
        encoder_layerdrop (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
            for more details.
        decoder_layerdrop (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
            for more details.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        attention_type (`str`, *optional*, defaults to `"block_sparse"`)
            Whether to use block sparse attention (with n complexity) as introduced in paper or original attention
            layer (with n^2 complexity) in encoder. Possible values are `"original_full"` and `"block_sparse"`.
        use_bias (`bool`, *optional*, defaults to `False`)
            Whether to use bias in query, key, value.
        block_size (`int`, *optional*, defaults to 64)
            Size of each block. Useful only when `attention_type == "block_sparse"`.
        num_random_blocks (`int`, *optional*, defaults to 3)
            Each query is going to attend these many number of random blocks. Useful only when `attention_type ==
            "block_sparse"`.
        scale_embeddings (`bool`, *optional*, defaults to `True`)
            Whether to rescale embeddings with (hidden_size ** 0.5).

    Example:

    ```python
    >>> from transformers import BigBirdPegasusConfig, BigBirdPegasusModel

    >>> # Initializing a BigBirdPegasus bigbird-pegasus-base style configuration
    >>> configuration = BigBirdPegasusConfig()

    >>> # Initializing a model (with random weights) from the bigbird-pegasus-base style configuration
    >>> model = BigBirdPegasusModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Zbigbird_pegasusÚpast_key_valuesÚ