code: 
  
  
  import sacrebleu as scb
  from packaging import version
  
  import datasets
  
  
  lowerCamelCase_     :     List[Any]    		=						"""\
  @inproceedings{post-2018-call,
      title = \"A Call for Clarity in Reporting {BLEU} Scores\",
      author = \"Post, Matt\",
      booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
      month = oct,
      year = \"2018\",
      address = \"Belgium, Brussels\",
      publisher = \"Association for Computational Linguistics\",
      url = \"https://www.aclweb.org/anthology/W18-6319\",
      pages = \"186--191\",
  }
  """
  
  lowerCamelCase_     :     List[str]    		=						"""\
  SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
  Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
  It also knows all the standard test sets and handles downloading, processing, and tokenization for you.
  
  See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
  """
  
  lowerCamelCase_     :     List[Any]    		=						"""
  Produces BLEU scores along with its sufficient statistics
  from a source against one or more references.
  
  Args:
      predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
      references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
      smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
          - `'none'`: no smoothing
          - `'floor'`: increment zero counts
          - `'add-k'`: increment num/denom by k for n>1
          - `'exp'`: exponential decay
      smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).
      tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:
          - `'none'`: No tokenization.
          - `'zh'`: Chinese tokenization.
          - `'13a'`: mimics the `mteval-v13a` script from Moses.
          - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
          - `'char'`: Language-agnostic character-level tokenization.
          - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
      lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
      force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
      use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.
  
  Returns:
      'score': BLEU score,
      'counts': Counts,
      'totals': Totals,
      'precisions': Precisions,
      'bp': Brevity penalty,
      'sys_len': predictions length,
      'ref_len': reference length,
  
  Examples:
  
      Example 1:
          >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
          >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
          >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
          >>> results = sacrebleu.compute(predictions=predictions, references=references)
          >>> print(list(results.keys()))
          ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
          >>> print(round(results[\"score\"], 1))
          100.0
  
      Example 2:
          >>> predictions = [\"hello there general kenobi\",
          ...                 \"on our way to ankh morpork\"]
          >>> references = [[\"hello there general kenobi\", \"hello there !\"],
          ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
          >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
          >>> results = sacrebleu.compute(predictions=predictions,
          ...                             references=references)
          >>> print(list(results.keys()))
          ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
          >>> print(round(results[\"score\"], 1))
          39.8
  """
  
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,					_KWARGS_DESCRIPTION    )
  class    __A       ( datasets.Metric    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
  
       if version.parse(scb.__version__		) < version.parse('''1.4.12'''		):
           raise ImportWarning(
               '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
               '''You can install it with `pip install \"sacrebleu>=1.4.12\"`.'''		)
       return datasets.MetricInfo(
           description=_DESCRIPTION				,					citation=_CITATION				,					homepage='''https://github.com/mjpost/sacreBLEU'''				,					inputs_description=_KWARGS_DESCRIPTION				,					features=datasets.Features(
               {
                   '''predictions''': datasets.Value('''string'''				,					id='''sequence'''		),
                   '''references''': datasets.Sequence(datasets.Value('''string'''				,					id='''sequence'''		)				,					id='''references'''		),
               }		)				,					codebase_urls=['''https://github.com/mjpost/sacreBLEU''']				,					reference_urls=[
               '''https://github.com/mjpost/sacreBLEU''',
               '''https://en.wikipedia.org/wiki/BLEU''',
               '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
           ]				,					)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A="exp"				,					__A=None				,					__A=False				,					__A=False				,					__A=None				,					__A=False				,					)      ->  Optional[Any]:
       a					=len(references[0]		)
       if any(len(_lowerCamelCase		) != references_per_prediction for refs in references		):
           raise ValueError('''Sacrebleu requires the same number of references for each prediction'''		)
       a					=[[refs[i] for refs in references] for i in range(_lowerCamelCase		)]
       a					=scb.corpus_bleu(
           _lowerCamelCase				,					_lowerCamelCase				,					smooth_method=_lowerCamelCase				,					smooth_value=_lowerCamelCase				,					force=_lowerCamelCase				,					lowercase=_lowerCamelCase				,					use_effective_order=_lowerCamelCase				,					**({'''tokenize''': tokenize} if tokenize else {})				,					)
       a					={
           """score""": output.score,
           """counts""": output.counts,
           """totals""": output.totals,
           """precisions""": output.precisions,
           """bp""": output.bp,
           """sys_len""": output.sys_len,
           """ref_len""": output.ref_len,
       }
       return output_dict
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from __future__ import annotations
  
  from random import randrange
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      if len(lowercase	) < 2:
          return collection
      a					=randrange(len(lowercase	)	)  # Use random element as pivot
      a					=collection[pivot_index]
      a					=[]  # All elements greater than pivot
      a					=[]  # All elements less than or equal to pivot
  
      for element in collection[:pivot_index]:
          (greater if element > pivot else lesser).append(lowercase	)
  
      for element in collection[pivot_index + 1 :]:
          (greater if element > pivot else lesser).append(lowercase	)
  
      return [*quick_sort(lowercase	), pivot, *quick_sort(lowercase	)]
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     Union[str, Any]    		=						input("""Enter numbers separated by a comma:\n""").strip()
         lowerCamelCase_     :     Union[str, Any]    		=						[int(item) for item in user_input.split(""",""")]
         print(quick_sort(unsorted))