





from sklearn.metrics import fa_score

import datasets


__lowerCamelCase       :     List[Any]   =     """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""


__lowerCamelCase       :     List[Any]   =     """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.

        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {'f1': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results['f1'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results['f1'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")
        >>> print(round(results['f1'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'f1': array([0.8, 0. , 0. ])}
"""


__lowerCamelCase       :     str   =     """
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
"""



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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return datasets.MetricInfo(
           description=_DESCRIPTION       ,							citation=_CITATION       ,							inputs_description=_KWARGS_DESCRIPTION       ,							features=datasets.Features(
               {
                   "predictions": datasets.Sequence(datasets.Value("int32"   )   ),
                   "references": datasets.Sequence(datasets.Value("int32"   )   ),
               }
               if self.config_name == "multilabel"
               else {
                   "predictions": datasets.Value("int32"   ),
                   "references": datasets.Value("int32"   ),
               }   )       ,							reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"]       ,							)





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_=None       ,							A_=1       ,							A_="binary"       ,							A_=None   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   fa_score(
           A_       ,							A_       ,							labels=A_       ,							pos_label=A_       ,							average=A_       ,							sample_weight=A_   )
       return {"f1": float(A_   ) if score.size == 1 else score}







import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCamelCase       :     List[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
},
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

__lowerCamelCase       :     Optional[int]   =     """\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


__lowerCamelCase       :     str   =     """
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
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
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\"]]
    >>> wiki_split = datasets.load_metric(\"wiki_split\")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}
"""



def 		A_				(     _lowerCAmelCase     )						-> str:

 def remove_articles(_lowerCAmelCase     ):
  UpperCamelCase	:					Tuple       	=   re.compile(r"\b(a|an|the)\b"							,   re.UNICODE     )
  return re.sub(_lowerCAmelCase							,   " "							,   _lowerCAmelCase     )

 def white_space_fix(_lowerCAmelCase     ):
  return " ".join(text.split()     )

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

 def lower(_lowerCAmelCase     ):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase     )     )     )     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> Any:
 return int(normalize_answer(_lowerCAmelCase     ) == normalize_answer(_lowerCAmelCase     )     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Tuple       	=   [any(compute_exact(_lowerCAmelCase							,   _lowerCAmelCase     ) for ref in refs     ) for pred, refs in zip(_lowerCAmelCase							,   _lowerCAmelCase     )]
 return (sum(_lowerCAmelCase     ) / len(_lowerCAmelCase     )) * 100



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> Union[str, Any]:
 UpperCamelCase	:					Union[str, Any]       	=   [rgram for rgrams in rgramslist for rgram in rgrams]
 UpperCamelCase	:					Union[str, Any]       	=   Counter(_lowerCAmelCase     )

 UpperCamelCase	:					Optional[int]       	=   Counter(_lowerCAmelCase     )
 UpperCamelCase	:					List[Any]       	=   Counter()
 for sgram, scount in sgramcounter.items():
  UpperCamelCase	:					Tuple       	=   scount * numref

 UpperCamelCase	:					Union[str, Any]       	=   Counter(_lowerCAmelCase     )
 UpperCamelCase	:					Tuple       	=   Counter()
 for cgram, ccount in cgramcounter.items():
  UpperCamelCase	:					Dict       	=   ccount * numref

 # KEEP
 UpperCamelCase	:					List[Any]       	=   sgramcounter_rep & cgramcounter_rep
 UpperCamelCase	:					Union[str, Any]       	=   keepgramcounter_rep & rgramcounter
 UpperCamelCase	:					Dict       	=   sgramcounter_rep & rgramcounter

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

 # DELETION
 UpperCamelCase	:					Any       	=   sgramcounter_rep - cgramcounter_rep
 UpperCamelCase	:					str       	=   delgramcounter_rep - rgramcounter
 UpperCamelCase	:					Any       	=   sgramcounter_rep - rgramcounter
 UpperCamelCase	:					Optional[int]       	=   0
 UpperCamelCase	:					Union[str, Any]       	=   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	:					Dict       	=   1
 if len(_lowerCAmelCase     ) > 0:
  UpperCamelCase	:					str       	=   deltmpscorea / len(_lowerCAmelCase     )

 # ADDITION
 UpperCamelCase	:					List[str]       	=   set(_lowerCAmelCase     ) - set(_lowerCAmelCase     )
 UpperCamelCase	:					List[str]       	=   set(_lowerCAmelCase     ) & set(_lowerCAmelCase     )
 UpperCamelCase	:					Dict       	=   set(_lowerCAmelCase     ) - set(_lowerCAmelCase     )

 UpperCamelCase	:					Optional[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.
 UpperCamelCase	:					Tuple       	=   1
 UpperCamelCase	:					Tuple       	=   1
 if len(_lowerCAmelCase     ) > 0:
  UpperCamelCase	:					Dict       	=   addtmpscore / len(_lowerCAmelCase     )
 if len(_lowerCAmelCase     ) > 0:
  UpperCamelCase	:					Tuple       	=   addtmpscore / len(_lowerCAmelCase     )
 UpperCamelCase	:					List[str]       	=   0
 if addscore_precision > 0 or addscore_recall > 0:
  UpperCamelCase	:					List[str]       	=   2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

 return (keepscore, delscore_precision, addscore)



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> List[str]:
 UpperCamelCase	:					int       	=   len(_lowerCAmelCase     )

 UpperCamelCase	:					Optional[Any]       	=   ssent.split(" "     )
 UpperCamelCase	:					Dict       	=   csent.split(" "     )
 UpperCamelCase	:					str       	=   []
 UpperCamelCase	:					Any       	=   []
 UpperCamelCase	:					Any       	=   []
 UpperCamelCase	:					Union[str, Any]       	=   []
 UpperCamelCase	:					str       	=   []
 UpperCamelCase	:					str       	=   []

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

 for i in range(0							,   len(_lowerCAmelCase     ) - 1     ):
  if i < len(_lowerCAmelCase     ) - 1:
   UpperCamelCase	:					Union[str, Any]       	=   sagrams[i] + " " + sagrams[i + 1]
   sagrams.append(_lowerCAmelCase     )
  if i < len(_lowerCAmelCase     ) - 2:
   UpperCamelCase	:					List[str]       	=   sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
   sagrams.append(_lowerCAmelCase     )
  if i < len(_lowerCAmelCase     ) - 3:
   UpperCamelCase	:					Optional[int]       	=   sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
   sagrams.append(_lowerCAmelCase     )

 for i in range(0							,   len(_lowerCAmelCase     ) - 1     ):
  if i < len(_lowerCAmelCase     ) - 1:
   UpperCamelCase	:					Union[str, Any]       	=   cagrams[i] + " " + cagrams[i + 1]
   cagrams.append(_lowerCAmelCase     )
  if i < len(_lowerCAmelCase     ) - 2:
   UpperCamelCase	:					Optional[Any]       	=   cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
   cagrams.append(_lowerCAmelCase     )
  if i < len(_lowerCAmelCase     ) - 3:
   UpperCamelCase	:					Union[str, Any]       	=   cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
   cagrams.append(_lowerCAmelCase     )

 ((UpperCamelCase)	,    (UpperCamelCase)	,    (UpperCamelCase))	:					Optional[int]       	=   SARIngram(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )
 ((UpperCamelCase)	,    (UpperCamelCase)	,    (UpperCamelCase))	:					Optional[Any]       	=   SARIngram(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )
 ((UpperCamelCase)	,    (UpperCamelCase)	,    (UpperCamelCase))	:					str       	=   SARIngram(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )
 ((UpperCamelCase)	,    (UpperCamelCase)	,    (UpperCamelCase))	:					Optional[int]       	=   SARIngram(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )
 UpperCamelCase	:					Tuple       	=   sum([keepascore, keepascore, keepascore, keepascore]     ) / 4
 UpperCamelCase	:					str       	=   sum([delascore, delascore, delascore, delascore]     ) / 4
 UpperCamelCase	:					Union[str, Any]       	=   sum([addascore, addascore, addascore, addascore]     ) / 4
 UpperCamelCase	:					Union[str, Any]       	=   (avgkeepscore + avgdelscore + avgaddscore) / 3
 return finalscore



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase = True							,   _lowerCAmelCase = "13a"							,   _lowerCAmelCase = True     )						-> Optional[Any]:
 # 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	:					Dict       	=   sentence.lower()

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

 if not return_str:
  UpperCamelCase	:					Tuple       	=   normalized_sent.split()

 return normalized_sent



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



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



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





      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       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   {}
       result.update({"sari": compute_sari(sources=A_       ,							predictions=A_       ,							references=A_   )}   )
       result.update({"sacrebleu": compute_sacrebleu(predictions=A_       ,							references=A_   )}   )
       result.update({"exact": compute_em(predictions=A_       ,							references=A_   )}   )
       return result
