


import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCAmelCase      =   '\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

UpperCAmelCase      =   '\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n'


UpperCAmelCase      =   '\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\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\n    sacrebleu: sacrebleu score\n    exact: exact score\n\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 ."]]\n    >>> wiki_split = datasets.load_metric("wiki_split")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}\n'






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      def remove_articles(SCREAMING_SNAKE_CASE ):
            snake_case_	=				re.compile(R'''\b(a|an|the)\b''' , re.UNICODE )
            return re.sub(_A , ''' ''' , _A )

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

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

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

      return white_space_fix(remove_articles(remove_punc(lower(_A ) ) ) )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      return int(normalize_answer(_A ) == normalize_answer(_A ) )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      snake_case_	=				[any(compute_exact(_A , _A ) for ref in refs ) for pred, refs in zip(_A , _A )]
      return (sum(_A ) / len(_A )) * 100






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Tuple:




      """simple docstring"""

      snake_case_	=				[rgram for rgrams in rgramslist for rgram in rgrams]
      snake_case_	=				Counter(_A )

      snake_case_	=				Counter(_A )
      snake_case_	=				Counter()
      for sgram, scount in sgramcounter.items():
            snake_case_	=				scount * numref

      snake_case_	=				Counter(_A )
      snake_case_	=				Counter()
      for cgram, ccount in cgramcounter.items():
            snake_case_	=				ccount * numref

      # KEEP
      snake_case_	=				sgramcounter_rep & cgramcounter_rep
      snake_case_	=				keepgramcounter_rep & rgramcounter
      snake_case_	=				sgramcounter_rep & rgramcounter

      snake_case_	=				0
      snake_case_	=				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_	=				1
      snake_case_	=				1
      if len(_A ) > 0:
            snake_case_	=				keeptmpscorea / len(_A )
      if len(_A ) > 0:
            # Fix an alleged bug [2] in the keep score computation.
            # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
            snake_case_	=				keeptmpscorea / sum(keepgramcounterall_rep.values() )
      snake_case_	=				0
      if keepscore_precision > 0 or keepscore_recall > 0:
            snake_case_	=				2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

      # DELETION
      snake_case_	=				sgramcounter_rep - cgramcounter_rep
      snake_case_	=				delgramcounter_rep - rgramcounter
      snake_case_	=				sgramcounter_rep - rgramcounter
      snake_case_	=				0
      snake_case_	=				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_	=				1
      if len(_A ) > 0:
            snake_case_	=				deltmpscorea / len(_A )

      # ADDITION
      snake_case_	=				set(_A ) - set(_A )
      snake_case_	=				set(_A ) & set(_A )
      snake_case_	=				set(_A ) - set(_A )

      snake_case_	=				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_	=				1
      snake_case_	=				1
      if len(_A ) > 0:
            snake_case_	=				addtmpscore / len(_A )
      if len(_A ) > 0:
            snake_case_	=				addtmpscore / len(_A )
      snake_case_	=				0
      if addscore_precision > 0 or addscore_recall > 0:
            snake_case_	=				2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

      return (keepscore, delscore_precision, addscore)






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Optional[Any]:




      """simple docstring"""

      snake_case_	=				len(_A )

      snake_case_	=				ssent.split(''' ''' )
      snake_case_	=				csent.split(''' ''' )
      snake_case_	=				[]
      snake_case_	=				[]
      snake_case_	=				[]
      snake_case_	=				[]
      snake_case_	=				[]
      snake_case_	=				[]

      snake_case_	=				[]
      snake_case_	=				[]
      snake_case_	=				[]
      snake_case_	=				[]
      for rsent in rsents:
            snake_case_	=				rsent.split(''' ''' )
            snake_case_	=				[]
            snake_case_	=				[]
            snake_case_	=				[]
            ragramslist.append(_A )
            for i in range(0 , len(_A ) - 1 ):
                  if i < len(_A ) - 1:
                        snake_case_	=				ragrams[i] + ' ' + ragrams[i + 1]
                        ragrams.append(_A )
                  if i < len(_A ) - 2:
                        snake_case_	=				ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
                        ragrams.append(_A )
                  if i < len(_A ) - 3:
                        snake_case_	=				ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
                        ragrams.append(_A )
            ragramslist.append(_A )
            ragramslist.append(_A )
            ragramslist.append(_A )

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

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

      (snake_case_)	=				SARIngram(_A , _A , _A , _A )
      (snake_case_)	=				SARIngram(_A , _A , _A , _A )
      (snake_case_)	=				SARIngram(_A , _A , _A , _A )
      (snake_case_)	=				SARIngram(_A , _A , _A , _A )
      snake_case_	=				sum([keepascore, keepascore, keepascore, keepascore] ) / 4
      snake_case_	=				sum([delascore, delascore, delascore, delascore] ) / 4
      snake_case_	=				sum([addascore, addascore, addascore, addascore] ) / 4
      snake_case_	=				(avgkeepscore + avgdelscore + avgaddscore) / 3
      return finalscore






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = True , SCREAMING_SNAKE_CASE = "13a" , SCREAMING_SNAKE_CASE = True )->  Dict:




      """simple docstring"""

      if lowercase:
            snake_case_	=				sentence.lower()

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

      if not return_str:
            snake_case_	=				normalized_sent.split()

      return normalized_sent






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE="exp" , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=False , )->  Optional[Any]:




      """simple docstring"""

      snake_case_	=				len(references[0] )
      if any(len(_A ) != references_per_prediction for refs in references ):
            raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
      snake_case_	=				[[refs[i] for refs in references] for i in range(_A )]
      snake_case_	=				sacrebleu.corpus_bleu(
          _A , _A , smooth_method=_A , smooth_value=_A , force=_A , lowercase=_A , use_effective_order=_A , )
      return output.score







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

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       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		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				{}
       result.update({'''sari''': compute_sari(sources=__snake_case		,				predictions=__snake_case		,				references=__snake_case )} )
       result.update({'''sacrebleu''': compute_sacrebleu(predictions=__snake_case		,				references=__snake_case )} )
       result.update({'''exact''': compute_em(predictions=__snake_case		,				references=__snake_case )} )
       return result



import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase      =   get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")







@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  PegasusTokenizer
 __snake_case												=  PegasusTokenizerFast
 __snake_case												=  True
 __snake_case												=  True



 def 						UpperCamelCase__					(     self ):
       super().setUp()

       # We have a SentencePiece fixture for testing
       snake_case_	=				PegasusTokenizer(_UpperCAmelCase )
       tokenizer.save_pretrained(self.tmpdirname )



 @cached_property
 def 						UpperCamelCase__					(     self ):
       return PegasusTokenizer.from_pretrained('''google/pegasus-large''' )



 def 						UpperCamelCase__					(     self		,				**_UpperCAmelCase ):
       return PegasusTokenizer.from_pretrained(self.tmpdirname		,				**_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       return ("This is a test", "This is a test")



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''</s>'''
       snake_case_	=				1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase )		,				_UpperCAmelCase )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase )		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				list(self.get_tokenizer().get_vocab().keys() )

       self.assertEqual(vocab_keys[0]		,				'''<pad>''' )
       self.assertEqual(vocab_keys[1]		,				'''</s>''' )
       self.assertEqual(vocab_keys[-1]		,				'''v''' )
       self.assertEqual(len(_UpperCAmelCase )		,				11_03 )



 def 						UpperCamelCase__					(     self ):
       self.assertEqual(self.get_tokenizer().vocab_size		,				11_03 )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				self.tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				(
           '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
           ''' </s> <pad> <pad> <pad>'''
       )
       snake_case_	=				rust_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       snake_case_	=				py_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self._large_tokenizer
       # <mask_1> masks whole sentence while <mask_2> masks single word
       snake_case_	=				'''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
       snake_case_	=				[2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
       snake_case_	=				tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self._large_tokenizer
       # The tracebacks for the following asserts are **better** without messages or self.assertEqual
       assert tokenizer.vocab_size == 9_61_03
       assert tokenizer.pad_token_id == 0
       assert tokenizer.eos_token_id == 1
       assert tokenizer.offset == 1_03
       assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
       assert tokenizer.unk_token == "<unk>"
       assert tokenizer.model_max_length == 10_24
       snake_case_	=				'''To ensure a smooth flow of bank resolutions.'''
       snake_case_	=				[4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
       snake_case_	=				tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				['''This is going to be way too long.''' * 1_50, '''short example''']
       snake_case_	=				['''not super long but more than 5 tokens''', '''tiny''']
       snake_case_	=				self._large_tokenizer(_UpperCAmelCase		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )
       snake_case_	=				self._large_tokenizer(
           text_target=_UpperCAmelCase		,				max_length=5		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )

       assert batch.input_ids.shape == (2, 10_24)
       assert batch.attention_mask.shape == (2, 10_24)
       assert targets["input_ids"].shape == (2, 5)
       assert len(_UpperCAmelCase ) == 2  # input_ids, attention_mask.





 @slow
 def 						UpperCamelCase__					(     self ):
       # fmt: off
       snake_case_	=				{'''input_ids''': [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=_UpperCAmelCase		,				model_name='''google/bigbird-pegasus-large-arxiv'''		,				revision='''ba85d0851d708441f91440d509690f1ab6353415'''		,				)







@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  PegasusTokenizer
 __snake_case												=  PegasusTokenizerFast
 __snake_case												=  True
 __snake_case												=  True



 def 						UpperCamelCase__					(     self ):
       super().setUp()

       # We have a SentencePiece fixture for testing
       snake_case_	=				PegasusTokenizer(_UpperCAmelCase		,				offset=0		,				mask_token_sent=_UpperCAmelCase		,				mask_token='''[MASK]''' )
       tokenizer.save_pretrained(self.tmpdirname )



 @cached_property
 def 						UpperCamelCase__					(     self ):
       return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv''' )



 def 						UpperCamelCase__					(     self		,				**_UpperCAmelCase ):
       return PegasusTokenizer.from_pretrained(self.tmpdirname		,				**_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       return ("This is a test", "This is a test")



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				self.tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				(
           '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
           ''' <pad> <pad> <pad>'''
       )
       snake_case_	=				rust_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       snake_case_	=				py_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				['''This is going to be way too long.''' * 10_00, '''short example''']
       snake_case_	=				['''not super long but more than 5 tokens''', '''tiny''']
       snake_case_	=				self._large_tokenizer(_UpperCAmelCase		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )
       snake_case_	=				self._large_tokenizer(
           text_target=_UpperCAmelCase		,				max_length=5		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )

       assert batch.input_ids.shape == (2, 40_96)
       assert batch.attention_mask.shape == (2, 40_96)
       assert targets["input_ids"].shape == (2, 5)
       assert len(_UpperCAmelCase ) == 2  # input_ids, attention_mask.





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				(
           '''This is an example string that is used to test the original TF implementation against the HF'''
           ''' implementation'''
       )

       snake_case_	=				self._large_tokenizer(_UpperCAmelCase ).input_ids

       self.assertListEqual(
           _UpperCAmelCase		,				[1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]		,				)