



import sacrebleu as scb
from packaging import version

import datasets


_lowerCamelCase         =	"""\
@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\",
}
"""

_lowerCamelCase         =	"""\
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.
"""

_lowerCamelCase         =	"""
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    _SCREAMING_SNAKE_CASE						(datasets.Metric			):





     def 						__snake_case     (							self			:				Optional[Any]					)->Union[str, 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 						__snake_case     (							self			:				Dict  ,	UpperCamelCase			:				List[str]  ,	UpperCamelCase			:				str  ,	UpperCamelCase			:				str="exp"  ,	UpperCamelCase			:				Optional[int]=None  ,	UpperCamelCase			:				Optional[int]=False  ,	UpperCamelCase			:				Optional[int]=False  ,	UpperCamelCase			:				List[Any]=None  ,	UpperCamelCase			:				Tuple=False  ,	)->Tuple:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						len(references[0]					)
      if any(len(UpperCamelCase					) != references_per_prediction for refs in references					):
       raise ValueError("Sacrebleu requires the same number of references for each prediction"					)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						[[refs[i] for refs in references] for i in range(UpperCamelCase					)]
      __SCREAMING_SNAKE_CASE							:     List[str]							=						scb.corpus_bleu(
          UpperCamelCase  ,	UpperCamelCase  ,	smooth_method=UpperCamelCase  ,	smooth_value=UpperCamelCase  ,	force=UpperCamelCase  ,	lowercase=UpperCamelCase  ,	use_effective_order=UpperCamelCase  ,	**({"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














'''simple docstring'''





def  lowerCamelCase_  (     lowercase__):
 return int(lowercase__) if x - int(lowercase__) <= 0 else int(lowercase__) + 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()

