

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

import datasets
from datasets.config import importlib_metadata, version


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


lowerCamelCase			:				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'

lowerCamelCase			:				Optional[Any] 			=					'\\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'

lowerCamelCase			:				List[Any] 			=					'\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 						A  (		self				:     Any			)     ->   Optional[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 						A  (		self				:     Union[str, Any]							, _a				:     Dict			)     ->   Optional[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 						A  (		self				:     int							, _a				:     int							, _a				:     List[str]							, _a				:     Any=0.9							, _a				:     int=3							, _a				:     List[str]=0.5			)     ->   Optional[int]:





       '''simple docstring'''






       if NLTK_VERSION >= version.Version('3.6.5'			):
            _SCREAMING_SNAKE_CASE							   =[
                meteor_score.single_meteor_score(
                    word_tokenize(_lowercase			)							, word_tokenize(_lowercase			)							, alpha=_lowercase							, beta=_lowercase							, gamma=_lowercase			)
                for ref, pred in zip(_lowercase							, _lowercase			)
            ]
       else:
            _SCREAMING_SNAKE_CASE							   =[
                meteor_score.single_meteor_score(_lowercase							, _lowercase							, alpha=_lowercase							, beta=_lowercase							, gamma=_lowercase			)
                for ref, pred in zip(_lowercase							, _lowercase			)
            ]

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



'''simple docstring'''
import os


def 	_lowerCAmelCase (						)				->      List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =os.path.dirname(os.path.realpath(_UpperCamelCase    )    )
     _SCREAMING_SNAKE_CASE							   =os.path.join(_UpperCamelCase							,			'triangle.txt'    )

     with open(_UpperCamelCase    ) as f:
          _SCREAMING_SNAKE_CASE							   =f.readlines()

     _SCREAMING_SNAKE_CASE							   =[]
     for line in triangle:
          _SCREAMING_SNAKE_CASE							   =[]
          for number in line.strip().split(' '    ):
               numbers_from_line.append(int(_UpperCamelCase    )    )
          a.append(_UpperCamelCase    )

     for i in range(1							,			len(_UpperCamelCase    )    ):
          for j in range(len(a[i]    )    ):
               _SCREAMING_SNAKE_CASE							   =a[i - 1][j] if j != len(a[i - 1]    ) else 0
               _SCREAMING_SNAKE_CASE							   =a[i - 1][j - 1] if j > 0 else 0
               a[i][j] += max(_UpperCamelCase							,			_UpperCamelCase    )
     return max(a[-1]    )


if __name__ == "__main__":
     print(solution())
