
    2Vh                     L    d dl mZ d dlmZ  eddg       G d de             Zy)    )keras_export)BasePoolingzkeras.layers.MaxPooling1Dzkeras.layers.MaxPool1Dc                   .     e Zd ZdZ	 	 	 	 	 d fd	Z xZS )MaxPooling1Da
  Max pooling operation for 1D temporal data.

    Downsamples the input representation by taking the maximum value over a
    spatial window of size `pool_size`. The window is shifted by `strides`.

    The resulting output when using the `"valid"` padding option has a shape of:
    `output_shape = (input_shape - pool_size + 1) / strides)`.

    The resulting output shape when using the `"same"` padding option is:
    `output_shape = input_shape / strides`

    Args:
        pool_size: int, size of the max pooling window.
        strides: int or None. Specifies how much the pooling window moves
            for each pooling step. If None, it will default to `pool_size`.
        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 such that output has the same
            height/width dimension 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, steps, features)`
            while `"channels_first"` corresponds to inputs with shape
            `(batch, features, steps)`. 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"`.

    Input shape:

    - If `data_format="channels_last"`:
        3D tensor with shape `(batch_size, steps, features)`.
    - If `data_format="channels_first"`:
        3D tensor with shape `(batch_size, features, steps)`.

    Output shape:

    - If `data_format="channels_last"`:
        3D tensor with shape `(batch_size, downsampled_steps, features)`.
    - If `data_format="channels_first"`:
        3D tensor with shape `(batch_size, features, downsampled_steps)`.

    Examples:

    `strides=1` and `padding="valid"`:

    >>> x = np.array([1., 2., 3., 4., 5.])
    >>> x = np.reshape(x, [1, 5, 1])
    >>> max_pool_1d = keras.layers.MaxPooling1D(pool_size=2,
    ...    strides=1, padding="valid")
    >>> max_pool_1d(x)

    `strides=2` and `padding="valid"`:

    >>> x = np.array([1., 2., 3., 4., 5.])
    >>> x = np.reshape(x, [1, 5, 1])
    >>> max_pool_1d = keras.layers.MaxPooling1D(pool_size=2,
    ...    strides=2, padding="valid")
    >>> max_pool_1d(x)

    `strides=1` and `padding="same"`:

    >>> x = np.array([1., 2., 3., 4., 5.])
    >>> x = np.reshape(x, [1, 5, 1])
    >>> max_pool_1d = keras.layers.MaxPooling1D(pool_size=2,
    ...    strides=1, padding="same")
    >>> max_pool_1d(x)
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