
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


A__   :		int 		='\\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'

A__   :		int 		='\\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'

A__   :		List[str] 		='\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			):
 def 		lowercase_(   self			:    int       ):



   """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			:    Dict				,   lowerCamelCase			:    List[Any]       ):



   """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			:    Union[str, Any]				,   lowerCamelCase			:    int				,   lowerCamelCase			:    Dict				,   lowerCamelCase			:    Any=0.9				,   lowerCamelCase			:    Tuple=3				,   lowerCamelCase			:    Optional[int]=0.5       ):



   """simple docstring"""





   if NLTK_VERSION >= version.Version("""3.6.5"""       ):
     __A		:     Tuple												=		[
         meteor_score.single_meteor_score(
             word_tokenize(lowerCAmelCase__       )				,   word_tokenize(lowerCAmelCase__       )				,   alpha=lowerCAmelCase__				,   beta=lowerCAmelCase__				,   gamma=lowerCAmelCase__       )
         for ref, pred in zip(lowerCAmelCase__				,   lowerCAmelCase__       )
     ]
   else:
     __A		:     Dict												=		[
         meteor_score.single_meteor_score(lowerCAmelCase__				,   lowerCAmelCase__				,   alpha=lowerCAmelCase__				,   beta=lowerCAmelCase__				,   gamma=lowerCAmelCase__       )
         for ref, pred in zip(lowerCAmelCase__				,   lowerCAmelCase__       )
     ]

   return {"meteor": np.mean(lowerCAmelCase__       )}





'''simple docstring'''
def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       int:





  """simple docstring"""

  __A		:     Optional[int]												=		1
  for i in range(1			,     num + 1 ):
    fact *= i
  return fact







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       int:





  """simple docstring"""

  __A		:     List[str]												=		0
  while number > 0:
    __A		:     Union[str, Any]												=		number % 10
    sum_of_digits += last_digit
    __A		:     int												=		number // 10  # Removing the last_digit from the given number
  return sum_of_digits







def        A_     (						__SCREAMING_SNAKE_CASE						:  int = 100 )				->       int:





  """simple docstring"""

  __A		:     Union[str, Any]												=		factorial(__SCREAMING_SNAKE_CASE )
  __A		:     Any												=		split_and_add(__SCREAMING_SNAKE_CASE )
  return result


if __name__ == "__main__":
     print(solution(int(input('Enter the Number: ').strip())))



