




'''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


UpperCamelCase_    	=				logging.get_logger(__name__)
UpperCamelCase_    	=				{"vocab_file": "spiece.model"}

UpperCamelCase_    	=				{
    "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",
    }
}

UpperCamelCase_    	=				{
    "AI-Sweden/gpt-sw3-126m": 2_0_4_8,
    "AI-Sweden/gpt-sw3-350m": 2_0_4_8,
    "AI-Sweden/gpt-sw3-1.6b": 2_0_4_8,
    "AI-Sweden/gpt-sw3-6.7b": 2_0_4_8,
    "AI-Sweden/gpt-sw3-20b": 2_0_4_8,
}






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				int        =						VOCAB_FILES_NAMES
       A						:				Union[str, Any]        =						PRETRAINED_VOCAB_FILES_MAP
       A						:				Any        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A						:				Tuple        =						['''input_ids''', '''attention_mask''']







       def __init__(      self,							A,							A=False,							A=False,							A=False,							A=None,							A=None,							A=None,							A=None,							A = None,							**A,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						{} if sp_model_kwargs is None else sp_model_kwargs

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						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'  )
                   SCREAMING_SNAKE_CASE   :      Dict    					=						'None'

             # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'<|endoftext|>' if eos_token is None else eos_token
             SCREAMING_SNAKE_CASE   :      int    					=						'<unk>' if unk_token is None else unk_token
             if "gpt-sw3-7b" in name_or_path:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						unk_token if pad_token is None else pad_token
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						eos_token if bos_token is None else bos_token
             else:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						'<pad>' if pad_token is None else pad_token
                   SCREAMING_SNAKE_CASE   :      int    					=						'<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,							)

             SCREAMING_SNAKE_CASE   :      Tuple    					=						do_lower_case
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						remove_space
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						keep_accents
             SCREAMING_SNAKE_CASE   :      Tuple    					=						vocab_file

             SCREAMING_SNAKE_CASE   :      Dict    					=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
             self.sp_model.Load(A  )

             # Used for whitespace normalization in input texts
             # fmt : off
             SCREAMING_SNAKE_CASE   :      Any    					=						{' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
             # fmt : on

             # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
             SCREAMING_SNAKE_CASE   :      str    					=						re.compile(
                 F"[{''.join(map(A,							list(range(0,							9  )  ) + list(range(11,							32  )  ) + list(range(127,							160  )  ) + [160, 173, 8_203]  )  )}]"  )







       def __getstate__(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.__dict__.copy()
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						None
             return state







       def __setstate__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						d

             # for backward compatibility
             if not hasattr(self,							'sp_model_kwargs'  ):
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{}

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return len(self.sp_model  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.non_printing_characters_re.sub('',							A  )

             # Normalize whitespaces
             SCREAMING_SNAKE_CASE   :      Any    					=						''.join([char if char not in self.whitespaces else ' ' for char in text]  )

             # NFC Unicode normalization
             SCREAMING_SNAKE_CASE   :      List[str]    					=						unicodedata.normalize('NFC',							A  )
             return text







       def        UpperCamelCase_							(      self,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.preprocess_text(A  )
             return self.sp_model.encode(A,							out_type=A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             return self.sp_model.PieceToId(A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             return self.sp_model.IdToPiece(A  )







       @staticmethod
       def        UpperCamelCase_							(      A  ):







             '''simple docstring'''







             return out_string







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						[]
             SCREAMING_SNAKE_CASE   :      str    					=						''
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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
                         SCREAMING_SNAKE_CASE   :      str    					=						True
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						[]
                   else:
                         current_sub_tokens.append(A  )
                         SCREAMING_SNAKE_CASE   :      int    					=						False
             out_string += self.sp_model.decode(A  )

             return out_string







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{self.convert_ids_to_tokens(A  ): i for i in range(self.vocab_size  )}
             vocab.update(self.added_tokens_encoder  )
             return vocab







       def        UpperCamelCase_							(      self,							A,							A = None  ):







             '''simple docstring'''







             if not os.path.isdir(A  ):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
                   return
             SCREAMING_SNAKE_CASE   :      Any    					=						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:
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.sp_model.serialized_model_proto()
                         fi.write(A  )

             return (out_vocab_file,)







       def        UpperCamelCase_							(      self,							A,							A = False  ):







             '''simple docstring'''







             if isinstance(A,							A  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.preprocess_text(A  )
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						self.sp_model.encode(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						[self.preprocess_text(A  ) for t in text]
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.sp_model.encode(A  )

             if return_tensors is True or return_tensors == "pt":
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						torch.tensor(A  )

             return token_ids







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             return self.sp_model.decode(A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						[F"User: {text}" if is_user else F"Bot: {text}" for is_user, text in conversation.iter_texts()]
             SCREAMING_SNAKE_CASE   :      Any    					=						(
                 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   lowercase__(			__UpperCamelCase:					int = 1_00_00_00    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Tuple    					=						[i - 1 for i in range(limit + 1    )]

      for i in range(2      ,limit + 1    ):
            if phi[i] == i - 1:
                  for j in range(2 * i      ,limit + 1      ,__UpperCamelCase    ):
                        phi[j] -= phi[j] // i

      return sum(phi[2 : limit + 1]    )


if __name__ == "__main__":
      print(solution())


