






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


lowerCamelCase       :       List[str]        =				logging.get_logger(__name__)


lowerCamelCase       :       List[str]        =				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCamelCase       :       Tuple        =				{
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase       :       Any        =				{
    '''RUCAIBox/mvp''': 10_24,
}

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	int     =					VOCAB_FILES_NAMES
					_A  :	List[str]     =					PRETRAINED_VOCAB_FILES_MAP
					_A  :	Optional[int]     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_A  :	str     =					['''input_ids''', '''attention_mask''']
					_A  :	List[Any]     =					MvpTokenizer



					def __init__(     self :		Union[str, Any]						,   __a :		Optional[Any]=None						,   __a :		List[str]=None						,   __a :		Tuple=None						,   __a :		Union[str, Any]="replace"						,   __a :		int="<s>"						,   __a :		List[str]="</s>"						,   __a :		Optional[Any]="</s>"						,   __a :		Optional[int]="<s>"						,   __a :		Any="<unk>"						,   __a :		Union[str, Any]="<pad>"						,   __a :		List[Any]="<mask>"						,   __a :		Optional[int]=False						,   __a :		str=True						,   **__a :		Any						,   )	->     List[Any]:


												"""simple docstring"""

												super().__init__(
												    __a						,   __a						,   tokenizer_file=__a						,   errors=__a						,   bos_token=__a						,   eos_token=__a						,   sep_token=__a						,   cls_token=__a						,   unk_token=__a						,   pad_token=__a						,   mask_token=__a						,   add_prefix_space=__a						,   trim_offsets=__a						,   **__a						,   )

												__lowercase					:							List[Any]			=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
												if pre_tok_state.get("""add_prefix_space"""						,   __a							) != add_prefix_space:
																			__lowercase					:							Union[str, Any]			=   getattr(__a						,   pre_tok_state.pop("""type"""							)							)
																			__lowercase					:							Union[str, Any]			=   add_prefix_space
																			__lowercase					:							Any			=   pre_tok_class(**__a							)

												__lowercase					:							str			=   add_prefix_space

												# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
												__lowercase					:							str			=   """post_processor"""
												__lowercase					:							List[str]			=   getattr(self.backend_tokenizer						,   __a						,   __a							)
												if tokenizer_component_instance:
																			__lowercase					:							List[str]			=   json.loads(tokenizer_component_instance.__getstate__()							)

																			# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																			if "sep" in state:
																										__lowercase					:							Union[str, Any]			=   tuple(state["""sep"""]							)
																			if "cls" in state:
																										__lowercase					:							Tuple			=   tuple(state["""cls"""]							)

																			__lowercase					:							str			=   False

																			if state.get("""add_prefix_space"""						,   __a							) != add_prefix_space:
																										__lowercase					:							Optional[int]			=   add_prefix_space
																										__lowercase					:							Optional[Any]			=   True

																			if state.get("""trim_offsets"""						,   __a							) != trim_offsets:
																										__lowercase					:							Any			=   trim_offsets
																										__lowercase					:							Dict			=   True

																			if changes_to_apply:
																										__lowercase					:							List[str]			=   getattr(__a						,   state.pop("""type"""							)							)
																										__lowercase					:							Tuple			=   component_class(**__a							)
																										setattr(self.backend_tokenizer						,   __a						,   __a							)



					@property
					def 					lowerCAmelCase (     self :		List[Any]							)	->     str:


												"""simple docstring"""

												if self._mask_token is None:
																			if self.verbose:
																										logger.error("""Using mask_token, but it is not set yet."""							)
																			return None
												return str(self._mask_token							)



					@mask_token.setter
					def 					lowerCAmelCase (     self :		List[str]						,   __a :		Any							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							int			=   AddedToken(__a						,   lstrip=__a						,   rstrip=__a							) if isinstance(__a						,   __a							) else value
												__lowercase					:							Dict			=   value



					def 					lowerCAmelCase (     self :		Optional[int]						,   *__a :		Optional[Any]						,   **__a :		int							)	->     BatchEncoding:


												"""simple docstring"""

												__lowercase					:							Optional[Any]			=   kwargs.get("""is_split_into_words"""						,   __a							)

												if is_split_into_words and not self.add_prefix_space:
																			raise ValueError(
																			    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
																			    """to use it with pretokenized inputs."""							)

												return super()._batch_encode_plus(*__a						,   **__a							)



					def 					lowerCAmelCase (     self :		Optional[int]						,   *__a :		Union[str, Any]						,   **__a :		Tuple							)	->     BatchEncoding:


												"""simple docstring"""

												__lowercase					:							Tuple			=   kwargs.get("""is_split_into_words"""						,   __a							)

												if is_split_into_words and not self.add_prefix_space:
																			raise ValueError(
																			    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
																			    """to use it with pretokenized inputs."""							)

												return super()._encode_plus(*__a						,   **__a							)



					def 					lowerCAmelCase (     self :		Dict						,   __a :		str						,   __a :		Optional[str] = None							)	->     Tuple[str]:


												"""simple docstring"""

												__lowercase					:							int			=   self._tokenizer.model.save(__a						,   name=__a							)
												return tuple(__a							)



					def 					lowerCAmelCase (     self :		List[Any]						,   __a :		int						,   __a :		Optional[int]=None							)	->     Tuple:


												"""simple docstring"""

												__lowercase					:							Any			=   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
												if token_ids_a is None:
																			return output

												return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



					def 					lowerCAmelCase (     self :		List[str]						,   __a :		List[int]						,   __a :		Optional[List[int]] = None							)	->     List[int]:


												"""simple docstring"""

												__lowercase					:							List[str]			=   [self.sep_token_id]
												__lowercase					:							int			=   [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 + sep + token_ids_a + sep							) * [0]







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       :       Any        =				get_tests_dir('''fixtures/test_sentencepiece.model''')
lowerCamelCase       :       Any        =				get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

lowerCamelCase       :       Optional[int]        =				'''pt''' if is_torch_available() else '''tf'''

@require_sentencepiece
@require_tokenizers
class 						lowerCAmelCase   (       __a					,				unittest.TestCase	):

					'''simple docstring'''

					_A  :	Dict     =					CamembertTokenizer
					_A  :	Union[str, Any]     =					CamembertTokenizerFast
					_A  :	Union[str, Any]     =					True
					_A  :	Tuple     =					True



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


												"""simple docstring"""

												super().setUp()

												# We have a SentencePiece fixture for testing
												__lowercase					:							Union[str, Any]			=   CamembertTokenizer(__a							)
												tokenizer.save_pretrained(self.tmpdirname							)



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Dict			=   """<pad>"""
												__lowercase					:							Dict			=   1

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



					def 					lowerCAmelCase (     self :		Optional[Any]							)	->     Tuple:


												"""simple docstring"""

												__lowercase					:							int			=   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(__a							)						,   1004							)



					def 					lowerCAmelCase (     self :		str							)	->     List[Any]:


												"""simple docstring"""

												self.assertEqual(self.get_tokenizer().vocab_size						,   1005							)



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


												"""simple docstring"""

												__lowercase					:							Tuple			=   CamembertTokenizer(__a							)
												tokenizer.save_pretrained(self.tmpdirname							)
												__lowercase					:							Tuple			=   CamembertTokenizerFast.from_pretrained(self.tmpdirname							)

												__lowercase					:							List[str]			=   """I was born in 92000, and this is falsé."""

												__lowercase					:							Optional[Any]			=   tokenizer.encode(__a							)
												__lowercase					:							List[Any]			=   rust_tokenizer.encode(__a							)
												self.assertListEqual(__a						,   __a							)

												__lowercase					:							Tuple			=   tokenizer.encode(__a						,   add_special_tokens=__a							)
												__lowercase					:							Union[str, Any]			=   rust_tokenizer.encode(__a						,   add_special_tokens=__a							)
												self.assertListEqual(__a						,   __a							)

												# <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)
												__lowercase					:							Dict			=   tokenizer.convert_ids_to_tokens(__a							)
												__lowercase					:							Any			=   rust_tokenizer.tokenize(__a							)
												self.assertListEqual(__a						,   __a							)



					def 					lowerCAmelCase (     self :		Any							)	->     Dict:


												"""simple docstring"""

												if not self.test_rust_tokenizer:
																			return

												__lowercase					:							List[str]			=   self.get_tokenizer()
												__lowercase					:							Any			=   self.get_rust_tokenizer()

												__lowercase					:							Any			=   """I was born in 92000, and this is falsé."""

												__lowercase					:							Tuple			=   tokenizer.tokenize(__a							)
												__lowercase					:							Optional[Any]			=   rust_tokenizer.tokenize(__a							)
												self.assertListEqual(__a						,   __a							)

												__lowercase					:							Dict			=   tokenizer.encode(__a						,   add_special_tokens=__a							)
												__lowercase					:							Dict			=   rust_tokenizer.encode(__a						,   add_special_tokens=__a							)
												self.assertListEqual(__a						,   __a							)

												__lowercase					:							Any			=   self.get_rust_tokenizer()
												__lowercase					:							str			=   tokenizer.encode(__a							)
												__lowercase					:							Union[str, Any]			=   rust_tokenizer.encode(__a							)
												self.assertListEqual(__a						,   __a							)



					@slow
					def 					lowerCAmelCase (     self :		Optional[int]							)	->     List[Any]:


												"""simple docstring"""

												__lowercase					:							str			=   {"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 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.
												__lowercase					:							List[str]			=   [
												    """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=__a						,   model_name="""camembert-base"""						,   revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""						,   sequences=__a						,   )