





import sacrebleu as scb
from packaging import version

import datasets


_lowercase		:     Optional[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\",
}
"""

_lowercase		:     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.
"""

_lowercase		:     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 	UpperCamelCase_     (     datasets.Metric       ):



					def      __a    (		self    :    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      __a    (		self    :    str					,			lowerCamelCase    :    List[Any]					,			lowerCamelCase    :    List[str]					,			lowerCamelCase    :    Any="exp"					,			lowerCamelCase    :    str=None					,			lowerCamelCase    :    List[str]=False					,			lowerCamelCase    :    str=False					,			lowerCamelCase    :    Optional[int]=None					,			lowerCamelCase    :    Tuple=False					,			):
							lowerCamelCase_  :    List[str]   	=			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'	)
							lowerCamelCase_  :    Optional[Any]   	=			[[refs[i] for refs in references] for i in range(lowerCamelCase	)]
							lowerCamelCase_  :    Tuple   	=			scb.corpus_bleu(
							    lowerCamelCase					,			lowerCamelCase					,			smooth_method=lowerCamelCase					,			smooth_value=lowerCamelCase					,			force=lowerCamelCase					,			lowercase=lowerCamelCase					,			use_effective_order=lowerCamelCase					,			**({'tokenize': tokenize} if tokenize else {})					,			)
							lowerCamelCase_  :    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







from __future__ import annotations


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
		lowerCamelCase_  :    int
		lowerCamelCase_  :    list[str]   	=			list(lowerCAmelCase__	)
		lowerCamelCase_	,			lowerCamelCase_  :    List[str]   	=			_vector[:-1], int(_vector[-1]	)
		lowerCamelCase_  :    list[int]   	=			[int(lowerCAmelCase__	) for digit in __vector]

		vector.reverse()
		for i, digit in enumerate(lowerCAmelCase__	):
				if i & 1 == 0:
						lowerCamelCase_  :    int   	=			digit * 2
						if doubled > 9:
								doubled -= 9
						check_digit += doubled
				else:
						check_digit += digit

		return check_digit % 10 == 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		assert is_luhn("""79927398713""")
		assert not is_luhn("""79927398714""")
