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

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


@dataclass(frozen=_lowerCamelCase			)
class      lowerCamelCase_					(       _lowerCamelCase			):






				'''simple docstring'''






				a__     :						Dict      =				field(default="""image-classification"""						,     metadata={"""include_in_asdict_even_if_is_default""": True}			)
				a__     :						List[str]      =				Features({"""image""": Image()}			)
				a__     :						Union[str, Any]      =				Features({"""labels""": ClassLabel}			)
				a__     :						int      =				"""image"""
				a__     :						Tuple      =				"""labels"""







				def       UpperCamelCase__       (     self			,			__lowercase)	->     str:
								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]			,			lowercase_):
												raise ValueError(f"""Column {self.label_column} is not a ClassLabel.""")
								__UpperCamelCase					:str        =		copy.deepcopy(self)
								__UpperCamelCase					:List[str]        =		self.label_schema.copy()
								__UpperCamelCase					:List[Any]        =		features[self.label_column]
								__UpperCamelCase					:int        =		label_schema
								return task_template







				@property
				def       UpperCamelCase__       (     self)	->     List[Any]:
								return {
								    self.image_column: "image",
								    self.label_column: "labels",
								}






from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .notes_encoder import SpectrogramNotesEncoder
    from .continous_encoder import SpectrogramContEncoder
    from .pipeline_spectrogram_diffusion import (
        SpectrogramContEncoder,
        SpectrogramDiffusionPipeline,
        TaFilmDecoder,
    )

try:
    if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
    from .midi_utils import MidiProcessor





