


'''simple docstring'''





import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_   :				Union[str, Any]						      = get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
   from transformers.models.mbart.modeling_mbart import shift_tokens_right

UpperCAmelCase_   :				List[Any]						      = 25_0004
UpperCAmelCase_   :				Union[str, Any]						      = 25_0020
@require_sentencepiece
@require_tokenizers
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					unittest.TestCase    ):
      snake_case__       :  Optional[Any]       =		MBartTokenizer
      snake_case__       :  Optional[Any]       =		MBartTokenizerFast
      snake_case__       :  Tuple       =		True
      snake_case__       :  Any       =		True
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Optional[Any]:
             super().setUp()

             # We have a SentencePiece fixture for testing
             a_      :	Optional[int]							=						MBartTokenizer(SCREAMING_SNAKE_CASE__  ,							keep_accents=SCREAMING_SNAKE_CASE__  )
             tokenizer.save_pretrained(self.tmpdirname  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Tuple:
             a_      :	int							=						MBartTokenizer(SCREAMING_SNAKE_CASE__  ,							keep_accents=SCREAMING_SNAKE_CASE__  )

             a_      :	List[str]							=						tokenizer.tokenize('This is a test'  )
             self.assertListEqual(SCREAMING_SNAKE_CASE__  ,							['▁This', '▁is', '▁a', '▁t', 'est']  )

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__  )  ,							[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]  ,							)

             a_      :	Dict							=						tokenizer.tokenize('I was born in 92000, and this is falsé.'  )
             self.assertListEqual(
                 SCREAMING_SNAKE_CASE__  ,							[
                     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',
                     '.',
                 ]  ,							)
             a_      :	Union[str, Any]							=						tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__  )
             self.assertListEqual(
                 SCREAMING_SNAKE_CASE__  ,							[
                     value + tokenizer.fairseq_offset
                     for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
                     #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
                 ]  ,							)

             a_      :	Optional[Any]							=						tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__  )
             self.assertListEqual(
                 SCREAMING_SNAKE_CASE__  ,							[
                     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>',
                     '.',
                 ]  ,							)

      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    int:
             if not self.test_slow_tokenizer:
                    # as we don't have a slow version, we can't compare the outputs between slow and fast versions
                    return

             a_      :	Optional[int]							=						(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
                           a_      :	str							=						self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
                           a_      :	str							=						self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )

                           a_      :	int							=						tempfile.mkdtemp()

                           a_      :	Optional[Any]							=						tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__  )
                           a_      :	Optional[int]							=						tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__  )

                           # Checks it save with the same files + the tokenizer.json file for the fast one
                           self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files  )  )
                           a_      :	Optional[int]							=						tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f  )
                           self.assertSequenceEqual(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                           # Checks everything loads correctly in the same way
                           a_      :	int							=						tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__  )
                           a_      :	Union[str, Any]							=						tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__  )

                           # Check special tokens are set accordingly on Rust and Python
                           for key in tokenizer_pp.special_tokens_map:
                                  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )  )
                                  # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                                  # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                           shutil.rmtree(SCREAMING_SNAKE_CASE__  )

                           # Save tokenizer rust, legacy_format=True
                           a_      :	Dict							=						tempfile.mkdtemp()

                           a_      :	Optional[Any]							=						tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__  ,							legacy_format=SCREAMING_SNAKE_CASE__  )
                           a_      :	Optional[int]							=						tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__  )

                           # Checks it save with the same files
                           self.assertSequenceEqual(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                           # Checks everything loads correctly in the same way
                           a_      :	Any							=						tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__  )
                           a_      :	Optional[Any]							=						tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__  )

                           # Check special tokens are set accordingly on Rust and Python
                           for key in tokenizer_pp.special_tokens_map:
                                  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )  )

                           shutil.rmtree(SCREAMING_SNAKE_CASE__  )

                           # Save tokenizer rust, legacy_format=False
                           a_      :	int							=						tempfile.mkdtemp()

                           a_      :	Union[str, Any]							=						tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__  ,							legacy_format=SCREAMING_SNAKE_CASE__  )
                           a_      :	List[Any]							=						tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__  )

                           # Checks it saved the tokenizer.json file
                           self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files  )  )

                           # Checks everything loads correctly in the same way
                           a_      :	List[Any]							=						tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__  )
                           a_      :	int							=						tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__  )

                           # Check special tokens are set accordingly on Rust and Python
                           for key in tokenizer_pp.special_tokens_map:
                                  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )  )

                           shutil.rmtree(SCREAMING_SNAKE_CASE__  )
@require_torch
@require_sentencepiece
@require_tokenizers
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      snake_case__       :  Dict       =		'''facebook/mbart-large-en-ro'''
      snake_case__       :  str       =		[
          ''' UN Chief Says There Is No Military Solution in Syria''',
          ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
      ]
      snake_case__       :  Optional[int]       =		[
          '''Şeful ONU declară că nu există o soluţie militară în Siria''',
          '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
          ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
          ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
      ]
      snake_case__       :  Union[str, Any]       =		[8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]
      @classmethod
      def  SCREAMING_SNAKE_CASE							( cls       : Optional[int]  )	->    int:
             a_      :	MBartTokenizer							=						MBartTokenizer.from_pretrained(
                 cls.checkpoint_name  ,							src_lang='en_XX'  ,							tgt_lang='ro_RO'  )
             a_      :	Any							=						1
             return cls
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Dict:
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']  ,							2_5_0_0_0_1  )
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']  ,							2_5_0_0_0_4  )
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']  ,							2_5_0_0_2_0  )
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    List[Any]:
             a_      :	List[Any]							=						self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
             self.assertListEqual(self.expected_src_tokens  ,							SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    str:
             self.assertIn(SCREAMING_SNAKE_CASE__  ,							self.tokenizer.all_special_ids  )
             a_      :	Optional[int]							=						[RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
             a_      :	int							=						self.tokenizer.decode(SCREAMING_SNAKE_CASE__  ,							skip_special_tokens=SCREAMING_SNAKE_CASE__  )
             a_      :	Tuple							=						self.tokenizer.decode(generated_ids[1:]  ,							skip_special_tokens=SCREAMING_SNAKE_CASE__  )
             self.assertEqual(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
             self.assertNotIn(self.tokenizer.eos_token  ,							SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    List[Any]:
             a_      :	Any							=						['this is gunna be a long sentence ' * 2_0]
             assert isinstance(src_text[0]  ,							SCREAMING_SNAKE_CASE__  )
             a_      :	Any							=						1_0
             a_      :	str							=						self.tokenizer(SCREAMING_SNAKE_CASE__  ,							max_length=SCREAMING_SNAKE_CASE__  ,							truncation=SCREAMING_SNAKE_CASE__  ).input_ids[0]
             self.assertEqual(ids[-2]  ,							2  )
             self.assertEqual(ids[-1]  ,							SCREAMING_SNAKE_CASE__  )
             self.assertEqual(len(SCREAMING_SNAKE_CASE__  )  ,							SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Tuple:
             self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']  )  ,							[2_5_0_0_2_6, 2_5_0_0_0_1]  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    List[str]:
             a_      :	List[Any]							=						tempfile.mkdtemp()
             a_      :	List[Any]							=						self.tokenizer.fairseq_tokens_to_ids
             self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[int]							=						MBartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__  )
             self.assertDictEqual(new_tok.fairseq_tokens_to_ids  ,							SCREAMING_SNAKE_CASE__  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    List[str]:
             a_      :	Tuple							=						self.tokenizer(self.src_text  ,							text_target=self.tgt_text  ,							padding=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
             a_      :	int							=						shift_tokens_right(batch['labels']  ,							self.tokenizer.pad_token_id  )

             # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
             assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
             assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
             assert batch.decoder_input_ids[1][-1] == 2
             assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Optional[int]:
             a_      :	int							=						self.tokenizer(
                 self.src_text  ,							text_target=self.tgt_text  ,							padding=SCREAMING_SNAKE_CASE__  ,							truncation=SCREAMING_SNAKE_CASE__  ,							max_length=len(self.expected_src_tokens  )  ,							return_tensors='pt'  ,							)

             a_      :	Optional[Any]							=						shift_tokens_right(batch['labels']  ,							self.tokenizer.pad_token_id  )

             self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

             self.assertEqual((2, 1_4)  ,							batch.input_ids.shape  )
             self.assertEqual((2, 1_4)  ,							batch.attention_mask.shape  )
             a_      :	List[Any]							=						batch.input_ids.tolist()[0]
             self.assertListEqual(self.expected_src_tokens  ,							SCREAMING_SNAKE_CASE__  )
             self.assertEqual(2  ,							batch.decoder_input_ids[0, -1]  )  # EOS
             # Test that special tokens are reset
             self.assertEqual(self.tokenizer.prefix_tokens  ,							[]  )
             self.assertEqual(self.tokenizer.suffix_tokens  ,							[self.tokenizer.eos_token_id, EN_CODE]  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    List[str]:
             a_      :	Tuple							=						self.tokenizer(self.src_text  ,							padding=SCREAMING_SNAKE_CASE__  ,							truncation=SCREAMING_SNAKE_CASE__  ,							max_length=3  ,							return_tensors='pt'  )
             a_      :	Tuple							=						self.tokenizer(
                 text_target=self.tgt_text  ,							padding=SCREAMING_SNAKE_CASE__  ,							truncation=SCREAMING_SNAKE_CASE__  ,							max_length=1_0  ,							return_tensors='pt'  )
             a_      :	List[Any]							=						targets['input_ids']
             a_      :	List[Any]							=						shift_tokens_right(SCREAMING_SNAKE_CASE__  ,							self.tokenizer.pad_token_id  )

             self.assertEqual(batch.input_ids.shape[1]  ,							3  )
             self.assertEqual(batch.decoder_input_ids.shape[1]  ,							1_0  )

      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Union[str, Any]:
             a_      :	List[str]							=						self.tokenizer._build_translation_inputs(
                 'A test'  ,							return_tensors='pt'  ,							src_lang='en_XX'  ,							tgt_lang='ar_AR'  )

             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__  )  ,							{
                     # A, test, EOS, en_XX
                     'input_ids': [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
                     'attention_mask': [[1, 1, 1, 1]],
                     # ar_AR
                     'forced_bos_token_id': 2_5_0_0_0_1,
                 }  ,							)






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					unittest.TestCase    ):
      snake_case__       :  List[str]       =		MvpTokenizer
      snake_case__       :  Dict       =		MvpTokenizerFast
      snake_case__       :  Any       =		True
      snake_case__       :  Optional[int]       =		filter_roberta_detectors
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Optional[int]:
             super().setUp()
             a_      :	Any							=						[
                 'l',
                 'o',
                 'w',
                 'e',
                 'r',
                 's',
                 't',
                 'i',
                 'd',
                 'n',
                 '\u0120',
                 '\u0120l',
                 '\u0120n',
                 '\u0120lo',
                 '\u0120low',
                 'er',
                 '\u0120lowest',
                 '\u0120newer',
                 '\u0120wider',
                 '<unk>',
             ]
             a_      :	int							=						dict(zip(SCREAMING_SNAKE_CASE__  ,							range(len(SCREAMING_SNAKE_CASE__  )  )  )  )
             a_      :	int							=						['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
             a_      :	str							=						{'unk_token': '<unk>'}

             a_      :	Tuple							=						os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['vocab_file']  )
             a_      :	Optional[int]							=						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(SCREAMING_SNAKE_CASE__  ) + '\n'  )
             with open(self.merges_file  ,							'w'  ,							encoding='utf-8'  ) as fp:
                    fp.write('\n'.join(SCREAMING_SNAKE_CASE__  )  )
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							**SCREAMING_SNAKE_CASE__       : str  )	->    List[Any]:
             kwargs.update(self.special_tokens_map  )
             return self.tokenizer_class.from_pretrained(self.tmpdirname  ,							**SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							**SCREAMING_SNAKE_CASE__       : Optional[Any]  )	->    List[str]:
             kwargs.update(self.special_tokens_map  )
             return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,							**SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Any  )	->    Union[str, Any]:
             return "lower newer", "lower newer"
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    str:
             return MvpTokenizer.from_pretrained('RUCAIBox/mvp'  )
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Optional[int]:
             return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Union[str, Any]:
             a_      :	Union[str, Any]							=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
             a_      :	List[Any]							=						[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	List[str]							=						tokenizer(SCREAMING_SNAKE_CASE__  ,							max_length=len(SCREAMING_SNAKE_CASE__  )  ,							padding=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
                    self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    self.assertEqual((2, 9)  ,							batch.input_ids.shape  )
                    self.assertEqual((2, 9)  ,							batch.attention_mask.shape  )
                    a_      :	Optional[Any]							=						batch.input_ids.tolist()[0]
                    self.assertListEqual(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    # Test that special tokens are reset
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Optional[Any]:
             a_      :	Any							=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	Optional[Any]							=						tokenizer(SCREAMING_SNAKE_CASE__  ,							padding=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
                    # check if input_ids are returned and no labels
                    self.assertIn('input_ids'  ,							SCREAMING_SNAKE_CASE__  )
                    self.assertIn('attention_mask'  ,							SCREAMING_SNAKE_CASE__  )
                    self.assertNotIn('labels'  ,							SCREAMING_SNAKE_CASE__  )
                    self.assertNotIn('decoder_attention_mask'  ,							SCREAMING_SNAKE_CASE__  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    int:
             a_      :	Union[str, Any]							=						[
                 'Summary of the text.',
                 'Another summary.',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	List[str]							=						tokenizer(text_target=SCREAMING_SNAKE_CASE__  ,							max_length=3_2  ,							padding='max_length'  ,							return_tensors='pt'  )
                    self.assertEqual(3_2  ,							targets['input_ids'].shape[1]  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    List[str]:
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	Dict							=						tokenizer(
                        ['I am a small frog' * 1_0_2_4, 'I am a small frog']  ,							padding=SCREAMING_SNAKE_CASE__  ,							truncation=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
                    self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    self.assertEqual(batch.input_ids.shape  ,							(2, 1_0_2_4)  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Tuple:
             a_      :	Dict							=						['A long paragraph for summarization.']
             a_      :	Dict							=						[
                 'Summary of the text.',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	Union[str, Any]							=						tokenizer(SCREAMING_SNAKE_CASE__  ,							text_target=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
                    a_      :	Dict							=						inputs['input_ids']
                    a_      :	str							=						inputs['labels']
                    self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
                    self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
                    self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
                    self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Union[str, Any]:
             pass

      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    int:
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
                           a_      :	List[str]							=						self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
                           a_      :	Tuple							=						self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
                           a_      :	int							=						'A, <mask> AllenNLP sentence.'
                           a_      :	Optional[Any]							=						tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE__  ,							add_special_tokens=SCREAMING_SNAKE_CASE__  ,							return_token_type_ids=SCREAMING_SNAKE_CASE__  )
                           a_      :	int							=						tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE__  ,							add_special_tokens=SCREAMING_SNAKE_CASE__  ,							return_token_type_ids=SCREAMING_SNAKE_CASE__  )

                           # 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']  )  ,							)

                           a_      :	str							=						tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']  )
                           a_      :	Any							=						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_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]  )
                           self.assertSequenceEqual(tokens_r['input_ids']  ,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]  )

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


