

import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


_a	 =     logging.get_logger(__name__)

_a	 =     [
    'yjernite/retribert-base-uncased',
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]






class 							A_				(	snake_case__      ):
     _lowercase    :  List[Any]          =			RetriBertConfig
     _lowercase    :  Any          =			None
     _lowercase    :  List[Any]          =			'retribert'


     def 						UpperCAmelCase						(							self    :	Dict    ,						UpperCAmelCase    :	Optional[Any]      )							->			Dict:
      if isinstance(A_    ,						nn.Linear      ):
       module.weight.data.normal_(mean=0.0    ,						std=self.config.initializer_range      )
       if module.bias is not None:
        module.bias.data.zero_()
      elif isinstance(A_    ,						nn.Embedding      ):
       module.weight.data.normal_(mean=0.0    ,						std=self.config.initializer_range      )
       if module.padding_idx is not None:
        module.weight.data[module.padding_idx].zero_()
      elif isinstance(A_    ,						nn.LayerNorm      ):
       module.bias.data.zero_()
       module.weight.data.fill_(1.0      )







_a	 =     R'\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'






@add_start_docstrings(
    'Bert Based model to embed queries or document for document retrieval.'							,			snake_case__							,			)
class 							A_				(	snake_case__      ):


     def __init__(							self    :	int    ,						UpperCAmelCase    :	RetriBertConfig      )							->			Any:
      super().__init__(A_      )
      __lowerCAmelCase:     Tuple     			=   config.projection_dim

      __lowerCAmelCase:     Optional[int]     			=   BertModel(A_      )
      __lowerCAmelCase:     Tuple     			=   None if config.share_encoders else BertModel(A_      )
      __lowerCAmelCase:     Dict     			=   nn.Dropout(config.hidden_dropout_prob      )
      __lowerCAmelCase:     Union[str, Any]     			=   nn.Linear(config.hidden_size    ,						config.projection_dim    ,						bias=A_      )
      __lowerCAmelCase:     Union[str, Any]     			=   nn.Linear(config.hidden_size    ,						config.projection_dim    ,						bias=A_      )

      __lowerCAmelCase:     int     			=   nn.CrossEntropyLoss(reduction='mean'      )

      # Initialize weights and apply final processing
      self.post_init()


     def 						UpperCAmelCase						(							self    :	Tuple    ,						UpperCAmelCase    :	Union[str, Any]    ,						UpperCAmelCase    :	Any    ,						UpperCAmelCase    :	List[str]    ,						UpperCAmelCase    :	Any=-1    ,						)							->			str:
      # reproduces BERT forward pass with checkpointing
      if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
       return sent_encoder(A_    ,						attention_mask=A_      )[1]
      else:
       # prepare implicit variables
       __lowerCAmelCase:     Optional[Any]     			=   input_ids.device
       __lowerCAmelCase:     Dict     			=   input_ids.size()
       __lowerCAmelCase:     List[Any]     			=   torch.zeros(A_    ,						dtype=torch.long    ,						device=A_      )
       __lowerCAmelCase:     Optional[int]     			=   [None] * sent_encoder.config.num_hidden_layers
       __lowerCAmelCase:     List[str]     			=   sent_encoder.get_extended_attention_mask(
           A_    ,						A_      )

       # define function for checkpointing
       def partial_encode(*UpperCAmelCase    :	Optional[int]      ):
        __lowerCAmelCase:     str     			=   sent_encoder.encoder(
            inputs[0]    ,						attention_mask=inputs[1]    ,						head_mask=A_    ,						)
        __lowerCAmelCase:     List[str]     			=   encoder_outputs[0]
        __lowerCAmelCase:     Optional[Any]     			=   sent_encoder.pooler(A_      )
        return pooled_output

       # run embedding layer on everything at once
       __lowerCAmelCase:     Optional[int]     			=   sent_encoder.embeddings(
           input_ids=A_    ,						position_ids=A_    ,						token_type_ids=A_    ,						inputs_embeds=A_      )
       # run encoding and pooling on one mini-batch at a time
       __lowerCAmelCase:     Optional[int]     			=   []
       for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size      )      ):
        __lowerCAmelCase:     int     			=   embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
        __lowerCAmelCase:     Union[str, Any]     			=   extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
        __lowerCAmelCase:     Tuple     			=   checkpoint.checkpoint(A_    ,						A_    ,						A_      )
        pooled_output_list.append(A_      )
       return torch.cat(A_    ,						dim=0      )


     def 						UpperCAmelCase						(							self    :	str    ,						UpperCAmelCase    :	List[Any]    ,						UpperCAmelCase    :	Dict=None    ,						UpperCAmelCase    :	Optional[Any]=-1    ,						)							->			Any:
      __lowerCAmelCase:     List[str]     			=   self.embed_sentences_checkpointed(
          A_    ,						A_    ,						self.bert_query    ,						A_    ,						)
      return self.project_query(A_      )


     def 						UpperCAmelCase						(							self    :	Dict    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	List[str]=None    ,						UpperCAmelCase    :	int=-1    ,						)							->			Tuple:
      __lowerCAmelCase:     str     			=   self.embed_sentences_checkpointed(
          A_    ,						A_    ,						self.bert_query if self.bert_doc is None else self.bert_doc    ,						A_    ,						)
      return self.project_doc(A_      )


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	torch.LongTensor    ,						UpperCAmelCase    :	Optional[torch.FloatTensor]    ,						UpperCAmelCase    :	torch.LongTensor    ,						UpperCAmelCase    :	Optional[torch.FloatTensor]    ,						UpperCAmelCase    :	int = -1    ,						)							->			str:
      __lowerCAmelCase:     int     			=   input_ids_query.device
      __lowerCAmelCase:     Tuple     			=   self.embed_questions(A_    ,						A_    ,						A_      )
      __lowerCAmelCase:     Union[str, Any]     			=   self.embed_answers(A_    ,						A_    ,						A_      )
      __lowerCAmelCase:     int     			=   torch.mm(A_    ,						a_reps.t()      )
      __lowerCAmelCase:     int     			=   self.ce_loss(A_    ,						torch.arange(compare_scores.shape[1]      ).to(A_      )      )
      __lowerCAmelCase:     Tuple     			=   self.ce_loss(compare_scores.t()    ,						torch.arange(compare_scores.shape[0]      ).to(A_      )      )
      __lowerCAmelCase:     Tuple     			=   (loss_qa + loss_aq) / 2
      return loss








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}





