





"""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 		__snake_case (		datasets.BeamBasedBuilder					):





 """simple docstring"""


 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




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







 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     import apache_beam as beam

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






class 		__snake_case (		datasets.BeamBasedBuilder					):





 """simple docstring"""


 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




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







 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     import apache_beam as beam

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







def 		__lowercase					(       )       ->Optional[Any]:



    '''simple docstring'''







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







def 		__lowercase					(       )       ->Dict:



    '''simple docstring'''







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






class 		__snake_case (		_lowerCAmelCase					):





 """simple docstring"""


 @require_beam
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					len(get_test_dummy_examples()  )
     with tempfile.TemporaryDirectory() as tmp_cache_dir:
         __A   :						Optional[int]									=					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   :						Any									=					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  UpperCamelCase__(   self  ):



     '''simple docstring'''




     import apache_beam as beam

     __A   :						List[str]									=					beam.io.parquetio.WriteToParquet

     __A   :						Any									=					len(get_test_dummy_examples()  )
     with tempfile.TemporaryDirectory() as tmp_cache_dir:
         __A   :						Dict									=					DummyBeamDataset(cache_dir=SCREAMING_SNAKE_CASE_ ,						beam_runner='''DirectRunner'''  )
         with patch('''apache_beam.io.parquetio.WriteToParquet'''  ) as write_parquet_mock:
             __A   :						List[Any]									=					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   :						Any									=					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  UpperCamelCase__(   self  ):



     '''simple docstring'''




     with tempfile.TemporaryDirectory() as tmp_cache_dir:
         __A   :						int									=					DummyBeamDataset(cache_dir=SCREAMING_SNAKE_CASE_  )
         self.assertRaises(datasets.builder.MissingBeamOptions ,						builder.download_and_prepare  )







 @require_beam
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					len(get_test_nested_examples()  )
     with tempfile.TemporaryDirectory() as tmp_cache_dir:
         __A   :						int									=					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   :						Any									=					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











"""simple docstring"""



def 		__lowercase					(       snake_case_  :  int						)       ->Optional[Any]:



    '''simple docstring'''







    stooge(snake_case_    ,0    ,len(snake_case_						) - 1						)
    return arr







def 		__lowercase					(       snake_case_  :  Optional[Any]    ,snake_case_  :  Union[str, Any]    ,snake_case_  :  Any						)       ->Tuple:



    '''simple docstring'''







    if i >= h:
        return

    # If first element is smaller than the last then swap them
    if arr[i] > arr[h]:
        __A			,	__A   :						Optional[int]									=					arr[h], arr[i]

    # If there are more than 2 elements in the array
    if h - i + 1 > 2:
        __A   :						Any									=					(int)((h - i + 1) / 3						)

        # Recursively sort first 2/3 elements
        stooge(snake_case_    ,snake_case_    ,(h - t)						)

        # Recursively sort last 2/3 elements
        stooge(snake_case_    ,i + t    ,(snake_case_)						)

        # Recursively sort first 2/3 elements
        stooge(snake_case_    ,snake_case_    ,(h - t)						)


if __name__ == "__main__":
       a_			=      input("""Enter numbers separated by a comma:\n""").strip()
       a_			=      [int(item) for item in user_input.split(""",""")]
       print(stooge_sort(unsorted))




