



"""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


__snake_case				:  Any =       logging.get_logger(__name__)

__snake_case				:  Tuple =       {'vocab_file': 'spiece.model'}

__snake_case				:  Tuple =       {
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

__snake_case				:  List[str] =       {'bert_for_seq_generation': 512}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE        =      PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE        =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE        =      []
	SCREAMING_SNAKE_CASE        =      ['input_ids', 'attention_mask']

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

				"""simple docstring"""

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

				# Add extra_ids to the special token list
				super().__init__(
				    bos_token=_SCREAMING_SNAKE_CASE			,     eos_token=_SCREAMING_SNAKE_CASE			,     unk_token=_SCREAMING_SNAKE_CASE			,     pad_token=_SCREAMING_SNAKE_CASE			,     sep_token=_SCREAMING_SNAKE_CASE			,     sp_model_kwargs=self.sp_model_kwargs			,     **_SCREAMING_SNAKE_CASE			,     )

				__lowerCAmelCase :    Union[str, Any]				=			vocab_file

				__lowerCAmelCase :    Optional[Any]				=			spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.Load(_SCREAMING_SNAKE_CASE)

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			List[Any]:

				"""simple docstring"""

				return self.sp_model.get_piece_size()

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str])							->			Dict:

				"""simple docstring"""

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

	def __getstate__(	self:						Union[str, Any])							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			self.__dict__.copy()
				__lowerCAmelCase :    List[str]				=			None
				return state

	def __setstate__(	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[Any])							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			d

				# for backward compatibility
				if not hasattr(self			,     "sp_model_kwargs"):
							__lowerCAmelCase :    List[str]				=			{}

				__lowerCAmelCase :    Dict				=			spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.Load(self.vocab_file)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     _SCREAMING_SNAKE_CASE:						str)							->			List[str]:

				"""simple docstring"""

				return self.sp_model.encode(_SCREAMING_SNAKE_CASE			,     out_type=_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						str			,     _SCREAMING_SNAKE_CASE:						Any)							->			Dict:

				"""simple docstring"""

				return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						Optional[int])							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE)
				return token

	def 				_SCREAMING_SNAKE_CASE  (	self:						int			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			[]
				__lowerCAmelCase :    Union[str, Any]				=			""
				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(_SCREAMING_SNAKE_CASE) + token
										__lowerCAmelCase :    str				=			[]
							else:
										current_sub_tokens.append(_SCREAMING_SNAKE_CASE)
				out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE)
				return out_string.strip()







	def 				_SCREAMING_SNAKE_CASE  (	self:						str			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						Optional[str] = None)							->			Tuple[str]:

				"""simple docstring"""

				if not os.path.isdir(_SCREAMING_SNAKE_CASE):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
							return
				__lowerCAmelCase :    List[Any]				=			os.path.join(
				    _SCREAMING_SNAKE_CASE			,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

				if os.path.abspath(self.vocab_file) != os.path.abspath(_SCREAMING_SNAKE_CASE) and os.path.isfile(self.vocab_file):
							copyfile(self.vocab_file			,     _SCREAMING_SNAKE_CASE)
				elif not os.path.isfile(self.vocab_file):
							with open(_SCREAMING_SNAKE_CASE			,     "wb") as fi:
										__lowerCAmelCase :    Optional[Any]				=			self.sp_model.serialized_model_proto()
										fi.write(_SCREAMING_SNAKE_CASE)

				return (out_vocab_file,)




"""simple docstring"""






def      _lowercase							(    __snake_case   ,__snake_case      )		->  int:
			return int((input_a, input_a).count(1      ) != 0      )


def      _lowercase							(    )		->  None:
			assert or_gate(0   ,0      ) == 0
			assert or_gate(0   ,1      ) == 1
			assert or_gate(1   ,0      ) == 1
			assert or_gate(1   ,1      ) == 1


if __name__ == "__main__":
	print(or_gate(0, 1))
	print(or_gate(1, 0))
	print(or_gate(0, 0))
	print(or_gate(1, 1))