# 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.
"""


import numpy as np

from tensorboard.plugins.histogram import metadata
from tensorboard.plugins.histogram import summary_v2


# Export V3 versions.
histogram = summary_v2.histogram
histogram_pb = summary_v2.histogram_pb


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`.
    """
    # TODO(nickfelt): remove on-demand imports once dep situation is fixed.
    import tensorflow.compat.v1 as tf

    if bucket_count is None:
        bucket_count = summary_v2.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(input=data), 0)

        def when_empty():
            return tf.constant([], shape=(0, 3), dtype=tf.float64)

        def when_nonempty():
            min_ = tf.reduce_min(input_tensor=data)
            max_ = tf.reduce_max(input_tensor=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)
                # Use float64 instead of float32 to avoid accumulating floating point error
                # later in tf.reduce_sum when summing more than 2^24 individual `1.0` values.
                # See https://github.com/tensorflow/tensorflow/issues/51419 for details.
                one_hots = tf.one_hot(
                    clamped_indices, depth=bucket_count, dtype=tf.float64
                )
                bucket_counts = tf.cast(
                    tf.reduce_sum(input_tensor=one_hots, axis=0),
                    dtype=tf.float64,
                )
                edges = tf.linspace(min_, max_, bucket_count + 1)
                left_edges = edges[:-1]
                right_edges = edges[1:]
                return tf.transpose(
                    a=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(input=data), tf.float64)]
                )
                return tf.transpose(
                    a=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 legacy 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.
    """
    # TODO(nickfelt): remove on-demand imports once dep situation is fixed.
    import tensorflow.compat.v1 as tf

    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 legacy 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.
    """
    # TODO(nickfelt): remove on-demand imports once dep situation is fixed.
    import tensorflow.compat.v1 as tf

    if bucket_count is None:
        bucket_count = summary_v2.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 = tf.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
    )
    tf_summary_metadata = tf.SummaryMetadata.FromString(
        summary_metadata.SerializeToString()
    )

    summary = tf.Summary()
    summary.value.add(
        tag="%s/histogram_summary" % name,
        metadata=tf_summary_metadata,
        tensor=tensor,
    )
    return summary