


from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

_lowercase     							=						TypeVar('''T''')
class 				__A					( Generic[T]      ):
			UpperCamelCase					:int     =			42  # Cache store of keys
			UpperCamelCase					:Optional[Any]     =			42  # References of the keys in cache
			UpperCamelCase					:str     =			10  # Maximum capacity of cache





			def __init__(self							,    __magic_name__				):
						lowerCamelCase__ :      List[str]								=		deque()
						lowerCamelCase__ :      Any								=		set()
						if not n:
									lowerCamelCase__ :      List[Any]								=		sys.maxsize
						elif n < 0:
									raise ValueError("""n should be an integer greater than 0."""				)
						else:
									lowerCamelCase__ :      str								=		n





			def        _snake_case    (self							,    __magic_name__				):






						if x not in self.key_reference:
									if len(self.dq_store				) == LRUCache._MAX_CAPACITY:
												lowerCamelCase__ :      Any								=		self.dq_store.pop()
												self.key_reference.remove(lowercase__				)
						else:
									self.dq_store.remove(lowercase__				)

						self.dq_store.appendleft(lowercase__				)
						self.key_reference.add(lowercase__				)





			def        _snake_case    (self				):






						for k in self.dq_store:
									print(lowercase__				)





			def __repr__(self				):
						return f"LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store				)}"


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowercase     							=						LRUCache(4)
				lru_cache.refer('''A''')
				lru_cache.refer(2)
				lru_cache.refer(3)
				lru_cache.refer('''A''')
				lru_cache.refer(4)
				lru_cache.refer(5)
				lru_cache.display()

				print(lru_cache)
				assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"








import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


_lowercase     							=						'''\
@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},
}
'''

_lowercase     							=						'''\
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.
'''

_lowercase     							=						'''
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 				__A					( datasets.Metric      ):





			def        _snake_case    (self				):
						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							,    __magic_name__				):
						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							,    __magic_name__							,    __magic_name__							,    __magic_name__=0.9							,    __magic_name__=3							,    __magic_name__=0.5				):
						if NLTK_VERSION >= version.Version("""3.6.5"""				):
									lowerCamelCase__ :      List[str]								=		[
									    meteor_score.single_meteor_score(
									        word_tokenize(__magic_name__				)							,    word_tokenize(__magic_name__				)							,    alpha=__magic_name__							,    beta=__magic_name__							,    gamma=__magic_name__				)
									    for ref, pred in zip(__magic_name__							,    __magic_name__				)
									]
						else:
									lowerCamelCase__ :      int								=		[
									    meteor_score.single_meteor_score(__magic_name__							,    __magic_name__							,    alpha=__magic_name__							,    beta=__magic_name__							,    gamma=__magic_name__				)
									    for ref, pred in zip(__magic_name__							,    __magic_name__				)
									]

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



