


'''simple docstring'''





from __future__ import annotations

from functools import lru_cache
from math import ceil

UpperCAmelCase__  			=				1_0_0

UpperCAmelCase__  			=				set(range(3, NUM_PRIMES, 2))
primes.add(2)
UpperCAmelCase__  			=				42

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
					if prime not in primes:
										continue
					primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))






@lru_cache(maxsize=100 )
def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			if number_to_partition < 0:
						return set()
			elif number_to_partition == 0:
						return {1}

			__A=	set()
			__A=	42
			__A=	42

			for prime in primes:
						if prime > number_to_partition:
									continue
						for sub in partition(number_to_partition - prime ):
									ret.add(sub * prime )

			return ret






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int = 5000 ):






			"""simple docstring"""




			for number_to_partition in range(1,_SCREAMING_SNAKE_CASE ):
						if len(partition(_SCREAMING_SNAKE_CASE ) ) > number_unique_partitions:
									return number_to_partition
			return None


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







'''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__					(					a_   ):





			'''simple docstring'''

			A						:				int					      =			VOCAB_FILES_NAMES
			A						:				Optional[int]					      =			PRETRAINED_VOCAB_FILES_MAP
			A						:				Tuple					      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A						:				int					      =			['''input_ids''', '''attention_mask''']
			def __init__(  self							:			int			,    lowerCAmelCase_							:			List[str]			,    lowerCAmelCase_							:			Union[str, Any]=False			,    lowerCAmelCase_							:			List[str]=False			,    lowerCAmelCase_							:			List[Any]=False			,    lowerCAmelCase_							:			Union[str, Any]=None			,    lowerCAmelCase_							:			Union[str, Any]=None			,    lowerCAmelCase_							:			Union[str, Any]=None			,    lowerCAmelCase_							:			List[Any]=None			,    lowerCAmelCase_							:			Optional[Dict[str, Any]] = None			,    **lowerCAmelCase_							:			Optional[Any]			,    )      ->		None:
						__A=	{} if sp_model_kwargs is None else sp_model_kwargs

						__A=	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'	)
									__A=	'None'

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

						__A=	do_lower_case
						__A=	remove_space
						__A=	keep_accents
						__A=	vocab_file

						__A=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
						self.sp_model.Load(lowerCAmelCase_	)

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

						# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
						__A=	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							:			Optional[int]	)      ->		Tuple:
						__A=	self.__dict__.copy()
						__A=	None
						return state
			def __setstate__(  self							:			int			,    lowerCAmelCase_							:			int	)      ->		Tuple:
						__A=	d

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

						__A=	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 					lowerCAmelCase     (  self							:			Tuple	)      ->		int:
						return len(self.sp_model	)
			def 					lowerCAmelCase     (  self							:			int			,    lowerCAmelCase_							:			str	)      ->		str:
						__A=	self.non_printing_characters_re.sub(''			,    lowerCAmelCase_	)

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

						# NFC Unicode normalization
						__A=	unicodedata.normalize('NFC'			,    lowerCAmelCase_	)
						return text
			def 					lowerCAmelCase     (  self							:			Tuple			,    lowerCAmelCase_							:			str			,    **lowerCAmelCase_							:			Optional[Any]	)      ->		List[str]:
						__A=	self.preprocess_text(lowerCAmelCase_	)
						return self.sp_model.encode(lowerCAmelCase_			,    out_type=lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Any			,    lowerCAmelCase_							:			str	)      ->		int:
						return self.sp_model.PieceToId(lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			int	)      ->		str:
						return self.sp_model.IdToPiece(lowerCAmelCase_	)
			@staticmethod
			def 					lowerCAmelCase     (  lowerCAmelCase_							:			str	)      ->		str:
						return out_string
			def 					lowerCAmelCase     (  self							:			Union[str, Any]			,    lowerCAmelCase_							:			List[str]	)      ->		str:
						__A=	[]
						__A=	''
						__A=	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
												__A=	True
												__A=	[]
									else:
												current_sub_tokens.append(lowerCAmelCase_	)
												__A=	False
						out_string += self.sp_model.decode(lowerCAmelCase_	)

						return out_string
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		Dict[str, int]:
						__A=	{self.convert_ids_to_tokens(lowerCAmelCase_	): i for i in range(self.vocab_size	)}
						vocab.update(self.added_tokens_encoder	)
						return vocab
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Optional[str] = None	)      ->		Tuple[str]:
						if not os.path.isdir(lowerCAmelCase_	):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
									return
						__A=	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:
												__A=	self.sp_model.serialized_model_proto()
												fi.write(lowerCAmelCase_	)

						return (out_vocab_file,)
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			Union[str, List[str]]			,    lowerCAmelCase_							:			Union[str, bool] = False	)      ->		Union[List[int], List[List[int]], "torch.Tensor"]:

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

						if return_tensors is True or return_tensors == "pt":
									__A=	torch.tensor(lowerCAmelCase_	)

						return token_ids
			def 					lowerCAmelCase     (  self							:			Tuple			,    lowerCAmelCase_							:			Union[int, List[int]]	)      ->		str:
						return self.sp_model.decode(lowerCAmelCase_	)

			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			"Conversation"	)      ->		List[int]:
						__A=	[F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
						__A=	(
						    F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(lowerCAmelCase_	) + F"""{self.bos_token}Bot:"""
						)
						return self.encode(text=lowerCAmelCase_	)



