code: 
  
  
  from collections import Counter
  
  import sacrebleu
  import sacremoses
  from packaging import version
  
  import datasets
  
  
  A__	:      List[str]				=						"""\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n"""
  
  A__	:      str				=						"""\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n"""
  
  
  A__	:      Dict				=						"""\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]\n    >>> sari = datasets.load_metric(\"sari\")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 26.953601953601954}\n"""
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Optional[int]   ,					UpperCAmelCase_					:       Optional[Any]   ,					UpperCAmelCase_					:       int   ,					UpperCAmelCase_					:       Dict							)  ->	List[str]:
       __lowerCamelCase     :			Union[str, Any]              =	[rgram for rgrams in rgramslist for rgram in rgrams]
       __lowerCamelCase     :			Any              =	Counter(UpperCamelCase__							)
  
       __lowerCamelCase     :			List[Any]              =	Counter(UpperCamelCase__							)
       __lowerCamelCase     :			Optional[Any]              =	Counter()
       for sgram, scount in sgramcounter.items():
            __lowerCamelCase     :			Optional[Any]              =	scount * numref
  
       __lowerCamelCase     :			Any              =	Counter(UpperCamelCase__							)
       __lowerCamelCase     :			Optional[int]              =	Counter()
       for cgram, ccount in cgramcounter.items():
            __lowerCamelCase     :			Optional[int]              =	ccount * numref
  
       # KEEP
       __lowerCamelCase     :			int              =	sgramcounter_rep & cgramcounter_rep
       __lowerCamelCase     :			Optional[Any]              =	keepgramcounter_rep & rgramcounter
       __lowerCamelCase     :			Optional[int]              =	sgramcounter_rep & rgramcounter
  
       __lowerCamelCase     :			List[str]              =	0
       __lowerCamelCase     :			Optional[int]              =	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     :			str              =	1
       __lowerCamelCase     :			str              =	1
       if len(UpperCamelCase__							) > 0:
            __lowerCamelCase     :			Dict              =	keeptmpscorea / len(UpperCamelCase__							)
       if len(UpperCamelCase__							) > 0:
            # Fix an alleged bug [2] in the keep score computation.
            # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
            __lowerCamelCase     :			Tuple              =	keeptmpscorea / sum(keepgramcounterall_rep.values()							)
       __lowerCamelCase     :			Optional[int]              =	0
       if keepscore_precision > 0 or keepscore_recall > 0:
            __lowerCamelCase     :			Optional[int]              =	2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
  
       # DELETION
       __lowerCamelCase     :			Union[str, Any]              =	sgramcounter_rep - cgramcounter_rep
       __lowerCamelCase     :			str              =	delgramcounter_rep - rgramcounter
       __lowerCamelCase     :			int              =	sgramcounter_rep - rgramcounter
       __lowerCamelCase     :			Tuple              =	0
       __lowerCamelCase     :			Optional[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.
       __lowerCamelCase     :			Optional[int]              =	1
       if len(UpperCamelCase__							) > 0:
            __lowerCamelCase     :			Union[str, Any]              =	deltmpscorea / len(UpperCamelCase__							)
  
       # ADDITION
       __lowerCamelCase     :			Optional[Any]              =	set(UpperCamelCase__							) - set(UpperCamelCase__							)
       __lowerCamelCase     :			Any              =	set(UpperCamelCase__							) & set(UpperCamelCase__							)
       __lowerCamelCase     :			Optional[Any]              =	set(UpperCamelCase__							) - set(UpperCamelCase__							)
  
       __lowerCamelCase     :			Dict              =	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     :			int              =	1
       __lowerCamelCase     :			str              =	1
       if len(UpperCamelCase__							) > 0:
            __lowerCamelCase     :			Dict              =	addtmpscore / len(UpperCamelCase__							)
       if len(UpperCamelCase__							) > 0:
            __lowerCamelCase     :			Dict              =	addtmpscore / len(UpperCamelCase__							)
       __lowerCamelCase     :			str              =	0
       if addscore_precision > 0 or addscore_recall > 0:
            __lowerCamelCase     :			Optional[int]              =	2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
  
       return (keepscore, delscore_precision, addscore)
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Optional[Any]   ,					UpperCAmelCase_					:       List[Any]   ,					UpperCAmelCase_					:       Any							)  ->	Tuple:
       __lowerCamelCase     :			Dict              =	len(UpperCamelCase__							)
  
       __lowerCamelCase     :			Dict              =	ssent.split(' '							)
       __lowerCamelCase     :			Union[str, Any]              =	csent.split(' '							)
       __lowerCamelCase     :			List[str]              =	[]
       __lowerCamelCase     :			Union[str, Any]              =	[]
       __lowerCamelCase     :			List[str]              =	[]
       __lowerCamelCase     :			Any              =	[]
       __lowerCamelCase     :			Dict              =	[]
       __lowerCamelCase     :			Dict              =	[]
  
       __lowerCamelCase     :			List[Any]              =	[]
       __lowerCamelCase     :			Optional[int]              =	[]
       __lowerCamelCase     :			List[Any]              =	[]
       __lowerCamelCase     :			str              =	[]
       for rsent in rsents:
            __lowerCamelCase     :			Any              =	rsent.split(' '							)
            __lowerCamelCase     :			int              =	[]
            __lowerCamelCase     :			Dict              =	[]
            __lowerCamelCase     :			Any              =	[]
            ragramslist.append(UpperCamelCase__							)
            for i in range(0   ,					len(UpperCamelCase__							) - 1							):
                 if i < len(UpperCamelCase__							) - 1:
                      __lowerCamelCase     :			Optional[int]              =	ragrams[i] + " " + ragrams[i + 1]
                      ragrams.append(UpperCamelCase__							)
                 if i < len(UpperCamelCase__							) - 2:
                      __lowerCamelCase     :			str              =	ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
                      ragrams.append(UpperCamelCase__							)
                 if i < len(UpperCamelCase__							) - 3:
                      __lowerCamelCase     :			List[str]              =	ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
                      ragrams.append(UpperCamelCase__							)
            ragramslist.append(UpperCamelCase__							)
            ragramslist.append(UpperCamelCase__							)
            ragramslist.append(UpperCamelCase__							)
  
       for i in range(0   ,					len(UpperCamelCase__							) - 1							):
            if i < len(UpperCamelCase__							) - 1:
                 __lowerCamelCase     :			Union[str, Any]              =	sagrams[i] + " " + sagrams[i + 1]
                 sagrams.append(UpperCamelCase__							)
            if i < len(UpperCamelCase__							) - 2:
                 __lowerCamelCase     :			List[str]              =	sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
                 sagrams.append(UpperCamelCase__							)
            if i < len(UpperCamelCase__							) - 3:
                 __lowerCamelCase     :			List[Any]              =	sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
                 sagrams.append(UpperCamelCase__							)
  
       for i in range(0   ,					len(UpperCamelCase__							) - 1							):
            if i < len(UpperCamelCase__							) - 1:
                 __lowerCamelCase     :			List[Any]              =	cagrams[i] + " " + cagrams[i + 1]
                 cagrams.append(UpperCamelCase__							)
            if i < len(UpperCamelCase__							) - 2:
                 __lowerCamelCase     :			Any              =	cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
                 cagrams.append(UpperCamelCase__							)
            if i < len(UpperCamelCase__							) - 3:
                 __lowerCamelCase     :			List[str]              =	cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
                 cagrams.append(UpperCamelCase__							)
  
       (__lowerCamelCase)     :			Optional[int]              =	SARIngram(UpperCamelCase__   ,					UpperCamelCase__   ,					UpperCamelCase__   ,					UpperCamelCase__							)
       (__lowerCamelCase)     :			int              =	SARIngram(UpperCamelCase__   ,					UpperCamelCase__   ,					UpperCamelCase__   ,					UpperCamelCase__							)
       (__lowerCamelCase)     :			Optional[Any]              =	SARIngram(UpperCamelCase__   ,					UpperCamelCase__   ,					UpperCamelCase__   ,					UpperCamelCase__							)
       (__lowerCamelCase)     :			Tuple              =	SARIngram(UpperCamelCase__   ,					UpperCamelCase__   ,					UpperCamelCase__   ,					UpperCamelCase__							)
       __lowerCamelCase     :			Union[str, Any]              =	sum([keepascore, keepascore, keepascore, keepascore]							) / 4
       __lowerCamelCase     :			Union[str, Any]              =	sum([delascore, delascore, delascore, delascore]							) / 4
       __lowerCamelCase     :			Optional[int]              =	sum([addascore, addascore, addascore, addascore]							) / 4
       __lowerCamelCase     :			Optional[int]              =	(avgkeepscore + avgdelscore + avgaddscore) / 3
       return finalscore
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[Any]   ,					UpperCAmelCase_					:       bool = True   ,					UpperCAmelCase_					:       str = "13a"   ,					UpperCAmelCase_					:       bool = True							)  ->	List[str]:
       # 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:
            __lowerCamelCase     :			Optional[int]              =	sentence.lower()
  
       if tokenizer in ["13a", "intl"]:
            if version.parse(sacrebleu.__version__							).major >= 2:
                 __lowerCamelCase     :			Dict              =	sacrebleu.metrics.bleu._get_tokenizer(UpperCamelCase__							)()(UpperCamelCase__							)
            else:
                 __lowerCamelCase     :			Any              =	sacrebleu.TOKENIZERS[tokenizer]()(UpperCamelCase__							)
       elif tokenizer == "moses":
            __lowerCamelCase     :			Dict              =	sacremoses.MosesTokenizer().tokenize(UpperCamelCase__   ,					return_str=UpperCamelCase__   ,					escape=UpperCamelCase__							)
       elif tokenizer == "penn":
            __lowerCamelCase     :			Dict              =	sacremoses.MosesTokenizer().penn_tokenize(UpperCamelCase__   ,					return_str=UpperCamelCase__							)
       else:
            __lowerCamelCase     :			int              =	sentence
  
       if not return_str:
            __lowerCamelCase     :			Dict              =	normalized_sent.split()
  
       return normalized_sent
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,	_KWARGS_DESCRIPTION						)
  class      UpperCAmelCase_						(datasets.Metric						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           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    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Optional[Any]:
           if not (len(__lowerCamelCase			) == len(__lowerCamelCase			) == len(__lowerCamelCase			)):
                raise ValueError('Sources length must match predictions and references lengths.'			)
           __lowerCamelCase     :			List[Any]              =	0
           for src, pred, refs in zip(__lowerCamelCase    ,  __lowerCamelCase    ,  __lowerCamelCase			):
                sari_score += SARIsent(normalize(__lowerCamelCase			)    ,  normalize(__lowerCamelCase			)    ,  [normalize(__lowerCamelCase			) for sent in refs]			)
           __lowerCamelCase     :			Union[str, Any]              =	sari_score / len(__lowerCamelCase			)
           return {"sari": 1_00 * sari_score}
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available
  
  
  A__	:      Optional[Any]				=						{
      """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
  }
  
  try:
        if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      List[Any]				=						["""XLMProphetNetTokenizer"""]
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      Optional[Any]				=						[
            """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """XLMProphetNetDecoder""",
            """XLMProphetNetEncoder""",
            """XLMProphetNetForCausalLM""",
            """XLMProphetNetForConditionalGeneration""",
            """XLMProphetNetModel""",
            """XLMProphetNetPreTrainedModel""",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig
  
        try:
              if not is_sentencepiece_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_xlm_prophetnet import (
                  XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLMProphetNetDecoder,
                  XLMProphetNetEncoder,
                  XLMProphetNetForCausalLM,
                  XLMProphetNetForConditionalGeneration,
                  XLMProphetNetModel,
                  XLMProphetNetPreTrainedModel,
              )
  
  else:
        import sys
  
        A__	:      List[str]				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  