





import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version


def 							__snake_case							(    *lowerCAmelCase_ ,							lowerCAmelCase_ = None ,							lowerCAmelCase_=True ,							lowerCAmelCase_=2	)				->						int:
    from .. import __version__

    SCREAMING_SNAKE_CASE__											=			take_from
    SCREAMING_SNAKE_CASE__											=			()
    if not isinstance(args[0] ,							lowerCAmelCase_	):
        SCREAMING_SNAKE_CASE__											=			(args,)

    for attribute, version_name, message in args:
        if version.parse(version.parse(lowerCAmelCase_	).base_version	) >= version.parse(lowerCAmelCase_	):
            raise ValueError(
                f'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
                f''' version {__version__} is >= {version_name}'''	)

        SCREAMING_SNAKE_CASE__											=			None
        if isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	) and attribute in deprecated_kwargs:
            values += (deprecated_kwargs.pop(lowerCAmelCase_	),)
            SCREAMING_SNAKE_CASE__											=			f'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
        elif hasattr(lowerCAmelCase_ ,							lowerCAmelCase_	):
            values += (getattr(lowerCAmelCase_ ,							lowerCAmelCase_	),)
            SCREAMING_SNAKE_CASE__											=			f'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
        elif deprecated_kwargs is None:
            SCREAMING_SNAKE_CASE__											=			f'''`{attribute}` is deprecated and will be removed in version {version_name}.'''

        if warning is not None:
            SCREAMING_SNAKE_CASE__											=			warning + ''' ''' if standard_warn else ''''''
            warnings.warn(warning + message ,							lowerCAmelCase_ ,							stacklevel=lowerCAmelCase_	)

    if isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	) and len(lowerCAmelCase_	) > 0:
        SCREAMING_SNAKE_CASE__											=			inspect.getouterframes(inspect.currentframe()	)[1]
        SCREAMING_SNAKE_CASE__											=			call_frame.filename
        SCREAMING_SNAKE_CASE__											=			call_frame.lineno
        SCREAMING_SNAKE_CASE__											=			call_frame.function
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			next(iter(deprecated_kwargs.items()	)	)
        raise TypeError(f'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'''	)

    if len(lowerCAmelCase_	) == 0:
        return
    elif len(lowerCAmelCase_	) == 1:
        return values[0]
    return values








from ...configuration_utils import PretrainedConfig


lowerCamelCase__       		=  {
    """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       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					Tuple								=			"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__      ,    )			->   Any:






            '''simple docstring'''





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

            # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
            __snake_case      :Optional[Any] 							=    vocab_size
            __snake_case      :Tuple 							=    hidden_size
            __snake_case      :Tuple 							=    num_hidden_layers
            __snake_case      :Any 							=    num_attention_heads
            __snake_case      :List[str] 							=    hidden_act
            __snake_case      :List[str] 							=    intermediate_size
            __snake_case      :int 							=    hidden_dropout_prob
            __snake_case      :List[Any] 							=    attention_probs_dropout_prob
            __snake_case      :Any 							=    max_position_embeddings
            __snake_case      :Optional[int] 							=    type_vocab_sizes
            __snake_case      :Tuple 							=    initializer_range
            __snake_case      :Any 							=    layer_norm_eps

            # Fine-tuning task hyperparameters
            __snake_case      :Optional[int] 							=    positive_label_weight
            __snake_case      :Optional[Any] 							=    num_aggregation_labels
            __snake_case      :Dict 							=    aggregation_loss_weight
            __snake_case      :str 							=    use_answer_as_supervision
            __snake_case      :int 							=    answer_loss_importance
            __snake_case      :List[Any] 							=    use_normalized_answer_loss
            __snake_case      :List[str] 							=    huber_loss_delta
            __snake_case      :Any 							=    temperature
            __snake_case      :Optional[int] 							=    aggregation_temperature
            __snake_case      :List[str] 							=    use_gumbel_for_cells
            __snake_case      :Optional[int] 							=    use_gumbel_for_aggregation
            __snake_case      :Optional[Any] 							=    average_approximation_function
            __snake_case      :Union[str, Any] 							=    cell_selection_preference
            __snake_case      :Tuple 							=    answer_loss_cutoff
            __snake_case      :Union[str, Any] 							=    max_num_rows
            __snake_case      :Optional[int] 							=    max_num_columns
            __snake_case      :str 							=    average_logits_per_cell
            __snake_case      :Union[str, Any] 							=    select_one_column
            __snake_case      :Optional[int] 							=    allow_empty_column_selection
            __snake_case      :Optional[Any] 							=    init_cell_selection_weights_to_zero
            __snake_case      :Tuple 							=    reset_position_index_per_cell
            __snake_case      :Any 							=    disable_per_token_loss

            # Aggregation hyperparameters
            __snake_case      :List[str] 							=    aggregation_labels
            __snake_case      :int 							=    no_aggregation_label_index

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


