
"""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							      = '''▁'''

_lowercase							      = {'''vocab_file''': '''spiece.model'''}

_lowercase							      = {
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

_lowercase							      = {
    '''google/pegasus-xsum''': 5_12,
}


_lowercase							      = logging.get_logger(__name__)







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			List[str]					=			VOCAB_FILES_NAMES

						_lowerCamelCase:			Optional[Any]					=			VOCAB_FILES_NAMES
						_lowerCamelCase:			Union[str, Any]					=			PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:			Any					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCamelCase:			str					=			['''input_ids''', '''attention_mask''']


						def __init__(       self   :					Union[str, Any]     ,A_   :					int     ,A_   :					List[Any]="<pad>"     ,A_   :					List[str]="</s>"     ,A_   :					Any="<unk>"     ,A_   :					Union[str, Any]="<mask_2>"     ,A_   :					int="<mask_1>"     ,A_   :					str=None     ,A_   :					List[Any]=103     ,A_   :					Optional[Dict[str, Any]] = None     ,**A_   :					List[Any]     ,)   ->      None:
							A        =      offset
							if additional_special_tokens is not None:
								if not isinstance(A_     ,A_					):
									raise TypeError(
									    F'additional_special_tokens should be of type {type(A_					)}, but is'
									    F' {type(A_					)}'					)

								A        =      (
								    ([mask_token_sent] + additional_special_tokens)
								    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
								    else additional_special_tokens
								)
								# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
								additional_special_tokens_extended += [
								    F'<unk_{i}>' for i in range(len(A_					)     ,self.offset - 1					)
								]

								if len(set(A_					)					) != len(A_					):
									raise ValueError(
									    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
									    F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'					)
								A        =      additional_special_tokens_extended
							else:
								A        =      [mask_token_sent] if mask_token_sent is not None else []
								additional_special_tokens += [F'<unk_{i}>' for i in range(2     ,self.offset					)]

							A        =      {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=A_     ,unk_token=A_     ,mask_token=A_     ,pad_token=A_     ,mask_token_sent=A_     ,offset=A_     ,additional_special_tokens=A_     ,sp_model_kwargs=self.sp_model_kwargs     ,**A_     ,)
							A        =      mask_token_sent
							A        =      vocab_file
							A        =      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(A_					)

							# add special tokens to encoder dict
							A        =      {
							    0: self.pad_token,
							    1: self.eos_token,
							}

							if self.mask_token_sent is not None:
								self.encoder.update(
								    {
								        2: self.mask_token_sent,
								        3: self.mask_token,
								    }					)

							if self.offset > 0:
								# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
								# mask_token_sent is already added to list -> so start at 1
								self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1     ,self.offset - 1					)}					)

							A        =      {v: k for k, v in self.encoder.items()}


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      int:
							return len(self.sp_model					) + self.offset


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      Dict[str, int]:
							A        =      {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   :					Union[str, Any]					)   ->      Optional[int]:
							A        =      self.__dict__.copy()
							A        =      None
							return state


						def __setstate__(       self   :					str     ,A_   :					Tuple					)   ->      List[str]:
							A        =      d

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

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


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


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]     ,A_   :					str					)   ->      int:
							if token in self.decoder:
								return self.decoder[token]
							elif token in self.added_tokens_decoder:
								return self.added_tokens_decoder[token]
							A        =      self.sp_model.piece_to_id(A_					)
							return sp_id + self.offset


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]     ,A_   :					int					)   ->      str:
							if index in self.encoder:
								return self.encoder[index]
							elif index in self.added_tokens_encoder:
								return self.added_tokens_encoder[index]
							else:
								A        =      self.sp_model.IdToPiece(index - self.offset					)
							return token


						def   _SCREAMING_SNAKE_CASE					(       self   :					int     ,A_   :					int					)   ->      Any:
							A        =      []
							A        =      ''
							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
									A        =      []
								else:
									current_sub_tokens.append(A_					)
							out_string += self.sp_model.decode(A_					)
							return out_string.strip()


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict     ,A_   :					List[str]=False					)   ->      Optional[Any]:
							return 1


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]     ,A_   :					Dict					)   ->      Optional[int]:
							A        =      set(self.all_special_ids					)  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special

							return [1 if x in all_special_ids else 0 for x in seq]


						def   _SCREAMING_SNAKE_CASE					(       self   :					Any     ,A_   :					List     ,A_   :					Optional[List] = None     ,A_   :					bool = False					)   ->      List[int]:
							if already_has_special_tokens:
								return self._special_token_mask(A_					)
							elif token_ids_a is None:
								return self._special_token_mask(A_					) + [1]
							else:
								return self._special_token_mask(token_ids_a + token_ids_a					) + [1]


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]     ,A_   :					Any     ,A_   :					Optional[int]=None					)   ->      List[int]:
							if token_ids_a is None:
								return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]




						def   _SCREAMING_SNAKE_CASE					(       self   :					List[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
							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_					) 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        =      self.sp_model.serialized_model_proto()
									fi.write(A_					)

							return (out_vocab_file,)




'''simple docstring'''

def      __lowerCamelCase     (      )			->      Tuple:
		for n in range(1						,			1000000  ):
				yield n * (n + 1) // 2



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      List[Any]:
		_a     :      Any        =							1
		_a     :      Tuple        =							2
		while i * i <= n:
				_a     :      Tuple        =							0
				while n % i == 0:
						n //= i
						multiplicity += 1
				divisors_count *= multiplicity + 1
				i += 1
		if n > 1:
				divisors_count *= 2
		return divisors_count



def      __lowerCamelCase     (      )			->      str:
		return next(i for i in triangle_number_generator() if count_divisors(lowerCAmelCase_  ) > 500  )


if __name__ == "__main__":
							print(solution())




