






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


snake_case_           =      logging.get_logger(__name__)
snake_case_           =      {'vocab_file': 'spiece.model'}

snake_case_           =      {
    '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',
    }
}

snake_case_           =      {
    'AI-Sweden/gpt-sw3-126m': 2048,
    'AI-Sweden/gpt-sw3-350m': 2048,
    'AI-Sweden/gpt-sw3-1.6b': 2048,
    'AI-Sweden/gpt-sw3-6.7b': 2048,
    'AI-Sweden/gpt-sw3-20b': 2048,
}

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    int							=   VOCAB_FILES_NAMES
      A_						:    Any							=   PRETRAINED_VOCAB_FILES_MAP
      A_						:    List[str]							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A_						:    List[Any]							=   ['input_ids', 'attention_mask']


      def __init__(self	:				Tuple    ,      a__	:				str    ,      a__	:				Any=False    ,      a__	:				Any=False    ,      a__	:				List[str]=False    ,      a__	:				List[Any]=None    ,      a__	:				List[Any]=None    ,      a__	:				Optional[Any]=None    ,      a__	:				Tuple=None    ,      a__	:				Optional[Dict[str, Any]] = None    ,      **a__	:				str    ,      ):





         """simple docstring"""



         __snake_case								=       {} if sp_model_kwargs is None else sp_model_kwargs

         __snake_case								=       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'''				)
            __snake_case								=       '''None'''

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

         __snake_case								=       do_lower_case
         __snake_case								=       remove_space
         __snake_case								=       keep_accents
         __snake_case								=       vocab_file

         __snake_case								=       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
         self.sp_model.Load(a__				)

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

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


      def __getstate__(self	:				str				):





         """simple docstring"""



         __snake_case								=       self.__dict__.copy()
         __snake_case								=       None
         return state


      def __setstate__(self	:				Union[str, Any]    ,      a__	:				List[Any]				):





         """simple docstring"""



         __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				)


      @property
      # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         return len(self.sp_model				)


      def        a			(self	:				List[str]    ,      a__	:				str				):





         """simple docstring"""



         __snake_case								=       self.non_printing_characters_re.sub(''''''    ,      a__				)

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

         # NFC Unicode normalization
         __snake_case								=       unicodedata.normalize('''NFC'''    ,      a__				)
         return text


      def        a			(self	:				Dict    ,      a__	:				str    ,      **a__	:				Tuple				):





         """simple docstring"""



         __snake_case								=       self.preprocess_text(a__				)
         return self.sp_model.encode(a__    ,      out_type=a__				)


      def        a			(self	:				Optional[Any]    ,      a__	:				str				):





         """simple docstring"""



         return self.sp_model.PieceToId(a__				)


      def        a			(self	:				List[Any]    ,      a__	:				int				):





         """simple docstring"""



         return self.sp_model.IdToPiece(a__				)


      @staticmethod
      def        a			(a__	:				str				):





         """simple docstring"""



         return out_string


      def        a			(self	:				List[str]    ,      a__	:				List[str]				):





         """simple docstring"""



         __snake_case								=       []
         __snake_case								=       ''''''
         __snake_case								=       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
               __snake_case								=       True
               __snake_case								=       []
            else:
               current_sub_tokens.append(a__				)
               __snake_case								=       False
         out_string += self.sp_model.decode(a__				)

         return out_string


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       {self.convert_ids_to_tokens(a__				): i for i in range(self.vocab_size				)}
         vocab.update(self.added_tokens_encoder				)
         return vocab


      def        a			(self	:				Tuple    ,      a__	:				str    ,      a__	:				Optional[str] = None				):





         """simple docstring"""



         if not os.path.isdir(a__				):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
            return
         __snake_case								=       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:
               __snake_case								=       self.sp_model.serialized_model_proto()
               fi.write(a__				)

         return (out_vocab_file,)


      def        a			(self	:				str    ,      a__	:				Union[str, List[str]]    ,      a__	:				Union[str, bool] = False				):





         """simple docstring"""



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

         if return_tensors is True or return_tensors == "pt":
            __snake_case								=       torch.tensor(a__				)

         return token_ids


      def        a			(self	:				Any    ,      a__	:				Union[int, List[int]]				):





         """simple docstring"""



         return self.sp_model.decode(a__				)


      def        a			(self	:				Dict    ,      a__	:				"Conversation"				):





         """simple docstring"""



         __snake_case								=       [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
         __snake_case								=       (
             f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(a__				) + f"""{self.bos_token}Bot:"""
         )
         return self.encode(text=a__				)








from __future__ import annotations

snake_case_           =      [True] * 1000001
snake_case_           =      2
while i * i <= 1000000:
    if seive[i]:
        for j in range(i * i, 1000001, i):
            snake_case_           =      False
    i += 1






def lowerCamelCase__   (							snake_case_     :  int	)				->    bool:
   return seive[n]






def lowerCamelCase__   (							snake_case_     :  int	)				->    bool:
   return any(digit in '''02468''' for digit in str(snake_case_	)	)






def lowerCamelCase__   (							snake_case_     :  int = 100_0000	)				->    list[int]:
   __snake_case								=       [2]  # result already includes the number 2.
   for num in range(3	,				limit + 1	,				2	):
      if is_prime(snake_case_	) and not contains_an_even_digit(snake_case_	):
         __snake_case								=       str(snake_case_	)
         __snake_case								=       [int(str_num[j:] + str_num[:j]	) for j in range(len(snake_case_	)	)]
         if all(is_prime(snake_case_	) for i in list_nums	):
            result.append(snake_case_	)
   return result






def lowerCamelCase__   (							)				->    int:
   return len(find_circular_primes()	)


if __name__ == "__main__":
    print(F'{len(find_circular_primes()) = }')

