'''simple docstring'''




def 							lowerCamelCase	(      UpperCAmelCase__       :					Union[str, Any]      ,      UpperCAmelCase__       :					Tuple      ,      UpperCAmelCase__       :					List[str]      ,      UpperCAmelCase__       :					Union[str, Any] )			->      Any:
							global f  # a global dp table for knapsack
							if f[i][j] < 0:
														if j < wt[i - 1]:
																					lowercase_					:			Any										=       mf_knapsack(i - 1      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__ )
														else:
																					lowercase_					:			List[str]										=       max(
																					    mf_knapsack(i - 1      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__ )      ,      mf_knapsack(i - 1      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      j - wt[i - 1] ) + val[i - 1]      ,      )
														lowercase_					:			Tuple										=       val
							return f[i][j]




def 							lowerCamelCase	(      UpperCAmelCase__       :					Union[str, Any]      ,      UpperCAmelCase__       :					List[str]      ,      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					Tuple )			->      str:
							lowercase_					:			Dict										=       [[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_:
																												lowercase_					:			Tuple										=       max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]      ,      dp[i - 1][w_] )
																					else:
																												lowercase_					:			int										=       dp[i - 1][w_]

							return dp[n][w_], dp




def 							lowerCamelCase	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					list      ,      UpperCAmelCase__       :					list )			->      Any:
							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""" )

							lowercase_					:			Any										=       len(UpperCAmelCase__ )
							if num_items != len(UpperCAmelCase__ ):
														lowercase_					:			Dict										=       (
														    """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__ ):
																					lowercase_					:			List[str]										=       (
																					    """All weights must be integers but got weight of """
																					    F'''type {type(wt[i] )} at index {i}'''
																					)
																					raise TypeError(UpperCAmelCase__ )

							lowercase_						,						lowercase_					:			Tuple										=       knapsack(UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__ )
							lowercase_					:			set										=       set()
							_construct_solution(UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__ )

							return optimal_val, example_optional_set




def 							lowerCamelCase	(      UpperCAmelCase__       :					list      ,      UpperCAmelCase__       :					list      ,      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					set )			->      List[Any]:
							# 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__":
						_lowercase						: Optional[Any]						=						[3, 2, 4, 4]
						_lowercase						: Optional[int]						=						[4, 3, 2, 3]
						_lowercase						: Optional[Any]						=						4
						_lowercase						: str						=						6
						_lowercase						: Dict						=						[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
						_lowercase				,       _lowercase						: Optional[int]						=						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
						_lowercase				,       _lowercase						: Dict						=						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)






'''simple docstring'''




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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_albert import AlbertTokenizer
else:
						_lowercase						: int						=						None

_lowercase						: Union[str, Any]						=						logging.get_logger(__name__)
_lowercase						: Tuple						=						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase						: Dict						=						{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
    },
}

_lowercase						: int						=						{
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

_lowercase						: Tuple						=						"▁"





class     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       VOCAB_FILES_NAMES
			UpperCamelCase__				=       PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__				=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__				=       AlbertTokenizer







			def __init__(	self				:  Optional[Any]				,			lowercase_				:  Union[str, Any]=None				,			lowercase_				:  str=None				,			lowercase_				:  Any=True				,			lowercase_				:  Optional[int]=True				,			lowercase_				:  List[str]=False				,			lowercase_				:  Optional[int]="[CLS]"				,			lowercase_				:  Any="[SEP]"				,			lowercase_				:  int="<unk>"				,			lowercase_				:  Any="[SEP]"				,			lowercase_				:  int="<pad>"				,			lowercase_				:  Tuple="[CLS]"				,			lowercase_				:  Dict="[MASK]"				,			**lowercase_				:  Optional[int]				,			):
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										lowercase_					:			Tuple										=       (
										    AddedToken(lowercase_				,			lstrip=lowercase_				,			rstrip=lowercase_				,			normalized=lowercase_    )
										    if isinstance(lowercase_				,			lowercase_    )
										    else mask_token
										)

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

										lowercase_					:			Optional[int]										=       do_lower_case
										lowercase_					:			Any										=       remove_space
										lowercase_					:			Dict										=       keep_accents
										lowercase_					:			Optional[int]										=       vocab_file
										lowercase_					:			Any										=       False if not self.vocab_file else True







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  List[int]				,			lowercase_				:  Optional[List[int]] = None    ):
										lowercase_					:			Tuple										=       [self.sep_token_id]
										lowercase_					:			str										=       [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 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  List[int]				,			lowercase_				:  Optional[List[int]] = None    ):
										lowercase_					:			Union[str, Any]										=       [self.sep_token_id]
										lowercase_					:			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 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]				,			lowercase_				:  str				,			lowercase_				:  Optional[str] = None    ):
										if not self.can_save_slow_tokenizer:
																	raise ValueError(
																	    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																	    """tokenizer."""    )

										if not os.path.isdir(lowercase_    ):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
																	return
										lowercase_					:			Optional[Any]										=       os.path.join(
										    lowercase_				,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

										if os.path.abspath(self.vocab_file    ) != os.path.abspath(lowercase_    ):
																	copyfile(self.vocab_file				,			lowercase_    )

										return (out_vocab_file,)





