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

from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=a_ )
class    A_ (       a_ ):
		# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
		_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 		_UpperCAmelCase   (		self :      Optional[int]       ):
			return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
  from .pipeline_unclip import UnCLIPPipeline
  from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
  from .text_proj import UnCLIPTextProjModel


