text: # Copyright 2017 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.
  # ==============================================================================
  """Histogram summaries and TensorFlow operations to create them.
  
  A histogram summary stores a list of buckets. Each bucket is encoded as
  a triple `[left_edge, right_edge, count]`. Thus, a full histogram is
  encoded as a tensor of dimension `[k, 3]`.
  
  In general, the value of `k` (the number of buckets) will be a constant,
  like 30. There are two edge cases: if there is no data, then there are
  no buckets (the shape is `[0, 3]`); and if there is data but all points
  have the same value, then there is one bucket whose left and right
  endpoints are the same (the shape is `[1, 3]`).
  
  NOTE: This module is in beta, and its API is subject to change, but the
  data that it stores to disk will be supported forever.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  import numpy as np
  
  from tensorboard.plugins.histogram import metadata
  from tensorboard.util import tensor_util
  
  
  DEFAULT_BUCKET_COUNT = 30
  
  
  def _buckets(data, bucket_count=None):
    """Create a TensorFlow op to group data into histogram buckets.
  
    Arguments:
      data: A `Tensor` of any shape. Must be castable to `float64`.
      bucket_count: Optional positive `int` or scalar `int32` `Tensor`.
    Returns:
      A `Tensor` of shape `[k, 3]` and type `float64`. The `i`th row is
      a triple `[left_edge, right_edge, count]` for a single bucket.
      The value of `k` is either `bucket_count` or `1` or `0`.
    """
    if bucket_count is None:
      bucket_count = DEFAULT_BUCKET_COUNT
    with tf.name_scope('buckets', values=[data, bucket_count]), \
         tf.control_dependencies([tf.assert_scalar(bucket_count),
                                  tf.assert_type(bucket_count, tf.int32)]):
      data = tf.reshape(data, shape=[-1])  # flatten
      data = tf.cast(data, tf.float64)
      is_empty = tf.equal(tf.size(data), 0)
  
      def when_empty():
        return tf.constant([], shape=(0, 3), dtype=tf.float64)
  
      def when_nonempty():
        min_ = tf.reduce_min(data)
        max_ = tf.reduce_max(data)
        range_ = max_ - min_
        is_singular = tf.equal(range_, 0)
  
        def when_nonsingular():
          bucket_width = range_ / tf.cast(bucket_count, tf.float64)
          offsets = data - min_
          bucket_indices = tf.cast(tf.floor(offsets / bucket_width),
                                   dtype=tf.int32)
          clamped_indices = tf.minimum(bucket_indices, bucket_count - 1)
          one_hots = tf.one_hot(clamped_indices, depth=bucket_count)
          bucket_counts = tf.cast(tf.reduce_sum(one_hots, axis=0),
                                  dtype=tf.float64)
          edges = tf.lin_space(min_, max_, bucket_count + 1)
          left_edges = edges[:-1]
          right_edges = edges[1:]
          return tf.transpose(tf.stack(
              [left_edges, right_edges, bucket_counts]))
  
        def when_singular():
          center = min_
          bucket_starts = tf.stack([center - 0.5])
          bucket_ends = tf.stack([center + 0.5])
          bucket_counts = tf.stack([tf.cast(tf.size(data), tf.float64)])
          return tf.transpose(
              tf.stack([bucket_starts, bucket_ends, bucket_counts]))
  
        return tf.cond(is_singular, when_singular, when_nonsingular)
  
      return tf.cond(is_empty, when_empty, when_nonempty)
  
  
  def op(name,
         data,
         bucket_count=None,
         display_name=None,
         description=None,
         collections=None):
    """Create a histogram summary op.
  
    Arguments:
      name: A unique name for the generated summary node.
      data: A `Tensor` of any shape. Must be castable to `float64`.
      bucket_count: Optional positive `int`. The output will have this
        many buckets, except in two edge cases. If there is no data, then
        there are no buckets. If there is data but all points have the
        same value, then there is one bucket whose left and right
        endpoints are the same.
      display_name: Optional name for this summary in TensorBoard, as a
        constant `str`. Defaults to `name`.
      description: Optional long-form description for this summary, as a
        constant `str`. Markdown is supported. Defaults to empty.
      collections: Optional list of graph collections keys. The new
        summary op is added to these collections. Defaults to
        `[Graph Keys.SUMMARIES]`.
  
    Returns:
      A TensorFlow summary op.
    """
    if display_name is None:
      display_name = name
    summary_metadata = metadata.create_summary_metadata(
        display_name=display_name, description=description)
    with tf.name_scope(name):
      tensor = _buckets(data, bucket_count=bucket_count)
      return tf.summary.tensor_summary(name='histogram_summary',
                                       tensor=tensor,
                                       collections=collections,
                                       summary_metadata=summary_metadata)
  
  
  def pb(name, data, bucket_count=None, display_name=None, description=None):
    """Create a histogram summary protobuf.
  
    Arguments:
      name: A unique name for the generated summary, including any desired
        name scopes.
      data: A `np.array` or array-like form of any shape. Must have type
        castable to `float`.
      bucket_count: Optional positive `int`. The output will have this
        many buckets, except in two edge cases. If there is no data, then
        there are no buckets. If there is data but all points have the
        same value, then there is one bucket whose left and right
        endpoints are the same.
      display_name: Optional name for this summary in TensorBoard, as a
        `str`. Defaults to `name`.
      description: Optional long-form description for this summary, as a
        `str`. Markdown is supported. Defaults to empty.
  
    Returns:
      A `tf.Summary` protobuf object.
    """
    if bucket_count is None:
      bucket_count = DEFAULT_BUCKET_COUNT
    data = np.array(data).flatten().astype(float)
    if data.size == 0:
      buckets = np.array([]).reshape((0, 3))
    else:
      min_ = np.min(data)
      max_ = np.max(data)
      range_ = max_ - min_
      if range_ == 0:
        center = min_
        buckets = np.array([[center - 0.5, center + 0.5, float(data.size)]])
      else:
        bucket_width = range_ / bucket_count
        offsets = data - min_
        bucket_indices = np.floor(offsets / bucket_width).astype(int)
        clamped_indices = np.minimum(bucket_indices, bucket_count - 1)
        one_hots = (np.array([clamped_indices]).transpose()
                    == np.arange(0, bucket_count))  # broadcast
        assert one_hots.shape == (data.size, bucket_count), (
            one_hots.shape, (data.size, bucket_count))
        bucket_counts = np.sum(one_hots, axis=0)
        edges = np.linspace(min_, max_, bucket_count + 1)
        left_edges = edges[:-1]
        right_edges = edges[1:]
        buckets = np.array([left_edges, right_edges, bucket_counts]).transpose()
    tensor = tensor_util.make_tensor_proto(buckets, dtype=tf.float64)
  
    if display_name is None:
      display_name = name
    summary_metadata = metadata.create_summary_metadata(
        display_name=display_name, description=description)
  
    summary = tf.Summary()
    summary.value.add(tag='%s/histogram_summary' % name,
                      metadata=summary_metadata,
                      tensor=tensor)
    return summary
  