"""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       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   Optional[Any]:
     snake_case_       =      [rgram for rgrams in rgramslist for rgram in rgrams]
     snake_case_       =      Counter(UpperCAmelCase      )

     snake_case_       =      Counter(UpperCAmelCase      )
     snake_case_       =      Counter()
     for sgram, scount in sgramcounter.items():
          snake_case_       =      scount * numref

     snake_case_       =      Counter(UpperCAmelCase      )
     snake_case_       =      Counter()
     for cgram, ccount in cgramcounter.items():
          snake_case_       =      ccount * numref

     # KEEP
     snake_case_       =      sgramcounter_rep & cgramcounter_rep
     snake_case_       =      keepgramcounter_rep & rgramcounter
     snake_case_       =      sgramcounter_rep & rgramcounter

     snake_case_       =      0
     snake_case_       =      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_       =      1
     snake_case_       =      1
     if len(UpperCAmelCase      ) > 0:
          snake_case_       =      keeptmpscorea / len(UpperCAmelCase      )
     if len(UpperCAmelCase      ) > 0:
          # Fix an alleged bug [2] in the keep score computation.
          # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
          snake_case_       =      keeptmpscorea / sum(keepgramcounterall_rep.values()      )
     snake_case_       =      0
     if keepscore_precision > 0 or keepscore_recall > 0:
          snake_case_       =      2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

     # DELETION
     snake_case_       =      sgramcounter_rep - cgramcounter_rep
     snake_case_       =      delgramcounter_rep - rgramcounter
     snake_case_       =      sgramcounter_rep - rgramcounter
     snake_case_       =      0
     snake_case_       =      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_       =      1
     if len(UpperCAmelCase      ) > 0:
          snake_case_       =      deltmpscorea / len(UpperCAmelCase      )

     # ADDITION
     snake_case_       =      set(UpperCAmelCase      ) - set(UpperCAmelCase      )
     snake_case_       =      set(UpperCAmelCase      ) & set(UpperCAmelCase      )
     snake_case_       =      set(UpperCAmelCase      ) - set(UpperCAmelCase      )

     snake_case_       =      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_       =      1
     snake_case_       =      1
     if len(UpperCAmelCase      ) > 0:
          snake_case_       =      addtmpscore / len(UpperCAmelCase      )
     if len(UpperCAmelCase      ) > 0:
          snake_case_       =      addtmpscore / len(UpperCAmelCase      )
     snake_case_       =      0
     if addscore_precision > 0 or addscore_recall > 0:
          snake_case_       =      2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

     return (keepscore, delscore_precision, addscore)







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   List[Any]:
     snake_case_       =      len(UpperCAmelCase      )

     snake_case_       =      ssent.split(' '      )
     snake_case_       =      csent.split(' '      )
     snake_case_       =      []
     snake_case_       =      []
     snake_case_       =      []
     snake_case_       =      []
     snake_case_       =      []
     snake_case_       =      []

     snake_case_       =      []
     snake_case_       =      []
     snake_case_       =      []
     snake_case_       =      []
     for rsent in rsents:
          snake_case_       =      rsent.split(' '      )
          snake_case_       =      []
          snake_case_       =      []
          snake_case_       =      []
          ragramslist.append(UpperCAmelCase      )
          for i in range(0		,      len(UpperCAmelCase      ) - 1      ):
               if i < len(UpperCAmelCase      ) - 1:
                    snake_case_       =      ragrams[i] + ' ' + ragrams[i + 1]
                    ragrams.append(UpperCAmelCase      )
               if i < len(UpperCAmelCase      ) - 2:
                    snake_case_       =      ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
                    ragrams.append(UpperCAmelCase      )
               if i < len(UpperCAmelCase      ) - 3:
                    snake_case_       =      ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
                    ragrams.append(UpperCAmelCase      )
          ragramslist.append(UpperCAmelCase      )
          ragramslist.append(UpperCAmelCase      )
          ragramslist.append(UpperCAmelCase      )

     for i in range(0		,      len(UpperCAmelCase      ) - 1      ):
          if i < len(UpperCAmelCase      ) - 1:
               snake_case_       =      sagrams[i] + ' ' + sagrams[i + 1]
               sagrams.append(UpperCAmelCase      )
          if i < len(UpperCAmelCase      ) - 2:
               snake_case_       =      sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
               sagrams.append(UpperCAmelCase      )
          if i < len(UpperCAmelCase      ) - 3:
               snake_case_       =      sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
               sagrams.append(UpperCAmelCase      )

     for i in range(0		,      len(UpperCAmelCase      ) - 1      ):
          if i < len(UpperCAmelCase      ) - 1:
               snake_case_       =      cagrams[i] + ' ' + cagrams[i + 1]
               cagrams.append(UpperCAmelCase      )
          if i < len(UpperCAmelCase      ) - 2:
               snake_case_       =      cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
               cagrams.append(UpperCAmelCase      )
          if i < len(UpperCAmelCase      ) - 3:
               snake_case_       =      cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
               cagrams.append(UpperCAmelCase      )

     ((snake_case_) ,			(snake_case_) ,			(snake_case_))       =      SARIngram(UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )
     ((snake_case_) ,			(snake_case_) ,			(snake_case_))       =      SARIngram(UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )
     ((snake_case_) ,			(snake_case_) ,			(snake_case_))       =      SARIngram(UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )
     ((snake_case_) ,			(snake_case_) ,			(snake_case_))       =      SARIngram(UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )
     snake_case_       =      sum([keepascore, keepascore, keepascore, keepascore]      ) / 4
     snake_case_       =      sum([delascore, delascore, delascore, delascore]      ) / 4
     snake_case_       =      sum([addascore, addascore, addascore, addascore]      ) / 4
     snake_case_       =      (avgkeepscore + avgdelscore + avgaddscore) / 3
     return finalscore







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase = True		,      UpperCAmelCase = "13a"		,      UpperCAmelCase = True      )       ->   str:
     # 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_       =      sentence.lower()

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

     if not return_str:
          snake_case_       =      normalized_sent.split()

     return normalized_sent




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







       def 	a_		(				self)   ->   List[Any]:
            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__)   ->   Union[str, Any]:
            if not (len(lowerCAmelCase__) == len(lowerCAmelCase__) == len(lowerCAmelCase__)):
                 raise ValueError('Sources length must match predictions and references lengths.')
            snake_case_       =      0
            for src, pred, refs in zip(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__):
                 sari_score += SARIsent(normalize(lowerCAmelCase__),							normalize(lowerCAmelCase__),							[normalize(lowerCAmelCase__) for sent in refs])
            snake_case_       =      sari_score / len(lowerCAmelCase__)
            return {"sari": 100 * sari_score}


"""simple docstring"""


import random
import sys

from . import cryptomath_module as cryptomath

__UpperCamelCase								=    (
    r''' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`'''
    r'''abcdefghijklmnopqrstuvwxyz{|}~'''
)







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   None:
     if mode == "encrypt":
          if key_a == 1:
               sys.exit(
                   'The affine cipher becomes weak when key '
                   'A is set to 1. Choose different key'      )
          if key_b == 0:
               sys.exit(
                   'The affine cipher becomes weak when key '
                   'B is set to 0. Choose different key'      )
     if key_a < 0 or key_b < 0 or key_b > len(UpperCAmelCase      ) - 1:
          sys.exit(
              'Key A must be greater than 0 and key B must '
              f'be between 0 and {len(UpperCAmelCase      ) - 1}.'      )
     if cryptomath.gcd(UpperCAmelCase		,      len(UpperCAmelCase      )      ) != 1:
          sys.exit(
              f'Key A {key_a} and the symbol set size {len(UpperCAmelCase      )} '
              'are not relatively prime. Choose a different key.'      )







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase      )       ->   str:
     snake_case_ ,			snake_case_       =      divmod(UpperCAmelCase		,      len(UpperCAmelCase      )      )
     check_keys(UpperCAmelCase		,      UpperCAmelCase		,      'encrypt'      )
     snake_case_       =      ''
     for symbol in message:
          if symbol in SYMBOLS:
               snake_case_       =      SYMBOLS.find(UpperCAmelCase      )
               cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(UpperCAmelCase      )]
          else:
               cipher_text += symbol
     return cipher_text







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase      )       ->   str:
     snake_case_ ,			snake_case_       =      divmod(UpperCAmelCase		,      len(UpperCAmelCase      )      )
     check_keys(UpperCAmelCase		,      UpperCAmelCase		,      'decrypt'      )
     snake_case_       =      ''
     snake_case_       =      cryptomath.find_mod_inverse(UpperCAmelCase		,      len(UpperCAmelCase      )      )
     for symbol in message:
          if symbol in SYMBOLS:
               snake_case_       =      SYMBOLS.find(UpperCAmelCase      )
               plain_text += SYMBOLS[
                   (sym_index - key_b) * mod_inverse_of_key_a % len(UpperCAmelCase      )
               ]
          else:
               plain_text += symbol
     return plain_text







def       UpperCAmelCase					(	)       ->   int:
     while True:
          snake_case_       =      random.randint(2		,      len(UpperCAmelCase      )      )
          snake_case_       =      random.randint(2		,      len(UpperCAmelCase      )      )
          if cryptomath.gcd(UpperCAmelCase		,      len(UpperCAmelCase      )      ) == 1 and key_b % len(UpperCAmelCase      ) != 0:
               return key_b * len(UpperCAmelCase      ) + key_b







def       UpperCAmelCase					(	)       ->   None:
     snake_case_       =      input('Enter message: '      ).strip()
     snake_case_       =      int(input('Enter key [2000 - 9000]: '      ).strip()      )
     snake_case_       =      input('Encrypt/Decrypt [E/D]: '      ).strip().lower()

     if mode.startswith('e'      ):
          snake_case_       =      'encrypt'
          snake_case_       =      encrypt_message(UpperCAmelCase		,      UpperCAmelCase      )
     elif mode.startswith('d'      ):
          snake_case_       =      'decrypt'
          snake_case_       =      decrypt_message(UpperCAmelCase		,      UpperCAmelCase      )
     print(f'\n{mode.title()}ed text: \n{translated}'      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    # main()

