# coding=utf-8
# Copyright 2023 The TensorFlow GAN 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.

"""Evaluates a TF-GAN trained CIFAR model."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections

import tensorflow.compat.v1 as tf
import tensorflow_gan as tfgan
from tensorflow_gan.examples import evaluation_helper as evaluation

from tensorflow_gan.examples.cifar import data_provider
from tensorflow_gan.examples.cifar import networks
from tensorflow_gan.examples.cifar import util

HParams = collections.namedtuple('HParams', [
    'master', 'checkpoint_dir', 'eval_dir', 'num_images_generated',
    'num_inception_images', 'eval_real_images',
    'eval_frechet_inception_distance', 'max_number_of_evaluations',
    'write_to_disk'
])


def evaluate(hparams, run_eval_loop=True):
  """Runs an evaluation loop.

  Args:
    hparams: An HParams instance containing the eval hyperparameters.
    run_eval_loop: Whether to run the full eval loop. Set to False for testing.
  """
  # Fetch and generate images to run through Inception.
  with tf.name_scope('inputs'):
    real_data, _ = data_provider.provide_data(
        'test', hparams.num_images_generated, shuffle=False)
    generated_data = _get_generated_data(hparams.num_images_generated)

  # Compute Frechet Inception Distance.
  if hparams.eval_frechet_inception_distance:
    fid = util.get_frechet_inception_distance(real_data, generated_data,
                                              hparams.num_images_generated,
                                              hparams.num_inception_images)
    tf.summary.scalar('frechet_inception_distance', fid)

  # Compute normal Inception scores.
  if hparams.eval_real_images:
    inc_score = util.get_inception_scores(real_data,
                                          hparams.num_images_generated,
                                          hparams.num_inception_images)
  else:
    inc_score = util.get_inception_scores(generated_data,
                                          hparams.num_images_generated,
                                          hparams.num_inception_images)
  tf.summary.scalar('inception_score', inc_score)

  # Create ops that write images to disk.
  image_write_ops = None
  if hparams.num_images_generated >= 100 and hparams.write_to_disk:
    reshaped_imgs = tfgan.eval.image_reshaper(generated_data[:100], num_cols=10)
    uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
    image_write_ops = tf.io.write_file(
        '%s/%s' % (hparams.eval_dir, 'unconditional_cifar10.png'),
        tf.image.encode_png(uint8_images[0]))

  # For unit testing, use `run_eval_loop=False`.
  if not run_eval_loop: return
  evaluation.evaluate_repeatedly(
      hparams.checkpoint_dir,
      master=hparams.master,
      hooks=[
          evaluation.SummaryAtEndHook(hparams.eval_dir),
          evaluation.StopAfterNEvalsHook(1)
      ],
      eval_ops=image_write_ops,
      max_number_of_evaluations=hparams.max_number_of_evaluations)


def _get_generated_data(num_images_generated):
  """Get generated images."""
  noise = tf.random.normal([num_images_generated, 64])
  generator_inputs = noise
  generator_fn = networks.generator
  # In order for variables to load, use the same variable scope as in the
  # train job.
  with tf.variable_scope('Generator'):
    data = generator_fn(generator_inputs, is_training=False)

  return data
