



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


lowercase_	       =			'''\
@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",
}
'''

lowercase_	       =			'''\
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
'''

lowercase_	       =			'''
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 						__a (     datasets.Metric				):



					def 		UpperCamelCase							(						self							:    int)->							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 		UpperCamelCase							(						self							:    int				,	snake_case_							:    Dict				,	snake_case_							:    str				,	snake_case_							:    Union[str, Any]=None				,	snake_case_							:    Dict=True				,	snake_case_							:    Dict=False)->							Tuple:
										if rouge_types is None:
															__lowerCAmelCase	   =["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

										__lowerCAmelCase	   =rouge_scorer.RougeScorer(rouge_types=snake_case_				,	use_stemmer=snake_case_)
										if use_aggregator:
															__lowerCAmelCase	   =scoring.BootstrapAggregator()
										else:
															__lowerCAmelCase	   =[]

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

										if use_aggregator:
															__lowerCAmelCase	   =aggregator.aggregate()
										else:
															__lowerCAmelCase	   ={}
															for key in scores[0]:
																				__lowerCAmelCase	   =[score[key] for score in scores]

										return result









def 					__lowerCAmelCase		(  __lowerCamelCase	:    list	)  ->						list:
					if len(__lowerCamelCase	) <= 1:
										return lst

					__lowerCAmelCase	   =1

					while i < len(__lowerCamelCase	):
										if lst[i - 1] <= lst[i]:
															i += 1
										else:
															__lowerCAmelCase   ,	__lowerCAmelCase	   =lst[i], lst[i - 1]
															i -= 1
															if i == 0:
																				__lowerCAmelCase	   =1

					return lst


if __name__ == "__main__":
				lowercase_	       =			input('''Enter numbers separated by a comma:\n''').strip()
				lowercase_	       =			[int(item) for item in user_input.split(''',''')]
				print(gnome_sort(unsorted))




