






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 						SCREAMING_SNAKE_CASE				( datasets.BeamBasedBuilder					):





							"""simple docstring"""

							def       __A (  self:   Dict   )    ->				int:
												return datasets.DatasetInfo(
												    features=datasets.Features({'''content''': datasets.Value('''string'''   )}   )		,     supervised_keys=__A		,     )

							def       __A (  self:   Optional[int]		,     __A:   Any		,     __A:   List[str]   )    ->				str:
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,     gen_kwargs={'''examples''': get_test_dummy_examples()}   )]



							def       __A (  self:   List[str]		,     __A:   str		,     __A:   List[str]   )    ->				Dict:
												import apache_beam as beam

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





class 						SCREAMING_SNAKE_CASE				( datasets.BeamBasedBuilder					):





							"""simple docstring"""

							def       __A (  self:   Optional[Any]   )    ->				Optional[int]:
												return datasets.DatasetInfo(
												    features=datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string'''   )}   )}   )		,     supervised_keys=__A		,     )

							def       __A (  self:   int		,     __A:   str		,     __A:   str   )    ->				Tuple:
												return [
												    datasets.SplitGenerator(name=datasets.Split.TRAIN		,     gen_kwargs={'''examples''': get_test_nested_examples()}   )
												]



							def       __A (  self:   Optional[Any]		,     __A:   Dict		,     __A:   int   )    ->				Tuple:
												import apache_beam as beam

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




def      __A					(     ):

					'''simple docstring'''







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




def      __A					(     ):

					'''simple docstring'''







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





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							@require_beam
							def       __A (  self:   Union[str, Any]   )    ->				str:
												_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:   Tuple   )    ->				Dict:
												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:   List[str]   )    ->				int:
												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:   List[str]   )    ->				List[Any]:
												_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











import math




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= []
					_A								= 2
					_A								= int(math.sqrt(_lowercase ) )  # Size of every segment
					_A								= [True] * (end + 1)
					_A								= []

					while start <= end:
										if temp[start] is True:
															in_prime.append(_lowercase )
															for i in range(start * start					,			end + 1					,			_lowercase ):
																				_A								= False
										start += 1
					prime += in_prime

					_A								= end + 1
					_A								= min(2 * end					,			_lowercase )

					while low <= n:
										_A								= [True] * (high - low + 1)
										for each in in_prime:
															_A								= math.floor(low / each ) * each
															if t < low:
																				t += each

															for j in range(_lowercase					,			high + 1					,			_lowercase ):
																				_A								= False

										for j in range(len(_lowercase ) ):
															if temp[j] is True:
																				prime.append(j + low )

										_A								= high + 1
										_A								= min(high + end					,			_lowercase )

					return prime


print(sieve(10**6))



