






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


_a    :   str        =			logging.get_logger(__name__)
_a    :   Tuple        =			{"""vocab_file""": """spiece.model"""}

_a    :   Any        =			{
    """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""",
    }
}

_a    :   List[str]        =			{
    """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 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    VOCAB_FILES_NAMES
      A          =    PRETRAINED_VOCAB_FILES_MAP
      A          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A          =    ['''input_ids''', '''attention_mask''']
      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase = None    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				{} if sp_model_kwargs is None else sp_model_kwargs

          lowerCAmelCase__   :Optional[int]      							=				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"		)
              lowerCAmelCase__   :str      							=				"None"

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

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

          lowerCAmelCase__   :Any      							=				do_lower_case
          lowerCAmelCase__   :List[Any]      							=				remove_space
          lowerCAmelCase__   :Dict      							=				keep_accents
          lowerCAmelCase__   :Optional[Any]      							=				vocab_file

          lowerCAmelCase__   :Optional[int]      							=				spm.SentencePieceProcessor(**self.sp_model_kwargs		)
          self.sp_model.Load(_lowerCAmelCase		)

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

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



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				self.__dict__.copy()
          lowerCAmelCase__   :Union[str, Any]      							=				None
          return state
      def __setstate__(   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				d

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

          lowerCAmelCase__   :int      							=				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 			snake_case_      (   self		):



          '''simple docstring'''
          return len(self.sp_model		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Any      							=				self.non_printing_characters_re.sub(""    ,	_lowerCAmelCase		)

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

          # NFC Unicode normalization
          lowerCAmelCase__   :List[Any]      							=				unicodedata.normalize("NFC"    ,	_lowerCAmelCase		)
          return text
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				self.preprocess_text(_lowerCAmelCase		)
          return self.sp_model.encode(_lowerCAmelCase    ,	out_type=_lowerCAmelCase		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          return self.sp_model.PieceToId(_lowerCAmelCase		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          return self.sp_model.IdToPiece(_lowerCAmelCase		)
      @staticmethod
      def 			snake_case_      (   _lowerCAmelCase		):



          '''simple docstring'''
          return out_string
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Any      							=				[]
          lowerCAmelCase__   :List[Any]      							=				""
          lowerCAmelCase__   :str      							=				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(_lowerCAmelCase		) + token
                  lowerCAmelCase__   :List[Any]      							=				True
                  lowerCAmelCase__   :Any      							=				[]
              else:
                  current_sub_tokens.append(_lowerCAmelCase		)
                  lowerCAmelCase__   :Union[str, Any]      							=				False
          out_string += self.sp_model.decode(_lowerCAmelCase		)

          return out_string
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[Any]      							=				{self.convert_ids_to_tokens(_lowerCAmelCase		): i for i in range(self.vocab_size		)}
          vocab.update(self.added_tokens_encoder		)
          return vocab
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None		):



          '''simple docstring'''
          if not os.path.isdir(_lowerCAmelCase		):
              logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
              return
          lowerCAmelCase__   :str      							=				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:
                  lowerCAmelCase__   :str      							=				self.sp_model.serialized_model_proto()
                  fi.write(_lowerCAmelCase		)

          return (out_vocab_file,)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = False		):



          '''simple docstring'''

          if isinstance(_lowerCAmelCase    ,	_lowerCAmelCase		):
              lowerCAmelCase__   :List[str]      							=				self.preprocess_text(_lowerCAmelCase		)
              lowerCAmelCase__   :List[str]      							=				self.sp_model.encode(_lowerCAmelCase		)
          else:
              lowerCAmelCase__   :int      							=				[self.preprocess_text(_lowerCAmelCase		) for t in text]
              lowerCAmelCase__   :List[str]      							=				self.sp_model.encode(_lowerCAmelCase		)

          if return_tensors is True or return_tensors == "pt":
              lowerCAmelCase__   :Tuple      							=				torch.tensor(_lowerCAmelCase		)

          return token_ids
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          return self.sp_model.decode(_lowerCAmelCase		)



      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				[f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
          lowerCAmelCase__   :str      							=				(
              f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(_lowerCAmelCase		) + f'''{self.bos_token}Bot:'''
          )
          return self.encode(text=_lowerCAmelCase		)











from math import isqrt, loga


def 				snake_case__							(							UpperCAmelCase							:  int       ):
    lowerCAmelCase__   :Optional[int]      							=				[True] * max_number
    for i in range(2    ,			isqrt(max_number - 1       ) + 1       ):
        if is_prime[i]:
            for j in range(i**2    ,			UpperCAmelCase    ,			UpperCAmelCase       ):
                lowerCAmelCase__   :int      							=				False

    return [i for i in range(2    ,			UpperCAmelCase       ) if is_prime[i]]


def 				snake_case__							(							UpperCAmelCase							:  int = 8_0_0_8_0_0    ,			UpperCAmelCase							:  int = 8_0_0_8_0_0       ):
    lowerCAmelCase__   :str      							=				degree * loga(UpperCAmelCase       )
    lowerCAmelCase__   :Any      							=				int(UpperCAmelCase       )
    lowerCAmelCase__   :List[Any]      							=				calculate_prime_numbers(UpperCAmelCase       )

    lowerCAmelCase__   :List[str]      							=				0
    lowerCAmelCase__   :List[str]      							=				0
    lowerCAmelCase__   :Optional[Any]      							=				len(UpperCAmelCase       ) - 1
    while left < right:
        while (
            prime_numbers[right] * loga(prime_numbers[left]       )
            + prime_numbers[left] * loga(prime_numbers[right]       )
            > upper_bound
        ):
            right -= 1
        hybrid_integers_count += right - left
        left += 1

    return hybrid_integers_count


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



