






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      :    Dict	,   __lowercase      :    List	,   __lowercase      :    bool	,   __lowercase      :    int = None	,   __lowercase      :    int = None	,   __lowercase      :    int = None	,   __lowercase      :    str = "longest"	,   __lowercase      :    bool = True	,   __lowercase      :    int = 512	,   __lowercase      :    int = None	,   __lowercase      :    bool = True	,   __lowercase      :    bool = True	,   __lowercase      :    bool = True	,   **__lowercase      :    List[str]	,   )							->   List[Any]:
        super().__init__()
        if use_fast_bert_tokenizer:
          __UpperCAmelCase							:			Any     =						FastBertTokenizer(
              __lowercase	,   token_out_type=tf.intaa	,   lower_case_nfd_strip_accents=__lowercase	,   **__lowercase      )
        else:
          __UpperCAmelCase							:			Optional[Any]     =						tf.lookup.StaticVocabularyTable(
              tf.lookup.KeyValueTensorInitializer(
                  keys=__lowercase	,   key_dtype=tf.string	,   values=tf.range(tf.size(__lowercase	,   out_type=tf.intaa      )	,   dtype=tf.intaa      )	,   value_dtype=tf.intaa	,   )	,   num_oov_buckets=1	,   )
          __UpperCAmelCase							:			Union[str, Any]     =						BertTokenizerLayer(
              __lowercase	,   token_out_type=tf.intaa	,   lower_case=__lowercase	,   **__lowercase      )

        __UpperCAmelCase							:			int     =						vocab_list
        __UpperCAmelCase							:			str     =						do_lower_case
        __UpperCAmelCase							:			Optional[Any]     =						cls_token_id or vocab_list.index("""[CLS]"""      )
        __UpperCAmelCase							:			int     =						sep_token_id or vocab_list.index("""[SEP]"""      )
        __UpperCAmelCase							:			List[Any]     =						pad_token_id or vocab_list.index("""[PAD]"""      )
        __UpperCAmelCase							:			int     =						ShrinkLongestTrimmer(max_length - 3	,   axis=1      )  # Allow room for special tokens
        __UpperCAmelCase							:			Optional[Any]     =						max_length
        __UpperCAmelCase							:			List[str]     =						padding
        __UpperCAmelCase							:			int     =						truncation
        __UpperCAmelCase							:			str     =						pad_to_multiple_of
        __UpperCAmelCase							:			str     =						return_token_type_ids
        __UpperCAmelCase							:			Optional[Any]     =						return_attention_mask
      @classmethod
      def 			UpperCAmelCase   (   cls      :    int	,   __lowercase      :    "PreTrainedTokenizerBase"	,   **__lowercase      :    Any      )							->   Union[str, Any]:  # noqa: F821
        __UpperCAmelCase							:			Union[str, Any]     =						kwargs.pop("""do_lower_case"""	,   __lowercase      )
        __UpperCAmelCase							:			Dict     =						tokenizer.do_lower_case if do_lower_case is None else do_lower_case
        __UpperCAmelCase							:			Optional[Any]     =						kwargs.pop("""cls_token_id"""	,   __lowercase      )
        __UpperCAmelCase							:			Dict     =						tokenizer.cls_token_id if cls_token_id is None else cls_token_id
        __UpperCAmelCase							:			Union[str, Any]     =						kwargs.pop("""sep_token_id"""	,   __lowercase      )
        __UpperCAmelCase							:			Union[str, Any]     =						tokenizer.sep_token_id if sep_token_id is None else sep_token_id
        __UpperCAmelCase							:			Tuple     =						kwargs.pop("""pad_token_id"""	,   __lowercase      )
        __UpperCAmelCase							:			Tuple     =						tokenizer.pad_token_id if pad_token_id is None else pad_token_id

        __UpperCAmelCase							:			Any     =						tokenizer.get_vocab()
        __UpperCAmelCase							:			Tuple     =						sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]	,   key=lambda __lowercase      : x[1]      )
        __UpperCAmelCase							:			List[str]     =						[entry[0] for entry in vocab]
        return cls(
            vocab_list=__lowercase	,   do_lower_case=__lowercase	,   cls_token_id=__lowercase	,   sep_token_id=__lowercase	,   pad_token_id=__lowercase	,   **__lowercase	,   )
      @classmethod
      def 			UpperCAmelCase   (   cls      :    Optional[int]	,   __lowercase      :    Union[str, os.PathLike]	,   *__lowercase      :    Dict	,   **__lowercase      :    Optional[int]      )							->   Optional[Any]:
        try:
          __UpperCAmelCase							:			Optional[Any]     =						BertTokenizer.from_pretrained(__lowercase	,   *__lowercase	,   **__lowercase      )
        except:  # noqa: E722
          from .tokenization_bert_fast import BertTokenizerFast

          __UpperCAmelCase							:			str     =						BertTokenizerFast.from_pretrained(__lowercase	,   *__lowercase	,   **__lowercase      )
        return cls.from_tokenizer(__lowercase	,   **__lowercase      )
      def 			UpperCAmelCase   (   self      :    str	,   __lowercase      :    Any      )							->   List[str]:
        if self.do_lower_case:
          __UpperCAmelCase							:			Union[str, Any]     =						case_fold_utfa(__lowercase      )
        __UpperCAmelCase							:			Optional[int]     =						self.tf_tokenizer.tokenize(__lowercase      )
        return tokens.merge_dims(1	,   -1      )
      def 			UpperCAmelCase   (   self      :    str	,   __lowercase      :    str	,   __lowercase      :    Dict=None	,   __lowercase      :    Any=None	,   __lowercase      :    List[str]=None	,   __lowercase      :    Optional[int]=None	,   __lowercase      :    Optional[Any]=None	,   __lowercase      :    Optional[int]=None	,   __lowercase      :    Dict=None	,   )							->   Union[str, Any]:
        if padding is None:
          __UpperCAmelCase							:			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:
          __UpperCAmelCase							:			str     =						self.max_length
        if truncation is None:
          __UpperCAmelCase							:			Any     =						self.truncation
        if pad_to_multiple_of is None:
          __UpperCAmelCase							:			int     =						self.pad_to_multiple_of
        if return_token_type_ids is None:
          __UpperCAmelCase							:			List[str]     =						self.return_token_type_ids
        if return_attention_mask is None:
          __UpperCAmelCase							:			Tuple     =						self.return_attention_mask
        if not isinstance(__lowercase	,   tf.Tensor      ):
          __UpperCAmelCase							:			str     =						tf.convert_to_tensor(__lowercase      )
        if text_pair is not None and not isinstance(__lowercase	,   tf.Tensor      ):
          __UpperCAmelCase							:			List[str]     =						tf.convert_to_tensor(__lowercase      )
        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							:			Dict     =						text[:, 0], text[:, 1]
        __UpperCAmelCase							:			List[Any]     =						self.unpaired_tokenize(__lowercase      )
        if text_pair is None:  # Unpaired text
          if truncation:
            __UpperCAmelCase							:			int     =						text[:, : max_length - 2]  # Allow room for special tokens
          __UpperCAmelCase							:			Union[str, Any]     =						combine_segments(
              (text,)	,   start_of_sequence_id=self.cls_token_id	,   end_of_segment_id=self.sep_token_id      )
        else:  # Paired text
          __UpperCAmelCase							:			str     =						self.unpaired_tokenize(__lowercase      )
          if truncation:
            __UpperCAmelCase							:			str     =						self.paired_trimmer.trim([text, text_pair]      )
          __UpperCAmelCase							:			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":
          __UpperCAmelCase							:			Optional[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
            __UpperCAmelCase							:			str     =						pad_to_multiple_of * (-tf.math.floordiv(-pad_length	,   __lowercase      ))
        else:
          __UpperCAmelCase							:			int     =						max_length

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




      def 			UpperCAmelCase   (   self      :    Dict      )							->   List[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,
        }










class 					a					:



      """simple docstring"""


      def __init__(   self      :    int	,   __lowercase      :    int      )							->   None:
        __UpperCAmelCase							:			str     =						data
        __UpperCAmelCase							:			BinaryTreeNode | None     =						None
        __UpperCAmelCase							:			BinaryTreeNode | None     =						None






def        lowerCamelCase__   (					__lowerCamelCase    :  BinaryTreeNode | None						,	__lowerCamelCase    :  int	):
  if node is None:
    __UpperCAmelCase							:			Optional[int]     =						BinaryTreeNode(__lowerCamelCase	)
    return node

  # binary search tree is not empty,
  # so we will insert it into the tree
  # if new_value is less than value of data in node,
  #  add it to left subtree and proceed recursively
  if new_value < node.data:
    __UpperCAmelCase							:			int     =						insert(node.left_child						,	__lowerCamelCase	)
  else:
    # if new_value is greater than value of data in node,
    #  add it to right subtree and proceed recursively
    __UpperCAmelCase							:			Dict     =						insert(node.right_child						,	__lowerCamelCase	)
  return node






def        lowerCamelCase__   (					__lowerCamelCase    :  None | BinaryTreeNode	):  # if node is None,return
  if node:
    __UpperCAmelCase							:			Optional[Any]     =						inorder(node.left_child	)
    __UpperCAmelCase							:			str     =						[*inorder_array, node.data]
    __UpperCAmelCase							:			Optional[Any]     =						inorder_array + inorder(node.right_child	)
  else:
    __UpperCAmelCase							:			Tuple     =						[]
  return inorder_array






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			Any     =						insert(__lowerCamelCase						,	15	)
  insert(__lowerCamelCase						,	10	)
  insert(__lowerCamelCase						,	25	)
  insert(__lowerCamelCase						,	6	)
  insert(__lowerCamelCase						,	14	)
  insert(__lowerCamelCase						,	20	)
  insert(__lowerCamelCase						,	60	)
  return root






def        lowerCamelCase__   (					):
  # main function
  __UpperCAmelCase							:			int     =						make_tree()
  print("""Printing values of binary search tree in Inorder Traversal."""	)
  inorder(__lowerCamelCase	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()


