# Copyright 2015 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.
# ==============================================================================
"""Contains the Dense layer."""
# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import

from keras import activations
from keras import backend as K
from keras import constraints
from keras import initializers
from keras import regularizers
from keras.engine.base_layer import Layer
from keras.engine.input_spec import InputSpec
import tensorflow.compat.v2 as tf

from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.layers.Dense')
class Dense(Layer):
  """Just your regular densely-connected NN layer.

  `Dense` implements the operation:
  `output = activation(dot(input, kernel) + bias)`
  where `activation` is the element-wise activation function
  passed as the `activation` argument, `kernel` is a weights matrix
  created by the layer, and `bias` is a bias vector created by the layer
  (only applicable if `use_bias` is `True`). These are all attributes of
  `Dense`.

  Note: If the input to the layer has a rank greater than 2, then `Dense`
  computes the dot product between the `inputs` and the `kernel` along the
  last axis of the `inputs` and axis 0 of the `kernel` (using `tf.tensordot`).
  For example, if input has dimensions `(batch_size, d0, d1)`,
  then we create a `kernel` with shape `(d1, units)`, and the `kernel` operates
  along axis 2 of the `input`, on every sub-tensor of shape `(1, 1, d1)`
  (there are `batch_size * d0` such sub-tensors).
  The output in this case will have shape `(batch_size, d0, units)`.

  Besides, layer attributes cannot be modified after the layer has been called
  once (except the `trainable` attribute).
  When a popular kwarg `input_shape` is passed, then keras will create
  an input layer to insert before the current layer. This can be treated
  equivalent to explicitly defining an `InputLayer`.

  Example:

  >>> # Create a `Sequential` model and add a Dense layer as the first layer.
  >>> model = tf.keras.models.Sequential()
  >>> model.add(tf.keras.Input(shape=(16,)))
  >>> model.add(tf.keras.layers.Dense(32, activation='relu'))
  >>> # Now the model will take as input arrays of shape (None, 16)
  >>> # and output arrays of shape (None, 32).
  >>> # Note that after the first layer, you don't need to specify
  >>> # the size of the input anymore:
  >>> model.add(tf.keras.layers.Dense(32))
  >>> model.output_shape
  (None, 32)

  Args:
    units: Positive integer, dimensionality of the output space.
    activation: Activation function to use.
      If you don't specify anything, no activation is applied
      (ie. "linear" activation: `a(x) = x`).
    use_bias: Boolean, whether the layer uses a bias vector.
    kernel_initializer: Initializer for the `kernel` weights matrix.
    bias_initializer: Initializer for the bias vector.
    kernel_regularizer: Regularizer function applied to
      the `kernel` weights matrix.
    bias_regularizer: Regularizer function applied to the bias vector.
    activity_regularizer: Regularizer function applied to
      the output of the layer (its "activation").
    kernel_constraint: Constraint function applied to
      the `kernel` weights matrix.
    bias_constraint: Constraint function applied to the bias vector.

  Input shape:
    N-D tensor with shape: `(batch_size, ..., input_dim)`.
    The most common situation would be
    a 2D input with shape `(batch_size, input_dim)`.

  Output shape:
    N-D tensor with shape: `(batch_size, ..., units)`.
    For instance, for a 2D input with shape `(batch_size, input_dim)`,
    the output would have shape `(batch_size, units)`.
  """

  def __init__(self,
               units,
               activation=None,
               use_bias=True,
               kernel_initializer='glorot_uniform',
               bias_initializer='zeros',
               kernel_regularizer=None,
               bias_regularizer=None,
               activity_regularizer=None,
               kernel_constraint=None,
               bias_constraint=None,
               **kwargs):
    super(Dense, self).__init__(
        activity_regularizer=activity_regularizer, **kwargs)

    self.units = int(units) if not isinstance(units, int) else units
    if self.units < 0:
      raise ValueError(f'Received an invalid value for `units`, expected '
                       f'a positive integer. Received: units={units}')
    self.activation = activations.get(activation)
    self.use_bias = use_bias
    self.kernel_initializer = initializers.get(kernel_initializer)
    self.bias_initializer = initializers.get(bias_initializer)
    self.kernel_regularizer = regularizers.get(kernel_regularizer)
    self.bias_regularizer = regularizers.get(bias_regularizer)
    self.kernel_constraint = constraints.get(kernel_constraint)
    self.bias_constraint = constraints.get(bias_constraint)

    self.input_spec = InputSpec(min_ndim=2)
    self.supports_masking = True

  def build(self, input_shape):
    dtype = tf.as_dtype(self.dtype or K.floatx())
    if not (dtype.is_floating or dtype.is_complex):
      raise TypeError('A Dense layer can only be built with a floating-point '
                      f'dtype. Received: dtype={dtype}')

    input_shape = tf.TensorShape(input_shape)
    last_dim = tf.compat.dimension_value(input_shape[-1])
    if last_dim is None:
      raise ValueError('The last dimension of the inputs to a Dense layer '
                       'should be defined. Found None. '
                       f'Full input shape received: {input_shape}')
    self.input_spec = InputSpec(min_ndim=2, axes={-1: last_dim})
    self.kernel = self.add_weight(
        'kernel',
        shape=[last_dim, self.units],
        initializer=self.kernel_initializer,
        regularizer=self.kernel_regularizer,
        constraint=self.kernel_constraint,
        dtype=self.dtype,
        trainable=True)
    if self.use_bias:
      self.bias = self.add_weight(
          'bias',
          shape=[self.units,],
          initializer=self.bias_initializer,
          regularizer=self.bias_regularizer,
          constraint=self.bias_constraint,
          dtype=self.dtype,
          trainable=True)
    else:
      self.bias = None
    self.built = True

  def call(self, inputs):
    if inputs.dtype.base_dtype != self._compute_dtype_object.base_dtype:
      inputs = tf.cast(inputs, dtype=self._compute_dtype_object)

    if isinstance(inputs, tf.RaggedTensor) and inputs.shape[-1] is not None:
      # In case we encounter a RaggedTensor with a fixed last dimension (last
      # dimension not ragged), we can map the call method to the flat values.
      return tf.ragged.map_flat_values(self.call, inputs)

    rank = inputs.shape.rank
    if rank == 2 or rank is None:
      # We use embedding_lookup_sparse as a more efficient matmul operation for
      # large sparse input tensors. The op will result in a sparse gradient, as
      # opposed to sparse_ops.sparse_tensor_dense_matmul which results in dense
      # gradients. This can lead to sigfinicant speedups, see b/171762937.
      if isinstance(inputs, tf.SparseTensor):
        # We need to fill empty rows, as the op assumes at least one id per row.
        inputs, _ = tf.sparse.fill_empty_rows(inputs, 0)
        # We need to do some munging of our input to use the embedding lookup as
        # a matrix multiply. We split our input matrix into separate ids and
        # weights tensors. The values of the ids tensor should be the column
        # indices of our input matrix and the values of the weights tensor
        # can continue to the actual matrix weights.
        # The column arrangement of ids and weights
        # will be summed over and does not matter. See the documentation for
        # sparse_ops.sparse_tensor_dense_matmul a more detailed explanation
        # of the inputs to both ops.
        ids = tf.SparseTensor(
            indices=inputs.indices,
            values=inputs.indices[:, 1],
            dense_shape=inputs.dense_shape)
        weights = inputs
        outputs = tf.nn.embedding_lookup_sparse(
            self.kernel, ids, weights, combiner='sum')
      else:
        outputs = tf.raw_ops.MatMul(a=inputs, b=self.kernel)
    # Broadcast kernel to inputs.
    else:
      outputs = tf.tensordot(inputs, self.kernel, [[rank - 1], [0]])
      # Reshape the output back to the original ndim of the input.
      if not tf.executing_eagerly():
        shape = inputs.shape.as_list()
        output_shape = shape[:-1] + [self.kernel.shape[-1]]
        outputs.set_shape(output_shape)

    if self.use_bias:
      outputs = tf.nn.bias_add(outputs, self.bias)

    if self.activation is not None:
      outputs = self.activation(outputs)
    return outputs

  def compute_output_shape(self, input_shape):
    input_shape = tf.TensorShape(input_shape)
    input_shape = input_shape.with_rank_at_least(2)
    if tf.compat.dimension_value(input_shape[-1]) is None:
      raise ValueError('The last dimension of the input shape of a Dense layer '
                       'should be defined. Found None. '
                       f'Received: input_shape={input_shape}')
    return input_shape[:-1].concatenate(self.units)

  def get_config(self):
    config = super(Dense, self).get_config()
    config.update({
        'units': self.units,
        'activation': activations.serialize(self.activation),
        'use_bias': self.use_bias,
        'kernel_initializer': initializers.serialize(self.kernel_initializer),
        'bias_initializer': initializers.serialize(self.bias_initializer),
        'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
        'bias_regularizer': regularizers.serialize(self.bias_regularizer),
        'activity_regularizer':
            regularizers.serialize(self.activity_regularizer),
        'kernel_constraint': constraints.serialize(self.kernel_constraint),
        'bias_constraint': constraints.serialize(self.bias_constraint)
    })
    return config
