

"""simple docstring"""

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

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


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    '''▁'''

_UpperCamelCase		     =    {'''vocab_file''': '''prophetnet.tokenizer'''}

_UpperCamelCase		     =    {
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

_UpperCamelCase		     =    {
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

_UpperCamelCase		     =    {
    '''microsoft/xprophetnet-large-wiki100-cased''': 5_1_2,
}



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						str:
 lowerCAmelCase__   :				List[str] 			=				collections.OrderedDict()
 with open(_lowerCAmelCase					,					"r"					,					encoding="utf-8"   ) as reader:
  lowerCAmelCase__   :				Optional[Any] 			=				reader.readlines()
 for index, token in enumerate(_lowerCAmelCase   ):
  lowerCAmelCase__   :				List[str] 			=				token.rstrip("\n"   )
  lowerCAmelCase__   :				Dict 			=				index
 return vocab






class 			__a					(						lowerCamelCase__       ):





       """simple docstring"""


       __UpperCamelCase					:      List[str]    =						VOCAB_FILES_NAMES
       __UpperCamelCase					:      str    =						PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase					:      Optional[Any]    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase					:      str    =						['input_ids', 'attention_mask']






       def __init__(   self			,					snake_case			,					snake_case="[SEP]"			,					snake_case="[SEP]"			,					snake_case="[SEP]"			,					snake_case="[UNK]"			,					snake_case="[PAD]"			,					snake_case="[CLS]"			,					snake_case="[MASK]"			,					snake_case = None			,					**snake_case			,					):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				{} if sp_model_kwargs is None else sp_model_kwargs

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

        try:
         import sentencepiece as spm
        except ImportError:
         logger.warning(
             "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
             " pip install sentencepiece"   )
         raise

        lowerCAmelCase__   :				List[str] 			=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
        self.sp_model.Load(str(snake_case   )   )
        lowerCAmelCase__   :				str 			=				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'

        # put special tokens and [unused] tokens into the vocab
        lowerCAmelCase__   :				Union[str, Any] 			=				{"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

        for i in range(10   ):
         lowerCAmelCase__   :				Optional[Any] 			=				F"""[unused{i}]"""
         lowerCAmelCase__   :				Tuple 			=				5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        lowerCAmelCase__   :				str 			=				12
        lowerCAmelCase__   :				List[Any] 			=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
        for k in self.fairseq_tokens_to_ids.keys():
         self.unique_no_split_tokens.append(snake_case   )






       def __getstate__(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				self.__dict__.copy()
        lowerCAmelCase__   :				int 			=				None
        return state






       def __setstate__(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				d
        try:
         import sentencepiece as spm
        except ImportError:
         logger.warning(
             "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
             " pip install sentencepiece"   )
         raise

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

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None			,					snake_case = False   ):





        """simple docstring"""

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

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				[self.sep_token_id]

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






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return len(self.sp_model   ) + self.fairseq_offset






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				{self.convert_ids_to_tokens(snake_case   ): i for i in range(self.vocab_size   )}
        vocab.update(self.added_tokens_encoder   )
        return vocab






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
        lowerCAmelCase__   :				List[str] 			=				self.sp_model.PieceToId(snake_case   )

        # 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			,					snake_case   ):





        """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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				"".join(snake_case   ).replace(snake_case			,					" "   ).strip()
        return out_string






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        if not os.path.isdir(snake_case   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
         return
        lowerCAmelCase__   :				int 			=				os.path.join(
            snake_case			,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

        if os.path.abspath(self.vocab_file   ) != os.path.abspath(snake_case   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file			,					snake_case   )
        elif not os.path.isfile(self.vocab_file   ):
         with open(snake_case			,					"wb"   ) as fi:
          lowerCAmelCase__   :				str 			=				self.sp_model.serialized_model_proto()
          fi.write(snake_case   )

        return (out_vocab_file,)






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        if token_ids_a is None:
         return token_ids_a + [self.sep_token_id]
        lowerCAmelCase__   :				List[Any] 			=				[self.sep_token_id]
        return token_ids_a + sep + token_ids_a + sep













from __future__ import annotations

def   __snake_case    (       _lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			int						)			->    list[list[int]]:
   A_       :					list[list[int]]        =      []
   A_       :					list[int]        =      []
   A_       :					Dict        =      0
   A_       :					str        =      sum(_lowerCAmelCase						)
   create_state_space_tree(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase						)
   return result

def   __snake_case    (       _lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			list[list[int]]					,					_lowerCAmelCase			:			int					,					)			->    None:
   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]					,					)
_lowerCAmelCase				:   List[Any]						       =						[3, 34, 4, 12, 5, 2]
_lowerCAmelCase				:   Dict						       =						9
_lowerCAmelCase				:   Dict						       =						generate_sum_of_subsets_soln(nums, max_sum)





print(*result)
