
"""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		:       Union[str, Any]     						=       logging.get_logger(__name__)

a		:       Union[str, Any]     						=       {'vocab_file': 'sentencepiece.bpe.model'}

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

a		:       int     						=       {
    'camembert-base': 5_12,
}

a		:       Dict     						=       '▁'







class     lowercase(_lowercase       ):
     __snake_case:	Optional[Any]						=     VOCAB_FILES_NAMES
     __snake_case:	Tuple						=     PRETRAINED_VOCAB_FILES_MAP
     __snake_case:	Union[str, Any]						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __snake_case:	Optional[Any]						=     ['input_ids', 'attention_mask']



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE="<s>"     ,				__SCREAMING_SNAKE_CASE="</s>"     ,				__SCREAMING_SNAKE_CASE="</s>"     ,				__SCREAMING_SNAKE_CASE="<s>"     ,				__SCREAMING_SNAKE_CASE="<unk>"     ,				__SCREAMING_SNAKE_CASE="<pad>"     ,				__SCREAMING_SNAKE_CASE="<mask>"     ,				__SCREAMING_SNAKE_CASE=["<s>NOTUSED", "</s>NOTUSED"]     ,				__SCREAMING_SNAKE_CASE = None     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     None:







         """simple docstring"""



         a__		=			AddedToken(__SCREAMING_SNAKE_CASE     ,				lstrip=__SCREAMING_SNAKE_CASE     ,				rstrip=__SCREAMING_SNAKE_CASE   ) if isinstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ) else mask_token

         a__		=			{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=__SCREAMING_SNAKE_CASE     ,				eos_token=__SCREAMING_SNAKE_CASE     ,				unk_token=__SCREAMING_SNAKE_CASE     ,				sep_token=__SCREAMING_SNAKE_CASE     ,				cls_token=__SCREAMING_SNAKE_CASE     ,				pad_token=__SCREAMING_SNAKE_CASE     ,				mask_token=__SCREAMING_SNAKE_CASE     ,				additional_special_tokens=__SCREAMING_SNAKE_CASE     ,				sp_model_kwargs=self.sp_model_kwargs     ,				**__SCREAMING_SNAKE_CASE     ,				)
         a__		=			spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(str(__SCREAMING_SNAKE_CASE   )   )
         a__		=			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>
         a__		=			{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
         a__		=			len(self.fairseq_tokens_to_ids   )
         a__		=			len(self.sp_model   ) + len(self.fairseq_tokens_to_ids   )
         a__		=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None   )		->     List[int]:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = False   )		->     List[int]:







         """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 None:
             return [1] + ([0] * len(__SCREAMING_SNAKE_CASE   )) + [1]
         return [1] + ([0] * len(__SCREAMING_SNAKE_CASE   )) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE   )) + [1]



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None   )		->     List[int]:







         """simple docstring"""



         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]



     @property
     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



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



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         a__		=			{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE   ): i for i in range(self.vocab_size   )}
         vocab.update(self.added_tokens_encoder   )
         return vocab



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     List[str]:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Any:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Dict:







         """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 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Dict:







         """simple docstring"""



         a__		=			[]
         a__		=			''
         a__		=			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(__SCREAMING_SNAKE_CASE   ) + token
                 a__		=			True
                 a__		=			[]
             else:
                 current_sub_tokens.append(__SCREAMING_SNAKE_CASE   )
                 a__		=			False
         out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE   )
         return out_string.strip()



     def __getstate__(							self   )		->     Optional[int]:







         """simple docstring"""



         a__		=			self.__dict__.copy()
         a__		=			None
         return state



     def __setstate__(							self     ,				__SCREAMING_SNAKE_CASE   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			d

         # for backward compatibility
         if not hasattr(self     ,				'sp_model_kwargs'   ):
             a__		=			{}

         a__		=			spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(self.vocab_file   )




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None   )		->     Tuple[str]:







         """simple docstring"""



         if not os.path.isdir(__SCREAMING_SNAKE_CASE   ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
             return
         a__		=			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   ) and os.path.isfile(self.vocab_file   ):
             copyfile(self.vocab_file     ,				__SCREAMING_SNAKE_CASE   )
         elif not os.path.isfile(self.vocab_file   ):
             with open(__SCREAMING_SNAKE_CASE     ,				'wb'   ) as fi:
                 a__		=			self.sp_model.serialized_model_proto()
                 fi.write(__SCREAMING_SNAKE_CASE   )

         return (out_vocab_file,)




"""simple docstring"""




def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:

    def count_of_possible_combinations(UpperCamelCase     :		int     ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        return sum(count_of_possible_combinations(target - item     ) for item in array     )

    return count_of_possible_combinations(UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:

    def count_of_possible_combinations_with_dp_array(
        UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]     ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        if dp_array[target] != -1:
            return dp_array[target]
        a__		=			sum(
            count_of_possible_combinations_with_dp_array(target - item							,		UpperCamelCase     )
            for item in array     )
        a__		=			answer
        return answer

    a__		=			[-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(UpperCamelCase							,		UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:
    a__		=			[0] * (target + 1)
    a__		=			1

    for i in range(1							,		target + 1     ):
        for j in range(UpperCamelCase     ):
            if i - array[j] >= 0:
                dp_array[i] += dp_array[i - array[j]]

    return dp_array[target]


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    a		:       Optional[Any]     						=       3
    a		:       List[str]     						=       5
    a		:       Dict     						=       [1, 2, 5]
    print(combination_sum_iv(n, array, target))


