




'''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


a    :							int										=    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 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[int]:

					'''simple docstring'''

					return (abs(source - target      ) / target) < 0.0_1


@pytest.mark.integration
def 						__magic_name__       (      __UpperCAmelCase      )						->   Tuple:

					'''simple docstring'''

					snake_case_		     =							_TestCommandArgs(dataset=__UpperCAmelCase, all_configs=__UpperCAmelCase, save_infos=__UpperCAmelCase      )
					snake_case_		     =							TestCommand(*__UpperCAmelCase      )
					test_command.run()
					snake_case_		     =							os.path.join(__UpperCAmelCase, '''README.md'''      )
					assert os.path.exists(__UpperCAmelCase      )
					snake_case_		     =							DatasetInfosDict.from_directory(__UpperCAmelCase      )
					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:
										snake_case_     ,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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a    :							Tuple										=    {
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a    :							str										=    [
							    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'EncodecModel',
							    'EncodecPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_encodec import (
							    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    EncodecConfig,
							)
							from .feature_extraction_encodec import EncodecFeatureExtractor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_encodec import (
														    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EncodecModel,
														    EncodecPreTrainedModel,
														)

else:
							import sys

							a    :							List[Any]										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

