
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__		:     Optional[Any]     			=      {
    'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__		:     List[Any]     			=      [
       'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TimesformerModel',
       'TimesformerForVideoClassification',
       'TimesformerPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_timesformer import (
          TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TimesformerForVideoClassification,
          TimesformerModel,
          TimesformerPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase__		:     List[str]     			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





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'''
   # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
   __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  __magic_name__				(					self     :							List[str]     ):

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



