






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_fnet import FNetTokenizer
else:
				__A      =				None

__A      =				logging.get_logger(__name__)
__A      =				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__A      =				{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

__A      =				{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

__A      =				'▁'





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					VOCAB_FILES_NAMES
							A_						  =					PRETRAINED_VOCAB_FILES_MAP
							A_						  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							A_						  =					["input_ids", "token_type_ids"]
							A_						  =					FNetTokenizer

							def __init__(  self:   Optional[int]		,     __A:   str=None		,     __A:   Optional[Any]=None		,     __A:   Dict=False		,     __A:   Dict=True		,     __A:   Optional[int]=True		,     __A:   Optional[Any]="<unk>"		,     __A:   Any="[SEP]"		,     __A:   Dict="<pad>"		,     __A:   Tuple="[CLS]"		,     __A:   List[str]="[MASK]"		,     **__A:   Any		,     )    ->				Optional[Any]:
												# 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.
												_A								= (
												    AddedToken(__A		,     lstrip=__A		,     rstrip=__A		,     normalized=__A   )
												    if isinstance(__A		,     __A   )
												    else mask_token
												)

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

												_A								= do_lower_case
												_A								= remove_space
												_A								= keep_accents
												_A								= vocab_file
												_A								= False if not self.vocab_file else True

							def       __A (  self:   int		,     __A:   List[int]		,     __A:   Optional[List[int]] = None   )    ->				List[int]:
												_A								= [self.sep_token_id]
												_A								= [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:   List[Any]		,     __A:   List[int]		,     __A:   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   ) * [0] + len(token_ids_a + sep   ) * [1]



							def       __A (  self:   Dict		,     __A:   str		,     __A:   Optional[str] = None   )    ->				Tuple[str]:
												if not os.path.isdir(__A   ):
																	logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
																	return
												_A								= 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   ):
																	copyfile(self.vocab_file		,     __A   )

												return (out_vocab_file,)











def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					global f  # a global dp table for knapsack
					if f[i][j] < 0:
										if j < wt[i - 1]:
															_A								= mf_knapsack(i - 1					,			_lowercase					,			_lowercase					,			_lowercase )
										else:
															_A								= max(
															    mf_knapsack(i - 1					,			_lowercase					,			_lowercase					,			_lowercase )					,			mf_knapsack(i - 1					,			_lowercase					,			_lowercase					,			j - wt[i - 1] ) + val[i - 1]					,			)
										_A								= val
					return f[i][j]




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= [[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_:
																				_A								= max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]					,			dp[i - 1][w_] )
															else:
																				_A								= dp[i - 1][w_]

					return dp[n][w_], dp




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					if not (isinstance(_lowercase					,			(list, tuple) ) and isinstance(_lowercase					,			(list, tuple) )):
										raise ValueError(
										    '''Both the weights and values vectors must be either lists or tuples''' )

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

					_A							,_A								= knapsack(_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )
					_A								= set()
					_construct_solution(_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )

					return optimal_val, example_optional_set




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					if i > 0 and j > 0:
										if dp[i - 1][j] == dp[i][j]:
															_construct_solution(_lowercase					,			_lowercase					,			i - 1					,			_lowercase					,			_lowercase )
										else:
															optimal_set.add(_lowercase )
															_construct_solution(_lowercase					,			_lowercase					,			i - 1					,			j - wt[i - 1]					,			_lowercase )


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)



