:
        raise ValueError('virtual_batch_size must be a positive integer that '
                         'divides the true batch size of the input tensor')
      # If using virtual batches, the first dimension must be the batch
      # dimension and cannot be the batch norm axis
      if 0 in self.axis:
        raise ValueError('When using virtual_batch_size, the batch dimension '
                         'must be 0 and thus axis cannot include 0. '
                         'Received axis=%s' % (self.axis,))
      if self.adjustment is not None:
        raise ValueError('When using virtual_batch_size, adjustment cannot '
                         'be specified')

    if self.fused in (None, True):
      # TODO(yaozhang): if input is not 4D, reshape it to 4D and reshape the
      # output back to its original shape accordingly.
      if self._USE_V2_BEHAVIOR:
        if self.fused is None:
          self.fused = ndims in (4, 5)
        elif self.fused and ndims not in (4, 5):
          raise ValueError('Batch normalization layers with `fused=True` only '
                           'support 4D or 5D input tensors. '
                           'Received tensor with shape: %s' %
                           (tuple(input_shape),))
      else:
        assert self.fused is not None
        self.fused = (ndims in (4, 5) and self._fused_can_be_used())
      # TODO(chrisying): fused batch norm is currently not supported for
      # multi-axis batch norm and by extension virtual batches. In some cases,
      # it might be possible to use fused batch norm but would require reshaping
      # the Tensor to 4D with the axis in 1 or 3 (preferred 1) which is
      # particularly tricky. A compromise might be to just support the most
      # common use case (turning 5D w/ virtual batch to NCHW)

    if self.fused:
      if self.axis == [1] and ndims == 4:
        self._data_format = 'NCHW'
      elif self.axis == [1] and ndims == 5:
        self._data_format = 'NCDHW'
      elif self.axis == [3] and ndims == 4:
        self._data_format = 'NHWC'
      elif self.axis == [4] and ndims == 5:
        self._data_format = 'NDHWC'
      elif ndims == 5:
        # 5D tensors that can be passed in but should not use fused batch norm
        # due to unsupported axis.
        self.fused = False
      else:
        if ndims == 4:
          raise ValueError(
              'Unsupported axis. The use of `fused=True` is only possible with '
              '`axis=1` or `axis=3` for 4D input tensors. Received '
              'axis=%s' % (self.axis,))
        else:
          raise ValueError(
              'Unsupported axis. The use of `fused=True` is only possible with '
              '`axis=1` or `axis=4` for 5D input tensors. Received '
              'axis=%s' % (self.axis,))

    axis_to_dim = {x: input_shape.dims[x].value for x in self.axis}
    for x in axis_to_dim:
      if axis_to_dim[x] is None:
        raise ValueError('Input has undefined `axis` dimension. Received input '
                         'with shape %s. Axis value: %s' %
                         (tuple(input_shape), self.axis))
    self.input_spec = InputSpec(ndim=ndims, axes=axis_to_dim)

    if len(axis_to_dim) == 1 and self.virtual_batch_size is None:
      # Single axis batch norm (most common/default use-case)
      param_shape = (list(axis_to_dim.values())[0],)
    else:
      # Parameter shape is the original shape but with 1 in all non-axis dims
      param_shape = [
          axis_to_dim[i] if i in axis_to_dim else 1 for i in range(ndims)
      ]
      if self.virtual_batch_size is not None:
        # When using virtual batches, add an extra dim at index 1
        param_shape.insert(1, 1)
        for idx, x in enumerate(self.axis):
          self.axis[idx] = x + 1  # Account for added dimension

    if self.scale:
      self.gamma = self.add_weight(
          name='gamma',
          shape=param_shape,
          dtype=self._param_dtype,
          initializer=self.gamma_initializer,
          regularizer=self.gamma_regularizer,
          constraint=self.gamma_constraint,
          trainable=True,
          experimental_autocast=False)
    else:
      self.gamma = None
      if self.fused:
        self._gamma_const = backend.constant(
            1.0, dtype=self._param_dtype, shape=param_shape)

    if self.center:
      self.beta = self.add_weight(
          name='beta',
          shape=param_shape,
          dtype=self._param_dtype,
          initializer=self.beta_initializer,
          regularizer=self.beta_regularizer,
          constraint=self.beta_constraint,
          trainable=True,
          experimental_autocast=False)
    else:
      self.beta = None
      if self.fused:
        self._beta_const = backend.constant(
            0.0, dtype=self._param_dtype, shape=param_shape)

    try:
      # Disable variable partitioning when creating the moving mean and variance
      if hasattr(self, '_scope') and self._scope:
        partitioner = self._scope.partitioner
        self._scope.set_partitioner(None)
      else:
        partitioner = None
      self.moving_mean = self.add_weight(
          name='moving_mean',
          shape=param_shape,
          dtype=self._param_dtype,
          initializer=self.moving_mean_initializer,
          synchronization=tf.VariableSynchronization.ON_READ,
          trainable=False,
          aggregation=tf.VariableAggregation.MEAN,
          experimental_autocast=False)

      self.moving_variance = self.add_weight(
          name='moving_variance',
          shape=param_shape,
          dtype=self._param_dtype,
          initializer=self.moving_variance_initializer,
          synchronization=tf.VariableSynchronization.ON_READ,
          trainable=False,
          aggregation=tf.VariableAggregation.MEAN,
          experimental_autocast=False)

      if self.renorm:
        # In batch renormalization we track the inference moving stddev instead
        # of the moving variance to more closely align with the paper.
        def moving_stddev_initializer(*args, **kwargs):
          return tf.sqrt(
              self.moving_variance_initializer(*args, **kwargs))

        with tf.distribute.get_strategy(
        ).extended.colocate_vars_with(self.moving_variance):
          self.moving_stddev = self.add_weight(
              name='moving_stddev',
              shape=param_shape,
              dtype=self._param_dtype,
              initializer=moving_stddev_initializer,
              synchronization=tf.VariableSynchronization.ON_READ,
              trainable=False,
              aggregation=tf.VariableAggregation.MEAN,
              experimental_autocast=False)

        # Create variables to maintain the moving mean and standard deviation.
        # These are used in training and thus are different from the moving
        # averages above. The renorm variables are colocated with moving_mean
        # and moving_stddev.
        # NOTE: below, the outer `with device` block causes the current device
        # stack to be cleared. The nested ones use a `lambda` to set the desired
        # device and ignore any devices that may be set by the custom getter.
        def _renorm_variable(name,
                             shape,
                             initializer=tf.compat.v1.zeros_initializer()):
          """Create a renorm variable."""
          var = self.add_weight(
              name=name,
              shape=shape,
              dtype=self._param_dtype,
              initializer=initializer,
              synchronization=tf.VariableSynchronization.ON_READ,
              trainable=False,
              aggregation=tf.VariableAggregation.MEAN,
              experimental_autocast=False)
          return var

        with tf.distribute.get_strategy(
        ).extended.colocate_vars_with(self.moving_mean):
          self.renorm_mean = _renorm_variable('renorm_mean', param_shape,
                                              self.moving_mean_initializer)
        with tf.distribute.get_strategy(
        ).extended.colocate_vars_with(self.moving_stddev):
          self.renorm_stddev = _renorm_variable('renorm_stddev', param_shape,
                                                moving_stddev_initializer)
    finally:
      if partitioner:
        self._scope.set_partitioner(partitioner)
    self.built = True

  def _assign_moving_average(self, variable, value, momentum, inputs_size):

    def calculate_update_delta():
      decay = tf.convert_to_tensor(
          1.0 - momentum, name='decay')
      if decay.dtype != variable.dtype.base_dtype:
        decay = tf.cast(decay, variable.dtype.base_dtype)
      update_delta = (variable - tf.cast(value, variable.dtype)) * decay
      if inputs_size is not None:
        update_delta = tf.where(inputs_size > 0, update_delta,
                                backend.zeros_like(update_delta))
      return update_delta

    with backend.name_scope('AssignMovingAvg') as scope:
      if tf.compat.v1.executing_eagerly_outside_functions():
        return variable.assign_sub(calculate_update_delta(), name=scope)
      else:
        with tf.compat.v1.colocate_with(variable):  # pylint: disable=protected-access
          return tf.compat.v1.assign_sub(
              variable, calculate_update_delta(), name=scope)

  def _assign_new_value(self, variable, value):
    with backend.name_scope('AssignNewValue') as scope:
      if tf.compat.v1.executing_eagerly_outside_functions():
        return variable.assign(value, name=scope)
      else:
        with tf.compat.v1.colocate_with(variable):  # pylint: disable=protected-access
          return tf.compat.v1.assign(variable, value, name=scope)

  def _fused_batch_norm(self, inputs, training):
    """Returns the output of fused batch norm."""
    beta = self.beta if self.center else self._beta_const
    gamma = self.gamma if self.scale else self._gamma_const

    # TODO(b/129279393): Support zero batch input in non DistributionStrategy
    # code as well.
    if self._support_zero_size_input():
      # Keras assumes that batch dimension is the first dimension for Batch
      # Normalization.
      input_batch_size = tf.shape(inputs)[0]
    else:
      input_batch_size = None

    # TODO(rmlarsen): Support using fused avg updates for non-eager execution
    # after fixing graph pattern matching and enabling fused_batch_norm to
    # take exponential_avg_factor as a tensor input.
    use_fused_avg_updates = (
        tf.compat.v1.executing_eagerly_outside_functions() and
        isinstance(self.momentum,
                   (float, int)) and get_enclosing_xla_context() is None)
    if use_fused_avg_updates:
      exponential_avg_factor = 1.0 - self.momentum
    else:
      exponential_avg_factor = None

    def _maybe_add_or_remove_bessels_correction(variance, remove=True):
      r"""Add or remove Bessel's correction."""
      # Removes Bessel's correction if remove == True, adds it otherwise.
      # This is to be consistent with non-fused batch norm. Note that the
      # variance computed by fused batch norm is with Bessel's correction.
      # This is only used in legacy V1 batch norm tests.
      if self._bessels_correction_test_only:
        return variance
      sample_size = tf.cast(
          tf.size(inputs) / tf.size(variance), variance.dtype)
      if remove:
        factor = (sample_size -
                  tf.cast(1.0, variance.dtype)) / sample_size
      else:
        factor = sample_size / (
            sample_size - tf.cast(1.0, variance.dtype))
      return variance * factor

    def _fused_batch_norm_training():
      return tf.compat.v1.nn.fused_batch_norm(
          inputs,
          gamma,
          beta,
          mean=self.moving_mean,
          variance=_maybe_add_or_remove_bessels_correction(
              self.moving_variance, remove=False),
          epsilon=self.epsilon,
          is_training=True,
          data_format=self._data_format,
          exponential_avg_factor=exponential_avg_factor)

    def _fused_batch_norm_training_empty():
      return inputs, self.moving_mean, self.moving_variance

    def _fused_batch_norm_inference():
      return tf.compat.v1.nn.fused_batch_norm(
          inputs,
          gamma,
          beta,
          mean=self.moving_mean,
          variance=self.moving_variance,
          epsilon=self.epsilon,
          is_training=False,
          data_format=self._data_format)

    train_op = _fused_batch_norm_training
    if use_fused_avg_updates and input_batch_size is not None:
      # pylint: disable=g-long-lambda
      train_op = lambda: control_flow_util.smart_cond(
          input_batch_size > 0, _fused_batch_norm_training,
          _fused_batch_norm_training_empty)
      # pylint: enable=g-long-lambda

    output, mean, variance = control_flow_util.smart_cond(
        training, train_op, _fused_batch_norm_inference)
    variance = _maybe_add_or_remove_bessels_correction(variance, remove=True)

    training_value = control_flow_util.constant_value(training)
    if training_value or training_value is None:
      if not use_fused_avg_updates:
        if training_value is None:
          momentum = control_flow_util.smart_cond(training,
                                                  lambda: self.momentum,
                                                  lambda: 1.0)
        else:
          momentum = tf.convert_to_tensor(self.momentum)

      def mean_update():
        """Update self.moving_mean with the most recent data point."""
        if use_fused_avg_updates:
          return self._assign_new_value(self.moving_mean, mean)
        else:
          return self._assign_moving_average(self.moving_mean, mean, momentum,
                                             input_batch_size)

      def variance_update():
        """Update self.moving_variance with the most recent data point."""
        if use_fused_avg_updates:
          return self._assign_new_value(self.moving_variance, variance)
        else:
          return self._assign_moving_average(self.moving_variance, variance,
                                             momentum, input_batch_size)

      self.add_update(mean_update)
      self.add_update(variance_update)

    return output

  def _renorm_correction_and_moments(self, mean, variance, training,
                                     inputs_size):
    """Returns the correction and update values for renorm."""
    stddev = tf.sqrt(variance + self.epsilon)
    # Compute the average mean and standard deviation, as if they were
    # initialized with this batch's moments.
    renorm_mean = self.renorm_mean
    # Avoid divide by zero early on in training.
    renorm_stddev = tf.maximum(self.renorm_stddev, tf.sqrt(self.epsilon))
    # Compute the corrections for batch renorm.
    r = stddev / renorm_stddev
    d = (mean - renorm_mean) / renorm_stddev
    # Ensure the corrections use pre-update moving averages.
    with tf.control_dependencies([r, d]):
      mean = tf.identity(mean)
      stddev = tf.identity(stddev)
    rmin, rmax, dmax = [
        self.renorm_clipping.get(key) for key in ['rmin', 'rmax', 'dmax']
    ]
    if rmin is not None:
      r = tf.maximum(r, rmin)
    if rmax is not None:
      r = tf.minimum(r, rmax)
    if dmax is not None:
      d = tf.maximum(d, -dmax)
      d = tf.minimum(d, dmax)
    # When not training, use r=1, d=0.
    r = control_flow_util.smart_cond(training, lambda: r,
                                     lambda: tf.ones_like(r))
    d = control_flow_util.smart_cond(training, lambda: d,
                                     lambda: tf.zeros_like(d))

    def _update_renorm_variable(var, value, inputs_size):
      """Updates a moving average and weight, returns the unbiased value."""
      value = tf.identity(value)

      def _do_update():
        """Updates the var, returns the updated value."""
        new_var = self._assign_moving_average(var, value, self.renorm_momentum,
                                              inputs_size)
        return new_var

      def _fake_update():
        return tf.identity(var)

      return control_flow_util.smart_cond(training, _do_update, _fake_update)

    # TODO(yuefengz): colocate the operations
    update_new_mean = _update_renorm_variable(self.renorm_mean, mean,
                                              inputs_size)
    update_new_stddev = _update_renorm_variable(self.renorm_stddev, stddev,
                                                inputs_size)

    # Update the inference mode moving averages with the batch value.
    with tf.control_dependencies([update_new_mean, update_new_stddev]):
      out_mean = tf.identity(mean)
      out_variance = tf.identity(variance)

    return (r, d, out_mean, out_variance)

  def _calculate_mean_and_var(self, inputs, reduction_axes, keep_dims):
    return tf.nn.moments(inputs, reduction_axes, keepdims=keep_dims)

  def _moments(self, inputs, reduction_axes, keep_dims):
    mean, variance = self._calculate_mean_and_var(inputs, reduction_axes,
                                                  keep_dims)
    # TODO(b/129279393): Support zero batch input in non DistributionStrategy
    # code as well.
    if self._support_zero_size_input():
      input_batch_size = tf.shape(inputs)[0]
      mean = tf.where(input_batch_size > 0, mean, backend.zeros_like(mean))
      variance = tf.where(input_batch_size > 0, variance,
                          backend.zeros_like(variance))
    return mean, variance

  def _get_training_value(self, training=None):
    if training is None:
      training = backend.learning_phase()
    if self._USE_V2_BEHAVIOR:
      if isinstance(training, int):
        training = bool(training)
      if not self.trainable:
        # When the layer is not trainable, it overrides the value passed from
        # model.
        training = False
    return training

  def call(self, inputs, training=None):
    training = self._get_training_value(training)

    if self.virtual_batch_size is not None:
      # Virtual batches (aka ghost batches) can be simulated by reshaping the
      # Tensor and reusing the existing batch norm implementation
      original_shape = tf.shape(inputs)
      original_shape = tf.concat(
          [tf.constant([-1]), original_shape[1:]], axis=0)
      expanded_shape = tf.concat([
          tf.constant([self.virtual_batch_size, -1]),
          original_shape[1:]
      ], axis=0)

      # Will cause errors if virtual_batch_size does not divide the batch size
      inputs = tf.reshape(inputs, expanded_shape)

      def undo_virtual_batching(outputs):
        outputs = tf.reshape(outputs, original_shape)
        return outputs

    if self.fused:
      outputs = self._fused_batch_norm(inputs, training=training)
      if self.virtual_batch_size is not None:
        # Currently never reaches here since fused_batch_norm does not support
        # virtual batching
        outputs = undo_virtual_batching(outputs)
      return outputs

    inputs_dtype = inputs.dtype.base_dtype
    if inputs_dtype in (tf.float16, tf.bfloat16):
      # Do all math in float32 if given 16-bit inputs for numeric stability.
      # In particular, it's very easy for variance to overflow in float16 and
      # for safety we also choose to cast bfloat16 to float32.
      inputs = tf.cast(inputs, tf.float32)

    # Compute the axes along which to reduce the mean / variance
    input_shape = inputs.shape
    ndims = len(input_shape)
    reduction_axes = [i for i in range(ndims) if i not in self.axis]
    if self.virtual_batch_size is not None:
      del reduction_axes[1]  # Do not reduce along virtual batch dim

    # Broadcasting only necessary for single-axis batch norm where the axis is
    # not the last dimension
    broadcast_shape = [1] * ndims
    broadcast_shape[self.axis[0]] = input_shape.dims[self.axis[0]].value

    def _broadcast(v):
      if (v is not None and len(v.shape) != ndims and
          reduction_axes != list(range(ndims - 1))):
        return tf.reshape(v, broadcast_shape)
      return v

    scale, offset = _broadcast(self.gamma), _broadcast(self.beta)

    def _compose_transforms(scale, offset, then_scale, then_offset):
      if then_scale is not None:
        scale *= then_scale
        offset *= then_scale
      if then_offset is not None:
        offset += then_offset
      return (scale, offset)

    # Determine a boolean value for `training`: could be True, False, or None.
    training_value = control_flow_util.constant_value(training)
    if training_value == False:  # pylint: disable=singleton-comparison,g-explicit-bool-comparison
      mean, variance = self.moving_mean, self.moving_variance
    else:
      if self.adjustment:
        adj_scale, adj_bias = self.adjustment(tf.shape(inputs))
        # Adjust only during training.
        adj_scale = control_flow_util.smart_cond(
            training, lambda: adj_scale, lambda: tf.ones_like(adj_scale))
        adj_bias = control_flow_util.smart_cond(
            training, lambda: adj_bias, lambda: tf.zeros_like(adj_bias))
        scale, offset = _compose_transforms(adj_scale, adj_bias, scale, offset)

      # Some of the computations here are not necessary when training==False
      # but not a constant. However, this makes the code simpler.
      keep_dims = self.virtual_batch_size is not None or len(self.axis) > 1
      mean, variance = self._moments(
          tf.cast(inputs, self._param_dtype),
          reduction_axes,
          keep_dims=keep_dims)

      moving_mean = self.moving_mean
      moving_variance = self.moving_variance

      mean = control_flow_util.smart_cond(
          training, lambda: mean,
          lambda: tf.convert_to_tensor(moving_mean))
      variance = control_flow_util.smart_cond(
          training, lambda: variance,
          lambda: tf.convert_to_tensor(moving_variance))

      if self.virtual_batch_size is not None:
        # This isn't strictly correct since in ghost batch norm, you are
        # supposed to sequentially update the moving_mean and moving_variance
        # with each sub-batch. However, since the moving statistics are only
        # used during evaluation, it is more efficient to just update in one
        # step and should not make a significant difference in the result.
        new_mean = tf.reduce_mean(mean, axis=1, keepdims=True)
        new_variance = tf.reduce_mean(variance, axis=1, keepdims=True)
      else:
        new_mean, new_variance = mean, variance

      if self._support_zero_size_input():
        # Keras assumes that batch dimension is the first dimension for Batch
        # Normalization.
        input_batch_size = tf.shape(inputs)[0]
      else:
        input_batch_size = None

      if self.renorm:
        r, d, new_mean, new_variance = self._renorm_correction_and_moments(
            new_mean, new_variance, training, input_batch_size)
        # When training, the normalized values (say, x) will be transformed as
        # x * gamma + beta without renorm, and (x * r + d) * gamma + beta
        # = x * (r * gamma) + (d * gamma + beta) with renorm.
        r = _broadcast(tf.stop_gradient(r, name='renorm_r'))
        d = _broadcast(tf.stop_gradient(d, name='renorm_d'))
        scale, offset = _compose_transforms(r, d, scale, offset)

      def _do_update(var, value):
        """Compute the updates for mean and variance."""
        return self._assign_moving_average(var, value, self.momentum,
                                           input_batch_size)

      def mean_update():
        true_branch = lambda: _do_update(self.moving_mean, new_mean)
        false_branch = lambda: self.moving_mean
        return control_flow_util.smart_cond(training, true_branch, false_branch)

      def variance_update():
        """Update the moving variance."""

        def true_branch_renorm():
          # We apply epsilon as part of the moving_stddev to mirror the training
          # code path.
          moving_stddev = _do_update(self.moving_stddev,
                                     tf.sqrt(new_variance + self.epsilon))
          return self._assign_new_value(
              self.moving_variance,
              # Apply relu in case floating point rounding causes it to go
              # negative.
              backend.relu(moving_stddev * moving_stddev - self.epsilon))

        if self.renorm:
          true_branch = true_branch_renorm
        else:
          true_branch = lambda: _do_update(self.moving_variance, new_variance)

        false_branch = lambda: self.moving_variance
        return control_flow_util.smart_cond(training, true_branch, false_branch)

      self.add_update(mean_update)
      self.add_update(variance_update)

    mean = tf.cast(mean, inputs.dtype)
    variance = tf.cast(variance, inputs.dtype)
    if offset is not None:
      offset = tf.cast(offset, inputs.dtype)
    if scale is not None:
      scale = tf.cast(scale, inputs.dtype)
    outputs = tf.nn.batch_normalization(inputs, _broadcast(mean),
                                        _broadcast(variance), offset, scale,
                                        self.epsilon)
    if inputs_dtype in (tf.float16, tf.bfloat16):
      outputs = tf.cast(outputs, inputs_dtype)

    # If some components of the shape got lost due to adjustments, fix that.
    outputs.set_shape(input_shape)

    if self.virtual_batch_size is not None:
      outputs = undo_virtual_batching(outputs)
    return outputs

  def compute_output_shape(self, input_shape):
    return input_shape

  def get_config(self):
    config = {
        'axis': self.axis,
        'momentum': self.momentum,
        'epsilon': self.epsilon,
        'center': self.center,
        'scale': self.scale,
        'beta_initializer': initializers.serialize(self.beta_initializer),
        'gamma_initializer': initializers.serialize(self.gamma_initializer),
        'moving_mean_initializer':
            initializers.serialize(self.moving_mean_initializer),
        'moving_variance_initializer':
            initializers.serialize(self.moving_variance_initializer),
        'beta_regularizer': regularizers.serialize(self.beta_regularizer),
        'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
        'beta_constraint': constraints.serialize(self.beta_constraint),
        'gamma_constraint': constraints.serialize(self.gamma_constraint)
    }
    # Only add TensorFlow-specific parameters if they are set, so as to preserve
    # model compatibility with external Keras.
    if self.renorm:
      config['renorm'] = True
      config['renorm_clipping'] = self.renorm_clipping
      config['renorm_momentum'] = self.renorm_momentum
    if self.virtual_batch_size is not None:
      config['virtual_batch_size'] = self.virtual_batch_size
    # Note: adjustment is not serializable.
    if self.adjustment is not None:
      logging.warning('The `adjustment` function of this `BatchNormalization` '
                      'layer cannot be serialized and has been omitted from '
                      'the layer config. It will not be included when '
                      're-creating the layer from the saved config.')
    base_config = super(BatchNormalizationBase, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


# pylint: disable=g-classes-have-attributes
@keras_export('keras.layers.experimental.SyncBatchNormalization', v1=[])
class SyncBatchNormalization(BatchNormalizationBase):
  r"""Normalize and scale inputs or activations synchronously across replicas.

  Applies batch normalization to activations of the previous layer at each batch
  by synchronizing the global batch statistics across all devices that are
  training the model. For specific details about batch normalization please
  refer to the `tf.keras.layers.BatchNormalization` layer docs.

  If this layer is used when using tf.distribute strategy to train models
  across devices/workers, there will be an allreduce call to aggregate batch
  statistics across all replicas at every training step. Without tf.distribute
  strategy, this layer behaves as a regular `tf.keras.layers.BatchNormalization`
  layer.

  Example usage:

  ```python
  strategy = tf.distribute.MirroredStrategy()

  with strategy.scope():
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Dense(16))
    model.add(tf.keras.layers.experimental.SyncBatchNormalization())
  ```

  Args:
    axis: Integer, the axis that should be normalized
      (typically the features axis).
      For instance, after a `Conv2D` layer with
      `data_format="channels_first"`,
      set `axis=1` in `BatchNormalization`.
    momentum: Momentum for the moving average.
    epsilon: Small float added to variance to avoid dividing by zero.
    center: If True, add offset of `beta` to normalized tensor.
      If False, `beta` is ignored.
    scale: If True, multiply by `gamma`.
      If False, `gamma` is not used.
      When the next layer is linear (also e.g. `nn.relu`),
      this can be disabled since the scaling
      will be done by the next layer.
    beta_initializer: Initializer for the beta weight.
    gamma_initializer: Initializer for the gamma weight.
    moving_mean_initializer: Initializer for the moving mean.
    moving_variance_initializer: Initializer for the moving variance.
    beta_regularizer: Optional regularizer for the beta weight.
    gamma_regularizer: Optional regularizer for the gamma weight.
    beta_constraint: Optional constraint for the beta weight.
    gamma_constraint: Optional constraint for the gamma weight.

  Call arguments:
    inputs: Input tensor (of any rank).
    training: Python boolean indicating whether the layer should behave in
      training mode or in inference mode.
      - `training=True`: The layer will normalize its inputs using the
        mean and variance of the current batch of inputs.
      - `training=False`: The layer will normalize its inputs using the
        mean and variance of its moving statistics, learned during training.

  Input shape:
    Arbitrary. Use the keyword argument `input_shape`
    (tuple of integers, does not include the samples axis)
    when using this layer as the first layer in a model.

  Output shape:
    Same shape as input.

  """

  def __init__(self,
               axis=-1,
               momentum=0.99,
               epsilon=1e-3,
               center=True,
               scale=True,
               beta_initializer='zeros',
               gamma_initializer='ones',
               moving_mean_initializer='zeros',
               moving_variance_initializer='ones',
               beta_regularizer=None,
               gamma_regularizer=None,
               beta_constraint=None,
               gamma_constraint=None,
               **kwargs):
    if kwargs.pop('fused', None):
      raise ValueError(
          '`fused` argument cannot be True for SyncBatchNormalization.')

    # Currently we only support aggregating over the global batch size.
    super(SyncBatchNormalization, self).__init__(
        axis=axis,
        momentum=momentum,
        epsilon=epsilon,
        center=center,
        scale=scale,
        beta_initializer=beta_initializer,
        gamma_initializer=gamma_initializer,
        moving_mean_initializer=moving_mean_initializer,
        moving_variance_initializer=moving_variance_initializer,
        beta_regularizer=beta_regularizer,
        gamma_regularizer=gamma_regularizer,
        beta_constraint=beta_constraint,
        gamma_constraint=gamma_constraint,
        fused=False,
        **kwargs)

  def _calculate_mean_and_var(self, x, axes, keep_dims):

    with backend.name_scope('moments'):
      # The dynamic range of fp16 is too limited to support the collection of
      # sufficient statistics. As a workaround we simply perform the operations
      # on 32-bit floats before converting the mean and variance back to fp16
      y = tf.cast(x, tf.float32) if x.dtype == tf.float16 else x
      replica_ctx = tf.distribute.get_replica_context()
      if replica_ctx:
        local_sum = tf.reduce_sum(y, axis=axes, keepdims=True)
        local_squared_sum = tf.reduce_sum(tf.square(y), axis=axes,
                                                keepdims=True)
        batch_size = tf.cast(tf.shape(y)[axes[0]],
                                   tf.float32)
        # TODO(b/163099951): batch the all-reduces once we sort out the ordering
        # issue for NCCL. We don't have a mechanism to launch NCCL in the same
        # order in each replica nowadays, so we limit NCCL to batch all-reduces.
        y_sum = replica_ctx.all_reduce(tf.distribute.ReduceOp.SUM, local_sum)
        y_squared_sum = replica_ctx.all_reduce(tf.distribute.ReduceOp.SUM,
                                               local_squared_sum)
        global_batch_size = replica_ctx.all_reduce(tf.distribute.ReduceOp.SUM,
                                                   batch_size)

        axes_vals = [(tf.shape(y))[axes[i]]
                     for i in range(1, len(axes))]
        multiplier = tf.cast(tf.reduce_prod(axes_vals),
                                   tf.float32)
        multiplier = multiplier * global_batch_size

        mean = y_sum / multiplier
        y_squared_mean = y_squared_sum / multiplier
        # var = E(x^2) - E(x)^2
        variance = y_squared_mean - tf.square(mean)
      else:
        # Compute true mean while keeping the dims for proper broadcasting.
        mean = tf.reduce_mean(y, axes, keepdims=True, name='mean')
        # sample variance, not unbiased variance
        # Note: stop_gradient does not change the gradient that gets
        #       backpropagated to the mean from the variance calculation,
        #       because that gradient is zero
        variance = tf.reduce_mean(
            tf.math.squared_difference(y, tf.stop_gradient(mean)),
            axes,
            keepdims=True,
            name='variance')
      if not keep_dims:
        mean = tf.squeeze(mean, axes)
        variance = tf.squeeze(variance, axes)
      if x.dtype == tf.float16:
        return (tf.cast(mean, tf.float16),
                tf.cast(variance, tf.float16))
      else:
        return (mean, variance)


@keras_export('keras.layers.BatchNormalization', v1=[])
class BatchNormalization(BatchNormalizationBase):
  """Layer that normalizes its inputs.

  Batch normalization applies a transformation that maintains the mean output
  close to 0 and the output standard deviation close to 1.

  Importantly, batch normalization works differently during training and
  during inference.

  **During training** (i.e. when using `fit()` or when calling the layer/model
  with the argument `training=True`), the layer normalizes its output using
  the mean and standard deviation of the current batch of inputs. That is to
  say, for each channel being normalized, the layer returns
  `gamma * (batch - mean(batch)) / sqrt(var(batch) + epsilon) + beta`, where:

  - `epsilon` is small constant (configurable as part of the constructor
  arguments)
  - `gamma` is a learned scaling factor (initialized as 1), which
  can be disabled by passing `scale=False` to the constructor.
  - `beta` is a learned offset factor (initialized as 0), which
  can be disabled by passing `center=False` to the constructor.

  **During inference** (i.e. when using `evaluate()` or `predict()` or when
  calling the layer/model with the argument `training=False` (which is the
  default), the layer normalizes its output using a moving average of the
  mean and standard deviation of the batches it has seen during training. That
  is to say, it returns
  `gamma * (batch - self.moving_mean) / sqrt(self.moving_var + epsilon) + beta`.

  `self.moving_mean` and `self.moving_var` are non-trainable variables that
  are updated each time the layer in called in training mode, as such:

  - `moving_mean = moving_mean * momentum + mean(batch) * (1 - momentum)`
  - `moving_var = moving_var * momentum + var(batch) * (1 - momentum)`

  As such, the layer will only normalize its inputs during inference
  *after having been trained on data that has similar statistics as the
  inference data*.

  Args:
    axis: Integer, the axis that should be normalized (typically the features
      axis). For instance, after a `Conv2D` layer with
      `data_format="channels_first"`, set `axis=1` in `BatchNormalization`.
    momentum: Momentum for the moving average.
    epsilon: Small float added to variance to avoid dividing by zero.
    center: If True, add offset of `beta` to normalized tensor. If False, `beta`
      is ignored.
    scale: If True, multiply by `gamma`. If False, `gamma` is not used. When the
      next layer is linear (also e.g. `nn.relu`), this can be disabled since the
      scaling will be done by the next layer.
    beta_initializer: Initializer for the beta weight.
    gamma_initializer: Initializer for the gamma weight.
    moving_mean_initializer: Initializer for the moving mean.
    moving_variance_initializer: Initializer for the moving variance.
    beta_regularizer: Optional regularizer for the beta weight.
    gamma_regularizer: Optional regularizer for the gamma weight.
    beta_constraint: Optional constraint for the beta weight.
    gamma_constraint: Optional constraint for the gamma weight.

  Call arguments:
    inputs: Input tensor (of any rank).
    training: Python boolean indicating whether the layer should behave in
      training mode or in inference mode.
      - `training=True`: The layer will normalize its inputs using the mean and
        variance of the current batch of inputs.
      - `training=False`: The layer will normalize its inputs using the mean and
        variance of its moving statistics, learned during training.

  Input shape:
    Arbitrary. Use the keyword argument `input_shape` (tuple of
    integers, does not include the samples axis) when using this layer as the
    first layer in a model.

  Output shape:
    Same shape as input.

  Reference:
    - [Ioffe and Szegedy, 2015](https://arxiv.org/abs/1502.03167).

  **About setting `layer.trainable = False` on a `BatchNormalization` layer:**

  The meaning of setting `layer.trainable = False` is to freeze the layer,
  i.e. its internal state will not change during training:
  its trainable weights will not be updated
  during `fit()` or `train_on_batch()`, and its state updates will not be run.

  Usually, this does not necessarily mean that the layer is run in inference
  mode (which is normally controlled by the `training` argument that can
  be passed when calling a layer). "Frozen state" and "inference mode"
  are two separate concepts.

  However, in the case of the `BatchNormalization` layer, **setting
  `trainable = False` on the layer means that the layer will be
  subsequently run in inference mode** (meaning that it will use
  the moving mean and the moving variance to normalize the current batch,
  rather than using the mean and variance of the current batch).

  This behavior has been introduced in TensorFlow 2.0, in order
  to enable `layer.trainable = False` to produce the most commonly
  expected behavior in the convnet fine-tuning use case.

  Note that:
    - Setting `trainable` on an model containing other layers will
      recursively set the `trainable` value of all inner layers.
    - If the value of the `trainable`
      attribute is changed after calling `compile()` on a model,
      the new value doesn't take effect for this model
      until `compile()` is called again.
  """
  _USE_V2_BEHAVIOR = True

  def __init__(self,
               axis=-1,
               momentum=0.99,
               epsilon=1e-3,
               center=True,
               scale=True,
               beta_initializer='zeros',
               gamma_initializer='ones',
               moving_mean_initializer='zeros',
               moving_variance_initializer='ones',
               beta_regularizer=None,
               gamma_regularizer=None,
               beta_constraint=None,
               gamma_constraint=None,
               **kwargs):
    super(BatchNormalization, self).__init__(
        axis=axis,
        momentum=momentum,
        epsilon=epsilon,
        center=center,
        scale=scale,
        beta_initializer=beta_initializer,
        gamma_initializer=gamma_initializer,
        moving_mean_initializer=moving_mean_initializer,
        moving_variance_initializer=moving_variance_initializer,
        beta_regularizer=beta_regularizer,
        gamma_regularizer=gamma_regularizer,
        beta_constraint=beta_constraint,
        gamma_constraint=gamma_constraint,
        **kwargs)
