


'''simple docstring'''







from ...configuration_utils import PretrainedConfig


a__  :					Dict        =       {
    'google/tapas-base-finetuned-sqa': (
        'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-wtq': (
        'https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-wikisql-supervised': (
        'https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-tabfact': (
        'https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'
    ),
}
class 					lowercase_     (    a__   ):
    __UpperCAmelCase   			=	'tapas'


    def __init__(     self  ,					a=3_05_22  ,					a=7_68  ,					a=12  ,					a=12  ,					a=30_72  ,					a="gelu"  ,					a=0.1  ,					a=0.1  ,					a=10_24  ,					a=[3, 2_56, 2_56, 2, 2_56, 2_56, 10]  ,					a=0.02  ,					a=1e-12  ,					a=0  ,					a=10.0  ,					a=0  ,					a=1.0  ,					a=None  ,					a=1.0  ,					a=False  ,					a=None  ,					a=1.0  ,					a=1.0  ,					a=False  ,					a=False  ,					a="ratio"  ,					a=None  ,					a=None  ,					a=64  ,					a=32  ,					a=False  ,					a=True  ,					a=False  ,					a=False  ,					a=True  ,					a=False  ,					a=None  ,					a=None  ,					**a  ,					):
          super().__init__(pad_token_id=a  ,					**a    )

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

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

          # Aggregation hyperparameters
          UpperCamelCase__										=				aggregation_labels
          UpperCamelCase__										=				no_aggregation_label_index

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









import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


A      :    List[str]		=					logging.get_logger(__name__)







class 			_lowercase (							lowercase__):


       """simple docstring"""




       def __init__(   self							:			Optional[int]					,      *__lowerCamelCase							:			List[str]					,      **__lowerCamelCase							:			str       ):


             '''simple docstring'''



             warnings.warn(
                 "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                 " use BeitImageProcessor instead."					,      __lowerCamelCase					,      )
             super().__init__(*__lowerCamelCase					,      **__lowerCamelCase       )


