'''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


SCREAMING_SNAKE_CASE_:	Optional[Any]    =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:	Optional[int]    ={'''vocab_file''': '''spiece.model'''}

SCREAMING_SNAKE_CASE_:	int    ={
    '''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_:	Optional[int]    ={
    '''AI-Sweden/gpt-sw3-126m''': 20_48,
    '''AI-Sweden/gpt-sw3-350m''': 20_48,
    '''AI-Sweden/gpt-sw3-1.6b''': 20_48,
    '''AI-Sweden/gpt-sw3-6.7b''': 20_48,
    '''AI-Sweden/gpt-sw3-20b''': 20_48,
}




class 			__A (					__lowerCAmelCase      ):
    a__			:		List[str]			  =    VOCAB_FILES_NAMES
    a__			:		Optional[int]			  =    PRETRAINED_VOCAB_FILES_MAP
    a__			:		Any			  =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__			:		Tuple			  =    ["""input_ids""", """attention_mask"""]



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

         UpperCAmelCase_			       =  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"					)
              UpperCAmelCase_			       =  "None"

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

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

         UpperCAmelCase_			       =  do_lower_case
         UpperCAmelCase_			       =  remove_space
         UpperCAmelCase_			       =  keep_accents
         UpperCAmelCase_			       =  vocab_file

         UpperCAmelCase_			       =  spm.SentencePieceProcessor(**self.sp_model_kwargs					)
         self.sp_model.Load(_SCREAMING_SNAKE_CASE					)

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

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



    def __getstate__(self       :       Union[str, Any]					):
         UpperCAmelCase_			       =  self.__dict__.copy()
         UpperCAmelCase_			       =  None
         return state



    def __setstate__(self       :       List[Any]  ,     __a       :       Union[str, Any]					):
         UpperCAmelCase_			       =  d

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

         UpperCAmelCase_			       =  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       :       int					):
         return len(self.sp_model					)



    def    _lowercase		(self       :       Optional[Any]  ,     __a       :       Optional[Any]					):
         UpperCAmelCase_			       =  self.non_printing_characters_re.sub(""  ,     _SCREAMING_SNAKE_CASE					)

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

         # NFC Unicode normalization
         UpperCAmelCase_			       =  unicodedata.normalize("NFC"  ,     _SCREAMING_SNAKE_CASE					)
         return text



    def    _lowercase		(self       :       Optional[Any]  ,     __a       :       Dict  ,     **__a       :       Union[str, Any]					):
         UpperCAmelCase_			       =  self.preprocess_text(_SCREAMING_SNAKE_CASE					)
         return self.sp_model.encode(_SCREAMING_SNAKE_CASE  ,     out_type=_SCREAMING_SNAKE_CASE					)



    def    _lowercase		(self       :       Any  ,     __a       :       List[str]					):
         return self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE					)



    def    _lowercase		(self       :       Optional[int]  ,     __a       :       Tuple					):
         return self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE					)



    @staticmethod
    def    _lowercase		(__a       :       List[Any]					):
         return out_string



    def    _lowercase		(self       :       Optional[int]  ,     __a       :       List[str]					):
         UpperCAmelCase_			       =  []
         UpperCAmelCase_			       =  ""
         UpperCAmelCase_			       =  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(_SCREAMING_SNAKE_CASE					) + token
                   UpperCAmelCase_			       =  True
                   UpperCAmelCase_			       =  []
              else:
                   current_sub_tokens.append(_SCREAMING_SNAKE_CASE					)
                   UpperCAmelCase_			       =  False
         out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE					)

         return out_string



    def    _lowercase		(self       :       str					):
         UpperCAmelCase_			       =  {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE					): i for i in range(self.vocab_size					)}
         vocab.update(self.added_tokens_encoder					)
         return vocab



    def    _lowercase		(self       :       Any  ,     __a       :       Dict  ,     __a       :       int = None					):
         if not os.path.isdir(_SCREAMING_SNAKE_CASE					):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
              return
         UpperCAmelCase_			       =  os.path.join(
             _SCREAMING_SNAKE_CASE  ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)

         if os.path.abspath(self.vocab_file					) != os.path.abspath(_SCREAMING_SNAKE_CASE					) and os.path.isfile(self.vocab_file					):
              copyfile(self.vocab_file  ,     _SCREAMING_SNAKE_CASE					)
         elif not os.path.isfile(self.vocab_file					):
              with open(_SCREAMING_SNAKE_CASE  ,     "wb"					) as fi:
                   UpperCAmelCase_			       =  self.sp_model.serialized_model_proto()
                   fi.write(_SCREAMING_SNAKE_CASE					)

         return (out_vocab_file,)



    def    _lowercase		(self       :       Union[str, Any]  ,     __a       :       Any  ,     __a       :       List[str] = False					):

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

         if return_tensors is True or return_tensors == "pt":
              UpperCAmelCase_			       =  torch.tensor(_SCREAMING_SNAKE_CASE					)

         return token_ids



    def    _lowercase		(self       :       Optional[Any]  ,     __a       :       List[Any]					):
         return self.sp_model.decode(_SCREAMING_SNAKE_CASE					)






    def    _lowercase		(self       :       Union[str, Any]  ,     __a       :       List[str]					):
         UpperCAmelCase_			       =  [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
         UpperCAmelCase_			       =  (
             f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(_SCREAMING_SNAKE_CASE					) + f"""{self.bos_token}Bot:"""
         )
         return self.encode(text=_SCREAMING_SNAKE_CASE					)










'''simple docstring'''





from __future__ import annotations

from typing import Any

class   _a							:






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     None:
          _snake_case				= num_of_nodes
          _snake_case				= []
          _snake_case				= {}






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     None:
          self.m_edges.append([u_node, v_node, weight]       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     int:

          if self.m_component[u_node] == u_node:
               return u_node
          return self.find_component(self.m_component[u_node]       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     None:

          if self.m_component[u_node] != u_node:
               for k in self.m_component:
                    _snake_case				= self.find_component(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     None:

          if component_size[u_node] <= component_size[v_node]:
               _snake_case				= v_node
               component_size[v_node] += component_size[u_node]
               self.set_component(_SCREAMING_SNAKE_CASE       )

          elif component_size[u_node] >= component_size[v_node]:
               _snake_case				= self.find_component(_SCREAMING_SNAKE_CASE       )
               component_size[u_node] += component_size[v_node]
               self.set_component(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     None:
          _snake_case				= []
          _snake_case				= 0

          _snake_case				= [-1] * self.m_num_of_nodes

          # A list of components (initialized to all of the nodes)
          for node in range(self.m_num_of_nodes       ):
               self.m_component.update({node: node}       )
               component_size.append(1       )

          _snake_case				= self.m_num_of_nodes

          while num_of_components > 1:
               for edge in self.m_edges:
                    _snake_case				,   _snake_case				,   _snake_case				= edge

                    _snake_case				= self.m_component[u]
                    _snake_case				= self.m_component[v]

                    if u_component != v_component:

                         for component in (u_component, v_component):
                              if (
                                  minimum_weight_edge[component] == -1
                                  or minimum_weight_edge[component][2] > w
                              ):
                                   _snake_case				= [u, v, w]

               for edge in minimum_weight_edge:
                    if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
                         _snake_case				,   _snake_case				,   _snake_case				= edge

                         _snake_case				= self.m_component[u]
                         _snake_case				= self.m_component[v]

                         if u_component != v_component:
                              mst_weight += w
                              self.union(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
                              print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""       )
                              num_of_components -= 1

               _snake_case				= [-1] * self.m_num_of_nodes
          print(f"""The total weight of the minimal spanning tree is: {mst_weight}"""       )







def       __a      (  )		->					None:

     """simple docstring"""






if __name__ == "__main__":
   import doctest

   doctest.testmod()


