


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



"""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))