

"""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


lowerCAmelCase							:  Any            =						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_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						return (abs(source - target   ) / target) < 0.0_1







@pytest.mark.integration
def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Any  		=	_TestCommandArgs(dataset=_UpperCAmelCase    ,     all_configs=_UpperCAmelCase    ,     save_infos=_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			int  		=	TestCommand(*_UpperCAmelCase   )
						test_command.run()
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	os.path.join(_UpperCAmelCase    ,     "README.md"   )
						assert os.path.exists(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Any  		=	DatasetInfosDict.from_directory(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			str  		=	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:
												SCREAMING_SNAKE_CASE_:			Dict  		=	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









def       A_				(   _UpperCAmelCase = 10**9   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	1
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	2

						SCREAMING_SNAKE_CASE_:			int  		=	0
						SCREAMING_SNAKE_CASE_:			Dict  		=	0
						SCREAMING_SNAKE_CASE_:			List[str]  		=	0
						while perimeter <= max_perimeter:
												perimeters_sum += perimeter

												prev_value += 2 * value
												value += prev_value

												SCREAMING_SNAKE_CASE_:			Any  		=	2 * value + 2 if i % 2 == 0 else 2 * value - 2
												i += 1

						return perimeters_sum


if __name__ == "__main__":
						print(f'''{solution() = }''')






