


from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__snake_case     =		'''\
@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},
}
'''

__snake_case     =		'''\
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
'''


__snake_case     =		'''
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						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				int:


     """simple docstring"""


     __UpperCamelCase				=	[rgram for rgrams in rgramslist for rgram in rgrams]
     __UpperCamelCase				=	Counter(_lowercase				)

     __UpperCamelCase				=	Counter(_lowercase				)
     __UpperCamelCase				=	Counter()
     for sgram, scount in sgramcounter.items():
          __UpperCamelCase				=	scount * numref

     __UpperCamelCase				=	Counter(_lowercase				)
     __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(_lowercase				) > 0:
          __UpperCamelCase				=	keeptmpscorea / len(_lowercase				)
     if len(_lowercase				) > 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(_lowercase				) > 0:
          __UpperCamelCase				=	deltmpscorea / len(_lowercase				)

     # ADDITION
     __UpperCamelCase				=	set(_lowercase				) - set(_lowercase				)
     __UpperCamelCase				=	set(_lowercase				) & set(_lowercase				)
     __UpperCamelCase				=	set(_lowercase				) - set(_lowercase				)

     __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(_lowercase				) > 0:
          __UpperCamelCase				=	addtmpscore / len(_lowercase				)
     if len(_lowercase				) > 0:
          __UpperCamelCase				=	addtmpscore / len(_lowercase				)
     __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 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				Optional[Any]:


     """simple docstring"""


     __UpperCamelCase				=	len(_lowercase				)

     __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(_lowercase				)
          for i in range(0			,       len(_lowercase				) - 1				):
               if i < len(_lowercase				) - 1:
                    __UpperCamelCase				=	ragrams[i] + ' ' + ragrams[i + 1]
                    ragrams.append(_lowercase				)
               if i < len(_lowercase				) - 2:
                    __UpperCamelCase				=	ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
                    ragrams.append(_lowercase				)
               if i < len(_lowercase				) - 3:
                    __UpperCamelCase				=	ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
                    ragrams.append(_lowercase				)
          ragramslist.append(_lowercase				)
          ragramslist.append(_lowercase				)
          ragramslist.append(_lowercase				)

     for i in range(0			,       len(_lowercase				) - 1				):
          if i < len(_lowercase				) - 1:
               __UpperCamelCase				=	sagrams[i] + ' ' + sagrams[i + 1]
               sagrams.append(_lowercase				)
          if i < len(_lowercase				) - 2:
               __UpperCamelCase				=	sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
               sagrams.append(_lowercase				)
          if i < len(_lowercase				) - 3:
               __UpperCamelCase				=	sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
               sagrams.append(_lowercase				)

     for i in range(0			,       len(_lowercase				) - 1				):
          if i < len(_lowercase				) - 1:
               __UpperCamelCase				=	cagrams[i] + ' ' + cagrams[i + 1]
               cagrams.append(_lowercase				)
          if i < len(_lowercase				) - 2:
               __UpperCamelCase				=	cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
               cagrams.append(_lowercase				)
          if i < len(_lowercase				) - 3:
               __UpperCamelCase				=	cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
               cagrams.append(_lowercase				)

     ((__UpperCamelCase),  (__UpperCamelCase),  (__UpperCamelCase))				=	SARIngram(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)
     ((__UpperCamelCase),  (__UpperCamelCase),  (__UpperCamelCase))				=	SARIngram(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)
     ((__UpperCamelCase),  (__UpperCamelCase),  (__UpperCamelCase))				=	SARIngram(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)
     ((__UpperCamelCase),  (__UpperCamelCase),  (__UpperCamelCase))				=	SARIngram(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)
     __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 						_A						(						_lowercase			,       _lowercase = True			,       _lowercase = "13a"			,       _lowercase = True				)			->				int:


     """simple docstring"""


     if lowercase:
          __UpperCamelCase				=	sentence.lower()

     if tokenizer in ["13a", "intl"]:
          if version.parse(sacrebleu.__version__				).major >= 2:
               __UpperCamelCase				=	sacrebleu.metrics.bleu._get_tokenizer(_lowercase				)()(_lowercase				)
          else:
               __UpperCamelCase				=	sacrebleu.TOKENIZERS[tokenizer]()(_lowercase				)
     elif tokenizer == "moses":
          __UpperCamelCase				=	sacremoses.MosesTokenizer().tokenize(_lowercase			,       return_str=_lowercase			,       escape=_lowercase				)
     elif tokenizer == "penn":
          __UpperCamelCase				=	sacremoses.MosesTokenizer().penn_tokenize(_lowercase			,       return_str=_lowercase				)
     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 					__lowerCamelCase	(datasets.Metric		):







       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




            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 						snake_case_					(	self:				Union[str, Any],A_:				str,A_:				Union[str, Any],A_:				str			):






            '''simple docstring'''




            if not (len(A_			) == len(A_			) == len(A_			)):
                 raise ValueError('Sources length must match predictions and references lengths.'			)
            __UpperCamelCase				=	0
            for src, pred, refs in zip(A_,A_,A_			):
                 sari_score += SARIsent(normalize(A_			),normalize(A_			),[normalize(A_			) for sent in refs]			)
            __UpperCamelCase				=	sari_score / len(A_			)
            return {"sari": 100 * sari_score}






import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin



class 					__lowerCamelCase	(_a							,     unittest.TestCase		):
       _lowercase    							=      SpeechaTextaTokenizer
       _lowercase    							=      False







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            super().setUp()

            __UpperCamelCase				=	'<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'.split(' '			)
            __UpperCamelCase				=	['he re</w> 123', 'here a 1456']
            __UpperCamelCase				=	dict(zip(A_,range(len(A_			)			)			)			)

            __UpperCamelCase				=	{'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

            __UpperCamelCase				=	tempfile.mkdtemp()
            __UpperCamelCase				=	os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['vocab_file']			)
            __UpperCamelCase				=	os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['merges_file']			)

            with open(self.vocab_file,'w',encoding='utf-8'			) as fp:
                 fp.write(json.dumps(A_			) + '\n'			)

            with open(self.merges_file,'w'			) as fp:
                 fp.write('\n'.join(A_			)			)







       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




            __UpperCamelCase				=	list(self.get_tokenizer().get_vocab().keys()			)

            self.assertEqual(vocab_keys[0],'<s>'			)
            self.assertEqual(vocab_keys[1],'<pad>'			)
            self.assertEqual(vocab_keys[-1],'vocab'			)
            self.assertEqual(len(A_			),14			)







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size,14			)







       def 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




            __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(self.tmpdirname			)

            # make sure @@ is correctly concatenated
            __UpperCamelCase				=	[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
            __UpperCamelCase				=	tokenizer.decode(A_			)

            self.assertTrue(output_string == 'herecouple words ofthe'			)







       def 						snake_case_					(	self:				int			):






            '''simple docstring'''




            __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(self.tmpdirname			)

            with tempfile.TemporaryDirectory() as tmp_dirname:
                 tokenizer.save_pretrained(A_			)
                 os.remove(os.path.join(A_,'merges.txt'			)			)

                 # load tokenizer without merges file should not throw an error
                 __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(A_			)

            with tempfile.TemporaryDirectory() as tmp_dirname:
                 # save tokenizer and load again
                 tokenizer.save_pretrained(A_			)
                 __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(A_			)

            self.assertIsNotNone(A_			)







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            if not self.test_slow_tokenizer:
                 return

            __UpperCamelCase				=	inspect.signature(self.tokenizer_class.__init__			)
            __UpperCamelCase				=	self.get_tokenizer()

            for parameter_name, parameter in signature.parameters.items():
                 if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
                      self.assertIn(A_,tokenizer.init_kwargs			)


