






from typing import TYPE_CHECKING

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


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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase    				=   [
	    "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:
	lowercase    				=   [
	    "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

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













from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate




@dataclass(frozen=snake_case_							)
class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					field(default='''language-modeling'''			,   metadata={'''include_in_asdict_even_if_is_default''': True}							)

	lowerCAmelCase						=					Features({'''text''': Value('''string'''							)}							)
	lowerCAmelCase						=					Features({}							)
	lowerCAmelCase						=					"text"




	@property
	def 				_UpperCamelCase     (			self   )			->       Dict[str, str]:
								return {self.text_column: "text"}





