





"""simple docstring"""




from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCamelCase_	 ="""\
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415},
}
"""

UpperCamelCase_	 ="""\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow's tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
"""


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





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   List[str]      =       [rgram for rgrams in rgramslist for rgram in rgrams]
						_UpperCamelCase     :   List[Any]      =       Counter(_lowercase  )

						_UpperCamelCase     :   Tuple      =       Counter(_lowercase  )
						_UpperCamelCase     :   Union[str, Any]      =       Counter()
						for sgram, scount in sgramcounter.items():
												_UpperCamelCase     :   List[Any]      =       scount * numref

						_UpperCamelCase     :   Union[str, Any]      =       Counter(_lowercase  )
						_UpperCamelCase     :   Any      =       Counter()
						for cgram, ccount in cgramcounter.items():
												_UpperCamelCase     :   str      =       ccount * numref

						# KEEP
						_UpperCamelCase     :   List[str]      =       sgramcounter_rep & cgramcounter_rep
						_UpperCamelCase     :   Optional[int]      =       keepgramcounter_rep & rgramcounter
						_UpperCamelCase     :   Optional[int]      =       sgramcounter_rep & rgramcounter

						_UpperCamelCase     :   Tuple      =       0
						_UpperCamelCase     :   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.
						_UpperCamelCase     :   Dict      =       1
						_UpperCamelCase     :   List[str]      =       1
						if len(_lowercase  ) > 0:
												_UpperCamelCase     :   Dict      =       keeptmpscorea / len(_lowercase  )
						if len(_lowercase  ) > 0:
												# Fix an alleged bug [2] in the keep score computation.
												# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
												_UpperCamelCase     :   Optional[int]      =       keeptmpscorea / sum(keepgramcounterall_rep.values()  )
						_UpperCamelCase     :   List[Any]      =       0
						if keepscore_precision > 0 or keepscore_recall > 0:
												_UpperCamelCase     :   Optional[int]      =       2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

						# DELETION
						_UpperCamelCase     :   int      =       sgramcounter_rep - cgramcounter_rep
						_UpperCamelCase     :   str      =       delgramcounter_rep - rgramcounter
						_UpperCamelCase     :   Optional[Any]      =       sgramcounter_rep - rgramcounter
						_UpperCamelCase     :   List[Any]      =       0
						_UpperCamelCase     :   Optional[Any]      =       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.
						_UpperCamelCase     :   List[Any]      =       1
						if len(_lowercase  ) > 0:
												_UpperCamelCase     :   Any      =       deltmpscorea / len(_lowercase  )

						# ADDITION
						_UpperCamelCase     :   Optional[Any]      =       set(_lowercase  ) - set(_lowercase  )
						_UpperCamelCase     :   Optional[Any]      =       set(_lowercase  ) & set(_lowercase  )
						_UpperCamelCase     :   Any      =       set(_lowercase  ) - set(_lowercase  )

						_UpperCamelCase     :   Optional[int]      =       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.
						_UpperCamelCase     :   List[Any]      =       1
						_UpperCamelCase     :   List[str]      =       1
						if len(_lowercase  ) > 0:
												_UpperCamelCase     :   List[str]      =       addtmpscore / len(_lowercase  )
						if len(_lowercase  ) > 0:
												_UpperCamelCase     :   str      =       addtmpscore / len(_lowercase  )
						_UpperCamelCase     :   Optional[Any]      =       0
						if addscore_precision > 0 or addscore_recall > 0:
												_UpperCamelCase     :   int      =       2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

						return (keepscore, delscore_precision, addscore)





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   List[Any]      =       len(_lowercase  )

						_UpperCamelCase     :   List[Any]      =       ssent.split(''' '''  )
						_UpperCamelCase     :   Tuple      =       csent.split(''' '''  )
						_UpperCamelCase     :   List[Any]      =       []
						_UpperCamelCase     :   Dict      =       []
						_UpperCamelCase     :   int      =       []
						_UpperCamelCase     :   Tuple      =       []
						_UpperCamelCase     :   Union[str, Any]      =       []
						_UpperCamelCase     :   Any      =       []

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

						for i in range(0			,		len(_lowercase  ) - 1  ):
												if i < len(_lowercase  ) - 1:
																		_UpperCamelCase     :   Dict      =       sagrams[i] + ''' ''' + sagrams[i + 1]
																		sagrams.append(_lowercase  )
												if i < len(_lowercase  ) - 2:
																		_UpperCamelCase     :   Optional[Any]      =       sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
																		sagrams.append(_lowercase  )
												if i < len(_lowercase  ) - 3:
																		_UpperCamelCase     :   Union[str, Any]      =       sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
																		sagrams.append(_lowercase  )

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

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





def 			a_							(				_lowercase			,		_lowercase = True			,		_lowercase = "13a"			,		_lowercase = 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:
												_UpperCamelCase     :   Optional[Any]      =       sentence.lower()

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

						if not return_str:
												_UpperCamelCase     :   Any      =       normalized_sent.split()

						return normalized_sent






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






				def 							snake_case      (					self						:       Any				)      -> Tuple:




										'''simple docstring'''



										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 							snake_case      (					self						:       str,					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       List[str]				)      -> List[str]:




										'''simple docstring'''



										if not (len(lowerCAmelCase__				) == len(lowerCAmelCase__				) == len(lowerCAmelCase__				)):
																raise ValueError('''Sources length must match predictions and references lengths.'''				)
										_UpperCamelCase     :   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]				)
										_UpperCamelCase     :   Dict      =       sari_score / len(lowerCAmelCase__				)
										return {"sari": 1_0_0 * sari_score}










"""simple docstring"""




from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime




class 					_a		( _lowerCAmelCase							):






				def __init__(					self						:       Tuple,					*lowerCAmelCase__						:       Tuple,					**lowerCAmelCase__						:       Optional[int]				)      -> Union[str, Any]:




										'''simple docstring'''



										super().__init__(*lowerCAmelCase__,					**lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any,					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Tuple				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       (
										    next_prime(value % self.size_table				)
										    if not is_prime(value % self.size_table				)
										    else value % self.size_table
										)  # gt = bigger than
										return next_prime_gt - (data % next_prime_gt)






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Tuple				)      -> Any:




										'''simple docstring'''



										return (increment * self.__hash_function_a(lowerCAmelCase__,					lowerCAmelCase__				)) % self.size_table






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Optional[Any]=None				)      -> Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       1
										_UpperCamelCase     :   Dict      =       self.hash_function(lowerCAmelCase__				)

										while self.values[new_key] is not None and self.values[new_key] != key:
																_UpperCamelCase     :   Optional[int]      =       (
																    self.__hash_double_function(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)
																    if self.balanced_factor() >= self.lim_charge
																    else None
																)
																if new_key is None:
																						break
																else:
																						i += 1

										return new_key



