


"""simple docstring"""




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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase_			     =  logging.get_logger(__name__)

lowercase_			     =  {"""vocab_file""": """sentencepiece.model"""}

lowercase_			     =  {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

lowercase_			     =  {
    """google/rembert""": 2_5_6,
}
class 					__lowerCAmelCase					(   _lowerCamelCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			str										=				VOCAB_FILES_NAMES
		__UpperCAmelCase		:			Any										=				PRETRAINED_VOCAB_FILES_MAP
		__UpperCAmelCase		:			Optional[Any]										=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

		def __init__(  self							, _a							, _a=False							, _a=True							, _a=True							, _a="[CLS]"							, _a="[SEP]"							, _a="[UNK]"							, _a="[SEP]"							, _a="[PAD]"							, _a="[CLS]"							, _a="[MASK]"							, **_a							, ):
				super().__init__(
				    do_lower_case=a_							, remove_space=a_							, keep_accents=a_							, bos_token=a_							, eos_token=a_							, unk_token=a_							, sep_token=a_							, pad_token=a_							, cls_token=a_							, mask_token=a_							, **a_							, )

				__a           =			do_lower_case
				__a           =			remove_space
				__a           =			keep_accents
				__a           =			vocab_file

				__a           =			spm.SentencePieceProcessor()
				self.sp_model.Load(a_      )

		@property
		def 			__UpperCAmelCase       (  self      ):
				return len(self.sp_model      )

		def 			__UpperCAmelCase       (  self      ):
				__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      ):
				__a           =			self.__dict__.copy()
				__a           =			None
				return state

		def __setstate__(  self							, _a      ):
				__a           =			d
				__a           =			spm.SentencePieceProcessor()
				self.sp_model.Load(self.vocab_file      )

		def 			__UpperCAmelCase       (  self							, _a							, _a=False      ):
				__a           =			self.sp_model.EncodeAsPieces(a_      )
				return pieces

		def 			__UpperCAmelCase       (  self							, _a      ):
				return self.sp_model.PieceToId(a_      )

		def 			__UpperCAmelCase       (  self							, _a      ):
				return self.sp_model.IdToPiece(a_      )

		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			self.sp_model.decode_pieces(a_      )
				return out_string

		def 			__UpperCAmelCase       (  self							, _a							, _a = None      ):
				__a           =			[self.sep_token_id]
				__a           =			[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 			__UpperCAmelCase       (  self							, _a							, _a = None							, _a = False      ):

				if already_has_special_tokens:
						if token_ids_a is not None:
								raise ValueError(
								    '''You should not supply a second sequence if the provided sequence of '''
								    '''ids is already formatted with special tokens for the model.'''      )
						return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

				if token_ids_a is not None:
						return [1] + ([0] * len(a_      )) + [1] + ([0] * len(a_      )) + [1]
				return [1] + ([0] * len(a_      )) + [1]

		def 			__UpperCAmelCase       (  self							, _a							, _a = None      ):
				__a           =			[self.sep_token_id]
				__a           =			[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 			__UpperCAmelCase       (  self							, _a							, _a = None      ):
				if not os.path.isdir(a_      ):
						logger.error('''Vocabulary path ({}) should be a directory'''.format(a_      )      )
						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_      ):
						copyfile(self.vocab_file							, a_      )

				return (out_vocab_file,)











'''simple docstring'''






from __future__ import annotations

import typing
from collections import Counter




def     snake_case_					(     lowerCAmelCase_		)->		typing.Counter[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						typing.Counter[int]			     = Counter()
       for base in range(1       ,   max_perimeter + 1		):
              for perpendicular in range(lowerCAmelCase_       ,   max_perimeter + 1		):
                     _UpperCAmelCase  :						List[str]			     = (base * base + perpendicular * perpendicular) ** 0.5
                     if hypotenuse == int(lowerCAmelCase_		):
                            _UpperCAmelCase  :						Optional[Any]			     = int(base + perpendicular + hypotenuse		)
                            if perimeter > max_perimeter:
                                   continue
                            triplets[perimeter] += 1
       return triplets




def     snake_case_					(     lowerCAmelCase_ = 1000		)->		int:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = pythagorean_triple(lowerCAmelCase_		)
       return triplets.most_common(1		)[0][0]


if __name__ == "__main__":
   print(f"""Perimeter {solution()} has maximum solutions""")



