


"""simple docstring"""







import math


def 					lowercase      (    A_  ,       A_ = 0  ,       A_ = 0	)->				list:
  '''simple docstring'''







  a						:		Optional[int]											=      end or len(A_	)
  for i in range(A_  ,       A_	):
    a						:		Optional[int]											=      i
    a						:		Tuple											=      array[i]
    while temp_index != start and temp_index_value < array[temp_index - 1]:
      a						:		List[Any]											=      array[temp_index - 1]
      temp_index -= 1
    a						:		int											=      temp_index_value
  return array


def 					lowercase      (    A_  ,       A_  ,       A_	)->				None:  # Max Heap
  '''simple docstring'''







  a						:		Optional[int]											=      index
  a						:		str											=      2 * index + 1  # Left Node
  a						:		Union[str, Any]											=      2 * index + 2  # Right Node

  if left_index < heap_size and array[largest] < array[left_index]:
    a						:		Union[str, Any]											=      left_index

  if right_index < heap_size and array[largest] < array[right_index]:
    a						:		List[Any]											=      right_index

  if largest != index:
    a							,		a						:		Tuple											=      array[largest], array[index]
    heapify(A_  ,       A_  ,       A_	)


def 					lowercase      (    A_	)->				list:
  '''simple docstring'''







  a						:		Tuple											=      len(A_	)

  for i in range(n // 2  ,       -1  ,       -1	):
    heapify(A_  ,       A_  ,       A_	)

  for i in range(n - 1  ,       0  ,       -1	):
    a							,		a						:		int											=      array[0], array[i]
    heapify(A_  ,       0  ,       A_	)

  return array


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_	)->				int:
  '''simple docstring'''







  if (array[first_index] > array[middle_index]) != (
      array[first_index] > array[last_index]
  ):
    return array[first_index]
  elif (array[middle_index] > array[first_index]) != (
      array[middle_index] > array[last_index]
  ):
    return array[middle_index]
  else:
    return array[last_index]


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_	)->				int:
  '''simple docstring'''







  a						:		str											=      low
  a						:		Any											=      high
  while True:
    while array[i] < pivot:
      i += 1
    j -= 1
    while pivot < array[j]:
      j -= 1
    if i >= j:
      return i
    a							,		a						:		Tuple											=      array[j], array[i]
    i += 1


def 					lowercase      (    A_	)->				list:
  '''simple docstring'''







  if len(A_	) == 0:
    return array
  a						:		Dict											=      2 * math.ceil(math.loga(len(A_	)	)	)
  a						:		Union[str, Any]											=      16
  return intro_sort(A_  ,       0  ,       len(A_	)  ,       A_  ,       A_	)


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_	)->				list:
  '''simple docstring'''







  while end - start > size_threshold:
    if max_depth == 0:
      return heap_sort(A_	)
    max_depth -= 1
    a						:		Optional[int]											=      median_of_a(A_  ,       A_  ,       start + ((end - start) // 2) + 1  ,       end - 1	)
    a						:		Tuple											=      partition(A_  ,       A_  ,       A_  ,       A_	)
    intro_sort(A_  ,       A_  ,       A_  ,       A_  ,       A_	)
    a						:		Optional[Any]											=      p
  return insertion_sort(A_  ,       A_  ,       A_	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 __lowercase    =				input("""Enter numbers separated by a comma : """).strip()
 __lowercase    =				[float(item) for item in user_input.split(""",""")]
 print(sort(unsorted))










"""simple docstring"""







import datasets


__lowercase    =				"""\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

__lowercase    =				"""\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

__lowercase    =				"""
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""


def 					lowercase      (    A_  ,       A_	)->				List[str]:
  '''simple docstring'''







  return (preds == labels).mean()







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,_KWARGS_DESCRIPTION  )
class 						_A  (  datasets.Metric  ):







    """simple docstring"""




    def      __snake_case							( self    :   List[str]):
      return datasets.MetricInfo(
          description=_DESCRIPTION    ,			citation=_CITATION    ,			inputs_description=_KWARGS_DESCRIPTION    ,			features=datasets.Features(
              {
                  "predictions": datasets.Value("int64" if self.config_name != "sts-b" else "float32"),
                  "references": datasets.Value("int64" if self.config_name != "sts-b" else "float32"),
              })    ,			codebase_urls=[]    ,			reference_urls=[]    ,			format="numpy"    ,			)






    def      __snake_case							( self    :   str    ,			__UpperCAmelCase    :   List[Any]    ,			__UpperCAmelCase    :   Tuple):
      return {"accuracy": simple_accuracy(__UpperCAmelCase    ,			__UpperCAmelCase)}






