



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


__A       =						logging.get_logger(__name__)

__A       =						'''▁'''

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

__A       =						{
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

__A       =						{
    '''facebook/xglm-564M''': 2048,
}






class 		lowercase						(			lowercase__):






	"""simple docstring"""


	a__				: Any		       =							VOCAB_FILES_NAMES
	a__				: str		       =							PRETRAINED_VOCAB_FILES_MAP
	a__				: Optional[Any]		       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a__				: Dict		       =							['''input_ids''', '''attention_mask''']




	def __init__(     self     :						Optional[Any]						,  __UpperCAmelCase     :						Optional[int]						,  __UpperCAmelCase     :						Dict="<s>"						,  __UpperCAmelCase     :						Optional[int]="</s>"						,  __UpperCAmelCase     :						Optional[int]="</s>"						,  __UpperCAmelCase     :						Any="<s>"						,  __UpperCAmelCase     :						str="<unk>"						,  __UpperCAmelCase     :						Tuple="<pad>"						,  __UpperCAmelCase     :						Optional[Dict[str, Any]] = None						,  **__UpperCAmelCase     :						Optional[Any]						,  )      ->			Optional[Any]:
							UpperCAmelCase_=       {} if sp_model_kwargs is None else sp_model_kwargs

							# Compatibility with the original tokenizer
							UpperCAmelCase_=       7
							UpperCAmelCase_=       [F"""<madeupword{i}>""" for i in range(self.num_madeup_words					)]

							UpperCAmelCase_=       kwargs.get("""additional_special_tokens"""						,  []					)
							kwargs["additional_special_tokens"] += [
							    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
							]

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

							UpperCAmelCase_=       spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(str(_a					)					)
							UpperCAmelCase_=       vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							UpperCAmelCase_=       1

							# Mimic fairseq token-to-id alignment for the first 4 token
							UpperCAmelCase_=       {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

							UpperCAmelCase_=       len(self.sp_model					)
							UpperCAmelCase_=       {F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words					)}
							self.fairseq_tokens_to_ids.update(_a					)

							UpperCAmelCase_=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}




	def __getstate__(     self     :						Dict					)      ->			Optional[Any]:
							UpperCAmelCase_=       self.__dict__.copy()
							UpperCAmelCase_=       None
							UpperCAmelCase_=       self.sp_model.serialized_model_proto()
							return state




	def __setstate__(     self     :						List[Any]						,  __UpperCAmelCase     :						Tuple					)      ->			Optional[Any]:
							UpperCAmelCase_=       d

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

							UpperCAmelCase_=       spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None					)      ->			List[Any]:






							if token_ids_a is None:
													return [self.sep_token_id] + token_ids_a
							UpperCAmelCase_=       [self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None						,  __UpperCAmelCase     :						bool = False					)      ->			List[str]:






							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					))
							return [1] + ([0] * len(_a					)) + [1, 1] + ([0] * len(_a					))




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None					)      ->			List[str]:
							UpperCAmelCase_=       [self.sep_token_id]

							if token_ids_a is None:
													return len(sep + token_ids_a					) * [0]
							return len(sep + token_ids_a + sep + sep + token_ids_a					) * [0]




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Optional[Any]:
							return len(self.sp_model					) + self.fairseq_offset + self.num_madeup_words




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Optional[Any]:
							UpperCAmelCase_=       {self.convert_ids_to_tokens(_a					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						str					)      ->			Tuple:
							return self.sp_model.encode(_a						,  out_type=_a					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						Optional[Any]					)      ->			Dict:






							if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
							UpperCAmelCase_=       self.sp_model.PieceToId(_a					)

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  __UpperCAmelCase     :						int					)      ->			List[Any]:






							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   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						List[str]					)      ->			List[Any]:
							UpperCAmelCase_=       ''.join(_a					).replace(_a						,  """ """					).strip()
							return out_string




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						Optional[str] = None					)      ->			Any:






							if not os.path.isdir(_a					):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
													return
							UpperCAmelCase_=       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:
																			UpperCAmelCase_=       self.sp_model.serialized_model_proto()
																			fi.write(_a					)

							return (out_vocab_file,)




from __future__ import annotations



def 	__a					(   lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : int    )				->			list[list[int]]:






						'''simple docstring'''



						UpperCAmelCase_=       []
						UpperCAmelCase_=       []
						UpperCAmelCase_=       0
						UpperCAmelCase_=       sum(lowerCAmelCase_    )
						create_state_space_tree(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
						return result



def 	__a					(   lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : list[list[int]]      ,lowerCAmelCase_       : int      ,)				->			None:






						'''simple docstring'''



						if sum(lowerCAmelCase_    ) > max_sum or (remaining_nums_sum + sum(lowerCAmelCase_    )) < max_sum:
												return
						if sum(lowerCAmelCase_    ) == max_sum:
												result.append(lowerCAmelCase_    )
												return
						for index in range(lowerCAmelCase_      ,len(lowerCAmelCase_    )    ):
												create_state_space_tree(
												    lowerCAmelCase_      ,lowerCAmelCase_      ,index + 1      ,[*path, nums[index]]      ,lowerCAmelCase_      ,remaining_nums_sum - nums[index]      ,)
__A       =						[3, 34, 4, 12, 5, 2]
__A       =						9
__A       =						generate_sum_of_subsets_soln(nums, max_sum)



print(*result)




