



import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


UpperCAmelCase__       =	'\\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'

UpperCAmelCase__       =	'\\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'

UpperCAmelCase__       =	'\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       __lowerCAmelCase   (      datasets.Metric    ):



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Union[str, 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    _lowerCamelCase	(						self       :  List[str]			,		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    _lowerCamelCase	(						self       :  Any			,		A       :  List[Any]			,		A       :  List[str]			,		A       :  List[str]=0.9			,		A       :  Optional[int]=3			,		A       :  Union[str, Any]=0.5)       ->   str:



						"""simple docstring"""






						if NLTK_VERSION >= version.Version('3.6.5'):
									_UpperCAmelCase			=       [
									    meteor_score.single_meteor_score(
									        word_tokenize(lowercase_)			,		word_tokenize(lowercase_)			,		alpha=lowercase_			,		beta=lowercase_			,		gamma=lowercase_)
									    for ref, pred in zip(lowercase_			,		lowercase_)
									]
						else:
									_UpperCAmelCase			=       [
									    meteor_score.single_meteor_score(lowercase_			,		lowercase_			,		alpha=lowercase_			,		beta=lowercase_			,		gamma=lowercase_)
									    for ref, pred in zip(lowercase_			,		lowercase_)
									]

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





def 							A     (					_UpperCAmelCase    :       int   )    ->			"list[int]":







			'''simple docstring'''
			if upper_limit < 0:
						raise ValueError('Limit for the Catalan sequence must be ≥ 0'   )

			_UpperCAmelCase			=       [0] * (upper_limit + 1)

			# Base case: C(0) = C(1) = 1
			_UpperCAmelCase			=       1
			if upper_limit > 0:
						_UpperCAmelCase			=       1

			# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
			for i in range(2							,       upper_limit + 1   ):
						for j in range(_UpperCAmelCase   ):
									catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

			return catalan_list


if __name__ == "__main__":
							print("\n********* Catalan Numbers Using Dynamic Programming ************\n")
							print("\n*** Enter -1 at any time to quit ***")
							print("\nEnter the upper limit (≥ 0) for the Catalan number sequence: ", end="")
							try:
														while True:
																					UpperCAmelCase__       =	int(input().strip())
																					if N < 0:
																												print("\n********* Goodbye!! ************")
																												break
																					else:
																												print(f"""The Catalan numbers from 0 through {N} are:""")
																												print(catalan_numbers(N))
																												print("Try another upper limit for the sequence: ", end="")
							except (NameError, ValueError):
														print("\n********* Invalid input, goodbye! ************\n")

							import doctest

							doctest.testmod()
