





"""simple docstring"""



from typing import List

import numpy as np







def 						__lowercase	(  snake_case_    :    dict		)  ->int:



    '''simple docstring'''







    __A       :		Optional[int]							=	{key: len(snake_case_		) for key, value in gen_kwargs.items() if isinstance(snake_case_							,snake_case_		)}
    if len(set(lists_lengths.values()		)		) > 1:
        raise RuntimeError(
            (
                '''Sharding is ambiguous for this dataset: '''
                + '''we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'''
                + '''\n'''.join(F"""\t- key {key} has length {length}""" for key, length in lists_lengths.items()		)
                + '''\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '''
                + '''and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'''
            )		)
    __A       :		int							=	max(lists_lengths.values()							,default=0		)
    return max(1							,snake_case_		)







def 						__lowercase	(  snake_case_    :    int							,snake_case_    :    int		)  ->List[range]:



    '''simple docstring'''







    __A       :		Any							=	[]
    for group_idx in range(snake_case_		):
        __A       :		Optional[int]							=	num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
        if num_shards_to_add == 0:
            break
        __A       :		Optional[Any]							=	shards_indices_per_group[-1].stop if shards_indices_per_group else 0
        __A       :		List[str]							=	range(snake_case_							,start + num_shards_to_add		)
        shards_indices_per_group.append(snake_case_		)
    return shards_indices_per_group







def 						__lowercase	(  snake_case_    :    dict							,snake_case_    :    int		)  ->List[dict]:



    '''simple docstring'''







    __A       :		List[str]							=	_number_of_shards_in_gen_kwargs(snake_case_		)
    if num_shards == 1:
        return [dict(snake_case_		)]
    else:
        __A       :		Dict							=	_distribute_shards(num_shards=snake_case_							,max_num_jobs=snake_case_		)
        return [
            {
                key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                if isinstance(snake_case_							,snake_case_		)
                else value
                for key, value in gen_kwargs.items()
            }
            for group_idx in range(len(snake_case_		)		)
        ]







def 						__lowercase	(  snake_case_    :    List[dict]		)  ->dict:



    '''simple docstring'''







    return {
        key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
        if isinstance(gen_kwargs_list[0][key]							,snake_case_		)
        else gen_kwargs_list[0][key]
        for key in gen_kwargs_list[0]
    }







def 						__lowercase	(  snake_case_    :    np.random.Generator							,snake_case_    :    dict		)  ->dict:



    '''simple docstring'''







    __A       :		Dict							=	{len(snake_case_		) for value in gen_kwargs.values() if isinstance(snake_case_							,snake_case_		)}
    __A       :		Optional[int]							=	{}
    for size in list_sizes:
        __A       :		Optional[Any]							=	list(range(snake_case_		)		)
        rng.shuffle(indices_per_size[size]		)
    # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
    __A       :		Tuple							=	dict(snake_case_		)
    for key, value in shuffled_kwargs.items():
        if isinstance(snake_case_							,snake_case_		):
            __A       :		List[Any]							=	[value[i] for i in indices_per_size[len(snake_case_		)]]
    return shuffled_kwargs











"""simple docstring"""



import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


a_					=     """\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

a_					=     """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

a_					=     """
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,				_KWARGS_DESCRIPTION	)
class 						__snake_case   (						datasets.Metric	):





 """simple docstring"""


 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return datasets.MetricInfo(
         description=_DESCRIPTION   ,		citation=_CITATION   ,		inputs_description=_KWARGS_DESCRIPTION   ,		features=datasets.Features(
             {
                 '''predictions''': datasets.Value('''string'''   ,		id='''sequence'''    ),
                 '''references''': datasets.Value('''string'''   ,		id='''sequence'''    ),
             }    )   ,		codebase_urls=['''https://github.com/google-research/google-research/tree/master/rouge''']   ,		reference_urls=[
             '''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
             '''https://github.com/google-research/google-research/tree/master/rouge''',
         ]   ,		)







 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase=None   ,		__lowerCamelCase=True   ,		__lowerCamelCase=False    ):



     '''simple docstring'''




     if rouge_types is None:
         __A       :		Dict							=	['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

     __A       :		Dict							=	rouge_scorer.RougeScorer(rouge_types=__lowerCamelCase   ,		use_stemmer=__lowerCamelCase    )
     if use_aggregator:
         __A       :		Dict							=	scoring.BootstrapAggregator()
     else:
         __A       :		Optional[Any]							=	[]

     for ref, pred in zip(__lowerCamelCase   ,		__lowerCamelCase    ):
         __A       :		List[Any]							=	scorer.score(__lowerCamelCase   ,		__lowerCamelCase    )
         if use_aggregator:
             aggregator.add_scores(__lowerCamelCase    )
         else:
             scores.append(__lowerCamelCase    )

     if use_aggregator:
         __A       :		Optional[Any]							=	aggregator.aggregate()
     else:
         __A       :		List[Any]							=	{}
         for key in scores[0]:
             __A       :		Tuple							=	[score[key] for score in scores]

     return result




