'''simple docstring'''

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCAmelCase_ : Union[str, Any]						  =      "\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"

lowerCAmelCase_ : Union[str, Any]						  =      "\\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"


lowerCAmelCase_ : int						  =      "\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 __a (	__lowerCamelCase			:						Dict				)	->							List[str]:
      '''simple docstring'''


      def remove_articles(__lowerCamelCase			:						Dict				):
            lowercase_     =      re.compile(R"\b(a|an|the)\b" ,		re.UNICODE				)
            return re.sub(__lowerCamelCase ,		" " ,		__lowerCamelCase				)

      def white_space_fix(__lowerCamelCase			:						Any				):
            return " ".join(text.split()				)

      def remove_punc(__lowerCamelCase			:						Optional[int]				):
            lowercase_     =      set(string.punctuation				)
            return "".join(ch for ch in text if ch not in exclude				)

      def lower(__lowerCamelCase			:						List[Any]				):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(__lowerCamelCase				)				)				)				)


def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Dict				)	->							Optional[int]:
      '''simple docstring'''


      return int(normalize_answer(__lowerCamelCase				) == normalize_answer(__lowerCamelCase				)				)


def __a (	__lowerCamelCase			:						Tuple ,		__lowerCamelCase			:						Optional[int]				)	->							List[Any]:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						int ,		__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						Optional[int]				)	->							str:
      '''simple docstring'''


      lowercase_     =      [rgram for rgrams in rgramslist for rgram in rgrams]
      lowercase_     =      Counter(__lowerCamelCase				)

      lowercase_     =      Counter(__lowerCamelCase				)
      lowercase_     =      Counter()
      for sgram, scount in sgramcounter.items():
            lowercase_     =      scount * numref

      lowercase_     =      Counter(__lowerCamelCase				)
      lowercase_     =      Counter()
      for cgram, ccount in cgramcounter.items():
            lowercase_     =      ccount * numref

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

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

      # DELETION
      lowercase_     =      sgramcounter_rep - cgramcounter_rep
      lowercase_     =      delgramcounter_rep - rgramcounter
      lowercase_     =      sgramcounter_rep - rgramcounter
      lowercase_     =      0
      lowercase_     =      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.
      lowercase_     =      1
      if len(__lowerCamelCase				) > 0:
            lowercase_     =      deltmpscorea / len(__lowerCamelCase				)

      # ADDITION
      lowercase_     =      set(__lowerCamelCase				) - set(__lowerCamelCase				)
      lowercase_     =      set(__lowerCamelCase				) & set(__lowerCamelCase				)
      lowercase_     =      set(__lowerCamelCase				) - set(__lowerCamelCase				)

      lowercase_     =      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.
      lowercase_     =      1
      lowercase_     =      1
      if len(__lowerCamelCase				) > 0:
            lowercase_     =      addtmpscore / len(__lowerCamelCase				)
      if len(__lowerCamelCase				) > 0:
            lowercase_     =      addtmpscore / len(__lowerCamelCase				)
      lowercase_     =      0
      if addscore_precision > 0 or addscore_recall > 0:
            lowercase_     =      2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

      return (keepscore, delscore_precision, addscore)


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						Tuple				)	->							List[str]:
      '''simple docstring'''


      lowercase_     =      len(__lowerCamelCase				)

      lowercase_     =      ssent.split(" "				)
      lowercase_     =      csent.split(" "				)
      lowercase_     =      []
      lowercase_     =      []
      lowercase_     =      []
      lowercase_     =      []
      lowercase_     =      []
      lowercase_     =      []

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

      for i in range(0 ,		len(__lowerCamelCase				) - 1				):
            if i < len(__lowerCamelCase				) - 1:
                  lowercase_     =      sagrams[i] + " " + sagrams[i + 1]
                  sagrams.append(__lowerCamelCase				)
            if i < len(__lowerCamelCase				) - 2:
                  lowercase_     =      sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
                  sagrams.append(__lowerCamelCase				)
            if i < len(__lowerCamelCase				) - 3:
                  lowercase_     =      sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
                  sagrams.append(__lowerCamelCase				)

      for i in range(0 ,		len(__lowerCamelCase				) - 1				):
            if i < len(__lowerCamelCase				) - 1:
                  lowercase_     =      cagrams[i] + " " + cagrams[i + 1]
                  cagrams.append(__lowerCamelCase				)
            if i < len(__lowerCamelCase				) - 2:
                  lowercase_     =      cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
                  cagrams.append(__lowerCamelCase				)
            if i < len(__lowerCamelCase				) - 3:
                  lowercase_     =      cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
                  cagrams.append(__lowerCamelCase				)

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


def __a (	__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						bool = True ,		__lowerCamelCase			:						str = "13a" ,		__lowerCamelCase			:						bool = True				)	->							Optional[Any]:
      '''simple docstring'''


      if lowercase:
            lowercase_     =      sentence.lower()

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

      if not return_str:
            lowercase_     =      normalized_sent.split()

      return normalized_sent


def __a (	__lowerCamelCase			:						int ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						Union[str, Any]				)	->							Optional[Any]:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						Union[str, Any]="exp" ,		__lowerCamelCase			:						str=None ,		__lowerCamelCase			:						Dict=False ,		__lowerCamelCase			:						int=False ,		__lowerCamelCase			:						Union[str, Any]=False ,		)	->							Union[str, Any]:
      '''simple docstring'''


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






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


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					List[Any]:
         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				:	List[str]   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	Any)      ->					List[Any]:
         lowercase_     =      {}
         result.update({"sari": compute_sari(sources=__lowerCAmelCase   ,		predictions=__lowerCAmelCase   ,		references=__lowerCAmelCase)})
         result.update({"sacrebleu": compute_sacrebleu(predictions=__lowerCAmelCase   ,		references=__lowerCAmelCase)})
         result.update({"exact": compute_em(predictions=__lowerCAmelCase   ,		references=__lowerCAmelCase)})
         return result







'''simple docstring'''

import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
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


lowerCAmelCase_ : List[Any]						  =      get_tests_dir("fixtures/test_sentencepiece.model")





@require_sentencepiece
@require_tokenizers
class 							lowercase						(						__lowerCamelCase ,				unittest.TestCase ):
   lowerCamelCase_    					=ReformerTokenizer
   lowerCamelCase_    					=ReformerTokenizerFast
   lowerCamelCase_    					=True
   lowerCamelCase_    					=False
   lowerCamelCase_    					=True


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Optional[Any]:
         super().setUp()

         lowercase_     =      ReformerTokenizer(__lowerCAmelCase   ,		keep_accents=__lowerCAmelCase)
         tokenizer.save_pretrained(self.tmpdirname)


   def __UpperCAmelCase       (   self				:	int)      ->					int:
         lowercase_     =      "<s>"
         lowercase_     =      1

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


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Optional[int]:
         lowercase_     =      list(self.get_tokenizer().get_vocab().keys())

         self.assertEqual(vocab_keys[0]   ,		"<unk>")
         self.assertEqual(vocab_keys[1]   ,		"<s>")
         self.assertEqual(vocab_keys[-1]   ,		"j")
         self.assertEqual(len(__lowerCAmelCase)   ,		1000)


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					Optional[Any]:
         self.assertEqual(self.get_tokenizer().vocab_size   ,		1000)


   def __UpperCAmelCase       (   self				:	List[Any])      ->					List[Any]:
         if not self.test_rust_tokenizer:
               return

         lowercase_     =      self.get_tokenizer()
         lowercase_     =      self.get_rust_tokenizer()

         lowercase_     =      "I was born in 92000, and this is falsé."

         lowercase_     =      tokenizer.tokenize(__lowerCAmelCase)
         lowercase_     =      rust_tokenizer.tokenize(__lowerCAmelCase)
         self.assertListEqual(__lowerCAmelCase   ,		__lowerCAmelCase)

         lowercase_     =      tokenizer.encode(__lowerCAmelCase   ,		add_special_tokens=__lowerCAmelCase)
         lowercase_     =      rust_tokenizer.encode(__lowerCAmelCase   ,		add_special_tokens=__lowerCAmelCase)
         self.assertListEqual(__lowerCAmelCase   ,		__lowerCAmelCase)

         lowercase_     =      self.get_rust_tokenizer()
         lowercase_     =      tokenizer.encode(__lowerCAmelCase)
         lowercase_     =      rust_tokenizer.encode(__lowerCAmelCase)
         self.assertListEqual(__lowerCAmelCase   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	List[str]=15)      ->					Tuple:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
                     lowercase_     =      self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase   ,		**__lowerCAmelCase)

                     # Simple input
                     lowercase_     =      "This is a simple input"
                     lowercase_     =      ["This is a simple input 1", "This is a simple input 2"]
                     lowercase_     =      ("This is a simple input", "This is a pair")
                     lowercase_     =      [
                         ("This is a simple input 1", "This is a simple input 2"),
                         ("This is a simple pair 1", "This is a simple pair 2"),
                     ]

                     # Simple input tests
                     self.assertRaises(__lowerCAmelCase   ,		tokenizer_r.encode   ,		__lowerCAmelCase   ,		max_length=__lowerCAmelCase   ,		padding="max_length")

                     # Simple input
                     self.assertRaises(__lowerCAmelCase   ,		tokenizer_r.encode_plus   ,		__lowerCAmelCase   ,		max_length=__lowerCAmelCase   ,		padding="max_length")

                     # Simple input
                     self.assertRaises(
                         __lowerCAmelCase   ,		tokenizer_r.batch_encode_plus   ,		__lowerCAmelCase   ,		max_length=__lowerCAmelCase   ,		padding="max_length"   ,		)

                     # Pair input
                     self.assertRaises(__lowerCAmelCase   ,		tokenizer_r.encode   ,		__lowerCAmelCase   ,		max_length=__lowerCAmelCase   ,		padding="max_length")

                     # Pair input
                     self.assertRaises(__lowerCAmelCase   ,		tokenizer_r.encode_plus   ,		__lowerCAmelCase   ,		max_length=__lowerCAmelCase   ,		padding="max_length")

                     # Pair input
                     self.assertRaises(
                         __lowerCAmelCase   ,		tokenizer_r.batch_encode_plus   ,		__lowerCAmelCase   ,		max_length=__lowerCAmelCase   ,		padding="max_length"   ,		)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Tuple:
         pass


   def __UpperCAmelCase       (   self				:	Any)      ->					Optional[Any]:
         lowercase_     =      ReformerTokenizer(__lowerCAmelCase   ,		keep_accents=__lowerCAmelCase)

         lowercase_     =      tokenizer.tokenize("This is a test")
         self.assertListEqual(__lowerCAmelCase   ,		["▁This", "▁is", "▁a", "▁t", "est"])

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(__lowerCAmelCase)   ,		[285, 46, 10, 170, 382]   ,		)

         lowercase_     =      tokenizer.tokenize("I was born in 92000, and this is falsé.")
         self.assertListEqual(
             __lowerCAmelCase   ,		[
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "9",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "é",
                 ".",
             ]   ,		)
         lowercase_     =      tokenizer.convert_tokens_to_ids(__lowerCAmelCase)
         self.assertListEqual(
             __lowerCAmelCase   ,		[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]   ,		)

         lowercase_     =      tokenizer.convert_ids_to_tokens(__lowerCAmelCase)
         self.assertListEqual(
             __lowerCAmelCase   ,		[
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "<unk>",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "<unk>",
                 ".",
             ]   ,		)


   @cached_property
   def __UpperCAmelCase       (   self				:	List[str])      ->					Any:
         return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")


   @slow
   def __UpperCAmelCase       (   self				:	Tuple)      ->					str:
         lowercase_     =      "Hello World!"
         lowercase_     =      [126, 32, 262, 152, 38, 72, 287]

         self.assertListEqual(__lowerCAmelCase   ,		self.big_tokenizer.encode(__lowerCAmelCase))


   @slow
   def __UpperCAmelCase       (   self				:	Any)      ->					Dict:
         lowercase_     =      (
             "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
             " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
         )
         lowercase_     =      [
             108,
             265,
             24,
             111,
             4,
             258,
             156,
             35,
             28,
             275,
             3,
             259,
             297,
             260,
             84,
             4,
             35,
             110,
             44,
             8,
             259,
             91,
             268,
             21,
             11,
             209,
             274,
             109,
             266,
             277,
             117,
             86,
             93,
             315,
             258,
             278,
             258,
             277,
             258,
             0,
             258,
             288,
             258,
             319,
             258,
             0,
             258,
             0,
             258,
             0,
             258,
             0,
             258,
             287,
             258,
             315,
             258,
             289,
             258,
             278,
             99,
             269,
             266,
             262,
             8,
             259,
             241,
             4,
             217,
             230,
             268,
             266,
             55,
             168,
             106,
             75,
             193,
             266,
             223,
             27,
             49,
             26,
             282,
             25,
             264,
             299,
             19,
             26,
             0,
             258,
             277,
             117,
             86,
             93,
             176,
             183,
             270,
             11,
             262,
             42,
             61,
             265,
         ]

         self.assertListEqual(__lowerCAmelCase   ,		self.big_tokenizer.encode(__lowerCAmelCase))


   @require_torch
   @slow
   def __UpperCAmelCase       (   self				:	Any)      ->					Tuple:
         import torch

         from transformers import ReformerConfig, ReformerModel

         # Build sequence
         lowercase_     =      list(self.big_tokenizer.get_vocab().keys())[:10]
         lowercase_     =      " ".join(__lowerCAmelCase)
         lowercase_     =      self.big_tokenizer.encode_plus(__lowerCAmelCase   ,		return_tensors="pt")
         lowercase_     =      self.big_tokenizer.batch_encode_plus([sequence, sequence]   ,		return_tensors="pt")

         lowercase_     =      ReformerConfig()
         # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
         lowercase_     =      encoded_sequence["input_ids"].shape
         lowercase_     =      ReformerModel(__lowerCAmelCase)

         # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
         assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

         with torch.no_grad():
               model(**__lowerCAmelCase)
               model(**__lowerCAmelCase)


   @slow
   def __UpperCAmelCase       (   self				:	List[Any])      ->					Tuple:
         # fmt: off
         lowercase_     =      {"input_ids": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], "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]]}  # noqa: E501
         # fmt: on

         # This tokenizer does not know some characters like ")".
         # That is the reason why we use very simple texts here.
         # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
         lowercase_     =      [
             "This is a very simple sentence.",
             "The quick brown fox jumps over the lazy dog.",
         ]

         self.tokenizer_integration_test_util(
             expected_encoding=__lowerCAmelCase   ,		model_name="google/reformer-crime-and-punishment"   ,		revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"   ,		padding=__lowerCAmelCase   ,		sequences=__lowerCAmelCase   ,		)






