# Copyright 2017 The TensorFlow Agents Authors.
#
# 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.
"""Normalize tensors based on streaming estimates of mean and variance."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tf.compat.v1 as tf


class StreamingNormalize(object):
  """Normalize tensors based on streaming estimates of mean and variance."""

  def __init__(self, template, center=True, scale=True, clip=10, name='normalize'):
    """Normalize tensors based on streaming estimates of mean and variance.

    Centering the value, scaling it by the standard deviation, and clipping
    outlier values are optional.

    Args:
      template: Example tensor providing shape and dtype of the vaule to track.
      center: Python boolean indicating whether to subtract mean from values.
      scale: Python boolean indicating whether to scale values by stddev.
      clip: If and when to clip normalized values.
      name: Parent scope of operations provided by this class.
    """
    self._center = center
    self._scale = scale
    self._clip = clip
    self._name = name
    with tf.name_scope(name):
      self._count = tf.Variable(0, False)
      self._mean = tf.Variable(tf.zeros_like(template), False)
      self._var_sum = tf.Variable(tf.zeros_like(template), False)

  def transform(self, value):
    """Normalize a single or batch tensor.

    Applies the activated transformations in the constructor using current
    estimates of mean and variance.

    Args:
      value: Batch or single value tensor.

    Returns:
      Normalized batch or single value tensor.
    """
    with tf.name_scope(self._name + '/transform'):
      no_batch_dim = value.shape.ndims == self._mean.shape.ndims
      if no_batch_dim:
        # Add a batch dimension if necessary.
        value = value[None, ...]
      if self._center:
        value -= self._mean[None, ...]
      if self._scale:
        # We cannot scale before seeing at least two samples.
        value /= tf.cond(
            self._count > 1, lambda: self._std() + 1e-8, lambda: tf.ones_like(self._var_sum))[None]
      if self._clip:
        value = tf.clip_by_value(value, -self._clip, self._clip)
      # Remove batch dimension if necessary.
      if no_batch_dim:
        value = value[0]
      return tf.check_numerics(value, 'value')

  def update(self, value):
    """Update the mean and variance estimates.

    Args:
      value: Batch or single value tensor.

    Returns:
      Summary tensor.
    """
    with tf.name_scope(self._name + '/update'):
      if value.shape.ndims == self._mean.shape.ndims:
        # Add a batch dimension if necessary.
        value = value[None, ...]
      count = tf.shape(value)[0]
      with tf.control_dependencies([self._count.assign_add(count)]):
        step = tf.cast(self._count, tf.float32)
        mean_delta = tf.reduce_sum(value - self._mean[None, ...], 0)
        new_mean = self._mean + mean_delta / step
        new_mean = tf.cond(self._count > 1, lambda: new_mean, lambda: value[0])
        var_delta = (value - self._mean[None, ...]) * (value - new_mean[None, ...])
        new_var_sum = self._var_sum + tf.reduce_sum(var_delta, 0)
      with tf.control_dependencies([new_mean, new_var_sum]):
        update = self._mean.assign(new_mean), self._var_sum.assign(new_var_sum)
      with tf.control_dependencies(update):
        if value.shape.ndims == 1:
          value = tf.reduce_mean(value)
        return self._summary('value', tf.reduce_mean(value))

  def reset(self):
    """Reset the estimates of mean and variance.

    Resets the full state of this class.

    Returns:
      Operation.
    """
    with tf.name_scope(self._name + '/reset'):
      return tf.group(self._count.assign(0), self._mean.assign(tf.zeros_like(self._mean)),
                      self._var_sum.assign(tf.zeros_like(self._var_sum)))

  def summary(self):
    """Summary string of mean and standard deviation.

    Returns:
      Summary tensor.
    """
    with tf.name_scope(self._name + '/summary'):
      mean_summary = tf.cond(self._count > 0, lambda: self._summary('mean', self._mean), str)
      std_summary = tf.cond(self._count > 1, lambda: self._summary('stddev', self._std()), str)
      return tf.summary.merge([mean_summary, std_summary])

  def _std(self):
    """Computes the current estimate of the standard deviation.

    Note that the standard deviation is not defined until at least two samples
    were seen.

    Returns:
      Tensor of current variance.
    """
    variance = tf.cond(self._count > 1, lambda: self._var_sum / tf.cast(
        self._count - 1, tf.float32), lambda: tf.ones_like(self._var_sum) * float('nan'))
    # The epsilon corrects for small negative variance values caused by
    # the algorithm. It was empirically chosen to work with all environments
    # tested.
    return tf.sqrt(variance + 1e-4)

  def _summary(self, name, tensor):
    """Create a scalar or histogram summary matching the rank of the tensor.

    Args:
      name: Name for the summary.
      tensor: Tensor to summarize.

    Returns:
      Summary tensor.
    """
    if tensor.shape.ndims == 0:
      return tf.summary.scalar(name, tensor)
    else:
      return tf.summary.histogram(name, tensor)
