






"""simple docstring"""
def 							lowercase__		(				lowerCamelCase,	lowerCamelCase					):
				# The longer word should come first
				if len(lowerCamelCase					) < len(lowerCamelCase					):
								return levenshtein_distance(lowerCamelCase,	lowerCamelCase					)

				if len(lowerCamelCase					) == 0:
								return len(lowerCamelCase					)

				_SCREAMING_SNAKE_CASE      :		Optional[int]							=						list(range(len(lowerCamelCase					) + 1					)					)

				for i, ca in enumerate(lowerCamelCase					):
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						[i + 1]

								for j, ca in enumerate(lowerCamelCase					):
												# Calculate insertions, deletions and substitutions
												_SCREAMING_SNAKE_CASE      :		List[Any]							=						previous_row[j + 1] + 1
												_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						current_row[j] + 1
												_SCREAMING_SNAKE_CASE      :		Optional[int]							=						previous_row[j] + (ca != ca)

												# Get the minimum to append to the current row
												current_row.append(min(lowerCamelCase,	lowerCamelCase,	lowerCamelCase					)					)

								# Store the previous row
								_SCREAMING_SNAKE_CASE      :		str							=						current_row

				# Returns the last element (distance)
				return previous_row[-1]


if __name__ == "__main__":
			lowerCAmelCase__							= input('Enter the first word:\n').strip()
			lowerCAmelCase__							= input('Enter the second word:\n').strip()

			lowerCAmelCase__							= levenshtein_distance(first_word, second_word)
			print(F'Levenshtein distance between {first_word} and {second_word} is {result}')









"""simple docstring"""
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCAmelCase__							= '\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n'

lowerCAmelCase__							= '\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n'


lowerCAmelCase__							= '\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\nExamples:\n    >>> sources=["About 95 species are currently accepted ."]\n    >>> predictions=["About 95 you now get in ."]\n    >>> references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]\n    >>> sari = datasets.load_metric("sari")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 26.953601953601954}\n'





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		int							=						[rgram for rgrams in rgramslist for rgram in rgrams]
				_SCREAMING_SNAKE_CASE      :		Any							=						Counter(lowerCamelCase					)

				_SCREAMING_SNAKE_CASE      :		List[Any]							=						Counter(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		int							=						Counter()
				for sgram, scount in sgramcounter.items():
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						scount * numref

				_SCREAMING_SNAKE_CASE      :		Dict							=						Counter(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						Counter()
				for cgram, ccount in cgramcounter.items():
								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						ccount * numref

				# KEEP
				_SCREAMING_SNAKE_CASE      :		List[str]							=						sgramcounter_rep & cgramcounter_rep
				_SCREAMING_SNAKE_CASE      :		str							=						keepgramcounter_rep & rgramcounter
				_SCREAMING_SNAKE_CASE      :		Tuple							=						sgramcounter_rep & rgramcounter

				_SCREAMING_SNAKE_CASE      :		Dict							=						0
				_SCREAMING_SNAKE_CASE      :		Any							=						0
				for keepgram in keepgramcountergood_rep:
								keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
								# Fix an alleged bug [2] in the keep score computation.
								# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
								keeptmpscorea += keepgramcountergood_rep[keepgram]
				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				#      a target exactly.
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						1
				_SCREAMING_SNAKE_CASE      :		Dict							=						1
				if len(lowerCamelCase					) > 0:
								_SCREAMING_SNAKE_CASE      :		str							=						keeptmpscorea / len(lowerCamelCase					)
				if len(lowerCamelCase					) > 0:
								# Fix an alleged bug [2] in the keep score computation.
								# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
								_SCREAMING_SNAKE_CASE      :		List[str]							=						keeptmpscorea / sum(keepgramcounterall_rep.values()					)
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						0
				if keepscore_precision > 0 or keepscore_recall > 0:
								_SCREAMING_SNAKE_CASE      :		Dict							=						2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

				# DELETION
				_SCREAMING_SNAKE_CASE      :		int							=						sgramcounter_rep - cgramcounter_rep
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						delgramcounter_rep - rgramcounter
				_SCREAMING_SNAKE_CASE      :		str							=						sgramcounter_rep - rgramcounter
				_SCREAMING_SNAKE_CASE      :		Tuple							=						0
				_SCREAMING_SNAKE_CASE      :		Optional[int]							=						0
				for delgram in delgramcountergood_rep:
								deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
								deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				# a target exactly.
				_SCREAMING_SNAKE_CASE      :		str							=						1
				if len(lowerCamelCase					) > 0:
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						deltmpscorea / len(lowerCamelCase					)

				# ADDITION
				_SCREAMING_SNAKE_CASE      :		Any							=						set(lowerCamelCase					) - set(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						set(lowerCamelCase					) & set(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						set(lowerCamelCase					) - set(lowerCamelCase					)

				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						0
				for addgram in addgramcountergood:
								addtmpscore += 1

				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				# a target exactly.
				_SCREAMING_SNAKE_CASE      :		Tuple							=						1
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						1
				if len(lowerCamelCase					) > 0:
								_SCREAMING_SNAKE_CASE      :		str							=						addtmpscore / len(lowerCamelCase					)
				if len(lowerCamelCase					) > 0:
								_SCREAMING_SNAKE_CASE      :		str							=						addtmpscore / len(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		List[str]							=						0
				if addscore_precision > 0 or addscore_recall > 0:
								_SCREAMING_SNAKE_CASE      :		Dict							=						2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

				return (keepscore, delscore_precision, addscore)





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		Tuple							=						len(lowerCamelCase					)

				_SCREAMING_SNAKE_CASE      :		Tuple							=						ssent.split(' '					)
				_SCREAMING_SNAKE_CASE      :		Tuple							=						csent.split(' '					)
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						[]
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						[]
				_SCREAMING_SNAKE_CASE      :		List[str]							=						[]
				_SCREAMING_SNAKE_CASE      :		Tuple							=						[]
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						[]
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						[]

				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						[]
				_SCREAMING_SNAKE_CASE      :		int							=						[]
				_SCREAMING_SNAKE_CASE      :		int							=						[]
				_SCREAMING_SNAKE_CASE      :		int							=						[]
				for rsent in rsents:
								_SCREAMING_SNAKE_CASE      :		int							=						rsent.split(' '					)
								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						[]
								_SCREAMING_SNAKE_CASE      :		str							=						[]
								_SCREAMING_SNAKE_CASE      :		List[str]							=						[]
								ragramslist.append(lowerCamelCase					)
								for i in range(0,	len(lowerCamelCase					) - 1					):
												if i < len(lowerCamelCase					) - 1:
																_SCREAMING_SNAKE_CASE      :		List[str]							=						ragrams[i] + ' ' + ragrams[i + 1]
																ragrams.append(lowerCamelCase					)
												if i < len(lowerCamelCase					) - 2:
																_SCREAMING_SNAKE_CASE      :		Any							=						ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
																ragrams.append(lowerCamelCase					)
												if i < len(lowerCamelCase					) - 3:
																_SCREAMING_SNAKE_CASE      :		List[Any]							=						ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
																ragrams.append(lowerCamelCase					)
								ragramslist.append(lowerCamelCase					)
								ragramslist.append(lowerCamelCase					)
								ragramslist.append(lowerCamelCase					)

				for i in range(0,	len(lowerCamelCase					) - 1					):
								if i < len(lowerCamelCase					) - 1:
												_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						sagrams[i] + ' ' + sagrams[i + 1]
												sagrams.append(lowerCamelCase					)
								if i < len(lowerCamelCase					) - 2:
												_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
												sagrams.append(lowerCamelCase					)
								if i < len(lowerCamelCase					) - 3:
												_SCREAMING_SNAKE_CASE      :		Tuple							=						sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
												sagrams.append(lowerCamelCase					)

				for i in range(0,	len(lowerCamelCase					) - 1					):
								if i < len(lowerCamelCase					) - 1:
												_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						cagrams[i] + ' ' + cagrams[i + 1]
												cagrams.append(lowerCamelCase					)
								if i < len(lowerCamelCase					) - 2:
												_SCREAMING_SNAKE_CASE      :		Optional[int]							=						cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
												cagrams.append(lowerCamelCase					)
								if i < len(lowerCamelCase					) - 3:
												_SCREAMING_SNAKE_CASE      :		Any							=						cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
												cagrams.append(lowerCamelCase					)

				((_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE))      :		Tuple							=						SARIngram(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					)
				((_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE))      :		int							=						SARIngram(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					)
				((_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE))      :		Optional[int]							=						SARIngram(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					)
				((_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE))      :		Union[str, Any]							=						SARIngram(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		int							=						sum([keepascore, keepascore, keepascore, keepascore]					) / 4
				_SCREAMING_SNAKE_CASE      :		List[str]							=						sum([delascore, delascore, delascore, delascore]					) / 4
				_SCREAMING_SNAKE_CASE      :		List[str]							=						sum([addascore, addascore, addascore, addascore]					) / 4
				_SCREAMING_SNAKE_CASE      :		Dict							=						(avgkeepscore + avgdelscore + avgaddscore) / 3
				return finalscore





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase = True,	lowerCamelCase = "13a",	lowerCamelCase = True					):
				# Normalization is requried for the ASSET dataset (one of the primary
				# datasets in sentence simplification) to allow using space
				# to split the sentence. Even though Wiki-Auto and TURK datasets,
				# do not require normalization, we do it for consistency.
				# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
				# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

				if lowercase:
								_SCREAMING_SNAKE_CASE      :		List[str]							=						sentence.lower()

				if tokenizer in ["13a", "intl"]:
								if version.parse(sacrebleu.__version__					).major >= 2:
												_SCREAMING_SNAKE_CASE      :		Dict							=						sacrebleu.metrics.bleu._get_tokenizer(lowerCamelCase					)()(lowerCamelCase					)
								else:
												_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						sacrebleu.TOKENIZERS[tokenizer]()(lowerCamelCase					)
				elif tokenizer == "moses":
								_SCREAMING_SNAKE_CASE      :		Tuple							=						sacremoses.MosesTokenizer().tokenize(lowerCamelCase,	return_str=lowerCamelCase,	escape=lowerCamelCase					)
				elif tokenizer == "penn":
								_SCREAMING_SNAKE_CASE      :		str							=						sacremoses.MosesTokenizer().penn_tokenize(lowerCamelCase,	return_str=lowerCamelCase					)
				else:
								_SCREAMING_SNAKE_CASE      :		Any							=						sentence

				if not return_str:
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						normalized_sent.split()

				return normalized_sent






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







						def  A			(						self       )		->       Dict:
										return datasets.MetricInfo(
										    description=_DESCRIPTION      ,							citation=_CITATION      ,							inputs_description=_KWARGS_DESCRIPTION      ,							features=datasets.Features(
										        {
										            'sources': datasets.Value('string'      ,							id='sequence'       ),
										            'predictions': datasets.Value('string'      ,							id='sequence'       ),
										            'references': datasets.Sequence(datasets.Value('string'      ,							id='sequence'       )      ,							id='references'       ),
										        }       )      ,							codebase_urls=[
										        'https://github.com/cocoxu/simplification/blob/master/SARI.py',
										        'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
										    ]      ,							reference_urls=['https://www.aclweb.org/anthology/Q16-1029.pdf']      ,							)







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )		->       Optional[int]:
										if not (len(lowerCAmelCase_       ) == len(lowerCAmelCase_       ) == len(lowerCAmelCase_       )):
														raise ValueError('Sources length must match predictions and references lengths.'       )
										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						0
										for src, pred, refs in zip(lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_       ):
														sari_score += SARIsent(normalize(lowerCAmelCase_       )      ,							normalize(lowerCAmelCase_       )      ,							[normalize(lowerCAmelCase_       ) for sent in refs]       )
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						sari_score / len(lowerCAmelCase_       )
										return {"sari": 1_0_0 * sari_score}

