






"""simple docstring"""



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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__A		:       List[Any]  				=		logging.get_logger(__name__)

__A		:       List[str]  				=		'''▁'''

__A		:       Any  				=		{'''vocab_file''': '''sentencepiece.bpe.model'''}

__A		:       Union[str, Any]  				=		{
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

__A		:       Optional[Any]  				=		{
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Dict							    =   VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE_		:				str							    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE_		:				Tuple							    =   ["input_ids", "attention_mask"]




					def __init__(				self     :						Optional[int]    ,						A     :						List[Any]    ,						A     :						List[str]="<s>"    ,						A     :						Optional[int]="</s>"    ,						A     :						Optional[int]="</s>"    ,						A     :						Tuple="<s>"    ,						A     :						List[str]="<unk>"    ,						A     :						Tuple="<pad>"    ,						A     :						Tuple="<mask>"    ,						A     :						Optional[Dict[str, Any]] = None    ,						**A     :						List[Any]    ,						)  ->  None:
							# Mask token behave like a normal word, i.e. include the space before it
							lowercase_ : Any							       =   AddedToken(A    ,						lstrip=A    ,						rstrip=A   ) if isinstance(A    ,						A   ) else mask_token

							lowercase_ : Optional[int]							       =   {} if sp_model_kwargs is None else sp_model_kwargs

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

							lowercase_ : int							       =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(str(A   )   )
							lowercase_ : Optional[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'

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

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							lowercase_ : List[Any]							       =   1

							lowercase_ : Tuple							       =   len(self.sp_model   ) + self.fairseq_offset
							lowercase_ : Optional[int]							       =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}




					def __getstate__(				self     :						Dict   )  ->  Dict:
							lowercase_ : Tuple							       =   self.__dict__.copy()
							lowercase_ : str							       =   None
							lowercase_ : str							       =   self.sp_model.serialized_model_proto()
							return state




					def __setstate__(				self     :						Tuple    ,						A     :						Tuple   )  ->  int:
							lowercase_ : Optional[int]							       =   d

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

							lowercase_ : List[str]							       =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )




					def   A  (				self     :						Tuple    ,						A     :						List[int]    ,						A     :						Optional[List[int]] = None   )  ->  List[int]:

							if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							lowercase_ : Optional[int]							       =   [self.cls_token_id]
							lowercase_ : List[str]							       =   [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep




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

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

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




					def   A  (				self     :						int    ,						A     :						List[int]    ,						A     :						Optional[List[int]] = None   )  ->  List[int]:
							lowercase_ : str							       =   [self.sep_token_id]
							lowercase_ : List[Any]							       =   [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 + sep + token_ids_a + sep   ) * [0]




					@property
					def   A  (				self     :						Optional[int]   )  ->  Optional[int]:
							return len(self.sp_model   ) + self.fairseq_offset + 1  # Add the <mask> token




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




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




					def   A  (				self     :						Any    ,						A     :						int   )  ->  List[Any]:
							if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
							lowercase_ : Dict							       =   self.sp_model.PieceToId(A   )

							# 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   A  (				self     :						Dict    ,						A     :						str   )  ->  Union[str, Any]:
							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   A  (				self     :						List[str]    ,						A     :						Tuple   )  ->  List[Any]:
							lowercase_ : Tuple							       =   ''''''.join(A   ).replace(A    ,						''' '''   ).strip()
							return out_string






					def   A  (				self     :						Any    ,						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
							lowercase_ : int							       =   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:
											lowercase_ : Optional[Any]							       =   self.sp_model.serialized_model_proto()
											fi.write(A   )

							return (out_vocab_file,)











"""simple docstring"""



def 						lowercase    (				__snake_case				: int	):
		if not isinstance(__snake_case		,					__snake_case	):
				raise ValueError('''Input must be an integer'''	)
		if input_num <= 0:
				raise ValueError('''Input must be positive'''	)
		return sum(
		    divisor for divisor in range(1		,					input_num // 2 + 1	) if input_num % divisor == 0	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



