'''simple docstring'''





import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
    import torch


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


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

SCREAMING_SNAKE_CASE_:     List[str]  ={
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

SCREAMING_SNAKE_CASE_:     Union[str, Any]  ={
    'AI-Sweden/gpt-sw3-126m': 20_48,
    'AI-Sweden/gpt-sw3-350m': 20_48,
    'AI-Sweden/gpt-sw3-1.6b': 20_48,
    'AI-Sweden/gpt-sw3-6.7b': 20_48,
    'AI-Sweden/gpt-sw3-20b': 20_48,
}




class        __A      ( UpperCamelCase__	):
    a__       :						List[str]          =  VOCAB_FILES_NAMES
    a__       :						Any          =  PRETRAINED_VOCAB_FILES_MAP
    a__       :						int          =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__       :						Optional[int]          =  ["""input_ids""", """attention_mask"""]



    def __init__(self    :    Tuple   ,			__a    :    Dict   ,			__a    :    Union[str, Any]=False   ,			__a    :    int=False   ,			__a    :    Dict=False   ,			__a    :    Any=None   ,			__a    :    Tuple=None   ,			__a    :    List[str]=None   ,			__a    :    Optional[Any]=None   ,			__a    :    Optional[Dict[str, Any]] = None   ,			**__a    :    Optional[int]   ,			):
         UpperCAmelCase_           =   {} if sp_model_kwargs is None else sp_model_kwargs

         UpperCAmelCase_           =   kwargs.get("name_or_path" )
         if name_or_path is None:
              logger.warning(
                  "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
                  " you are testing the model, this can safely be ignored" )
              UpperCAmelCase_           =   "None"

         # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
         UpperCAmelCase_           =   "<|endoftext|>" if eos_token is None else eos_token
         UpperCAmelCase_           =   "<unk>" if unk_token is None else unk_token
         if "gpt-sw3-7b" in name_or_path:
              UpperCAmelCase_           =   unk_token if pad_token is None else pad_token
              UpperCAmelCase_           =   eos_token if bos_token is None else bos_token
         else:
              UpperCAmelCase_           =   "<pad>" if pad_token is None else pad_token
              UpperCAmelCase_           =   "<s>" if bos_token is None else bos_token

         super().__init__(
             do_lower_case=__a   ,			remove_space=__a   ,			keep_accents=__a   ,			bos_token=__a   ,			eos_token=__a   ,			unk_token=__a   ,			pad_token=__a   ,			sp_model_kwargs=self.sp_model_kwargs   ,			**__a   ,			)

         UpperCAmelCase_           =   do_lower_case
         UpperCAmelCase_           =   remove_space
         UpperCAmelCase_           =   keep_accents
         UpperCAmelCase_           =   vocab_file

         UpperCAmelCase_           =   spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(__a )

         # Used for whitespace normalization in input texts
         # fmt : off
         UpperCAmelCase_           =   {" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
         # fmt : on

         # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
         UpperCAmelCase_           =   re.compile(
             f"""[{"".join(map(__a   ,			list(range(0   ,			9 ) ) + list(range(11   ,			32 ) ) + list(range(127   ,			160 ) ) + [160, 173, 8203] ) )}]""" )



    def __getstate__(self    :    Optional[int] ):
         UpperCAmelCase_           =   self.__dict__.copy()
         UpperCAmelCase_           =   None
         return state



    def __setstate__(self    :    Optional[Any]   ,			__a    :    int ):
         UpperCAmelCase_           =   d

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

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



    @property
    # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
    def 					_lowercase						(self    :    int ):
         return len(self.sp_model )



    def 					_lowercase						(self    :    List[str]   ,			__a    :    str ):
         UpperCAmelCase_           =   self.non_printing_characters_re.sub(""   ,			__a )

         # Normalize whitespaces
         UpperCAmelCase_           =   "".join([char if char not in self.whitespaces else " " for char in text] )

         # NFC Unicode normalization
         UpperCAmelCase_           =   unicodedata.normalize("NFC"   ,			__a )
         return text



    def 					_lowercase						(self    :    int   ,			__a    :    str   ,			**__a    :    str ):
         UpperCAmelCase_           =   self.preprocess_text(__a )
         return self.sp_model.encode(__a   ,			out_type=__a )



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    str ):
         return self.sp_model.PieceToId(__a )



    def 					_lowercase						(self    :    List[Any]   ,			__a    :    int ):
         return self.sp_model.IdToPiece(__a )



    @staticmethod
    def 					_lowercase						(__a    :    str ):
         return out_string



    def 					_lowercase						(self    :    Dict   ,			__a    :    List[str] ):
         UpperCAmelCase_           =   []
         UpperCAmelCase_           =   ""
         UpperCAmelCase_           =   False
         for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                   # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                   if not prev_is_special:
                        out_string += " "

                   out_string += self.sp_model.decode(__a ) + token
                   UpperCAmelCase_           =   True
                   UpperCAmelCase_           =   []
              else:
                   current_sub_tokens.append(__a )
                   UpperCAmelCase_           =   False
         out_string += self.sp_model.decode(__a )

         return out_string



    def 					_lowercase						(self    :    List[str] ):
         UpperCAmelCase_           =   {self.convert_ids_to_tokens(__a ): i for i in range(self.vocab_size )}
         vocab.update(self.added_tokens_encoder )
         return vocab



    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    str   ,			__a    :    Optional[str] = None ):
         if not os.path.isdir(__a ):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
              return
         UpperCAmelCase_           =   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 ) and os.path.isfile(self.vocab_file ):
              copyfile(self.vocab_file   ,			__a )
         elif not os.path.isfile(self.vocab_file ):
              with open(__a   ,			"wb" ) as fi:
                   UpperCAmelCase_           =   self.sp_model.serialized_model_proto()
                   fi.write(__a )

         return (out_vocab_file,)



    def 					_lowercase						(self    :    str   ,			__a    :    Union[str, List[str]]   ,			__a    :    Union[str, bool] = False ):

         if isinstance(__a   ,			__a ):
              UpperCAmelCase_           =   self.preprocess_text(__a )
              UpperCAmelCase_           =   self.sp_model.encode(__a )
         else:
              UpperCAmelCase_           =   [self.preprocess_text(__a ) for t in text]
              UpperCAmelCase_           =   self.sp_model.encode(__a )

         if return_tensors is True or return_tensors == "pt":
              UpperCAmelCase_           =   torch.tensor(__a )

         return token_ids



    def 					_lowercase						(self    :    List[Any]   ,			__a    :    Union[int, List[int]] ):
         return self.sp_model.decode(__a )






    def 					_lowercase						(self    :    int   ,			__a    :    "Conversation" ):
         UpperCAmelCase_           =   [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
         UpperCAmelCase_           =   (
             f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(__a ) + f"""{self.bos_token}Bot:"""
         )
         return self.encode(text=__a )





'''simple docstring'''





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : int			)    ->      int:


     '''simple docstring'''




     return x if y == 0 else greatest_common_divisor(snake_case_					,    x % y			)





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : int			)    ->      int:


     '''simple docstring'''




     return (x * y) // greatest_common_divisor(snake_case_					,    snake_case_			)





def 	lowerCAmelCase_				(					snake_case_       : int = 20			)    ->      int:


     '''simple docstring'''




     UpperCAmelCase_           =   1
     for i in range(1					,    n + 1			):
          UpperCAmelCase_           =   lcm(snake_case_					,    snake_case_			)
     return g


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




