

"""simple docstring"""




import sacrebleu as scb
from packaging import version

import datasets


__A	:       Any        =						'\\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'

__A	:       Optional[int]        =						'\\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'

__A	:       List[Any]        =						'\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      __UpperCamelCase					(  datasets.Metric		):


		def    SCREAMING_SNAKE_CASE__  (self					:						str):


							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					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						Tuple="exp"	,	__SCREAMING_SNAKE_CASE					:						Tuple=None	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]=False	,	__SCREAMING_SNAKE_CASE					:						Dict=False	,	__SCREAMING_SNAKE_CASE					:						List[str]=None	,	__SCREAMING_SNAKE_CASE					:						Dict=False	,	):
							A     =   len(references[0])
							if any(len(_A) != 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(_A)]
							A     =   scb.corpus_bleu(
							    _A	,	_A	,	smooth_method=_A	,	smooth_value=_A	,	force=_A	,	lowercase=_A	,	use_effective_order=_A	,	**({"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












def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	if len(str(__a ) ) == 1 or len(str(__a ) ) == 1:
		return a * b

	snake_case_		:      Dict           =						max(len(str(__a ) ) ,			len(str(__a ) ) )
	snake_case_		:      Tuple           =						ma // 2

	snake_case_    ,snake_case_		:      Tuple           =						divmod(__a ,			10**ma )
	snake_case_    ,snake_case_		:      int           =						divmod(__a ,			10**ma )

	snake_case_		:      str           =						karatsuba(__a ,			__a )
	snake_case_		:      List[str]           =						karatsuba((aa + aa) ,			(ba + ba) )
	snake_case_		:      Optional[Any]           =						karatsuba(__a ,			__a )

	return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)


def 				SCREAMING_SNAKE_CASE__    (     ):
	print(karatsuba(1_54_63 ,			2_34_89 ) )


if __name__ == "__main__":
						main()

