


import argparse
from collections import defaultdict

import yaml


__lowerCAmelCase			=							'''docs/source/en/_toctree.yml'''




def        snake_case_					(  snake_case					)  ->    Dict:
       lowercase__:       List[str]        =       defaultdict(snake_case					)
       lowercase__:       Dict        =       []
       lowercase__:       Optional[int]        =       []
       for doc in doc_list:
              if "local" in doc:
                     counts[doc["local"]] += 1

              if doc["title"].lower() == "overview":
                     overview_doc.append({'local': doc['local'], 'title': doc['title']}					)
              else:
                     new_doc_list.append(snake_case					)

       lowercase__:       str        =       new_doc_list
       lowercase__:       int        =       [key for key, value in counts.items() if value > 1]

       lowercase__:       str        =       []
       for duplicate_key in duplicates:
              lowercase__:       Dict        =       list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}					)
              if len(snake_case					) > 1:
                     raise ValueError(
                         f'{duplicate_key} is present several times in the documentation table of content at '
                         '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                         'others.'					)
              # Only add this once
              new_doc.append({'local': duplicate_key, 'title': titles[0]}					)

       # Add none duplicate-keys
       new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]					)
       lowercase__:       str        =       sorted(snake_case      ,							key=lambda snake_case					: s["title"].lower()					)

       # "overview" gets special treatment and is always first
       if len(snake_case					) > 1:
              raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'					)

       overview_doc.extend(snake_case					)

       # Sort
       return overview_doc




def        snake_case_					(  snake_case=False					)  ->    Union[str, Any]:
       with open(snake_case      ,							encoding='utf-8'					) as f:
              lowercase__:       str        =       yaml.safe_load(f.read()					)

       # Get to the API doc
       lowercase__:       List[str]        =       0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       lowercase__:       Union[str, Any]        =       content[api_idx]['sections']

       # Then to the model doc
       lowercase__:       Optional[int]        =       0
       while api_doc[scheduler_idx]["title"] != "Schedulers":
              scheduler_idx += 1

       lowercase__:       str        =       api_doc[scheduler_idx]['sections']
       lowercase__:       Optional[int]        =       clean_doc_toc(snake_case					)

       lowercase__:       Tuple        =       False
       if new_scheduler_doc != scheduler_doc:
              lowercase__:       int        =       True
              if overwrite:
                     lowercase__:       Union[str, Any]        =       new_scheduler_doc

       if diff:
              if overwrite:
                     lowercase__:       Any        =       api_doc
                     with open(snake_case      ,							'w'      ,							encoding='utf-8'					) as f:
                            f.write(yaml.dump(snake_case      ,							allow_unicode=snake_case					)					)
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'					)




def        snake_case_					(  snake_case=False					)  ->    int:
       with open(snake_case      ,							encoding='utf-8'					) as f:
              lowercase__:       Dict        =       yaml.safe_load(f.read()					)

       # Get to the API doc
       lowercase__:       Dict        =       0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       lowercase__:       Dict        =       content[api_idx]['sections']

       # Then to the model doc
       lowercase__:       Dict        =       0
       while api_doc[pipeline_idx]["title"] != "Pipelines":
              pipeline_idx += 1

       lowercase__:       Union[str, Any]        =       False
       lowercase__:       Dict        =       api_doc[pipeline_idx]['sections']
       lowercase__:       Optional[int]        =       []

       # sort sub pipeline docs
       for pipeline_doc in pipeline_docs:
              if "section" in pipeline_doc:
                     lowercase__:       str        =       pipeline_doc['section']
                     lowercase__:       Any        =       clean_doc_toc(snake_case					)
                     if overwrite:
                            lowercase__:       List[Any]        =       new_sub_pipeline_doc
              new_pipeline_docs.append(snake_case					)

       # sort overall pipeline doc
       lowercase__:       str        =       clean_doc_toc(snake_case					)

       if new_pipeline_docs != pipeline_docs:
              lowercase__:       Union[str, Any]        =       True
              if overwrite:
                     lowercase__:       List[str]        =       new_pipeline_docs

       if diff:
              if overwrite:
                     lowercase__:       Any        =       api_doc
                     with open(snake_case      ,							'w'      ,							encoding='utf-8'					) as f:
                            f.write(yaml.dump(snake_case      ,							allow_unicode=snake_case					)					)
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'					)


if __name__ == "__main__":
 __lowerCAmelCase			=							argparse.ArgumentParser()
 parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
 __lowerCAmelCase			=							parser.parse_args()

 check_scheduler_doc(args.fix_and_overwrite)
 check_pipeline_doc(args.fix_and_overwrite)






import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


__lowerCAmelCase			=							'''\
@inproceedings{popovic-2015-chrf,
    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W15-3049",
    doi = "10.18653/v1/W15-3049",
    pages = "392--395",
}
@inproceedings{popovic-2017-chrf,
    title = "chr{F}++: words helping character n-grams",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-4770",
    doi = "10.18653/v1/W17-4770",
    pages = "612--618",
}
@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			=							'''\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
'''

__lowerCAmelCase			=							'''
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    \'score\' (float): The chrF (chrF++) score,
    \'char_order\' (int): The character n-gram order,
    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    \'beta\' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}
'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,	_KWARGS_DESCRIPTION				)
class   __a      (   datasets.Metric				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''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#chrf--chrf'    ,			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#chrf--chrf']    ,			reference_urls=[
                'https://github.com/m-popovic/chrF',
            ]    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = CHRF.CHAR_ORDER    ,			lowerCAmelCase__ = CHRF.WORD_ORDER    ,			lowerCAmelCase__ = CHRF.BETA    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = False    ,			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       str        =       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__:       List[str]        =       [[refs[i] for refs in references] for i in range(lowerCAmelCase__			)]

        lowercase__:       Union[str, Any]        =       CHRF(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			)
        lowercase__:       str        =       sb_chrf.corpus_score(lowerCAmelCase__    ,			lowerCAmelCase__			)

        return {
            "score": output.score,
            "char_order": output.char_order,
            "word_order": output.word_order,
            "beta": output.beta,
        }


