


'''simple docstring'''







from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):






			def __init__( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[NestedDataStructureLike[PathLike]] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[NamedSplit] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[Features] = None  ,						__SCREAMING_SNAKE_CASE      :			str = None  ,						__SCREAMING_SNAKE_CASE      :			bool = False  ,						__SCREAMING_SNAKE_CASE      :			bool = False  ,						__SCREAMING_SNAKE_CASE      :			Optional[int] = None  ,						**__SCREAMING_SNAKE_CASE      :			List[str]  ,						)			->			Optional[Any]:
							a_		:  List[str]											=      path_or_paths
							a_		:  int											=      split if split or isinstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					) else '''train'''
							a_		:  Optional[int]											=      features
							a_		:  List[Any]											=      cache_dir
							a_		:  int											=      keep_in_memory
							a_		:  int											=      streaming
							a_		:  List[Any]											=      num_proc
							a_		:  str											=      kwargs







			@abstractmethod
			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
							pass





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):






			def __init__( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[Features] = None  ,						__SCREAMING_SNAKE_CASE      :			str = None  ,						__SCREAMING_SNAKE_CASE      :			bool = False  ,						__SCREAMING_SNAKE_CASE      :			bool = False  ,						__SCREAMING_SNAKE_CASE      :			Optional[int] = None  ,						**__SCREAMING_SNAKE_CASE      :			Any  ,						)			->			Tuple:
							a_		:  List[Any]											=      features
							a_		:  Any											=      cache_dir
							a_		:  Optional[Any]											=      keep_in_memory
							a_		:  Tuple											=      streaming
							a_		:  int											=      num_proc
							a_		:  Dict											=      kwargs







			@abstractmethod
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			Union[Dataset, IterableDataset]:
							pass












'''simple docstring'''







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

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





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






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Any:
							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]  ,						__SCREAMING_SNAKE_CASE					):
											raise ValueError(f'Column {self.label_column} is not a ClassLabel.'					)
							a_		:  Optional[int]											=      copy.deepcopy(self					)
							a_		:  int											=      self.label_schema.copy()
							a_		:  Tuple											=      features[self.label_column]
							a_		:  str											=      label_schema
							return task_template







			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Dict[str, str]:
							return {
							    self.image_column: "image",
							    self.label_column: "labels",
							}




