


'''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_camembert import CamembertTokenizer
else:
   A				:					Optional[int]    						=			None


A				:					Union[str, Any]    						=			logging.get_logger(__name__)

A				:					Tuple    						=			{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

A				:					List[Any]    						=			{
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

A				:					Optional[Any]    						=			{
    '''camembert-base''': 512,
}

A				:					Union[str, Any]    						=			'''▁'''


class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   VOCAB_FILES_NAMES
    a													=   PRETRAINED_VOCAB_FILES_MAP
    a													=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a													=   ["input_ids", "attention_mask"]
    a													=   CamembertTokenizer


    def __init__(  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Tuple=None				,							SCREAMING_SNAKE_CASE      :							Tuple=None				,							SCREAMING_SNAKE_CASE      :							Any="<s>"				,							SCREAMING_SNAKE_CASE      :							Any="</s>"				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]="</s>"				,							SCREAMING_SNAKE_CASE      :							List[Any]="<s>"				,							SCREAMING_SNAKE_CASE      :							List[str]="<unk>"				,							SCREAMING_SNAKE_CASE      :							Optional[Any]="<pad>"				,							SCREAMING_SNAKE_CASE      :							List[Any]="<mask>"				,							SCREAMING_SNAKE_CASE      :							Tuple=["<s>NOTUSED", "</s>NOTUSED"]				,							**SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							):
           # Mask token behave like a normal word, i.e. include the space before it
           _A					:     int     = 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				,							bos_token=SCREAMING_SNAKE_CASE				,							eos_token=SCREAMING_SNAKE_CASE				,							sep_token=SCREAMING_SNAKE_CASE				,							cls_token=SCREAMING_SNAKE_CASE				,							unk_token=SCREAMING_SNAKE_CASE				,							pad_token=SCREAMING_SNAKE_CASE				,							mask_token=SCREAMING_SNAKE_CASE				,							additional_special_tokens=SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE				,							)

           _A					:     Optional[Any]     = vocab_file
           _A					:     List[str]     = False if not self.vocab_file else True


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[int]				,							SCREAMING_SNAKE_CASE      :							Optional[List[int]] = None):

           if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           _A					:     Union[str, Any]     = [self.cls_token_id]
           _A					:     Optional[int]     = [self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[int]				,							SCREAMING_SNAKE_CASE      :							Optional[List[int]] = None):
           _A					:     Tuple     = [self.sep_token_id]
           _A					:     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]


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Optional[str] = None):
           if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                      'tokenizer.')

           if not os.path.isdir(SCREAMING_SNAKE_CASE):
                  logger.error(F'Vocabulary path ({save_directory}) should be a directory')
                  return
           _A					:     Dict     = 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,)










'''simple docstring'''







def 				lowerCAmelCase__	(    lowerCamelCase					:      int = 10       ):
       if not isinstance(lowerCamelCase  ,lowerCamelCase       ) or n < 0:
              raise ValueError('Invalid input'       )
       _A					:     Optional[Any]     = 10**n
       _A					:     List[str]     = 28433 * (pow(2  ,7830457  ,lowerCamelCase       )) + 1
       return str(number % modulus       )


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   print(f"""{solution(10) = }""")






