






"""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


lowercase    			=       logging.get_logger(__name__)

lowercase    			=       '''▁'''

lowercase    			=       {'''vocab_file''': '''sentencepiece.bpe.model'''}

lowercase    			=       {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

lowercase    			=       {
    '''facebook/xglm-564M''': 2048,
}

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				VOCAB_FILES_NAMES
				UpperCAmelCase__        =				PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase__        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase__        =				['''input_ids''', '''attention_mask''']







				def __init__(							self							:						Optional[Any]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Union[str, Any]="<s>"						,    __lowerCamelCase							:						str="</s>"						,    __lowerCamelCase							:						str="</s>"						,    __lowerCamelCase							:						Dict="<s>"						,    __lowerCamelCase							:						List[Any]="<unk>"						,    __lowerCamelCase							:						int="<pad>"						,    __lowerCamelCase							:						Optional[Dict[str, Any]] = None						,    **__lowerCamelCase							:						Optional[Any]						,    )				->					None:
										__magic_name__     :   Dict            =     {} if sp_model_kwargs is None else sp_model_kwargs

										# Compatibility with the original tokenizer
										__magic_name__     :   Any            =     7
										__magic_name__     :   Dict            =     [f'''<madeupword{i}>''' for i in range(self.num_madeup_words	)]

										__magic_name__     :   Dict            =     kwargs.get("additional_special_tokens"						,    []	)
										kwargs["additional_special_tokens"] += [
										    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
										]

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

										__magic_name__     :   Optional[int]            =     spm.SentencePieceProcessor(**self.sp_model_kwargs	)
										self.sp_model.Load(str(__lowerCamelCase	)	)
										__magic_name__     :   Any            =     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'

										# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
										__magic_name__     :   int            =     1

										# Mimic fairseq token-to-id alignment for the first 4 token
										__magic_name__     :   List[str]            =     {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

										__magic_name__     :   int            =     len(self.sp_model	)
										__magic_name__     :   str            =     {f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words	)}
										self.fairseq_tokens_to_ids.update(__lowerCamelCase	)

										__magic_name__     :   Tuple            =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}







				def __getstate__(							self							:						Dict	)				->					Optional[int]:
										__magic_name__     :   Any            =     self.__dict__.copy()
										__magic_name__     :   str            =     None
										__magic_name__     :   Any            =     self.sp_model.serialized_model_proto()
										return state







				def __setstate__(							self							:						Optional[int]						,    __lowerCamelCase							:						Union[str, Any]	)				->					Union[str, Any]:
										__magic_name__     :   List[Any]            =     d

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

										__magic_name__     :   Dict            =     spm.SentencePieceProcessor(**self.sp_model_kwargs	)
										self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)







				def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						List[int]						,    __lowerCamelCase							:						Optional[List[int]] = None	)				->					List[int]:

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







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						List[int]						,    __lowerCamelCase							:						Optional[List[int]] = None						,    __lowerCamelCase							:						bool = False	)				->					List[int]:

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

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







				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						List[int]						,    __lowerCamelCase							:						Optional[List[int]] = None	)				->					List[int]:
										__magic_name__     :   Dict            =     [self.sep_token_id]

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







				@property
				def     _snake_case					(							self							:						List[str]	)				->					List[str]:
										return len(self.sp_model	) + self.fairseq_offset + self.num_madeup_words







				def     _snake_case					(							self							:						List[Any]	)				->					List[str]:
										__magic_name__     :   Optional[Any]            =     {self.convert_ids_to_tokens(__lowerCamelCase	): i for i in range(self.vocab_size	)}
										vocab.update(self.added_tokens_encoder	)
										return vocab







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						str	)				->					List[str]:
										return self.sp_model.encode(__lowerCamelCase						,    out_type=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]						,    __lowerCamelCase							:						List[str]	)				->					Optional[Any]:
										if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
										__magic_name__     :   Dict            =     self.sp_model.PieceToId(__lowerCamelCase	)

										# 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     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						Any	)				->					List[str]:
										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     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						Optional[int]	)				->					str:
										__magic_name__     :   Any            =     "".join(__lowerCamelCase	).replace(__lowerCamelCase						,    " "	).strip()
										return out_string







				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						Optional[str] = None	)				->					Tuple[str]:
										if not os.path.isdir(__lowerCamelCase	):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
																return
										__magic_name__     :   Optional[int]            =     os.path.join(
										    __lowerCamelCase						,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

										if os.path.abspath(self.vocab_file	) != os.path.abspath(__lowerCamelCase	) and os.path.isfile(self.vocab_file	):
																copyfile(self.vocab_file						,    __lowerCamelCase	)
										elif not os.path.isfile(self.vocab_file	):
																with open(__lowerCamelCase						,    "wb"	) as fi:
																						__magic_name__     :   Optional[int]            =     self.sp_model.serialized_model_proto()
																						fi.write(__lowerCamelCase	)

										return (out_vocab_file,)











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     n ** (1 / 3)
						return (val * val * val) == n


if __name__ == "__main__":
			print(perfect_cube(27))
			print(perfect_cube(4))



