



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


__snake_case     :Dict			       =logging.get_logger(__name__)

__snake_case     :Any			       ={'vocab_file': 'sentencepiece.bpe.model'}

__snake_case     :Union[str, Any]			       ={
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

__snake_case     :Optional[int]			       ={
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

__snake_case     :str			       ='▁'



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Union[str, Any]								=     VOCAB_FILES_NAMES
				A_   :     Optional[Any]								=     PRETRAINED_VOCAB_FILES_MAP
				A_   :     Dict								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A_   :     str								=     ['input_ids', 'attention_mask']


				def __init__(							self      :  str						, __UpperCamelCase      :  Tuple						, __UpperCamelCase      :  List[str]="<s>"						, __UpperCamelCase      :  Optional[int]="</s>"						, __UpperCamelCase      :  Any="</s>"						, __UpperCamelCase      :  int="<s>"						, __UpperCamelCase      :  Optional[Any]="<unk>"						, __UpperCamelCase      :  Dict="<pad>"						, __UpperCamelCase      :  str="<mask>"						, __UpperCamelCase      :  Optional[Dict[str, Any]] = None						, **__UpperCamelCase      :  Optional[Any]						, )		->     None:
											# Mask token behave like a normal word, i.e. include the space before it
											A      						=    AddedToken(__UpperCamelCase						, lstrip=__UpperCamelCase						, rstrip=__UpperCamelCase       ) if isinstance(__UpperCamelCase						, __UpperCamelCase       ) else mask_token

											A      						=    {} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    bos_token=__UpperCamelCase						, eos_token=__UpperCamelCase						, unk_token=__UpperCamelCase						, sep_token=__UpperCamelCase						, cls_token=__UpperCamelCase						, pad_token=__UpperCamelCase						, mask_token=__UpperCamelCase						, sp_model_kwargs=self.sp_model_kwargs						, **__UpperCamelCase						, )

											A      						=    vocab_file
											A      						=    spm.SentencePieceProcessor(**self.sp_model_kwargs       )
											self.sp_model.Load(str(__UpperCamelCase       )       )

											A      						=    {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

											A      						=    len(self.sp_model       ) - 1
											A      						=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}


				def __UpperCamelCase						(							self      :  Dict						, __UpperCamelCase      :  List[int]						, __UpperCamelCase      :  Optional[List[int]] = None       )		->     List[int]:

											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 __UpperCamelCase						(							self      :  Any						, __UpperCamelCase      :  List[int]						, __UpperCamelCase      :  Optional[List[int]] = None						, __UpperCamelCase      :  bool = False       )		->     List[int]:
											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=__UpperCamelCase						, token_ids_a=__UpperCamelCase						, already_has_special_tokens=__UpperCamelCase       )

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


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  List[int]						, __UpperCamelCase      :  Optional[List[int]] = 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]


				@property
				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Dict:
											return len(self.sp_model       )


				def __UpperCamelCase						(							self      :  Tuple       )		->     str:
											A      						=    {self.convert_ids_to_tokens(__UpperCamelCase       ): i for i in range(self.vocab_size       )}
											vocab.update(self.added_tokens_encoder       )
											return vocab


				def __UpperCamelCase						(							self      :  Union[str, Any]						, __UpperCamelCase      :  str       )		->     List[str]:
											return self.sp_model.encode(__UpperCamelCase						, out_type=__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Dict						, __UpperCamelCase      :  Any       )		->     Dict:
											if token in self.fairseq_tokens_to_ids:
																		return self.fairseq_tokens_to_ids[token]
											A      						=    self.sp_model.PieceToId(__UpperCamelCase       )

											return spm_id if spm_id else self.unk_token_id


				def __UpperCamelCase						(							self      :  Optional[Any]						, __UpperCamelCase      :  int       )		->     str:
											if index in self.fairseq_ids_to_tokens:
																		return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Any						, __UpperCamelCase      :  Optional[int]       )		->     Optional[int]:
											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(__UpperCamelCase       ) + token
																									A      						=    True
																									A      						=    []
																		else:
																									current_sub_tokens.append(__UpperCamelCase       )
																									A      						=    False
											out_string += self.sp_model.decode(__UpperCamelCase       )
											return out_string.strip()


				def __getstate__(							self      :  Any       )		->     int:
											A      						=    self.__dict__.copy()
											A      						=    None
											return state


				def __setstate__(							self      :  Any						, __UpperCamelCase      :  Dict       )		->     int:
											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 __UpperCamelCase						(							self      :  Tuple						, __UpperCamelCase      :  str						, __UpperCamelCase      :  Optional[str] = None       )		->     Tuple[str]:
											if not os.path.isdir(__UpperCamelCase       ):
																		logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
																		return
											A      						=    os.path.join(
											    __UpperCamelCase						, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

											if os.path.abspath(self.vocab_file       ) != os.path.abspath(__UpperCamelCase       ) and os.path.isfile(self.vocab_file       ):
																		copyfile(self.vocab_file						, __UpperCamelCase       )
											elif not os.path.isfile(self.vocab_file       ):
																		with open(__UpperCamelCase						, 'wb'       ) as fi:
																									A      						=    self.sp_model.serialized_model_proto()
																									fi.write(__UpperCamelCase       )

											return (out_vocab_file,)




from collections.abc import Sequence






def  lowerCamelCase_	(      lowerCAmelCase__	:	Sequence[int] | None = None				)       ->							int:

							'''simple docstring'''






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

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

							return ans


if __name__ == "__main__":
				import doctest

				doctest.testmod()

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