


'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


lowercase__					  =		logging.get_logger(__name__)

lowercase__					  =		{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowercase__					  =		{
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "allenai/longformer-base-4096": (
            "https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
    },
}

lowercase__					  =		{
    "allenai/longformer-base-4096": 4_096,
    "allenai/longformer-large-4096": 4_096,
    "allenai/longformer-large-4096-finetuned-triviaqa": 4_096,
    "allenai/longformer-base-4096-extra.pos.embd.only": 4_096,
    "allenai/longformer-large-4096-extra.pos.embd.only": 4_096,
}





class     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			):
     UpperCAmelCase			       =					VOCAB_FILES_NAMES
     UpperCAmelCase			       =					PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase			       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase			       =					['''input_ids''', '''attention_mask''']
     UpperCAmelCase			       =					LongformerTokenizer






     def __init__(      self  ,				__UpperCamelCase=None  ,				__UpperCamelCase=None  ,				__UpperCamelCase=None  ,				__UpperCamelCase="replace"  ,				__UpperCamelCase="<s>"  ,				__UpperCamelCase="</s>"  ,				__UpperCamelCase="</s>"  ,				__UpperCamelCase="<s>"  ,				__UpperCamelCase="<unk>"  ,				__UpperCamelCase="<pad>"  ,				__UpperCamelCase="<mask>"  ,				__UpperCamelCase=False  ,				__UpperCamelCase=True  ,				**__UpperCamelCase  ,				)							->     Dict:
          super().__init__(
              __UpperCamelCase  ,				__UpperCamelCase  ,				tokenizer_file=__UpperCamelCase  ,				errors=__UpperCamelCase  ,				bos_token=__UpperCamelCase  ,				eos_token=__UpperCamelCase  ,				sep_token=__UpperCamelCase  ,				cls_token=__UpperCamelCase  ,				unk_token=__UpperCamelCase  ,				pad_token=__UpperCamelCase  ,				mask_token=__UpperCamelCase  ,				add_prefix_space=__UpperCamelCase  ,				trim_offsets=__UpperCamelCase  ,				**__UpperCamelCase  ,				)

          _a            =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
          if pre_tok_state.get("add_prefix_space"  ,				__UpperCamelCase		) != add_prefix_space:
               _a            =		getattr(__UpperCamelCase  ,				pre_tok_state.pop("type"		)		)
               _a            =		add_prefix_space
               _a            =		pre_tok_class(**__UpperCamelCase		)

          _a            =		add_prefix_space

          _a            =		"post_processor"
          _a            =		getattr(self.backend_tokenizer  ,				__UpperCamelCase  ,				__UpperCamelCase		)
          if tokenizer_component_instance:
               _a            =		json.loads(tokenizer_component_instance.__getstate__()		)

               # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
               if "sep" in state:
                    _a            =		tuple(state["sep"]		)
               if "cls" in state:
                    _a            =		tuple(state["cls"]		)

               _a            =		False

               if state.get("add_prefix_space"  ,				__UpperCamelCase		) != add_prefix_space:
                    _a            =		add_prefix_space
                    _a            =		True

               if state.get("trim_offsets"  ,				__UpperCamelCase		) != trim_offsets:
                    _a            =		trim_offsets
                    _a            =		True

               if changes_to_apply:
                    _a            =		getattr(__UpperCamelCase  ,				state.pop("type"		)		)
                    _a            =		component_class(**__UpperCamelCase		)
                    setattr(self.backend_tokenizer  ,				__UpperCamelCase  ,				__UpperCamelCase		)






     @property
     def 			a_       (      self		)							->     str:
          if self._mask_token is None:
               if self.verbose:
                    logger.error("Using mask_token, but it is not set yet."		)
               return None
          return str(self._mask_token		)






     @mask_token.setter
     def 			a_       (      self  ,				__UpperCamelCase		)							->     Dict:
          _a            =		AddedToken(__UpperCamelCase  ,				lstrip=__UpperCamelCase  ,				rstrip=__UpperCamelCase		) if isinstance(__UpperCamelCase  ,				__UpperCamelCase		) else value
          _a            =		value






     def 			a_       (      self  ,				*__UpperCamelCase  ,				**__UpperCamelCase		)							->     BatchEncoding:
          _a            =		kwargs.get("is_split_into_words"  ,				__UpperCamelCase		)
          assert self.add_prefix_space or not is_split_into_words, (
              f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
              "to use it with pretokenized inputs."
          )

          return super()._batch_encode_plus(*__UpperCamelCase  ,				**__UpperCamelCase		)






     def 			a_       (      self  ,				*__UpperCamelCase  ,				**__UpperCamelCase		)							->     BatchEncoding:
          _a            =		kwargs.get("is_split_into_words"  ,				__UpperCamelCase		)

          assert self.add_prefix_space or not is_split_into_words, (
              f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
              "to use it with pretokenized inputs."
          )

          return super()._encode_plus(*__UpperCamelCase  ,				**__UpperCamelCase		)






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase = None		)							->     Tuple[str]:
          _a            =		self._tokenizer.model.save(__UpperCamelCase  ,				name=__UpperCamelCase		)
          return tuple(__UpperCamelCase		)






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=None		)							->     Dict:
          _a            =		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
               return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase = None		)							->     List[int]:
          _a            =		[self.sep_token_id]
          _a            =		[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]





'''simple docstring'''

from __future__ import annotations






def __UpperCamelCase (      __lowerCamelCase		:			float | int   ,	__lowerCamelCase		:			float | int   ,	__lowerCamelCase		:			float | int   ,	)   ->  list[float | int]:







     '''simple docstring'''






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


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   lowercase__					  =		float(input("Enter the last number (n term) of the Geometric Series"))
   lowercase__					  =		float(input("Enter the starting term (a) of the Geometric Series"))
   lowercase__					  =		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))

