





"""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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase						=			logging.get_logger(__name__)

__UpperCAmelCase						=			'''▁'''

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

__UpperCAmelCase						=			{
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

__UpperCAmelCase						=			{
    '''facebook/mbart-large-50-one-to-many-mmt''': 1024,
}

# fmt: off
__UpperCAmelCase						=			['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']



class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	Optional[int]				     =				VOCAB_FILES_NAMES
				__lowerCamelCase				:	Tuple				     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCamelCase				:	Optional[Any]				     =				PRETRAINED_VOCAB_FILES_MAP
				__lowerCamelCase				:	int				     =				["input_ids", "attention_mask"]

				__lowerCamelCase				:	List[int]				     =				[]
				__lowerCamelCase				:	List[int]				     =				[]
				def __init__(		self    :			Optional[int]	, a_    :			Optional[Any]	, a_    :			Dict=None	, a_    :			int=None	, a_    :			List[Any]="</s>"	, a_    :			List[Any]="</s>"	, a_    :			Union[str, Any]="<s>"	, a_    :			Tuple="<unk>"	, a_    :			str="<pad>"	, a_    :			str="<mask>"	, a_    :			Optional[Dict[str, Any]] = None	, **a_    :			int	, )	->	None:



							'''simple docstring'''



							a__  :	List[str]		 =			AddedToken(a_	, lstrip=a_	, rstrip=a_    ) if isinstance(a_	, a_    ) else mask_token

							a__  :	str		 =			{} if sp_model_kwargs is None else sp_model_kwargs

							a__  :	Tuple		 =			kwargs.get("additional_special_tokens"	, []    )
							kwargs["additional_special_tokens"] += [
							    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    src_lang=a_	, tgt_lang=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_	, )

							a__  :	List[Any]		 =			spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(str(a_    )    )
							a__  :	List[str]		 =			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
							a__  :	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
							a__  :	Dict		 =			1

							a__  :	Dict		 =			len(self.sp_model    )
							a__  :	int		 =			{
							    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(a_    )
							}
							a__  :	Optional[int]		 =			{v: k for k, v in self.lang_code_to_id.items()}
							a__  :	Tuple		 =			len(self.sp_model    ) + len(self.lang_code_to_id    ) + self.fairseq_offset

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

							a__  :	Any		 =			src_lang if src_lang is not None else "en_XX"
							a__  :	int		 =			self.lang_code_to_id[self._src_lang]
							a__  :	List[str]		 =			tgt_lang
							self.set_src_lang_special_tokens(self._src_lang    )
				@property
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	int:



							'''simple docstring'''



							return len(self.sp_model    ) + len(self.lang_code_to_id    ) + self.fairseq_offset + 1  # Plus 1 for the mask token
				@property
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	str:



							'''simple docstring'''



							return self._src_lang
				@src_lang.setter
				def       UpperCAmelCase      (		self    :			Any	, a_    :			str    )	->	None:



							'''simple docstring'''



							a__  :	List[Any]		 =			new_src_lang
							self.set_src_lang_special_tokens(self._src_lang    )
				def __getstate__(		self    :			Dict    )	->	Dict:



							'''simple docstring'''



							a__  :	int		 =			self.__dict__.copy()
							a__  :	List[str]		 =			None
							return state
				def __setstate__(		self    :			List[str]	, a_    :			Dict    )	->	None:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			d

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

							a__  :	Dict		 =			spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(self.vocab_file    )
				def       UpperCAmelCase      (		self    :			Any    )	->	Dict:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			{self.convert_ids_to_tokens(a_    ): i for i in range(self.vocab_size    )}
							vocab.update(self.added_tokens_encoder    )
							return vocab
				def       UpperCAmelCase      (		self    :			Tuple	, a_    :			str    )	->	List[str]:



							'''simple docstring'''



							return self.sp_model.encode(a_	, out_type=a_    )
				def       UpperCAmelCase      (		self    :			Dict	, a_    :			str    )	->	int:



							'''simple docstring'''



							if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
							a__  :	Optional[int]		 =			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       UpperCAmelCase      (		self    :			List[Any]	, a_    :			int    )	->	str:



							'''simple docstring'''



							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       UpperCAmelCase      (		self    :			Tuple	, a_    :			int    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			[]
							a__  :	Tuple		 =			""
							a__  :	int		 =			False
							for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
													if not prev_is_special:
																out_string += " "
													out_string += self.sp_model.decode(a_    ) + token
													a__  :	Dict		 =			True
													a__  :	int		 =			[]
										else:
													current_sub_tokens.append(a_    )
													a__  :	str		 =			False
							out_string += self.sp_model.decode(a_    )
							return out_string.strip()
				def       UpperCAmelCase      (		self    :			Union[str, Any]	, a_    :			str	, a_    :			Optional[str] = None    )	->	Tuple[str]:



							'''simple docstring'''



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

							return (out_vocab_file,)
				def       UpperCAmelCase      (		self    :			Any	, a_    :			List[int]	, a_    :			Optional[List[int]] = None	, a_    :			bool = False    )	->	List[int]:



							'''simple docstring'''



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

							a__  :	Any		 =			[1] * len(self.prefix_tokens    )
							a__  :	Optional[int]		 =			[1] * len(self.suffix_tokens    )
							if token_ids_a is None:
										return prefix_ones + ([0] * len(a_    )) + suffix_ones
							return prefix_ones + ([0] * len(a_    )) + ([0] * len(a_    )) + suffix_ones
				def       UpperCAmelCase      (		self    :			str	, a_    :			List[int]	, a_    :			Optional[List[int]] = None    )	->	List[int]:



							'''simple docstring'''



							if token_ids_a is None:
										return self.prefix_tokens + token_ids_a + self.suffix_tokens
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
				def       UpperCAmelCase      (		self    :			Dict	, a_    :			Optional[int]	, a_    :			str	, a_    :			Optional[str]	, a_    :			Optional[str]	, **a_    :			Optional[Any]    )	->	str:



							'''simple docstring'''



							if src_lang is None or tgt_lang is None:
										raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"    )
							a__  :	Optional[Any]		 =			src_lang
							a__  :	Optional[Any]		 =			self(a_	, add_special_tokens=a_	, return_tensors=a_	, **a_    )
							a__  :	str		 =			self.convert_tokens_to_ids(a_    )
							a__  :	Optional[int]		 =			tgt_lang_id
							return inputs
				def       UpperCAmelCase      (		self    :			Dict	, a_    :			List[str]	, a_    :			str = "en_XX"	, a_    :			Optional[List[str]] = None	, a_    :			str = "ro_RO"	, **a_    :			Optional[int]	, )	->	BatchEncoding:



							'''simple docstring'''



							a__  :	List[str]		 =			src_lang
							a__  :	List[Any]		 =			tgt_lang
							return super().prepare_seqaseq_batch(a_	, a_	, **a_    )
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Optional[Any]:



							'''simple docstring'''



							return self.set_src_lang_special_tokens(self.src_lang    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	Any:



							'''simple docstring'''



							return self.set_tgt_lang_special_tokens(self.tgt_lang    )
				def       UpperCAmelCase      (		self    :			Optional[Any]	, a_    :			str    )	->	None:



							'''simple docstring'''



							a__  :	Tuple		 =			self.lang_code_to_id[src_lang]
							a__  :	Tuple		 =			[self.cur_lang_code_id]
							a__  :	str		 =			[self.eos_token_id]






				def       UpperCAmelCase      (		self    :			Union[str, Any]	, a_    :			str    )	->	None:



							'''simple docstring'''



							a__  :	List[Any]		 =			self.lang_code_to_id[tgt_lang]
							a__  :	Optional[int]		 =			[self.cur_lang_code_id]
							a__  :	str		 =			[self.eos_token_id]






"""simple docstring"""






from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors






def      lowercase__ (					lowerCAmelCase__   :			int			)    ->			int:



			'''simple docstring'''
			a__  :	Tuple		 =			prime_factors(lowerCAmelCase__			)
			if is_square_free(lowerCAmelCase__			):
						return -1 if len(lowerCAmelCase__			) % 2 else 1
			return 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()