






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 logging


A__			:    List[Any]         =				logging.get_logger(__name__)
A__			:    str         =				{'vocab_file': 'spiece.model'}

A__			:    Tuple         =				{
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    }
}

A__			:    Optional[int]         =				{
    'albert-base-v1': 5_12,
    'albert-large-v1': 5_12,
    'albert-xlarge-v1': 5_12,
    'albert-xxlarge-v1': 5_12,
    'albert-base-v2': 5_12,
    'albert-large-v2': 5_12,
    'albert-xlarge-v2': 5_12,
    'albert-xxlarge-v2': 5_12,
}

A__			:    str         =				'▁'
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= VOCAB_FILES_NAMES
				lowercase__       							= PRETRAINED_VOCAB_FILES_MAP
				lowercase__       							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




				def __init__(  self		:					List[str],						lowerCamelCase		:					int,						lowerCamelCase		:					Union[str, Any]=True,						lowerCamelCase		:					Union[str, Any]=True,						lowerCamelCase		:					Tuple=False,						lowerCamelCase		:					Tuple="[CLS]",						lowerCamelCase		:					int="[SEP]",						lowerCamelCase		:					Any="<unk>",						lowerCamelCase		:					List[str]="[SEP]",						lowerCamelCase		:					Optional[Any]="<pad>",						lowerCamelCase		:					int="[CLS]",						lowerCamelCase		:					List[str]="[MASK]",						lowerCamelCase		:					Optional[Dict[str, Any]] = None,						**lowerCamelCase		:					Optional[int],						):

								'''simple docstring'''
								lowercase__     				=    (
								    AddedToken(lowerCamelCase,						lstrip=lowerCamelCase,						rstrip=lowerCamelCase,						normalized=lowerCamelCase						)
								    if isinstance(lowerCamelCase,						lowerCamelCase						)
								    else mask_token
								)

								lowercase__     				=    {} 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,						sp_model_kwargs=self.sp_model_kwargs,						**lowerCamelCase,						)

								lowercase__     				=    do_lower_case
								lowercase__     				=    remove_space
								lowercase__     				=    keep_accents
								lowercase__     				=    vocab_file

								lowercase__     				=    spm.SentencePieceProcessor(**self.sp_model_kwargs						)
								self.sp_model.Load(lowerCamelCase						)




				@property
				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								return len(self.sp_model						)




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    {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		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    self.__dict__.copy()
								lowercase__     				=    None
								return state




				def __setstate__(  self		:					List[str],						lowerCamelCase		:					int						):

								'''simple docstring'''
								lowercase__     				=    d

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

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




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					List[str]						):

								'''simple docstring'''

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

								if not self.keep_accents:
												lowercase__     				=    unicodedata.normalize('''NFKD''',						lowerCamelCase						)
												lowercase__     				=    ''''''.join([c for c in outputs if not unicodedata.combining(lowerCamelCase						)]						)
								if self.do_lower_case:
												lowercase__     				=    outputs.lower()

								return outputs




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    self.preprocess_text(lowerCamelCase						)
								lowercase__     				=    self.sp_model.encode(lowerCamelCase,						out_type=lowerCamelCase						)
								lowercase__     				=    []
								for piece in pieces:
												if len(lowerCamelCase						) > 1 and piece[-1] == str(''','''						) and piece[-2].isdigit():
																lowercase__     				=    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:
																								lowercase__     				=    cur_pieces[1:]
																				else:
																								lowercase__     				=    cur_pieces[0][1:]
																cur_pieces.append(piece[-1]						)
																new_pieces.extend(lowerCamelCase						)
												else:
																new_pieces.append(lowerCamelCase						)

								return new_pieces




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					List[Any]						):

								'''simple docstring'''
								return self.sp_model.PieceToId(lowerCamelCase						)




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					List[str]						):

								'''simple docstring'''
								return self.sp_model.IdToPiece(lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    []
								lowercase__     				=    ''''''
								lowercase__     				=    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(lowerCamelCase						) + token
																lowercase__     				=    True
																lowercase__     				=    []
												else:
																current_sub_tokens.append(lowerCamelCase						)
																lowercase__     				=    False
								out_string += self.sp_model.decode(lowerCamelCase						)
								return out_string.strip()




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					List[int],						lowerCamelCase		:					Optional[List[int]] = None						):

								'''simple docstring'''
								lowercase__     				=    [self.sep_token_id]
								lowercase__     				=    [self.cls_token_id]
								if token_ids_a is None:
												return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					List[int],						lowerCamelCase		:					Optional[List[int]] = None,						lowerCamelCase		:					bool = False						):

								'''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 [1] + ([0] * len(lowerCamelCase						)) + [1] + ([0] * len(lowerCamelCase						)) + [1]
								return [1] + ([0] * len(lowerCamelCase						)) + [1]




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					List[int],						lowerCamelCase		:					Optional[List[int]] = None						):

								'''simple docstring'''
								lowercase__     				=    [self.sep_token_id]
								lowercase__     				=    [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						) * [0] + len(token_ids_a + sep						) * [1]




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					str,						lowerCamelCase		:					Optional[str] = None						):

								'''simple docstring'''

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

								return (out_vocab_file,)












from collections import defaultdict
from math import gcd






def 						a    (		lowerCamelCase_ = 150_0000       ):






				'''simple docstring'''
				lowercase__     				=    defaultdict(lowerCamelCase_       )
				lowercase__     				=    2
				while 2 * euclid_m * (euclid_m + 1) <= limit:
								for euclid_n in range((euclid_m % 2) + 1	,     lowerCamelCase_	,     2       ):
												if gcd(lowerCamelCase_	,     lowerCamelCase_       ) > 1:
																continue
												lowercase__     				=    2 * euclid_m * (euclid_m + euclid_n)
												for perimeter in range(lowerCamelCase_	,     limit + 1	,     lowerCamelCase_       ):
																frequencies[perimeter] += 1
								euclid_m += 1
				return sum(1 for frequency in frequencies.values() if frequency == 1       )


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




