# Copyright 2025 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.

"""Tests for Centernet nn_blocks.

It is a literal translation of the PyTorch implementation.
"""

from absl.testing import parameterized
import numpy as np
import tensorflow as tf, tf_keras

from official.projects.centernet.modeling.layers import cn_nn_blocks
from official.vision.modeling.layers import nn_blocks


class HourglassBlockPyTorch(tf_keras.layers.Layer):
  """An CornerNet-style implementation of the hourglass block."""

  def __init__(self, dims, modules, k=0, **kwargs):
    """An CornerNet-style implementation of the hourglass block.

    Args:
      dims: input sizes of residual blocks
      modules: number of repetitions of the residual blocks in each hourglass
        upsampling and downsampling
      k: recursive parameter
      **kwargs: Additional keyword arguments to be passed.
    """
    super(HourglassBlockPyTorch).__init__()

    if len(dims) != len(modules):
      raise ValueError('dims and modules lists must have the same length')

    self.n = len(dims) - 1
    self.k = k
    self.modules = modules
    self.dims = dims

    self._kwargs = kwargs

  def build(self, input_shape):
    modules = self.modules
    dims = self.dims
    k = self.k
    kwargs = self._kwargs

    curr_mod = modules[k]
    next_mod = modules[k + 1]

    curr_dim = dims[k + 0]
    next_dim = dims[k + 1]

    self.up1 = self.make_up_layer(3, curr_dim, curr_dim, curr_mod, **kwargs)
    self.max1 = tf_keras.layers.MaxPool2D(strides=2)
    self.low1 = self.make_hg_layer(3, curr_dim, next_dim, curr_mod, **kwargs)
    if self.n - k > 1:
      self.low2 = type(self)(dims, modules, k=k + 1, **kwargs)
    else:
      self.low2 = self.make_low_layer(
          3, next_dim, next_dim, next_mod, **kwargs)
    self.low3 = self.make_hg_layer_revr(
        3, next_dim, curr_dim, curr_mod, **kwargs)
    self.up2 = tf_keras.layers.UpSampling2D(2)
    self.merge = tf_keras.layers.Add()

    super(HourglassBlockPyTorch, self).build(input_shape)

  def call(self, x):
    up1 = self.up1(x)
    max1 = self.max1(x)
    low1 = self.low1(max1)
    low2 = self.low2(low1)
    low3 = self.low3(low2)
    up2 = self.up2(low3)
    return self.merge([up1, up2])

  def make_layer(self, k, inp_dim, out_dim, modules, **kwargs):
    layers = [
        nn_blocks.ResidualBlock(out_dim, 1, use_projection=True, **kwargs)]
    for _ in range(1, modules):
      layers.append(nn_blocks.ResidualBlock(out_dim, 1, **kwargs))
    return tf_keras.Sequential(layers)

  def make_layer_revr(self, k, inp_dim, out_dim, modules, **kwargs):
    layers = []
    for _ in range(modules - 1):
      layers.append(
          nn_blocks.ResidualBlock(inp_dim, 1, **kwargs))
    layers.append(
        nn_blocks.ResidualBlock(out_dim, 1, use_projection=True, **kwargs))
    return tf_keras.Sequential(layers)

  def make_up_layer(self, k, inp_dim, out_dim, modules, **kwargs):
    return self.make_layer(k, inp_dim, out_dim, modules, **kwargs)

  def make_low_layer(self, k, inp_dim, out_dim, modules, **kwargs):
    return self.make_layer(k, inp_dim, out_dim, modules, **kwargs)

  def make_hg_layer(self, k, inp_dim, out_dim, modules, **kwargs):
    return self.make_layer(k, inp_dim, out_dim, modules, **kwargs)

  def make_hg_layer_revr(self, k, inp_dim, out_dim, modules, **kwargs):
    return self.make_layer_revr(k, inp_dim, out_dim, modules, **kwargs)


class NNBlocksTest(parameterized.TestCase, tf.test.TestCase):

  def test_hourglass_block(self):
    dims = [256, 256, 384, 384, 384, 512]
    modules = [2, 2, 2, 2, 2, 4]
    model = cn_nn_blocks.HourglassBlock(dims, modules)
    test_input = tf_keras.Input((512, 512, 256))
    _ = model(test_input)

    filter_sizes = [256, 256, 384, 384, 384, 512]
    rep_sizes = [2, 2, 2, 2, 2, 4]

    hg_test_input_shape = (1, 512, 512, 256)
    # bb_test_input_shape = (1, 512, 512, 3)
    x_hg = tf.ones(shape=hg_test_input_shape)
    # x_bb = tf.ones(shape=bb_test_input_shape)

    hg = cn_nn_blocks.HourglassBlock(
        channel_dims_per_stage=filter_sizes,
        blocks_per_stage=rep_sizes)

    hg.build(input_shape=hg_test_input_shape)
    out = hg(x_hg)
    self.assertAllEqual(
        tf.shape(out), hg_test_input_shape,
        'Hourglass module output shape and expected shape differ')

    # ODAPI Test
    layer = cn_nn_blocks.HourglassBlock(
        blocks_per_stage=[2, 3, 4, 5, 6],
        channel_dims_per_stage=[4, 6, 8, 10, 12])
    output = layer(np.zeros((2, 64, 64, 4), dtype=np.float32))
    self.assertEqual(output.shape, (2, 64, 64, 4))


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