



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_   						=   {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   ['FNetTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   ['FNetTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'FNetForMaskedLM',
							    'FNetForMultipleChoice',
							    'FNetForNextSentencePrediction',
							    'FNetForPreTraining',
							    'FNetForQuestionAnswering',
							    'FNetForSequenceClassification',
							    'FNetForTokenClassification',
							    'FNetLayer',
							    'FNetModel',
							    'FNetPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet import FNetTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet_fast import FNetTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_fnet import (
														    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FNetForMaskedLM,
														    FNetForMultipleChoice,
														    FNetForNextSentencePrediction,
														    FNetForPreTraining,
														    FNetForQuestionAnswering,
														    FNetForSequenceClassification,
														    FNetForTokenClassification,
														    FNetLayer,
														    FNetModel,
														    FNetPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase_   						=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""simple docstring"""







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        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					str   	=				"tapas"







		def __init__(	self						:  List[Any]			,_snake_case						:  Dict=30_522			,_snake_case						:  Union[str, Any]=768			,_snake_case						:  int=12			,_snake_case						:  Union[str, Any]=12			,_snake_case						:  Union[str, Any]=3_072			,_snake_case						:  List[Any]="gelu"			,_snake_case						:  Optional[int]=0.1			,_snake_case						:  Tuple=0.1			,_snake_case						:  List[Any]=1_024			,_snake_case						:  Any=[3, 256, 256, 2, 256, 256, 10]			,_snake_case						:  List[Any]=0.02			,_snake_case						:  Union[str, Any]=1e-12			,_snake_case						:  str=0			,_snake_case						:  Any=10.0			,_snake_case						:  int=0			,_snake_case						:  Optional[Any]=1.0			,_snake_case						:  List[str]=None			,_snake_case						:  Tuple=1.0			,_snake_case						:  Tuple=False			,_snake_case						:  List[Any]=None			,_snake_case						:  int=1.0			,_snake_case						:  List[Any]=1.0			,_snake_case						:  Optional[int]=False			,_snake_case						:  Optional[int]=False			,_snake_case						:  Optional[int]="ratio"			,_snake_case						:  Any=None			,_snake_case						:  Union[str, Any]=None			,_snake_case						:  List[str]=64			,_snake_case						:  Optional[Any]=32			,_snake_case						:  Optional[Any]=False			,_snake_case						:  Optional[int]=True			,_snake_case						:  Dict=False			,_snake_case						:  Tuple=False			,_snake_case						:  int=True			,_snake_case						:  List[str]=False			,_snake_case						:  Dict=None			,_snake_case						:  Optional[int]=None			,**_snake_case						:  int			,)					->		List[Any]:






				"""simple docstring"""



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

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

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

				# Aggregation hyperparameters
				lowercase__   :    Optional[Any]				=			aggregation_labels
				lowercase__   :    List[Any]				=			no_aggregation_label_index

				if isinstance(self.aggregation_labels			,_snake_case						):
						lowercase__   :    Union[str, Any]				=			{int(_snake_case						): v for k, v in aggregation_labels.items()}
