




'''simple docstring'''






from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowercase						:   Union[str, Any]      						=							'''\
@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},
}
'''

lowercase						:   Dict      						=							'''\
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
'''


lowercase						:   int      						=							'''
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_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )					->List[str]:






       '''simple docstring'''




       lowercase	:		str    =   [rgram for rgrams in rgramslist for rgram in rgrams]
       lowercase	:		Optional[int]    =   Counter(lowerCamelCase   )

       lowercase	:		int    =   Counter(lowerCamelCase   )
       lowercase	:		Optional[int]    =   Counter()
       for sgram, scount in sgramcounter.items():
              lowercase	:		int    =   scount * numref

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

       # KEEP
       lowercase	:		List[str]    =   sgramcounter_rep & cgramcounter_rep
       lowercase	:		Optional[Any]    =   keepgramcounter_rep & rgramcounter
       lowercase	:		Union[str, Any]    =   sgramcounter_rep & rgramcounter

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

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

       # ADDITION
       lowercase	:		str    =   set(lowerCamelCase   ) - set(lowerCamelCase   )
       lowercase	:		Dict    =   set(lowerCamelCase   ) & set(lowerCamelCase   )
       lowercase	:		int    =   set(lowerCamelCase   ) - set(lowerCamelCase   )

       lowercase	:		Tuple    =   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.
       lowercase	:		Tuple    =   1
       lowercase	:		Tuple    =   1
       if len(lowerCamelCase   ) > 0:
              lowercase	:		int    =   addtmpscore / len(lowerCamelCase   )
       if len(lowerCamelCase   ) > 0:
              lowercase	:		Tuple    =   addtmpscore / len(lowerCamelCase   )
       lowercase	:		Optional[int]    =   0
       if addscore_precision > 0 or addscore_recall > 0:
              lowercase	:		Dict    =   2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

       return (keepscore, delscore_precision, addscore)




def 		a_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )					->Dict:






       '''simple docstring'''




       lowercase	:		List[str]    =   len(lowerCamelCase   )

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

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

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




def 		a_(			lowerCamelCase ,					lowerCamelCase = True ,					lowerCamelCase = "13a" ,					lowerCamelCase = True   )					->Optional[int]:






       '''simple docstring'''




       if lowercase:
              lowercase	:		str    =   sentence.lower()

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

       if not return_str:
              lowercase	:		List[str]    =   normalized_sent.split()

       return normalized_sent





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



 def  SCREAMING_SNAKE_CASE(	self			):






        '''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  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        if not (len(A			) == len(A			) == len(A			)):
               raise ValueError('''Sources length must match predictions and references lengths.'''			)
        lowercase	:		Optional[int]    =   0
        for src, pred, refs in zip(A ,		A ,		A			):
               sari_score += SARIsent(normalize(A			) ,		normalize(A			) ,		[normalize(A			) for sent in refs]			)
        lowercase	:		Dict    =   sari_score / len(A			)
        return {"sari": 100 * sari_score}










'''simple docstring'''






from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
lowercase						:   Any      						=							TypeVar('''T''', bound='''Enum''')

@unique
class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):
 __snake_case						:  Union[str, Any]       =   8_0
 __snake_case						:  Optional[Any]       =   7_0
 __snake_case						:  List[str]       =   6_0
 __snake_case						:  str       =   5_0
 __snake_case						:  Tuple       =   4_0
 __snake_case						:  Union[str, Any]       =   3_0
 __snake_case						:  Dict       =   2_0
 __snake_case						:  str       =   1_0






@unique
class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):
 __snake_case						:  Optional[Any]       =   2_4
 __snake_case						:  List[str]       =   2_1
 __snake_case						:  Any       =   1_8
 __snake_case						:  Union[str, Any]       =   1_5
 __snake_case						:  Dict       =   1_2
 __snake_case						:  Union[str, Any]       =   9
 __snake_case						:  List[str]       =   6
 __snake_case						:  Optional[int]       =   3
 __snake_case						:  Tuple       =   2
 __snake_case						:  Any       =   1
 __snake_case						:  Optional[int]       =   -1
 __snake_case						:  Optional[int]       =   -2
 __snake_case						:  str       =   -3
 __snake_case						:  Optional[Any]       =   -6
 __snake_case						:  Optional[int]       =   -9
 __snake_case						:  List[Any]       =   -1_2
 __snake_case						:  str       =   -1_5
 __snake_case						:  str       =   -1_8
 __snake_case						:  Dict       =   -2_1
 __snake_case						:  int       =   -2_4



 @classmethod
 def  SCREAMING_SNAKE_CASE(	cls			):






        '''simple docstring'''




        return {unit.name: unit.value for unit in cls if unit.value > 0}





 @classmethod
 def  SCREAMING_SNAKE_CASE(	cls			):






        '''simple docstring'''




        return {unit.name: unit.value for unit in cls if unit.value < 0}






def 		a_(			lowerCamelCase   )					->str:






       '''simple docstring'''




       lowercase	:		Tuple    =   SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
       for name_prefix, value_prefix in prefixes.items():
              lowercase	:		Dict    =   value / (10**value_prefix)
              if numerical_part > 1:
                     return f"""{numerical_part!s} {name_prefix}"""
       return str(lowerCamelCase   )




def 		a_(			lowerCamelCase   )					->str:






       '''simple docstring'''




       for prefix in BinaryUnit:
              lowercase	:		Dict    =   value / (2**prefix.value)
              if numerical_part > 1:
                     return f"""{numerical_part!s} {prefix.name}"""
       return str(lowerCamelCase   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()




