






"""simple docstring"""



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

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





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




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











"""simple docstring"""



from ...utils import is_torch_available, is_transformers_available


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



