
"""simple docstring"""





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


__magic_name__       =			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 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		return (abs(source - target				) / target) < 0.01



@pytest.mark.integration
def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = _TestCommandArgs(dataset=UpperCamelCase_  ,				all_configs=UpperCamelCase_  ,				save_infos=UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = TestCommand(*UpperCamelCase_				)
		test_command.run()
		__SCREAMING_SNAKE_CASE            = os.path.join(UpperCamelCase_  ,				"""README.md"""				)
		assert os.path.exists(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = DatasetInfosDict.from_directory(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = 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""": 235_1563,
		                    """num_examples""": 1_0000,
		                },
		                {
		                    """name""": """validation""",
		                    """num_bytes""": 23_8418,
		                    """num_examples""": 1000,
		                },
		            ]  ,				download_size=394_0680  ,				dataset_size=258_9981  ,				)
		    }				)
		assert dataset_infos.keys() == expected_dataset_infos.keys()
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = getattr(dataset_infos["""default"""]  ,				UpperCamelCase_				), getattr(expected_dataset_infos["""default"""]  ,				UpperCamelCase_				)
				if key == "num_bytes":
						assert is_apercent_close(UpperCamelCase_  ,				UpperCamelCase_				)
				elif key == "splits":
						assert list(UpperCamelCase_				) == list(UpperCamelCase_				)
						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






"""simple docstring"""





from ..utils import DummyObject, requires_backends
class 						SCREAMING_SNAKE_CASE_ (						metaclass=__a ):




		"""simple docstring"""





		__lowercase							:  Tuple						   =		['''keras_nlp''']




		def __init__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				requires_backends(self							,  ["""keras_nlp"""])




