





"""simple docstring"""





import numpy as np


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Tuple							,						SCREAMING_SNAKE_CASE					:						Dict							,						SCREAMING_SNAKE_CASE					:						Optional[Any]      ):







      '''simple docstring'''




      lowerCAmelCase  						=	n_draws * class_counts / class_counts.sum()
      # floored means we don't overshoot n_samples, but probably undershoot
      lowerCAmelCase  						=	np.floor(SCREAMING_SNAKE_CASE      )
      # we add samples according to how much "left over" probability
      # they had, until we arrive at n_samples
      lowerCAmelCase  						=	int(n_draws - floored.sum()      )
      if need_to_add > 0:
            lowerCAmelCase  						=	continuous - floored
            lowerCAmelCase  						=	np.sort(np.unique(SCREAMING_SNAKE_CASE      )      )[::-1]
            # add according to remainder, but break ties
            # randomly to avoid biases
            for value in values:
                  ((lowerCAmelCase)						,		)  						=	np.where(remainder == value      )
                  # if we need_to_add less than what's in inds
                  # we draw randomly from them.
                  # if we need to add more, we add them all and
                  # go to the next value
                  lowerCAmelCase  						=	min(len(SCREAMING_SNAKE_CASE      )							,						SCREAMING_SNAKE_CASE      )
                  lowerCAmelCase  						=	rng.choice(SCREAMING_SNAKE_CASE							,						size=SCREAMING_SNAKE_CASE							,						replace=SCREAMING_SNAKE_CASE      )
                  floored[inds] += 1
                  need_to_add -= add_now
                  if need_to_add == 0:
                        break
      return floored.astype(np.intaa      )






def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Any							,						SCREAMING_SNAKE_CASE					:						Union[str, Any]							,						SCREAMING_SNAKE_CASE					:						Optional[Any]							,						SCREAMING_SNAKE_CASE					:						Optional[int]							,						SCREAMING_SNAKE_CASE					:						Optional[int]=10      ):







      '''simple docstring'''




      lowerCAmelCase						,		lowerCAmelCase  						=	np.unique(SCREAMING_SNAKE_CASE							,						return_inverse=SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	classes.shape[0]
      lowerCAmelCase  						=	np.bincount(SCREAMING_SNAKE_CASE      )
      if np.min(SCREAMING_SNAKE_CASE      ) < 2:
            raise ValueError("""Minimum class count error"""      )
      if n_train < n_classes:
            raise ValueError(
                """The train_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_train, n_classes)      )
      if n_test < n_classes:
            raise ValueError(
                """The test_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_test, n_classes)      )
      lowerCAmelCase  						=	np.split(np.argsort(SCREAMING_SNAKE_CASE							,						kind="""mergesort"""      )							,						np.cumsum(SCREAMING_SNAKE_CASE      )[:-1]      )
      for _ in range(SCREAMING_SNAKE_CASE      ):
            lowerCAmelCase  						=	approximate_mode(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      )
            lowerCAmelCase  						=	class_counts - n_i
            lowerCAmelCase  						=	approximate_mode(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      )

            lowerCAmelCase  						=	[]
            lowerCAmelCase  						=	[]

            for i in range(SCREAMING_SNAKE_CASE      ):
                  lowerCAmelCase  						=	rng.permutation(class_counts[i]      )
                  lowerCAmelCase  						=	class_indices[i].take(SCREAMING_SNAKE_CASE							,						mode="""clip"""      )
                  train.extend(perm_indices_class_i[: n_i[i]]      )
                  test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
            lowerCAmelCase  						=	rng.permutation(SCREAMING_SNAKE_CASE      )
            lowerCAmelCase  						=	rng.permutation(SCREAMING_SNAKE_CASE      )

            yield train, test













"""simple docstring"""





import sacrebleu as scb
from packaging import version

import datasets


SCREAMING_SNAKE_CASE__ =       "\\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"

SCREAMING_SNAKE_CASE__ =       "\\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"

SCREAMING_SNAKE_CASE__ =       "\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 ):





      def    _snake_case      (	self			)		->					List[str]:
            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				,						lowercase				,						lowercase				,						lowercase="exp"				,						lowercase=None				,						lowercase=False				,						lowercase=False				,						lowercase=None				,						lowercase=False				,						)		->					int:
            lowerCAmelCase  						=	len(references[0]			)
            if any(len(lowercase			) != references_per_prediction for refs in references			):
                  raise ValueError("""Sacrebleu requires the same number of references for each prediction"""			)
            lowerCAmelCase  						=	[[refs[i] for refs in references] for i in range(lowercase			)]
            lowerCAmelCase  						=	scb.corpus_bleu(
                lowercase				,						lowercase				,						smooth_method=lowercase				,						smooth_value=lowercase				,						force=lowercase				,						lowercase=lowercase				,						use_effective_order=lowercase				,						**({"""tokenize""": tokenize} if tokenize else {})				,						)
            lowerCAmelCase  						=	{
                """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






