




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 			a__    (					datasets.Metric      ):






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""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 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A=0.9		,			_A=3		,			_A=0.5			):




											"""simple docstring"""



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








from __future__ import annotations


class        _a							:

							def __init__(							self:	Union[str, Any]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str							)			->   List[Any]:





									"""simple docstring"""







									lowercase__     ,		lowercase__            =      text, pattern
									lowercase__     ,		lowercase__            =      len(UpperCamelCase_							), len(UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	str							)			->   int:





									"""simple docstring"""







									for i in range(self.patLen - 1    ,						-1    ,						-1							):
											if char == self.pattern[i]:
													return i
									return -1

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	int							)			->   int:





									"""simple docstring"""







									for i in range(self.patLen - 1    ,						-1    ,						-1							):
											if self.pattern[i] != self.text[current_pos + i]:
													return current_pos + i
									return -1


							def       lowerCamelCase_ (							self:	List[Any]							)			->   list[int]:





									"""simple docstring"""







									lowercase__            =      []
									for i in range(self.textLen - self.patLen + 1							):
											lowercase__            =      self.mismatch_in_text(UpperCamelCase_							)
											if mismatch_index == -1:
													positions.append(UpperCamelCase_							)
											else:
													lowercase__            =      self.match_in_pattern(self.text[mismatch_index]							)
													lowercase__            =      (
													    mismatch_index - match_index
													)  # shifting index lgtm [py/multiple-definition]
									return positions
lowerCAmelCase       =							'ABAABA'
lowerCAmelCase       =							'AB'
lowerCAmelCase       =							BoyerMooreSearch(text, pattern)
lowerCAmelCase       =							bms.bad_character_heuristic()

if len(positions) == 0:
			print('No match found')
else:
			print('Pattern found in following positions: ')
			print(positions)


