



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

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






@dataclass(frozen=_lowercase       )
class SCREAMING_SNAKE_CASE__	(						_lowercase       ):




    '''simple docstring'''






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






    @property
    def      _lowerCAmelCase   (			self						):
        return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_							:					int		 =   {
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					List[str]		 =   [
			    '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''InformerForPrediction''',
			    '''InformerModel''',
			    '''InformerPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_informer import (
						    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    InformerForPrediction,
						    InformerModel,
						    InformerPreTrainedModel,
						)

else:
			import sys

			UpperCAmelCase_							:					Optional[int]		 =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





