





import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
   from .tokenization_deberta_va import DebertaVaTokenizer
else:
   a__   :       str   						=			None

a__   :       List[str]   						=			logging.get_logger(__name__)

a__   :       List[str]   						=			{"""vocab_file""": """spm.model""", """tokenizer_file""": """tokenizer.json"""}

a__   :       List[str]   						=			{
    """vocab_file""": {
        """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model""",
        """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model""",
        """microsoft/deberta-v2-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"""
        ),
        """microsoft/deberta-v2-xxlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"""
        ),
    }
}

a__   :       Dict   						=			{
    """microsoft/deberta-v2-xlarge""": 5_1_2,
    """microsoft/deberta-v2-xxlarge""": 5_1_2,
    """microsoft/deberta-v2-xlarge-mnli""": 5_1_2,
    """microsoft/deberta-v2-xxlarge-mnli""": 5_1_2,
}

a__   :       Optional[Any]   						=			{
    """microsoft/deberta-v2-xlarge""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xxlarge""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xlarge-mnli""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xxlarge-mnli""": {"""do_lower_case""": False},
}



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							List[str]										=       VOCAB_FILES_NAMES
       snake_case__			:							Tuple										=       PRETRAINED_VOCAB_FILES_MAP
       snake_case__			:							Tuple										=       PRETRAINED_INIT_CONFIGURATION
       snake_case__			:							List[Any]										=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case__			:							List[Any]										=       DebertaVaTokenizer

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=None				,   __SCREAMING_SNAKE_CASE=None				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE="[CLS]"				,   __SCREAMING_SNAKE_CASE="[SEP]"				,   __SCREAMING_SNAKE_CASE="[UNK]"				,   __SCREAMING_SNAKE_CASE="[SEP]"				,   __SCREAMING_SNAKE_CASE="[PAD]"				,   __SCREAMING_SNAKE_CASE="[CLS]"				,   __SCREAMING_SNAKE_CASE="[MASK]"				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

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

        snake_case__      							=			do_lower_case
        snake_case__      							=			split_by_punct
        snake_case__      							=			vocab_file
        snake_case__      							=			False if not self.vocab_file else True

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE=None       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE=None				,   __SCREAMING_SNAKE_CASE=False       ):






        '''simple docstring'''

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

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

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE=None       ):






        '''simple docstring'''

        snake_case__      							=			[self.sep_token_id]
        snake_case__      							=			[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       ) * [0] + len(token_ids_a + sep       ) * [1]

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE = 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(__SCREAMING_SNAKE_CASE       ):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory"       )
         return
        snake_case__      							=			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"""


import math





def 							a__     (					_UpperCAmelCase		)      ->					list:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive integers have prime factors"		)
 snake_case__      							=			[]
 while n % 2 == 0:
  pf.append(2		)
  snake_case__      							=			int(n / 2		)
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  while n % i == 0:
   pf.append(_UpperCAmelCase		)
   snake_case__      							=			int(n / i		)
 if n > 2:
  pf.append(_UpperCAmelCase		)
 return pf





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive numbers are accepted"		)
 snake_case__      							=			1
 snake_case__      							=			1
 while n % 2 == 0:
  temp += 1
  snake_case__      							=			int(n / 2		)
 div *= temp
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  snake_case__      							=			1
  while n % i == 0:
   temp += 1
   snake_case__      							=			int(n / i		)
  div *= temp
 if n > 1:
  div *= 2
 return div





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive numbers are accepted"		)
 snake_case__      							=			1
 snake_case__      							=			1
 while n % 2 == 0:
  temp += 1
  snake_case__      							=			int(n / 2		)
 if temp > 1:
  s *= (2**temp - 1) / (2 - 1)
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  snake_case__      							=			1
  while n % i == 0:
   temp += 1
   snake_case__      							=			int(n / i		)
  if temp > 1:
   s *= (i**temp - 1) / (i - 1)
 return int(_UpperCAmelCase		)





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 snake_case__      							=			n
 for x in set(prime_factors(_UpperCAmelCase		)		):
  s *= (x - 1) / x
 return int(_UpperCAmelCase		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

