




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__				:      Optional[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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					List[str]:
     return (abs(source - target) / target) < 0.01





@pytest.mark.integration
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Union[str, Any]:
     a     =				_TestCommandArgs(dataset=__UpperCamelCase		, all_configs=__UpperCamelCase		, save_infos=__UpperCamelCase)
     a     =				TestCommand(*__UpperCamelCase)
     test_command.run()
     a     =				os.path.join(__UpperCamelCase		, "README.md")
     assert os.path.exists(__UpperCamelCase)
     a     =				DatasetInfosDict.from_directory(__UpperCamelCase)
     a     =				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:
          a  ,				a     =				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







import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property




@unittest.skipUnless(os.path.exists(UpperCamelCase__				)       , """Tatoeba directory does not exist."""				)
class       a__	( unittest.TestCase				):







     @cached_property
     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				tempfile.mkdtemp()
          return TatoebaConverter(save_dir=A			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          self.resolver.convert_models(["heb-eng"]			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a  ,				a     =				self.resolver.write_model_card("opus-mt-he-en"  ,		dry_run=A			)
          assert mmeta["long_pair"] == "heb-eng"

