





"""simple docstring"""


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__    :   int    						= None

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

a__    :   Dict    						= {"""vocab_file""": """spm.model""", """tokenizer_file""": """tokenizer.json"""}

a__    :   Union[str, Any]    						= {
    """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__    :   Any    						= {
    """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__    :   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__ :							str 							=   VOCAB_FILES_NAMES
       snake_case__ :							str 							=   PRETRAINED_VOCAB_FILES_MAP
       snake_case__ :							str 							=   PRETRAINED_INIT_CONFIGURATION
       snake_case__ :							Optional[int] 							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case__ :							List[str] 							=   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"""


from __future__ import annotations





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   )			->     list[float | int]:

 '''simple docstring'''






 if not all((nth_term, start_term_a, common_ratio_r)		):
  return []
 snake_case__										= []
 snake_case__										= 1
 snake_case__										= common_ratio_r
 for _ in range(int(_UpperCAmelCase		)		):
  if not series:
   series.append(_UpperCAmelCase		)
  else:
   power += 1
   series.append(float(start_term_a * multiple		)		)
   snake_case__										= pow(float(_UpperCAmelCase		)		,   _UpperCAmelCase		)
 return series


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   a__    :   int    						= float(input("""Enter the last number (n term) of the Geometric Series"""))
   a__    :   str    						= float(input("""Enter the starting term (a) of the Geometric Series"""))
   a__    :   str    						= float(
       input("""Enter the common ratio between two terms (r) of the Geometric Series""")
   )
   print("""Formula of Geometric Series => a + ar + ar^2 ... +ar^n""")
   print(geometric_series(nth_term, start_term_a, common_ratio_r))

