

import sacrebleu as scb
from packaging import version

import datasets


__A    	=			'''\
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

__A    	=			'''\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich\'s `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
'''

__A    	=			'''
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    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).
    smooth_method (`str`): The smoothing method to use, defaults to `\'exp\'`. Possible values are:
        - `\'none\'`: no smoothing
        - `\'floor\'`: increment zero counts
        - `\'add-k\'`: increment num/denom by k for n>1
        - `\'exp\'`: exponential decay
    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`).
    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:
        - `\'none\'`: No tokenization.
        - `\'zh\'`: Chinese tokenization.
        - `\'13a\'`: mimics the `mteval-v13a` script from Moses.
        - `\'intl\'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `\'char\'`: Language-agnostic character-level tokenization.
        - `\'ja-mecab\'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    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`.

Returns:
    \'score\': BLEU score,
    \'counts\': Counts,
    \'totals\': Totals,
    \'precisions\': Precisions,
    \'bp\': Brevity penalty,
    \'sys_len\': predictions length,
    \'ref_len\': reference length,

Examples:

    Example 1:
        >>> predictions = ["hello there general kenobi", "foo bar foobar"]
        >>> references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]]
        >>> sacrebleu = datasets.load_metric("sacrebleu")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']
        >>> print(round(results["score"], 1))
        100.0

    Example 2:
        >>> predictions = ["hello there general kenobi",
        ...                 "on our way to ankh morpork"]
        >>> references = [["hello there general kenobi", "hello there !"],
        ...                 ["goodbye ankh morpork", "ankh morpork"]]
        >>> sacrebleu = datasets.load_metric("sacrebleu")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']
        >>> print(round(results["score"], 1))
        39.8
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,							_KWARGS_DESCRIPTION  )
class 						lowercase_					(						datasets.Metric  ):



      def 		UpperCamelCase_	(    self   :       Optional[int]	)					->  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 		UpperCamelCase_	(    self   :       str    ,      A__   :       List[str]    ,      A__   :       Tuple    ,      A__   :       Dict="exp"    ,      A__   :       Optional[Any]=None    ,      A__   :       List[str]=False    ,      A__   :       str=False    ,      A__   :       int=None    ,      A__   :       Union[str, Any]=False    ,      )					->  int:
        _snake_case		=							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'''	)
        _snake_case		=							[[refs[i] for refs in references] for i in range(A__	)]
        _snake_case		=							scb.corpus_bleu(
            A__    ,      A__    ,      smooth_method=A__    ,      smooth_value=A__    ,      force=A__    ,      lowercase=A__    ,      use_effective_order=A__    ,      **({'''tokenize''': tokenize} if tokenize else {})    ,      )
        _snake_case		=							{
            '''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 __future__ import annotations



def    snake_case_(_UpperCamelCase  )							->			int | float:



  """simple docstring"""






  if len(_UpperCamelCase  ) == 0:
    raise ValueError('''find_max() arg is an empty sequence'''  )
  _snake_case		=							nums[0]
  for x in nums:
    if x > max_num:
      _snake_case		=							x
  return max_num


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)

