




"""simple docstring"""





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

import sentencepiece as spm

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


A   =       logging.get_logger(__name__)

A   =       {"""vocab_file""": """sentencepiece.model"""}

A   =       {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

A   =       {
    """google/rembert""": 256,
}



class      a__				(	__magic_name__   ):
			lowercase_    =	VOCAB_FILES_NAMES
			lowercase_    =	PRETRAINED_VOCAB_FILES_MAP
			lowercase_    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


			def __init__(      self      :    Any      ,  UpperCamelCase_      :    Optional[Any]      ,  UpperCamelCase_      :    List[Any]=False      ,  UpperCamelCase_      :    Optional[Any]=True      ,  UpperCamelCase_      :    str=True      ,  UpperCamelCase_      :    int="[CLS]"      ,  UpperCamelCase_      :    List[Any]="[SEP]"      ,  UpperCamelCase_      :    str="[UNK]"      ,  UpperCamelCase_      :    Any="[SEP]"      ,  UpperCamelCase_      :    Union[str, Any]="[PAD]"      ,  UpperCamelCase_      :    List[Any]="[CLS]"      ,  UpperCamelCase_      :    Union[str, Any]="[MASK]"      ,  **UpperCamelCase_      :    Dict      ,  ):


						"""simple docstring"""




						super().__init__(
						    do_lower_case=UpperCamelCase_      ,  remove_space=UpperCamelCase_      ,  keep_accents=UpperCamelCase_      ,  bos_token=UpperCamelCase_      ,  eos_token=UpperCamelCase_      ,  unk_token=UpperCamelCase_      ,  sep_token=UpperCamelCase_      ,  pad_token=UpperCamelCase_      ,  cls_token=UpperCamelCase_      ,  mask_token=UpperCamelCase_      ,  **UpperCamelCase_      ,  )

						__UpperCAmelCase	: Any		=					do_lower_case
						__UpperCAmelCase	: str		=					remove_space
						__UpperCAmelCase	: str		=					keep_accents
						__UpperCAmelCase	: Dict		=					vocab_file

						__UpperCAmelCase	: Union[str, Any]		=					spm.SentencePieceProcessor()
						self.sp_model.Load(UpperCamelCase_)


			@property
			def       a_     (      self      :    List[str]):


						"""simple docstring"""




						return len(self.sp_model)


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					{self.convert_ids_to_tokens(UpperCamelCase_): i for i in range(self.vocab_size)}
						vocab.update(self.added_tokens_encoder)
						return vocab


			def __getstate__(      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					self.__dict__.copy()
						__UpperCAmelCase	: Tuple		=					None
						return state


			def __setstate__(      self      :    Any      ,  UpperCamelCase_      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					d
						__UpperCAmelCase	: int		=					spm.SentencePieceProcessor()
						self.sp_model.Load(self.vocab_file)


			def       a_     (      self      :    Dict      ,  UpperCamelCase_      :    List[str]      ,  UpperCamelCase_      :    Optional[Any]=False):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					self.sp_model.EncodeAsPieces(UpperCamelCase_)
						return pieces


			def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    Dict):


						"""simple docstring"""




						return self.sp_model.PieceToId(UpperCamelCase_)


			def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    List[Any]):


						"""simple docstring"""




						return self.sp_model.IdToPiece(UpperCamelCase_)


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[int]		=					self.sp_model.decode_pieces(UpperCamelCase_)
						return out_string


			def       a_     (      self      :    str      ,  UpperCamelCase_      :    List[int]      ,  UpperCamelCase_      :    Optional[List[int]] = None):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					[self.sep_token_id]
						__UpperCAmelCase	: Optional[Any]		=					[self.cls_token_id]
						if token_ids_a is None:
									return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    List[int]      ,  UpperCamelCase_      :    Optional[List[int]] = None      ,  UpperCamelCase_      :    bool = False):


						"""simple docstring"""




						if already_has_special_tokens:
									if token_ids_a is not None:
												raise ValueError(
												    "You should not supply a second sequence if the provided sequence of "
												    "ids is already formatted with special tokens for the model.")
									return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

						if token_ids_a is not None:
									return [1] + ([0] * len(UpperCamelCase_)) + [1] + ([0] * len(UpperCamelCase_)) + [1]
						return [1] + ([0] * len(UpperCamelCase_)) + [1]


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    List[int]      ,  UpperCamelCase_      :    Optional[List[int]] = None):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					[self.sep_token_id]
						__UpperCAmelCase	: Optional[int]		=					[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) * [0] + len(token_ids_a + sep) * [1]


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    str      ,  UpperCamelCase_      :    Optional[str] = None):


						"""simple docstring"""




						if not os.path.isdir(UpperCamelCase_):
									logger.error("Vocabulary path ({}) should be a directory".format(UpperCamelCase_))
									return
						__UpperCAmelCase	: Union[str, Any]		=					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_):
									copyfile(self.vocab_file      ,  UpperCamelCase_)

						return (out_vocab_file,)








"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


			"""simple docstring"""
			global f  # a global dp table for knapsack
			if f[i][j] < 0:
						if j < wt[i - 1]:
									__UpperCAmelCase	: Optional[int]		=					mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									__UpperCAmelCase	: Any		=					max(
									    mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)				,       mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       j - wt[i - 1]							) + val[i - 1]				,       )
						__UpperCAmelCase	: str		=					val
			return f[i][j]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			List[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: List[Any]		=					[[0] * (w + 1) for _ in range(n + 1							)]

			for i in range(1				,       n + 1							):
						for w_ in range(1				,       w + 1							):
									if wt[i - 1] <= w_:
												__UpperCAmelCase	: Tuple		=					max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]				,       dp[i - 1][w_]							)
									else:
												__UpperCAmelCase	: int		=					dp[i - 1][w_]

			return dp[n][w_], dp

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			if not (isinstance(UpperCamelCase				,       (list, tuple)							) and isinstance(UpperCamelCase				,       (list, tuple)							)):
						raise ValueError(
						    "Both the weights and values vectors must be either lists or tuples"							)

			__UpperCAmelCase	: List[Any]		=					len(UpperCamelCase							)
			if num_items != len(UpperCamelCase							):
						__UpperCAmelCase	: int		=					(
						    "The number of weights must be the same as the number of values.\n"
						    f"But got {num_items} weights and {len(UpperCamelCase							)} values"
						)
						raise ValueError(UpperCamelCase							)
			for i in range(UpperCamelCase							):
						if not isinstance(wt[i]				,       UpperCamelCase							):
									__UpperCAmelCase	: Tuple		=					(
									    "All weights must be integers but got weight of "
									    f"type {type(wt[i]							)} at index {i}"
									)
									raise TypeError(UpperCamelCase							)

			__UpperCAmelCase	,     __UpperCAmelCase	: Tuple		=					knapsack(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
			__UpperCAmelCase	: set		=					set()
			_construct_solution(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)

			return optimal_val, example_optional_set

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Dict:


			"""simple docstring"""
			# for the current item i at a maximum weight j to be part of an optimal subset,
			# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
			# where i - 1 means considering only the previous items at the given maximum weight
			if i > 0 and j > 0:
						if dp[i - 1][j] == dp[i][j]:
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									optimal_set.add(UpperCamelCase							)
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       j - wt[i - 1]				,       UpperCamelCase							)


if __name__ == "__main__":
					A   =       [3, 2, 4, 4]
					A   =       [4, 3, 2, 3]
					A   =       4
					A   =       6
					A   =       [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					A ,						A   =       knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					A ,						A   =       knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)


