# Copyright 2022 The TensorFlow Recommenders Authors.
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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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"""Implements `Cross` Layer, the cross layer in Deep & Cross Network (DCN)."""

from typing import Union, Text, Optional

import tensorflow as tf


@tf.keras.utils.register_keras_serializable()
class Cross(tf.keras.layers.Layer):
  """Cross Layer in Deep & Cross Network to learn explicit feature interactions.

    A layer that creates explicit and bounded-degree feature interactions
    efficiently. The `call` method accepts `inputs` as a tuple of size 2
    tensors. The first input `x0` is the base layer that contains the original
    features (usually the embedding layer); the second input `xi` is the output
    of the previous `Cross` layer in the stack, i.e., the i-th `Cross`
    layer. For the first `Cross` layer in the stack, x0 = xi.

    The output is x_{i+1} = x0 .* (W * xi + bias + diag_scale * xi) + xi,
    where .* designates elementwise multiplication, W could be a full-rank
    matrix, or a low-rank matrix U*V to reduce the computational cost, and
    diag_scale increases the diagonal of W to improve training stability (
    especially for the low-rank case).

    References:
        1. [R. Wang et al.](https://arxiv.org/pdf/2008.13535.pdf)
          See Eq. (1) for full-rank and Eq. (2) for low-rank version.
        2. [R. Wang et al.](https://arxiv.org/pdf/1708.05123.pdf)

    Example:

        ```python
        # after embedding layer in a functional model:
        input = tf.keras.Input(shape=(None,), name='index', dtype=tf.int64)
        x0 = tf.keras.layers.Embedding(input_dim=32, output_dim=6)
        x1 = Cross()(x0, x0)
        x2 = Cross()(x0, x1)
        logits = tf.keras.layers.Dense(units=10)(x2)
        model = tf.keras.Model(input, logits)
        ```

    Args:
        projection_dim: project dimension to reduce the computational cost.
          Default is `None` such that a full (`input_dim` by `input_dim`) matrix
          W is used. If enabled, a low-rank matrix W = U*V will be used, where U
          is of size `input_dim` by `projection_dim` and V is of size
          `projection_dim` by `input_dim`. `projection_dim` need to be smaller
          than `input_dim`/2 to improve the model efficiency. In practice, we've
          observed that `projection_dim` = d/4 consistently preserved the
          accuracy of a full-rank version.
        diag_scale: a non-negative float used to increase the diagonal of the
          kernel W by `diag_scale`, that is, W + diag_scale * I, where I is an
          identity matrix.
        use_bias: whether to add a bias term for this layer. If set to False,
          no bias term will be used.
        preactivation: Activation applied to output matrix of the layer, before
          multiplication with the input. Can be used to control the scale of the
          layer's outputs and improve stability.
        kernel_initializer: Initializer to use on the kernel matrix.
        bias_initializer: Initializer to use on the bias vector.
        kernel_regularizer: Regularizer to use on the kernel matrix.
        bias_regularizer: Regularizer to use on bias vector.

    Input shape: A tuple of 2 (batch_size, `input_dim`) dimensional inputs.
    Output shape: A single (batch_size, `input_dim`) dimensional output.
  """

  def __init__(
      self,
      projection_dim: Optional[int] = None,
      diag_scale: Optional[float] = 0.0,
      use_bias: bool = True,
      preactivation: Optional[Union[str, tf.keras.layers.Activation]] = None,
      kernel_initializer: Union[
          Text, tf.keras.initializers.Initializer] = "truncated_normal",
      bias_initializer: Union[Text,
                              tf.keras.initializers.Initializer] = "zeros",
      kernel_regularizer: Union[Text, None,
                                tf.keras.regularizers.Regularizer] = None,
      bias_regularizer: Union[Text, None,
                              tf.keras.regularizers.Regularizer] = None,
      **kwargs):

    super(Cross, self).__init__(**kwargs)

    self._projection_dim = projection_dim
    self._diag_scale = diag_scale
    self._use_bias = use_bias
    self._preactivation = tf.keras.activations.get(preactivation)
    self._kernel_initializer = tf.keras.initializers.get(kernel_initializer)
    self._bias_initializer = tf.keras.initializers.get(bias_initializer)
    self._kernel_regularizer = tf.keras.regularizers.get(kernel_regularizer)
    self._bias_regularizer = tf.keras.regularizers.get(bias_regularizer)
    self._input_dim = None

    self._supports_masking = True

    if self._diag_scale < 0:  # pytype: disable=unsupported-operands
      raise ValueError(
          "`diag_scale` should be non-negative. Got `diag_scale` = {}".format(
              self._diag_scale))

  def build(self, input_shape):
    last_dim = input_shape[-1]

    if self._projection_dim is None:
      self._dense = tf.keras.layers.Dense(
          last_dim,
          kernel_initializer=_clone_initializer(self._kernel_initializer),
          bias_initializer=self._bias_initializer,
          kernel_regularizer=self._kernel_regularizer,
          bias_regularizer=self._bias_regularizer,
          use_bias=self._use_bias,
          dtype=self.dtype,
          activation=self._preactivation,
      )
    else:
      self._dense_u = tf.keras.layers.Dense(
          self._projection_dim,
          kernel_initializer=_clone_initializer(self._kernel_initializer),
          kernel_regularizer=self._kernel_regularizer,
          use_bias=False,
          dtype=self.dtype,
      )
      self._dense_v = tf.keras.layers.Dense(
          last_dim,
          kernel_initializer=_clone_initializer(self._kernel_initializer),
          bias_initializer=self._bias_initializer,
          kernel_regularizer=self._kernel_regularizer,
          bias_regularizer=self._bias_regularizer,
          use_bias=self._use_bias,
          dtype=self.dtype,
          activation=self._preactivation,
      )
    self.built = True

  def call(self, x0: tf.Tensor, x: Optional[tf.Tensor] = None) -> tf.Tensor:
    """Computes the feature cross.

    Args:
      x0: The input tensor
      x: Optional second input tensor. If provided, the layer will compute
        crosses between x0 and x; if not provided, the layer will compute
        crosses between x0 and itself.

    Returns:
     Tensor of crosses.
    """

    if not self.built:
      self.build(x0.shape)

    if x is None:
      x = x0

    if x0.shape[-1] != x.shape[-1]:
      raise ValueError(
          "`x0` and `x` dimension mismatch! Got `x0` dimension {}, and x "
          "dimension {}. This case is not supported yet.".format(
              x0.shape[-1], x.shape[-1]))

    if self._projection_dim is None:
      prod_output = self._dense(x)
    else:
      prod_output = self._dense_v(self._dense_u(x))

    prod_output = tf.cast(prod_output, self.compute_dtype)

    if self._diag_scale:
      prod_output = prod_output + self._diag_scale * x

    return x0 * prod_output + x

  def get_config(self):
    config = {
        "projection_dim":
            self._projection_dim,
        "diag_scale":
            self._diag_scale,
        "use_bias":
            self._use_bias,
        "preactivation":
            tf.keras.activations.serialize(self._preactivation),
        "kernel_initializer":
            tf.keras.initializers.serialize(self._kernel_initializer),
        "bias_initializer":
            tf.keras.initializers.serialize(self._bias_initializer),
        "kernel_regularizer":
            tf.keras.regularizers.serialize(self._kernel_regularizer),
        "bias_regularizer":
            tf.keras.regularizers.serialize(self._bias_regularizer),
    }
    base_config = super().get_config()
    return dict(list(base_config.items()) + list(config.items()))


def _clone_initializer(initializer):
  return initializer.__class__.from_config(initializer.get_config())
