'''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=  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  _UpperCamelCase (     _a	:	Dict				,     _a	:	Any			):




    """simple docstring"""



    return (abs(source - target			) / target) < 0.01







@pytest.mark.integration
def  _UpperCamelCase (     _a	:	Tuple			):




    """simple docstring"""



    __UpperCamelCase	:	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	:	Optional[Any]		    =  DatasetInfosDict.from_directory(_a			)
    __UpperCamelCase	:	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_3_5_1_5_6_3,
                        'num_examples': 1_0_0_0_0,
                    },
                    {
                        'name': 'validation',
                        'num_bytes': 2_3_8_4_1_8,
                        'num_examples': 1_0_0_0,
                    },
                ]				,     download_size=3_9_4_0_6_8_0				,     dataset_size=2_5_8_9_9_8_1				,     )
        }			)
    assert dataset_infos.keys() == expected_dataset_infos.keys()
    for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
        __UpperCamelCase	:	List[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_torch_available


a=  {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  [
      '''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''ViTMSNModel''',
      '''ViTMSNForImageClassification''',
      '''ViTMSNPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

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



