






'''simple docstring'''







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_A     :      Optional[int]   					=version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
			from nltk import word_tokenize


_A     :      Tuple   					='''\
@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},
}
'''

_A     :      Optional[int]   					='''\
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.
'''

_A     :      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 _lowercase     (    datasets.Metric		):




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														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 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    Any					):
														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 		lowerCamelCase_    (							self:    List[Any]      ,							UpperCamelCase__:    Tuple      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Optional[Any]=0.9      ,							UpperCamelCase__:    Union[str, Any]=3      ,							UpperCamelCase__:    Optional[Any]=0.5					):
														if NLTK_VERSION >= version.Version("""3.6.5"""					):
																					lowerCamelCase__ :     Union[str, Any]	  =	[
																					    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:
																					lowerCamelCase__ :     Optional[Any]	  =	[
																					    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__					)}









'''simple docstring'''







from math import asin, atan, cos, radians, sin, sqrt, tan

_A     :      List[str]   					=637_8137.0
_A     :      Dict   					=635_6752.31_4245
_A     :      int   					=6_378_137





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	float:
							lowerCamelCase__ :     str	  =	(AXIS_A - AXIS_B) / AXIS_A
							lowerCamelCase__ :     Dict	  =	atan((1 - flattening) * tan(radians(UpperCamelCase				)				)				)
							lowerCamelCase__ :     Dict	  =	atan((1 - flattening) * tan(radians(UpperCamelCase				)				)				)
							lowerCamelCase__ :     Optional[Any]	  =	radians(UpperCamelCase				)
							lowerCamelCase__ :     List[Any]	  =	radians(UpperCamelCase				)
							# Equation
							lowerCamelCase__ :     Tuple	  =	sin((phi_a - phi_a) / 2				)
							lowerCamelCase__ :     List[Any]	  =	sin((lambda_a - lambda_a) / 2				)
							# Square both values
							sin_sq_phi *= sin_sq_phi
							sin_sq_lambda *= sin_sq_lambda
							lowerCamelCase__ :     Tuple	  =	sqrt(sin_sq_phi + (cos(UpperCamelCase				) * cos(UpperCamelCase				) * sin_sq_lambda)				)
							return 2 * RADIUS * asin(UpperCamelCase				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()

