# Copyright 2018 Google LLC. 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.
# ==============================================================================
"""Tests of padding ops."""

import numpy as np
import tensorflow.compat.v1 as tf

from tensorflow.python.framework import test_util
from tensorflow_compression.python.ops import padding_ops


@test_util.deprecated_graph_mode_only
class PaddingOpsTest(tf.test.TestCase):

  def test_same_padding_corr(self):
    for ishape in [[10], [11]]:
      inputs = np.zeros(ishape, dtype=np.float32)
      inputs[len(inputs) // 2] = 1
      for kshape in [[4], [5]]:
        kernel = np.zeros(kshape, dtype=np.float32)
        kernel[len(kernel) // 2] = 1
        outputs = tf.nn.convolution(
            tf.reshape(inputs, (1, 1, -1, 1)),
            tf.reshape(kernel, (1, -1, 1, 1)),
            padding="VALID", data_format="NHWC")
        with self.cached_session() as sess:
          outputs = np.squeeze(sess.run(outputs))
        pos_inp = np.squeeze(np.nonzero(inputs))
        pos_out = np.squeeze(np.nonzero(outputs))
        padding = padding_ops.same_padding_for_kernel(kshape, True)
        self.assertEqual(padding[0][0], pos_inp - pos_out)

  def test_same_padding_conv(self):
    for ishape in [[10], [11]]:
      inputs = np.zeros(ishape, dtype=np.float32)
      inputs[len(inputs) // 2] = 1
      for kshape in [[4], [5]]:
        kernel = np.zeros(kshape, dtype=np.float32)
        kernel[len(kernel) // 2] = 1
        outputs = tf.nn.conv2d_transpose(
            tf.reshape(inputs, (1, 1, -1, 1)),
            tf.reshape(kernel, (1, -1, 1, 1)),
            (1, 1, ishape[0] + kshape[0] - 1, 1),
            strides=(1, 1, 1, 1), padding="VALID", data_format="NHWC")
        outputs = outputs[:, :, (kshape[0] - 1):-(kshape[0] - 1), :]
        with self.cached_session() as sess:
          outputs = np.squeeze(sess.run(outputs))
        pos_inp = np.squeeze(np.nonzero(inputs))
        pos_out = np.squeeze(np.nonzero(outputs))
        padding = padding_ops.same_padding_for_kernel(kshape, False)
        self.assertEqual(padding[0][0], pos_inp - pos_out)


if __name__ == "__main__":
  tf.test.main()
