





"""simple docstring"""




import sacrebleu as scb
from packaging import version

import datasets


_UpperCamelCase    =	"\\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

_UpperCamelCase    =	"\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n"

_UpperCamelCase    =	"\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    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).\n    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:\n        - `'none'`: no smoothing\n        - `'floor'`: increment zero counts\n        - `'add-k'`: increment num/denom by k for n>1\n        - `'exp'`: exponential decay\n    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`).\n    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:\n        - `'none'`: No tokenization.\n        - `'zh'`: Chinese tokenization.\n        - `'13a'`: mimics the `mteval-v13a` script from Moses.\n        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `'char'`: Language-agnostic character-level tokenization.\n        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    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`.\n\nReturns:\n    'score': BLEU score,\n    'counts': Counts,\n    'totals': Totals,\n    'precisions': Precisions,\n    'bp': Brevity penalty,\n    'sys_len': predictions length,\n    'ref_len': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = [\"hello there general kenobi\",\n        ...                 \"on our way to ankh morpork\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        39.8\n"




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



      """simple docstring"""






      def   UpperCAmelCase						(	self				)    ->				Tuple:






        '''simple docstring'''






        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   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase="exp"			,  UpperCAmelCase=None			,  UpperCAmelCase=False			,  UpperCAmelCase=False			,  UpperCAmelCase=None			,  UpperCAmelCase=False			,  )    ->				Union[str, Any]:






        '''simple docstring'''






        __snake_case				:			List[Any]            =    len(references[0]				)
        if any(len(SCREAMING_SNAKE_CASE_				) != references_per_prediction for refs in references				):
          raise ValueError("Sacrebleu requires the same number of references for each prediction"				)
        __snake_case				:			Union[str, Any]            =    [[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE_				)]
        __snake_case				:			Optional[Any]            =    scb.corpus_bleu(
            SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  smooth_method=SCREAMING_SNAKE_CASE_			,  smooth_value=SCREAMING_SNAKE_CASE_			,  force=SCREAMING_SNAKE_CASE_			,  lowercase=SCREAMING_SNAKE_CASE_			,  use_effective_order=SCREAMING_SNAKE_CASE_			,  **({"tokenize": tokenize} if tokenize else {})			,  )
        __snake_case				:			Any            =    {
            "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








def  lowerCAmelCase__(		lowercase    :      int = 10**6			)	->      int:

  if n <= 0:
    raise ValueError("Please enter an integer greater than 0"			)

  __snake_case				:			List[Any]            =    list(range(n + 1			)			)
  for number in range(2				,       n + 1			):
    if phi[number] == number:
      phi[number] -= 1
      for multiple in range(number * 2				,       n + 1				,       lowercase			):
        __snake_case				:			Optional[int]            =    (phi[multiple] // number) * (number - 1)

  __snake_case				:			List[Any]            =    1
  for number in range(1				,       n + 1			):
    if (answer / phi[answer]) < (number / phi[number]):
      __snake_case				:			List[str]            =    number

  return answer


if __name__ == "__main__":
     print(solution())

