'''simple docstring'''







import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    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_      	=	get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
		from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCamelCase_      	=	25_60_47
UpperCamelCase_      	=	25_61_45




@require_sentencepiece
@require_tokenizers
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							List[Any]     		= NllbTokenizer
						A_			:							Optional[int]     		= NllbTokenizerFast
						A_			:							str     		= True
						A_			:							str     		= True
						A_			:							Any     		= {}




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				List[str]:
													super().setUp()

													# We have a SentencePiece fixture for testing
													SCREAMING_SNAKE_CASE__     :str						    = NllbTokenizer(UpperCamelCase_							,							keep_accents=UpperCamelCase_      )
													tokenizer.save_pretrained(self.tmpdirname      )




						def      __lowerCamelCase       (    self :       List[str]      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :List[str]						    = NllbTokenizer(UpperCamelCase_							,							keep_accents=UpperCamelCase_      )

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

													self.assertListEqual(
													    tokenizer.convert_tokens_to_ids(UpperCamelCase_      )							,							[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]							,							)

													SCREAMING_SNAKE_CASE__     :List[Any]						    = tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
													self.assertListEqual(
													    UpperCamelCase_							,							[
													        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',
													        '.',
													    ]							,							)
													SCREAMING_SNAKE_CASE__     :List[Any]						    = tokenizer.convert_tokens_to_ids(UpperCamelCase_      )
													self.assertListEqual(
													    UpperCamelCase_							,							[
													        value + tokenizer.fairseq_offset
													        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
													    ]							,							)

													SCREAMING_SNAKE_CASE__     :Dict						    = tokenizer.convert_ids_to_tokens(UpperCamelCase_      )
													self.assertListEqual(
													    UpperCamelCase_							,							[
													        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      __lowerCamelCase       (    self :       Dict      )     ->				int:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																				with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																											SCREAMING_SNAKE_CASE__     :Any						    = self.rust_tokenizer_class.from_pretrained(UpperCamelCase_							,							**UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :Tuple						    = self.tokenizer_class.from_pretrained(UpperCamelCase_							,							**UpperCamelCase_      )

																											SCREAMING_SNAKE_CASE__     :Tuple						    = tempfile.mkdtemp()

																											SCREAMING_SNAKE_CASE__     :List[str]						    = tokenizer_r.save_pretrained(UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :str						    = tokenizer_p.save_pretrained(UpperCamelCase_      )

																											# 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      )      )
																											SCREAMING_SNAKE_CASE__     :List[Any]						    = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f      )
																											self.assertSequenceEqual(UpperCamelCase_							,							UpperCamelCase_      )

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

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

																											shutil.rmtree(UpperCamelCase_      )

																											# Save tokenizer rust, legacy_format=True
																											SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = tempfile.mkdtemp()

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

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

																											# Checks everything loads correctly in the same way
																											SCREAMING_SNAKE_CASE__     :Tuple						    = tokenizer_r.from_pretrained(UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :str						    = tokenizer_p.from_pretrained(UpperCamelCase_      )

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

																											shutil.rmtree(UpperCamelCase_      )

																											# Save tokenizer rust, legacy_format=False
																											SCREAMING_SNAKE_CASE__     :List[Any]						    = tempfile.mkdtemp()

																											SCREAMING_SNAKE_CASE__     :Tuple						    = tokenizer_r.save_pretrained(UpperCamelCase_							,							legacy_format=UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :List[Any]						    = tokenizer_p.save_pretrained(UpperCamelCase_      )

																											# 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
																											SCREAMING_SNAKE_CASE__     :Tuple						    = tokenizer_r.from_pretrained(UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :List[str]						    = tokenizer_p.from_pretrained(UpperCamelCase_      )

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

																											shutil.rmtree(UpperCamelCase_      )




						@require_torch
						def      __lowerCamelCase       (    self :       int      )     ->				Optional[int]:
													if not self.test_seqaseq:
																				return

													SCREAMING_SNAKE_CASE__     :List[str]						    = self.get_tokenizers()
													for tokenizer in tokenizers:
																				with self.subTest(f'''{tokenizer.__class__.__name__}'''      ):
																											# Longer text that will definitely require truncation.
																											SCREAMING_SNAKE_CASE__     :Tuple						    = [
																											    ' 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.',
																											]
																											SCREAMING_SNAKE_CASE__     :str						    = [
																											    '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.',
																											]
																											try:
																																		SCREAMING_SNAKE_CASE__     :Tuple						    = tokenizer.prepare_seqaseq_batch(
																																		    src_texts=UpperCamelCase_							,							tgt_texts=UpperCamelCase_							,							max_length=3							,							max_target_length=10							,							return_tensors='pt'							,							src_lang='eng_Latn'							,							tgt_lang='ron_Latn'							,							)
																											except NotImplementedError:
																																		return
																											self.assertEqual(batch.input_ids.shape[1]							,							3      )
																											self.assertEqual(batch.labels.shape[1]							,							10      )
																											# max_target_length will default to max_length if not specified
																											SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tokenizer.prepare_seqaseq_batch(
																											    UpperCamelCase_							,							tgt_texts=UpperCamelCase_							,							max_length=3							,							return_tensors='pt'      )
																											self.assertEqual(batch.input_ids.shape[1]							,							3      )
																											self.assertEqual(batch.labels.shape[1]							,							3      )

																											SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tokenizer.prepare_seqaseq_batch(
																											    src_texts=UpperCamelCase_							,							max_length=3							,							max_target_length=10							,							return_tensors='pt'      )
																											self.assertEqual(batch_encoder_only.input_ids.shape[1]							,							3      )
																											self.assertEqual(batch_encoder_only.attention_mask.shape[1]							,							3      )
																											self.assertNotIn('decoder_input_ids'							,							UpperCamelCase_      )




						@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.'      )
						def      __lowerCamelCase       (    self :       Any      )     ->				List[Any]:
													pass




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				List[Any]:
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																				with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																											SCREAMING_SNAKE_CASE__     :Any						    = [AddedToken('<special>'							,							lstrip=UpperCamelCase_      )]

																											SCREAMING_SNAKE_CASE__     :List[str]						    = self.rust_tokenizer_class.from_pretrained(
																											    UpperCamelCase_							,							additional_special_tokens=UpperCamelCase_							,							**UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tokenizer_r.encode('Hey this is a <special> token'      )

																											SCREAMING_SNAKE_CASE__     :str						    = tokenizer_r.encode('<special>'							,							add_special_tokens=UpperCamelCase_      )[0]

																											self.assertTrue(special_token_id in r_output      )

																											if self.test_slow_tokenizer:
																																		SCREAMING_SNAKE_CASE__     :Any						    = self.rust_tokenizer_class.from_pretrained(
																																		    UpperCamelCase_							,							additional_special_tokens=UpperCamelCase_							,							**UpperCamelCase_							,							)
																																		SCREAMING_SNAKE_CASE__     :Dict						    = self.tokenizer_class.from_pretrained(
																																		    UpperCamelCase_							,							additional_special_tokens=UpperCamelCase_							,							**UpperCamelCase_      )

																																		SCREAMING_SNAKE_CASE__     :Tuple						    = tokenizer_p.encode('Hey this is a <special> token'      )

																																		SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tokenizer_cr.encode('Hey this is a <special> token'      )

																																		self.assertEqual(UpperCamelCase_							,							UpperCamelCase_      )
																																		self.assertEqual(UpperCamelCase_							,							UpperCamelCase_      )
																																		self.assertTrue(special_token_id in p_output      )
																																		self.assertTrue(special_token_id in cr_output      )




@require_torch
@require_sentencepiece
@require_tokenizers
class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):
						A_			:							Optional[Any]     		= 'facebook/nllb-200-distilled-600M'
						A_			:							List[Any]     		= [
						    ' 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.',
						]
						A_			:							List[Any]     		= [
						    '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.',
						]
						A_			:							Optional[Any]     		= [
						    256_047,
						    16_297,
						    134_408,
						    8_165,
						    248_066,
						    14_734,
						    950,
						    1_135,
						    105_721,
						    3_573,
						    83,
						    27_352,
						    108,
						    49_486,
						    2,
						]




						@classmethod
						def      __lowerCamelCase       (    cls :       Union[str, Any]      )     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :NllbTokenizer						    = NllbTokenizer.from_pretrained(
													    cls.checkpoint_name							,							src_lang='eng_Latn'							,							tgt_lang='ron_Latn'      )
													SCREAMING_SNAKE_CASE__     :Any						    = 1
													return cls




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Optional[int]:
													self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']							,							25_60_01      )
													self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']							,							25_60_02      )
													self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']							,							25_60_57      )




						def      __lowerCamelCase       (    self :       Dict      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
													self.assertListEqual(self.expected_src_tokens							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       List[str]      )     ->				Optional[int]:
													self.assertIn(UpperCamelCase_							,							self.tokenizer.all_special_ids      )
													# fmt: off
													SCREAMING_SNAKE_CASE__     :Tuple						    = [RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
													# fmt: on

													SCREAMING_SNAKE_CASE__     :str						    = self.tokenizer.decode(UpperCamelCase_							,							skip_special_tokens=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = self.tokenizer.decode(generated_ids[1:]							,							skip_special_tokens=UpperCamelCase_      )
													self.assertEqual(UpperCamelCase_							,							UpperCamelCase_      )
													self.assertNotIn(self.tokenizer.eos_token							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = ['this is gunna be a long sentence ' * 20]
													assert isinstance(src_text[0]							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = 10
													SCREAMING_SNAKE_CASE__     :Any						    = self.tokenizer(UpperCamelCase_							,							max_length=UpperCamelCase_							,							truncation=UpperCamelCase_      ).input_ids[0]
													self.assertEqual(ids[-1]							,							2      )
													self.assertEqual(ids[0]							,							UpperCamelCase_      )
													self.assertEqual(len(UpperCamelCase_      )							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       List[str]      )     ->				List[str]:
													self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']      )							,							[25_62_03, 3]      )




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = tempfile.mkdtemp()
													SCREAMING_SNAKE_CASE__     :str						    = self.tokenizer.fairseq_tokens_to_ids
													self.tokenizer.save_pretrained(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = NllbTokenizer.from_pretrained(UpperCamelCase_      )
													self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,							UpperCamelCase_      )




						@require_torch
						def      __lowerCamelCase       (    self :       Any      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :Dict						    = self.tokenizer(
													    self.src_text							,							text_target=self.tgt_text							,							padding=UpperCamelCase_							,							truncation=UpperCamelCase_							,							max_length=len(self.expected_src_tokens      )							,							return_tensors='pt'							,							)
													SCREAMING_SNAKE_CASE__     :str						    = shift_tokens_right(
													    batch['labels']							,							self.tokenizer.pad_token_id							,							self.tokenizer.lang_code_to_id['ron_Latn']      )

													self.assertIsInstance(UpperCamelCase_							,							UpperCamelCase_      )

													self.assertEqual((2, 15)							,							batch.input_ids.shape      )
													self.assertEqual((2, 15)							,							batch.attention_mask.shape      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = batch.input_ids.tolist()[0]
													self.assertListEqual(self.expected_src_tokens							,							UpperCamelCase_      )
													self.assertEqual(UpperCamelCase_							,							batch.decoder_input_ids[0, 0]      )  # EOS
													# Test that special tokens are reset
													self.assertEqual(self.tokenizer.prefix_tokens							,							[EN_CODE]      )
													self.assertEqual(self.tokenizer.suffix_tokens							,							[self.tokenizer.eos_token_id]      )




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Dict:
													SCREAMING_SNAKE_CASE__     :Any						    = self.tokenizer(self.src_text							,							padding=UpperCamelCase_							,							truncation=UpperCamelCase_							,							max_length=3							,							return_tensors='pt'      )
													SCREAMING_SNAKE_CASE__     :int						    = self.tokenizer(
													    text_target=self.tgt_text							,							padding=UpperCamelCase_							,							truncation=UpperCamelCase_							,							max_length=10							,							return_tensors='pt'      )
													SCREAMING_SNAKE_CASE__     :int						    = targets['input_ids']
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = shift_tokens_right(
													    UpperCamelCase_							,							self.tokenizer.pad_token_id							,							decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]							,							)

													self.assertEqual(batch.input_ids.shape[1]							,							3      )
													self.assertEqual(batch.decoder_input_ids.shape[1]							,							10      )




						@require_torch
						def      __lowerCamelCase       (    self :       int      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Dict						    = self.tokenizer._build_translation_inputs(
													    'A test'							,							return_tensors='pt'							,							src_lang='eng_Latn'							,							tgt_lang='fra_Latn'      )

													self.assertEqual(
													    nested_simplify(UpperCamelCase_      )							,							{
													        # A, test, EOS, en_XX
													        'input_ids': [[25_60_47, 70, 73_56, 2]],
													        'attention_mask': [[1, 1, 1, 1]],
													        # ar_AR
													        'forced_bos_token_id': 25_60_57,
													    }							,							)




						@require_torch
						def      __lowerCamelCase       (    self :       int      )     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = True
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.tokenizer(
													    'UN Chief says there is no military solution in Syria'							,							src_lang='eng_Latn'							,							tgt_lang='fra_Latn'      )
													self.assertEqual(
													    inputs.input_ids							,							[1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47]      )

													SCREAMING_SNAKE_CASE__     :List[Any]						    = False
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.tokenizer(
													    'UN Chief says there is no military solution in Syria'							,							src_lang='eng_Latn'							,							tgt_lang='fra_Latn'      )
													self.assertEqual(
													    inputs.input_ids							,							[25_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2]      )







'''simple docstring'''







import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase_      	=	get_tests_dir('''fixtures/test_sentencepiece.model''')
UpperCamelCase_      	=	get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

UpperCamelCase_      	=	'''pt''' if is_torch_available() else '''tf'''




@require_sentencepiece
@require_tokenizers
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							Tuple     		= CamembertTokenizer
						A_			:							Dict     		= CamembertTokenizerFast
						A_			:							Any     		= True
						A_			:							List[Any]     		= True




						def      __lowerCamelCase       (    self :       int      )     ->				Tuple:
													super().setUp()

													# We have a SentencePiece fixture for testing
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = CamembertTokenizer(UpperCamelCase_      )
													tokenizer.save_pretrained(self.tmpdirname      )




						def      __lowerCamelCase       (    self :       int      )     ->				int:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = '<pad>'
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 1

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




						def      __lowerCamelCase       (    self :       int      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Any						    = list(self.get_tokenizer().get_vocab().keys()      )

													self.assertEqual(vocab_keys[0]							,							'<s>NOTUSED'      )
													self.assertEqual(vocab_keys[1]							,							'<pad>'      )
													self.assertEqual(vocab_keys[-1]							,							'<mask>'      )
													self.assertEqual(len(UpperCamelCase_      )							,							10_04      )




						def      __lowerCamelCase       (    self :       List[str]      )     ->				List[str]:
													self.assertEqual(self.get_tokenizer().vocab_size							,							10_05      )




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Any						    = CamembertTokenizer(UpperCamelCase_      )
													tokenizer.save_pretrained(self.tmpdirname      )
													SCREAMING_SNAKE_CASE__     :Dict						    = CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = 'I was born in 92000, and this is falsé.'

													SCREAMING_SNAKE_CASE__     :List[str]						    = tokenizer.encode(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = rust_tokenizer.encode(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :int						    = tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = rust_tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													# <unk> tokens are not the same for `rust` than for `slow`.
													# Because spm gives back raw token instead of `unk` in EncodeAsPieces
													# tokens = tokenizer.tokenize(sequence)
													SCREAMING_SNAKE_CASE__     :int						    = tokenizer.convert_ids_to_tokens(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = rust_tokenizer.tokenize(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )




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

													SCREAMING_SNAKE_CASE__     :List[str]						    = self.get_tokenizer()
													SCREAMING_SNAKE_CASE__     :Dict						    = self.get_rust_tokenizer()

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 'I was born in 92000, and this is falsé.'

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = tokenizer.tokenize(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = rust_tokenizer.tokenize(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = rust_tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :str						    = self.get_rust_tokenizer()
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = tokenizer.encode(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = rust_tokenizer.encode(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )




						@slow
						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Any:
													# fmt: off
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = {'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], '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, 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, 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

													# camembert is a french model. So we also use french texts.
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = [
													    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
													    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
													    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
													    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
													    'telles que la traduction et la synthèse de texte.',
													]

													self.tokenizer_integration_test_util(
													    expected_encoding=UpperCamelCase_							,							model_name='camembert-base'							,							revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'							,							sequences=UpperCamelCase_							,							)






