
'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:       Any						=			logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE							:       Any						=			"""▁"""

_SCREAMING_SNAKE_CASE							:       str						=			{"""vocab_file""": """spiece.model"""}

_SCREAMING_SNAKE_CASE							:       Dict						=			{
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

_SCREAMING_SNAKE_CASE							:       List[Any]						=			{
    """google/reformer-crime-and-punishment""": 52_4288,
}




class 			_snake_case		(     lowerCAmelCase__							):
			lowerCAmelCase_					:				List[Any]							=   VOCAB_FILES_NAMES
			lowerCAmelCase_					:				List[str]							=   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase_					:				List[str]							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase_					:				str							=   ["input_ids", "attention_mask"]




			def __init__( self    ,							a__    ,							a__="</s>"    ,							a__="<unk>"    ,							a__=[]    ,							a__ = None    ,							**a__    ,							)    ->    int:

										'''simple docstring'''




										snake_case_      =				{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    eos_token=a__    ,							unk_token=a__    ,							additional_special_tokens=a__    ,							sp_model_kwargs=self.sp_model_kwargs    ,							**a__    ,							)

										snake_case_      =				vocab_file
										snake_case_      =				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(a__							)




			@property
			def        lowerCAmelCase__    ( self							)    ->    Tuple:

										'''simple docstring'''




										return self.sp_model.get_piece_size()




			def        lowerCAmelCase__    ( self							)    ->    Tuple:

										'''simple docstring'''




										snake_case_      =				{self.convert_ids_to_tokens(a__							): i for i in range(self.vocab_size							)}
										vocab.update(self.added_tokens_encoder							)
										return vocab




			def __getstate__( self							)    ->    Any:

										'''simple docstring'''




										snake_case_      =				self.__dict__.copy()
										snake_case_      =				None
										return state




			def __setstate__( self    ,							a__							)    ->    int:

										'''simple docstring'''




										snake_case_      =				d

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

										snake_case_      =				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(self.vocab_file							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Optional[int]:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Tuple:

										'''simple docstring'''




										return self.sp_model.piece_to_id(a__							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Dict:

										'''simple docstring'''




										if index < self.sp_model.get_piece_size():
																	snake_case_      =				self.sp_model.IdToPiece(a__							)
										return token




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    str:

										'''simple docstring'''




										snake_case_      =				[]
										snake_case_      =				""
										for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																								out_string += self.sp_model.decode(a__							) + token
																								snake_case_      =				[]
																	else:
																								current_sub_tokens.append(a__							)
										out_string += self.sp_model.decode(a__							)
										return out_string.strip()




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    Tuple:

										'''simple docstring'''




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

										return (out_vocab_file,)










'''simple docstring'''





def 					UpperCamelCase_(     snake_case							:	list[int]							,    snake_case							:	int					):

							'''simple docstring'''






							snake_case_      =				len(snake_case					)
							snake_case_      =				[[False] * (required_sum + 1) for _ in range(arr_len + 1					)]

							# for each arr value, a sum of zero(0) can be formed by not taking any element
							# hence True/1
							for i in range(arr_len + 1					):
														snake_case_      =				True

							# sum is not zero and set is empty then false
							for i in range(1							,    required_sum + 1					):
														snake_case_      =				False

							for i in range(1							,    arr_len + 1					):
														for j in range(1							,    required_sum + 1					):
																					if arr[i - 1] > j:
																												snake_case_      =				subset[i - 1][j]
																					if arr[i - 1] <= j:
																												snake_case_      =				subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

							return subset[arr_len][required_sum]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




