
'''simple docstring'''



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

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




@dataclass(frozen=_SCREAMING_SNAKE_CASE					)
class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






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




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



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


'''simple docstring'''



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    ImageTextPipelineOutput,
						    UniDiffuserPipeline,
						)
else:
						from .modeling_text_decoder import UniDiffuserTextDecoder
						from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
						from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
