


'''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:
       __lowercase:		Dict    						=  None


__lowercase:		str    						=  logging.get_logger(__name__)

__lowercase:		Optional[int]    						=  {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__lowercase:		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"
        ),
    },
}

__lowercase:		Union[str, Any]    						=  {
    "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 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):
 _lowerCamelCase			:       Union[str, Any]            =       VOCAB_FILES_NAMES
 _lowerCamelCase			:       Optional[int]            =       PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase			:       str            =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase			:       List[str]            =       ['input_ids', 'attention_mask']
 _lowerCamelCase			:       Dict            =       XLMRobertaTokenizer

 def __init__(			self			:						List[str],       a_			:						List[str]=None,       a_			:						Any=None,       a_			:						Any="<s>",       a_			:						List[str]="</s>",       a_			:						int="</s>",       a_			:						Dict="<s>",       a_			:						Tuple="<unk>",       a_			:						List[str]="<pad>",       a_			:						str="<mask>",       **a_			:						str,       ):





    """simple docstring"""



    UpperCamelCase__			    =    AddedToken(a_,       lstrip=a_,       rstrip=a_       ) if isinstance(a_,       a_       ) else mask_token

    super().__init__(
        a_,       tokenizer_file=a_,       bos_token=a_,       eos_token=a_,       sep_token=a_,       cls_token=a_,       unk_token=a_,       pad_token=a_,       mask_token=a_,       **a_,       )

    UpperCamelCase__			    =    vocab_file
    UpperCamelCase__			    =    False if not self.vocab_file else True

 def 		lowercase_    (			self			:						int,       a_			:						List[int],       a_			:						Optional[List[int]] = None       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						List[str],       a_			:						List[int],       a_			:						Optional[List[int]] = None       ):





    """simple docstring"""



    UpperCamelCase__			    =    [self.sep_token_id]
    UpperCamelCase__			    =    [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 		lowercase_    (			self			:						Dict,       a_			:						str,       a_			:						Optional[str] = None       ):





    """simple docstring"""



    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(a_       ):
       logger.error(f'Vocabulary path ({save_directory}) should be a directory.'       )
       return
    UpperCamelCase__			    =    os.path.join(
        a_,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

    if os.path.abspath(self.vocab_file       ) != os.path.abspath(a_       ):
       copyfile(self.vocab_file,       a_       )

    return (out_vocab_file,)





"""simple docstring"""
import math






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Tuple						)    ->       Any:
 _UpperCamelCase      :	str					      =  0
 if n % 2 == 0:
  count += 1
  while n % 2 == 0:
   _UpperCamelCase      :	List[Any]					      =  int(n / 2						)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

 _UpperCamelCase      :	Tuple					      =  3

 while i <= int(math.sqrt(__lowerCAmelCase						)						):
  if n % i == 0:
   count += 1
   while n % i == 0:
    _UpperCamelCase      :	Any					      =  int(n / i						)
  _UpperCamelCase      :	Optional[Any]					      =  i + 2

 # this condition checks the prime
 # number n is greater than 2

 if n > 2:
  count += 1
 return count


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE						 =     5_1_2_4_2_1_8_3
    print(f'The number of distinct prime factors is/are {exact_prime_factor_count(n)}')
    print(f'The value of log(log(n)) is {math.log(math.log(n)):.4f}')



