'''simple docstring'''






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


SCREAMING_SNAKE_CASE_	   =			version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
    from nltk import word_tokenize


SCREAMING_SNAKE_CASE_	   =			'\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n'

SCREAMING_SNAKE_CASE_	   =			'\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'

SCREAMING_SNAKE_CASE_	   =			'\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    \'meteor\': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric(\'meteor\')\n    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results["meteor"], 4))\n    0.6944\n'




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



  """simple docstring"""







  def    lowercase_  (							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/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"""]   ,    reference_urls=[
            """https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score""",
            """https://en.wikipedia.org/wiki/METEOR""",
        ]   ,    )







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    import nltk

    nltk.download("""wordnet"""					)
    if NLTK_VERSION >= version.Version("""3.6.5"""					):
      nltk.download("""punkt"""					)
    if NLTK_VERSION >= version.Version("""3.6.6"""					):
      nltk.download("""omw-1.4"""					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_=0.9   ,    snake_case_=3   ,    snake_case_=0.5					):

    '''simple docstring'''







    if NLTK_VERSION >= version.Version("""3.6.5"""					):
      __UpperCAmelCase:					Union[str, Any]		= [
          meteor_score.single_meteor_score(
              word_tokenize(snake_case_					)   ,    word_tokenize(snake_case_					)   ,    alpha=snake_case_   ,    beta=snake_case_   ,    gamma=snake_case_					)
          for ref, pred in zip(snake_case_   ,    snake_case_					)
      ]
    else:
      __UpperCAmelCase:					Tuple		= [
          meteor_score.single_meteor_score(snake_case_   ,    snake_case_   ,    alpha=snake_case_   ,    beta=snake_case_   ,    gamma=snake_case_					)
          for ref, pred in zip(snake_case_   ,    snake_case_					)
      ]

    return {"meteor": np.mean(snake_case_					)}
'''simple docstring'''






def  UpperCamelCase__					(					_lowercase    :				list     )							->	list:
  if len(_lowercase     ) <= 1:
    return lst

  __UpperCAmelCase:					List[str]		= 1

  while i < len(_lowercase     ):
    if lst[i - 1] <= lst[i]:
      i += 1
    else:
      __UpperCAmelCase, __UpperCAmelCase:					Tuple		= lst[i], lst[i - 1]
      i -= 1
      if i == 0:
        __UpperCAmelCase:					List[str]		= 1

  return lst


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