code: 
  
  
  
  
  import sacrebleu as scb
  from packaging import version
  
  import datasets
  
  
  SCREAMING_SNAKE_CASE__    :    int								=					"\\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__    :    str								=					"\\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__    :    Tuple								=					"\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 			a_					(    datasets.Metric    ):
  
  
  
  
  
   def 	A_(       self			)     ->				Tuple:
  
  
  
  
      """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 	A_(       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			,    )     ->				Any:
  
  
  
  
      """simple docstring"""
  
  
  
      SCREAMING_SNAKE_CASE_			=	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'			)
      SCREAMING_SNAKE_CASE_			=	[[refs[i] for refs in references] for i in range(_lowerCamelCase			)]
      SCREAMING_SNAKE_CASE_			=	scb.corpus_bleu(
          _lowerCamelCase			,    _lowerCamelCase			,    smooth_method=_lowerCamelCase			,    smooth_value=_lowerCamelCase			,    force=_lowerCamelCase			,    lowercase=_lowerCamelCase			,    use_effective_order=_lowerCamelCase			,    **({'tokenize': tokenize} if tokenize else {})			,    )
      SCREAMING_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
  
style_context: 
  
  
  
  
  from __future__ import annotations
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       None:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	m - 1, 0
  			while i >= 0 and j < n:
  						if key == mat[i][j]:
  									print(F'Key {key} found at row- {i + 1} column- {j + 1}'   )
  									return
  						if key < mat[i][j]:
  									i -= 1
  						else:
  									j += 1
  			print(F'Key {key} not found'   )
  
  def lowercase						(     ) ->       None:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
  			SCREAMING_SNAKE_CASE_			=	int(input('Enter the element to be searched:'   )   )
  			print(SCREAMING_SNAKE_CASE   )
  			search_in_a_sorted_matrix(SCREAMING_SNAKE_CASE					,						len(SCREAMING_SNAKE_CASE   )					,						len(mat[0]   )					,						SCREAMING_SNAKE_CASE   )
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  				main()
  