
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_  :	List[str] 				=					get_tests_dir("""fixtures/test_sentencepiece.model""")
lowerCamelCase_  :	List[str] 				=					get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

lowerCamelCase_  :	Tuple 				=					"""pt""" if is_torch_available() else """tf"""

@require_sentencepiece
@require_tokenizers
class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  List[str]    =	CamembertTokenizer
			__UpperCamelCase :  List[str]    =	CamembertTokenizerFast
			__UpperCamelCase :  Dict    =	True
			__UpperCamelCase :  Dict    =	True

			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							super().setUp()

							# We have a SentencePiece fixture for testing
							UpperCamelCase_:							List[str]		=  CamembertTokenizer(snake_case_ )
							tokenizer.save_pretrained(self.tmpdirname )

			def  lowerCAmelCase__		(    self	:    Any ):
							UpperCamelCase_:							Any		=  """<pad>"""
							UpperCamelCase_:							Optional[int]		=  1

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

			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							Union[str, 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(snake_case_ )  ,		1004 )

			def  lowerCAmelCase__		(    self	:    Dict ):
							self.assertEqual(self.get_tokenizer().vocab_size  ,		1005 )

			def  lowerCAmelCase__		(    self	:    Tuple ):
							UpperCamelCase_:							Union[str, Any]		=  CamembertTokenizer(snake_case_ )
							tokenizer.save_pretrained(self.tmpdirname )
							UpperCamelCase_:							str		=  CamembertTokenizerFast.from_pretrained(self.tmpdirname )

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

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

							UpperCamelCase_:							List[str]		=  tokenizer.encode(snake_case_  ,		add_special_tokens=snake_case_ )
							UpperCamelCase_:							Optional[int]		=  rust_tokenizer.encode(snake_case_  ,		add_special_tokens=snake_case_ )
							self.assertListEqual(snake_case_  ,		snake_case_ )

							# <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)
							UpperCamelCase_:							int		=  tokenizer.convert_ids_to_tokens(snake_case_ )
							UpperCamelCase_:							Dict		=  rust_tokenizer.tokenize(snake_case_ )
							self.assertListEqual(snake_case_  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Any ):
							if not self.test_rust_tokenizer:
											return

							UpperCamelCase_:							str		=  self.get_tokenizer()
							UpperCamelCase_:							str		=  self.get_rust_tokenizer()

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

							UpperCamelCase_:							Optional[Any]		=  tokenizer.tokenize(snake_case_ )
							UpperCamelCase_:							Optional[Any]		=  rust_tokenizer.tokenize(snake_case_ )
							self.assertListEqual(snake_case_  ,		snake_case_ )

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

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




			@slow
			def  lowerCAmelCase__		(    self	:    str ):
							# fmt: off
							UpperCamelCase_:							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, 2_7575, 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, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 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.
							UpperCamelCase_:							List[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=snake_case_  ,		model_name="""camembert-base"""  ,		revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""  ,		sequences=snake_case_  ,		)








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_  :	Any 				=					logging.get_logger(__name__)


lowerCamelCase_  :	Optional[int] 				=					{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCamelCase_  :	str 				=					{
    """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_  :	Union[str, Any] 				=					{
    """RUCAIBox/mvp""": 10_24,
}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  List[Any]    =	VOCAB_FILES_NAMES
			__UpperCamelCase :  int    =	PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase :  Any    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase :  Any    =	["""input_ids""", """attention_mask"""]
			__UpperCamelCase :  int    =	MvpTokenizer

			def __init__(    self	:    Union[str, Any]  ,		snake_case_	:    Any=None  ,		snake_case_	:    Any=None  ,		snake_case_	:    Union[str, Any]=None  ,		snake_case_	:    Tuple="replace"  ,		snake_case_	:    Dict="<s>"  ,		snake_case_	:    Dict="</s>"  ,		snake_case_	:    Tuple="</s>"  ,		snake_case_	:    int="<s>"  ,		snake_case_	:    Tuple="<unk>"  ,		snake_case_	:    Optional[int]="<pad>"  ,		snake_case_	:    Any="<mask>"  ,		snake_case_	:    Union[str, Any]=False  ,		snake_case_	:    Optional[int]=True  ,		**snake_case_	:    Union[str, Any]  ,		):
							super().__init__(
							    snake_case_  ,		snake_case_  ,		tokenizer_file=snake_case_  ,		errors=snake_case_  ,		bos_token=snake_case_  ,		eos_token=snake_case_  ,		sep_token=snake_case_  ,		cls_token=snake_case_  ,		unk_token=snake_case_  ,		pad_token=snake_case_  ,		mask_token=snake_case_  ,		add_prefix_space=snake_case_  ,		trim_offsets=snake_case_  ,		**snake_case_  ,		)

							UpperCamelCase_:							Any		=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
							if pre_tok_state.get("""add_prefix_space"""  ,		snake_case_ ) != add_prefix_space:
											UpperCamelCase_:							str		=  getattr(snake_case_  ,		pre_tok_state.pop("""type""" ) )
											UpperCamelCase_:							int		=  add_prefix_space
											UpperCamelCase_:							int		=  pre_tok_class(**snake_case_ )

							UpperCamelCase_:							int		=  add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							UpperCamelCase_:							Tuple		=  """post_processor"""
							UpperCamelCase_:							Optional[int]		=  getattr(self.backend_tokenizer  ,		snake_case_  ,		snake_case_ )
							if tokenizer_component_instance:
											UpperCamelCase_:							Optional[int]		=  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:
															UpperCamelCase_:							Union[str, Any]		=  tuple(state["""sep"""] )
											if "cls" in state:
															UpperCamelCase_:							List[Any]		=  tuple(state["""cls"""] )

											UpperCamelCase_:							List[str]		=  False

											if state.get("""add_prefix_space"""  ,		snake_case_ ) != add_prefix_space:
															UpperCamelCase_:							str		=  add_prefix_space
															UpperCamelCase_:							Union[str, Any]		=  True

											if state.get("""trim_offsets"""  ,		snake_case_ ) != trim_offsets:
															UpperCamelCase_:							Optional[int]		=  trim_offsets
															UpperCamelCase_:							Any		=  True

											if changes_to_apply:
															UpperCamelCase_:							Optional[int]		=  getattr(snake_case_  ,		state.pop("""type""" ) )
															UpperCamelCase_:							Union[str, Any]		=  component_class(**snake_case_ )
															setattr(self.backend_tokenizer  ,		snake_case_  ,		snake_case_ )

			@property
			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							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	:    Union[str, Any]  ,		snake_case_	:    Dict ):
							UpperCamelCase_:							List[str]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_ ) if isinstance(snake_case_  ,		snake_case_ ) else value
							UpperCamelCase_:							Optional[int]		=  value

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		*snake_case_	:    Dict  ,		**snake_case_	:    Optional[int] ):
							UpperCamelCase_:							Optional[int]		=  kwargs.get("""is_split_into_words"""  ,		snake_case_ )

							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(*snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		*snake_case_	:    Dict  ,		**snake_case_	:    Tuple ):
							UpperCamelCase_:							Any		=  kwargs.get("""is_split_into_words"""  ,		snake_case_ )

							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(*snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Tuple  ,		snake_case_	:    str  ,		snake_case_	:    Optional[str] = None ):
							UpperCamelCase_:							Optional[int]		=  self._tokenizer.model.save(snake_case_  ,		name=snake_case_ )
							return tuple(snake_case_ )

			def  lowerCAmelCase__		(    self	:    Tuple  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    Any=None ):
							UpperCamelCase_:							str		=  [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	:    Any  ,		snake_case_	:    List[int]  ,		snake_case_	:    Optional[List[int]] = None ):
							UpperCamelCase_:							Tuple		=  [self.sep_token_id]
							UpperCamelCase_:							List[str]		=  [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]






