

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						:  Optional[Any]									=			logging.get_logger(__name__)
_UpperCAmelCase						:  Dict									=			{"""vocab_file""": """spiece.model"""}

_UpperCAmelCase						:  Tuple									=			{
    """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						:  List[Any]									=			{
    """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 					lowercase							(			a_     ):
				__SCREAMING_SNAKE_CASE   :					int			     =     VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE   :					int			     =     ['''input_ids''', '''attention_mask''']




				def __init__(   self ,  snake_case ,  snake_case=False ,  snake_case=False ,  snake_case=False ,  snake_case=None ,  snake_case=None ,  snake_case=None ,  snake_case=None ,  snake_case = None ,  **snake_case ,  ):
											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=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_ ,  )

											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(lowerCAmelCase_			)

											# 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(lowerCAmelCase_ ,  list(range(0 ,  9			)			) + list(range(11 ,  32			)			) + list(range(127 ,  160			)			) + [160, 173, 8203]			)			)}]'''			)




				def __getstate__(   self			):
											snake_case_     							=       self.__dict__.copy()
											snake_case_     							=       None
											return state




				def __setstate__(   self ,  snake_case			):
											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			):
											return len(self.sp_model			)




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self.non_printing_characters_re.sub('' ,  lowerCAmelCase_			)

											# 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' ,  lowerCAmelCase_			)
											return text




				def        a				(   self ,  snake_case ,  **snake_case			):
											snake_case_     							=       self.preprocess_text(lowerCAmelCase_			)
											return self.sp_model.encode(lowerCAmelCase_ ,  out_type=lowerCAmelCase_			)




				def        a				(   self ,  snake_case			):
											return self.sp_model.PieceToId(lowerCAmelCase_			)




				def        a				(   self ,  snake_case			):
											return self.sp_model.IdToPiece(lowerCAmelCase_			)




				@staticmethod
				def        a				(   snake_case			):
											return out_string




				def        a				(   self ,  snake_case			):
											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(lowerCAmelCase_			) + token
																									snake_case_     							=       True
																									snake_case_     							=       []
																		else:
																									current_sub_tokens.append(lowerCAmelCase_			)
																									snake_case_     							=       False
											out_string += self.sp_model.decode(lowerCAmelCase_			)

											return out_string




				def        a				(   self			):
											snake_case_     							=       {self.convert_ids_to_tokens(lowerCAmelCase_			): i for i in range(self.vocab_size			)}
											vocab.update(self.added_tokens_encoder			)
											return vocab




				def        a				(   self ,  snake_case ,  snake_case = None			):
											if not os.path.isdir(lowerCAmelCase_			):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
																		return
											snake_case_     							=       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:
																									snake_case_     							=       self.sp_model.serialized_model_proto()
																									fi.write(lowerCAmelCase_			)

											return (out_vocab_file,)




				def        a				(   self ,  snake_case ,  snake_case = False			):

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

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

											return token_ids




				def        a				(   self ,  snake_case			):
											return self.sp_model.decode(lowerCAmelCase_			)




				def        a				(   self ,  snake_case			):
											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(lowerCAmelCase_			) + F'''{self.bos_token}Bot:'''
											)
											return self.encode(text=lowerCAmelCase_			)








'''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() = }""")



