



from ...configuration_utils import PretrainedConfig


lowerCAmelCase__		:					str							={
    '''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_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			List[str]      =  '''tapas'''






						def __init__(   self				,							_A=30_522				,							_A=768				,							_A=12				,							_A=12				,							_A=3_072				,							_A="gelu"				,							_A=0.1				,							_A=0.1				,							_A=1_024				,							_A=[3, 256, 256, 2, 256, 256, 10]				,							_A=0.0_2				,							_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				,							):







													'''simple docstring'''

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

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

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

													# Aggregation hyperparameters
													__SCREAMING_SNAKE_CASE									= aggregation_labels
													__SCREAMING_SNAKE_CASE									= no_aggregation_label_index

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











from typing import TYPE_CHECKING

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


lowerCAmelCase__		:					Optional[int]							={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

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

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Tuple							=[
			    '''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__		:					Any							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






