






import sacrebleu as scb
from packaging import version

import datasets


UpperCamelCase__  :					str  						=    """\\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__  :					Tuple  						=    """\\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  lowerCamelCase_   (		datasets.Metric   ):
							def        SCREAMING_SNAKE_CASE_       (  self						:    List[str]    ):





								'''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        SCREAMING_SNAKE_CASE_       (  self						:    List[Any]							,__lowerCamelCase						:    str							,__lowerCamelCase						:    List[str]							,__lowerCamelCase						:    Any="exp"							,__lowerCamelCase						:    Tuple=None							,__lowerCamelCase						:    Tuple=False							,__lowerCamelCase						:    Optional[int]=False							,__lowerCamelCase						:    List[Any]=None							,__lowerCamelCase						:    List[Any]=False							,):





								'''simple docstring'''



								a				=	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'''    )
								a				=	[[refs[i] for refs in references] for i in range(__lowerCamelCase    )]
								a				=	scb.corpus_bleu(
								    __lowerCamelCase							,__lowerCamelCase							,smooth_method=__lowerCamelCase							,smooth_value=__lowerCamelCase							,force=__lowerCamelCase							,lowercase=__lowerCamelCase							,use_effective_order=__lowerCamelCase							,**({'''tokenize''': tokenize} if tokenize else {})							,)
								a				=	{
								    "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












import sys

sys.set_int_max_str_digits(0)  # type: ignore






def 			lowerCamelCase__	(__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		int  			=							[0] * 11
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							[0] * 11

     # mark last 9 numbers
     for _ in range(9		):
          _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							1
          _SCREAMING_SNAKE_CASE				:		str  			=							number // 10
     # flag
     _SCREAMING_SNAKE_CASE				:		int  			=							True

     # check last 9 numbers for pandigitality

     for x in range(9		):
          if not check_last[x + 1]:
               _SCREAMING_SNAKE_CASE				:		Any  			=							False
     if not f:
          return f

     # mark first 9 numbers
     _SCREAMING_SNAKE_CASE				:		str  			=							int(str(__lowerCamelCase		)[:9]		)

     for _ in range(9		):
          _SCREAMING_SNAKE_CASE				:		str  			=							1
          _SCREAMING_SNAKE_CASE				:		List[Any]  			=							number // 10

     # check first 9 numbers for pandigitality

     for x in range(9		):
          if not check_front[x + 1]:
               _SCREAMING_SNAKE_CASE				:		Any  			=							False
     return f






def 			lowerCamelCase__	(__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							[0] * 11

     # mark last 9 numbers
     for _ in range(9		):
          _SCREAMING_SNAKE_CASE				:		Dict  			=							1
          _SCREAMING_SNAKE_CASE				:		List[str]  			=							number // 10
     # flag
     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							True

     # check last 9 numbers for pandigitality

     for x in range(9		):
          if not check_last[x + 1]:
               _SCREAMING_SNAKE_CASE				:		str  			=							False
     return f






def 			lowerCamelCase__	():
     _SCREAMING_SNAKE_CASE				:		Dict  			=							1
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							1
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							2
     # temporary Fibonacci numbers

     _SCREAMING_SNAKE_CASE				:		Any  			=							1
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							1
     _SCREAMING_SNAKE_CASE				:		str  			=							2
     # temporary Fibonacci numbers mod 1e9

     # mod m=1e9, done for fast optimisation
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							[0] * 1000000
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							1000000000

     for x in range(1000000		):
          _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							(aa + ba) % m
          _SCREAMING_SNAKE_CASE				:		int  			=							ba % m
          _SCREAMING_SNAKE_CASE				:		List[str]  			=							ca % m
          if checka(__lowerCamelCase		):
               _SCREAMING_SNAKE_CASE				:		List[Any]  			=							1

     for x in range(1000000		):
          _SCREAMING_SNAKE_CASE				:		Any  			=							a + b
          _SCREAMING_SNAKE_CASE				:		Dict  			=							b
          _SCREAMING_SNAKE_CASE				:		int  			=							c
          # perform check only if in tocheck
          if tocheck[x + 3] and check(__lowerCamelCase		):
               return x + 3  # first 2 already done
     return -1


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