






from typing import Dict

from .base import GenericTensor, Pipeline





class 	snake_case__ (							_lowerCAmelCase				):
	def       __magic_name__    (    self      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       **lowerCAmelCase__			)							->				Union[str, Any]:
							if tokenize_kwargs is None:
													__magic_name__      :       int       						= {}

							if truncation is not None:
													if "truncation" in tokenize_kwargs:
																			raise ValueError(
																			    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""			)
													__magic_name__      :       Any       						= truncation

							__magic_name__      :       Tuple       						= tokenize_kwargs

							__magic_name__      :       List[Any]       						= {}
							if return_tensors is not None:
													__magic_name__      :       List[str]       						= return_tensors

							return preprocess_params, {}, postprocess_params
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				Dict[str, GenericTensor]:
							__magic_name__      :       str       						= self.framework
							__magic_name__      :       Optional[Any]       						= self.tokenizer(lowerCAmelCase__      ,       return_tensors=lowerCAmelCase__      ,       **lowerCAmelCase__			)
							return model_inputs
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Tuple:
							__magic_name__      :       Tuple       						= self.model(**lowerCAmelCase__			)
							return model_outputs
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=False			)							->				Tuple:
							# [0] is the first available tensor, logits or last_hidden_state.
							if return_tensors:
													return model_outputs[0]
							if self.framework == "pt":
													return model_outputs[0].tolist()
							elif self.framework == "tf":
													return model_outputs[0].numpy().tolist()







	def __call__(    self      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				Optional[Any]:
							return super().__call__(*lowerCAmelCase__      ,       **lowerCAmelCase__			)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__magic_name__:					Tuple							=     {
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					Dict							=     [
		    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TapasForMaskedLM",
		    "TapasForQuestionAnswering",
		    "TapasForSequenceClassification",
		    "TapasModel",
		    "TapasPreTrainedModel",
		    "load_tf_weights_in_tapas",
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					int							=     [
		    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFTapasForMaskedLM",
		    "TFTapasForQuestionAnswering",
		    "TFTapasForSequenceClassification",
		    "TFTapasModel",
		    "TFTapasPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
		from .tokenization_tapas import TapasTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tapas import (
				    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TapasForMaskedLM,
				    TapasForQuestionAnswering,
				    TapasForSequenceClassification,
				    TapasModel,
				    TapasPreTrainedModel,
				    load_tf_weights_in_tapas,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_tapas import (
				    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTapasForMaskedLM,
				    TFTapasForQuestionAnswering,
				    TFTapasForSequenceClassification,
				    TFTapasModel,
				    TFTapasPreTrainedModel,
				)


else:
		import sys

		__magic_name__:					Tuple							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
