


"""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 			lowerCamelCase__						(   datasets.BeamBasedBuilder			):







						def __a						(							self   :    str		):
								return datasets.DatasetInfo(
								    features=datasets.Features({'content': datasets.Value('string'		)}		)			,			supervised_keys=__snake_case			,			)







						def __a						(							self   :    int			,			_lowercase   :    Optional[Any]			,			_lowercase   :    Optional[Any]		):
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,			gen_kwargs={'examples': get_test_dummy_examples()}		)]







						def __a						(							self   :    int			,			_lowercase   :    int			,			_lowercase   :    Dict		):
								import apache_beam as beam

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



class 			lowerCamelCase__						(   datasets.BeamBasedBuilder			):







						def __a						(							self   :    Union[str, Any]		):
								return datasets.DatasetInfo(
								    features=datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'		)}		)}		)			,			supervised_keys=__snake_case			,			)







						def __a						(							self   :    Optional[int]			,			_lowercase   :    Dict			,			_lowercase   :    Union[str, Any]		):
								return [
								    datasets.SplitGenerator(name=datasets.Split.TRAIN			,			gen_kwargs={'examples': get_test_nested_examples()}		)
								]







						def __a						(							self   :    int			,			_lowercase   :    Optional[Any]			,			_lowercase   :    Dict		):
								import apache_beam as beam

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







def  __snake_case       (   )      ->		Optional[int]:







		"""simple docstring"""





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







def  __snake_case       (   )      ->		List[Any]:







		"""simple docstring"""





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



class 			lowerCamelCase__						(   __SCREAMING_SNAKE_CASE			):







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







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







						@require_beam
						def __a						(							self   :    Any		):
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
										A					       = DummyBeamDataset(cache_dir=__snake_case		)
										self.assertRaises(datasets.builder.MissingBeamOptions			,			builder.download_and_prepare		)







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









"""simple docstring"""


def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ = " "	)      ->		list:







		"""simple docstring"""





		A					       = []

		A					       = 0
		for index, char in enumerate(UpperCamelCase__	):
				if char == separator:
						split_words.append(string[last_index:index]	)
						A					       = index + 1
				elif index + 1 == len(UpperCamelCase__	):
						split_words.append(string[last_index : index + 1]	)
		return split_words


if __name__ == "__main__":
	from doctest import testmod

	testmod()





