# 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.
# ==============================================================================
"""Keras base class for convolution layers."""
# pylint: disable=g-classes-have-attributes

from keras import activations
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
from keras.utils import conv_utils
import tensorflow.compat.v2 as tf


class Conv(Layer):
  """Abstract N-D convolution layer (private, used as implementation base).

  This layer creates a convolution kernel that is convolved
  (actually cross-correlated) with the layer input to produce a tensor of
  outputs. If `use_bias` is True (and a `bias_initializer` is provided),
  a bias vector is created and added to the outputs. Finally, if
  `activation` is not `None`, it is applied to the outputs as well.

  Note: layer attributes cannot be modified after the layer has been called
  once (except the `trainable` attribute).

  Args:
    rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
    filters: Integer, the dimensionality of the output space (i.e. the number
      of filters in the convolution). Could be "None", eg in the case of
      depth wise convolution.
    kernel_size: An integer or tuple/list of n integers, specifying the
      length of the convolution window.
    strides: An integer or tuple/list of n integers,
      specifying the stride length of the convolution.
      Specifying any stride value != 1 is incompatible with specifying
      any `dilation_rate` value != 1.
    padding: One of `"valid"`,  `"same"`, or `"causal"` (case-insensitive).
      `"valid"` means no padding. `"same"` results in padding with zeros
      evenly to the left/right or up/down of the input such that output has the
      same height/width dimension as the input. `"causal"` results in causal
      (dilated) convolutions, e.g. `output[t]` does not depend on `input[t+1:]`.
    data_format: A string, one of `channels_last` (default) or `channels_first`.
      The ordering of the dimensions in the inputs.
      `channels_last` corresponds to inputs with shape
      `(batch_size, ..., channels)` while `channels_first` corresponds to
      inputs with shape `(batch_size, channels, ...)`.
    dilation_rate: An integer or tuple/list of n integers, specifying
      the dilation rate to use for dilated convolution.
      Currently, specifying any `dilation_rate` value != 1 is
      incompatible with specifying any `strides` value != 1.
    groups: A positive integer specifying the number of groups in which the
      input is split along the channel axis. Each group is convolved
      separately with `filters / groups` filters. The output is the
      concatenation of all the `groups` results along the channel axis.
      Input channels and `filters` must both be divisible by `groups`.
    activation: Activation function to use.
      If you don't specify anything, no activation is applied.
    use_bias: Boolean, whether the layer uses a bias.
    kernel_initializer: An initializer for the convolution kernel. If None, the
      default initializer (glorot_uniform) will be used.
    bias_initializer: An initializer for the bias vector. If None, the default
      initializer (zeros) will be used.
    kernel_regularizer: Optional regularizer for the convolution kernel.
    bias_regularizer: Optional regularizer for the bias vector.
    activity_regularizer: Optional regularizer function for the output.
    kernel_constraint: Optional projection function to be applied to the
        kernel after being updated by an `Optimizer` (e.g. used to implement
        norm constraints or value constraints for layer weights). The function
        must take as input the unprojected variable and must return the
        projected variable (which must have the same shape). Constraints are
        not safe to use when doing asynchronous distributed training.
    bias_constraint: Optional projection function to be applied to the
        bias after being updated by an `Optimizer`.
  """

  def __init__(self,
               rank,
               filters,
               kernel_size,
               strides=1,
               padding='valid',
               data_format=None,
               dilation_rate=1,
               groups=1,
               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,
               trainable=True,
               name=None,
               conv_op=None,
               **kwargs):
    super(Conv, self).__init__(
        trainable=trainable,
        name=name,
        activity_regularizer=regularizers.get(activity_regularizer),
        **kwargs)
    self.rank = rank

    if isinstance(filters, float):
      filters = int(filters)
    if filters is not None and filters < 0:
      raise ValueError(f'Received a negative value for `filters`.'
                       f'Was expecting a positive value. Received {filters}.')
    self.filters = filters
    self.groups = groups or 1
    self.kernel_size = conv_utils.normalize_tuple(
        kernel_size, rank, 'kernel_size')
    self.strides = conv_utils.normalize_tuple(
        strides, rank, 'strides', allow_zero=True)
    self.padding = conv_utils.normalize_padding(padding)
    self.data_format = conv_utils.normalize_data_format(data_format)
    self.dilation_rate = conv_utils.normalize_tuple(
        dilation_rate, rank, 'dilation_rate')

    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=self.rank + 2)

    self._validate_init()
    self._is_causal = self.padding == 'causal'
    self._channels_first = self.data_format == 'channels_first'
    self._tf_data_format = conv_utils.convert_data_format(
        self.data_format, self.rank + 2)

  def _validate_init(self):
    if self.filters is not None and self.filters % self.groups != 0:
      raise ValueError(
          'The number of filters must be evenly divisible by the number of '
          'groups. Received: groups={}, filters={}'.format(
              self.groups, self.filters))

    if not all(self.kernel_size):
      raise ValueError('The argument `kernel_size` cannot contain 0(s). '
                       'Received: %s' % (self.kernel_size,))

    if not all(self.strides):
      raise ValueError('The argument `strides` cannot contains 0(s). '
                       'Received: %s' % (self.strides,))

    if self.padding == 'causal':
      # pylint: disable=g-import-not-at-top
      from keras.layers.convolutional.conv1d import Conv1D
      from keras.layers.convolutional.separable_conv1d import SeparableConv1D
      # pylint: enable=g-import-not-at-top
      if not isinstance(self, (Conv1D, SeparableConv1D)):
        raise ValueError('Causal padding is only supported for `Conv1D`'
                         'and `SeparableConv1D`.')

  def build(self, input_shape):
    input_shape = tf.TensorShape(input_shape)
    input_channel = self._get_input_channel(input_shape)
    if input_channel % self.groups != 0:
      raise ValueError(
          'The number of input channels must be evenly divisible by the number '
          'of groups. Received groups={}, but the input has {} channels '
          '(full input shape is {}).'.format(self.groups, input_channel,
                                             input_shape))
    kernel_shape = self.kernel_size + (input_channel // self.groups,
                                       self.filters)

    # compute_output_shape contains some validation logic for the input shape,
    # and make sure the output shape has all positive dimensions.
    self.compute_output_shape(input_shape)

    self.kernel = self.add_weight(
        name='kernel',
        shape=kernel_shape,
        initializer=self.kernel_initializer,
        regularizer=self.kernel_regularizer,
        constraint=self.kernel_constraint,
        trainable=True,
        dtype=self.dtype)
    if self.use_bias:
      self.bias = self.add_weight(
          name='bias',
          shape=(self.filters,),
          initializer=self.bias_initializer,
          regularizer=self.bias_regularizer,
          constraint=self.bias_constraint,
          trainable=True,
          dtype=self.dtype)
    else:
      self.bias = None
    channel_axis = self._get_channel_axis()
    self.input_spec = InputSpec(min_ndim=self.rank + 2,
                                axes={channel_axis: input_channel})
    self.built = True

  def convolution_op(self, inputs, kernel):
    if self.padding == 'causal':
      tf_padding = 'VALID'  # Causal padding handled in `call`.
    elif isinstance(self.padding, str):
      tf_padding = self.padding.upper()
    else:
      tf_padding = self.padding

    return tf.nn.convolution(
        inputs,
        kernel,
        strides=list(self.strides),
        padding=tf_padding,
        dilations=list(self.dilation_rate),
        data_format=self._tf_data_format,
        name=self.__class__.__name__)

  # TODO(b/213173659): remove this when grouped convolutions are fully supported
  # on the CPU for compiled functions. For now, we need this as a workaround for
  # CPU support.
  @tf.function(jit_compile=True)
  def _jit_compiled_convolution_op(self, inputs, kernel):
    return self.convolution_op(inputs, kernel)

  def call(self, inputs):
    input_shape = inputs.shape

    if self._is_causal:  # Apply causal padding to inputs for Conv1D.
      inputs = tf.pad(inputs, self._compute_causal_padding(inputs))

    if self.groups > 1:
      outputs = self._jit_compiled_convolution_op(inputs, self.kernel)
    else:
      outputs = self.convolution_op(inputs, self.kernel)

    if self.use_bias:
      output_rank = outputs.shape.rank
      if self.rank == 1 and self._channels_first:
        # nn.bias_add does not accept a 1D input tensor.
        bias = tf.reshape(self.bias, (1, self.filters, 1))
        outputs += bias
      else:
        # Handle multiple batch dimensions.
        if output_rank is not None and output_rank > 2 + self.rank:

          def _apply_fn(o):
            return tf.nn.bias_add(
                o, self.bias, data_format=self._tf_data_format)

          outputs = conv_utils.squeeze_batch_dims(
              outputs, _apply_fn, inner_rank=self.rank + 1)
        else:
          outputs = tf.nn.bias_add(
              outputs, self.bias, data_format=self._tf_data_format)

    if not tf.executing_eagerly():
      # Infer the static output shape:
      out_shape = self.compute_output_shape(input_shape)
      outputs.set_shape(out_shape)

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

  def _spatial_output_shape(self, spatial_input_shape):
    return [
        conv_utils.conv_output_length(  # pylint: disable=g-complex-comprehension
            length,
            self.kernel_size[i],
            padding=self.padding,
            stride=self.strides[i],
            dilation=self.dilation_rate[i])
        for i, length in enumerate(spatial_input_shape)
    ]

  def compute_output_shape(self, input_shape):
    input_shape = tf.TensorShape(input_shape).as_list()
    batch_rank = len(input_shape) - self.rank - 1
    try:
      if self.data_format == 'channels_last':
        return tf.TensorShape(
            input_shape[:batch_rank] +
            self._spatial_output_shape(input_shape[batch_rank:-1]) +
            [self.filters])
      else:
        return tf.TensorShape(
            input_shape[:batch_rank] + [self.filters] +
            self._spatial_output_shape(input_shape[batch_rank + 1:]))

    except ValueError:
      raise ValueError(
          f'One of the dimensions in the output is <= 0 '
          f'due to downsampling in {self.name}. Consider '
          f'increasing the input size. '
          f'Received input shape {input_shape} which would produce '
          f'output shape with a zero or negative value in a '
          f'dimension.')

  def _recreate_conv_op(self, inputs):  # pylint: disable=unused-argument
    return False

  def get_config(self):
    config = {
        'filters':
            self.filters,
        'kernel_size':
            self.kernel_size,
        'strides':
            self.strides,
        'padding':
            self.padding,
        'data_format':
            self.data_format,
        'dilation_rate':
            self.dilation_rate,
        'groups':
            self.groups,
        '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)
    }
    base_config = super(Conv, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def _compute_causal_padding(self, inputs):
    """Calculates padding for 'causal' option for 1-d conv layers."""
    left_pad = self.dilation_rate[0] * (self.kernel_size[0] - 1)
    if getattr(inputs.shape, 'ndims', None) is None:
      batch_rank = 1
    else:
      batch_rank = len(inputs.shape) - 2
    if self.data_format == 'channels_last':
      causal_padding = [[0, 0]] * batch_rank + [[left_pad, 0], [0, 0]]
    else:
      causal_padding = [[0, 0]] * batch_rank + [[0, 0], [left_pad, 0]]
    return causal_padding

  def _get_channel_axis(self):
    if self.data_format == 'channels_first':
      return -1 - self.rank
    else:
      return -1

  def _get_input_channel(self, input_shape):
    channel_axis = self._get_channel_axis()
    if input_shape.dims[channel_axis].value is None:
      raise ValueError('The channel dimension of the inputs should be defined. '
                       f'The input_shape received is {input_shape}, '
                       f'where axis {channel_axis} (0-based) '
                       'is the channel dimension, which found to be `None`.')
    return int(input_shape[channel_axis])

  def _get_padding_op(self):
    if self.padding == 'causal':
      op_padding = 'valid'
    else:
      op_padding = self.padding
    if not isinstance(op_padding, (list, tuple)):
      op_padding = op_padding.upper()
    return op_padding
