src/transformers/models/tapas/configuration_tapas.py/TapasConfig/__init__
class TapasConfig:    def __init__(
        self,
        vocab_size=30522,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=1024,
        type_vocab_sizes=[3, 256, 256, 2, 256, 256, 10],
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        gradient_checkpointing=False,
        positive_label_weight=10.0,
        num_aggregation_labels=0,
        aggregation_loss_weight=1.0,
        use_answer_as_supervision=None,
        answer_loss_importance=1.0,
        use_normalized_answer_loss=False,
        huber_loss_delta=None,
        temperature=1.0,
        aggregation_temperature=1.0,
        use_gumbel_for_cells=False,
        use_gumbel_for_aggregation=False,
        average_approximation_function="ratio",
        cell_selection_preference=None,
        answer_loss_cutoff=None,
        max_num_rows=64,
        max_num_columns=32,
        average_logits_per_cell=False,
        select_one_column=True,
        allow_empty_column_selection=False,
        init_cell_selection_weights_to_zero=False,
        reset_position_index_per_cell=True,
        disable_per_token_loss=False,
        aggregation_labels=None,
        no_aggregation_label_index=None,
        **kwargs
    ):

        super().__init__(pad_token_id=pad_token_id, **kwargs)

        # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_sizes = type_vocab_sizes
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.gradient_checkpointing = gradient_checkpointing

        # Fine-tuning task hyperparameters
        self.positive_label_weight = positive_label_weight
        self.num_aggregation_labels = num_aggregation_labels
        self.aggregation_loss_weight = aggregation_loss_weight
        self.use_answer_as_supervision = use_answer_as_supervision
        self.answer_loss_importance = answer_loss_importance
        self.use_normalized_answer_loss = use_normalized_answer_loss
        self.huber_loss_delta = huber_loss_delta
        self.temperature = temperature
        self.aggregation_temperature = aggregation_temperature
        self.use_gumbel_for_cells = use_gumbel_for_cells
        self.use_gumbel_for_aggregation = use_gumbel_for_aggregation
        self.average_approximation_function = average_approximation_function
        self.cell_selection_preference = cell_selection_preference
        self.answer_loss_cutoff = answer_loss_cutoff
        self.max_num_rows = max_num_rows
        self.max_num_columns = max_num_columns
        self.average_logits_per_cell = average_logits_per_cell
        self.select_one_column = select_one_column
        self.allow_empty_column_selection = allow_empty_column_selection
        self.init_cell_selection_weights_to_zero = init_cell_selection_weights_to_zero
        self.reset_position_index_per_cell = reset_position_index_per_cell
        self.disable_per_token_loss = disable_per_token_loss

        # Aggregation hyperparameters
        self.aggregation_labels = aggregation_labels
        self.no_aggregation_label_index = no_aggregation_label_index

        if isinstance(self.aggregation_labels, dict):
            self.aggregation_labels = {int(k): v for k, v in aggregation_labels.items()}