



'''simple docstring'''

from __future__ import annotations

from cmath import sqrt






def    __lowercase	(_SCREAMING_SNAKE_CASE   :int     ,						_SCREAMING_SNAKE_CASE   :int     ,						_SCREAMING_SNAKE_CASE   :int							):





				if a == 0:
								raise ValueError('''Coefficient \'a\' must not be zero.'''							)
				SCREAMING_SNAKE_CASE	:    Tuple          =			b * b - 4 * a * c

				SCREAMING_SNAKE_CASE	:    int          =			(-b + sqrt(UpperCAmelCase_							)) / (2 * a)
				SCREAMING_SNAKE_CASE	:    List[str]          =			(-b - sqrt(UpperCAmelCase_							)) / (2 * a)

				return (
				    root_a.real if not root_a.imag else root_a,
				    root_a.real if not root_a.imag else root_a,
				)






def    __lowercase	():
				SCREAMING_SNAKE_CASE	:    Optional[int]          =			quadratic_roots(a=5     ,						b=6     ,						c=1							)
				print(F'''The solutions are: {solutiona} and {solutiona}'''							)


if __name__ == "__main__":
	main()








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 						_snake_case    ( datasets.BeamBasedBuilder				):




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




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




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       import apache_beam as beam

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







class 						_snake_case    ( datasets.BeamBasedBuilder				):




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




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




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       import apache_beam as beam

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







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 						_snake_case    ( _snake_case				):




 @require_beam
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Dict									=			len(get_test_dummy_examples()       )
       with tempfile.TemporaryDirectory() as tmp_cache_dir:
             a		:Optional[Any]									=			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'''       )}       )       )
             a		:str									=			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 			SCREAMING_SNAKE_CASE__							(						self       ):
       import apache_beam as beam

       a		:Any									=			beam.io.parquetio.WriteToParquet

       a		:Any									=			len(get_test_dummy_examples()       )
       with tempfile.TemporaryDirectory() as tmp_cache_dir:
             a		:Union[str, Any]									=			DummyBeamDataset(cache_dir=_lowerCamelCase   ,    beam_runner='''DirectRunner'''       )
             with patch('''apache_beam.io.parquetio.WriteToParquet'''       ) as write_parquet_mock:
                   a		:str									=			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'''       )}       )       )
             a		:List[str]									=			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 			SCREAMING_SNAKE_CASE__							(						self       ):
       with tempfile.TemporaryDirectory() as tmp_cache_dir:
             a		:Dict									=			DummyBeamDataset(cache_dir=_lowerCamelCase       )
             self.assertRaises(datasets.builder.MissingBeamOptions   ,    builder.download_and_prepare       )




 @require_beam
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:str									=			len(get_test_nested_examples()       )
       with tempfile.TemporaryDirectory() as tmp_cache_dir:
             a		:List[Any]									=			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'''       )}       )}       )       )
             a		:Any									=			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





