
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


_SCREAMING_SNAKE_CASE    :      Tuple          =			logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			{"vocab_file": "spiece.model"}

_SCREAMING_SNAKE_CASE    :      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",
    }
}

_SCREAMING_SNAKE_CASE    :      str          =			{
    "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  UpperCamelCase__		(    UpperCAmelCase_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				VOCAB_FILES_NAMES
	_lowerCAmelCase							       =				PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase							       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase							       =				["input_ids", "attention_mask"]





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__=False				,       UpperCamelCase__=False				,       UpperCamelCase__=False				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__ = None				,       **UpperCamelCase__				,       ):
					A__  :	Tuple         =							{} if sp_model_kwargs is None else sp_model_kwargs

					A__  :	Union[str, Any]         =							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__  :	str         =							'''None'''

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

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

					A__  :	List[Any]         =							do_lower_case
					A__  :	Tuple         =							remove_space
					A__  :	Optional[Any]         =							keep_accents
					A__  :	List[str]         =							vocab_file

					A__  :	Dict         =							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
					self.sp_model.Load(_lowercase		)

					# Used for whitespace normalization in input texts
					# fmt : off
					A__  :	Optional[Any]         =							{''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
					# fmt : on

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





	def __getstate__(			self		):
					A__  :	Optional[Any]         =							self.__dict__.copy()
					A__  :	Any         =							None
					return state





	def __setstate__(			self				,       UpperCamelCase__		):
					A__  :	Union[str, Any]         =							d

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

					A__  :	Union[str, Any]         =							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		):
					return len(self.sp_model		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	Optional[int]         =							self.non_printing_characters_re.sub(''''''				,       _lowercase		)

					# Normalize whitespaces
					A__  :	List[Any]         =							''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]		)

					# NFC Unicode normalization
					A__  :	Optional[int]         =							unicodedata.normalize('''NFC'''				,       _lowercase		)
					return text





	def   __snake_case				(			self				,       UpperCamelCase__				,       **UpperCamelCase__		):
					A__  :	int         =							self.preprocess_text(_lowercase		)
					return self.sp_model.encode(_lowercase				,       out_type=_lowercase		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					return self.sp_model.PieceToId(_lowercase		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					return self.sp_model.IdToPiece(_lowercase		)





	@staticmethod
	def   __snake_case				(			UpperCamelCase__		):
					return out_string





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	str         =							[]
					A__  :	Any         =							''''''
					A__  :	Dict         =							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(_lowercase		) + token
													A__  :	Any         =							True
													A__  :	Tuple         =							[]
									else:
													current_sub_tokens.append(_lowercase		)
													A__  :	Union[str, Any]         =							False
					out_string += self.sp_model.decode(_lowercase		)

					return out_string





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							{self.convert_ids_to_tokens(_lowercase		): i for i in range(self.vocab_size		)}
					vocab.update(self.added_tokens_encoder		)
					return vocab





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None		):
					if not os.path.isdir(_lowercase		):
									logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
									return
					A__  :	Tuple         =							os.path.join(
					    _lowercase				,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

					if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowercase		) and os.path.isfile(self.vocab_file		):
									copyfile(self.vocab_file				,       _lowercase		)
					elif not os.path.isfile(self.vocab_file		):
									with open(_lowercase				,       '''wb'''		) as fi:
													A__  :	Tuple         =							self.sp_model.serialized_model_proto()
													fi.write(_lowercase		)

					return (out_vocab_file,)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = False		):

					if isinstance(_lowercase				,       _lowercase		):
									A__  :	Union[str, Any]         =							self.preprocess_text(_lowercase		)
									A__  :	Optional[Any]         =							self.sp_model.encode(_lowercase		)
					else:
									A__  :	Dict         =							[self.preprocess_text(_lowercase		) for t in text]
									A__  :	Union[str, Any]         =							self.sp_model.encode(_lowercase		)

					if return_tensors is True or return_tensors == "pt":
									A__  :	Optional[int]         =							torch.tensor(_lowercase		)

					return token_ids





	def   __snake_case				(			self				,       UpperCamelCase__		):
					return self.sp_model.decode(_lowercase		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	Optional[int]         =							[F"User: {text}" if is_user else F"Bot: {text}" for is_user, text in conversation.iter_texts()]
					A__  :	Dict         =							(
					    F"{self.eos_token}{self.bos_token}" + F"{self.bos_token}".join(_lowercase		) + F"{self.bos_token}Bot:"
					)
					return self.encode(text=_lowercase		)

def 				SCREAMING_SNAKE_CASE     (      ) ->       Optional[int]:

				"""simple docstring"""







				A__  :	Optional[Any]         =							0
				for i in range(1							,						10_01       ):
								total += i**i
				return str(__UpperCamelCase       )[-10:]


if __name__ == "__main__":
					print(solution())