
"""simple docstring"""




import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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


a		:       List[Any]     						=       '▁'

a		:       str     						=       get_tests_dir('fixtures/test_sentencepiece.model')







@require_sentencepiece
@require_tokenizers
class     lowercase(_lowercase    ,						unittest.TestCase       ):
     __snake_case:	List[Any]						=     BigBirdTokenizer
     __snake_case:	Any						=     BigBirdTokenizerFast
     __snake_case:	List[Any]						=     True
     __snake_case:	List[str]						=     True



     def 			lowercase__  (							self   )		->     List[Any]:







         """simple docstring"""



         super().setUp()

         a__		=			self.tokenizer_class(__SCREAMING_SNAKE_CASE     ,				keep_accents=__SCREAMING_SNAKE_CASE   )
         tokenizer.save_pretrained(self.tmpdirname   )



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         a__		=			'<s>'
         a__		=			1

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



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



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

         self.assertEqual(vocab_keys[0]     ,				'<unk>'   )
         self.assertEqual(vocab_keys[1]     ,				'<s>'   )
         self.assertEqual(vocab_keys[-1]     ,				'[MASK]'   )
         self.assertEqual(len(__SCREAMING_SNAKE_CASE   )     ,				1_0_0_4   )



     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         self.assertEqual(self.get_tokenizer().vocab_size     ,				1_0_0_0   )



     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         if not self.test_rust_tokenizer:
             return

         a__		=			self.get_tokenizer()
         a__		=			self.get_rust_tokenizer()

         a__		=			'I was born in 92000, and this is falsé.'

         a__		=			tokenizer.tokenize(__SCREAMING_SNAKE_CASE   )
         a__		=			rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE   )
         self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         a__		=			tokenizer.encode(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
         a__		=			rust_tokenizer.encode(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
         self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         a__		=			self.get_rust_tokenizer()
         a__		=			tokenizer.encode(__SCREAMING_SNAKE_CASE   )
         a__		=			rust_tokenizer.encode(__SCREAMING_SNAKE_CASE   )
         self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         a__		=			BigBirdTokenizer(__SCREAMING_SNAKE_CASE     ,				keep_accents=__SCREAMING_SNAKE_CASE   )

         a__		=			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   )     ,				[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]     ,				)

         a__		=			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__		=			tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE   )
         self.assertListEqual(
             __SCREAMING_SNAKE_CASE     ,				[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]     ,				)

         a__		=			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>',
                 '.',
             ]     ,				)



     @cached_property
     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         return BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'   )



     @slow
     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         a__		=			'Hello World!'
         a__		=			[6_5, 1_8_5_3_6, 2_2_6_0, 1_0_1, 6_6]

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



     @slow
     def 			lowercase__  (							self   )		->     Optional[Any]:







         """simple docstring"""



         a__		=			(
             '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'
         )
         # fmt: off
         a__		=			[6_5, 8_7_1, 4_1_9, 3_5_8, 9_4_6, 9_9_1, 2_5_2_1, 4_5_2, 3_5_8, 1_3_5_7, 3_8_7, 7_7_5_1, 3_5_3_6, 1_1_2, 9_8_5, 4_5_6, 1_2_6, 8_6_5, 9_3_8, 5_4_0_0, 5_7_3_4, 4_5_8, 1_3_6_8, 4_6_7, 7_8_6, 2_4_6_2, 5_2_4_6, 1_1_5_9, 6_3_3, 8_6_5, 4_5_1_9, 4_5_7, 5_8_2, 8_5_2, 2_5_5_7, 4_2_7, 9_1_6, 5_0_8, 4_0_5, 3_4_3_2_4, 4_9_7, 3_9_1, 4_0_8, 1_1_3_4_2, 1_2_4_4, 3_8_5, 1_0_0, 9_3_8, 9_8_5, 4_5_6, 5_7_4, 3_6_2, 1_2_5_9_7, 3_2_0_0, 3_1_2_9, 1_1_7_2, 6_6]  # noqa: E231
         # fmt: on
         self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE   )   )



     @require_torch
     @slow
     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         import torch

         from transformers import BigBirdConfig, BigBirdModel

         # Build sequence
         a__		=			list(self.big_tokenizer.get_vocab().keys()   )[:1_0]
         a__		=			' '.join(__SCREAMING_SNAKE_CASE   )
         a__		=			self.big_tokenizer.encode_plus(__SCREAMING_SNAKE_CASE     ,				return_tensors='pt'     ,				return_token_type_ids=__SCREAMING_SNAKE_CASE   )
         a__		=			self.big_tokenizer.batch_encode_plus(
             [sequence + ' ' + sequence]     ,				return_tensors='pt'     ,				return_token_type_ids=__SCREAMING_SNAKE_CASE   )

         a__		=			BigBirdConfig(attention_type='original_full'   )
         a__		=			BigBirdModel(__SCREAMING_SNAKE_CASE   )

         assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

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



     @slow
     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'   )
         a__		=			tokenizer.decode(tokenizer('Paris is the [MASK].'   ).input_ids   )

         self.assertTrue(decoded_text == '[CLS] Paris is the[MASK].[SEP]'   )




     @slow
     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			{'input_ids': [[6_5, 3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4, 6_6], [6_5, 4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 6_6, 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], [6_5, 4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 6_6, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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, 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=__SCREAMING_SNAKE_CASE     ,				model_name='google/bigbird-roberta-base'     ,				revision='215c99f1600e06f83acce68422f2035b2b5c3510'     ,				)




"""simple docstring"""




import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
    a		:       str     						=       'pt'
elif is_tf_available():
    a		:       Dict     						=       'tf'
else:
    a		:       Optional[Any]     						=       'jax'







class     lowercase(_lowercase    ,						unittest.TestCase       ):
     __snake_case:	List[Any]						=     PerceiverTokenizer
     __snake_case:	Optional[Any]						=     False



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         super().setUp()
         a__		=			PerceiverTokenizer()
         tokenizer.save_pretrained(self.tmpdirname   )



     @cached_property
     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'   )



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     PerceiverTokenizer:







         """simple docstring"""



         return self.tokenizer_class.from_pretrained(self.tmpdirname     ,				**__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=False     ,				__SCREAMING_SNAKE_CASE=2_0     ,				__SCREAMING_SNAKE_CASE=5   )		->     Tuple[str, list]:







         """simple docstring"""



         a__		=			[]
         for i in range(len(__SCREAMING_SNAKE_CASE   )   ):
             try:
                 a__		=			tokenizer.decode([i]     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
             except UnicodeDecodeError:
                 pass
             toks.append((i, tok)   )

         a__		=			list(filter(lambda __SCREAMING_SNAKE_CASE   : re.match(R'^[ a-zA-Z]+$'     ,				t[1]   )     ,				__SCREAMING_SNAKE_CASE   )   )
         a__		=			list(filter(lambda __SCREAMING_SNAKE_CASE   : [t[0]] == tokenizer.encode(t[1]     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )     ,				__SCREAMING_SNAKE_CASE   )   )
         if max_length is not None and len(__SCREAMING_SNAKE_CASE   ) > max_length:
             a__		=			toks[:max_length]
         if min_length is not None and len(__SCREAMING_SNAKE_CASE   ) < min_length and len(__SCREAMING_SNAKE_CASE   ) > 0:
             while len(__SCREAMING_SNAKE_CASE   ) < min_length:
                 a__		=			toks + toks
        # toks_str = [t[1] for t in toks]
         a__		=			[t[0] for t in toks]

         # Ensure consistency
         a__		=			tokenizer.decode(__SCREAMING_SNAKE_CASE     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
         if " " not in output_txt and len(__SCREAMING_SNAKE_CASE   ) > 1:
             a__		=			(
                 tokenizer.decode([toks_ids[0]]     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
                 + ' '
                 + tokenizer.decode(toks_ids[1:]     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
             )
         if with_prefix_space:
             a__		=			' ' + output_txt
         a__		=			tokenizer.encode(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
         return output_txt, output_ids



     def 			lowercase__  (							self   )		->     Optional[Any]:







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         a__		=			'Unicode €.'
         a__		=			tokenizer(__SCREAMING_SNAKE_CASE   )
         a__		=			[4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
         self.assertEqual(encoded['input_ids']     ,				__SCREAMING_SNAKE_CASE   )

         # decoding
         a__		=			tokenizer.decode(__SCREAMING_SNAKE_CASE   )
         self.assertEqual(__SCREAMING_SNAKE_CASE     ,				'[CLS]Unicode €.[SEP]'   )

         a__		=			tokenizer('e è é ê ë'   )
         a__		=			[4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
         self.assertEqual(encoded['input_ids']     ,				__SCREAMING_SNAKE_CASE   )
         # decoding
         a__		=			tokenizer.decode(__SCREAMING_SNAKE_CASE   )
         self.assertEqual(__SCREAMING_SNAKE_CASE     ,				'[CLS]e è é ê ë[SEP]'   )

         # encode/decode, but with `encode` instead of `__call__`
         self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'   )   )     ,				'[CLS]e è é ê ë[SEP]'   )



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         a__		=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
         # fmt: off
         a__		=			[4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
         # fmt: on
         a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				padding=__SCREAMING_SNAKE_CASE     ,				return_tensors=__SCREAMING_SNAKE_CASE   )
         self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         if FRAMEWORK != "jax":
             a__		=			list(batch.input_ids.numpy()[0]   )
         else:
             a__		=			list(batch.input_ids.tolist()[0]   )

         self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         self.assertEqual((2, 3_8)     ,				batch.input_ids.shape   )
         self.assertEqual((2, 3_8)     ,				batch.attention_mask.shape   )



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         a__		=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
         a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				padding=__SCREAMING_SNAKE_CASE     ,				return_tensors=__SCREAMING_SNAKE_CASE   )
         # check if input_ids are returned and no decoder_input_ids
         self.assertIn('input_ids'     ,				__SCREAMING_SNAKE_CASE   )
         self.assertIn('attention_mask'     ,				__SCREAMING_SNAKE_CASE   )
         self.assertNotIn('decoder_input_ids'     ,				__SCREAMING_SNAKE_CASE   )
         self.assertNotIn('decoder_attention_mask'     ,				__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         a__		=			[
             'Summary of the text.',
             'Another summary.',
         ]
         a__		=			tokenizer(
             text_target=__SCREAMING_SNAKE_CASE     ,				max_length=3_2     ,				padding='max_length'     ,				truncation=__SCREAMING_SNAKE_CASE     ,				return_tensors=__SCREAMING_SNAKE_CASE   )
         self.assertEqual(3_2     ,				targets['input_ids'].shape[1]   )



     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         a__		=			self.get_tokenizers()
         for tokenizer in tokenizers:
             with self.subTest(f'{tokenizer.__class__.__name__}'   ):
                 self.assertNotEqual(tokenizer.model_max_length     ,				4_2   )

        # Now let's start the test
         a__		=			self.get_tokenizers()
         for tokenizer in tokenizers:
             with self.subTest(f'{tokenizer.__class__.__name__}'   ):
                 # Isolate this from the other tests because we save additional tokens/etc
                 a__		=			tempfile.mkdtemp()

                 a__		=			' He is very happy, UNwant\u00E9d,running'
                 a__		=			tokenizer.encode(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
                 tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE   )

                 a__		=			tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE   )
                 a__		=			after_tokenizer.encode(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
                 self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

                 shutil.rmtree(__SCREAMING_SNAKE_CASE   )

         a__		=			self.get_tokenizers(model_max_length=4_2   )
         for tokenizer in tokenizers:
             with self.subTest(f'{tokenizer.__class__.__name__}'   ):
                 # Isolate this from the other tests because we save additional tokens/etc
                 a__		=			tempfile.mkdtemp()

                 a__		=			' He is very happy, UNwant\u00E9d,running'
                 tokenizer.add_tokens(['bim', 'bambam']   )
                 a__		=			tokenizer.additional_special_tokens
                 additional_special_tokens.append('new_additional_special_token'   )
                 tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}   )
                 a__		=			tokenizer.encode(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
                 tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE   )

                 a__		=			tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE   )
                 a__		=			after_tokenizer.encode(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
                 self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
                 self.assertIn('new_additional_special_token'     ,				after_tokenizer.additional_special_tokens   )
                 self.assertEqual(after_tokenizer.model_max_length     ,				4_2   )

                 a__		=			tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE     ,				model_max_length=4_3   )
                 self.assertEqual(tokenizer.model_max_length     ,				4_3   )

                 shutil.rmtree(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     List[Any]:







         """simple docstring"""



         a__		=			[]
         if self.test_slow_tokenizer:
             tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())   )

         if self.test_rust_tokenizer:
             tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())   )

         for tokenizer_class, tokenizer_utils in tokenizer_list:
             with tempfile.TemporaryDirectory() as tmp_dir:
                 tokenizer_utils.save_pretrained(__SCREAMING_SNAKE_CASE   )

                 with open(os.path.join(__SCREAMING_SNAKE_CASE     ,				'special_tokens_map.json'   )     ,				encoding='utf-8'   ) as json_file:
                     a__		=			json.load(__SCREAMING_SNAKE_CASE   )

                 with open(os.path.join(__SCREAMING_SNAKE_CASE     ,				'tokenizer_config.json'   )     ,				encoding='utf-8'   ) as json_file:
                     a__		=			json.load(__SCREAMING_SNAKE_CASE   )

                 a__		=			[f'<extra_id_{i}>' for i in range(1_2_5   )]

                 a__		=			added_tokens_extra_ids + [
                     'an_additional_special_token'
                 ]
                 a__		=			added_tokens_extra_ids + [
                     'an_additional_special_token'
                 ]

                 with open(os.path.join(__SCREAMING_SNAKE_CASE     ,				'special_tokens_map.json'   )     ,				'w'     ,				encoding='utf-8'   ) as outfile:
                     json.dump(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
                 with open(os.path.join(__SCREAMING_SNAKE_CASE     ,				'tokenizer_config.json'   )     ,				'w'     ,				encoding='utf-8'   ) as outfile:
                     json.dump(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

                 # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                 # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                 # "special_tokens_map.json" files
                 a__		=			tokenizer_class.from_pretrained(
                     __SCREAMING_SNAKE_CASE     ,				)
                 self.assertIn(
                     'an_additional_special_token'     ,				tokenizer_without_change_in_init.additional_special_tokens   )
                 self.assertEqual(
                     ['an_additional_special_token']     ,				tokenizer_without_change_in_init.convert_ids_to_tokens(
                         tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']   )   )     ,				)

                 # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                 a__		=			added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'     ,				lstrip=__SCREAMING_SNAKE_CASE   )]
                 a__		=			tokenizer_class.from_pretrained(
                     __SCREAMING_SNAKE_CASE     ,				additional_special_tokens=__SCREAMING_SNAKE_CASE     ,				)

                 self.assertIn('a_new_additional_special_token'     ,				tokenizer.additional_special_tokens   )
                 self.assertEqual(
                     ['a_new_additional_special_token']     ,				tokenizer.convert_ids_to_tokens(
                         tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']   )   )     ,				)



     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			self.perceiver_tokenizer
         self.assertEqual(tokenizer.decode([1_7_8]   )     ,				'�'   )



     def 			lowercase__  (							self   )		->     str:







         """simple docstring"""



         pass



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         pass



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         pass



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         pass




     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			self.get_tokenizers(fast=__SCREAMING_SNAKE_CASE     ,				do_lower_case=__SCREAMING_SNAKE_CASE   )
         for tokenizer in tokenizers:
             with self.subTest(f'{tokenizer.__class__.__name__}'   ):
                 a__		=			['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
                 a__		=			tokenizer.convert_tokens_to_string(__SCREAMING_SNAKE_CASE   )

                 self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )


