from keras.src.api_export import keras_export
from keras.src.layers.convolutional.base_conv import BaseConv


@keras_export(["keras.layers.Conv2D", "keras.layers.Convolution2D"])
class Conv2D(BaseConv):
    """2D convolution layer.

    This layer creates a convolution kernel that is convolved with the layer
    input over a 2D spatial (or temporal) dimension (height and width) to
    produce a tensor of outputs. If `use_bias` is True, 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 on numerical precision: While in general Keras operation execution
    results are identical across backends up to 1e-7 precision in float32,
    `Conv2D` operations may show larger variations. Due to the large
    number of element-wise multiplications and additions in convolution
    operations, especially with large inputs or kernel sizes, accumulated
    floating-point differences can exceed this 1e-7 threshold. These variations
    are particularly noticeable when using different backends (e.g., TensorFlow
    vs JAX) or different hardware.

    Args:
        filters: int, the dimension of the output space (the number of filters
            in the convolution).
        kernel_size: int or tuple/list of 2 integer, specifying the size of the
            convolution window.
        strides: int or tuple/list of 2 integer, specifying the stride length
            of the convolution. `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_size, height, width, channels)`
            while `"channels_first"` corresponds to inputs with shape
            `(batch_size, 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.
        groups: A positive int 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. If `None`, no activation is applied.
        use_bias: bool, if `True`, bias will be added to the output.
        kernel_initializer: Initializer for the convolution kernel. If `None`,
            the default initializer (`"glorot_uniform"`) will be used.
        bias_initializer: 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`.

    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(conv2d(inputs, kernel) + bias)`.

    Raises:
        ValueError: when both `strides > 1` and `dilation_rate > 1`.

    Example:

    >>> x = np.random.rand(4, 10, 10, 128)
    >>> y = keras.layers.Conv2D(32, 3, activation='relu')(x)
    >>> print(y.shape)
    (4, 8, 8, 32)
    """

    def __init__(
        self,
        filters,
        kernel_size,
        strides=(1, 1),
        padding="valid",
        data_format=None,
        dilation_rate=(1, 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,
        **kwargs,
    ):
        super().__init__(
            rank=2,
            filters=filters,
            kernel_size=kernel_size,
            strides=strides,
            padding=padding,
            data_format=data_format,
            dilation_rate=dilation_rate,
            groups=groups,
            activation=activation,
            use_bias=use_bias,
            kernel_initializer=kernel_initializer,
            bias_initializer=bias_initializer,
            kernel_regularizer=kernel_regularizer,
            bias_regularizer=bias_regularizer,
            activity_regularizer=activity_regularizer,
            kernel_constraint=kernel_constraint,
            bias_constraint=bias_constraint,
            **kwargs,
        )