



'''simple docstring'''





import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam






class __A     (    datasets.BeamBasedBuilder		):



 '''simple docstring'''







 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      return datasets.DatasetInfo(
          features=datasets.Features({'''content''': datasets.Value('''string'''			)}			)					,  supervised_keys=A					,  )



 def    a__    (self					,  A					,  A			)       ->	Optional[Any]:


      """simple docstring"""



      return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,  gen_kwargs={'''examples''': get_test_dummy_examples()}			)]



 def    a__    (self					,  A					,  A			)       ->	str:


      """simple docstring"""



      import apache_beam as beam

      return pipeline | "Load Examples" >> beam.Create(A			)






class __A     (    datasets.BeamBasedBuilder		):



 '''simple docstring'''







 def    a__    (self			)       ->	int:


      """simple docstring"""



      return datasets.DatasetInfo(
          features=datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string'''			)}			)}			)					,  supervised_keys=A					,  )



 def    a__    (self					,  A					,  A			)       ->	str:


      """simple docstring"""



      return [
          datasets.SplitGenerator(name=datasets.Split.TRAIN					,  gen_kwargs={'''examples''': get_test_nested_examples()}			)
      ]



 def    a__    (self					,  A					,  A			)       ->	int:


      """simple docstring"""



      import apache_beam as beam

      return pipeline | "Load Examples" >> beam.Create(A			)





def lowerCAmelCase    ():



     """simple docstring"""



     return [(i, {"content": content}) for i, content in enumerate(['''foo''', '''bar''', '''foobar'''])]




def lowerCAmelCase    ():



     """simple docstring"""



     return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['''foo''', '''bar''', '''foobar'''])]






class __A     (    A		):



 '''simple docstring'''







 @require_beam
 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    len(get_test_dummy_examples()			)
      with tempfile.TemporaryDirectory() as tmp_cache_dir:
           _a           =    DummyBeamDataset(cache_dir=A					,  beam_runner='''DirectRunner'''			)
           builder.download_and_prepare()
           self.assertTrue(
               os.path.exists(
                   os.path.join(A					,  builder.name					,  '''default'''					,  '''0.0.0'''					,  f'''{builder.name}-train.arrow'''			)			)			)
           self.assertDictEqual(builder.info.features					,  datasets.Features({'''content''': datasets.Value('''string'''			)}			)			)
           _a           =    builder.as_dataset()
           self.assertEqual(dset['''train'''].num_rows					,  A			)
           self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples					,  A			)
           self.assertDictEqual(dset['''train'''][0]					,  get_test_dummy_examples()[0][1]			)
           self.assertDictEqual(
               dset['''train'''][expected_num_examples - 1]					,  get_test_dummy_examples()[expected_num_examples - 1][1]			)
           self.assertTrue(
               os.path.exists(os.path.join(A					,  builder.name					,  '''default'''					,  '''0.0.0'''					,  '''dataset_info.json'''			)			)			)
           del dset



 @require_beam
 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      import apache_beam as beam

      _a           =    beam.io.parquetio.WriteToParquet

      _a           =    len(get_test_dummy_examples()			)
      with tempfile.TemporaryDirectory() as tmp_cache_dir:
           _a           =    DummyBeamDataset(cache_dir=A					,  beam_runner='''DirectRunner'''			)
           with patch('''apache_beam.io.parquetio.WriteToParquet'''			) as write_parquet_mock:
                _a           =    partial(A					,  num_shards=2			)
                builder.download_and_prepare()
           self.assertTrue(
               os.path.exists(
                   os.path.join(
                       A					,  builder.name					,  '''default'''					,  '''0.0.0'''					,  f'''{builder.name}-train-00000-of-00002.arrow'''			)			)			)
           self.assertTrue(
               os.path.exists(
                   os.path.join(
                       A					,  builder.name					,  '''default'''					,  '''0.0.0'''					,  f'''{builder.name}-train-00000-of-00002.arrow'''			)			)			)
           self.assertDictEqual(builder.info.features					,  datasets.Features({'''content''': datasets.Value('''string'''			)}			)			)
           _a           =    builder.as_dataset()
           self.assertEqual(dset['''train'''].num_rows					,  A			)
           self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples					,  A			)
           # Order is not preserved when sharding, so we just check that all the elements are there
           self.assertListEqual(sorted(dset['''train''']['''content''']			)					,  sorted(['''foo''', '''bar''', '''foobar''']			)			)
           self.assertTrue(
               os.path.exists(os.path.join(A					,  builder.name					,  '''default'''					,  '''0.0.0'''					,  '''dataset_info.json'''			)			)			)
           del dset



 @require_beam
 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      with tempfile.TemporaryDirectory() as tmp_cache_dir:
           _a           =    DummyBeamDataset(cache_dir=A			)
           self.assertRaises(datasets.builder.MissingBeamOptions					,  builder.download_and_prepare			)



 @require_beam
 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    len(get_test_nested_examples()			)
      with tempfile.TemporaryDirectory() as tmp_cache_dir:
           _a           =    NestedBeamDataset(cache_dir=A					,  beam_runner='''DirectRunner'''			)
           builder.download_and_prepare()
           self.assertTrue(
               os.path.exists(
                   os.path.join(A					,  builder.name					,  '''default'''					,  '''0.0.0'''					,  f'''{builder.name}-train.arrow'''			)			)			)
           self.assertDictEqual(
               builder.info.features					,  datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string'''			)}			)}			)			)
           _a           =    builder.as_dataset()
           self.assertEqual(dset['''train'''].num_rows					,  A			)
           self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples					,  A			)
           self.assertDictEqual(dset['''train'''][0]					,  get_test_nested_examples()[0][1]			)
           self.assertDictEqual(
               dset['''train'''][expected_num_examples - 1]					,  get_test_nested_examples()[expected_num_examples - 1][1]			)
           self.assertTrue(
               os.path.exists(os.path.join(A					,  builder.name					,  '''default'''					,  '''0.0.0'''					,  '''dataset_info.json'''			)			)			)
           del dset








'''simple docstring'''





def lowerCAmelCase    (__A):



     """simple docstring"""



     if not isinstance(__A    ,      __A):
          raise ValueError('''multiplicative_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''multiplicative_persistence() does not accept negative values''')

     _a           =    0
     _a           =    str(__A)

     while len(__A) != 1:
          _a           =    [int(__A) for i in num_string]

          _a           =    1
          for i in range(0    ,      len(__A)):
               total *= numbers[i]

          _a           =    str(__A)

          steps += 1
     return steps




def lowerCAmelCase    (__A):



     """simple docstring"""



     if not isinstance(__A    ,      __A):
          raise ValueError('''additive_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''additive_persistence() does not accept negative values''')

     _a           =    0
     _a           =    str(__A)

     while len(__A) != 1:
          _a           =    [int(__A) for i in num_string]

          _a           =    0
          for i in range(0    ,      len(__A)):
               total += numbers[i]

          _a           =    str(__A)

          steps += 1
     return steps


if __name__ == "__main__":
  import doctest

  doctest.testmod()



