"""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


__UpperCamelCase									=			logging.get_logger(__name__)

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

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

__UpperCamelCase									=			{'''bert_for_seq_generation''': 512}




class  UpperCamelCase					(				lowerCAmelCase__   ):
       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,							lowerCAmelCase__,							lowerCAmelCase__="<s>",							lowerCAmelCase__="</s>",							lowerCAmelCase__="<unk>",							lowerCAmelCase__="<pad>",							lowerCAmelCase__="<::::>",							lowerCAmelCase__ = None,							**lowerCAmelCase__,							)      ->      None:
            snake_case_					       =					{} if sp_model_kwargs is None else sp_model_kwargs

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

            snake_case_					       =					vocab_file

            snake_case_					       =					spm.SentencePieceProcessor(**self.sp_model_kwargs)
            self.sp_model.Load(lowerCAmelCase__)







       @property
       def 		a_						( self)      ->      Tuple:
            return self.sp_model.get_piece_size()







       def 		a_						( self)      ->      Optional[Any]:
            snake_case_					       =					{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)      ->      Any:
            snake_case_					       =					self.__dict__.copy()
            snake_case_					       =					None
            return state







       def __setstate__( self,							lowerCAmelCase__)      ->      Optional[Any]:
            snake_case_					       =					d

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

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







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







       def 		a_						( self,							lowerCAmelCase__)      ->      Union[str, Any]:
            return self.sp_model.piece_to_id(lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__)      ->      Tuple:
            snake_case_					       =					self.sp_model.IdToPiece(lowerCAmelCase__)
            return token







       def 		a_						( self,							lowerCAmelCase__)      ->      Any:
            snake_case_					       =					[]
            snake_case_					       =					''
            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(lowerCAmelCase__) + token
                      snake_case_					       =					[]
                 else:
                      current_sub_tokens.append(lowerCAmelCase__)
            out_string += self.sp_model.decode(lowerCAmelCase__)
            return out_string.strip()







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = None)      ->      Tuple[str]:
            if not os.path.isdir(lowerCAmelCase__):
                 logger.error(f'Vocabulary path ({save_directory}) should be a directory')
                 return
            snake_case_					       =					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:
                      snake_case_					       =					self.sp_model.serialized_model_proto()
                      fi.write(lowerCAmelCase__)

            return (out_vocab_file,)









'''simple docstring'''







def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       int:
							return abs(UpperCamelCase      ) if a == 0 else greatest_common_divisor(b % a				,			UpperCamelCase      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       int:
							while y:  # --> when y=0 then loop will terminate and return x as final GCD.
														lowerCamelCase__  ,		lowerCamelCase__					:	Tuple         =       y, x % y
							return abs(UpperCamelCase      )





def  SCREAMING_SNAKE_CASE_       ()      ->       Tuple:
							try:
														lowerCamelCase__					:	Dict         =       input("""Enter two integers separated by comma (,): """      ).split(""","""      )
														lowerCamelCase__					:	Any         =       int(nums[0]      )
														lowerCamelCase__					:	Optional[Any]         =       int(nums[1]      )
														print(
														    f'''greatest_common_divisor({num_a}, {num_a}) = '''
														    f'''{greatest_common_divisor(UpperCamelCase				,			UpperCamelCase      )}'''      )
														print(f'''By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(UpperCamelCase				,			UpperCamelCase      )}'''      )
							except (IndexError, UnboundLocalError, ValueError):
														print("""Wrong input"""      )


if __name__ == "__main__":
			main()

