
'''simple docstring'''

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      ):




       def __init__(       self				,				snake_case_				,				snake_case_				,				snake_case_ = None				,				snake_case_ = None				,				snake_case_ = None				,				snake_case_ = "longest"				,				snake_case_ = True				,				snake_case_ = 5_1_2				,				snake_case_ = None				,				snake_case_ = True				,				snake_case_ = True				,				snake_case_ = True				,				**snake_case_				,				):
         super().__init__()
         if use_fast_bert_tokenizer:
           _lowerCAmelCase    :					int							=   FastBertTokenizer(
               snake_case_				,				token_out_type=tf.intaa				,				lower_case_nfd_strip_accents=snake_case_				,				**snake_case_					)
         else:
           _lowerCAmelCase    :					Optional[int]							=   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    :					Optional[Any]							=   BertTokenizerLayer(
               snake_case_				,				token_out_type=tf.intaa				,				lower_case=snake_case_				,				**snake_case_					)

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




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

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

           _lowerCAmelCase    :					Optional[int]							=   BertTokenizerFast.from_pretrained(snake_case_				,				*snake_case_				,				**snake_case_					)
         return cls.from_tokenizer(snake_case_				,				**snake_case_					)




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         if self.do_lower_case:
           _lowerCAmelCase    :					List[Any]							=   case_fold_utfa(snake_case_					)
         _lowerCAmelCase    :					List[Any]							=   self.tf_tokenizer.tokenize(snake_case_					)
         return tokens.merge_dims(1				,				-1					)




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_=None				,				snake_case_=None				,				snake_case_=None				,				snake_case_=None				,				snake_case_=None				,				snake_case_=None				,				snake_case_=None				,				):
         if padding is None:
           _lowerCAmelCase    :					str							=   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    :					Optional[int]							=   self.max_length
         if truncation is None:
           _lowerCAmelCase    :					Any							=   self.truncation
         if pad_to_multiple_of is None:
           _lowerCAmelCase    :					Optional[int]							=   self.pad_to_multiple_of
         if return_token_type_ids is None:
           _lowerCAmelCase    :					Union[str, Any]							=   self.return_token_type_ids
         if return_attention_mask is None:
           _lowerCAmelCase    :					Optional[int]							=   self.return_attention_mask
         if not isinstance(snake_case_				,				tf.Tensor					):
           _lowerCAmelCase    :					Dict							=   tf.convert_to_tensor(snake_case_					)
         if text_pair is not None and not isinstance(snake_case_				,				tf.Tensor					):
           _lowerCAmelCase    :					Dict							=   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    :					Union[str, Any]							=   text[:, 0], text[:, 1]
         _lowerCAmelCase    :					Union[str, Any]							=   self.unpaired_tokenize(snake_case_					)
         if text_pair is None:  # Unpaired text
           if truncation:
             _lowerCAmelCase    :					Union[str, Any]							=   text[:, : max_length - 2]  # Allow room for special tokens
           _lowerCAmelCase    :					Optional[int]							=   combine_segments(
               (text,)				,				start_of_sequence_id=self.cls_token_id				,				end_of_segment_id=self.sep_token_id					)
         else:  # Paired text
           _lowerCAmelCase    :					Optional[int]							=   self.unpaired_tokenize(snake_case_					)
           if truncation:
             _lowerCAmelCase    :					List[str]							=   self.paired_trimmer.trim([text, text_pair]					)
           _lowerCAmelCase    :					Optional[Any]							=   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    :					str							=   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    :					Optional[Any]							=   pad_to_multiple_of * (-tf.math.floordiv(-pad_length				,				snake_case_					))
         else:
           _lowerCAmelCase    :					Any							=   max_length

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







       def 							__UpperCamelCase    (       self					):
         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,
         }



'''simple docstring'''

from __future__ import annotations


class 					a_					:




       def __init__(       self				,				snake_case_ = 0					):
         _lowerCAmelCase    :					Optional[Any]							=   value
         _lowerCAmelCase    :					Node | None							=   None
         _lowerCAmelCase    :					Node | None							=   None



def 		_UpperCAmelCase			(					_lowerCamelCase  :    Node | None					, _lowerCamelCase  :    Node | None						)      ->  Node | None:
  if treea is None:
    return treea
  if treea is None:
    return treea

  _lowerCAmelCase    :					Any							=   treea.value + treea.value
  _lowerCAmelCase    :					Tuple							=   merge_two_binary_trees(treea.left					, treea.left						)
  _lowerCAmelCase    :					List[str]							=   merge_two_binary_trees(treea.right					, treea.right						)
  return treea


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Node | None						)      ->  None:
  if root:
    print(root.value						)
    print_preorder(root.left						)
    print_preorder(root.right						)


if __name__ == "__main__":
     UpperCamelCase_            =    Node(1)
     UpperCamelCase_            =    Node(2)
     UpperCamelCase_            =    Node(3)
     UpperCamelCase_            =    Node(4)

     UpperCamelCase_            =    Node(2)
     UpperCamelCase_            =    Node(4)
     UpperCamelCase_            =    Node(6)
     UpperCamelCase_            =    Node(9)
     UpperCamelCase_            =    Node(5)

     print("""Tree1 is: """)
     print_preorder(treea)
     print("""Tree2 is: """)
     print_preorder(treea)
     UpperCamelCase_            =    merge_two_binary_trees(treea, treea)
     print("""Merged Tree is: """)
     print_preorder(merged_tree)


