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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""SimCLR prediction heads."""

from typing import Text, Optional

import tensorflow as tf

from official.vision.beta.projects.simclr.modeling.layers import nn_blocks

regularizers = tf.keras.regularizers
layers = tf.keras.layers


class ProjectionHead(tf.keras.layers.Layer):
  """Projection head."""

  def __init__(
      self,
      num_proj_layers: int = 3,
      proj_output_dim: Optional[int] = None,
      ft_proj_idx: int = 0,
      kernel_initializer: Text = 'VarianceScaling',
      kernel_regularizer: Optional[regularizers.Regularizer] = None,
      bias_regularizer: Optional[regularizers.Regularizer] = None,
      use_sync_bn: bool = False,
      norm_momentum: float = 0.99,
      norm_epsilon: float = 0.001,
      **kwargs):
    """The projection head used during pretraining of SimCLR.

    Args:
      num_proj_layers: `int` number of Dense layers used.
      proj_output_dim: `int` output dimension of projection head, i.e., output
        dimension of the final layer.
      ft_proj_idx: `int` index of layer to use during fine-tuning. 0 means no
        projection head during fine tuning, -1 means the final layer.
      kernel_initializer: kernel_initializer for convolutional layers.
      kernel_regularizer: tf.keras.regularizers.Regularizer object for Conv2D.
        Default to None.
      bias_regularizer: tf.keras.regularizers.Regularizer object for Conv2d.
        Default to None.
      use_sync_bn: if True, use synchronized batch normalization.
      norm_momentum: `float` normalization omentum for the moving average.
      norm_epsilon: `float` small float added to variance to avoid dividing by
        zero.
      **kwargs: keyword arguments to be passed.
    """
    super(ProjectionHead, self).__init__(**kwargs)

    assert proj_output_dim is not None or num_proj_layers == 0
    assert ft_proj_idx <= num_proj_layers, (num_proj_layers, ft_proj_idx)

    self._proj_output_dim = proj_output_dim
    self._num_proj_layers = num_proj_layers
    self._ft_proj_idx = ft_proj_idx
    self._kernel_initializer = kernel_initializer
    self._kernel_regularizer = kernel_regularizer
    self._bias_regularizer = bias_regularizer
    self._use_sync_bn = use_sync_bn
    self._norm_momentum = norm_momentum
    self._norm_epsilon = norm_epsilon
    self._layers = []

  def get_config(self):
    config = {
        'proj_output_dim': self._proj_output_dim,
        'num_proj_layers': self._num_proj_layers,
        'ft_proj_idx': self._ft_proj_idx,
        'kernel_initializer': self._kernel_initializer,
        'kernel_regularizer': self._kernel_regularizer,
        'bias_regularizer': self._bias_regularizer,
        'norm_momentum': self._norm_momentum,
        'norm_epsilon': self._norm_epsilon
    }
    base_config = super(ProjectionHead, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def build(self, input_shape):
    self._layers = []
    if self._num_proj_layers > 0:
      intermediate_dim = int(input_shape[-1])
      for j in range(self._num_proj_layers):
        if j != self._num_proj_layers - 1:
          # for the middle layers, use bias and relu for the output.
          layer = nn_blocks.DenseBN(
              output_dim=intermediate_dim,
              use_bias=True,
              use_normalization=True,
              activation='relu',
              kernel_initializer=self._kernel_initializer,
              kernel_regularizer=self._kernel_regularizer,
              bias_regularizer=self._bias_regularizer,
              use_sync_bn=self._use_sync_bn,
              norm_momentum=self._norm_momentum,
              norm_epsilon=self._norm_epsilon,
              name='nl_%d' % j)
        else:
          # for the final layer, neither bias nor relu is used.
          layer = nn_blocks.DenseBN(
              output_dim=self._proj_output_dim,
              use_bias=False,
              use_normalization=True,
              activation=None,
              kernel_regularizer=self._kernel_regularizer,
              kernel_initializer=self._kernel_initializer,
              use_sync_bn=self._use_sync_bn,
              norm_momentum=self._norm_momentum,
              norm_epsilon=self._norm_epsilon,
              name='nl_%d' % j)
        self._layers.append(layer)
    super(ProjectionHead, self).build(input_shape)

  def call(self, inputs, training=None):
    hiddens_list = [tf.identity(inputs, 'proj_head_input')]

    if self._num_proj_layers == 0:
      proj_head_output = inputs
      proj_finetune_output = inputs
    else:
      for j in range(self._num_proj_layers):
        hiddens = self._layers[j](hiddens_list[-1], training)
        hiddens_list.append(hiddens)
      proj_head_output = tf.identity(
          hiddens_list[-1], 'proj_head_output')
      proj_finetune_output = tf.identity(
          hiddens_list[self._ft_proj_idx], 'proj_finetune_output')

    # The first element is the output of the projection head.
    # The second element is the input of the finetune head.
    return proj_head_output, proj_finetune_output


class ClassificationHead(tf.keras.layers.Layer):
  """Classification Head."""

  def __init__(
      self,
      num_classes: int,
      kernel_initializer: Text = 'random_uniform',
      kernel_regularizer: Optional[regularizers.Regularizer] = None,
      bias_regularizer: Optional[regularizers.Regularizer] = None,
      name: Text = 'head_supervised',
      **kwargs):
    """The classification head used during pretraining or fine tuning.

    Args:
      num_classes: `int` size of the output dimension or number of classes
        for classification task.
      kernel_initializer: kernel_initializer for convolutional layers.
      kernel_regularizer: tf.keras.regularizers.Regularizer object for Conv2D.
        Default to None.
      bias_regularizer: tf.keras.regularizers.Regularizer object for Conv2d.
        Default to None.
      name: `str`, name of the layer.
      **kwargs: keyword arguments to be passed.
    """
    super(ClassificationHead, self).__init__(name=name, **kwargs)
    self._num_classes = num_classes
    self._kernel_initializer = kernel_initializer
    self._kernel_regularizer = kernel_regularizer
    self._bias_regularizer = bias_regularizer
    self._name = name

  def get_config(self):
    config = {
        'num_classes': self._num_classes,
        'kernel_initializer': self._kernel_initializer,
        'kernel_regularizer': self._kernel_regularizer,
        'bias_regularizer': self._bias_regularizer,
    }
    base_config = super(ClassificationHead, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def build(self, input_shape):
    self._dense0 = layers.Dense(
        units=self._num_classes,
        kernel_initializer=self._kernel_initializer,
        kernel_regularizer=self._kernel_regularizer,
        bias_regularizer=self._bias_regularizer,
        activation=None)
    super(ClassificationHead, self).build(input_shape)

  def call(self, inputs, training=None):
    inputs = self._dense0(inputs)
    return inputs
