





from __future__ import annotations







class      lowerCamelCase:
     '''simple docstring'''


     def __init__(    self				,     snake_case_=None							):
         _A     =							data
         _A     =							None




     def __repr__(    self							):
         _A     =							[]
         _A     =							self
         while temp:
             string_rep.append(F"{temp.data}"							)
             _A     =							temp.next
         return "->".join(snake_case_							)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    if not elements_list:
        raise Exception('The Elements List is empty'				)

    _A     =							_A     =							Node(elements_list[0]				)
    for i in range(1							,     len(_SCREAMING_SNAKE_CASE				)				):
        _A     =							Node(elements_list[i]				)
        _A     =							current.next
    return head





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> None:




    """simple docstring"""




    if head_node is not None and isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        print_reverse(head_node.next				)
        print(head_node.data				)





def 				__lowerCAmelCase(       )							-> Any:




    """simple docstring"""




    from doctest import testmod

    testmod()

    _A     =							make_linked_list([14, 52, 14, 12, 43]				)
    print('Linked List:'				)
    print(_SCREAMING_SNAKE_CASE				)
    print('Elements in Reverse:'				)
    print_reverse(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
 main()






'''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


UpperCamelCase_      	=	'''\
@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",
}
'''

UpperCamelCase_      	=	'''\
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
'''

UpperCamelCase_      	=	'''
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 _SCREAMING_SNAKE_CASE(							datasets.Metric					):




						def      __lowerCamelCase       (    self :       Any      )     ->				Union[str, Any]:
													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      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       Tuple=None							,							UpperCamelCase_ :       Tuple=True							,							UpperCamelCase_ :       str=False      )     ->				Optional[Any]:
													if rouge_types is None:
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = rouge_scorer.RougeScorer(rouge_types=UpperCamelCase_							,							use_stemmer=UpperCamelCase_      )
													if use_aggregator:
																				SCREAMING_SNAKE_CASE__     :Tuple						    = scoring.BootstrapAggregator()
													else:
																				SCREAMING_SNAKE_CASE__     :Any						    = []

													for ref, pred in zip(UpperCamelCase_							,							UpperCamelCase_      ):
																				SCREAMING_SNAKE_CASE__     :Tuple						    = scorer.score(UpperCamelCase_							,							UpperCamelCase_      )
																				if use_aggregator:
																											aggregator.add_scores(UpperCamelCase_      )
																				else:
																											scores.append(UpperCamelCase_      )

													if use_aggregator:
																				SCREAMING_SNAKE_CASE__     :Tuple						    = aggregator.aggregate()
													else:
																				SCREAMING_SNAKE_CASE__     :List[str]						    = {}
																				for key in scores[0]:
																											SCREAMING_SNAKE_CASE__     :int						    = [score[key] for score in scores]

													return result






