




'''simple docstring'''






from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_lowerCAmelCase				     = '''\
@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},
}
'''

_lowerCAmelCase				     = '''\
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
'''


_lowerCAmelCase				     = '''
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       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__						):
			__UpperCamelCase				:      List[Any]									=       [rgram for rgrams in rgramslist for rgram in rgrams]
			__UpperCamelCase				:      int									=       Counter(snake_case__						)

			__UpperCamelCase				:      Optional[Any]									=       Counter(snake_case__						)
			__UpperCamelCase				:      int									=       Counter()
			for sgram, scount in sgramcounter.items():
						__UpperCamelCase				:      Optional[Any]									=       scount * numref

			__UpperCamelCase				:      Dict									=       Counter(snake_case__						)
			__UpperCamelCase				:      Optional[int]									=       Counter()
			for cgram, ccount in cgramcounter.items():
						__UpperCamelCase				:      Any									=       ccount * numref

			# KEEP
			__UpperCamelCase				:      Optional[Any]									=       sgramcounter_rep & cgramcounter_rep
			__UpperCamelCase				:      Any									=       keepgramcounter_rep & rgramcounter
			__UpperCamelCase				:      Optional[int]									=       sgramcounter_rep & rgramcounter

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

			# DELETION
			__UpperCamelCase				:      Optional[int]									=       sgramcounter_rep - cgramcounter_rep
			__UpperCamelCase				:      Tuple									=       delgramcounter_rep - rgramcounter
			__UpperCamelCase				:      Dict									=       sgramcounter_rep - rgramcounter
			__UpperCamelCase				:      Tuple									=       0
			__UpperCamelCase				:      Tuple									=       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				:      Union[str, Any]									=       1
			if len(snake_case__						) > 0:
						__UpperCamelCase				:      int									=       deltmpscorea / len(snake_case__						)

			# ADDITION
			__UpperCamelCase				:      Optional[int]									=       set(snake_case__						) - set(snake_case__						)
			__UpperCamelCase				:      str									=       set(snake_case__						) & set(snake_case__						)
			__UpperCamelCase				:      Union[str, Any]									=       set(snake_case__						) - set(snake_case__						)

			__UpperCamelCase				:      str									=       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				:      Optional[int]									=       1
			__UpperCamelCase				:      str									=       1
			if len(snake_case__						) > 0:
						__UpperCamelCase				:      str									=       addtmpscore / len(snake_case__						)
			if len(snake_case__						) > 0:
						__UpperCamelCase				:      Dict									=       addtmpscore / len(snake_case__						)
			__UpperCamelCase				:      str									=       0
			if addscore_precision > 0 or addscore_recall > 0:
						__UpperCamelCase				:      Optional[Any]									=       2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

			return (keepscore, delscore_precision, addscore)

def       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__						):
			__UpperCamelCase				:      Any									=       len(snake_case__						)

			__UpperCamelCase				:      List[str]									=       ssent.split(" "						)
			__UpperCamelCase				:      str									=       csent.split(" "						)
			__UpperCamelCase				:      Optional[Any]									=       []
			__UpperCamelCase				:      Optional[Any]									=       []
			__UpperCamelCase				:      Tuple									=       []
			__UpperCamelCase				:      List[Any]									=       []
			__UpperCamelCase				:      Optional[int]									=       []
			__UpperCamelCase				:      Dict									=       []

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

			for i in range(0 ,				len(snake_case__						) - 1						):
						if i < len(snake_case__						) - 1:
									__UpperCamelCase				:      Dict									=       sagrams[i] + " " + sagrams[i + 1]
									sagrams.append(snake_case__						)
						if i < len(snake_case__						) - 2:
									__UpperCamelCase				:      Tuple									=       sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
									sagrams.append(snake_case__						)
						if i < len(snake_case__						) - 3:
									__UpperCamelCase				:      Union[str, Any]									=       sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
									sagrams.append(snake_case__						)

			for i in range(0 ,				len(snake_case__						) - 1						):
						if i < len(snake_case__						) - 1:
									__UpperCamelCase				:      Dict									=       cagrams[i] + " " + cagrams[i + 1]
									cagrams.append(snake_case__						)
						if i < len(snake_case__						) - 2:
									__UpperCamelCase				:      Union[str, Any]									=       cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
									cagrams.append(snake_case__						)
						if i < len(snake_case__						) - 3:
									__UpperCamelCase				:      List[str]									=       cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
									cagrams.append(snake_case__						)

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

def       __lowerCAmelCase (   snake_case__ ,				snake_case__ = True ,				snake_case__ = "13a" ,				snake_case__ = 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				:      Dict									=       sentence.lower()

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

			if not return_str:
						__UpperCamelCase				:      List[Any]									=       normalized_sent.split()

			return normalized_sent




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




				'''simple docstring'''





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









'''simple docstring'''






from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_lowerCAmelCase				     = TypeVar('''T''', bound=bool)



class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      T									=       value
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
				@property
				def 	a_	(self      )     ->	T:
							return self._value





				@staticmethod
				def 	a_	(_UpperCAmelCase			,  _UpperCAmelCase      )     ->	RandomizedHeapNode[T] | None:
							if not roota:
										return roota

							if not roota:
										return roota

							if roota.value > roota.value:
										__UpperCamelCase		, __UpperCamelCase				:      int									=       roota, roota

							if random.choice([True, False]      ):
										__UpperCamelCase		, __UpperCamelCase				:      Dict									=       roota.right, roota.left

							__UpperCamelCase				:      Any									=       RandomizedHeapNode.merge(roota.left			,  _UpperCAmelCase      )

							return roota







class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase = ()      )     ->	None:
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None

							if data:
										for item in data:
													self.insert(_UpperCAmelCase      )
				def 	a_	(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      str									=       RandomizedHeapNode.merge(self._root			,  RandomizedHeapNode(_UpperCAmelCase      )      )
				def 	a_	(self      )     ->	T | None:
							__UpperCamelCase				:      Dict									=       self.top()

							if self._root is None:
										return None

							__UpperCamelCase				:      List[str]									=       RandomizedHeapNode.merge(self._root.left			,  self._root.right      )

							return result
				def 	a_	(self      )     ->	T:
							if not self._root:
										raise IndexError("Can't get top element for the empty heap."      )
							return self._root.value
				def 	a_	(self      )     ->	None:
							__UpperCamelCase				:      int									=       None
				def 	a_	(self      )     ->	list[Any]:
							__UpperCamelCase				:      int									=       []
							while self:
										result.append(self.pop()      )

							return result





				def __bool__(self      )     ->	bool:
							return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()



