



"""simple docstring"""






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_lowercase				:				List[str]   		=		version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
		from nltk import word_tokenize


_lowercase				:				str   		=		"\\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"

_lowercase				:				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"

_lowercase				:				int   		=		"\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  __SCREAMING_SNAKE_CASE    (   datasets.Metric     ):


					'''simple docstring'''





					def 	snake_case ( self							:						Dict  )->      List[str]:
								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 	snake_case ( self							:						str,						lowerCamelCase							:						Dict  )->      Optional[int]:
								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 	snake_case ( self							:						int,						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						List[str]=0.9,						lowerCamelCase							:						str=3,						lowerCamelCase							:						Union[str, Any]=0.5  )->      int:
								if NLTK_VERSION >= version.Version('''3.6.5'''  ):
											lowerCamelCase__	:	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:
											lowerCamelCase__	:	List[Any]				=[
											    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"""






import os






def   snake_case__     (	):
			"""simple docstring"""
			with open(os.path.dirname(__lowerCamelCase			) + '''/p022_names.txt'''			) as file:
						lowerCamelCase__	:	Tuple				=str(file.readlines()[0]			)
						lowerCamelCase__	:	int				=names.replace('''"'''							,	''''''			).split(''','''			)

			names.sort()

			lowerCamelCase__	:	Union[str, Any]				=0
			lowerCamelCase__	:	str				=0

			for i, name in enumerate(__lowerCamelCase			):
						for letter in name:
									name_score += ord(__lowerCamelCase			) - 64

						total_score += (i + 1) * name_score
						lowerCamelCase__	:	Dict				=0
			return total_score


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




