src/transformers/models/bigbird_pegasus/configuration_bigbird_pegasus.py/BigBirdPegasusConfig/__init__
class BigBirdPegasusConfig:    def __init__(
        self,
        vocab_size=96103,
        max_position_embeddings=4096,
        encoder_layers=16,
        encoder_ffn_dim=4096,
        encoder_attention_heads=16,
        decoder_layers=16,
        decoder_ffn_dim=4096,
        decoder_attention_heads=16,
        encoder_layerdrop=0.0,
        decoder_layerdrop=0.0,
        use_cache=True,
        is_encoder_decoder=True,
        activation_function="gelu_new",
        d_model=1024,
        dropout=0.1,
        attention_dropout=0.0,
        activation_dropout=0.0,
        init_std=0.02,
        decoder_start_token_id=2,
        classifier_dropout=0.0,
        scale_embedding=True,
        gradient_checkpointing=False,
        pad_token_id=0,
        bos_token_id=2,
        eos_token_id=1,
        attention_type="block_sparse",  # only for encoder
        block_size=64,
        num_random_blocks=3,
        use_bias=False,
        **kwargs
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.d_model = d_model
        self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_layers = encoder_layers
        self.encoder_attention_heads = encoder_attention_heads
        self.decoder_ffn_dim = decoder_ffn_dim
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.activation_function = activation_function
        self.init_std = init_std
        self.encoder_layerdrop = encoder_layerdrop
        self.decoder_layerdrop = decoder_layerdrop
        self.classifier_dropout = classifier_dropout
        self.use_cache = use_cache
        self.num_hidden_layers = encoder_layers
        self.gradient_checkpointing = gradient_checkpointing
        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True

        # extra config
        self.attention_type = attention_type
        self.block_size = block_size
        self.num_random_blocks = num_random_blocks
        self.use_bias = use_bias

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            is_encoder_decoder=is_encoder_decoder,
            decoder_start_token_id=decoder_start_token_id,
            **kwargs,
        )