o
    bi;                     @   sP   d dl mZ d dl mZ d dlmZ d dlmZ eddgG dd deZd	S )
    )backend)ops)keras_export)BaseGlobalPoolingz#keras.layers.GlobalAveragePooling1Dzkeras.layers.GlobalAvgPool1Dc                       s6   e Zd ZdZd
 fdd	ZdddZddd	Z  ZS )GlobalAveragePooling1Da;  Global average pooling operation for temporal data.

    Args:
        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"`.
        keepdims: A boolean, whether to keep the temporal dimension or not.
            If `keepdims` is `False` (default), the rank of the tensor is
            reduced for spatial dimensions. If `keepdims` is `True`, the
            temporal dimension are retained with length 1.
            The behavior is the same as for `tf.reduce_mean` or `np.mean`.

    Call arguments:
        inputs: A 3D tensor.
        mask: Binary tensor of shape `(batch_size, steps)` indicating whether
            a given step should be masked (excluded from the average).

    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 `keepdims=False`:
        2D tensor with shape `(batch_size, features)`.
    - If `keepdims=True`:
        - If `data_format="channels_last"`:
            3D tensor with shape `(batch_size, 1, features)`
        - If `data_format="channels_first"`:
            3D tensor with shape `(batch_size, features, 1)`

    Example:

    >>> x = np.random.rand(2, 3, 4)
    >>> y = keras.layers.GlobalAveragePooling1D()(x)
    >>> y.shape
    (2, 4)
    NFc                    s$   t  jdd||d| d| _d S )N   )pool_dimensionsdata_formatkeepdimsT )super__init__supports_masking)selfr	   r
   kwargs	__class__r   e/home/ubuntu/.local/lib/python3.10/site-packages/keras/src/layers/pooling/global_average_pooling1d.pyr   >   s   
zGlobalAveragePooling1D.__init__c                 C   s   | j dkrdnd}|d ur9t||d j}t|| j dkr dnd}||9 }tj||| jdtj||| jd S tj||| jdS )Nchannels_lastr      r   )axisr
   )	r	   r   castdtyper   expand_dimssumr
   mean)r   inputsmask
steps_axisr   r   r   callG   s   zGlobalAveragePooling1D.callc                 C   s   d S Nr   )r   r   r   r   r   r   compute_maskU   s   z#GlobalAveragePooling1D.compute_mask)NFr    )__name__
__module____qualname____doc__r   r   r!   __classcell__r   r   r   r   r      s
    0
	r   N)	keras.srcr   r   keras.src.api_exportr   ,keras.src.layers.pooling.base_global_poolingr   r   r   r   r   r   <module>   s    