


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

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


@dataclass(frozen=UpperCamelCase__     )
class 			_a				(     UpperCamelCase__     ):
							_lowercase      :     str     =					field(default='''audio-classification'''	,      metadata={'''include_in_asdict_even_if_is_default''': True}     )
							_lowercase      :     ClassVar[Features]     =					Features({'''audio''': Audio()}     )
							_lowercase      :     ClassVar[Features]     =					Features({'''labels''': ClassLabel}     )
							_lowercase      :     str     =					"audio"
							_lowercase      :     str     =					"labels"

							def  lowerCamelCase_					(				self:      str       ,		UpperCamelCase_:      Tuple				)     ->  List[str]:





									"""simple docstring"""







									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.'				)
									lowercase__				= copy.deepcopy(self				)
									lowercase__				= self.label_schema.copy()
									lowercase__				= features[self.label_column]
									lowercase__				= label_schema
									return task_template


							@property
							def  lowerCamelCase_					(				self:      Tuple				)     ->  Dict[str, str]:





									"""simple docstring"""







									return {
									    self.audio_column: "audio",
									    self.label_column: "labels",
									}




from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class       _lowerCAmelCase   (      __a			):
				_lowercase     						=42
				_lowercase     						=42


if is_transformers_available() and is_torch_available():
			from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline



