'''simple docstring'''


from binascii import hexlify
from hashlib import shaaaa
from os import urandom

# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526

snake_case_ :  Optional[Any]     =					{
    # 1536-bit
    5: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 2048-bit
    14: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AACAA68FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 3072-bit
    15: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
            + 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
            + 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
            + 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
            + '43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 4096-bit
    16: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
            + 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
            + 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
            + 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
            + '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
            + '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
            + '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
            + '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
            + '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
            + '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199'
            + 'FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 6144-bit
    17: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08'
            + '8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B'
            + '302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9'
            + 'A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6'
            + '49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8'
            + 'FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C'
            + '180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718'
            + '3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D'
            + '04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D'
            + 'B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226'
            + '1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC'
            + 'E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26'
            + '99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB'
            + '04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2'
            + '233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127'
            + 'D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
            + '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406'
            + 'AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918'
            + 'DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151'
            + '2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03'
            + 'F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F'
            + 'BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
            + 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B'
            + 'B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632'
            + '387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E'
            + '6DCC4024FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 8192-bit
    18: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
            + 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
            + 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
            + 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
            + '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
            + '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
            + '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
            + '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
            + '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
            + '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
            + '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD'
            + 'F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831'
            + '179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B'
            + 'DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF'
            + '5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6'
            + 'D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3'
            + '23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
            + 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328'
            + '06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C'
            + 'DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE'
            + '12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4'
            + '38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300'
            + '741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568'
            + '3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9'
            + '22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B'
            + '4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A'
            + '062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36'
            + '4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1'
            + 'B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92'
            + '4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47'
            + '9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71'
            + '60C980DD98EDD3DFFFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
}


class 						lowercase__	:






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ = 1_4   ):

      '''simple docstring'''







      if group not in primes:
         raise ValueError('''Unsupported Group'''   )
      UpperCamelCase       =  primes[group]["prime"]
      UpperCamelCase       =  primes[group]["generator"]

      UpperCamelCase       =  int(hexlify(urandom(3_2   )   ) ,   base=1_6   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return hex(self.__private_key   )[2:]







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  pow(self.generator ,   self.__private_key ,   self.prime   )
      return hex(lowerCamelCase__   )[2:]







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return (
          2 <= key <= self.prime - 2
          and pow(lowerCamelCase__ ,   (self.prime - 1) // 2 ,   self.prime   ) == 1
      )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  int(lowerCamelCase__ ,   base=1_6   )
      if not self.is_valid_public_key(lowerCamelCase__   ):
         raise ValueError('''Invalid public key'''   )
      UpperCamelCase       =  pow(lowerCamelCase__ ,   self.__private_key ,   self.prime   )
      return shaaaa(str(lowerCamelCase__   ).encode()   ).hexdigest()







   @staticmethod
   def       UpperCAmelCase				(			lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      return (
          2 <= remote_public_key_str <= prime - 2
          and pow(lowerCamelCase__ ,   (prime - 1) // 2 ,   lowerCamelCase__   ) == 1
      )







   @staticmethod
   def       UpperCAmelCase				(			lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ = 1_4   ):

      '''simple docstring'''







      UpperCamelCase       =  int(lowerCamelCase__ ,   base=1_6   )
      UpperCamelCase       =  int(lowerCamelCase__ ,   base=1_6   )
      UpperCamelCase       =  primes[group]["prime"]
      if not DiffieHellman.is_valid_public_key_static(lowerCamelCase__ ,   lowerCamelCase__   ):
         raise ValueError('''Invalid public key'''   )
      UpperCamelCase       =  pow(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   )
      return shaaaa(str(lowerCamelCase__   ).encode()   ).hexdigest()


if __name__ == "__main__":
   import doctest

   doctest.testmod()







'''simple docstring'''


import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


snake_case_ :  int     =					'\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

snake_case_ :  Optional[int]     =					'\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n'


snake_case_ :  Optional[Any]     =					'\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=["About 95 species are currently accepted ."]\n    >>> predictions=["About 95 you now get in ."]\n    >>> references=[["About 95 species are currently known ."]]\n    >>> wiki_split = datasets.load_metric("wiki_split")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}\n'



def  __snake_case     (    _UpperCAmelCase					:       Optional[Any]):

   def remove_articles(_UpperCAmelCase					:       str):
      UpperCamelCase       =  re.compile(R'''\b(a|an|the)\b''',      re.UNICODE)
      return re.sub(_UpperCAmelCase,      ''' ''',      _UpperCAmelCase)

   def white_space_fix(_UpperCAmelCase					:       Union[str, Any]):
      return " ".join(text.split())

   def remove_punc(_UpperCAmelCase					:       Dict):
      UpperCamelCase       =  set(string.punctuation)
      return "".join(ch for ch in text if ch not in exclude)

   def lower(_UpperCAmelCase					:       List[str]):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_UpperCAmelCase))))



def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       List[str]):
   return int(normalize_answer(_UpperCAmelCase) == normalize_answer(_UpperCAmelCase))



def  __snake_case     (    _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Optional[int]):
   UpperCamelCase       =  [any(compute_exact(_UpperCAmelCase,      _UpperCAmelCase) for ref in refs) for pred, refs in zip(_UpperCAmelCase,      _UpperCAmelCase)]
   return (sum(_UpperCAmelCase) / len(_UpperCAmelCase)) * 100



def  __snake_case     (    _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       List[str]):
   UpperCamelCase       =  [rgram for rgrams in rgramslist for rgram in rgrams]
   UpperCamelCase       =  Counter(_UpperCAmelCase)

   UpperCamelCase       =  Counter(_UpperCAmelCase)
   UpperCamelCase       =  Counter()
   for sgram, scount in sgramcounter.items():
      UpperCamelCase       =  scount * numref

   UpperCamelCase       =  Counter(_UpperCAmelCase)
   UpperCamelCase       =  Counter()
   for cgram, ccount in cgramcounter.items():
      UpperCamelCase       =  ccount * numref

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

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

   # DELETION
   UpperCamelCase       =  sgramcounter_rep - cgramcounter_rep
   UpperCamelCase       =  delgramcounter_rep - rgramcounter
   UpperCamelCase       =  sgramcounter_rep - rgramcounter
   UpperCamelCase       =  0
   UpperCamelCase       =  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       =  1
   if len(_UpperCAmelCase) > 0:
      UpperCamelCase       =  deltmpscorea / len(_UpperCAmelCase)

   # ADDITION
   UpperCamelCase       =  set(_UpperCAmelCase) - set(_UpperCAmelCase)
   UpperCamelCase       =  set(_UpperCAmelCase) & set(_UpperCAmelCase)
   UpperCamelCase       =  set(_UpperCAmelCase) - set(_UpperCAmelCase)

   UpperCamelCase       =  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       =  1
   UpperCamelCase       =  1
   if len(_UpperCAmelCase) > 0:
      UpperCamelCase       =  addtmpscore / len(_UpperCAmelCase)
   if len(_UpperCAmelCase) > 0:
      UpperCamelCase       =  addtmpscore / len(_UpperCAmelCase)
   UpperCamelCase       =  0
   if addscore_precision > 0 or addscore_recall > 0:
      UpperCamelCase       =  2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

   return (keepscore, delscore_precision, addscore)



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       str):
   UpperCamelCase       =  len(_UpperCAmelCase)

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

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

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



def  __snake_case     (    _UpperCAmelCase					:       List[str],      _UpperCAmelCase					:       bool = True,      _UpperCAmelCase					:       str = "13a",      _UpperCAmelCase					:       bool = 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       =  sentence.lower()

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

   if not return_str:
      UpperCamelCase       =  normalized_sent.split()

   return normalized_sent



def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       Optional[Any]):
   if not (len(_UpperCAmelCase) == len(_UpperCAmelCase) == len(_UpperCAmelCase)):
      raise ValueError('''Sources length must match predictions and references lengths.''')
   UpperCamelCase       =  0
   for src, pred, refs in zip(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase):
      sari_score += SARIsent(normalize(_UpperCAmelCase),      normalize(_UpperCAmelCase),      [normalize(_UpperCAmelCase) for sent in refs])
   UpperCamelCase       =  sari_score / len(_UpperCAmelCase)
   return 100 * sari_score



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Any="exp",      _UpperCAmelCase					:       str=None,      _UpperCAmelCase					:       Optional[Any]=False,      _UpperCAmelCase					:       Union[str, Any]=False,      _UpperCAmelCase					:       List[Any]=False,      ):
   UpperCamelCase       =  len(references[0])
   if any(len(_UpperCAmelCase) != references_per_prediction for refs in references):
      raise ValueError('''Sacrebleu requires the same number of references for each prediction''')
   UpperCamelCase       =  [[refs[i] for refs in references] for i in range(_UpperCAmelCase)]
   UpperCamelCase       =  sacrebleu.corpus_bleu(
       _UpperCAmelCase,      _UpperCAmelCase,      smooth_method=_UpperCAmelCase,      smooth_value=_UpperCAmelCase,      force=_UpperCAmelCase,      lowercase=_UpperCAmelCase,      use_effective_order=_UpperCAmelCase,      )
   return output.score


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






   '''simple docstring'''







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return datasets.MetricInfo(
          description=_DESCRIPTION ,   citation=_CITATION ,   inputs_description=_KWARGS_DESCRIPTION ,   features=datasets.Features(
              {
                  '''predictions''': datasets.Value('''string''' ,   id='''sequence'''   ),
                  '''references''': datasets.Sequence(datasets.Value('''string''' ,   id='''sequence'''   ) ,   id='''references'''   ),
              }   ) ,   codebase_urls=[
              '''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
              '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
              '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
              '''https://github.com/mjpost/sacreBLEU''',
          ] ,   reference_urls=[
              '''https://www.aclweb.org/anthology/Q16-1029.pdf''',
              '''https://github.com/mjpost/sacreBLEU''',
              '''https://en.wikipedia.org/wiki/BLEU''',
              '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
          ] ,   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  {}
      result.update({'''sari''': compute_sari(sources=lowerCamelCase__ ,   predictions=lowerCamelCase__ ,   references=lowerCamelCase__   )}   )
      result.update({'''sacrebleu''': compute_sacrebleu(predictions=lowerCamelCase__ ,   references=lowerCamelCase__   )}   )
      result.update({'''exact''': compute_em(predictions=lowerCamelCase__ ,   references=lowerCamelCase__   )}   )
      return result





