






"""simple docstring"""



import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


a  :						str					       =							logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
a  :						List[Any]					       =							direct_transformers_import(Path(__file__).parent)


a  :						Optional[Any]					       =							{
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}


class 	_UpperCamelCase ( __UpperCamelCase							):



    '''simple docstring'''


    __lowercase    :   int											=						['feature_extractor', 'tokenizer']
    # Names need to be attr_class for attr in attributes
    __lowercase    :   List[str]											=						None
    __lowercase    :   Optional[Any]											=						None
    __lowercase    :   Any											=						None


    def __init__(      self   ,			*__lowercase   ,			**__lowercase			):
     # Sanitize args and kwargs
     for key in kwargs:
      if key not in self.attributes:
       raise TypeError(F'''Unexpected keyword argument {key}.'''			)
     for arg, attribute_name in zip(__lowercase   ,			self.attributes			):
      if attribute_name in kwargs:
       raise TypeError(F'''Got multiple values for argument {attribute_name}.'''			)
      else:
       UpperCAmelCase__ 						=				arg

     if len(__lowercase			) != len(self.attributes			):
      raise ValueError(
          F'''This processor requires {len(self.attributes			)} arguments: {', '.join(self.attributes			)}. Got '''
          F'''{len(__lowercase			)} arguments instead.'''			)

     # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
     for attribute_name, arg in kwargs.items():
      UpperCAmelCase__ 						=				getattr(self   ,			F'''{attribute_name}_class'''			)
      # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
      UpperCAmelCase__ 						=				AUTO_TO_BASE_CLASS_MAPPING.get(__lowercase   ,			__lowercase			)
      if isinstance(__lowercase   ,			__lowercase			):
       UpperCAmelCase__ 						=				tuple(getattr(__lowercase   ,			__lowercase			) for n in class_name if n is not None			)
      else:
       UpperCAmelCase__ 						=				getattr(__lowercase   ,			__lowercase			)

      if not isinstance(__lowercase   ,			__lowercase			):
       raise ValueError(
           F'''Received a {type(__lowercase			).__name__} for argument {attribute_name}, but a {class_name} was expected.'''			)

      setattr(self   ,			__lowercase   ,			__lowercase			)


    def __repr__(      self			):
     UpperCAmelCase__ 						=				[F'''- {name}: {repr(getattr(self   ,			__lowercase			)			)}''' for name in self.attributes]
     UpperCAmelCase__ 						=				"""\n""".join(__lowercase			)
     return F'''{self.__class__.__name__}:\n{attributes_repr}'''


    def 		A__      (      self   ,			__lowercase   ,			__lowercase = False   ,			**__lowercase			):
     os.makedirs(__lowercase   ,			exist_ok=__lowercase			)

     if push_to_hub:
      UpperCAmelCase__ 						=				kwargs.pop("""commit_message"""   ,			__lowercase			)
      UpperCAmelCase__ 						=				kwargs.pop("""repo_id"""   ,			save_directory.split(os.path.sep			)[-1]			)
      UpperCAmelCase__ 						=				self._create_repo(__lowercase   ,			**__lowercase			)
      UpperCAmelCase__ 						=				self._get_files_timestamps(__lowercase			)
     # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
     # loaded from the Hub.
     if self._auto_class is not None:
      UpperCAmelCase__ 						=				[getattr(self   ,			__lowercase			) for attribute_name in self.attributes]
      UpperCAmelCase__ 						=				[(a.init_kwargs if isinstance(__lowercase   ,			__lowercase			) else a) for a in attrs]
      custom_object_save(self   ,			__lowercase   ,			config=__lowercase			)

     for attribute_name in self.attributes:
      UpperCAmelCase__ 						=				getattr(self   ,			__lowercase			)
      # Include the processor class in the attribute config so this processor can then be reloaded with the
      # `AutoProcessor` API.
      if hasattr(__lowercase   ,			"""_set_processor_class"""			):
       attribute._set_processor_class(self.__class__.__name__			)
      attribute.save_pretrained(__lowercase			)

     if self._auto_class is not None:
      # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
      for attribute_name in self.attributes:
       UpperCAmelCase__ 						=				getattr(self   ,			__lowercase			)
       if isinstance(__lowercase   ,			__lowercase			):
        del attribute.init_kwargs["auto_map"]

     if push_to_hub:
      self._upload_modified_files(
          __lowercase   ,			__lowercase   ,			__lowercase   ,			commit_message=__lowercase   ,			token=kwargs.get("""use_auth_token"""			)   ,			)


    @classmethod
    def 		A__      (      cls   ,			__lowercase   ,			__lowercase = None   ,			__lowercase = False   ,			__lowercase = False   ,			__lowercase = None   ,			__lowercase = "main"   ,			**__lowercase   ,			):
     UpperCAmelCase__ 						=				cache_dir
     UpperCAmelCase__ 						=				force_download
     UpperCAmelCase__ 						=				local_files_only
     UpperCAmelCase__ 						=				revision

     UpperCAmelCase__ 						=				kwargs.pop("""use_auth_token"""   ,			__lowercase			)
     if use_auth_token is not None:
      warnings.warn(
          """The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers."""   ,			__lowercase			)
      if token is not None:
       raise ValueError(
           """`token` and `use_auth_token` are both specified. Please set only the argument `token`."""			)
      UpperCAmelCase__ 						=				use_auth_token

     if token is not None:
      # change to `token` in a follow-up PR
      UpperCAmelCase__ 						=				token

     UpperCAmelCase__ 						=				cls._get_arguments_from_pretrained(__lowercase   ,			**__lowercase			)
     return cls(*__lowercase			)


    @classmethod
    def 		A__      (      cls   ,			__lowercase="AutoProcessor"			):
     if not isinstance(__lowercase   ,			__lowercase			):
      UpperCAmelCase__ 						=				auto_class.__name__

     import transformers.models.auto as auto_module

     if not hasattr(__lowercase   ,			__lowercase			):
      raise ValueError(F'''{auto_class} is not a valid auto class.'''			)

     UpperCAmelCase__ 						=				auto_class


    @classmethod
    def 		A__      (      cls   ,			__lowercase   ,			**__lowercase			):
     UpperCAmelCase__ 						=				[]
     for attribute_name in cls.attributes:
      UpperCAmelCase__ 						=				getattr(cls   ,			F'''{attribute_name}_class'''			)
      if isinstance(__lowercase   ,			__lowercase			):
       UpperCAmelCase__ 						=				tuple(getattr(__lowercase   ,			__lowercase			) if n is not None else None for n in class_name			)
       UpperCAmelCase__ 						=				kwargs.get("""use_fast"""   ,			__lowercase			)
       if use_fast and classes[1] is not None:
        UpperCAmelCase__ 						=				classes[1]
       else:
        UpperCAmelCase__ 						=				classes[0]
      else:
       UpperCAmelCase__ 						=				getattr(__lowercase   ,			__lowercase			)

      args.append(attribute_class.from_pretrained(__lowercase   ,			**__lowercase			)			)
     return args






    @property
    def 		A__      (      self			):
     UpperCAmelCase__ 						=				getattr(self   ,			self.attributes[0]			)
     return getattr(__lowercase   ,			"""model_input_names"""   ,			__lowercase			)



a  :						List[Any]					       =							copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
 a  :						str					       =							ProcessorMixin.push_to_hub.__doc__.format(
     object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
 )











"""simple docstring"""



from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


a  :						Optional[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},
}
'''

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       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Dict:
 UpperCAmelCase__ 						=				[rgram for rgrams in rgramslist for rgram in rgrams]
 UpperCAmelCase__ 						=				Counter(_SCREAMING_SNAKE_CASE			)

 UpperCAmelCase__ 						=				Counter(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				Counter()
 for sgram, scount in sgramcounter.items():
  UpperCAmelCase__ 						=				scount * numref

 UpperCAmelCase__ 						=				Counter(_SCREAMING_SNAKE_CASE			)
 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(_SCREAMING_SNAKE_CASE			) > 0:
  UpperCAmelCase__ 						=				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)
  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(_SCREAMING_SNAKE_CASE			) > 0:
  UpperCAmelCase__ 						=				deltmpscorea / len(_SCREAMING_SNAKE_CASE			)

 # ADDITION
 UpperCAmelCase__ 						=				set(_SCREAMING_SNAKE_CASE			) - set(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				set(_SCREAMING_SNAKE_CASE			) & set(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				set(_SCREAMING_SNAKE_CASE			) - set(_SCREAMING_SNAKE_CASE			)

 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(_SCREAMING_SNAKE_CASE			) > 0:
  UpperCAmelCase__ 						=				addtmpscore / len(_SCREAMING_SNAKE_CASE			)
 if len(_SCREAMING_SNAKE_CASE			) > 0:
  UpperCAmelCase__ 						=				addtmpscore / len(_SCREAMING_SNAKE_CASE			)
 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__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Dict:
 UpperCAmelCase__ 						=				len(_SCREAMING_SNAKE_CASE			)

 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(_SCREAMING_SNAKE_CASE			)
  for i in range(0       ,	len(_SCREAMING_SNAKE_CASE			) - 1			):
   if i < len(_SCREAMING_SNAKE_CASE			) - 1:
    UpperCAmelCase__ 						=				ragrams[i] + """ """ + ragrams[i + 1]
    ragrams.append(_SCREAMING_SNAKE_CASE			)
   if i < len(_SCREAMING_SNAKE_CASE			) - 2:
    UpperCAmelCase__ 						=				ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
    ragrams.append(_SCREAMING_SNAKE_CASE			)
   if i < len(_SCREAMING_SNAKE_CASE			) - 3:
    UpperCAmelCase__ 						=				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:
   UpperCAmelCase__ 						=				sagrams[i] + """ """ + sagrams[i + 1]
   sagrams.append(_SCREAMING_SNAKE_CASE			)
  if i < len(_SCREAMING_SNAKE_CASE			) - 2:
   UpperCAmelCase__ 						=				sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
   sagrams.append(_SCREAMING_SNAKE_CASE			)
  if i < len(_SCREAMING_SNAKE_CASE			) - 3:
   UpperCAmelCase__ 						=				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:
   UpperCAmelCase__ 						=				cagrams[i] + """ """ + cagrams[i + 1]
   cagrams.append(_SCREAMING_SNAKE_CASE			)
  if i < len(_SCREAMING_SNAKE_CASE			) - 2:
   UpperCAmelCase__ 						=				cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
   cagrams.append(_SCREAMING_SNAKE_CASE			)
  if i < len(_SCREAMING_SNAKE_CASE			) - 3:
   UpperCAmelCase__ 						=				cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
   cagrams.append(_SCREAMING_SNAKE_CASE			)

 ((UpperCAmelCase__)						,	(UpperCAmelCase__)						,	(UpperCAmelCase__)) 						=				SARIngram(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)
 ((UpperCAmelCase__)						,	(UpperCAmelCase__)						,	(UpperCAmelCase__)) 						=				SARIngram(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)
 ((UpperCAmelCase__)						,	(UpperCAmelCase__)						,	(UpperCAmelCase__)) 						=				SARIngram(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)
 ((UpperCAmelCase__)						,	(UpperCAmelCase__)						,	(UpperCAmelCase__)) 						=				SARIngram(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)
 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__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = True       ,	_SCREAMING_SNAKE_CASE = "13a"       ,	_SCREAMING_SNAKE_CASE = True			)    ->Dict:
 # 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(_SCREAMING_SNAKE_CASE			)()(_SCREAMING_SNAKE_CASE			)
  else:
   UpperCAmelCase__ 						=				sacrebleu.TOKENIZERS[tokenizer]()(_SCREAMING_SNAKE_CASE			)
 elif tokenizer == "moses":
  UpperCAmelCase__ 						=				sacremoses.MosesTokenizer().tokenize(_SCREAMING_SNAKE_CASE       ,	return_str=_SCREAMING_SNAKE_CASE       ,	escape=_SCREAMING_SNAKE_CASE			)
 elif tokenizer == "penn":
  UpperCAmelCase__ 						=				sacremoses.MosesTokenizer().penn_tokenize(_SCREAMING_SNAKE_CASE       ,	return_str=_SCREAMING_SNAKE_CASE			)
 else:
  UpperCAmelCase__ 						=				sentence

 if not return_str:
  UpperCAmelCase__ 						=				normalized_sent.split()

 return normalized_sent


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



    '''simple docstring'''


    def 		A__      (      self			):
     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   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     if not (len(__lowercase			) == len(__lowercase			) == len(__lowercase			)):
      raise ValueError("""Sources length must match predictions and references lengths."""			)
     UpperCAmelCase__ 						=				0
     for src, pred, refs in zip(__lowercase   ,			__lowercase   ,			__lowercase			):
      sari_score += SARIsent(normalize(__lowercase			)   ,			normalize(__lowercase			)   ,			[normalize(__lowercase			) for sent in refs]			)
     UpperCAmelCase__ 						=				sari_score / len(__lowercase			)
     return {"sari": 100 * sari_score}



