
'''simple docstring'''







import sacrebleu as scb
from packaging import version

import datasets


_lowercase				:						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"

_lowercase				:						Union[str, 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"

_lowercase				:						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 				lowerCAmelCase__     (						datasets.Metric  ):






					def 			_snake_case					(     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 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE="exp"       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=False       ,							__SCREAMING_SNAKE_CASE=False       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=False       ,							):


											"""simple docstring"""





											lowercase_			:  Any									= 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'''	)
											lowercase_			:  Optional[int]									= [[refs[i] for refs in references] for i in range(_lowerCamelCase	)]
											lowercase_			:  Optional[Any]									= scb.corpus_bleu(
											    _lowerCamelCase       ,							_lowerCamelCase       ,							smooth_method=_lowerCamelCase       ,							smooth_value=_lowerCamelCase       ,							force=_lowerCamelCase       ,							lowercase=_lowerCamelCase       ,							use_effective_order=_lowerCamelCase       ,							**({'''tokenize''': tokenize} if tokenize else {})       ,							)
											lowercase_			:  str									= {
											    """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'''







from itertools import combinations, product
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				int							):



						"""simple docstring"""





						if xa == ya == 0 or xa == ya == 0:
												return False
						lowercase_			:  Optional[Any]									= xa * xa + ya * ya
						lowercase_			:  List[str]									= xa * xa + ya * ya
						lowercase_			:  Any									= (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
						return (
						    a_square + b_square == c_square
						    or a_square + c_square == b_square
						    or b_square + c_square == a_square
						)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int = 50							):



						"""simple docstring"""





						return sum(
						    1
						    for pta, pta in combinations(product(range(limit + 1							)   ,   repeat=2							)   ,   2							)
						    if is_right(*__SCREAMING_SNAKE_CASE   ,   *__SCREAMING_SNAKE_CASE							)							)


if __name__ == "__main__":
							print(f"""{solution() = }""")



