





"""simple docstring"""



import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


a_			=      logging.get_logger(__name__)

a_			=      {"""vocab_file""": """spiece.model"""}

a_			=      {
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    }
}






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase=False ,						__lowerCamelCase=True ,						__lowerCamelCase=False ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="<unk>" ,						__lowerCamelCase="<sep>" ,						__lowerCamelCase="<pad>" ,						__lowerCamelCase="<cls>" ,						__lowerCamelCase="<mask>" ,						__lowerCamelCase=["<eop>", "<eod>"] ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     __A   :						Union[str, Any]									=					AddedToken(__lowerCamelCase ,						lstrip=__lowerCamelCase ,						rstrip=__lowerCamelCase  ) if isinstance(__lowerCamelCase ,						__lowerCamelCase  ) else mask_token

     __A   :						Any									=					{} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         do_lower_case=__lowerCamelCase ,						remove_space=__lowerCamelCase ,						keep_accents=__lowerCamelCase ,						bos_token=__lowerCamelCase ,						eos_token=__lowerCamelCase ,						unk_token=__lowerCamelCase ,						sep_token=__lowerCamelCase ,						pad_token=__lowerCamelCase ,						cls_token=__lowerCamelCase ,						mask_token=__lowerCamelCase ,						additional_special_tokens=__lowerCamelCase ,						sp_model_kwargs=self.sp_model_kwargs ,						**__lowerCamelCase ,						)

     __A   :						int									=					3

     __A   :						Dict									=					do_lower_case
     __A   :						Dict									=					remove_space
     __A   :						Dict									=					keep_accents
     __A   :						List[Any]									=					vocab_file

     __A   :						Optional[int]									=					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
     self.sp_model.Load(__lowerCamelCase  )

     try:
         import jieba
     except ModuleNotFoundError as error:
         raise error.__class__(
             '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
             '''See https://pypi.org/project/jieba/ for installation.'''  )
     __A   :						Any									=					jieba
     __A   :						Dict									=					str.maketrans(''' \n''' ,						'''\u2582\u2583'''  )

 @property
 # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return len(self.sp_model  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					{self.convert_ids_to_tokens(__lowerCamelCase  ): i for i in range(self.vocab_size  )}
     vocab.update(self.added_tokens_encoder  )
     return vocab

 def __getstate__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					self.__dict__.copy()
     __A   :						Optional[int]									=					None
     return state

 def __setstate__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						List[Any]									=					d

     # for backward compatibility
     if not hasattr(self ,						'''sp_model_kwargs'''  ):
         __A   :						Any									=					{}

     __A   :						Any									=					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
     self.sp_model.Load(self.vocab_file  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     if self.remove_space:
         __A   :						int									=					''' '''.join(inputs.strip().split()  )
     else:
         __A   :						Optional[Any]									=					inputs
     __A   :						Dict									=					outputs.replace('''``''' ,						'''"'''  ).replace('''\'\'''' ,						'''"'''  )

     if not self.keep_accents:
         __A   :						List[str]									=					unicodedata.normalize('''NFKD''' ,						__lowerCamelCase  )
         __A   :						Any									=					''''''.join([c for c in outputs if not unicodedata.combining(__lowerCamelCase  )]  )
     if self.do_lower_case:
         __A   :						str									=					outputs.lower()

     return outputs

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						int									=					self.preprocess_text(__lowerCamelCase  )
     __A   :						List[Any]									=					self.sp_model.encode(__lowerCamelCase ,						out_type=__lowerCamelCase  )
     __A   :						str									=					[]
     for piece in pieces:
         if len(__lowerCamelCase  ) > 1 and piece[-1] == str(''','''  ) and piece[-2].isdigit():
             __A   :						Optional[int]									=					self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCamelCase ,						''''''  )  )
             if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                 if len(cur_pieces[0]  ) == 1:
                     __A   :						str									=					cur_pieces[1:]
                 else:
                     __A   :						Dict									=					cur_pieces[0][1:]
             cur_pieces.append(piece[-1]  )
             new_pieces.extend(__lowerCamelCase  )
         else:
             new_pieces.append(__lowerCamelCase  )

     return new_pieces

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     return self.sp_model.PieceToId(__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     return self.sp_model.IdToPiece(__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					''''''.join(__lowerCamelCase  ).replace(__lowerCamelCase ,						''' '''  ).strip()
     return out_string

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = False  ):



     '''simple docstring'''




     if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=__lowerCamelCase ,						token_ids_a=__lowerCamelCase ,						already_has_special_tokens=__lowerCamelCase  )

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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None  ):



     '''simple docstring'''




     __A   :						List[Any]									=					[self.sep_token_id]
     __A   :						int									=					[2]

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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None  ):



     '''simple docstring'''




     if not os.path.isdir(__lowerCamelCase  ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
         return
     __A   :						Union[str, Any]									=					os.path.join(
         __lowerCamelCase ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

     if os.path.abspath(self.vocab_file  ) != os.path.abspath(__lowerCamelCase  ) and os.path.isfile(self.vocab_file  ):
         copyfile(self.vocab_file ,						__lowerCamelCase  )
     elif not os.path.isfile(self.vocab_file  ):
         with open(__lowerCamelCase ,						'''wb'''  ) as fi:
             __A   :						Union[str, Any]									=					self.sp_model.serialized_model_proto()
             fi.write(__lowerCamelCase  )

     return (out_vocab_file,)







 def  UpperCamelCase__(   self ,						*__lowerCamelCase ,						**__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						List[str]									=					super()._decode(*__lowerCamelCase ,						**__lowerCamelCase  )
     __A   :						Tuple									=					text.replace(''' ''' ,						''''''  ).replace('''\u2582''' ,						''' '''  ).replace('''\u2583''' ,						'''\n'''  )
     return text











"""simple docstring"""



import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					BarthezTokenizer
 _lowerCamelCase				=					BarthezTokenizerFast
 _lowerCamelCase				=					True
 _lowerCamelCase				=					True

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     super().setUp()

     __A   :						List[Any]									=					BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''  )
     tokenizer.save_pretrained(self.tmpdirname  )
     tokenizer.save_pretrained(self.tmpdirname ,						legacy_format=__lowerCamelCase  )
     __A   :						Any									=					tokenizer

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					'''<pad>'''
     __A   :						Union[str, Any]									=					1

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

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					list(self.get_tokenizer().get_vocab().keys()  )

     self.assertEqual(vocab_keys[0] ,						'''<s>'''  )
     self.assertEqual(vocab_keys[1] ,						'''<pad>'''  )
     self.assertEqual(vocab_keys[-1] ,						'''<mask>'''  )
     self.assertEqual(len(__lowerCamelCase  ) ,						10_1122  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     self.assertEqual(self.get_tokenizer().vocab_size ,						10_1122  )

 @require_torch
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     __A   :						Any									=					[0, 57, 3018, 7_0307, 91, 2]

     __A   :						List[Any]									=					self.tokenizer(
         __lowerCamelCase ,						max_length=len(__lowerCamelCase  ) ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						return_tensors='''pt'''  )
     self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase  )

     self.assertEqual((2, 6) ,						batch.input_ids.shape  )
     self.assertEqual((2, 6) ,						batch.attention_mask.shape  )
     __A   :						Any									=					batch.input_ids.tolist()[0]
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     if not self.test_rust_tokenizer:
         return

     __A   :						Tuple									=					self.get_tokenizer()
     __A   :						Dict									=					self.get_rust_tokenizer()

     __A   :						Union[str, Any]									=					'''I was born in 92000, and this is falsé.'''

     __A   :						List[Any]									=					tokenizer.tokenize(__lowerCamelCase  )
     __A   :						Optional[int]									=					rust_tokenizer.tokenize(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  )
     __A   :						Union[str, Any]									=					rust_tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					self.get_rust_tokenizer()
     __A   :						Optional[int]									=					tokenizer.encode(__lowerCamelCase  )
     __A   :						Any									=					rust_tokenizer.encode(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )







 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					{'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], '''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, 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, 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

     # moussaKam/mbarthez is a french model. So we also use french texts.
     __A   :						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=__lowerCamelCase ,						model_name='''moussaKam/mbarthez''' ,						revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' ,						sequences=__lowerCamelCase ,						)




