

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

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






@dataclass(frozen=_lowercase	)
class      lowerCamelCase_      (	_lowercase	):
	# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
	_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        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		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


A__ :       List[str]				= {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__ :       Dict				= [
      'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BioGptForCausalLM',
      'BioGptForTokenClassification',
      'BioGptForSequenceClassification',
      'BioGptModel',
      'BioGptPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

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