

def _a				( SCREAMING_SNAKE_CASE	:    str	)					->					int:

 """simple docstring"""






 __lowerCAmelCase:     Dict     			=   len(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Optional[int]     			=   [0] * length
 __lowerCAmelCase:     List[Any]     			=   [[False for i in range(SCREAMING_SNAKE_CASE	)] for j in range(SCREAMING_SNAKE_CASE	)]
 for i, c in enumerate(SCREAMING_SNAKE_CASE	):
  __lowerCAmelCase:     Any     			=   i
  for j in range(i + 1	):
   if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
    __lowerCAmelCase:     Dict     			=   True
    __lowerCAmelCase:     str     			=   min(SCREAMING_SNAKE_CASE					,      0 if j == 0 else (cut[j - 1] + 1)	)
  __lowerCAmelCase:     Tuple     			=   mincut
 return cut[length - 1]


if __name__ == "__main__":
  _a	 =     input('''Enter the string: ''').strip()
  _a	 =     find_minimum_partitions(s)
  print(f"Minimum number of partitions required for the '{s}' is {ans}")








from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_a	 =     '''\
@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	 =     '''\
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	 =     '''
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				( SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Optional[Any]					,      SCREAMING_SNAKE_CASE	:    Tuple					,      SCREAMING_SNAKE_CASE	:    Optional[Any]	)					->					Dict:

 """simple docstring"""






 __lowerCAmelCase:     str     			=   [rgram for rgrams in rgramslist for rgram in rgrams]
 __lowerCAmelCase:     List[Any]     			=   Counter(SCREAMING_SNAKE_CASE	)

 __lowerCAmelCase:     List[str]     			=   Counter(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     int     			=   Counter()
 for sgram, scount in sgramcounter.items():
  __lowerCAmelCase:     Optional[int]     			=   scount * numref

 __lowerCAmelCase:     Optional[Any]     			=   Counter(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Optional[Any]     			=   Counter()
 for cgram, ccount in cgramcounter.items():
  __lowerCAmelCase:     Tuple     			=   ccount * numref

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

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

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

 # ADDITION
 __lowerCAmelCase:     Optional[Any]     			=   set(SCREAMING_SNAKE_CASE	) - set(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     int     			=   set(SCREAMING_SNAKE_CASE	) & set(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Tuple     			=   set(SCREAMING_SNAKE_CASE	) - set(SCREAMING_SNAKE_CASE	)

 __lowerCAmelCase:     Union[str, 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.
 __lowerCAmelCase:     Optional[Any]     			=   1
 __lowerCAmelCase:     Union[str, Any]     			=   1
 if len(SCREAMING_SNAKE_CASE	) > 0:
  __lowerCAmelCase:     int     			=   addtmpscore / len(SCREAMING_SNAKE_CASE	)
 if len(SCREAMING_SNAKE_CASE	) > 0:
  __lowerCAmelCase:     str     			=   addtmpscore / len(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Dict     			=   0
 if addscore_precision > 0 or addscore_recall > 0:
  __lowerCAmelCase:     str     			=   2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

 return (keepscore, delscore_precision, addscore)







def _a				( SCREAMING_SNAKE_CASE	:    Optional[int]					,      SCREAMING_SNAKE_CASE	:    List[Any]					,      SCREAMING_SNAKE_CASE	:    Optional[Any]	)					->					List[str]:

 """simple docstring"""






 __lowerCAmelCase:     List[Any]     			=   len(SCREAMING_SNAKE_CASE	)

 __lowerCAmelCase:     int     			=   ssent.split(' '	)
 __lowerCAmelCase:     List[str]     			=   csent.split(' '	)
 __lowerCAmelCase:     Dict     			=   []
 __lowerCAmelCase:     Any     			=   []
 __lowerCAmelCase:     Optional[int]     			=   []
 __lowerCAmelCase:     str     			=   []
 __lowerCAmelCase:     Dict     			=   []
 __lowerCAmelCase:     str     			=   []

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

 for i in range(0					,      len(SCREAMING_SNAKE_CASE	) - 1	):
  if i < len(SCREAMING_SNAKE_CASE	) - 1:
   __lowerCAmelCase:     Optional[int]     			=   sagrams[i] + ' ' + sagrams[i + 1]
   sagrams.append(SCREAMING_SNAKE_CASE	)
  if i < len(SCREAMING_SNAKE_CASE	) - 2:
   __lowerCAmelCase:     Any     			=   sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
   sagrams.append(SCREAMING_SNAKE_CASE	)
  if i < len(SCREAMING_SNAKE_CASE	) - 3:
   __lowerCAmelCase:     Optional[int]     			=   sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
   sagrams.append(SCREAMING_SNAKE_CASE	)

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

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







def _a				( SCREAMING_SNAKE_CASE	:    int					,      SCREAMING_SNAKE_CASE	:    bool = True					,      SCREAMING_SNAKE_CASE	:    str = "13a"					,      SCREAMING_SNAKE_CASE	:    bool = True	)					->					Optional[Any]:

 """simple docstring"""






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

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

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

 return normalized_sent






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


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





