




import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


a__		     =     namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)





def 				A__ (snake_case   :		Any		,	snake_case   :		Tuple				)     ->						Dict:
					return (abs(source - target				) / target) < 0.01







@pytest.mark.integration
def 				A__ (snake_case   :		Any				)     ->						int:
					__UpperCamelCase					:	Optional[int]             =      _TestCommandArgs(dataset=snake_case		,	all_configs=snake_case		,	save_infos=snake_case				)
					__UpperCamelCase					:	Dict             =      TestCommand(*snake_case				)
					test_command.run()
					__UpperCamelCase					:	List[Any]             =      os.path.join(snake_case		,	"""README.md"""				)
					assert os.path.exists(snake_case				)
					__UpperCamelCase					:	List[Any]             =      DatasetInfosDict.from_directory(snake_case				)
					__UpperCamelCase					:	Tuple             =      DatasetInfosDict(
					    {
					        """default""": DatasetInfo(
					            features=Features(
					                {
					                    """tokens""": Sequence(Value("""string"""				)				),
					                    """ner_tags""": Sequence(
					                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]				)				),
					                    """langs""": Sequence(Value("""string"""				)				),
					                    """spans""": Sequence(Value("""string"""				)				),
					                }				)		,	splits=[
					                {
					                    """name""": """train""",
					                    """num_bytes""": 2_35_15_63,
					                    """num_examples""": 1_00_00,
					                },
					                {
					                    """name""": """validation""",
					                    """num_bytes""": 23_84_18,
					                    """num_examples""": 10_00,
					                },
					            ]		,	download_size=3_94_06_80		,	dataset_size=2_58_99_81		,	)
					    }				)
					assert dataset_infos.keys() == expected_dataset_infos.keys()
					for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
										__UpperCamelCase      ,			__UpperCamelCase					:	int             =      getattr(dataset_infos["""default"""]		,	snake_case				), getattr(expected_dataset_infos["""default"""]		,	snake_case				)
										if key == "num_bytes":
															assert is_apercent_close(snake_case		,	snake_case				)
										elif key == "splits":
															assert list(snake_case				) == list(snake_case				)
															for split in result:
																				assert result[split].name == expected[split].name
																				assert result[split].num_examples == expected[split].num_examples
																				assert is_apercent_close(result[split].num_bytes		,	expected[split].num_bytes				)
										else:
															result == expected












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

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


@dataclass(frozen=_UpperCamelCase   )
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       str      	=		field(default='image-classification'	,  metadata={'include_in_asdict_even_if_is_default': True}   )
	__magic_name__   :       ClassVar[Features]      	=		Features({'image': Image()}   )
	__magic_name__   :       ClassVar[Features]      	=		Features({'labels': ClassLabel}   )
	__magic_name__   :       str      	=		"image"
	__magic_name__   :       str      	=		"labels"






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						Optional[Any]	)	->       Union[str, Any]:







						"""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]					,     lowerCAmelCase	):
											raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''	)
						__UpperCamelCase					:	List[str]             =      copy.deepcopy(self	)
						__UpperCamelCase					:	List[Any]             =      self.label_schema.copy()
						__UpperCamelCase					:	List[str]             =      features[self.label_column]
						__UpperCamelCase					:	List[str]             =      label_schema
						return task_template






	@property
	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Dict[str, str]:







						"""simple docstring"""







						return {
						    self.image_column: "image",
						    self.label_column: "labels",
						}






