


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


A      :    List[Any]		=					logging.get_logger(__name__)

A      :    Optional[int]		=					{"tokenizer_file": "tokenizer.json"}

A      :    List[Any]		=					{
    "tokenizer_file": {
        "bigscience/tokenizer": "https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json",
        "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json",
        "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json",
        "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json",
        "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json",
        "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json",
        "bigscience/bloom": "https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json",
    },
}







class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			VOCAB_FILES_NAMES
       A__											=			PRETRAINED_VOCAB_FILES_MAP
       A__											=			["input_ids", "attention_mask"]
       A__											=			None





       def __init__(   self							:			Optional[int]					,      __lowerCamelCase							:			int=None					,      __lowerCamelCase							:			str=None					,      __lowerCamelCase							:			List[Any]=None					,      __lowerCamelCase							:			Any="<unk>"					,      __lowerCamelCase							:			Optional[int]="<s>"					,      __lowerCamelCase							:			List[Any]="</s>"					,      __lowerCamelCase							:			str="<pad>"					,      __lowerCamelCase							:			List[Any]=False					,      __lowerCamelCase							:			Optional[Any]=False					,      **__lowerCamelCase							:			int					,      ):


             '''simple docstring'''



             super().__init__(
                 __lowerCamelCase					,      __lowerCamelCase					,      tokenizer_file=__lowerCamelCase					,      unk_token=__lowerCamelCase					,      bos_token=__lowerCamelCase					,      eos_token=__lowerCamelCase					,      pad_token=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      clean_up_tokenization_spaces=__lowerCamelCase					,      **__lowerCamelCase					,      )
             lowerCamelCase__		:				int    =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
             if pre_tok_state.get("add_prefix_space"					,      __lowerCamelCase       ) != add_prefix_space:
                   lowerCamelCase__		:				Tuple    =  getattr(__lowerCamelCase					,      pre_tok_state.pop("type"       )       )
                   lowerCamelCase__		:				Union[str, Any]    =  add_prefix_space
                   lowerCamelCase__		:				Any    =  pre_tok_class(**__lowerCamelCase       )

             lowerCamelCase__		:				Tuple    =  add_prefix_space





       def 						lowerCAmelCase     (   self							:			Dict					,      *__lowerCamelCase							:			Any					,      **__lowerCamelCase							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  kwargs.get("is_split_into_words"					,      __lowerCamelCase       )
             if not (self.add_prefix_space or not is_split_into_words):
                   raise Exception(
                       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
                       " pretokenized inputs."       )

             return super()._batch_encode_plus(*__lowerCamelCase					,      **__lowerCamelCase       )





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


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  kwargs.get("is_split_into_words"					,      __lowerCamelCase       )

             if not (self.add_prefix_space or not is_split_into_words):
                   raise Exception(
                       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
                       " pretokenized inputs."       )

             return super()._encode_plus(*__lowerCamelCase					,      **__lowerCamelCase       )





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


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  self._tokenizer.model.save(__lowerCamelCase					,      name=__lowerCamelCase       )
             return tuple(__lowerCamelCase       )





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


             '''simple docstring'''



             lowerCamelCase__		:				int    =  []
             for is_user, text in conversation.iter_texts():
                   input_ids.extend(self.encode(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase       ) + [self.eos_token_id]       )

             if len(__lowerCamelCase       ) > self.model_max_length:
                   lowerCamelCase__		:				Optional[int]    =  input_ids[-self.model_max_length :]
             return input_ids






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"))


