def        __UpperCAmelCase    (       UpperCAmelCase       )->       str:



			"""simple docstring"""







			lowercase							=		0

			# if input_string is "aba" than new_input_string become "a|b|a"
			lowercase							=		''''''
			lowercase							=		''''''

			# append each character + "|" in new_string for range(0, length-1)
			for i in input_string[: len(UpperCAmelCase       ) - 1]:
						new_input_string += i + "|"
			# append last character
			new_input_string += input_string[-1]

			# we will store the starting and ending of previous furthest ending palindromic
			# substring
			lowercase		,lowercase							=		0, 0

			# length[i] shows the length of palindromic substring with center i
			lowercase							=		[1 for i in range(len(UpperCAmelCase       )       )]

			# for each character in new_string find corresponding palindromic string
			lowercase							=		0
			for j in range(len(UpperCAmelCase       )       ):
						lowercase							=		1 if j > r else min(length[l + r - j] // 2,		r - j + 1       )
						while (
						    j - k >= 0
						    and j + k < len(UpperCAmelCase       )
						    and new_input_string[k + j] == new_input_string[j - k]
						):
									k += 1

						lowercase							=		2 * k - 1

						# does this string is ending after the previously explored end (that is r) ?
						# if yes the update the new r to the last index of this
						if j + k - 1 > r:
									lowercase							=		j - k + 1  # noqa: E741
									lowercase							=		j + k - 1

						# update max_length and start position
						if max_length < length[j]:
									lowercase							=		length[j]
									lowercase							=		j

    # create that string
			lowercase							=		new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
			for i in s:
						if i != "|":
									output_string += i

			return output_string


if __name__ == "__main__":
						import doctest

						doctest.testmod()






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      __lowercase					(    datasets.BeamBasedBuilder    ):

						def        __a		(	self				:      Tuple   )							->		Tuple:


									'''simple docstring'''

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

						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Tuple   )							->		int:


									'''simple docstring'''

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



						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Union[str, Any]   )							->		Optional[int]:


									'''simple docstring'''

									import apache_beam as beam

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






class      __lowercase					(    datasets.BeamBasedBuilder    ):

						def        __a		(	self				:      int   )							->		Any:


									'''simple docstring'''

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

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Optional[int]   )							->		int:


									'''simple docstring'''

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



						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Union[str, Any]   )							->		Tuple:


									'''simple docstring'''

									import apache_beam as beam

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






def        __UpperCAmelCase    (       )->       Dict:



			"""simple docstring"""







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






def        __UpperCAmelCase    (       )->       List[str]:



			"""simple docstring"""







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






class      __lowercase					(    _A    ):

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


									'''simple docstring'''

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

						@require_beam
						def        __a		(	self				:      Union[str, Any]   )							->		List[Any]:


									'''simple docstring'''

									import apache_beam as beam

									lowercase							=		beam.io.parquetio.WriteToParquet

									lowercase							=		len(get_test_dummy_examples()   )
									with tempfile.TemporaryDirectory() as tmp_cache_dir:
												lowercase							=		DummyBeamDataset(cache_dir=__lowerCamelCase							,							beam_runner='''DirectRunner'''   )
												with patch('''apache_beam.io.parquetio.WriteToParquet'''   ) as write_parquet_mock:
															lowercase							=		partial(__lowerCamelCase							,							num_shards=2   )
															builder.download_and_prepare()
												self.assertTrue(
												    os.path.exists(
												        os.path.join(
												            __lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							f'{builder.name}-train-00000-of-00002.arrow'   )   )   )
												self.assertTrue(
												    os.path.exists(
												        os.path.join(
												            __lowerCamelCase							,							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'''   )}   )   )
												lowercase							=		builder.as_dataset()
												self.assertEqual(dset['''train'''].num_rows							,							__lowerCamelCase   )
												self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples							,							__lowerCamelCase   )
												# 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(__lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							'''dataset_info.json'''   )   )   )
												del dset

						@require_beam
						def        __a		(	self				:      int   )							->		int:


									'''simple docstring'''

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



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


									'''simple docstring'''

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





