






"""simple docstring"""



import sacrebleu as scb
from packaging import version

import datasets


_UpperCamelCase						:    List[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						:    List[Any]		=						'\\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						:    Optional[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 			a	(  datasets.Metric      ):







    def      UpperCamelCase_ (     self			):
      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				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase="exp"				,						_lowerCamelCase=None				,						_lowerCamelCase=False				,						_lowerCamelCase=False				,						_lowerCamelCase=None				,						_lowerCamelCase=False				,						):
      lowercase				       =			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'			)
      lowercase				       =			[[refs[i] for refs in references] for i in range(_lowerCamelCase			)]
      lowercase				       =			scb.corpus_bleu(
          _lowerCamelCase				,						_lowerCamelCase				,						smooth_method=_lowerCamelCase				,						smooth_value=_lowerCamelCase				,						force=_lowerCamelCase				,						lowercase=_lowerCamelCase				,						use_effective_order=_lowerCamelCase				,						**({'tokenize': tokenize} if tokenize else {})				,						)
      lowercase				       =			{
          '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 unittest

from cer import CER


lowerCAmelCase__      		=					CER()
class 							a_					(		unittest.TestCase		):






 '''simple docstring'''






 def 						__snake_case		(     self   :    Any):







        '''simple docstring'''



        lowerCAmelCase__	      =   ['White House']
        lowerCAmelCase__	      =   ['white house']
        # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
        lowerCAmelCase__	      =   cer.compute(predictions=lowercase__     ,       references=lowercase__)
        self.assertTrue(abs(char_error_rate - 0.1_818_181_818) < 1e-6)




 def 						__snake_case		(     self   :    List[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   ['were wolf']
        lowerCAmelCase__	      =   ['werewolf']
        # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
        lowerCAmelCase__	      =   cer.compute(predictions=lowercase__     ,       references=lowercase__)
        self.assertTrue(abs(char_error_rate - 0.1_111_111) < 1e-6)

        lowerCAmelCase__	      =   ['werewolf']
        lowerCAmelCase__	      =   ['weae     wolf']
        # S = 1, D = 1, I = 0, N = 8, CER = 0.25
        lowerCAmelCase__	      =   cer.compute(predictions=lowercase__     ,       references=lowercase__)
        self.assertTrue(abs(char_error_rate - 0.25) < 1e-6)

        # consecutive whitespaces case 1
        lowerCAmelCase__	      =   ['were wolf']
        lowerCAmelCase__	      =   ['were               wolf']
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        lowerCAmelCase__	      =   cer.compute(predictions=lowercase__     ,       references=lowercase__)
        self.assertTrue(abs(char_error_rate - 0.0) < 1e-6)

        # consecutive whitespaces case 2
        lowerCAmelCase__	      =   ['were   wolf']
        lowerCAmelCase__	      =   ['were               wolf']
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        lowerCAmelCase__	      =   cer.compute(predictions=lowercase__     ,       references=lowercase__)
        self.assertTrue(abs(char_error_rate - 0.0) < 1e-6)




 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   ['werewolf']
        lowerCAmelCase__	      =   ['weaewolf']
        # S = 1, D = 0, I = 0, N = 8, CER = 0.125
        lowerCAmelCase__	      =   cer.compute(predictions=lowercase__     ,       references=lowercase__)
        self.assertTrue(abs(char_error_rate - 0.125) < 1e-6)




 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



        lowerCAmelCase__	      =   ['werewolf']
        lowerCAmelCase__	      =   ['wereawolf']
        # S = 0, D = 1, I = 0, N = 8, CER = 0.125
        lowerCAmelCase__	      =   cer.compute(predictions=lowercase__     ,       references=lowercase__)
        self.assertTrue(abs(char_error_rate - 0.125) < 1e-6)




 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__	      =   ['werewolf']
        lowerCAmelCase__	      =   ['wereolf']
        # S = 0, D = 0, I = 1, N = 8, CER = 0.125
        lowerCAmelCase__	      =   cer.compute(predictions=lowercase__     ,       references=lowercase__)
        self.assertTrue(abs(char_error_rate - 0.125) < 1e-6)




 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   ['werewolf']
        lowerCAmelCase__	      =   cer.compute(predictions=lowercase__     ,       references=lowercase__)
        self.assertEqual(lowercase__     ,       0.0)




 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



        lowerCAmelCase__	      =   ['werewolf', 'I am your father']
        lowerCAmelCase__	      =   cer.compute(predictions=lowercase__     ,       references=lowercase__)
        self.assertEqual(lowercase__     ,       0.0)

        lowerCAmelCase__	      =   ['werewolf', 'I am your father', 'doge']
        lowerCAmelCase__	      =   ['werxwolf', 'I       am your father', 'doge']
        # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
        lowerCAmelCase__	      =   cer.compute(predictions=lowercase__     ,       references=lowercase__)
        self.assertTrue(abs(char_error_rate - 0.03_571_428) < 1e-6)




 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



        lowerCAmelCase__	      =   ['My hovercraft', 'is full of eels']
        lowerCAmelCase__	      =   ['My hovercraft is full', ' of eels']
        # S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
        # whitespace at the front of " of eels" will be strip during preporcessing
        # so need to insert 2 whitespaces
        lowerCAmelCase__	      =   cer.compute(predictions=lowercase__     ,       references=lowercase__     ,       concatenate_texts=lowercase__)
        self.assertTrue(abs(char_error_rate - 0.071_428) < 1e-6)




 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        lowerCAmelCase__	      =   ['我能吞下玻璃而不伤身体']
        lowerCAmelCase__	      =   [' 能吞虾玻璃而 不霜身体啦']
        # S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
        lowerCAmelCase__	      =   cer.compute(predictions=lowercase__     ,       references=lowercase__)
        self.assertTrue(abs(char_error_rate - 0.4_545_454_545) < 1e-6)

        lowerCAmelCase__	      =   ['我能吞下玻璃', '而不伤身体']
        lowerCAmelCase__	      =   ['我    能 吞 下 玻 璃', '而不伤身体']
        # S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
        lowerCAmelCase__	      =   cer.compute(predictions=lowercase__     ,       references=lowercase__)
        self.assertTrue(abs(char_error_rate - 0.454_545_454_545) < 1e-6)

        lowerCAmelCase__	      =   ['我能吞下玻璃而不伤身体']
        lowerCAmelCase__	      =   cer.compute(predictions=lowercase__     ,       references=lowercase__)
        self.assertFalse(lowercase__     ,       0.0)




 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        lowerCAmelCase__	      =   ['']
        lowerCAmelCase__	      =   ['Hypothesis']
        with self.assertRaises(lowercase__):
               cer.compute(predictions=lowercase__     ,       references=lowercase__)


if __name__ == "__main__":
    unittest.main()
