
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


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

_a   :      Optional[Any]       =				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_a   :      Optional[int]       =				{
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

_a   :      Optional[int]       =				{
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

_a   :      Any       =				{
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			str	=       VOCAB_FILES_NAMES
    __lowercase				:			List[Any]	=       PRETRAINED_VOCAB_FILES_MAP
    __lowercase				:			List[str]	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowercase				:			str	=       ["""input_ids""", """attention_mask""", """token_type_ids"""]
    __lowercase				:			Tuple	=       DebertaTokenizer
    def __init__(     self    ,     snake_case_=None    ,     snake_case_=None    ,     snake_case_=None    ,     snake_case_="replace"    ,     snake_case_="[CLS]"    ,     snake_case_="[SEP]"    ,     snake_case_="[SEP]"    ,     snake_case_="[CLS]"    ,     snake_case_="[UNK]"    ,     snake_case_="[PAD]"    ,     snake_case_="[MASK]"    ,     snake_case_=False    ,     **snake_case_    ,     ):
      super().__init__(
          snake_case_    ,     snake_case_    ,     tokenizer_file=snake_case_    ,     errors=snake_case_    ,     bos_token=snake_case_    ,     eos_token=snake_case_    ,     unk_token=snake_case_    ,     sep_token=snake_case_    ,     cls_token=snake_case_    ,     pad_token=snake_case_    ,     mask_token=snake_case_    ,     add_prefix_space=snake_case_    ,     **snake_case_    ,     )
      _snake_case  :    Dict            = kwargs.pop("add_bos_token"    ,     snake_case_  )

      _snake_case  :    Dict            = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
      if pre_tok_state.get("add_prefix_space"    ,     snake_case_  ) != add_prefix_space:
        _snake_case  :    str            = getattr(snake_case_    ,     pre_tok_state.pop("type"  )  )
        _snake_case  :    Union[str, Any]            = add_prefix_space
        _snake_case  :    Any            = pre_tok_class(**snake_case_  )

      _snake_case  :    Optional[Any]            = add_prefix_space
    @property
    def        lowerCamelCase__						(     self  ):
      if self._mask_token is None:
        if self.verbose:
          logger.error("Using mask_token, but it is not set yet."  )
        return None
      return str(self._mask_token  )
    @mask_token.setter
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    List[Any]            = AddedToken(snake_case_    ,     lstrip=snake_case_    ,     rstrip=snake_case_  ) if isinstance(snake_case_    ,     snake_case_  ) else value
      _snake_case  :    Dict            = value
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_ = None  ):
      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _snake_case  :    List[str]            = [self.cls_token_id]
      _snake_case  :    List[str]            = [self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_ = None  ):
      _snake_case  :    Optional[int]            = [self.sep_token_id]
      _snake_case  :    Optional[Any]            = [self.cls_token_id]

      if token_ids_a is None:
        return len(cls + token_ids_a + sep  ) * [0]
      return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]
    def        lowerCamelCase__						(     self    ,     *snake_case_    ,     **snake_case_  ):
      _snake_case  :    str            = kwargs.get("is_split_into_words"    ,     snake_case_  )
      assert self.add_prefix_space or not is_split_into_words, (
          F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*snake_case_    ,     **snake_case_  )
    def        lowerCamelCase__						(     self    ,     *snake_case_    ,     **snake_case_  ):
      _snake_case  :    str            = kwargs.get("is_split_into_words"    ,     snake_case_  )

      assert self.add_prefix_space or not is_split_into_words, (
          F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*snake_case_    ,     **snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_ = None  ):
      _snake_case  :    List[str]            = self._tokenizer.model.save(snake_case_    ,     name=snake_case_  )
      return tuple(snake_case_  )


    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    Union[str, Any]            = []
      for is_user, text in conversation.iter_texts():
        input_ids.extend(self.encode(snake_case_    ,     add_special_tokens=snake_case_  ) + [self.eos_token_id]  )

      if len(snake_case_  ) > self.model_max_length:
        _snake_case  :    Dict            = input_ids[-self.model_max_length :]
      return input_ids









'''simple docstring'''





from __future__ import annotations

from queue import Queue
class 					lowercase							:


    '''simple docstring'''

    def __init__(       self  :  Optional[int]  )				->				None:

     '''simple docstring'''




     lowerCamelCase__	=  {}

    def  a__					(       self  :  Optional[int]  )				->				None:

     '''simple docstring'''




     for i in self.vertices:
      print(__lowerCamelCase					,  " : "					,  " -> ".join([str(__lowerCamelCase  ) for j in self.vertices[i]]  )  )

    def  a__					(       self  :  Optional[Any]					,  __lowerCamelCase  :  int					,  __lowerCamelCase  :  int  )				->				None:

     '''simple docstring'''




     if from_vertex in self.vertices:
      self.vertices[from_vertex].append(__lowerCamelCase  )
     else:
      lowerCamelCase__	=  [to_vertex]




    def  a__					(       self  :  Optional[int]					,  __lowerCamelCase  :  int  )				->				set[int]:

     '''simple docstring'''




     lowerCamelCase__	=  set()

     # create a first in first out queue to store all the vertices for BFS
     lowerCamelCase__	=  Queue()

     # mark the source node as visited and enqueue it
     visited.add(__lowerCamelCase  )
     queue.put(__lowerCamelCase  )

     while not queue.empty():
      lowerCamelCase__	=  queue.get()

      # loop through all adjacent vertex and enqueue it if not yet visited
      for adjacent_vertex in self.vertices[vertex]:
       if adjacent_vertex not in visited:
        queue.put(__lowerCamelCase  )
        visited.add(__lowerCamelCase  )
     return visited


if __name__ == "__main__":
 from doctest import testmod

 testmod(verbose=True)

 __A				:				Optional[Any]					=		Graph()
 g.add_edge(0, 1)
 g.add_edge(0, 2)
 g.add_edge(1, 2)
 g.add_edge(2, 0)
 g.add_edge(2, 3)
 g.add_edge(3, 3)

 g.print_graph()
 # 0  :  1 -> 2
 # 1  :  2
 # 2  :  0 -> 3
 # 3  :  3

 assert sorted(g.bfs(2)) == [0, 1, 2, 3]

