
import sacrebleu as scb
from packaging import version

import datasets


_UpperCAmelCase   :					Tuple    =			"\\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   :					str    =			"\\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   :					int    =			"\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 							lowercase   (						datasets.Metric  ):



		def __UpperCamelCase					(					self			)    ->   Dict:
				"""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							, A_							, A_							, A_="exp"							, A_=None							, A_=False							, A_=False							, A_=None							, A_=False							, )    ->   Optional[Any]:
				"""simple docstring"""
				UpperCamelCase            =	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'			)
				UpperCamelCase            =	[[refs[i] for refs in references] for i in range(A_			)]
				UpperCamelCase            =	scb.corpus_bleu(
				    A_							, A_							, smooth_method=A_							, smooth_value=A_							, force=A_							, lowercase=A_							, use_effective_order=A_							, **({'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  A			(   lowercase					)  ->    int:
		'''simple docstring'''

		barcode //= 10  # exclude the last digit
		UpperCamelCase            =	False
		UpperCamelCase            =	0

		# extract and check each digit
		while barcode != 0:
				UpperCamelCase            =	1 if checker else 3
				s += mult * (barcode % 10)
				barcode //= 10
				UpperCamelCase            =	not checker

		return (10 - (s % 10)) % 10


def  A			(   lowercase					)  ->    bool:
		'''simple docstring'''

		return len(str(lowercase					)					) == 13 and get_check_digit(lowercase					) == barcode % 10


def  A			(   lowercase					)  ->    int:
		'''simple docstring'''

		if str(lowercase					).isalpha():
				UpperCamelCase            =	f'''Barcode \'{barcode}\' has alphabetic characters.'''
				raise ValueError(lowercase					)
		elif int(lowercase					) < 0:
				raise ValueError('The entered barcode has a negative value. Try again.'					)
		else:
				return int(lowercase					)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					_UpperCAmelCase   :					List[str]    =			get_barcode(input("Barcode: ").strip())

					if is_valid(barcode):
										print(F'''\'{barcode}\' is a valid barcode.''')
					else:
										print(F'''\'{barcode}\' is NOT a valid barcode.''')
