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


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

__lowerCamelCase			: str							   =		'pt' if is_torch_available() else 'tf'







@require_sentencepiece
@require_tokenizers
class __snake_case				(						__snake_case   ,		unittest.TestCase		):
   lowerCAmelCase_														=							CamembertTokenizer
   lowerCAmelCase_														=							CamembertTokenizerFast
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							True




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     super().setUp()

     # We have a SentencePiece fixture for testing
     SCREAMING_SNAKE_CASE__										=      CamembertTokenizer(_lowercase      )
     tokenizer.save_pretrained(self.tmpdirname      )




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      '''<pad>'''
     SCREAMING_SNAKE_CASE__										=      1

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




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      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(_lowercase      )       ,	10_04      )




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     self.assertEqual(self.get_tokenizer().vocab_size       ,	10_05      )




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      CamembertTokenizer(_lowercase      )
     tokenizer.save_pretrained(self.tmpdirname      )
     SCREAMING_SNAKE_CASE__										=      CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

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

     SCREAMING_SNAKE_CASE__										=      tokenizer.encode(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      rust_tokenizer.encode(_lowercase      )
     self.assertListEqual(_lowercase       ,	_lowercase      )

     SCREAMING_SNAKE_CASE__										=      tokenizer.encode(_lowercase       ,	add_special_tokens=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      rust_tokenizer.encode(_lowercase       ,	add_special_tokens=_lowercase      )
     self.assertListEqual(_lowercase       ,	_lowercase      )

     # <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__										=      tokenizer.convert_ids_to_tokens(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      rust_tokenizer.tokenize(_lowercase      )
     self.assertListEqual(_lowercase       ,	_lowercase      )




   def 	__a						(    self			:				str      ):



     """simple docstring"""






     if not self.test_rust_tokenizer:
       return

     SCREAMING_SNAKE_CASE__										=      self.get_tokenizer()
     SCREAMING_SNAKE_CASE__										=      self.get_rust_tokenizer()

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

     SCREAMING_SNAKE_CASE__										=      tokenizer.tokenize(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      rust_tokenizer.tokenize(_lowercase      )
     self.assertListEqual(_lowercase       ,	_lowercase      )

     SCREAMING_SNAKE_CASE__										=      tokenizer.encode(_lowercase       ,	add_special_tokens=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      rust_tokenizer.encode(_lowercase       ,	add_special_tokens=_lowercase      )
     self.assertListEqual(_lowercase       ,	_lowercase      )

     SCREAMING_SNAKE_CASE__										=      self.get_rust_tokenizer()
     SCREAMING_SNAKE_CASE__										=      tokenizer.encode(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      rust_tokenizer.encode(_lowercase      )
     self.assertListEqual(_lowercase       ,	_lowercase      )




   @slow
   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      {'''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__										=      [
         '''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=_lowercase       ,	model_name="""camembert-base"""       ,	revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""       ,	sequences=_lowercase       ,	)


'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_rembert import RemBertTokenizer
else:
      lowercase			:				Optional[Any]    		=			None

lowercase			:				Tuple    		=			logging.get_logger(__name__)
lowercase			:				Dict    		=			{'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

lowercase			:				Tuple    		=			{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

lowercase			:				List[str]    		=			{
    'google/rembert': 2_56,
}

lowercase			:				Dict    		=			'▁'





class  A				(							__snake_case						):
     __magic_name__						=		VOCAB_FILES_NAMES
     __magic_name__						=		PRETRAINED_VOCAB_FILES_MAP
     __magic_name__						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__						=		RemBertTokenizer





     def __init__(							self						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE="[CLS]"						,			SCREAMING_SNAKE_CASE="[SEP]"						,			SCREAMING_SNAKE_CASE="<unk>"						,			SCREAMING_SNAKE_CASE="[SEP]"						,			SCREAMING_SNAKE_CASE="<pad>"						,			SCREAMING_SNAKE_CASE="[CLS]"						,			SCREAMING_SNAKE_CASE="[MASK]"						,			**SCREAMING_SNAKE_CASE						,			) ->				List[str]:






         """simple docstring"""






         A       :   Optional[Any]					=						AddedToken(SCREAMING_SNAKE_CASE						,			lstrip=SCREAMING_SNAKE_CASE						,			rstrip=SCREAMING_SNAKE_CASE	) if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) else mask_token

         super().__init__(
             SCREAMING_SNAKE_CASE						,			tokenizer_file=SCREAMING_SNAKE_CASE						,			do_lower_case=SCREAMING_SNAKE_CASE						,			remove_space=SCREAMING_SNAKE_CASE						,			keep_accents=SCREAMING_SNAKE_CASE						,			bos_token=SCREAMING_SNAKE_CASE						,			eos_token=SCREAMING_SNAKE_CASE						,			unk_token=SCREAMING_SNAKE_CASE						,			sep_token=SCREAMING_SNAKE_CASE						,			pad_token=SCREAMING_SNAKE_CASE						,			cls_token=SCREAMING_SNAKE_CASE						,			mask_token=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE						,			)

         A       :   List[Any]					=						do_lower_case
         A       :   str					=						remove_space
         A       :   int					=						keep_accents
         A       :   Union[str, Any]					=						vocab_file
         A       :   List[Any]					=						False if not self.vocab_file else True





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[int]:






         """simple docstring"""






         A       :   List[Any]					=						[self.sep_token_id]
         A       :   Tuple					=						[self.cls_token_id]
         if token_ids_a is None:
             return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = False	) ->				List[int]:






         """simple docstring"""






         if already_has_special_tokens:
             if token_ids_a is not None:
                 raise ValueError(
                     '''You should not supply a second sequence if the provided sequence of '''
                     '''ids is already formatted with special tokens for the model.'''	)
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1]
         return [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1]





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[int]:






         """simple docstring"""






         A       :   Tuple					=						[self.sep_token_id]
         A       :   Dict					=						[self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep	) * [0]
         return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				Tuple[str]:






         """simple docstring"""






         if not os.path.isdir(SCREAMING_SNAKE_CASE	):
             logger.error('''Vocabulary path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE	)	)
             return
         A       :   Any					=						os.path.join(
             SCREAMING_SNAKE_CASE						,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

         if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE	):
             copyfile(self.vocab_file						,			SCREAMING_SNAKE_CASE	)

         return (out_vocab_file,)




