






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__       		=  {
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__       		=  ["""RobertaTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__       		=  [
         """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """RobertaForCausalLM""",
         """RobertaForMaskedLM""",
         """RobertaForMultipleChoice""",
         """RobertaForQuestionAnswering""",
         """RobertaForSequenceClassification""",
         """RobertaForTokenClassification""",
         """RobertaModel""",
         """RobertaPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__       		=  [
         """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFRobertaForCausalLM""",
         """TFRobertaForMaskedLM""",
         """TFRobertaForMultipleChoice""",
         """TFRobertaForQuestionAnswering""",
         """TFRobertaForSequenceClassification""",
         """TFRobertaForTokenClassification""",
         """TFRobertaMainLayer""",
         """TFRobertaModel""",
         """TFRobertaPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__       		=  [
         """FlaxRobertaForCausalLM""",
         """FlaxRobertaForMaskedLM""",
         """FlaxRobertaForMultipleChoice""",
         """FlaxRobertaForQuestionAnswering""",
         """FlaxRobertaForSequenceClassification""",
         """FlaxRobertaForTokenClassification""",
         """FlaxRobertaModel""",
         """FlaxRobertaPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
     from .tokenization_roberta import RobertaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_roberta_fast import RobertaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roberta import (
              ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              RobertaForCausalLM,
              RobertaForMaskedLM,
              RobertaForMultipleChoice,
              RobertaForQuestionAnswering,
              RobertaForSequenceClassification,
              RobertaForTokenClassification,
              RobertaModel,
              RobertaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_roberta import (
              TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRobertaForCausalLM,
              TFRobertaForMaskedLM,
              TFRobertaForMultipleChoice,
              TFRobertaForQuestionAnswering,
              TFRobertaForSequenceClassification,
              TFRobertaForTokenClassification,
              TFRobertaMainLayer,
              TFRobertaModel,
              TFRobertaPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_roberta import (
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaModel,
              FlaxRobertaPreTrainedModel,
          )

else:
     import sys

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










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()}


