






"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a  :						int					       =							{
    '''configuration_bart''': ['''BART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BartConfig''', '''BartOnnxConfig'''],
    '''tokenization_bart''': ['''BartTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :						List[Any]					       =							['''BartTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :						Any					       =							[
     '''BART_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''BartForCausalLM''',
     '''BartForConditionalGeneration''',
     '''BartForQuestionAnswering''',
     '''BartForSequenceClassification''',
     '''BartModel''',
     '''BartPretrainedModel''',
     '''PretrainedBartModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :						List[str]					       =							[
     '''TFBartForConditionalGeneration''',
     '''TFBartForSequenceClassification''',
     '''TFBartModel''',
     '''TFBartPretrainedModel''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :						List[str]					       =							[
     '''FlaxBartDecoderPreTrainedModel''',
     '''FlaxBartForCausalLM''',
     '''FlaxBartForConditionalGeneration''',
     '''FlaxBartForQuestionAnswering''',
     '''FlaxBartForSequenceClassification''',
     '''FlaxBartModel''',
     '''FlaxBartPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
 from .tokenization_bart import BartTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_bart_fast import BartTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bart import (
      BART_PRETRAINED_MODEL_ARCHIVE_LIST,
      BartForCausalLM,
      BartForConditionalGeneration,
      BartForQuestionAnswering,
      BartForSequenceClassification,
      BartModel,
      BartPretrainedModel,
      PretrainedBartModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_bart import (
      TFBartForConditionalGeneration,
      TFBartForSequenceClassification,
      TFBartModel,
      TFBartPretrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_bart import (
      FlaxBartDecoderPreTrainedModel,
      FlaxBartForCausalLM,
      FlaxBartForConditionalGeneration,
      FlaxBartForQuestionAnswering,
      FlaxBartForSequenceClassification,
      FlaxBartModel,
      FlaxBartPreTrainedModel,
  )

else:
 import sys

 a  :						Optional[int]					       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)











"""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}



