



'''simple docstring'''






import sacrebleu as scb
from packaging import version

import datasets


UpperCamelCase__:       Optional[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"

UpperCamelCase__:       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"

UpperCamelCase__:       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     SCREAMING_SNAKE_CASE(							datasets.Metric      ):







	"""simple docstring"""






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






UpperCamelCase__:       str							     =      [0.485, 0.456, 0.406]
UpperCamelCase__:       List[str]							     =      [0.229, 0.224, 0.225]
UpperCamelCase__:       List[str]							     =      [0.5, 0.5, 0.5]
UpperCamelCase__:       Optional[Any]							     =      [0.5, 0.5, 0.5]
UpperCamelCase__:       List[str]							     =      [0.48145466, 0.4578275, 0.40821073]
UpperCamelCase__:       List[str]							     =      [0.26862954, 0.26130258, 0.27577711]





