
def a__    (       A_					):






  '''simple docstring'''





  if len(A_					) <= 1:
    return [tuple(A_					)]

  __magic_name__				     =       []

  def generate(A_,     A_					):
    __magic_name__				     =       [0] * n
    res.append(tuple(A_					)					)

    __magic_name__				     =       0
    while i < n:
      if c[i] < i:
        if i % 2 == 0:
          __magic_name__     ,    __magic_name__				     =       arr[i], arr[0]
        else:
          __magic_name__     ,    __magic_name__				     =       arr[i], arr[c[i]]
        res.append(tuple(A_					)					)
        c[i] += 1
        __magic_name__				     =       0
      else:
        __magic_name__				     =       0
        i += 1

  generate(len(A_					),     A_					)
  return res


if __name__ == "__main__":
    __lowerCAmelCase			:     Tuple           =   input('Enter numbers separated by a comma:\n').strip()
    __lowerCAmelCase			:     int           =   [int(item) for item in user_input.split(',')]
    print(heaps(arr))



import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


__lowerCAmelCase			:     Any           =   [
    {'dataset': 'wikipedia', 'config_name': '20220301.de'},
    {'dataset': 'wikipedia', 'config_name': '20220301.en'},
    {'dataset': 'wikipedia', 'config_name': '20220301.fr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.frr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.it'},
    {'dataset': 'wikipedia', 'config_name': '20220301.simple'},
    {'dataset': 'snli', 'config_name': 'plain_text'},
    {'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
    {'dataset': 'wiki40b', 'config_name': 'en'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
    {'dataset': 'natural_questions', 'config_name': 'default'},
]

def a__    (       A_=True					):






  '''simple docstring'''





  if with_config:
    return [
        {
            "testcase_name": d["dataset"] + "/" + d["config_name"],
            "dataset": d["dataset"],
            "config_name": d["config_name"],
        }
        for d in DATASETS_ON_HF_GCP
    ]
  else:
    return [
        {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
    ]




@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_A     )     )
class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		None
  a__           =		None
  def 							_lowercase							( self			: List[Any]			,							UpperCamelCase__			: int			,							UpperCamelCase__			: Union[str, Any]    )			->	Tuple:







    """simple docstring"""






    with TemporaryDirectory() as tmp_dir:
      __magic_name__				     =       dataset_module_factory(UpperCamelCase__			,							cache_dir=UpperCamelCase__    )

      __magic_name__				     =       import_main_class(dataset_module.module_path			,							dataset=UpperCamelCase__    )

      __magic_name__				     =       builder_cls(
          cache_dir=UpperCamelCase__			,							config_name=UpperCamelCase__			,							hash=dataset_module.hash			,							)

      __magic_name__				     =       """/""".join(
          [
              HF_GCP_BASE_URL,
              builder_instance._relative_data_dir(with_hash=UpperCamelCase__    ).replace(os.sep			,							"""/"""    ),
              config.DATASET_INFO_FILENAME,
          ]    )
      __magic_name__				     =       cached_path(UpperCamelCase__			,							cache_dir=UpperCamelCase__    )
      self.assertTrue(os.path.exists(UpperCamelCase__    )    )







@pytest.mark.integration
def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       tmp_path_factory.mktemp("""test_hf_gcp"""					) / """test_wikipedia_simple"""
  __magic_name__				     =       dataset_module_factory("""wikipedia""",     cache_dir=A_					)
  __magic_name__				     =       import_main_class(dataset_module.module_path					)
  __magic_name__				     =       builder_cls(
      cache_dir=A_,     config_name="""20220301.frr""",     hash=dataset_module.hash,     )
  # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
  __magic_name__				     =       None
  builder_instance.download_and_prepare()
  __magic_name__				     =       builder_instance.as_dataset()
  assert ds





@pytest.mark.integration
def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       dataset_module_factory("""wikipedia""",     cache_dir=A_					)
  __magic_name__				     =       import_main_class(dataset_module.module_path,     dataset=A_					)
  __magic_name__				     =       builder_cls(
      cache_dir=A_,     config_name="""20220301.frr""",     hash=dataset_module.hash,     )
  __magic_name__				     =       builder_instance.as_streaming_dataset()
  assert ds
  assert isinstance(A_,     A_					)
  assert "train" in ds
  assert isinstance(ds["""train"""],     A_					)
  assert next(iter(ds["""train"""]					)					)

