






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

a__       =     {
    """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__       =     {
    """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    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



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



				def __init__(      self    :    Optional[int]			,		UpperCamelCase__    :    List[Any]			,		UpperCamelCase__    :    int=False			,		UpperCamelCase__    :    Tuple=False			,		UpperCamelCase__    :    List[Any]=False			,		UpperCamelCase__    :    Union[str, Any]=None			,		UpperCamelCase__    :    Any=None			,		UpperCamelCase__    :    List[Any]=None			,		UpperCamelCase__    :    str=None			,		UpperCamelCase__    :    Optional[Dict[str, Any]] = None			,		**UpperCamelCase__    :    List[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=UpperCamelCase__			,		remove_space=UpperCamelCase__			,		keep_accents=UpperCamelCase__			,		bos_token=UpperCamelCase__			,		eos_token=UpperCamelCase__			,		unk_token=UpperCamelCase__			,		pad_token=UpperCamelCase__			,		sp_model_kwargs=self.sp_model_kwargs			,		**UpperCamelCase__			,		)

											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(UpperCamelCase__)

											# 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(UpperCamelCase__			,		list(range(0			,		9)) + list(range(1_1			,		3_2)) + list(range(1_2_7			,		1_6_0)) + [1_6_0, 1_7_3, 8_2_0_3]))}]''')



				def __getstate__(      self    :    Tuple):






											'''simple docstring'''







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



				def __setstate__(      self    :    Dict			,		UpperCamelCase__    :    Optional[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 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											return len(self.sp_model)



				def 					__magic_name__							(      self    :    List[str]			,		UpperCamelCase__    :    str):






											'''simple docstring'''







											snake_case__	  =       self.non_printing_characters_re.sub(""""""			,		UpperCamelCase__)

											# 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"""			,		UpperCamelCase__)
											return text



				def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    str			,		**UpperCamelCase__    :    List[str]):






											'''simple docstring'''







											snake_case__	  =       self.preprocess_text(UpperCamelCase__)
											return self.sp_model.encode(UpperCamelCase__			,		out_type=UpperCamelCase__)



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    str):






											'''simple docstring'''







											return self.sp_model.PieceToId(UpperCamelCase__)



				def 					__magic_name__							(      self    :    Tuple			,		UpperCamelCase__    :    int):






											'''simple docstring'''







											return self.sp_model.IdToPiece(UpperCamelCase__)



				@staticmethod
				def 					__magic_name__							(      UpperCamelCase__    :    str):






											'''simple docstring'''







											return out_string



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    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(UpperCamelCase__) + token
																									snake_case__	  =       True
																									snake_case__	  =       []
																		else:
																									current_sub_tokens.append(UpperCamelCase__)
																									snake_case__	  =       False
											out_string += self.sp_model.decode(UpperCamelCase__)

											return out_string



				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







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



				def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Optional[str] = None):






											'''simple docstring'''







											if not os.path.isdir(UpperCamelCase__):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
																		return
											snake_case__	  =       os.path.join(
											    UpperCamelCase__			,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

											if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase__) and os.path.isfile(self.vocab_file):
																		copyfile(self.vocab_file			,		UpperCamelCase__)
											elif not os.path.isfile(self.vocab_file):
																		with open(UpperCamelCase__			,		"""wb""") as fi:
																									snake_case__	  =       self.sp_model.serialized_model_proto()
																									fi.write(UpperCamelCase__)

											return (out_vocab_file,)



				def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    Union[str, List[str]]			,		UpperCamelCase__    :    Union[str, bool] = False):






											'''simple docstring'''







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

											if return_tensors is True or return_tensors == "pt":
																		snake_case__	  =       torch.tensor(UpperCamelCase__)

											return token_ids



				def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    Union[int, List[int]]):






											'''simple docstring'''







											return self.sp_model.decode(UpperCamelCase__)



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    "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(UpperCamelCase__) + F'''{self.bos_token}Bot:'''
											)
											return self.encode(text=UpperCamelCase__)








import math
from collections.abc import Iterator
from itertools import takewhile

def _UpperCAmelCase   (	a						:	int ):

							if 1 < number < 4:
														# 2 and 3 are primes
														return True
							elif number < 2 or number % 2 == 0 or number % 3 == 0:
														# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
														return False

							# All primes number are in format of 6k +/- 1
							for i in range(5  ,					int(math.sqrt(a ) + 1 )  ,					6 ):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True

def _UpperCAmelCase   (	):
							snake_case__	  =       2
							while True:
														if is_prime(a ):
																					yield num
														num += 1

def _UpperCAmelCase   (	a						:	int = 200_0000 ):
							return sum(takewhile(lambda a : x < n  ,					prime_generator() ) )


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