





import os
import unicodedata
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 SPIECE_UNDERLINE, logging


A    :					str =      logging.get_logger(__name__)

A    :					Union[str, Any] =      {'''vocab_file''': '''spiece.model'''}

A    :					Any =      {
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    }
}

A    :					List[str] =      {
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

# Segments (not really needed)
A    :					Dict =      0
A    :					Any =      1
A    :					str =      2
A    :					Optional[int] =      3
A    :					str =      4


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    VOCAB_FILES_NAMES
			__lowerCamelCase	:       List[Any]							=    PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase	:       List[str]							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase	:       int							=    '''left'''






			def __init__(			self   :    Optional[Any]							,	__lowerCAmelCase   :    Any							,	__lowerCAmelCase   :    Optional[int]=False							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    List[str]=False							,	__lowerCAmelCase   :    int="<s>"							,	__lowerCAmelCase   :    Dict="</s>"							,	__lowerCAmelCase   :    Optional[Any]="<unk>"							,	__lowerCAmelCase   :    Union[str, Any]="<sep>"							,	__lowerCAmelCase   :    Optional[Any]="<pad>"							,	__lowerCAmelCase   :    Union[str, Any]="<cls>"							,	__lowerCAmelCase   :    Optional[Any]="<mask>"							,	__lowerCAmelCase   :    Dict=["<eop>", "<eod>"]							,	__lowerCAmelCase   :    Optional[Dict[str, Any]] = None							,	**__lowerCAmelCase   :    int							,	)	->	None:







					"""simple docstring"""






					A__        =   AddedToken(__lowerCAmelCase							,	lstrip=__lowerCAmelCase							,	rstrip=__lowerCAmelCase   ) if isinstance(__lowerCAmelCase							,	__lowerCAmelCase   ) else mask_token

					A__        =   {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    do_lower_case=__lowerCAmelCase							,	remove_space=__lowerCAmelCase							,	keep_accents=__lowerCAmelCase							,	bos_token=__lowerCAmelCase							,	eos_token=__lowerCAmelCase							,	unk_token=__lowerCAmelCase							,	sep_token=__lowerCAmelCase							,	pad_token=__lowerCAmelCase							,	cls_token=__lowerCAmelCase							,	mask_token=__lowerCAmelCase							,	additional_special_tokens=__lowerCAmelCase							,	sp_model_kwargs=self.sp_model_kwargs							,	**__lowerCAmelCase							,	)

					A__        =   3

					A__        =   do_lower_case
					A__        =   remove_space
					A__        =   keep_accents
					A__        =   vocab_file

					A__        =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
					self.sp_model.Load(__lowerCAmelCase   )






			@property
			def    a_       (			self   :    List[Any]   )	->	Tuple:







					"""simple docstring"""






					return len(self.sp_model   )






			def    a_       (			self   :    str   )	->	str:







					"""simple docstring"""






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






			def __getstate__(			self   :    Optional[Any]   )	->	str:







					"""simple docstring"""






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






			def __setstate__(			self   :    str							,	__lowerCAmelCase   :    Optional[Any]   )	->	Dict:







					"""simple docstring"""






					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    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    int   )	->	str:







					"""simple docstring"""






					if self.remove_space:
							A__        =   """ """.join(inputs.strip().split()   )
					else:
							A__        =   inputs
					A__        =   outputs.replace("""``"""							,	"""\""""   ).replace("""''"""							,	"""\""""   )

					if not self.keep_accents:
							A__        =   unicodedata.normalize("""NFKD"""							,	__lowerCAmelCase   )
							A__        =   """""".join([c for c in outputs if not unicodedata.combining(__lowerCAmelCase   )]   )
					if self.do_lower_case:
							A__        =   outputs.lower()

					return outputs






			def    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    str   )	->	List[str]:







					"""simple docstring"""






					A__        =   self.preprocess_text(__lowerCAmelCase   )
					A__        =   self.sp_model.encode(__lowerCAmelCase							,	out_type=__lowerCAmelCase   )
					A__        =   []
					for piece in pieces:
							if len(__lowerCAmelCase   ) > 1 and piece[-1] == str(""","""   ) and piece[-2].isdigit():
									A__        =   self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCAmelCase							,	""""""   )   )
									if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
											if len(cur_pieces[0]   ) == 1:
													A__        =   cur_pieces[1:]
											else:
													A__        =   cur_pieces[0][1:]
									cur_pieces.append(piece[-1]   )
									new_pieces.extend(__lowerCAmelCase   )
							else:
									new_pieces.append(__lowerCAmelCase   )

					return new_pieces






			def    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    Dict   )	->	List[Any]:







					"""simple docstring"""






					return self.sp_model.PieceToId(__lowerCAmelCase   )






			def    a_       (			self   :    Union[str, Any]							,	__lowerCAmelCase   :    List[Any]   )	->	Dict:







					"""simple docstring"""






					return self.sp_model.IdToPiece(__lowerCAmelCase   )






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    Dict   )	->	Optional[Any]:







					"""simple docstring"""






					A__        =   """""".join(__lowerCAmelCase   ).replace(__lowerCAmelCase							,	""" """   ).strip()
					return out_string






			def    a_       (			self   :    int							,	__lowerCAmelCase   :    List[int]							,	__lowerCAmelCase   :    bool = False							,	__lowerCAmelCase   :    bool = None							,	__lowerCAmelCase   :    bool = True							,	**__lowerCAmelCase   :    int							,	)	->	str:







					"""simple docstring"""






					A__        =   kwargs.pop("""use_source_tokenizer"""							,	__lowerCAmelCase   )

					A__        =   self.convert_ids_to_tokens(__lowerCAmelCase							,	skip_special_tokens=__lowerCAmelCase   )

					# To avoid mixing byte-level and unicode for byte-level BPT
					# we need to build string separately for added tokens and byte-level tokens
					# cf. https://github.com/huggingface/transformers/issues/1133
					A__        =   []
					A__        =   []
					for token in filtered_tokens:
							if skip_special_tokens and token in self.all_special_ids:
									continue
							if token in self.added_tokens_encoder:
									if current_sub_text:
											sub_texts.append(self.convert_tokens_to_string(__lowerCAmelCase   )   )
											A__        =   []
									sub_texts.append(__lowerCAmelCase   )
							else:
									current_sub_text.append(__lowerCAmelCase   )
					if current_sub_text:
							sub_texts.append(self.convert_tokens_to_string(__lowerCAmelCase   )   )

					# Mimic the behavior of the Rust tokenizer:
					# By default, there are no spaces between special tokens
					A__        =   """""".join(__lowerCAmelCase   )

					A__        =   (
					    clean_up_tokenization_spaces
					    if clean_up_tokenization_spaces is not None
					    else self.clean_up_tokenization_spaces
					)
					if clean_up_tokenization_spaces:
							A__        =   self.clean_up_tokenization(__lowerCAmelCase   )
							return clean_text
					else:
							return text






			def    a_       (			self   :    Optional[int]							,	__lowerCAmelCase   :    List[int]							,	__lowerCAmelCase   :    Optional[List[int]] = None   )	->	List[int]:







					"""simple docstring"""






					A__        =   [self.sep_token_id]
					A__        =   [self.cls_token_id]
					if token_ids_a is None:
							return token_ids_a + sep + cls
					return token_ids_a + sep + token_ids_a + sep + cls






			def    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    List[int]							,	__lowerCAmelCase   :    Optional[List[int]] = None							,	__lowerCAmelCase   :    bool = False   )	->	List[int]:







					"""simple docstring"""






					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 not None:
							return ([0] * len(__lowerCAmelCase   )) + [1] + ([0] * len(__lowerCAmelCase   )) + [1, 1]
					return ([0] * len(__lowerCAmelCase   )) + [1, 1]






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







					"""simple docstring"""






					A__        =   [self.sep_token_id]
					A__        =   [2]

					if token_ids_a is None:
							return len(token_ids_a + sep   ) * [0] + cls_segment_id
					return len(token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1] + cls_segment_id







			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    Optional[str] = None   )	->	Tuple[str]:







					"""simple docstring"""






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

					return (out_vocab_file,)










def    __lowerCamelCase					(							__a   :int = 1_0_0_0_0_0_0    )		->				int:






		"""simple docstring"""

		A__        =   limit + 1
		A__        =   [0] * limit
		for first_term in range(1    ,     __a    ):
				for n in range(__a    ,     __a    ,     __a    ):
						A__        =   first_term + n / first_term
						if common_difference % 4:  # d must be divisble by 4
								continue
						else:
								common_difference /= 4
								if (
								    first_term > common_difference
								    and first_term < 4 * common_difference
								):  # since x,y,z are positive integers
										frequency[n] += 1  # so z>0 and a>d ,also 4d<a

		A__        =   sum(1 for x in frequency[1:limit] if x == 1_0    )

		return count


if __name__ == "__main__":
					print(F'''{solution() = }''')



