






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_       :Tuple       =						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     (       a_							,a_					)     ->						Dict:
					return (abs(source - target					) / target) < 0.01






@pytest.mark.integration
def 							A     (       a_					)     ->						Dict:
					__UpperCamelCase      :					Optional[int]       		=_TestCommandArgs(dataset=a_							,all_configs=a_							,save_infos=a_					)
					__UpperCamelCase      :					Any       		=TestCommand(*a_					)
					test_command.run()
					__UpperCamelCase      :					List[str]       		=os.path.join(a_							,'README.md'					)
					assert os.path.exists(a_					)
					__UpperCamelCase      :					Any       		=DatasetInfosDict.from_directory(a_					)
					__UpperCamelCase      :					int       		=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_351_563,
					                    'num_examples': 10_000,
					                },
					                {
					                    'name': 'validation',
					                    'num_bytes': 238_418,
					                    'num_examples': 1_000,
					                },
					            ]							,download_size=3_940_680							,dataset_size=2_589_981							,)
					    }					)
					assert dataset_infos.keys() == expected_dataset_infos.keys()
					for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
										__UpperCamelCase		,				__UpperCamelCase      :					Optional[Any]       		=getattr(dataset_infos['default']							,a_					), getattr(expected_dataset_infos['default']							,a_					)
										if key == "num_bytes":
															assert is_apercent_close(a_							,a_					)
										elif key == "splits":
															assert list(a_					) == list(a_					)
															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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


_A	:			Dict         ={'''tokenization_herbert''': ['''HerbertTokenizer''']}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			Optional[int]         =['''HerbertTokenizerFast''']


if TYPE_CHECKING:
			from .tokenization_herbert import HerbertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_herbert_fast import HerbertTokenizerFast

else:
			import sys

			_A	:			Optional[int]         =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

