


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[str]		=					{"vocab_file": "spiece.model"}

A      :    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",
    }
}

A      :    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 (							lowercase__):


       """simple docstring"""




       A__											=			VOCAB_FILES_NAMES
       A__											=			PRETRAINED_VOCAB_FILES_MAP
       A__											=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A__											=			["input_ids", "attention_mask"]





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


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  {} if sp_model_kwargs is None else sp_model_kwargs

             lowerCamelCase__		:				Tuple    =  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__		:				List[str]    =  "None"

             # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
             lowerCamelCase__		:				Optional[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__		:				Union[str, Any]    =  unk_token if pad_token is None else pad_token
                   lowerCamelCase__		:				Union[str, Any]    =  eos_token if bos_token is None else bos_token
             else:
                   lowerCamelCase__		:				Tuple    =  "<pad>" if pad_token is None else pad_token
                   lowerCamelCase__		:				Union[str, Any]    =  "<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					,      )

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

             lowerCamelCase__		:				int    =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
             self.sp_model.Load(__lowerCamelCase       )

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

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





       def __getstate__(   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  self.__dict__.copy()
             lowerCamelCase__		:				List[str]    =  None
             return state





       def __setstate__(   self							:			Dict					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  d

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

             lowerCamelCase__		:				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 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             return len(self.sp_model       )





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  self.non_printing_characters_re.sub(""					,      __lowerCamelCase       )

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

             # NFC Unicode normalization
             lowerCamelCase__		:				Optional[Any]    =  unicodedata.normalize("NFC"					,      __lowerCamelCase       )
             return text





       def 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			str					,      **__lowerCamelCase							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  self.preprocess_text(__lowerCamelCase       )
             return self.sp_model.encode(__lowerCamelCase					,      out_type=__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             return self.sp_model.PieceToId(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             return self.sp_model.IdToPiece(__lowerCamelCase       )





       @staticmethod
       def 						lowerCAmelCase     (   __lowerCamelCase							:			str       ):


             '''simple docstring'''



             return out_string





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			List[str]       ):


             '''simple docstring'''



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

             return out_string





       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  {self.convert_ids_to_tokens(__lowerCamelCase       ): i for i in range(self.vocab_size       )}
             vocab.update(self.added_tokens_encoder       )
             return vocab





       def 						lowerCAmelCase     (   self							:			str					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			Optional[str] = None       ):


             '''simple docstring'''



             if not os.path.isdir(__lowerCamelCase       ):
                   logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
                   return
             lowerCamelCase__		:				Optional[int]    =  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:
                         lowerCamelCase__		:				List[Any]    =  self.sp_model.serialized_model_proto()
                         fi.write(__lowerCamelCase       )

             return (out_vocab_file,)





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			Union[str, List[str]]					,      __lowerCamelCase							:			Union[str, bool] = False       ):


             '''simple docstring'''



             if isinstance(__lowerCamelCase					,      __lowerCamelCase       ):
                   lowerCamelCase__		:				List[Any]    =  self.preprocess_text(__lowerCamelCase       )
                   lowerCamelCase__		:				List[str]    =  self.sp_model.encode(__lowerCamelCase       )
             else:
                   lowerCamelCase__		:				Tuple    =  [self.preprocess_text(__lowerCamelCase       ) for t in text]
                   lowerCamelCase__		:				Any    =  self.sp_model.encode(__lowerCamelCase       )

             if return_tensors is True or return_tensors == "pt":
                   lowerCamelCase__		:				int    =  torch.tensor(__lowerCamelCase       )

             return token_ids





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			Union[int, List[int]]       ):


             '''simple docstring'''



             return self.sp_model.decode(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			"Conversation"       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  [f"User: {text}" if is_user else f"Bot: {text}" for is_user, text in conversation.iter_texts()]
             lowerCamelCase__		:				str    =  (
                 f"{self.eos_token}{self.bos_token}" + f"{self.bos_token}".join(__lowerCamelCase       ) + f"{self.bos_token}Bot:"
             )
             return self.encode(text=__lowerCamelCase       )






from __future__ import annotations

A      :    Union[str, Any]		=					{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			Tuple					,      __lowerCamelCase							:			dict[str, list[str]]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  graph
             # mapping node to its parent in resulting breadth first tree
             lowerCamelCase__		:				dict[str, str | None]    =  {}
             lowerCamelCase__		:				Dict    =  source_vertex





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  {self.source_vertex}
             lowerCamelCase__		:				Optional[int]    =  None
             lowerCamelCase__		:				Dict    =  [self.source_vertex]  # first in first out queue

             while queue:
                   lowerCamelCase__		:				Optional[int]    =  queue.pop(0       )
                   for adjacent_vertex in self.graph[vertex]:
                         if adjacent_vertex not in visited:
                               visited.add(__lowerCamelCase       )
                               lowerCamelCase__		:				List[str]    =  vertex
                               queue.append(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             if target_vertex == self.source_vertex:
                   return self.source_vertex

             lowerCamelCase__		:				Tuple    =  self.parent.get(__lowerCamelCase       )
             if target_vertex_parent is None:
                   lowerCamelCase__		:				Tuple    =  (
                       f"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
                   )
                   raise ValueError(__lowerCamelCase       )

             return self.shortest_path(__lowerCamelCase       ) + f"->{target_vertex}"


if __name__ == "__main__":
    A      :    List[str]		=					Graph(graph, "G")
    g.breath_first_search()
    print(g.shortest_path("D"))
    print(g.shortest_path("G"))
    print(g.shortest_path("Foo"))


