






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


__UpperCamelCase   :   Union[str, Any]      =    """\
@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},
}
"""

__UpperCamelCase   :   List[str]      =    """\
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.
"""

__UpperCamelCase   :   Optional[int]      =    """
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 							__UpperCamelCase     (  datasets.Metric ):







	def 			_a (	self   :				Optional[Any]      )		->			List[Any]:
		"""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   :				Optional[int]							,					_lowerCAmelCase   :				Union[str, Any]      )		->			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 			_a (	self   :				List[str]							,					_lowerCAmelCase   :				List[Any]							,					_lowerCAmelCase   :				Union[str, Any]							,					_lowerCAmelCase   :				Optional[int]=0.9							,					_lowerCAmelCase   :				Optional[Any]=3							,					_lowerCAmelCase   :				Tuple=0.5      )		->			Optional[Any]:
		"""simple docstring"""







		if NLTK_VERSION >= version.Version("""3.6.5"""      ):
			__lowercase   							=	[
			    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:
			__lowercase   							=	[
			    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      )}



from __future__ import annotations

lowerCamelCase__						=				"#"







class 							lowerCAmelCase__   :

						def __init__(      self					)       ->   None:


								'''simple docstring'''

								_UpperCamelCase         =						{}

						def 			A_					(      self							,				a					)       ->   None:


								'''simple docstring'''

								_UpperCamelCase         =						self._trie
								for char in text:
										if char not in trie:
												_UpperCamelCase         =						{}
										_UpperCamelCase         =						trie[char]
								_UpperCamelCase         =						True

						def 			A_					(      self							,				a					)       ->   tuple | list:


								'''simple docstring'''

								_UpperCamelCase         =						self._trie
								for char in prefix:
										if char in trie:
												_UpperCamelCase         =						trie[char]
										else:
												return []
								return self._elements(a					)







						def 			A_					(      self							,				a					)       ->   tuple:


								'''simple docstring'''

								_UpperCamelCase         =						[]
								for c, v in d.items():
										_UpperCamelCase         =						[""" """] if c == END else [(c + s) for s in self._elements(a					)]
										result.extend(a					)
								return tuple(a					)




lowerCamelCase__						=				Trie()
lowerCamelCase__						=				("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
		trie.insert_word(word)


def 				__A(lowerCAmelCase			)		->					tuple:

		"""simple docstring"""






		_UpperCamelCase         =						trie.find_word(lowerCAmelCase			)
		return tuple(string + word for word in suffixes			)


def 				__A()		->					None:

		"""simple docstring"""






		print(autocomplete_using_trie("""de"""			)			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()



