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

a__						:      Optional[Any]   				=				"\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

a__						:      Optional[Any]   				=				"\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    \'meteor\': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric(\'meteor\')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"


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






 def 		lowercase_  (	self     :List[str]	) ->	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 		lowercase_  (	self     :List[str]		, _A     :Tuple	) ->	Tuple:







       '''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 		lowercase_  (	self     :Any		, _A     :Union[str, Any]		, _A     :Dict		, _A     :str=0.9		, _A     :List[str]=3		, _A     :Optional[int]=0.5	) ->	Any:







       '''simple docstring'''



       if NLTK_VERSION >= version.Version('3.6.5'	):
             __A						=	[
                 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:
             __A						=	[
                 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_	)}







def  snake_case  (       snake_case__			:str = "The quick brown fox jumps over the lazy dog"   ,	)						->   bool:
       _A        =  set()

       # Replace all the whitespace in our sentence
       _A        =  input_str.replace(""" """   ,	"""""")
       for alpha in input_str:
              if "a" <= alpha.lower() <= "z":
                     frequency.add(alpha.lower())
       return len(snake_case__) == 26






def  snake_case  (       snake_case__			:str = "The quick brown fox jumps over the lazy dog"   ,	)						->   bool:
       _A        =  [False] * 26
       for char in input_str:
              if char.islower():
                     _A        =  True
              elif char.isupper():
                     _A        =  True
       return all(snake_case__)






def  snake_case  (       snake_case__			:str = "The quick brown fox jumps over the lazy dog"   ,	)						->   bool:
       return len({char for char in input_str.lower() if char.isalpha()}) == 26






def  snake_case  (       )						->   None:
       from timeit import timeit

       _A        =  """from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
       print(timeit("""is_pangram()"""   ,	setup=snake_case__))
       print(timeit("""is_pangram_faster()"""   ,	setup=snake_case__))
       print(timeit("""is_pangram_fastest()"""   ,	setup=snake_case__))
       # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
       # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()




