



'''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  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





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






    @property
    def  A       (  self      :					Dict ):


       """simple docstring"""







       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,
    is_vision_available,
)


_snake_case          =   {
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   ['PerceiverFeatureExtractor']
 _snake_case          =   ['PerceiverImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   [
     'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'PerceiverForImageClassificationConvProcessing',
     'PerceiverForImageClassificationFourier',
     'PerceiverForImageClassificationLearned',
     'PerceiverForMaskedLM',
     'PerceiverForMultimodalAutoencoding',
     'PerceiverForOpticalFlow',
     'PerceiverForSequenceClassification',
     'PerceiverLayer',
     'PerceiverModel',
     'PerceiverPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
 from .tokenization_perceiver import PerceiverTokenizer

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_perceiver import PerceiverFeatureExtractor
  from .image_processing_perceiver import PerceiverImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_perceiver import (
      PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
      PerceiverForImageClassificationConvProcessing,
      PerceiverForImageClassificationFourier,
      PerceiverForImageClassificationLearned,
      PerceiverForMaskedLM,
      PerceiverForMultimodalAutoencoding,
      PerceiverForOpticalFlow,
      PerceiverForSequenceClassification,
      PerceiverLayer,
      PerceiverModel,
      PerceiverPreTrainedModel,
  )

else:
 import sys

 _snake_case          =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

