




def   A		(__A  :       Dict  ,							__A  :       List[str]			)  -> Union[str, Any]:







     """simple docstring"""


     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   A		(__A  :       List[Any]  ,							__A  :       Union[str, Any]  ,							__A  :       List[str]			)  -> Any:







     """simple docstring"""


     UpperCAmelCase_		=				float('''inf'''			)
     UpperCAmelCase_		=				-1
     for i in range(__A			):
          if (not vset[i]) and mdist[i] < min_val:
               UpperCAmelCase_		=				i
               UpperCAmelCase_		=				mdist[i]
     return min_ind


def   A		(__A  :       Union[str, Any]  ,							__A  :       List[Any]  ,							__A  :       Tuple			)  -> Dict:







     """simple docstring"""


     UpperCAmelCase_		=				[float('''inf'''			) for _ in range(__A			)]
     UpperCAmelCase_		=				[False for _ in range(__A			)]
     UpperCAmelCase_		=				0.0

     for _ in range(v - 1			):
          UpperCAmelCase_		=				min_dist(__A  ,							__A  ,							__A			)
          UpperCAmelCase_		=				True

          for i in range(__A			):
               if (
                   (not vset[i])
                   and graph[u][i] != float('''inf'''			)
                   and mdist[u] + graph[u][i] < mdist[i]
               ):
                    UpperCAmelCase_		=				mdist[u] + graph[u][i]

     print_dist(__A  ,							__A			)


if __name__ == "__main__":
    snake_case_					:					Dict       						=			int(input("Enter number of vertices: ").strip())
    snake_case_					:					Any       						=			int(input("Enter number of edges: ").strip())

    snake_case_					:					List[Any]       						=			[[float("inf") for i in range(V)] for j in range(V)]

    for i in range(V):
        snake_case_					:					List[str]       						=			0.0

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

    snake_case_					:					Dict       						=			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 __snake_case     (     tf.keras.layers.Layer						):







   def __init__( self						:		Optional[int]   ,		_snake_case						:		List   ,		_snake_case						:		bool   ,		_snake_case						:		int = None   ,		_snake_case						:		int = None   ,		_snake_case						:		int = None   ,		_snake_case						:		str = "longest"   ,		_snake_case						:		bool = True   ,		_snake_case						:		int = 512   ,		_snake_case						:		int = None   ,		_snake_case						:		bool = True   ,		_snake_case						:		bool = True   ,		_snake_case						:		bool = True   ,		**_snake_case						:		Any   ,		):

        """simple docstring"""


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

        UpperCAmelCase_		=				vocab_list
        UpperCAmelCase_		=				do_lower_case
        UpperCAmelCase_		=				cls_token_id or vocab_list.index('''[CLS]''')
        UpperCAmelCase_		=				sep_token_id or vocab_list.index('''[SEP]''')
        UpperCAmelCase_		=				pad_token_id or vocab_list.index('''[PAD]''')
        UpperCAmelCase_		=				ShrinkLongestTrimmer(max_length - 3   ,		axis=1)  # Allow room for special tokens
        UpperCAmelCase_		=				max_length
        UpperCAmelCase_		=				padding
        UpperCAmelCase_		=				truncation
        UpperCAmelCase_		=				pad_to_multiple_of
        UpperCAmelCase_		=				return_token_type_ids
        UpperCAmelCase_		=				return_attention_mask







   @classmethod
   def lowerCamelCase		( cls						:		Optional[Any]   ,		_snake_case						:		"PreTrainedTokenizerBase"   ,		**_snake_case						:		str):  # noqa: F821

        """simple docstring"""


        UpperCAmelCase_		=				kwargs.pop('''do_lower_case'''   ,		_snake_case)
        UpperCAmelCase_		=				tokenizer.do_lower_case if do_lower_case is None else do_lower_case
        UpperCAmelCase_		=				kwargs.pop('''cls_token_id'''   ,		_snake_case)
        UpperCAmelCase_		=				tokenizer.cls_token_id if cls_token_id is None else cls_token_id
        UpperCAmelCase_		=				kwargs.pop('''sep_token_id'''   ,		_snake_case)
        UpperCAmelCase_		=				tokenizer.sep_token_id if sep_token_id is None else sep_token_id
        UpperCAmelCase_		=				kwargs.pop('''pad_token_id'''   ,		_snake_case)
        UpperCAmelCase_		=				tokenizer.pad_token_id if pad_token_id is None else pad_token_id

        UpperCAmelCase_		=				tokenizer.get_vocab()
        UpperCAmelCase_		=				sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]   ,		key=lambda _snake_case: x[1])
        UpperCAmelCase_		=				[entry[0] for entry in vocab]
        return cls(
            vocab_list=_snake_case   ,		do_lower_case=_snake_case   ,		cls_token_id=_snake_case   ,		sep_token_id=_snake_case   ,		pad_token_id=_snake_case   ,		**_snake_case   ,		)







   @classmethod
   def lowerCamelCase		( cls						:		List[str]   ,		_snake_case						:		Union[str, os.PathLike]   ,		*_snake_case						:		Union[str, Any]   ,		**_snake_case						:		Union[str, Any]):

        """simple docstring"""


        try:
             UpperCAmelCase_		=				BertTokenizer.from_pretrained(_snake_case   ,		*_snake_case   ,		**_snake_case)
        except:  # noqa: E722
             from .tokenization_bert_fast import BertTokenizerFast

             UpperCAmelCase_		=				BertTokenizerFast.from_pretrained(_snake_case   ,		*_snake_case   ,		**_snake_case)
        return cls.from_tokenizer(_snake_case   ,		**_snake_case)







   def lowerCamelCase		( self						:		int   ,		_snake_case						:		str):

        """simple docstring"""


        if self.do_lower_case:
             UpperCAmelCase_		=				case_fold_utfa(_snake_case)
        UpperCAmelCase_		=				self.tf_tokenizer.tokenize(_snake_case)
        return tokens.merge_dims(1   ,		-1)







   def lowerCamelCase		( self						:		int   ,		_snake_case						:		str   ,		_snake_case						:		str=None   ,		_snake_case						:		Union[str, Any]=None   ,		_snake_case						:		str=None   ,		_snake_case						:		int=None   ,		_snake_case						:		Optional[int]=None   ,		_snake_case						:		Optional[Any]=None   ,		_snake_case						:		List[str]=None   ,		):

        """simple docstring"""


        if padding is None:
             UpperCAmelCase_		=				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:
             UpperCAmelCase_		=				self.max_length
        if truncation is None:
             UpperCAmelCase_		=				self.truncation
        if pad_to_multiple_of is None:
             UpperCAmelCase_		=				self.pad_to_multiple_of
        if return_token_type_ids is None:
             UpperCAmelCase_		=				self.return_token_type_ids
        if return_attention_mask is None:
             UpperCAmelCase_		=				self.return_attention_mask
        if not isinstance(_snake_case   ,		tf.Tensor):
             UpperCAmelCase_		=				tf.convert_to_tensor(_snake_case)
        if text_pair is not None and not isinstance(_snake_case   ,		tf.Tensor):
             UpperCAmelCase_		=				tf.convert_to_tensor(_snake_case)
        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:
             UpperCAmelCase_	,			UpperCAmelCase_		=				text[:, 0], text[:, 1]
        UpperCAmelCase_		=				self.unpaired_tokenize(_snake_case)
        if text_pair is None:  # Unpaired text
             if truncation:
                  UpperCAmelCase_		=				text[:, : max_length - 2]  # Allow room for special tokens
             UpperCAmelCase_	,			UpperCAmelCase_		=				combine_segments(
                 (text,)   ,		start_of_sequence_id=self.cls_token_id   ,		end_of_segment_id=self.sep_token_id)
        else:  # Paired text
             UpperCAmelCase_		=				self.unpaired_tokenize(_snake_case)
             if truncation:
                  UpperCAmelCase_	,			UpperCAmelCase_		=				self.paired_trimmer.trim([text, text_pair])
             UpperCAmelCase_	,			UpperCAmelCase_		=				combine_segments(
                 (text, text_pair)   ,		start_of_sequence_id=self.cls_token_id   ,		end_of_segment_id=self.sep_token_id)
        if padding == "longest":
             UpperCAmelCase_		=				input_ids.bounding_shape(axis=1)
             if pad_to_multiple_of is not None:
                  # No ceiling division in tensorflow, so we negate floordiv instead
                  UpperCAmelCase_		=				pad_to_multiple_of * (-tf.math.floordiv(-pad_length   ,		_snake_case))
        else:
             UpperCAmelCase_		=				max_length

        UpperCAmelCase_	,			UpperCAmelCase_		=				pad_model_inputs(_snake_case   ,		max_seq_length=_snake_case   ,		pad_value=self.pad_token_id)
        UpperCAmelCase_		=				{'''input_ids''': input_ids}
        if return_attention_mask:
             UpperCAmelCase_		=				attention_mask
        if return_token_type_ids:
             UpperCAmelCase_	,			UpperCAmelCase_		=				pad_model_inputs(
                 _snake_case   ,		max_seq_length=_snake_case   ,		pad_value=self.pad_token_id)
             UpperCAmelCase_		=				token_type_ids
        return output







   def lowerCamelCase		( self						:		Optional[Any]):

        """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,
        }






