


"""simple docstring"""







import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def 					a__			(				SCREAMING_SNAKE_CASE    :    Optional[Any]				,      SCREAMING_SNAKE_CASE    :    List[Any]				):


  '''simple docstring'''







  assert isinstance(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
  assert dataset.num_rows == 4
  assert dataset.num_columns == 1
  assert dataset.column_names == ["text"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory"				,      [False, True]				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    List[Any]				,      SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    List[str]				):


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							tmp_path / "cache"
  lowerCAmelCase     :     Tuple								=							{"text": "string"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowerCAmelCase     :     Union[str, Any]								=							TextDatasetReader(SCREAMING_SNAKE_CASE				,      cache_dir=SCREAMING_SNAKE_CASE				,      keep_in_memory=SCREAMING_SNAKE_CASE				).read()
  _check_text_dataset(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
@pytest.mark.parametrize(
    "features"				,      [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]				,      )
def 					a__			(				SCREAMING_SNAKE_CASE    :    Optional[Any]				,      SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    Optional[int]				):


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							tmp_path / "cache"
  lowerCAmelCase     :     Any								=							{"text": "string"}
  lowerCAmelCase     :     Union[str, Any]								=							features.copy() if features else default_expected_features
  lowerCAmelCase     :     Union[str, Any]								=							(
      Features({feature: Value(SCREAMING_SNAKE_CASE				) for feature, dtype in features.items()}				) if features is not None else None
  )
  lowerCAmelCase     :     str								=							TextDatasetReader(SCREAMING_SNAKE_CASE				,      features=SCREAMING_SNAKE_CASE				,      cache_dir=SCREAMING_SNAKE_CASE				).read()
  _check_text_dataset(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
@pytest.mark.parametrize("split"				,      [None, NamedSplit("train"				), "train", "test"]				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    List[str]				,      SCREAMING_SNAKE_CASE    :    Any				,      SCREAMING_SNAKE_CASE    :    Optional[int]				):


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							tmp_path / "cache"
  lowerCAmelCase     :     Tuple								=							{"text": "string"}
  lowerCAmelCase     :     Union[str, Any]								=							TextDatasetReader(SCREAMING_SNAKE_CASE				,      cache_dir=SCREAMING_SNAKE_CASE				,      split=SCREAMING_SNAKE_CASE				).read()
  _check_text_dataset(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
  assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type"				,      [str, list]				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    Optional[Any]				,      SCREAMING_SNAKE_CASE    :    List[Any]				,      SCREAMING_SNAKE_CASE    :    Dict				):


  '''simple docstring'''







  if issubclass(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				):
    lowerCAmelCase     :     int								=							text_path
  elif issubclass(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				):
    lowerCAmelCase     :     Tuple								=							[text_path]
  lowerCAmelCase     :     List[Any]								=							tmp_path / "cache"
  lowerCAmelCase     :     int								=							{"text": "string"}
  lowerCAmelCase     :     List[Any]								=							TextDatasetReader(SCREAMING_SNAKE_CASE				,      cache_dir=SCREAMING_SNAKE_CASE				).read()
  _check_text_dataset(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    Any				,      SCREAMING_SNAKE_CASE    :    Any				,      SCREAMING_SNAKE_CASE    :    List[Any]=("train",)				):


  '''simple docstring'''







  assert isinstance(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
  for split in splits:
    lowerCAmelCase     :     List[str]								=							dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 1
    assert dataset.column_names == ["text"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory"				,      [False, True]				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    Tuple				,      SCREAMING_SNAKE_CASE    :    List[str]				,      SCREAMING_SNAKE_CASE    :    Union[str, Any]				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[Any]								=							tmp_path / "cache"
  lowerCAmelCase     :     str								=							{"text": "string"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowerCAmelCase     :     Optional[int]								=							TextDatasetReader({"train": text_path}				,      cache_dir=SCREAMING_SNAKE_CASE				,      keep_in_memory=SCREAMING_SNAKE_CASE				).read()
  _check_text_datasetdict(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
@pytest.mark.parametrize(
    "features"				,      [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]				,      )
def 					a__			(				SCREAMING_SNAKE_CASE    :    List[str]				,      SCREAMING_SNAKE_CASE    :    List[Any]				,      SCREAMING_SNAKE_CASE    :    Optional[Any]				):


  '''simple docstring'''







  lowerCAmelCase     :     Any								=							tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  lowerCAmelCase     :     Optional[Any]								=							{"text": "string"}
  lowerCAmelCase     :     Union[str, Any]								=							features.copy() if features else default_expected_features
  lowerCAmelCase     :     int								=							(
      Features({feature: Value(SCREAMING_SNAKE_CASE				) for feature, dtype in features.items()}				) if features is not None else None
  )
  lowerCAmelCase     :     List[Any]								=							TextDatasetReader({"train": text_path}				,      features=SCREAMING_SNAKE_CASE				,      cache_dir=SCREAMING_SNAKE_CASE				).read()
  _check_text_datasetdict(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)





@pytest.mark.parametrize("split"				,      [None, NamedSplit("train"				), "train", "test"]				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    List[Any]				,      SCREAMING_SNAKE_CASE    :    Optional[int]				,      SCREAMING_SNAKE_CASE    :    Tuple				):


  '''simple docstring'''







  if split:
    lowerCAmelCase     :     Dict								=							{split: text_path}
  else:
    lowerCAmelCase     :     List[str]								=							"train"
    lowerCAmelCase     :     int								=							{"train": text_path, "test": text_path}
  lowerCAmelCase     :     List[Any]								=							tmp_path / "cache"
  lowerCAmelCase     :     Optional[Any]								=							{"text": "string"}
  lowerCAmelCase     :     List[Any]								=							TextDatasetReader(SCREAMING_SNAKE_CASE				,      cache_dir=SCREAMING_SNAKE_CASE				).read()
  _check_text_datasetdict(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      splits=list(path.keys()				)				)
  assert all(dataset[split].split == split for split in path.keys()				)







"""simple docstring"""



from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class       lowerCAmelCase__				(						A_	):







     def __lt__(							self			:			Any    ,      _lowerCamelCase			:			int			):
       return self[-1] < other[-1]







     def __eq__(							self			:			int    ,      _lowerCamelCase			:			Optional[Any]			):
       return self[-1] == other[-1]






def 					_UpperCAmelCase      ( __lowerCamelCase   :       list		)     ->					list:
  _snake_case					     =       []
  # sort into stacks
  for element in collection:
    _snake_case					     =       Stack([element]		)
    _snake_case					     =       bisect_left(__lowerCamelCase ,					__lowerCamelCase		)
    if i != len(__lowerCamelCase		):
      stacks[i].append(__lowerCamelCase		)
    else:
      stacks.append(__lowerCamelCase		)

    # use a heap-based merge to merge stack efficiently
  _snake_case					     =       merge(*(reversed(__lowerCamelCase		) for stack in stacks)		)
  return collection


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



