from keras.api_export import keras_export
from keras.layers.convolutional.base_separable_conv import BaseSeparableConv


@keras_export(
    [
        "keras.layers.SeparableConv2D",
        "keras.layers.SeparableConvolution2D",
    ]
)
class SeparableConv2D(BaseSeparableConv):
    """2D separable convolution layer.

    This layer performs a depthwise convolution that acts separately on
    channels, followed by a pointwise convolution that mixes channels.
    If `use_bias` is True and a bias initializer is provided,
    it adds a bias vector to the output. It then optionally applies an
    activation function to produce the final output.

    Args:
        filters: int, the dimensionality of the output space (i.e. the number
            of filters in the pointwise convolution).
        kernel_size: int or tuple/list of 2 integers, specifying the size of the
            depthwise convolution window.
        strides: int or tuple/list of 2 integers, specifying the stride length
            of the depthwise convolution. If only one int is specified, the same
            stride size will be used for all dimensions. `strides > 1` is
            incompatible with `dilation_rate > 1`.
        padding: string, either `"valid"` or `"same"` (case-insensitive).
            `"valid"` means no padding. `"same"` results in padding evenly to
            the left/right or up/down of the input. When `padding="same"` and
            `strides=1`, the output has the same size as the input.
        data_format: string, either `"channels_last"` or `"channels_first"`.
            The ordering of the dimensions in the inputs. `"channels_last"`
            corresponds to inputs with shape `(batch, height, width, channels)`
            while `"channels_first"` corresponds to inputs with shape
            `(batch, channels, height, width)`. It defaults to the
            `image_data_format` value found in your Keras config file
            at `~/.keras/keras.json`.
            If you never set it, then it will be `"channels_last"`.
        dilation_rate: int or tuple/list of 2 integers, specifying the dilation
            rate to use for dilated convolution. If only one int is specified,
            the same dilation rate will be used for all dimensions.
        depth_multiplier: The number of depthwise convolution output channels
            for each input channel. The total number of depthwise convolution
            output channels will be equal to `input_channel * depth_multiplier`.
        activation: Activation function. If `None`, no activation is applied.
        use_bias: bool, if `True`, bias will be added to the output.
        depthwise_initializer: An initializer for the depthwise convolution
            kernel. If None, then the default initializer (`"glorot_uniform"`)
            will be used.
        pointwise_initializer: An initializer for the pointwise convolution
            kernel. If None, then 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.
        depthwise_regularizer: Optional regularizer for the depthwise
            convolution kernel.
        pointwise_regularizer: Optional regularizer for the pointwise
            convolution kernel.
        bias_regularizer: Optional regularizer for the bias vector.
        activity_regularizer: Optional regularizer function for the output.
        depthwise_constraint: Optional projection function to be applied to the
            depthwise kernel after being updated by an `Optimizer` (e.g. used
            for 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).
        pointwise_constraint: Optional projection function to be applied to the
            pointwise kernel after being updated by an `Optimizer`.
        bias_constraint: Optional projection function to be applied to the
            bias after being updated by an `Optimizer`.

    Input shape:
    - If `data_format="channels_last"`:
        A 4D tensor with shape: `(batch_size, height, width, channels)`
    - If `data_format="channels_first"`:
        A 4D tensor with shape: `(batch_size, channels, height, width)`

    Output shape:
    - If `data_format="channels_last"`:
        A 4D tensor with shape: `(batch_size, new_height, new_width, filters)`
    - If `data_format="channels_first"`:
        A 4D tensor with shape: `(batch_size, filters, new_height, new_width)`

    Returns:
        A 4D tensor representing
        `activation(separable_conv2d(inputs, kernel) + bias)`.

    Examples:

    >>> x = np.random.rand(4, 10, 10, 12)
    >>> y = keras.layers.SeparableConv2D(3, 4, 3, 2, activation='relu')(x)
    >>> print(y.shape)
    (4, 4, 4, 4)
    """

    def __init__(
        self,
        filters,
        kernel_size,
        strides=(1, 1),
        padding="valid",
        data_format=None,
        dilation_rate=(1, 1),
        depth_multiplier=1,
        activation=None,
        use_bias=True,
        depthwise_initializer="glorot_uniform",
        pointwise_initializer="glorot_uniform",
        bias_initializer="zeros",
        depthwise_regularizer=None,
        pointwise_regularizer=None,
        bias_regularizer=None,
        activity_regularizer=None,
        depthwise_constraint=None,
        pointwise_constraint=None,
        bias_constraint=None,
        **kwargs,
    ):
        super().__init__(
            rank=2,
            depth_multiplier=depth_multiplier,
            filters=filters,
            kernel_size=kernel_size,
            strides=strides,
            padding=padding,
            data_format=data_format,
            dilation_rate=dilation_rate,
            activation=activation,
            use_bias=use_bias,
            depthwise_initializer=depthwise_initializer,
            pointwise_initializer=pointwise_initializer,
            bias_initializer=bias_initializer,
            depthwise_regularizer=depthwise_regularizer,
            pointwise_regularizer=pointwise_regularizer,
            bias_regularizer=bias_regularizer,
            activity_regularizer=activity_regularizer,
            depthwise_constraint=depthwise_constraint,
            pointwise_constraint=pointwise_constraint,
            bias_constraint=bias_constraint,
            **kwargs,
        )
