'''simple docstring'''




from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__UpperCAmelCase												="\\n@misc{wu2016googles,\n      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n"

__UpperCAmelCase												="\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"

__UpperCAmelCase												="\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    'google_bleu': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.4\n"




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






     def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





         """simple docstring"""



         return datasets.MetricInfo(
             description=_DESCRIPTION  ,    citation=_CITATION  ,    inputs_description=_KWARGS_DESCRIPTION  ,    features=datasets.Features(
                 {
                     '''predictions''': datasets.Sequence(datasets.Value('''string'''  ,    id='''token'''   )  ,    id='''sequence'''   ),
                     '''references''': datasets.Sequence(
                         datasets.Sequence(datasets.Value('''string'''  ,    id='''token'''   )  ,    id='''sequence'''   )  ,    id='''references'''   ),
                 }   )  ,    )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]  ,    a		:		List[List[List[str]]]  ,    a		:		List[List[str]]  ,    a		:		int = 1  ,    a		:		int = 4  ,    ):





         """simple docstring"""



         return {
             "google_bleu": gleu_score.corpus_gleu(
                 list_of_references=a  ,    hypotheses=a  ,    min_len=a  ,    max_len=a   )
         }

'''simple docstring'''




import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class    a__				(       UpperCAmelCase__							,       unittest.TestCase			):
     lowerCamelCase  :					Optional[Any]							=LongformerTokenizer
     lowerCamelCase  :					Optional[Any]							=True
     lowerCamelCase  :					List[str]							=LongformerTokenizerFast
     lowerCamelCase  :					Union[str, Any]							=True






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __lowerCamelCase          =  [
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''\u0120''',
             '''\u0120l''',
             '''\u0120n''',
             '''\u0120lo''',
             '''\u0120low''',
             '''er''',
             '''\u0120lowest''',
             '''\u0120newer''',
             '''\u0120wider''',
             '''<unk>''',
         ]
         __lowerCamelCase          =  dict(zip(a  ,    range(len(a   )   )   )   )
         __lowerCamelCase          =  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
         __lowerCamelCase          =  {'''unk_token''': '''<unk>'''}

         __lowerCamelCase          =  os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES['''vocab_file''']   )
         __lowerCamelCase          =  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'''  ,    encoding='''utf-8'''   ) as fp:
             fp.write('''\n'''.join(a   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    **a		:		int   ):





         """simple docstring"""



         kwargs.update(self.special_tokens_map   )
         return self.tokenizer_class.from_pretrained(self.tmpdirname  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str  ,    **a		:		Dict   ):





         """simple docstring"""



         kwargs.update(self.special_tokens_map   )
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]  ,    a		:		int   ):





         """simple docstring"""



         __lowerCamelCase          =  '''lower newer'''
         __lowerCamelCase          =  '''lower newer'''
         return input_text, output_text






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.tokenizer_class(self.vocab_file  ,    self.merges_file  ,    **self.special_tokens_map   )
         __lowerCamelCase          =  '''lower newer'''
         __lowerCamelCase          =  ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
         __lowerCamelCase          =  tokenizer.tokenize(a   )  # , add_prefix_space=True)
         self.assertListEqual(a  ,    a   )

         __lowerCamelCase          =  tokens + [tokenizer.unk_token]
         __lowerCamelCase          =  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a   )  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_tokenizer()

         self.assertListEqual(tokenizer.encode('''Hello world!'''  ,    add_special_tokens=a   )  ,    [0, 3_14_14, 2_32, 3_28, 2]   )
         self.assertListEqual(
             tokenizer.encode('''Hello world! cécé herlolip 418'''  ,    add_special_tokens=a   )  ,    [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]  ,    )






     @slow
     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         __lowerCamelCase          =  self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''   )

         __lowerCamelCase          =  tokenizer.encode('''sequence builders'''  ,    add_special_tokens=a   )
         __lowerCamelCase          =  tokenizer.encode('''multi-sequence build'''  ,    add_special_tokens=a   )

         __lowerCamelCase          =  tokenizer.encode(
             '''sequence builders'''  ,    add_special_tokens=a  ,    add_prefix_space=a   )
         __lowerCamelCase          =  tokenizer.encode(
             '''sequence builders'''  ,    '''multi-sequence build'''  ,    add_special_tokens=a  ,    add_prefix_space=a   )

         __lowerCamelCase          =  tokenizer.build_inputs_with_special_tokens(a   )
         __lowerCamelCase          =  tokenizer.build_inputs_with_special_tokens(a  ,    a   )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_tokenizer()

         __lowerCamelCase          =  '''Encode this sequence.'''
         __lowerCamelCase          =  tokenizer.byte_encoder[''' '''.encode('''utf-8'''   )[0]]

         # Testing encoder arguments
         __lowerCamelCase          =  tokenizer.encode(a  ,    add_special_tokens=a  ,    add_prefix_space=a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(encoded[0]   )[0]
         self.assertNotEqual(a  ,    a   )

         __lowerCamelCase          =  tokenizer.encode(a  ,    add_special_tokens=a  ,    add_prefix_space=a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(encoded[0]   )[0]
         self.assertEqual(a  ,    a   )

         tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}   )
         __lowerCamelCase          =  tokenizer.encode(a  ,    add_special_tokens=a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(encoded[1]   )[0]
         self.assertNotEqual(a  ,    a   )

         # Testing spaces after special tokens
         __lowerCamelCase          =  '''<mask>'''
         tokenizer.add_special_tokens(
             {'''mask_token''': AddedToken(a  ,    lstrip=a  ,    rstrip=a   )}   )  # mask token has a left space
         __lowerCamelCase          =  tokenizer.convert_tokens_to_ids(a   )

         __lowerCamelCase          =  '''Encode <mask> sequence'''
         __lowerCamelCase          =  '''Encode <mask>sequence'''

         __lowerCamelCase          =  tokenizer.encode(a   )
         __lowerCamelCase          =  encoded.index(a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]   )[0]
         self.assertEqual(a  ,    a   )

         __lowerCamelCase          =  tokenizer.encode(a   )
         __lowerCamelCase          =  encoded.index(a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]   )[0]
         self.assertNotEqual(a  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         pass






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
                 __lowerCamelCase          =  self.rust_tokenizer_class.from_pretrained(a  ,    **a   )
                 __lowerCamelCase          =  self.tokenizer_class.from_pretrained(a  ,    **a   )
                 __lowerCamelCase          =  '''A, <mask> AllenNLP sentence.'''
                 __lowerCamelCase          =  tokenizer_r.encode_plus(a  ,    add_special_tokens=a  ,    return_token_type_ids=a   )
                 __lowerCamelCase          =  tokenizer_p.encode_plus(a  ,    add_special_tokens=a  ,    return_token_type_ids=a   )

                 # token_type_ids should put 0 everywhere
                 self.assertEqual(sum(tokens_r['''token_type_ids''']   )  ,    sum(tokens_p['''token_type_ids''']   )   )

                 # attention_mask should put 1 everywhere, so sum over length should be 1
                 self.assertEqual(
                     sum(tokens_r['''attention_mask''']   ) / len(tokens_r['''attention_mask''']   )  ,    sum(tokens_p['''attention_mask''']   ) / len(tokens_p['''attention_mask''']   )  ,    )

                 __lowerCamelCase          =  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']   )
                 __lowerCamelCase          =  tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']   )

                 # Rust correctly handles the space before the mask while python doesnt
                 self.assertSequenceEqual(tokens_p['''input_ids''']  ,    [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )
                 self.assertSequenceEqual(tokens_r['''input_ids''']  ,    [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )

                 self.assertSequenceEqual(
                     a  ,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )
                 self.assertSequenceEqual(
                     a  ,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         for trim_offsets, add_prefix_space in itertools.product([True, False]  ,    repeat=2   ):
             __lowerCamelCase          =  self.rust_tokenizer_class.from_pretrained(
                 self.tmpdirname  ,    use_fast=a  ,    add_prefix_space=a  ,    trim_offsets=a   )

             __lowerCamelCase          =  json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()   )
             __lowerCamelCase          =  json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()   )

             self.assertEqual(pre_tokenizer_state['''add_prefix_space''']  ,    a   )

             self.assertEqual(post_processor_state['''add_prefix_space''']  ,    a   )
             self.assertEqual(post_processor_state['''trim_offsets''']  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """simple docstring"""



         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
                 __lowerCamelCase          =  '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                 __lowerCamelCase          =  f"""{text_of_1_token} {text_of_1_token}"""

                 __lowerCamelCase          =  self.rust_tokenizer_class.from_pretrained(
                     a  ,    use_fast=a  ,    add_prefix_space=a  ,    trim_offsets=a   )
                 __lowerCamelCase          =  tokenizer_r(a  ,    return_offsets_mapping=a  ,    add_special_tokens=a   )
                 self.assertEqual(encoding.offset_mapping[0]  ,    (0, len(a   ))   )
                 self.assertEqual(
                     encoding.offset_mapping[1]  ,    (len(a   ) + 1, len(a   ) + 1 + len(a   ))  ,    )

                 __lowerCamelCase          =  self.rust_tokenizer_class.from_pretrained(
                     a  ,    use_fast=a  ,    add_prefix_space=a  ,    trim_offsets=a   )
                 __lowerCamelCase          =  tokenizer_r(a  ,    return_offsets_mapping=a  ,    add_special_tokens=a   )
                 self.assertEqual(encoding.offset_mapping[0]  ,    (0, len(a   ))   )
                 self.assertEqual(
                     encoding.offset_mapping[1]  ,    (len(a   ) + 1, len(a   ) + 1 + len(a   ))  ,    )

                 __lowerCamelCase          =  self.rust_tokenizer_class.from_pretrained(
                     a  ,    use_fast=a  ,    add_prefix_space=a  ,    trim_offsets=a   )
                 __lowerCamelCase          =  tokenizer_r(a  ,    return_offsets_mapping=a  ,    add_special_tokens=a   )
                 self.assertEqual(encoding.offset_mapping[0]  ,    (0, len(a   ))   )
                 self.assertEqual(
                     encoding.offset_mapping[1]  ,    (len(a   ), len(a   ) + 1 + len(a   ))  ,    )

                 __lowerCamelCase          =  self.rust_tokenizer_class.from_pretrained(
                     a  ,    use_fast=a  ,    add_prefix_space=a  ,    trim_offsets=a   )
                 __lowerCamelCase          =  tokenizer_r(a  ,    return_offsets_mapping=a  ,    add_special_tokens=a   )
                 self.assertEqual(encoding.offset_mapping[0]  ,    (0, len(a   ))   )
                 self.assertEqual(
                     encoding.offset_mapping[1]  ,    (len(a   ), len(a   ) + 1 + len(a   ))  ,    )

                 __lowerCamelCase          =  f""" {text}"""

                 # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                 #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                 # )
                 # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                 # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                 # self.assertEqual(
                 #     encoding.offset_mapping[1],
                 #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                 # )

                 __lowerCamelCase          =  self.rust_tokenizer_class.from_pretrained(
                     a  ,    use_fast=a  ,    add_prefix_space=a  ,    trim_offsets=a   )
                 __lowerCamelCase          =  tokenizer_r(a  ,    return_offsets_mapping=a  ,    add_special_tokens=a   )
                 self.assertEqual(encoding.offset_mapping[0]  ,    (1, 1 + len(a   ))   )
                 self.assertEqual(
                     encoding.offset_mapping[1]  ,    (1 + len(a   ) + 1, 1 + len(a   ) + 1 + len(a   ))  ,    )

                 __lowerCamelCase          =  self.rust_tokenizer_class.from_pretrained(
                     a  ,    use_fast=a  ,    add_prefix_space=a  ,    trim_offsets=a   )
                 __lowerCamelCase          =  tokenizer_r(a  ,    return_offsets_mapping=a  ,    add_special_tokens=a   )
                 self.assertEqual(encoding.offset_mapping[0]  ,    (0, 1 + len(a   ))   )
                 self.assertEqual(
                     encoding.offset_mapping[1]  ,    (1 + len(a   ), 1 + len(a   ) + 1 + len(a   ))  ,    )

                 __lowerCamelCase          =  self.rust_tokenizer_class.from_pretrained(
                     a  ,    use_fast=a  ,    add_prefix_space=a  ,    trim_offsets=a   )
                 __lowerCamelCase          =  tokenizer_r(a  ,    return_offsets_mapping=a  ,    add_special_tokens=a   )
                 self.assertEqual(encoding.offset_mapping[0]  ,    (0, 1 + len(a   ))   )
                 self.assertEqual(
                     encoding.offset_mapping[1]  ,    (1 + len(a   ), 1 + len(a   ) + 1 + len(a   ))  ,    )
