






"""simple docstring"""



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

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


if is_sentencepiece_available():
    from .tokenization_xglm import XGLMTokenizer
else:
    _UpperCamelCase						:    Optional[int]		=						None


_UpperCamelCase						:    Optional[int]		=						logging.get_logger(__name__)

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

_UpperCamelCase						:    List[str]		=						{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

_UpperCamelCase						:    List[str]		=						{
    'facebook/xglm-564M': 2_0_4_8,
}

class 			a	(  a_      ):
    UpperCAmelCase_					:	Dict      	=VOCAB_FILES_NAMES
    UpperCAmelCase_					:	Any      	=PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase_					:	List[str]      	=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase_					:	int      	=["input_ids", "attention_mask"]
    UpperCAmelCase_					:	Dict      	=XGLMTokenizer







    def __init__(     self				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase="<s>"				,						_lowerCamelCase="</s>"				,						_lowerCamelCase="</s>"				,						_lowerCamelCase="<s>"				,						_lowerCamelCase="<unk>"				,						_lowerCamelCase="<pad>"				,						**_lowerCamelCase				,						):
      # Compatibility with the original tokenizer
      lowercase				       =			7
      lowercase				       =			[F'<madeupword{i}>' for i in range(self.num_madeup_words			)]

      lowercase				       =			kwargs.get('additional_special_tokens'				,						[]			)
      kwargs["additional_special_tokens"] += [
          word for word in madeup_words if word not in kwargs["additional_special_tokens"]
      ]

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

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







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase = None			):

      if token_ids_a is None:
        return [self.sep_token_id] + token_ids_a
      lowercase				       =			[self.sep_token_id]
      return sep + token_ids_a + sep + sep + token_ids_a







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase = None			):
      lowercase				       =			[self.sep_token_id]

      if token_ids_a is None:
        return len(sep + token_ids_a			) * [0]
      return len(sep + token_ids_a + sep + sep + token_ids_a			) * [0]







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase = 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(_lowerCamelCase			):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory.'			)
        return
      lowercase				       =			os.path.join(
          _lowerCamelCase				,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

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

      return (out_vocab_file,)







def        a( A			:       int					,	A			:       int					,	A			:       int			)						->			float:
					"""simple docstring"""





					if common_ratio == 1:
										# Formula for sum if common ratio is 1
										return num_of_terms * first_term

					# Formula for finding sum of n terms of a GeometricProgression
					return (first_term / (1 - common_ratio)) * (1 - common_ratio**num_of_terms)
