






from ...configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE__		:							Tuple 				=				{
    """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_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  'tapas'




     def __init__(				self	,						_lowerCAmelCase=30522	,						_lowerCAmelCase=768	,						_lowerCAmelCase=12	,						_lowerCAmelCase=12	,						_lowerCAmelCase=3072	,						_lowerCAmelCase="gelu"	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=1024	,						_lowerCAmelCase=[3, 256, 256, 2, 256, 256, 10]	,						_lowerCAmelCase=0.0_2	,						_lowerCAmelCase=1e-12	,						_lowerCAmelCase=0	,						_lowerCAmelCase=1_0.0	,						_lowerCAmelCase=0	,						_lowerCAmelCase=1.0	,						_lowerCAmelCase=None	,						_lowerCAmelCase=1.0	,						_lowerCAmelCase=False	,						_lowerCAmelCase=None	,						_lowerCAmelCase=1.0	,						_lowerCAmelCase=1.0	,						_lowerCAmelCase=False	,						_lowerCAmelCase=False	,						_lowerCAmelCase="ratio"	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None	,						_lowerCAmelCase=64	,						_lowerCAmelCase=32	,						_lowerCAmelCase=False	,						_lowerCAmelCase=True	,						_lowerCAmelCase=False	,						_lowerCAmelCase=False	,						_lowerCAmelCase=True	,						_lowerCAmelCase=False	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None	,						**_lowerCAmelCase	,						):
          super().__init__(pad_token_id=_lowerCAmelCase	,						**_lowerCAmelCase		)

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

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

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

          if isinstance(self.aggregation_labels	,						_lowerCAmelCase		):
               UpperCAmelCase__      :  Optional[int]				      =			{int(_lowerCAmelCase		): v for k, v in aggregation_labels.items()}













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A					:				List[str]				=			{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Dict				=			[
      """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """WavLMForAudioFrameClassification""",
      """WavLMForCTC""",
      """WavLMForSequenceClassification""",
      """WavLMForXVector""",
      """WavLMModel""",
      """WavLMPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  _A					:				List[Any]				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
