




"""simple docstring"""


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A_						=				logging.get_logger(__name__)

A_						=				{'''vocab_file''': '''sentencepiece.bpe.model'''}

A_						=				{
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

A_						=				{
    '''camembert-base''': 5_12,
}

A_						=				'''▁'''






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					VOCAB_FILES_NAMES
 lowercase__ 					=					PRETRAINED_VOCAB_FILES_MAP
 lowercase__ 					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase__ 					=					["input_ids", "attention_mask"]


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




   '''simple docstring'''



   _snake_case							:		List[str]								=  AddedToken(a_,      lstrip=a_,      rstrip=a_		) if isinstance(a_,      a_		) else mask_token

   _snake_case							:		Tuple								=  {} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       bos_token=a_,      eos_token=a_,      unk_token=a_,      sep_token=a_,      cls_token=a_,      pad_token=a_,      mask_token=a_,      additional_special_tokens=a_,      sp_model_kwargs=self.sp_model_kwargs,      **a_,      )
   _snake_case							:		str								=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
   self.sp_model.Load(str(a_		)		)
   _snake_case							:		List[str]								=  vocab_file
   # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
   # sentencepiece vocabulary (this is the case for <s> and </s>
   _snake_case							:		List[str]								=  {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
   _snake_case							:		List[Any]								=  len(self.fairseq_tokens_to_ids		)
   _snake_case							:		List[str]								=  len(self.sp_model		) + len(self.fairseq_tokens_to_ids		)
   _snake_case							:		str								=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}


 def  UpperCamelCase_   (				self:		Optional[Any],      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]
   _snake_case							:		int								=  [self.cls_token_id]
   _snake_case							:		Any								=  [self.sep_token_id]
   return cls + token_ids_a + sep + sep + token_ids_a + sep


 def  UpperCamelCase_   (				self:		Optional[int],      a_:		List[int],      a_:		Optional[List[int]] = None,      a_:		bool = False		):




   '''simple docstring'''



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

   if token_ids_a is None:
     return [1] + ([0] * len(a_		)) + [1]
   return [1] + ([0] * len(a_		)) + [1, 1] + ([0] * len(a_		)) + [1]


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




   '''simple docstring'''



   _snake_case							:		str								=  [self.sep_token_id]
   _snake_case							:		str								=  [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]


 @property
 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   return len(self.fairseq_tokens_to_ids		) + len(self.sp_model		)


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  {self.convert_ids_to_tokens(a_		): i for i in range(self.vocab_size		)}
   vocab.update(self.added_tokens_encoder		)
   return vocab


 def  UpperCamelCase_   (				self:		List[str],      a_:		str		):




   '''simple docstring'''



   return self.sp_model.encode(a_,      out_type=a_		)


 def  UpperCamelCase_   (				self:		List[Any],      a_:		List[Any]		):




   '''simple docstring'''



   if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
   elif self.sp_model.PieceToId(a_		) == 0:
     # Convert sentence piece unk token to fairseq unk token index
     return self.unk_token_id
   return self.fairseq_offset + self.sp_model.PieceToId(a_		)


 def  UpperCamelCase_   (				self:		Any,      a_:		Optional[int]		):




   '''simple docstring'''



   if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
   return self.sp_model.IdToPiece(index - self.fairseq_offset		)


 def  UpperCamelCase_   (				self:		List[str],      a_:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  []
   _snake_case							:		Optional[int]								=  """"""
   _snake_case							:		Optional[Any]								=  False
   for token in tokens:
     # make sure that special tokens are not decoded using sentencepiece model
     if token in self.all_special_tokens:
       if not prev_is_special:
         out_string += " "
       out_string += self.sp_model.decode(a_		) + token
       _snake_case							:		Tuple								=  True
       _snake_case							:		Any								=  []
     else:
       current_sub_tokens.append(a_		)
       _snake_case							:		Optional[int]								=  False
   out_string += self.sp_model.decode(a_		)
   return out_string.strip()


 def __getstate__(				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		int								=  self.__dict__.copy()
   _snake_case							:		Optional[int]								=  None
   return state


 def __setstate__(				self:		int,      a_:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  d

   # for backward compatibility
   if not hasattr(self,      """sp_model_kwargs"""		):
     _snake_case							:		List[Any]								=  {}

   _snake_case							:		int								=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
   self.sp_model.Load(self.vocab_file		)


 def  UpperCamelCase_   (				self:		str,      a_:		str,      a_:		Optional[str] = None		):




   '''simple docstring'''



   if not os.path.isdir(a_		):
     logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
     return
   _snake_case							:		Optional[Any]								=  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_		) and os.path.isfile(self.vocab_file		):
     copyfile(self.vocab_file,      a_		)
   elif not os.path.isfile(self.vocab_file		):
     with open(a_,      """wb"""		) as fi:
       _snake_case							:		Dict								=  self.sp_model.serialized_model_proto()
       fi.write(a_		)

   return (out_vocab_file,)








"""simple docstring"""


from __future__ import annotations


def 					UpperCAmelCase__       (snake_case__							:					list[int]		,   snake_case__							:					int    ):
  """simple docstring"""

  if len(snake_case__    ) < k or k < 0:
    raise ValueError("""Invalid Input"""    )
  _snake_case							:		Optional[int]								=  sum(array[:k]    )
  for i in range(len(snake_case__    ) - k    ):
    _snake_case							:		Optional[Any]								=  current_sum - array[i] + array[i + k]
    _snake_case							:		List[str]								=  max(snake_case__		,   snake_case__    )
  return max_sum


if __name__ == "__main__":
     from doctest import testmod
     from random import randint

     testmod()
     A_						=				[randint(-10_00, 10_00) for i in range(1_00)]
     A_						=				randint(0, 1_10)
     print(F'''The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}''')


