


import sacrebleu as scb
from packaging import version

import datasets


lowerCAmelCase__ :			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'

lowerCAmelCase__ :			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'

lowerCAmelCase__ :			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   __lowercase						(datasets.Metric     ):





							"""simple docstring"""







							def 				UpperCamelCase__    (			self					):





										"""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 ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__="exp" ,	lowerCAmelCase__=None ,	lowerCAmelCase__=False ,	lowerCAmelCase__=False ,	lowerCAmelCase__=None ,	lowerCAmelCase__=False ,	):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    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'					)
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				[[refs[i] for refs in references] for i in range(lowerCAmelCase__					)]
										SCREAMING_SNAKE_CASE_		:    Dict 		=				scb.corpus_bleu(
										    lowerCAmelCase__ ,	lowerCAmelCase__ ,	smooth_method=lowerCAmelCase__ ,	smooth_value=lowerCAmelCase__ ,	force=lowerCAmelCase__ ,	lowercase=lowerCAmelCase__ ,	use_effective_order=lowerCAmelCase__ ,	**({'tokenize': tokenize} if tokenize else {}) ,	)
										SCREAMING_SNAKE_CASE_		:    List[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 collections import Counter






def 	a__ (							A__							):
			return sum(socks_by_color // 2 for socks_by_color in Counter(A__							).values()							)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				lowerCAmelCase__ :			Optional[int]         =[int(x) for x in input('Enter socks by color :').rstrip().split()]
				print(F"""sock_merchant({colors}) = {sock_merchant(colors)}""")





