content: # coding=utf-8
  # Copyright 2022 The Google Research 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.
  
  # Lint as: python2, python3
  """Minimal Reference implementation for the Frechet Video Distance (FVD).
  
  FVD is a metric for the quality of video generation models. It is inspired by
  the FID (Frechet Inception Distance) used for images, but uses a different
  embedding to be better suitable for videos.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  
  import six
  import tensorflow.compat.v1 as tf
  import tensorflow_gan as tfgan
  import tensorflow_hub as hub
  
  
  def preprocess(videos, target_resolution):
    """Runs some preprocessing on the videos for I3D model.
  
    Args:
      videos: <T>[batch_size, num_frames, height, width, depth] The videos to be
        preprocessed. We don't care about the specific dtype of the videos, it can
        be anything that tf.image.resize_bilinear accepts. Values are expected to
        be in the range 0-255.
      target_resolution: (width, height): target video resolution
  
    Returns:
      videos: <float32>[batch_size, num_frames, height, width, depth]
    """
    videos_shape = list(videos.shape)
    all_frames = tf.reshape(videos, [-1] + videos_shape[-3:])
    resized_videos = tf.image.resize_bilinear(all_frames, size=target_resolution)
    target_shape = [videos_shape[0], -1] + list(target_resolution) + [3]
    output_videos = tf.reshape(resized_videos, target_shape)
    scaled_videos = 2. * tf.cast(output_videos, tf.float32) / 255. - 1
    return scaled_videos
  
  
  def _is_in_graph(tensor_name):
    """Checks whether a given tensor does exists in the graph."""
    try:
      tf.get_default_graph().get_tensor_by_name(tensor_name)
    except KeyError:
      return False
    return True
  
  
  def create_id3_embedding(videos,warmup=False,batch_size=16):
    """Embeds the given videos using the Inflated 3D Convolution ne   twork.
  
    Downloads the graph of the I3D from tf.hub and adds it to the graph on the
    first call.
  
    Args:
      videos: <float32>[batch_size, num_frames, height=224, width=224, depth=3].
        Expected range is [-1, 1].
  
    Returns:
      embedding: <float32>[batch_size, embedding_size]. embedding_size depends
                 on the model used.
  
    Raises:
      ValueError: when a provided embedding_layer is not supported.
    """
  
    # batch_size = 16
    module_spec = "https://tfhub.dev/deepmind/i3d-kinetics-400/1"
  
  
    # Making sure that we import the graph separately for
    # each different input video tensor.
    module_name = "fvd_kinetics-400_id3_module_" + six.ensure_str(
        videos.name).replace(":", "_")
  
  
  
    assert_ops = [
        tf.Assert(
            tf.reduce_max(videos) <= 1.001,
            ["max value in frame is > 1", videos]),
        tf.Assert(
            tf.reduce_min(videos) >= -1.001,
            ["min value in frame is < -1", videos]),
        tf.assert_equal(
            tf.shape(videos)[0],
            batch_size, ["invalid frame batch size: ",
                         tf.shape(videos)],
            summarize=6),
    ]
    with tf.control_dependencies(assert_ops):
      videos = tf.identity(videos)
  
    module_scope = "%s_apply_default/" % module_name
  
    # To check whether the module has already been loaded into the graph, we look
    # for a given tensor name. If this tensor name exists, we assume the function
    # has been called before and the graph was imported. Otherwise we import it.
    # Note: in theory, the tensor could exist, but have wrong shapes.
    # This will happen if create_id3_embedding is called with a frames_placehoder
    # of wrong size/batch size, because even though that will throw a tf.Assert
    # on graph-execution time, it will insert the tensor (with wrong shape) into
    # the graph. This is why we need the following assert.
    if warmup:
        video_batch_size = int(videos.shape[0])
        assert video_batch_size in [batch_size, -1, None], f"Invalid batch size {video_batch_size}"
    tensor_name = module_scope + "RGB/inception_i3d/Mean:0"
    if not _is_in_graph(tensor_name):
      i3d_model = hub.Module(module_spec, name=module_name)
      i3d_model(videos)
  
    # gets the kinetics-i3d-400-logits layer
    tensor_name = module_scope + "RGB/inception_i3d/Mean:0"
    tensor = tf.get_default_graph().get_tensor_by_name(tensor_name)
    return tensor
  
  
  def calculate_fvd(real_activations,
                    generated_activations):
    """Returns a list of ops that compute metrics as funcs of activations.
  
    Args:
      real_activations: <float32>[num_samples, embedding_size]
      generated_activations: <float32>[num_samples, embedding_size]
  
    Returns:
      A scalar that contains the requested FVD.
    """
    return tfgan.eval.frechet_classifier_distance_from_activations(
        real_activations, generated_activations)
  