# Copyright 2015 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.
# ==============================================================================
"""Generate some standard test data for debugging TensorBoard."""


import bisect
import math
import os
import os.path
import random
import shutil

from absl import app
from absl import flags
import numpy as np
import tensorflow as tf


flags.DEFINE_string(
    "target",
    None,
    """The directory where serialized data will be written""",
)

flags.DEFINE_boolean(
    "overwrite",
    False,
    """Whether to remove and overwrite TARGET if it already exists.""",
)

FLAGS = flags.FLAGS

# Hardcode a start time and reseed so script always generates the same data.
_start_time = 0
random.seed(0)


def _MakeHistogramBuckets():
    v = 1e-12
    buckets = []
    neg_buckets = []
    while v < 1e20:
        buckets.append(v)
        neg_buckets.append(-v)
        v *= 1.1
    # Should include DBL_MAX, but won't bother for test data.
    return neg_buckets[::-1] + [0] + buckets


def _MakeHistogram(values):
    """Convert values into a histogram proto using logic from histogram.cc."""
    limits = _MakeHistogramBuckets()
    counts = [0] * len(limits)
    for v in values:
        idx = bisect.bisect_left(limits, v)
        counts[idx] += 1

    limit_counts = [
        (limits[i], counts[i]) for i in range(len(limits)) if counts[i]
    ]
    bucket_limit = [lc[0] for lc in limit_counts]
    bucket = [lc[1] for lc in limit_counts]
    sum_sq = sum(v * v for v in values)
    return tf.compat.v1.HistogramProto(
        min=min(values),
        max=max(values),
        num=len(values),
        sum=sum(values),
        sum_squares=sum_sq,
        bucket_limit=bucket_limit,
        bucket=bucket,
    )


def WriteScalarSeries(writer, tag, f, n=5):
    """Write a series of scalar events to writer, using f to create values."""
    step = 0
    wall_time = _start_time
    for i in range(n):
        v = f(i)
        value = tf.Summary.Value(tag=tag, simple_value=v)
        summary = tf.Summary(value=[value])
        event = tf.Event(wall_time=wall_time, step=step, summary=summary)
        writer.add_event(event)
        step += 1
        wall_time += 10


def WriteHistogramSeries(writer, tag, mu_sigma_tuples, n=20):
    """Write a sequence of normally distributed histograms to writer."""
    step = 0
    wall_time = _start_time
    for [mean, stddev] in mu_sigma_tuples:
        data = [random.normalvariate(mean, stddev) for _ in range(n)]
        histo = _MakeHistogram(data)
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=histo)])
        event = tf.Event(wall_time=wall_time, step=step, summary=summary)
        writer.add_event(event)
        step += 10
        wall_time += 100


def WriteImageSeries(writer, tag, n_images=1):
    """Write a few dummy images to writer."""
    step = 0
    session = tf.compat.v1.Session()
    p = tf.compat.v1.placeholder("uint8", (1, 4, 4, 3))
    s = tf.compat.v1.summary.image(tag, p)
    for _ in range(n_images):
        im = np.random.random_integers(0, 255, (1, 4, 4, 3))
        summ = session.run(s, feed_dict={p: im})
        writer.add_summary(summ, step)
        step += 20
    session.close()


def WriteAudioSeries(writer, tag, n_audio=1):
    """Write a few dummy audio clips to writer."""
    step = 0
    session = tf.compat.v1.Session()

    min_frequency_hz = 440
    max_frequency_hz = 880
    sample_rate = 4000
    duration_frames = sample_rate // 2  # 0.5 seconds.
    frequencies_per_run = 1
    num_channels = 2

    p = tf.compat.v1.placeholder(
        "float32", (frequencies_per_run, duration_frames, num_channels)
    )
    s = tf.compat.v1.summary.audio(tag, p, sample_rate)

    for _ in range(n_audio):
        # Generate a different frequency for each channel to show stereo works.
        frequencies = np.random.random_integers(
            min_frequency_hz,
            max_frequency_hz,
            size=(frequencies_per_run, num_channels),
        )
        tiled_frequencies = np.tile(frequencies, (1, duration_frames))
        tiled_increments = np.tile(
            np.arange(0, duration_frames), (num_channels, 1)
        ).T.reshape(1, duration_frames * num_channels)
        tones = np.sin(
            2.0 * np.pi * tiled_frequencies * tiled_increments / sample_rate
        )
        tones = tones.reshape(
            frequencies_per_run, duration_frames, num_channels
        )

        summ = session.run(s, feed_dict={p: tones})
        writer.add_summary(summ, step)
        step += 20
    session.close()


def GenerateTestData(path):
    """Generates the test data directory."""
    run1_path = os.path.join(path, "run1")
    os.makedirs(run1_path)
    writer1 = tf.summary.FileWriter(run1_path)
    WriteScalarSeries(writer1, "foo/square", lambda x: x * x)
    WriteScalarSeries(writer1, "bar/square", lambda x: x * x)
    WriteScalarSeries(writer1, "foo/sin", math.sin)
    WriteScalarSeries(writer1, "foo/cos", math.cos)
    WriteHistogramSeries(
        writer1, "histo1", [[0, 1], [0.3, 1], [0.5, 1], [0.7, 1], [1, 1]]
    )
    WriteImageSeries(writer1, "im1")
    WriteImageSeries(writer1, "im2")
    WriteAudioSeries(writer1, "au1")

    run2_path = os.path.join(path, "run2")
    os.makedirs(run2_path)
    writer2 = tf.summary.FileWriter(run2_path)
    WriteScalarSeries(writer2, "foo/square", lambda x: x * x * 2)
    WriteScalarSeries(writer2, "bar/square", lambda x: x * x * 3)
    WriteScalarSeries(writer2, "foo/cos", lambda x: math.cos(x) * 2)
    WriteHistogramSeries(
        writer2, "histo1", [[0, 2], [0.3, 2], [0.5, 2], [0.7, 2], [1, 2]]
    )
    WriteHistogramSeries(
        writer2, "histo2", [[0, 1], [0.3, 1], [0.5, 1], [0.7, 1], [1, 1]]
    )
    WriteImageSeries(writer2, "im1")
    WriteAudioSeries(writer2, "au2")

    graph_def = tf.compat.v1.GraphDef()
    node1 = graph_def.node.add()
    node1.name = "a"
    node1.op = "matmul"
    node2 = graph_def.node.add()
    node2.name = "b"
    node2.op = "matmul"
    node2.input.extend(["a:0"])

    writer1.add_graph(graph_def)
    node3 = graph_def.node.add()
    node3.name = "c"
    node3.op = "matmul"
    node3.input.extend(["a:0", "b:0"])
    writer2.add_graph(graph_def)
    writer1.close()
    writer2.close()


def main(unused_argv=None):
    target = FLAGS.target
    if not target:
        print("The --target flag is required.")
        return -1
    if os.path.exists(target):
        if FLAGS.overwrite:
            if os.path.isdir(target):
                shutil.rmtree(target)
            else:
                os.remove(target)
        else:
            print(
                "Refusing to overwrite target %s without --overwrite" % target
            )
            return -2
    GenerateTestData(target)
    return 0


if __name__ == "__main__":
    app.run(main)
