


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

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

@dataclass(frozen=_lowerCAmelCase )
class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				str	    =    field(default="question-answering-extractive"	,							metadata={"include_in_asdict_even_if_is_default": True} )
							__UpperCAmelCase       :				ClassVar[Features]	    =    Features({"question": Value("string" ), "context": Value("string" )} )
							__UpperCAmelCase       :				ClassVar[Features]	    =    Features(
							    {
							        "answers": Sequence(
							            {
							                "text": Value("string" ),
							                "answer_start": Value("int32" ),
							            } )
							    } )
							__UpperCAmelCase       :				str	    =    "question"
							__UpperCAmelCase       :				str	    =    "context"
							__UpperCAmelCase       :				str	    =    "answers"

							@property
							def        __snake_case   (				self			:    Optional[Any]							)      ->       Dict[str, str]:
														return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}





'''simple docstring'''
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE					:				List[str]									=		{
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		[
							    '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GraphormerForGraphClassification''',
							    '''GraphormerModel''',
							    '''GraphormerPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							__SCREAMING_SNAKE_CASE					:				Optional[int]									=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





