


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

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




@dataclass(frozen=__snake_case    )
class    UpperCAmelCase					(       __snake_case    ):
			# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
			lowercase						= field(default="""question-answering-extractive"""      , metadata={"""include_in_asdict_even_if_is_default""": True}    )
			lowercase						= Features({"""question""": Value("""string"""    ), """context""": Value("""string"""    )}    )
			lowercase						= Features(
			    {
			        """answers""": Sequence(
			            {
			                """text""": Value("""string"""    ),
			                """answer_start""": Value("""int32"""    ),
			            }    )
			    }    )
			lowercase						= """question"""
			lowercase						= """context"""
			lowercase						= """answers"""




			@property
			def   lowerCamelCase_					(						self							:		List[Any]						)     ->   Optional[Any]:







						"""simple docstring"""







						return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}








from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




