
from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

__lowerCAmelCase   :     Union[str, Any]      =						{
    "A": ["B", "C"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B"],
    "E": ["B", "F"],
    "F": ["C", "E"],
}


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  list[str]:
    __lowercase						:     int		      =    {start}
    __lowercase						:     Any		      =    [start]
    __lowercase						:     Queue		      =    Queue()
    queue.put(__lowerCAmelCase		)
    while not queue.empty():
        __lowercase						:     List[Any]		      =    queue.get()
        for w in graph[v]:
            if w not in explored:
                explored.add(__lowerCAmelCase		)
                result.append(__lowerCAmelCase		)
                queue.put(__lowerCAmelCase		)
    return result


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  list[str]:
    __lowercase						:     Union[str, Any]		      =    {start}
    __lowercase						:     Union[str, Any]		      =    [start]
    __lowercase						:     Dict		      =    deque([start]		)
    while queue:
        __lowercase						:     List[Any]		      =    queue.popleft()
        for child in graph[v]:
            if child not in visited:
                visited.add(__lowerCAmelCase		)
                result.append(__lowerCAmelCase		)
                queue.append(__lowerCAmelCase		)
    return result


def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  None:
    __lowercase						:     List[str]		      =    F'from __main__ import G, {name}'
    __lowercase						:     List[str]		      =    10_000
    __lowercase						:     List[Any]		      =    timeit(F'{name}(G, \'A\')'		,   setup=__lowerCAmelCase		,   number=__lowerCAmelCase		)
    print(F'{name:<35} finished {number} runs in {res:.5f} seconds'		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 benchmark_function("breadth_first_search")
 benchmark_function("breadth_first_search_with_deque")
 # breadth_first_search                finished 10000 runs in 0.20999 seconds
 # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds




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						:    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						:    int			,    ):
          super().__init__()
          if use_fast_bert_tokenizer:
              __lowercase						:     Optional[int]		      =    FastBertTokenizer(
                  _snake_case			,    token_out_type=tf.intaa			,    lower_case_nfd_strip_accents=_snake_case			,    **_snake_case			)
          else:
              __lowercase						:     List[str]		      =    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			,    )
              __lowercase						:     List[Any]		      =    BertTokenizerLayer(
                  _snake_case			,    token_out_type=tf.intaa			,    lower_case=_snake_case			,    **_snake_case			)

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




      @classmethod
      def 						snake_case_     ( cls						:    Optional[Any]			,    _snake_case						:    "PreTrainedTokenizerBase"			,    **_snake_case						:    Dict			):  # noqa: F821
          __lowercase						:     Tuple		      =    kwargs.pop('''do_lower_case'''			,    _snake_case			)
          __lowercase						:     Union[str, Any]		      =    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
          __lowercase						:     Optional[Any]		      =    kwargs.pop('''cls_token_id'''			,    _snake_case			)
          __lowercase						:     str		      =    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
          __lowercase						:     Tuple		      =    kwargs.pop('''sep_token_id'''			,    _snake_case			)
          __lowercase						:     Any		      =    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
          __lowercase						:     Dict		      =    kwargs.pop('''pad_token_id'''			,    _snake_case			)
          __lowercase						:     List[Any]		      =    tokenizer.pad_token_id if pad_token_id is None else pad_token_id

          __lowercase						:     Optional[int]		      =    tokenizer.get_vocab()
          __lowercase						:     Tuple		      =    sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,    key=lambda _snake_case			: x[1]			)
          __lowercase						:     Optional[int]		      =    [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 						snake_case_     ( cls						:    Any			,    _snake_case						:    Union[str, os.PathLike]			,    *_snake_case						:    List[str]			,    **_snake_case						:    Tuple			):
          try:
              __lowercase						:     List[Any]		      =    BertTokenizer.from_pretrained(_snake_case			,    *_snake_case			,    **_snake_case			)
          except:  # noqa: E722
              from .tokenization_bert_fast import BertTokenizerFast

              __lowercase						:     List[str]		      =    BertTokenizerFast.from_pretrained(_snake_case			,    *_snake_case			,    **_snake_case			)
          return cls.from_tokenizer(_snake_case			,    **_snake_case			)




      def 						snake_case_     ( self						:    Optional[int]			,    _snake_case						:    Union[str, Any]			):
          if self.do_lower_case:
              __lowercase						:     int		      =    case_fold_utfa(_snake_case			)
          __lowercase						:     Optional[int]		      =    self.tf_tokenizer.tokenize(_snake_case			)
          return tokens.merge_dims(1			,    -1			)




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

          __lowercase      , __lowercase						:     Optional[int]		      =    pad_model_inputs(_snake_case			,    max_seq_length=_snake_case			,    pad_value=self.pad_token_id			)
          __lowercase						:     Dict		      =    {'''input_ids''': input_ids}
          if return_attention_mask:
              __lowercase						:     Any		      =    attention_mask
          if return_token_type_ids:
              __lowercase      , __lowercase						:     List[str]		      =    pad_model_inputs(
                  _snake_case			,    max_seq_length=_snake_case			,    pad_value=self.pad_token_id			)
              __lowercase						:     Union[str, Any]		      =    token_type_ids
          return output




      def 						snake_case_     ( self						:    str			):
          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,
          }


