


import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


snake_case__			 =						'''\
@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},
}
'''

snake_case__			 =						'''\
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.
'''

snake_case__			 =						'''
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):




						def  _snake_case					(	self						:   int						)      ->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  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   Optional[int]						)      ->Union[str, 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  _snake_case					(	self						:   Optional[int]   ,     __A						:   Optional[Any]   ,     __A						:   Any   ,     __A						:   List[str]=0.9   ,     __A						:   Any=3   ,     __A						:   Dict=0.5						)      ->Any:






									"""simple docstring"""






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

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






def 				lowerCamelCase__	(      a	:   int	,  a	:   int				)    ->	Any:





			"""simple docstring"""
			# "extended trapezoidal rule"
			# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
			a__ :Optional[int]						  =    (boundary[1] - boundary[0]) / steps
			a__ :str						  =    boundary[0]
			a__ :Optional[int]						  =    boundary[1]
			a__ :str						  =    make_points(a	,  a	,  a				)
			a__ :Any						  =    0.0
			y += (h / 2.0) * f(a				)
			for i in x_i:
						# print(i)
						y += h * f(a				)
			y += (h / 2.0) * f(a				)
			return y


def 				lowerCamelCase__	(      a	:   Any	,  a	:   str	,  a	:   Dict				)    ->	int:





			"""simple docstring"""
			a__ :Union[str, Any]						  =    a + h
			while x < (b - h):
						yield x
						a__ :str						  =    x + h


def 				lowerCamelCase__	(      a	:   Optional[int]				)    ->	List[Any]:  # enter your function here





			"""simple docstring"""
			a__ :Tuple						  =    (x - 0) * (x - 0)
			return y


def 				lowerCamelCase__	(      )    ->	List[str]:





			"""simple docstring"""
			a__ :Optional[Any]						  =    0.0  # Lower bound of integration
			a__ :Optional[Any]						  =    1.0  # Upper bound of integration
			a__ :List[str]						  =    1_0.0  # define number of steps or resolution
			a__ :int						  =    [a, b]  # define boundary of integration
			a__ :str						  =    method_a(a	,  a				)
			print(F'''y = {y}'''				)


if __name__ == "__main__":
		main()


