



'''simple docstring'''





from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


A			:   str			=      """\
@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},
}
"""

A			:   str			=      """\
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
"""


A			:   List[str]			=      """
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_							):
   snake_case   :							Optional[int]						  =[rgram for rgrams in rgramslist for rgram in rgrams]
   snake_case   :							List[str]						  =Counter(lowerCamelCase_							)

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

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

   # KEEP
   snake_case   :							Union[str, Any]						  =sgramcounter_rep & cgramcounter_rep
   snake_case   :							int						  =keepgramcounter_rep & rgramcounter
   snake_case   :							str						  =sgramcounter_rep & rgramcounter

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

   # DELETION
   snake_case   :							Optional[int]						  =sgramcounter_rep - cgramcounter_rep
   snake_case   :							Dict						  =delgramcounter_rep - rgramcounter
   snake_case   :							int						  =sgramcounter_rep - rgramcounter
   snake_case   :							str						  =0
   snake_case   :							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.
   snake_case   :							Any						  =1
   if len(lowerCamelCase_							) > 0:
      snake_case   :							Tuple						  =deltmpscorea / len(lowerCamelCase_							)

   # ADDITION
   snake_case   :							List[Any]						  =set(lowerCamelCase_							) - set(lowerCamelCase_							)
   snake_case   :							Optional[int]						  =set(lowerCamelCase_							) & set(lowerCamelCase_							)
   snake_case   :							Any						  =set(lowerCamelCase_							) - set(lowerCamelCase_							)

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

   return (keepscore, delscore_precision, addscore)
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							):
   snake_case   :							Dict						  =len(lowerCamelCase_							)

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

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

   ((snake_case)  ,    (snake_case)  ,    (snake_case))   :							Union[str, Any]						  =SARIngram(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							)
   ((snake_case)  ,    (snake_case)  ,    (snake_case))   :							Union[str, Any]						  =SARIngram(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							)
   ((snake_case)  ,    (snake_case)  ,    (snake_case))   :							List[str]						  =SARIngram(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							)
   ((snake_case)  ,    (snake_case)  ,    (snake_case))   :							List[str]						  =SARIngram(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							)
   snake_case   :							List[str]						  =sum([keepascore, keepascore, keepascore, keepascore]							) / 4
   snake_case   :							Union[str, Any]						  =sum([delascore, delascore, delascore, delascore]							) / 4
   snake_case   :							Optional[Any]						  =sum([addascore, addascore, addascore, addascore]							) / 4
   snake_case   :							Optional[Any]						  =(avgkeepscore + avgdelscore + avgaddscore) / 3
   return finalscore
def 		_a   (  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:
      snake_case   :							Any						  =sentence.lower()

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

   if not return_str:
      snake_case   :							Optional[Any]						  =normalized_sent.split()

   return normalized_sent





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





    def 					__snake_case       (    self    :    Dict     ):



       '''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    :    List[str],    _snake_case    :    Optional[int],    _snake_case    :    int,    _snake_case    :    Any     ):



       '''simple docstring'''







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










'''simple docstring'''





from __future__ import annotations

import random
import string





class 							lowerCAmelCase_		:





    def __init__(    self    :    Union[str, Any],    _snake_case    :    str | None = None     ):



       '''simple docstring'''







       snake_case   :							Optional[int]						  =passcode or self.__passcode_creator()
       snake_case   :							List[str]						  =self.__make_key_list()
       snake_case   :							Dict						  =self.__make_shift_key()





    def __str__(    self    :    List[Any]     ):



       '''simple docstring'''







       return "".join(self.__passcode     )





    def 					__snake_case       (    self    :    int,    _snake_case    :    list[int]     ):



       '''simple docstring'''







       for i in range(1,    len(_snake_case     ),    2     ):
          iterlist[i] *= -1
       return iterlist





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							int						  =string.ascii_letters + string.digits
       snake_case   :							Union[str, Any]						  =[random.choice(_snake_case     ) for _ in range(random.randint(10,    20     )     )]
       return password





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							Dict						  =(
           string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
       )

       snake_case   :							Any						  =[]

       # creates points known as breakpoints to break the key_list_options at those
       # points and pivot each substring
       snake_case   :							Optional[int]						  =sorted(set(self.__passcode     )     )
       snake_case   :							list[str]						  =[]

       # algorithm for creating a new shuffled list, keys_l, out of key_list_options
       for i in key_list_options:
          temp_list.extend(_snake_case     )

          # checking breakpoints at which to pivot temporary sublist and add it into
          # keys_l
          if i in breakpoints or i == key_list_options[-1]:
             keys_l.extend(temp_list[::-1]     )
             temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
       return keys_l





    def 					__snake_case       (    self    :    Union[str, Any]     ):



       '''simple docstring'''







       snake_case   :							str						  =sum(self.__neg_pos([ord(_snake_case     ) for x in self.__passcode]     )     )
       return num if num > 0 else len(self.__passcode     )





    def 					__snake_case       (    self    :    Optional[int],    _snake_case    :    str     ):



       '''simple docstring'''







       snake_case   :							List[Any]						  =''''''

       # decoding shift like Caesar cipher algorithm implementing negative shift or
       # reverse shift or left shift
       for i in encoded_message:
          snake_case   :							Optional[int]						  =self.__key_list.index(_snake_case     )
          decoded_message += self.__key_list[
              (position - self.__shift_key) % -len(self.__key_list     )
          ]

       return decoded_message






    def 					__snake_case       (    self    :    Union[str, Any],    _snake_case    :    str     ):



       '''simple docstring'''







       snake_case   :							int						  =''''''

       # encoding shift like Caesar cipher algorithm implementing positive shift or
       # forward shift or right shift
       for i in plaintext:
          snake_case   :							int						  =self.__key_list.index(_snake_case     )
          encoded_message += self.__key_list[
              (position + self.__shift_key) % len(self.__key_list     )
          ]

       return encoded_message




def 		_a   (  lowerCamelCase_ = "Hello, this is a modified Caesar cipher"							):
   snake_case   :							List[Any]						  =ShuffledShiftCipher()
   return cipa.decrypt(cipa.encrypt(lowerCamelCase_							)							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





