

def    __lowercase (       _A  ,      _A     )       ->  Dict:
      print("""\nVertex Distance"""     )
      for i in range(_a     ):
            if dist[i] != float("""inf"""     ):
                  print(_a  ,      """\t"""  ,      int(dist[i]     )  ,      end="""\t"""     )
            else:
                  print(_a  ,      """\t"""  ,      """INF"""  ,      end="""\t"""     )
            print()






def    __lowercase (       _A  ,      _A  ,      _A     )       ->  Optional[int]:
      SCREAMING_SNAKE_CASE	: Optional[Any]					       =				float("""inf"""     )
      SCREAMING_SNAKE_CASE	: Tuple					       =				-1
      for i in range(_a     ):
            if (not vset[i]) and mdist[i] < min_val:
                  SCREAMING_SNAKE_CASE	: int					       =				i
                  SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				mdist[i]
      return min_ind






def    __lowercase (       _A  ,      _A  ,      _A     )       ->  Any:
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				[float("""inf"""     ) for _ in range(_a     )]
      SCREAMING_SNAKE_CASE	: str					       =				[False for _ in range(_a     )]
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				0.0

      for _ in range(v - 1     ):
            SCREAMING_SNAKE_CASE	: Dict					       =				min_dist(_a  ,      _a  ,      _a     )
            SCREAMING_SNAKE_CASE	: Optional[int]					       =				True

            for i in range(_a     ):
                  if (
                      (not vset[i])
                      and graph[u][i] != float("""inf"""     )
                      and mdist[u] + graph[u][i] < mdist[i]
                  ):
                        SCREAMING_SNAKE_CASE	: int					       =				mdist[u] + graph[u][i]

      print_dist(_a  ,      _a     )


if __name__ == "__main__":
       UpperCAmelCase__	:       int			      =      int(input("""Enter number of vertices: """).strip())
       UpperCAmelCase__	:       Union[str, Any]			      =      int(input("""Enter number of edges: """).strip())

       UpperCAmelCase__	:       Tuple			      =      [[float("""inf""") for i in range(V)] for j in range(V)]

       for i in range(V):
              UpperCAmelCase__	:       Optional[int]			      =      0.0

       for i in range(E):
              print("""\nEdge """, i + 1)
              UpperCAmelCase__	:       Tuple			      =      int(input("""Enter source:""").strip())
              UpperCAmelCase__	:       Dict			      =      int(input("""Enter destination:""").strip())
              UpperCAmelCase__	:       Union[str, Any]			      =      float(input("""Enter weight:""").strip())
              UpperCAmelCase__	:       List[Any]			      =      weight

       UpperCAmelCase__	:       Any			      =      int(input("""\nEnter shortest path source:""").strip())
       dijkstra(graph, V, gsrc)







import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer







class     a__    (				tf.keras.layers.Layer    ):







  """simple docstring"""




  def __init__(     self      :	str			,			UpperCAmelCase__      :	List			,			UpperCAmelCase__      :	bool			,			UpperCAmelCase__      :	int = None			,			UpperCAmelCase__      :	int = None			,			UpperCAmelCase__      :	int = None			,			UpperCAmelCase__      :	str = "longest"			,			UpperCAmelCase__      :	bool = True			,			UpperCAmelCase__      :	int = 5_1_2			,			UpperCAmelCase__      :	int = None			,			UpperCAmelCase__      :	bool = True			,			UpperCAmelCase__      :	bool = True			,			UpperCAmelCase__      :	bool = True			,			**UpperCAmelCase__      :	Optional[int]			,			)   ->Union[str, Any]:
        """simple docstring"""







        super().__init__()
        if use_fast_bert_tokenizer:
              SCREAMING_SNAKE_CASE	: Any					       =				FastBertTokenizer(
                  UpperCAmelCase__			,			token_out_type=tf.intaa			,			lower_case_nfd_strip_accents=UpperCAmelCase__			,			**UpperCAmelCase__						)
        else:
              SCREAMING_SNAKE_CASE	: Any					       =				tf.lookup.StaticVocabularyTable(
                  tf.lookup.KeyValueTensorInitializer(
                      keys=UpperCAmelCase__			,			key_dtype=tf.string			,			values=tf.range(tf.size(UpperCAmelCase__			,			out_type=tf.intaa						)			,			dtype=tf.intaa						)			,			value_dtype=tf.intaa			,			)			,			num_oov_buckets=1			,			)
              SCREAMING_SNAKE_CASE	: Dict					       =				BertTokenizerLayer(
                  UpperCAmelCase__			,			token_out_type=tf.intaa			,			lower_case=UpperCAmelCase__			,			**UpperCAmelCase__						)

        SCREAMING_SNAKE_CASE	: Any					       =				vocab_list
        SCREAMING_SNAKE_CASE	: int					       =				do_lower_case
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				cls_token_id or vocab_list.index("""[CLS]"""						)
        SCREAMING_SNAKE_CASE	: int					       =				sep_token_id or vocab_list.index("""[SEP]"""						)
        SCREAMING_SNAKE_CASE	: int					       =				pad_token_id or vocab_list.index("""[PAD]"""						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				ShrinkLongestTrimmer(max_length - 3			,			axis=1						)  # Allow room for special tokens
        SCREAMING_SNAKE_CASE	: Tuple					       =				max_length
        SCREAMING_SNAKE_CASE	: List[str]					       =				padding
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				truncation
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				pad_to_multiple_of
        SCREAMING_SNAKE_CASE	: Dict					       =				return_token_type_ids
        SCREAMING_SNAKE_CASE	: str					       =				return_attention_mask


  @classmethod
  def 	_lowercase (     cls      :	Optional[int]			,			UpperCAmelCase__      :	"PreTrainedTokenizerBase"			,			**UpperCAmelCase__      :	Tuple						)   ->str:  # noqa: F821
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Any					       =				kwargs.pop("""do_lower_case"""			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				tokenizer.do_lower_case if do_lower_case is None else do_lower_case
        SCREAMING_SNAKE_CASE	: Any					       =				kwargs.pop("""cls_token_id"""			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Any					       =				tokenizer.cls_token_id if cls_token_id is None else cls_token_id
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				kwargs.pop("""sep_token_id"""			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				tokenizer.sep_token_id if sep_token_id is None else sep_token_id
        SCREAMING_SNAKE_CASE	: int					       =				kwargs.pop("""pad_token_id"""			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Any					       =				tokenizer.pad_token_id if pad_token_id is None else pad_token_id

        SCREAMING_SNAKE_CASE	: List[str]					       =				tokenizer.get_vocab()
        SCREAMING_SNAKE_CASE	: List[str]					       =				sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,			key=lambda UpperCAmelCase__						: x[1]						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				[entry[0] for entry in vocab]
        return cls(
            vocab_list=UpperCAmelCase__			,			do_lower_case=UpperCAmelCase__			,			cls_token_id=UpperCAmelCase__			,			sep_token_id=UpperCAmelCase__			,			pad_token_id=UpperCAmelCase__			,			**UpperCAmelCase__			,			)


  @classmethod
  def 	_lowercase (     cls      :	str			,			UpperCAmelCase__      :	Union[str, os.PathLike]			,			*UpperCAmelCase__      :	Dict			,			**UpperCAmelCase__      :	int						)   ->Any:
        """simple docstring"""







        try:
              SCREAMING_SNAKE_CASE	: List[str]					       =				BertTokenizer.from_pretrained(UpperCAmelCase__			,			*UpperCAmelCase__			,			**UpperCAmelCase__						)
        except:  # noqa: E722
              from .tokenization_bert_fast import BertTokenizerFast

              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				BertTokenizerFast.from_pretrained(UpperCAmelCase__			,			*UpperCAmelCase__			,			**UpperCAmelCase__						)
        return cls.from_tokenizer(UpperCAmelCase__			,			**UpperCAmelCase__						)


  def 	_lowercase (     self      :	Tuple			,			UpperCAmelCase__      :	List[str]						)   ->List[str]:
        """simple docstring"""







        if self.do_lower_case:
              SCREAMING_SNAKE_CASE	: Optional[Any]					       =				case_fold_utfa(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: int					       =				self.tf_tokenizer.tokenize(UpperCAmelCase__						)
        return tokens.merge_dims(1			,			-1						)


  def 	_lowercase (     self      :	Tuple			,			UpperCAmelCase__      :	Any			,			UpperCAmelCase__      :	Dict=None			,			UpperCAmelCase__      :	Optional[int]=None			,			UpperCAmelCase__      :	Optional[Any]=None			,			UpperCAmelCase__      :	Union[str, Any]=None			,			UpperCAmelCase__      :	Dict=None			,			UpperCAmelCase__      :	int=None			,			UpperCAmelCase__      :	Tuple=None			,			)   ->Any:
        """simple docstring"""







        if padding is None:
              SCREAMING_SNAKE_CASE	: int					       =				self.padding
        if padding not in ("longest", "max_length"):
              raise ValueError("""Padding must be either 'longest' or 'max_length'!"""						)
        if max_length is not None and text_pair is not None:
              # Because we have to instantiate a Trimmer to do it properly
              raise ValueError("""max_length cannot be overridden at call time when truncating paired texts!"""						)
        if max_length is None:
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				self.max_length
        if truncation is None:
              SCREAMING_SNAKE_CASE	: str					       =				self.truncation
        if pad_to_multiple_of is None:
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.pad_to_multiple_of
        if return_token_type_ids is None:
              SCREAMING_SNAKE_CASE	: Any					       =				self.return_token_type_ids
        if return_attention_mask is None:
              SCREAMING_SNAKE_CASE	: int					       =				self.return_attention_mask
        if not isinstance(UpperCAmelCase__			,			tf.Tensor						):
              SCREAMING_SNAKE_CASE	: str					       =				tf.convert_to_tensor(UpperCAmelCase__						)
        if text_pair is not None and not isinstance(UpperCAmelCase__			,			tf.Tensor						):
              SCREAMING_SNAKE_CASE	: int					       =				tf.convert_to_tensor(UpperCAmelCase__						)
        if text_pair is not None:
              if text.shape.rank > 1:
                    raise ValueError("""text argument should not be multidimensional when a text pair is supplied!"""						)
              if text_pair.shape.rank > 1:
                    raise ValueError("""text_pair should not be multidimensional!"""						)
        if text.shape.rank == 2:
              SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Dict					       =				text[:, 0], text[:, 1]
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				self.unpaired_tokenize(UpperCAmelCase__						)
        if text_pair is None:  # Unpaired text
              if truncation:
                    SCREAMING_SNAKE_CASE	: int					       =				text[:, : max_length - 2]  # Allow room for special tokens
              SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: List[str]					       =				combine_segments(
                  (text,)			,			start_of_sequence_id=self.cls_token_id			,			end_of_segment_id=self.sep_token_id						)
        else:  # Paired text
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.unpaired_tokenize(UpperCAmelCase__						)
              if truncation:
                    SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Any					       =				self.paired_trimmer.trim([text, text_pair]						)
              SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: str					       =				combine_segments(
                  (text, text_pair)			,			start_of_sequence_id=self.cls_token_id			,			end_of_segment_id=self.sep_token_id						)
        if padding == "longest":
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				input_ids.bounding_shape(axis=1						)
              if pad_to_multiple_of is not None:
                    # No ceiling division in tensorflow, so we negate floordiv instead
                    SCREAMING_SNAKE_CASE	: Any					       =				pad_to_multiple_of * (-tf.math.floordiv(-pad_length			,			UpperCAmelCase__						))
        else:
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				max_length

        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Tuple					       =				pad_model_inputs(UpperCAmelCase__			,			max_seq_length=UpperCAmelCase__			,			pad_value=self.pad_token_id						)
        SCREAMING_SNAKE_CASE	: Any					       =				{"""input_ids""": input_ids}
        if return_attention_mask:
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				attention_mask
        if return_token_type_ids:
              SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Optional[Any]					       =				pad_model_inputs(
                  UpperCAmelCase__			,			max_seq_length=UpperCAmelCase__			,			pad_value=self.pad_token_id						)
              SCREAMING_SNAKE_CASE	: List[str]					       =				token_type_ids
        return output




  def 	_lowercase (     self      :	Optional[int]						)   ->Tuple:
        """simple docstring"""







        return {
            "vocab_list": self.vocab_list,
            "do_lower_case": self.do_lower_case,
            "cls_token_id": self.cls_token_id,
            "sep_token_id": self.sep_token_id,
            "pad_token_id": self.pad_token_id,
        }




