


from ...configuration_utils import PretrainedConfig


__a     =			{
    """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				(  __snake_case  ):
      UpperCamelCase								=       '''tapas'''





      def __init__(		self						:					str					,       __lowerCamelCase						:					Optional[Any]=3_0_5_2_2					,       __lowerCamelCase						:					Dict=7_6_8					,       __lowerCamelCase						:					List[Any]=1_2					,       __lowerCamelCase						:					Optional[int]=1_2					,       __lowerCamelCase						:					str=3_0_7_2					,       __lowerCamelCase						:					int="gelu"					,       __lowerCamelCase						:					Any=0.1					,       __lowerCamelCase						:					int=0.1					,       __lowerCamelCase						:					List[Any]=1_0_2_4					,       __lowerCamelCase						:					Dict=[3, 2_5_6, 2_5_6, 2, 2_5_6, 2_5_6, 1_0]					,       __lowerCamelCase						:					Dict=0.02					,       __lowerCamelCase						:					Union[str, Any]=1e-1_2					,       __lowerCamelCase						:					Union[str, Any]=0					,       __lowerCamelCase						:					int=10.0					,       __lowerCamelCase						:					Optional[int]=0					,       __lowerCamelCase						:					List[Any]=1.0					,       __lowerCamelCase						:					Tuple=None					,       __lowerCamelCase						:					Any=1.0					,       __lowerCamelCase						:					str=False					,       __lowerCamelCase						:					Dict=None					,       __lowerCamelCase						:					Any=1.0					,       __lowerCamelCase						:					Tuple=1.0					,       __lowerCamelCase						:					Any=False					,       __lowerCamelCase						:					List[str]=False					,       __lowerCamelCase						:					List[str]="ratio"					,       __lowerCamelCase						:					Tuple=None					,       __lowerCamelCase						:					List[Any]=None					,       __lowerCamelCase						:					str=6_4					,       __lowerCamelCase						:					List[Any]=3_2					,       __lowerCamelCase						:					str=False					,       __lowerCamelCase						:					Dict=True					,       __lowerCamelCase						:					int=False					,       __lowerCamelCase						:					Optional[int]=False					,       __lowerCamelCase						:					int=True					,       __lowerCamelCase						:					Optional[int]=False					,       __lowerCamelCase						:					Optional[int]=None					,       __lowerCamelCase						:					Tuple=None					,       **__lowerCamelCase						:					Any					,       )    ->   str:






         """simple docstring"""







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

         # 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					,       __lowerCamelCase       ):
            UpperCAmelCase							=     {int(__lowerCamelCase       ): v for k, v in aggregation_labels.items()}








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a     =			{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a     =			[
     """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """IBertForMaskedLM""",
     """IBertForMultipleChoice""",
     """IBertForQuestionAnswering""",
     """IBertForSequenceClassification""",
     """IBertForTokenClassification""",
     """IBertModel""",
     """IBertPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ibert import (
      IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      IBertForMaskedLM,
      IBertForMultipleChoice,
      IBertForQuestionAnswering,
      IBertForSequenceClassification,
      IBertForTokenClassification,
      IBertModel,
      IBertPreTrainedModel,
  )

else:
 import sys

 __a     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



