

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_a	 =     logging.getLogger(__name__)






@dataclass
class 							A_				:
     _lowercase    :  str          =			field(
         metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      )
     _lowercase    :  Optional[str]          =			field(
         default=snake_case__							,			metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
     _lowercase    :  Optional[str]          =			field(
         default=snake_case__							,			metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}      )
     _lowercase    :  Optional[str]          =			field(
         default=snake_case__							,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}							,			)







@dataclass
class 							A_				:
     _lowercase    :  str          =			field(
         metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()      )}      )
     _lowercase    :  str          =			field(
         metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}      )
     _lowercase    :  int          =			field(
         default=1_2_8							,			metadata={
             'help': (
                 'The maximum total input sequence length after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             )
         }							,			)
     _lowercase    :  bool          =			field(
         default=snake_case__							,			metadata={'help': 'Overwrite the cached training and evaluation sets'}      )







def _a				( SCREAMING_SNAKE_CASE	:    List[InputFeatures]	)					->					Dict[str, torch.Tensor]:

 """simple docstring"""






 __lowerCAmelCase:     Union[str, Any]     			=   default_data_collator(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Any     			=   batch.pop('pairID'					,      SCREAMING_SNAKE_CASE	)
 return batch







def _a				( )					->					Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:     List[Any]     			=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
 __lowerCAmelCase			,							__lowerCAmelCase			,							__lowerCAmelCase:     List[Any]     			=   parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir	)
     and os.listdir(training_args.output_dir	)
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
      ' --overwrite_output_dir to overcome.'	)

 # Setup logging
 logging.basicConfig(
     format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'					,      datefmt='%m/%d/%Y %H:%M:%S'					,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,      )
 logger.warning(
     'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'					,      training_args.local_rank					,      training_args.device					,      training_args.n_gpu					,      bool(training_args.local_rank != -1	)					,      training_args.fpaa					,      )
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank	):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info('Training/evaluation parameters %s'					,      SCREAMING_SNAKE_CASE	)

 # Set seed
 set_seed(training_args.seed	)

 try:
  __lowerCAmelCase:     List[str]     			=   hans_tasks_num_labels[data_args.task_name]
 except KeyError:
  raise ValueError('Task not found: %s' % (data_args.task_name)	)

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 __lowerCAmelCase:     List[Any]     			=   AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path					,      num_labels=SCREAMING_SNAKE_CASE					,      finetuning_task=data_args.task_name					,      cache_dir=model_args.cache_dir					,      )
 __lowerCAmelCase:     List[str]     			=   AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,      cache_dir=model_args.cache_dir					,      )
 __lowerCAmelCase:     Tuple     			=   AutoModelForSequenceClassification.from_pretrained(
     model_args.model_name_or_path					,      from_tf=bool('.ckpt' in model_args.model_name_or_path	)					,      config=SCREAMING_SNAKE_CASE					,      cache_dir=model_args.cache_dir					,      )

 # Get datasets
 __lowerCAmelCase:     List[str]     			=   (
     HansDataset(
         data_dir=data_args.data_dir					,      tokenizer=SCREAMING_SNAKE_CASE					,      task=data_args.task_name					,      max_seq_length=data_args.max_seq_length					,      overwrite_cache=data_args.overwrite_cache					,      )
     if training_args.do_train
     else None
 )
 __lowerCAmelCase:     str     			=   (
     HansDataset(
         data_dir=data_args.data_dir					,      tokenizer=SCREAMING_SNAKE_CASE					,      task=data_args.task_name					,      max_seq_length=data_args.max_seq_length					,      overwrite_cache=data_args.overwrite_cache					,      evaluate=SCREAMING_SNAKE_CASE					,      )
     if training_args.do_eval
     else None
 )

 # Initialize our Trainer
 __lowerCAmelCase:     str     			=   Trainer(
     model=SCREAMING_SNAKE_CASE					,      args=SCREAMING_SNAKE_CASE					,      train_dataset=SCREAMING_SNAKE_CASE					,      eval_dataset=SCREAMING_SNAKE_CASE					,      data_collator=SCREAMING_SNAKE_CASE					,      )

 # Training
 if training_args.do_train:
  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
  trainer.save_model()
  # For convenience, we also re-save the tokenizer to the same directory,
  # so that you can share your model easily on huggingface.co/models =)
  if trainer.is_world_master():
   tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
 if training_args.do_eval:
  logger.info('*** Evaluate ***'	)

  __lowerCAmelCase:     List[Any]     			=   trainer.predict(SCREAMING_SNAKE_CASE	)
  __lowerCAmelCase:     Tuple     			=   output.predictions
  __lowerCAmelCase:     int     			=   np.argmax(SCREAMING_SNAKE_CASE					,      axis=1	)

  __lowerCAmelCase:     Optional[int]     			=   [ex.pairID for ex in eval_dataset]
  __lowerCAmelCase:     Dict     			=   os.path.join(training_args.output_dir					,      'hans_predictions.txt'	)
  __lowerCAmelCase:     Union[str, Any]     			=   eval_dataset.get_labels()
  if trainer.is_world_master():
   with open(SCREAMING_SNAKE_CASE					,      'w'	) as writer:
    writer.write('pairID,gold_label\n'	)
    for pid, pred in zip(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE	):
     writer.write('ex' + str(SCREAMING_SNAKE_CASE	) + ',' + label_list[int(SCREAMING_SNAKE_CASE	)] + '\n'	)

  trainer._log(output.metrics	)







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

 """simple docstring"""






 main()


if __name__ == "__main__":
  main()








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}





