



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   ):



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

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





       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           import apache_beam as beam

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


class    _A	(     datasets.BeamBasedBuilder   ):



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


           '''simple docstring'''

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





       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           import apache_beam as beam

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





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





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

class    _A	(     __UpperCAmelCase   ):



       @require_beam
       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    len(get_test_dummy_examples())
           with tempfile.TemporaryDirectory() as tmp_cache_dir:
               __a							=    DummyBeamDataset(cache_dir=__SCREAMING_SNAKE_CASE					,		beam_runner='''DirectRunner''')
               builder.download_and_prepare()
               self.assertTrue(
                   os.path.exists(
                       os.path.join(__SCREAMING_SNAKE_CASE					,		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					,		__SCREAMING_SNAKE_CASE)
               self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples					,		__SCREAMING_SNAKE_CASE)
               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(__SCREAMING_SNAKE_CASE					,		builder.name					,		'''default'''					,		'''0.0.0'''					,		'''dataset_info.json''')))
               del dset



       @require_beam
       def 		_lowerCamelCase      (		self							:					List[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=__SCREAMING_SNAKE_CASE					,		beam_runner='''DirectRunner''')
               with patch('''apache_beam.io.parquetio.WriteToParquet''') as write_parquet_mock:
                   __a							=    partial(__SCREAMING_SNAKE_CASE					,		num_shards=2)
                   builder.download_and_prepare()
               self.assertTrue(
                   os.path.exists(
                       os.path.join(
                           __SCREAMING_SNAKE_CASE					,		builder.name					,		'''default'''					,		'''0.0.0'''					,		F'{builder.name}-train-00000-of-00002.arrow')))
               self.assertTrue(
                   os.path.exists(
                       os.path.join(
                           __SCREAMING_SNAKE_CASE					,		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					,		__SCREAMING_SNAKE_CASE)
               self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples					,		__SCREAMING_SNAKE_CASE)
               # 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(__SCREAMING_SNAKE_CASE					,		builder.name					,		'''default'''					,		'''0.0.0'''					,		'''dataset_info.json''')))
               del dset



       @require_beam
       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

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





       @require_beam
       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           __a							=    len(get_test_nested_examples())
           with tempfile.TemporaryDirectory() as tmp_cache_dir:
               __a							=    NestedBeamDataset(cache_dir=__SCREAMING_SNAKE_CASE					,		beam_runner='''DirectRunner''')
               builder.download_and_prepare()
               self.assertTrue(
                   os.path.exists(
                       os.path.join(__SCREAMING_SNAKE_CASE					,		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					,		__SCREAMING_SNAKE_CASE)
               self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples					,		__SCREAMING_SNAKE_CASE)
               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(__SCREAMING_SNAKE_CASE					,		builder.name					,		'''default'''					,		'''0.0.0'''					,		'''dataset_info.json''')))
               del dset










def 					__snake_case     (      _UpperCAmelCase    ):
    if not isinstance(_UpperCAmelCase       ,			_UpperCAmelCase    ):
        raise TypeError('''Input value must be an \'int\' type'''    )

    __a							=    0
    while number:
        position += 1
        number >>= 1

    return position


if __name__ == "__main__":
 import doctest

 doctest.testmod()





