

'''simple docstring'''



from collections.abc import Sequence


def       snake_case   (	snake_case      : int = None			) ->  str:


    """simple docstring"""


    if nums is None or not nums:
        raise ValueError('Input sequence should not be empty'			)

    lowerCAmelCase 						=							nums[0]
    for i in range(1					,		len(snake_case			)			):
        lowerCAmelCase 						=							nums[i]
        lowerCAmelCase 						=							max(snake_case					,		ans + num					,		snake_case			)

    return ans


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # Try on a sample input from the user
     _UpperCamelCase						:  Optional[int]							       =				int(input("Enter number of elements : ").strip())
     _UpperCamelCase						:  Any							       =				list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
     print(max_subsequence_sum(array))













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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__   :      str				=		logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__   :      Tuple				=		"""▁"""

SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		{"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE__   :      List[Any]				=		{
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

SCREAMING_SNAKE_CASE__   :      Optional[int]				=		{
    """google/reformer-crime-and-punishment""": 52_42_88,
}
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	VOCAB_FILES_NAMES
				a__       							=	PRETRAINED_VOCAB_FILES_MAP
				a__       							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				a__       							=	['''input_ids''', '''attention_mask''']







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase="</s>"							,       __lowerCAmelCase="<unk>"							,       __lowerCAmelCase=[]							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								__magic_name__		:int   =   {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=__lowerCAmelCase							,       unk_token=__lowerCAmelCase							,       additional_special_tokens=__lowerCAmelCase							,       sp_model_kwargs=self.sp_model_kwargs							,       **__lowerCAmelCase							,       )

								__magic_name__		:Optional[Any]   =   vocab_file
								__magic_name__		:int   =   spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(__lowerCAmelCase  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								return self.sp_model.get_piece_size()







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:str   =   {self.convert_ids_to_tokens(__lowerCAmelCase  ): i for i in range(self.vocab_size  )}
								vocab.update(self.added_tokens_encoder  )
								return vocab







				def __getstate__(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   self.__dict__.copy()
								__magic_name__		:Optional[Any]   =   None
								return state







				def __setstate__(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Any   =   d

								# for backward compatibility
								if not hasattr(self							,       '''sp_model_kwargs'''  ):
												__magic_name__		:Optional[int]   =   {}

								__magic_name__		:Union[str, Any]   =   spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(self.vocab_file  )







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								return self.sp_model.encode(__lowerCAmelCase							,       out_type=__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								return self.sp_model.piece_to_id(__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								if index < self.sp_model.get_piece_size():
												__magic_name__		:int   =   self.sp_model.IdToPiece(__lowerCAmelCase  )
								return token







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   []
								__magic_name__		:Tuple   =   ''''''
								for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
																out_string += self.sp_model.decode(__lowerCAmelCase  ) + token
																__magic_name__		:Optional[Any]   =   []
												else:
																current_sub_tokens.append(__lowerCAmelCase  )
								out_string += self.sp_model.decode(__lowerCAmelCase  )
								return out_string.strip()







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase = None  ):
								"""simple docstring"""
								if not os.path.isdir(__lowerCAmelCase  ):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
												return
								__magic_name__		:Optional[int]   =   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  ) and os.path.isfile(self.vocab_file  ):
												copyfile(self.vocab_file							,       __lowerCAmelCase  )
								elif not os.path.isfile(self.vocab_file  ):
												with open(__lowerCAmelCase							,       '''wb'''  ) as fi:
																__magic_name__		:Dict   =   self.sp_model.serialized_model_proto()
																fi.write(__lowerCAmelCase  )

								return (out_vocab_file,)






