






from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
SCREAMING_SNAKE_CASE       							=  TypeVar('T', bound='Enum')
@unique
class 							A_			(  a_	):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:	Tuple       =    8_0
 _SCREAMING_SNAKE_CASE							:	Dict       =    7_0
 _SCREAMING_SNAKE_CASE							:	Tuple       =    6_0
 _SCREAMING_SNAKE_CASE							:	List[Any]       =    5_0
 _SCREAMING_SNAKE_CASE							:	List[Any]       =    4_0
 _SCREAMING_SNAKE_CASE							:	Optional[Any]       =    3_0
 _SCREAMING_SNAKE_CASE							:	Dict       =    2_0
 _SCREAMING_SNAKE_CASE							:	str       =    1_0



@unique
class 							A_			(  a_	):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:	Optional[Any]       =    2_4
 _SCREAMING_SNAKE_CASE							:	Any       =    2_1
 _SCREAMING_SNAKE_CASE							:	Dict       =    1_8
 _SCREAMING_SNAKE_CASE							:	Optional[int]       =    1_5
 _SCREAMING_SNAKE_CASE							:	Tuple       =    1_2
 _SCREAMING_SNAKE_CASE							:	List[str]       =    9
 _SCREAMING_SNAKE_CASE							:	Optional[Any]       =    6
 _SCREAMING_SNAKE_CASE							:	str       =    3
 _SCREAMING_SNAKE_CASE							:	Optional[int]       =    2
 _SCREAMING_SNAKE_CASE							:	int       =    1
 _SCREAMING_SNAKE_CASE							:	List[str]       =    -1
 _SCREAMING_SNAKE_CASE							:	Any       =    -2
 _SCREAMING_SNAKE_CASE							:	Union[str, Any]       =    -3
 _SCREAMING_SNAKE_CASE							:	Optional[int]       =    -6
 _SCREAMING_SNAKE_CASE							:	List[str]       =    -9
 _SCREAMING_SNAKE_CASE							:	List[str]       =    -1_2
 _SCREAMING_SNAKE_CASE							:	Tuple       =    -1_5
 _SCREAMING_SNAKE_CASE							:	List[Any]       =    -1_8
 _SCREAMING_SNAKE_CASE							:	List[str]       =    -2_1
 _SCREAMING_SNAKE_CASE							:	Optional[Any]       =    -2_4


 @classmethod
 def        snake_case__ (		cls)       ->						List[str]:

     """simple docstring"""



     return {unit.name: unit.value for unit in cls if unit.value > 0}


 @classmethod
 def        snake_case__ (		cls)       ->						List[Any]:

     """simple docstring"""



     return {unit.name: unit.value for unit in cls if unit.value < 0}



def    _lowerCamelCase		(  __A			:							float       )					->    Dict:
    _UpperCAmelCase :					Any	     = SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
    for name_prefix, value_prefix in prefixes.items():
        _UpperCAmelCase :					Optional[Any]	     = value / (10**value_prefix)
        if numerical_part > 1:
            return f'''{numerical_part!s} {name_prefix}'''
    return str(__A       )

def    _lowerCamelCase		(  __A			:							float       )					->    Optional[int]:
    for prefix in BinaryUnit:
        _UpperCAmelCase :					Tuple	     = value / (2**prefix.value)
        if numerical_part > 1:
            return f'''{numerical_part!s} {prefix.name}'''
    return str(__A       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()








'''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_xlm_roberta import XLMRobertaTokenizer
else:
   A      :     Optional[int]										= None


A      :     Optional[Any]										= logging.get_logger(__name__)

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

A      :     Optional[int]										= {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

A      :     List[str]										= {
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}


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													=    XLMRobertaTokenizer


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

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


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






