





import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


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

_lowerCamelCase :    Optional[Any]  							= '''\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.

METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
'''

_lowerCamelCase :    List[Any]  							= '''
Computes METEOR score of translated segments against one or more 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.
    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
    gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
    \'meteor\': meteor score.
Examples:

    >>> meteor = datasets.load_metric(\'meteor\')
    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
    >>> results = meteor.compute(predictions=predictions, references=references)
    >>> print(round(results["meteor"], 4))
    0.6944
'''


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







							"""simple docstring"""




							def  A_     ( self     :					Dict						)				->			Tuple:





														"""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],			_UpperCAmelCase     :					Dict						)				->			List[str]:





														"""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     :					List[Any],			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					Union[str, Any]=0.9,			_UpperCAmelCase     :					str=3,			_UpperCAmelCase     :					str=0.5						)				->			int:





														"""simple docstring"""
														if NLTK_VERSION >= version.Version("3.6.5"						):
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						[
																					    meteor_score.single_meteor_score(
																					        word_tokenize(_UpperCAmelCase						),			word_tokenize(_UpperCAmelCase						),			alpha=_UpperCAmelCase,			beta=_UpperCAmelCase,			gamma=_UpperCAmelCase						)
																					    for ref, pred in zip(_UpperCAmelCase,			_UpperCAmelCase						)
																					]
														else:
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						[
																					    meteor_score.single_meteor_score(_UpperCAmelCase,			_UpperCAmelCase,			alpha=_UpperCAmelCase,			beta=_UpperCAmelCase,			gamma=_UpperCAmelCase						)
																					    for ref, pred in zip(_UpperCAmelCase,			_UpperCAmelCase						)
																					]

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













def _a (  SCREAMING_SNAKE_CASE__      :  int = 50_00_00_00       )	->    int:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						set()
							SCREAMING_SNAKE_CASE__       :      Dict     =						int((limit - 24) ** (1 / 2)       )

							SCREAMING_SNAKE_CASE__       :      Any     =						set(range(3			,				prime_square_limit + 1			,				2       )       )
							primes.add(2       )
							for p in range(3			,				prime_square_limit + 1			,				2       ):
														if p not in primes:
																					continue
														primes.difference_update(set(range(p * p			,				prime_square_limit + 1			,				SCREAMING_SNAKE_CASE__       )       )       )

							for primea in primes:
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						primea * primea
														for primea in primes:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						primea * primea * primea
																					if square + cube >= limit - 16:
																												break
																					for primea in primes:
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						primea * primea * primea * primea
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						square + cube + tetr
																												if total >= limit:
																																			break
																												ret.add(SCREAMING_SNAKE_CASE__       )

							return len(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
		print(f"{solution() = }")






