# Copyright 2018 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.

"""TensorBoard encoder helper module.

Encoder depends on TensorFlow.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


import numpy as np

from tensorboard.util import op_evaluator


class _TensorFlowPngEncoder(op_evaluator.PersistentOpEvaluator):
  """Encode an image to PNG.

  This function is thread-safe, and has high performance when run in
  parallel. See `encode_png_benchmark.py` for details.

  Arguments:
    image: A numpy array of shape `[height, width, channels]`, where
      `channels` is 1, 3, or 4, and of dtype uint8.

  Returns:
    A bytestring with PNG-encoded data.
  """

  def __init__(self):
    super(_TensorFlowPngEncoder, self).__init__()
    self._image_placeholder = None
    self._encode_op = None

  def initialize_graph(self):
    # TODO(nickfelt): remove on-demand imports once dep situation is fixed.
    import tensorflow.compat.v1 as tf
    self._image_placeholder = tf.placeholder(
        dtype=tf.uint8, name='image_to_encode')
    self._encode_op = tf.image.encode_png(self._image_placeholder)

  def run(self, image):  # pylint: disable=arguments-differ
    if not isinstance(image, np.ndarray):
      raise ValueError("'image' must be a numpy array: %r" % image)
    if image.dtype != np.uint8:
      raise ValueError("'image' dtype must be uint8, but is %r" % image.dtype)
    return self._encode_op.eval(feed_dict={self._image_placeholder: image})


encode_png = _TensorFlowPngEncoder()


class _TensorFlowWavEncoder(op_evaluator.PersistentOpEvaluator):
  """Encode an audio clip to WAV.

  This function is thread-safe and exhibits good parallel performance.

  Arguments:
    audio: A numpy array of shape `[samples, channels]`.
    samples_per_second: A positive `int`, in Hz.

  Returns:
    A bytestring with WAV-encoded data.
  """

  def __init__(self):
    super(_TensorFlowWavEncoder, self).__init__()
    self._audio_placeholder = None
    self._samples_per_second_placeholder = None
    self._encode_op = None

  def initialize_graph(self):
    # TODO(nickfelt): remove on-demand imports once dep situation is fixed.
    import tensorflow  # for contrib
    import tensorflow.compat.v1 as tf
    self._audio_placeholder = tf.placeholder(
        dtype=tf.float32, name='image_to_encode')
    self._samples_per_second_placeholder = tf.placeholder(
        dtype=tf.int32, name='samples_per_second')
    self._encode_op = tensorflow.contrib.ffmpeg.encode_audio(
        self._audio_placeholder,
        file_format='wav',
        samples_per_second=self._samples_per_second_placeholder)

  def run(self, audio, samples_per_second):  # pylint: disable=arguments-differ
    if not isinstance(audio, np.ndarray):
      raise ValueError("'audio' must be a numpy array: %r" % audio)
    if not isinstance(samples_per_second, int):
      raise ValueError("'samples_per_second' must be an int: %r"
                       % samples_per_second)
    feed_dict = {
        self._audio_placeholder: audio,
        self._samples_per_second_placeholder: samples_per_second,
    }
    return self._encode_op.eval(feed_dict=feed_dict)


encode_wav = _TensorFlowWavEncoder()
