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

SCREAMING_SNAKE_CASE__		:					str						=					{
    "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__		:					Dict						=					{
    "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       snake_case					(      _UpperCAmelCase						):
		lowercase_			=     VOCAB_FILES_NAMES
		lowercase_			=     PRETRAINED_VOCAB_FILES_MAP
		lowercase_			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase_			=     ['input_ids', 'attention_mask']




		def __init__(      self     :	Union[str, Any]     , a_     :	Dict     , a_     :	Dict=False     , a_     :	Any=False     , a_     :	List[Any]=False     , a_     :	int=None     , a_     :	List[str]=None     , a_     :	Optional[int]=None     , a_     :	Union[str, Any]=None     , a_     :	Optional[Any] = None     , **a_     :	int     , )->       Tuple:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Any			=		{} if sp_model_kwargs is None else sp_model_kwargs

							SCREAMING_SNAKE_CASE__				:				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'				)
												SCREAMING_SNAKE_CASE__				:				str			=		"None"

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

							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		do_lower_case
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		remove_space
							SCREAMING_SNAKE_CASE__				:				int			=		keep_accents
							SCREAMING_SNAKE_CASE__				:				List[Any]			=		vocab_file

							SCREAMING_SNAKE_CASE__				:				List[Any]			=		spm.SentencePieceProcessor(**self.sp_model_kwargs				)
							self.sp_model.Load(lowercase__				)

							# Used for whitespace normalization in input texts
							# fmt : off
							SCREAMING_SNAKE_CASE__				:				int			=		{" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
							# fmt : on

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




		def __getstate__(      self     :	Any				)->       Optional[int]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Tuple			=		self.__dict__.copy()
							SCREAMING_SNAKE_CASE__				:				Dict			=		None
							return state




		def __setstate__(      self     :	Optional[int]     , a_     :	int				)->       str:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				List[str]			=		d

							# for backward compatibility
							if not hasattr(self     , 'sp_model_kwargs'				):
												SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{}

							SCREAMING_SNAKE_CASE__				:				Optional[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 	__lowercase(      self     :	Optional[Any]				)->       Dict:




							"""simple docstring"""
							return len(self.sp_model				)




		def 	__lowercase(      self     :	Union[str, Any]     , a_     :	Optional[Any]				)->       Optional[Any]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				List[str]			=		self.non_printing_characters_re.sub(''     , lowercase__				)

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

							# NFC Unicode normalization
							SCREAMING_SNAKE_CASE__				:				List[Any]			=		unicodedata.normalize('NFC'     , lowercase__				)
							return text




		def 	__lowercase(      self     :	int     , a_     :	Dict     , **a_     :	Union[str, Any]				)->       List[str]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Dict			=		self.preprocess_text(lowercase__				)
							return self.sp_model.encode(lowercase__     , out_type=lowercase__				)




		def 	__lowercase(      self     :	Optional[Any]     , a_     :	List[str]				)->       Any:




							"""simple docstring"""
							return self.sp_model.PieceToId(lowercase__				)




		def 	__lowercase(      self     :	int     , a_     :	List[Any]				)->       Optional[Any]:




							"""simple docstring"""
							return self.sp_model.IdToPiece(lowercase__				)




		@staticmethod
		def 	__lowercase(      a_     :	Dict				)->       Any:




							"""simple docstring"""
							return out_string




		def 	__lowercase(      self     :	Optional[Any]     , a_     :	str				)->       List[str]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[]
							SCREAMING_SNAKE_CASE__				:				Any			=		""
							SCREAMING_SNAKE_CASE__				:				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
																	SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		True
																	SCREAMING_SNAKE_CASE__				:				int			=		[]
												else:
																	current_sub_tokens.append(lowercase__				)
																	SCREAMING_SNAKE_CASE__				:				List[str]			=		False
							out_string += self.sp_model.decode(lowercase__				)

							return out_string




		def 	__lowercase(      self     :	Optional[Any]				)->       Optional[int]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		{self.convert_ids_to_tokens(lowercase__				): i for i in range(self.vocab_size				)}
							vocab.update(self.added_tokens_encoder				)
							return vocab




		def 	__lowercase(      self     :	int     , a_     :	Optional[Any]     , a_     :	List[str] = None				)->       int:




							"""simple docstring"""






							if not os.path.isdir(lowercase__				):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
												return
							SCREAMING_SNAKE_CASE__				:				Any			=		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:
																	SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.sp_model.serialized_model_proto()
																	fi.write(lowercase__				)

							return (out_vocab_file,)




		def 	__lowercase(      self     :	Dict     , a_     :	List[str]     , a_     :	Any = False				)->       str:




							"""simple docstring"""






							if isinstance(lowercase__     , lowercase__				):
												SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.preprocess_text(lowercase__				)
												SCREAMING_SNAKE_CASE__				:				Any			=		self.sp_model.encode(lowercase__				)
							else:
												SCREAMING_SNAKE_CASE__				:				str			=		[self.preprocess_text(lowercase__				) for t in text]
												SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.sp_model.encode(lowercase__				)

							if return_tensors is True or return_tensors == "pt":
												SCREAMING_SNAKE_CASE__				:				str			=		torch.tensor(lowercase__				)

							return token_ids




		def 	__lowercase(      self     :	List[Any]     , a_     :	str				)->       int:




							"""simple docstring"""
							return self.sp_model.decode(lowercase__				)




		def 	__lowercase(      self     :	Any     , a_     :	List[str]				)->       List[str]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		[F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
							SCREAMING_SNAKE_CASE__				:				List[str]			=		(
							    F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(lowercase__				) + F'''{self.bos_token}Bot:'''
							)
							return self.encode(text=lowercase__				)




import heapq as hq
import math
from collections.abc import Iterator

class       snake_case					:




  def __init__(      self     :	str     , a_     :	str				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		str(id_				)
       SCREAMING_SNAKE_CASE__				:				Any			=		None
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		None
       SCREAMING_SNAKE_CASE__				:				Any			=		[]
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{}  # {vertex:distance}




  def __lt__(      self     :	int     , a_     :	Tuple				)->       Union[str, Any]:




       """simple docstring"""
       return self.key < other.key




  def __repr__(      self     :	Any				)->       Dict:




       """simple docstring"""
       return self.id




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	int				)->       List[str]:




       """simple docstring"""
       self.neighbors.append(a_				)




  def 	__lowercase(      self     :	int     , a_     :	int     , a_     :	Optional[Any]				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		weight







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			Union[str, Any]     ,  lowercase__  :			Tuple     ,  lowercase__  :			Dict ):







     '''simple docstring'''


     graph[a - 1].add_neighbor(graph[b - 1] )
     graph[b - 1].add_neighbor(graph[a - 1] )
     # add the edges:
     graph[a - 1].add_edge(graph[b - 1]     ,  lowercase__ )
     graph[b - 1].add_edge(graph[a - 1]     ,  lowercase__ )







def     _a				(      lowercase__  :			list     ,  lowercase__  :			Vertex ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		[]
     for u in graph:
          SCREAMING_SNAKE_CASE__				:				Dict			=		math.inf
          SCREAMING_SNAKE_CASE__				:				str			=		None
     SCREAMING_SNAKE_CASE__				:				List[str]			=		0
     SCREAMING_SNAKE_CASE__				:				int			=		graph[:]
     while q:
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		min(lowercase__ )
          q.remove(lowercase__ )
          for v in u.neighbors:
               if (v in q) and (u.edges[v.id] < v.key):
                    SCREAMING_SNAKE_CASE__				:				int			=		u
                    SCREAMING_SNAKE_CASE__				:				Any			=		u.edges[v.id]
     for i in range(1     ,  len(lowercase__ ) ):
          a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
     return a







def     _a				(      lowercase__  :			list     ,  lowercase__  :			Vertex ):







     '''simple docstring'''


     for u in graph:
          SCREAMING_SNAKE_CASE__				:				List[str]			=		math.inf
          SCREAMING_SNAKE_CASE__				:				int			=		None
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		0

     SCREAMING_SNAKE_CASE__				:				Tuple			=		list(lowercase__ )
     hq.heapify(lowercase__ )

     while h:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		hq.heappop(lowercase__ )
          for v in u.neighbors:
               if (v in h) and (u.edges[v.id] < v.key):
                    SCREAMING_SNAKE_CASE__				:				List[str]			=		u
                    SCREAMING_SNAKE_CASE__				:				Dict			=		u.edges[v.id]
                    hq.heapify(lowercase__ )

     for i in range(1     ,  len(lowercase__ ) ):
          yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)







def     _a				(      ):







     '''simple docstring'''




if __name__ == "__main__":
      import doctest

      doctest.testmod()



