






'''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


_A     :      str   					=logging.get_logger(__name__)
_A     :      List[Any]   					={'''vocab_file''': '''spiece.model'''}

_A     :      Union[str, 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     :      int   					={
    '''AI-Sweden/gpt-sw3-126m''': 2_048,
    '''AI-Sweden/gpt-sw3-350m''': 2_048,
    '''AI-Sweden/gpt-sw3-1.6b''': 2_048,
    '''AI-Sweden/gpt-sw3-6.7b''': 2_048,
    '''AI-Sweden/gpt-sw3-20b''': 2_048,
}

class _lowercase     (    _lowercase		):
							a    				=	VOCAB_FILES_NAMES
							a    				=	PRETRAINED_VOCAB_FILES_MAP
							a    				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a    				=	["""input_ids""", """attention_mask"""]




							def __init__(							self:    int      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Dict=False      ,							UpperCamelCase__:    List[str]=False      ,							UpperCamelCase__:    str=False      ,							UpperCamelCase__:    Dict=None      ,							UpperCamelCase__:    int=None      ,							UpperCamelCase__:    str=None      ,							UpperCamelCase__:    Any=None      ,							UpperCamelCase__:    Optional[Dict[str, Any]] = None      ,							**UpperCamelCase__:    Optional[int]      ,							):
														lowerCamelCase__ :     Any	  =	{} if sp_model_kwargs is None else sp_model_kwargs

														lowerCamelCase__ :     Dict	  =	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__ :     Any	  =	"""<|endoftext|>""" if eos_token is None else eos_token
														lowerCamelCase__ :     Any	  =	"""<unk>""" if unk_token is None else unk_token
														if "gpt-sw3-7b" in name_or_path:
																					lowerCamelCase__ :     Optional[Any]	  =	unk_token if pad_token is None else pad_token
																					lowerCamelCase__ :     Any	  =	eos_token if bos_token is None else bos_token
														else:
																					lowerCamelCase__ :     Any	  =	"""<pad>""" if pad_token is None else pad_token
																					lowerCamelCase__ :     List[str]	  =	"""<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__      ,							)

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

														lowerCamelCase__ :     List[str]	  =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
														self.sp_model.Load(UpperCamelCase__					)

														# Used for whitespace normalization in input texts
														# fmt : off
														lowerCamelCase__ :     Optional[int]	  =	{""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
														# fmt : on

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




							def __getstate__(							self:    Tuple					):
														lowerCamelCase__ :     List[str]	  =	self.__dict__.copy()
														lowerCamelCase__ :     Dict	  =	None
														return state




							def __setstate__(							self:    Tuple      ,							UpperCamelCase__:    Any					):
														lowerCamelCase__ :     Tuple	  =	d

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

														lowerCamelCase__ :     List[str]	  =	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:    List[Any]					):
														return len(self.sp_model					)




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    str					):
														lowerCamelCase__ :     int	  =	self.non_printing_characters_re.sub(""""""      ,							UpperCamelCase__					)

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

														# NFC Unicode normalization
														lowerCamelCase__ :     Dict	  =	unicodedata.normalize("""NFC"""      ,							UpperCamelCase__					)
														return text




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    str      ,							**UpperCamelCase__:    Optional[Any]					):
														lowerCamelCase__ :     List[str]	  =	self.preprocess_text(UpperCamelCase__					)
														return self.sp_model.encode(UpperCamelCase__      ,							out_type=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    List[Any]      ,							UpperCamelCase__:    str					):
														return self.sp_model.PieceToId(UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Dict      ,							UpperCamelCase__:    int					):
														return self.sp_model.IdToPiece(UpperCamelCase__					)




							@staticmethod
							def 		lowerCamelCase_    (							UpperCamelCase__:    str					):
														return out_string




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    List[str]					):
														lowerCamelCase__ :     List[Any]	  =	[]
														lowerCamelCase__ :     Union[str, Any]	  =	""""""
														lowerCamelCase__ :     int	  =	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
																												lowerCamelCase__ :     Tuple	  =	True
																												lowerCamelCase__ :     str	  =	[]
																					else:
																												current_sub_tokens.append(UpperCamelCase__					)
																												lowerCamelCase__ :     Union[str, Any]	  =	False
														out_string += self.sp_model.decode(UpperCamelCase__					)

														return out_string




							def 		lowerCamelCase_    (							self:    List[Any]					):
														lowerCamelCase__ :     Tuple	  =	{self.convert_ids_to_tokens(UpperCamelCase__					): i for i in range(self.vocab_size					)}
														vocab.update(self.added_tokens_encoder					)
														return vocab




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Optional[str] = None					):
														if not os.path.isdir(UpperCamelCase__					):
																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
																					return
														lowerCamelCase__ :     Optional[int]	  =	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:
																												lowerCamelCase__ :     List[Any]	  =	self.sp_model.serialized_model_proto()
																												fi.write(UpperCamelCase__					)

														return (out_vocab_file,)




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

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

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

														return token_ids




							def 		lowerCamelCase_    (							self:    Dict      ,							UpperCamelCase__:    Union[int, List[int]]					):
														return self.sp_model.decode(UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    List[Any]      ,							UpperCamelCase__:    "Conversation"					):
														lowerCamelCase__ :     List[str]	  =	[F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
														lowerCamelCase__ :     Optional[int]	  =	(
														    F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(UpperCamelCase__					) + F'''{self.bos_token}Bot:'''
														)
														return self.encode(text=UpperCamelCase__					)









'''simple docstring'''







from __future__ import annotations

from typing import Any

class _lowercase     (    _lowercase		):
							pass



class _lowercase     :




							def __init__(							self:    Optional[int]      ,							UpperCamelCase__:    Any					):
														lowerCamelCase__ :     Any	  =	data
														lowerCamelCase__ :     Node | None	  =	None




							def __iter__(							self:    List[Any]					):
														lowerCamelCase__ :     Optional[Any]	  =	self
														lowerCamelCase__ :     int	  =	[]
														while node:
																					if node in visited:
																												raise ContainsLoopError
																					visited.append(UpperCamelCase__					)
																					yield node.data
																					lowerCamelCase__ :     List[str]	  =	node.next_node




							@property
							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														try:
																					list(self					)
																					return False
														except ContainsLoopError:
																					return True


if __name__ == "__main__":
			_A     :      Any   					=Node(1)
			_A     :      Optional[int]   					=Node(2)
			_A     :      Dict   					=Node(3)
			_A     :      Optional[Any]   					=Node(4)
			print(root_node.has_loop)  # False
			_A     :      Any   					=root_node.next_node
			print(root_node.has_loop)  # True

			_A     :      Dict   					=Node(5)
			_A     :      Union[str, Any]   					=Node(6)
			_A     :      str   					=Node(5)
			_A     :      int   					=Node(6)
			print(root_node.has_loop)  # False

			_A     :      Optional[Any]   					=Node(1)
			print(root_node.has_loop)  # False

