





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

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate

@dataclass(frozen=a    )
class       __A     (						a    ):
				__A      =				field(default="""image-classification"""    ,					metadata={"""include_in_asdict_even_if_is_default""": True}    )
				__A      =				Features({"""image""": Image()}    )
				__A      =				Features({"""labels""": ClassLabel}    )
				__A      =				"image"
				__A      =				"labels"


				def _snake_case       (		self   , UpperCAmelCase_							):
								if self.label_column not in features:
												raise ValueError(f"""Column {self.label_column} is not present in features."""							)
								if not isinstance(features[self.label_column]   , UpperCAmelCase_							):
												raise ValueError(f"""Column {self.label_column} is not a ClassLabel."""							)
								lowerCamelCase	 =copy.deepcopy(self							)
								lowerCamelCase	 =self.label_schema.copy()
								lowerCamelCase	 =features[self.label_column]
								lowerCamelCase	 =label_schema
								return task_template


				@property
				def _snake_case       (		self							):
								return {
								    self.image_column: "image",
								    self.label_column: "labels",
								}












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


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
							from .pipeline_kandinsky import KandinskyPipeline
							from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
							from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
							from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
							from .text_encoder import MultilingualCLIP





