

import sacrebleu as scb
from packaging import version

import datasets


SCREAMING_SNAKE_CASE		:Optional[int]				  =    '\\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'

SCREAMING_SNAKE_CASE		:List[Any]				  =    '\\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'

SCREAMING_SNAKE_CASE		:List[str]				  =    '\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 	__magic_name__				(       datasets.Metric							):






     def   UpperCAmelCase_							(							self  )->    List[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   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase="exp"		,	_lowercase=None		,	_lowercase=False		,	_lowercase=False		,	_lowercase=None		,	_lowercase=False		,	)->    List[Any]:
         UpperCamelCase_      =	len(references[0]  )
         if any(len(_lowercase  ) != references_per_prediction for refs in references  ):
             raise ValueError("Sacrebleu requires the same number of references for each prediction"  )
         UpperCamelCase_      =	[[refs[i] for refs in references] for i in range(_lowercase  )]
         UpperCamelCase_      =	scb.corpus_bleu(
             _lowercase		,	_lowercase		,	smooth_method=_lowercase		,	smooth_value=_lowercase		,	force=_lowercase		,	lowercase=_lowercase		,	use_effective_order=_lowercase		,	**({"tokenize": tokenize} if tokenize else {})		,	)
         UpperCamelCase_      =	{
             '''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(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	str:



    """simple docstring"""






    if power < 0:
        return -1
    base %= mod
    UpperCamelCase_      =	1

    while power > 0:
        if power & 1:
            UpperCamelCase_      =	(result * base) % mod
        UpperCamelCase_      =	power >> 1
        UpperCamelCase_      =	(base * base) % mod

    return result




def  lowerCAmelCase(			)->	Tuple:



    """simple docstring"""






    print(modular_exponential(3 ,	2_0_0 ,	1_3 ) )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 main()
