
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


lowercase__          =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					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    Union[str, Any]  ):
 return (abs(source - target  ) / target) < 0.01




@pytest.mark.integration
def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[int]  ):
 __a			:  str		    =  _TestCommandArgs(dataset=snake_case_  ,       all_configs=snake_case_  ,       save_infos=snake_case_  )
 __a			:  str		    =  TestCommand(*snake_case_  )
 test_command.run()
 __a			:  Optional[Any]		    =  os.path.join(snake_case_  ,       '''README.md'''  )
 assert os.path.exists(snake_case_  )
 __a			:  int		    =  DatasetInfosDict.from_directory(snake_case_  )
 __a			:  Union[str, Any]		    =  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:
  __a    ,	__a			:  List[Any]		    =  getattr(dataset_infos['''default''']  ,       snake_case_  ), getattr(expected_dataset_infos['''default''']  ,       snake_case_  )
  if key == "num_bytes":
   assert is_apercent_close(snake_case_  ,       snake_case_  )
  elif key == "splits":
   assert list(snake_case_  ) == list(snake_case_  )
   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


from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
