

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     lowerCAmelCase				(    tf.keras.layers.Layer				):


       '''simple docstring'''







       def __init__(	self					:  Union[str, Any]						,	__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					:  Optional[int]						,	)   -> Union[str, Any]:







        '''simple docstring'''

        super().__init__()
        if use_fast_bert_tokenizer:
         lowerCamelCase      						=    FastBertTokenizer(
             __snake_case						,	token_out_type=tf.intaa						,	lower_case_nfd_strip_accents=__snake_case						,	**__snake_case     )
        else:
         lowerCamelCase      						=    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						,	)
         lowerCamelCase      						=    BertTokenizerLayer(
             __snake_case						,	token_out_type=tf.intaa						,	lower_case=__snake_case						,	**__snake_case     )

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

       @classmethod
       def   lowerCamelCase__						(	cls					:  str						,	__snake_case					:  "PreTrainedTokenizerBase"						,	**__snake_case					:  str     )   -> int:  # noqa: F821







        '''simple docstring'''

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

        lowerCamelCase      						=    tokenizer.get_vocab()
        lowerCamelCase      						=    sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]						,	key=lambda __snake_case     : x[1]     )
        lowerCamelCase      						=    [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					:  Tuple						,	__snake_case					:  Union[str, os.PathLike]						,	*__snake_case					:  Any						,	**__snake_case					:  int     )   -> str:







        '''simple docstring'''

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

         lowerCamelCase      						=    BertTokenizerFast.from_pretrained(__snake_case						,	*__snake_case						,	**__snake_case     )
        return cls.from_tokenizer(__snake_case						,	**__snake_case     )

       def   lowerCamelCase__						(	self					:  Tuple						,	__snake_case					:  Optional[Any]     )   -> Union[str, Any]:







        '''simple docstring'''

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

       def   lowerCamelCase__						(	self					:  Any						,	__snake_case					:  Optional[Any]						,	__snake_case					:  Optional[int]=None						,	__snake_case					:  Optional[int]=None						,	__snake_case					:  Optional[Any]=None						,	__snake_case					:  List[Any]=None						,	__snake_case					:  Optional[Any]=None						,	__snake_case					:  Any=None						,	__snake_case					:  List[str]=None						,	)   -> Any:







        '''simple docstring'''

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

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





       def   lowerCamelCase__						(	self					:  Any     )   -> int:







        '''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,
        }







from __future__ import annotations


class     lowerCAmelCase				:


       '''simple docstring'''







       def __init__(	self					:  Dict						,	__snake_case					:  int     )   -> None:







        '''simple docstring'''

        lowerCamelCase      						=    vertices
        lowerCamelCase      						=    [[0] * vertices for _ in range(__snake_case     )]

       def   lowerCamelCase__						(	self					:  List[str]						,	__snake_case					:  list[int]     )   -> None:







        '''simple docstring'''

        print('Vertex \t Distance from Source'     )
        for vertex in range(self.vertices     ):
         print(__snake_case						,	'\t\t'						,	distances_from_source[vertex]     )

       def   lowerCamelCase__						(	self					:  Optional[int]						,	__snake_case					:  list[int]						,	__snake_case					:  list[bool]     )   -> int:







        '''simple docstring'''

        lowerCamelCase      						=    1e7
        lowerCamelCase      						=    0

        # Search not nearest vertex not in the shortest path tree
        for vertex in range(self.vertices     ):
         if distances_from_source[vertex] < minimum and visited[vertex] is False:
          lowerCamelCase      						=    distances_from_source[vertex]
          lowerCamelCase      						=    vertex
        return min_index





       def   lowerCamelCase__						(	self					:  Optional[Any]						,	__snake_case					:  int     )   -> None:







        '''simple docstring'''

        lowerCamelCase      						=    [int(1e7     )] * self.vertices  # distances from the source
        lowerCamelCase      						=    0
        lowerCamelCase      						=    [False] * self.vertices

        for _ in range(self.vertices     ):
         lowerCamelCase      						=    self.minimum_distance(__snake_case						,	__snake_case     )
         lowerCamelCase      						=    True

         # Update dist value of the adjacent vertices
         # of the picked vertex only if the current
         # distance is greater than new distance and
         # the vertex in not in the shortest path tree
         for v in range(self.vertices     ):
          if (
              self.graph[u][v] > 0
              and visited[v] is False
              and distances[v] > distances[u] + self.graph[u][v]
          ):
           lowerCamelCase      						=    distances[u] + self.graph[u][v]

        self.print_solution(__snake_case     )


if __name__ == "__main__":
     _lowerCAmelCase :	Dict     				=    Graph(9)
     _lowerCAmelCase :	int     				=    [
         [0, 4, 0, 0, 0, 0, 0, 8, 0],
         [4, 0, 8, 0, 0, 0, 0, 1_1, 0],
         [0, 8, 0, 7, 0, 4, 0, 0, 2],
         [0, 0, 7, 0, 9, 1_4, 0, 0, 0],
         [0, 0, 0, 9, 0, 1_0, 0, 0, 0],
         [0, 0, 4, 1_4, 1_0, 0, 2, 0, 0],
         [0, 0, 0, 0, 0, 2, 0, 1, 6],
         [8, 1_1, 0, 0, 0, 0, 1, 0, 7],
         [0, 0, 2, 0, 0, 0, 6, 7, 0],
     ]
     graph.dijkstra(0)




