
'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_lowercase          =						{
    'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						['MobileViTFeatureExtractor']
						_lowercase          =						['MobileViTImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						[
						    'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'MobileViTForImageClassification',
						    'MobileViTForSemanticSegmentation',
						    'MobileViTModel',
						    'MobileViTPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						[
						    'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFMobileViTForImageClassification',
						    'TFMobileViTForSemanticSegmentation',
						    'TFMobileViTModel',
						    'TFMobileViTPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilevit import MobileViTFeatureExtractor
												from .image_processing_mobilevit import MobileViTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilevit import (
												    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileViTForImageClassification,
												    MobileViTForSemanticSegmentation,
												    MobileViTModel,
												    MobileViTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mobilevit import (
												    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMobileViTForImageClassification,
												    TFMobileViTForSemanticSegmentation,
												    TFMobileViTModel,
												    TFMobileViTPreTrainedModel,
												)


else:
						import sys

						_lowercase          =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

'''simple docstring'''

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

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


@dataclass(frozen=lowerCAmelCase )
class 					_lowercase						(   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 		lowerCAmelCase__       (					self				)      ->      Dict[str, str]:



         '''simple docstring'''



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




