
from ...configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE				:Optional[int]									=							{
    '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 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	snake_case_          =	"tapas"






	def __init__(			self   :					List[Any]				,A   :					Dict=3_05_22				,A   :					int=7_68				,A   :					List[Any]=12				,A   :					Any=12				,A   :					str=30_72				,A   :					Dict="gelu"				,A   :					str=0.1				,A   :					Any=0.1				,A   :					Union[str, Any]=10_24				,A   :					Dict=[3, 2_56, 2_56, 2, 2_56, 2_56, 10]				,A   :					str=0.02				,A   :					List[str]=1E-12				,A   :					List[Any]=0				,A   :					str=10.0				,A   :					Any=0				,A   :					Optional[int]=1.0				,A   :					Optional[Any]=None				,A   :					Any=1.0				,A   :					int=False				,A   :					str=None				,A   :					str=1.0				,A   :					Optional[Any]=1.0				,A   :					List[Any]=False				,A   :					Any=False				,A   :					Tuple="ratio"				,A   :					Optional[int]=None				,A   :					Any=None				,A   :					Optional[int]=64				,A   :					List[Any]=32				,A   :					int=False				,A   :					List[Any]=True				,A   :					str=False				,A   :					Optional[int]=False				,A   :					Any=True				,A   :					Tuple=False				,A   :					Tuple=None				,A   :					List[str]=None				,**A   :					Union[str, Any]				,):
					super().__init__(pad_token_id=A				,**A					)

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

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

					# Aggregation hyperparameters
					__A       =					aggregation_labels
					__A       =					no_aggregation_label_index

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


'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate


@dataclass(frozen=snake_case_)
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							field(default="""language-modeling"""							,       metadata={"""include_in_asdict_even_if_is_default""": True})

				lowerCAmelCase_     			=							Features({"""text""": Value("""string""")})
				lowerCAmelCase_     			=							Features({})
				lowerCAmelCase_     			=							"text"







				@property
				def 						UpperCAmelCase_    (       self     )->		Dict[str, str]:







											'''simple docstring'''






											return {self.text_column: "text"}




