




'''simple docstring'''






import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_	,					unittest.TestCase       ):
     A_       							= MPNetTokenizer
     A_       							= MPNetTokenizerFast
     A_       							= True
     A_       							= True





     def  _lowerCAmelCase			(    self    :				int ):
           super().setUp()

           snake_case__					:      Tuple  			=						[
               '[UNK]',
               '[CLS]',
               '[SEP]',
               '[PAD]',
               '[MASK]',
               'want',
               '##want',
               '##ed',
               'wa',
               'un',
               'runn',
               '##ing',
               ',',
               'low',
               'lowest',
           ]
           snake_case__					:      str  			=						os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES['vocab_file'] )
           with open(self.vocab_file    ,       'w'    ,       encoding='utf-8' ) as vocab_writer:
                 vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )





     def  _lowerCAmelCase			(    self    :				Dict    ,       __lowerCamelCase    :				Tuple ):
           snake_case__					:      Optional[Any]  			=						'UNwant\u00E9d,running'
           snake_case__					:      Optional[Any]  			=						'unwanted, running'
           return input_text, output_text





     def  _lowerCAmelCase			(    self    :				Tuple ):
           snake_case__					:      str  			=						self.tokenizer_class(self.vocab_file )

           snake_case__					:      List[str]  			=						tokenizer.tokenize('UNwant\u00E9d,running' )
           self.assertListEqual(__lowerCamelCase    ,       ['un', '##want', '##ed', ',', 'runn', '##ing'] )
           self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase )    ,       [9, 6, 7, 12, 10, 11] )





     @slow
     def  _lowerCAmelCase			(    self    :				str ):
           snake_case__					:      Optional[int]  			=						self.tokenizer_class.from_pretrained('microsoft/mpnet-base' )

           snake_case__					:      Optional[Any]  			=						tokenizer.encode('sequence builders'    ,       add_special_tokens=__lowerCamelCase )
           snake_case__					:      Optional[int]  			=						tokenizer.encode('multi-sequence build'    ,       add_special_tokens=__lowerCamelCase )

           snake_case__					:      Union[str, Any]  			=						tokenizer.build_inputs_with_special_tokens(__lowerCamelCase )
           snake_case__					:      Tuple  			=						tokenizer.build_inputs_with_special_tokens(__lowerCamelCase    ,       __lowerCamelCase )

           assert encoded_sentence == [0] + text + [2]
           assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]






'''simple docstring'''






from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


A_ 			=		"\\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_ 			=		"\\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_ 			=		"\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__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Dict:
      snake_case__					:      Dict  			=						[rgram for rgrams in rgramslist for rgram in rgrams]
      snake_case__					:      int  			=						Counter(__SCREAMING_SNAKE_CASE		)

      snake_case__					:      Tuple  			=						Counter(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Union[str, Any]  			=						Counter()
      for sgram, scount in sgramcounter.items():
            snake_case__					:      Any  			=						scount * numref

      snake_case__					:      Any  			=						Counter(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Dict  			=						Counter()
      for cgram, ccount in cgramcounter.items():
            snake_case__					:      Optional[int]  			=						ccount * numref

      # KEEP
      snake_case__					:      Union[str, Any]  			=						sgramcounter_rep & cgramcounter_rep
      snake_case__					:      Optional[int]  			=						keepgramcounter_rep & rgramcounter
      snake_case__					:      Tuple  			=						sgramcounter_rep & rgramcounter

      snake_case__					:      Optional[int]  			=						0
      snake_case__					:      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.
      snake_case__					:      List[Any]  			=						1
      snake_case__					:      Union[str, Any]  			=						1
      if len(__SCREAMING_SNAKE_CASE		) > 0:
            snake_case__					:      List[Any]  			=						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)
            snake_case__					:      Dict  			=						keeptmpscorea / sum(keepgramcounterall_rep.values()		)
      snake_case__					:      Optional[Any]  			=						0
      if keepscore_precision > 0 or keepscore_recall > 0:
            snake_case__					:      str  			=						2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

      # DELETION
      snake_case__					:      Optional[Any]  			=						sgramcounter_rep - cgramcounter_rep
      snake_case__					:      int  			=						delgramcounter_rep - rgramcounter
      snake_case__					:      Tuple  			=						sgramcounter_rep - rgramcounter
      snake_case__					:      List[str]  			=						0
      snake_case__					:      List[str]  			=						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.
      snake_case__					:      Optional[Any]  			=						1
      if len(__SCREAMING_SNAKE_CASE		) > 0:
            snake_case__					:      str  			=						deltmpscorea / len(__SCREAMING_SNAKE_CASE		)

      # ADDITION
      snake_case__					:      Optional[int]  			=						set(__SCREAMING_SNAKE_CASE		) - set(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      str  			=						set(__SCREAMING_SNAKE_CASE		) & set(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Any  			=						set(__SCREAMING_SNAKE_CASE		) - set(__SCREAMING_SNAKE_CASE		)

      snake_case__					:      List[str]  			=						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.
      snake_case__					:      Optional[Any]  			=						1
      snake_case__					:      Union[str, Any]  			=						1
      if len(__SCREAMING_SNAKE_CASE		) > 0:
            snake_case__					:      str  			=						addtmpscore / len(__SCREAMING_SNAKE_CASE		)
      if len(__SCREAMING_SNAKE_CASE		) > 0:
            snake_case__					:      List[Any]  			=						addtmpscore / len(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      str  			=						0
      if addscore_precision > 0 or addscore_recall > 0:
            snake_case__					:      int  			=						2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

      return (keepscore, delscore_precision, addscore)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Tuple:
      snake_case__					:      Any  			=						len(__SCREAMING_SNAKE_CASE		)

      snake_case__					:      str  			=						ssent.split(' '		)
      snake_case__					:      Optional[int]  			=						csent.split(' '		)
      snake_case__					:      Optional[Any]  			=						[]
      snake_case__					:      int  			=						[]
      snake_case__					:      Optional[int]  			=						[]
      snake_case__					:      Union[str, Any]  			=						[]
      snake_case__					:      Tuple  			=						[]
      snake_case__					:      Tuple  			=						[]

      snake_case__					:      List[str]  			=						[]
      snake_case__					:      int  			=						[]
      snake_case__					:      Union[str, Any]  			=						[]
      snake_case__					:      List[str]  			=						[]
      for rsent in rsents:
            snake_case__					:      Optional[Any]  			=						rsent.split(' '		)
            snake_case__					:      List[Any]  			=						[]
            snake_case__					:      Tuple  			=						[]
            snake_case__					:      Optional[Any]  			=						[]
            ragramslist.append(__SCREAMING_SNAKE_CASE		)
            for i in range(0							,			len(__SCREAMING_SNAKE_CASE		) - 1		):
                  if i < len(__SCREAMING_SNAKE_CASE		) - 1:
                        snake_case__					:      Optional[Any]  			=						ragrams[i] + ' ' + ragrams[i + 1]
                        ragrams.append(__SCREAMING_SNAKE_CASE		)
                  if i < len(__SCREAMING_SNAKE_CASE		) - 2:
                        snake_case__					:      List[Any]  			=						ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
                        ragrams.append(__SCREAMING_SNAKE_CASE		)
                  if i < len(__SCREAMING_SNAKE_CASE		) - 3:
                        snake_case__					:      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:
                  snake_case__					:      Optional[int]  			=						sagrams[i] + ' ' + sagrams[i + 1]
                  sagrams.append(__SCREAMING_SNAKE_CASE		)
            if i < len(__SCREAMING_SNAKE_CASE		) - 2:
                  snake_case__					:      Dict  			=						sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
                  sagrams.append(__SCREAMING_SNAKE_CASE		)
            if i < len(__SCREAMING_SNAKE_CASE		) - 3:
                  snake_case__					:      List[Any]  			=						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:
                  snake_case__					:      Any  			=						cagrams[i] + ' ' + cagrams[i + 1]
                  cagrams.append(__SCREAMING_SNAKE_CASE		)
            if i < len(__SCREAMING_SNAKE_CASE		) - 2:
                  snake_case__					:      Any  			=						cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
                  cagrams.append(__SCREAMING_SNAKE_CASE		)
            if i < len(__SCREAMING_SNAKE_CASE		) - 3:
                  snake_case__					:      Tuple  			=						cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
                  cagrams.append(__SCREAMING_SNAKE_CASE		)

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






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE = True							,			__SCREAMING_SNAKE_CASE = "13a"							,			__SCREAMING_SNAKE_CASE = True		)						->      Optional[int]:
      # 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:
            snake_case__					:      Dict  			=						sentence.lower()

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

      if not return_str:
            snake_case__					:      List[Any]  			=						normalized_sent.split()

      return normalized_sent






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





     def  _lowerCAmelCase			(    self    :				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  _lowerCAmelCase			(    self    :				List[Any]    ,       __lowerCamelCase    :				List[Any]    ,       __lowerCamelCase    :				Optional[int]    ,       __lowerCamelCase    :				List[str] ):
           if not (len(__lowerCamelCase ) == len(__lowerCamelCase ) == len(__lowerCamelCase )):
                 raise ValueError('Sources length must match predictions and references lengths.' )
           snake_case__					:      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] )
           snake_case__					:      int  			=						sari_score / len(__lowerCamelCase )
           return {"sari": 100 * sari_score}
