

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 		__magic_name__ (				tf.keras.layers.Layer							):






     def __init__(							self						,		_lowercase						,		_lowercase						,		_lowercase = None						,		_lowercase = None						,		_lowercase = None						,		_lowercase = "longest"						,		_lowercase = True						,		_lowercase = 512						,		_lowercase = None						,		_lowercase = True						,		_lowercase = True						,		_lowercase = True						,		**_lowercase						,		)->			int:
         super().__init__()
         if use_fast_bert_tokenizer:
             UpperCamelCase_          =		FastBertTokenizer(
                 _lowercase						,		token_out_type=tf.intaa						,		lower_case_nfd_strip_accents=_lowercase						,		**_lowercase     )
         else:
             UpperCamelCase_          =		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_          =		BertTokenizerLayer(
                 _lowercase						,		token_out_type=tf.intaa						,		lower_case=_lowercase						,		**_lowercase     )

         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      UpperCAmelCase_							(							cls						,		_lowercase						,		**_lowercase     )->			List[str]:  # noqa: F821
         UpperCamelCase_          =		kwargs.pop("do_lower_case"						,		_lowercase     )
         UpperCamelCase_          =		tokenizer.do_lower_case if do_lower_case is None else do_lower_case
         UpperCamelCase_          =		kwargs.pop("cls_token_id"						,		_lowercase     )
         UpperCamelCase_          =		tokenizer.cls_token_id if cls_token_id is None else cls_token_id
         UpperCamelCase_          =		kwargs.pop("sep_token_id"						,		_lowercase     )
         UpperCamelCase_          =		tokenizer.sep_token_id if sep_token_id is None else sep_token_id
         UpperCamelCase_          =		kwargs.pop("pad_token_id"						,		_lowercase     )
         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 _lowercase     : x[1]     )
         UpperCamelCase_          =		[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						,		_lowercase						,		*_lowercase						,		**_lowercase     )->			Any:
         try:
             UpperCamelCase_          =		BertTokenizer.from_pretrained(_lowercase						,		*_lowercase						,		**_lowercase     )
         except:  # noqa: E722
             from .tokenization_bert_fast import BertTokenizerFast

             UpperCamelCase_          =		BertTokenizerFast.from_pretrained(_lowercase						,		*_lowercase						,		**_lowercase     )
         return cls.from_tokenizer(_lowercase						,		**_lowercase     )






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			str:
         if self.do_lower_case:
             UpperCamelCase_          =		case_fold_utfa(_lowercase     )
         UpperCamelCase_          =		self.tf_tokenizer.tokenize(_lowercase     )
         return tokens.merge_dims(1						,		-1     )






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase=None						,		_lowercase=None						,		_lowercase=None						,		_lowercase=None						,		_lowercase=None						,		_lowercase=None						,		_lowercase=None						,		)->			int:
         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(_lowercase						,		tf.Tensor     ):
             UpperCamelCase_          =		tf.convert_to_tensor(_lowercase     )
         if text_pair is not None and not isinstance(_lowercase						,		tf.Tensor     ):
             UpperCamelCase_          =		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_   ,    UpperCamelCase_          =		text[:, 0], text[:, 1]
         UpperCamelCase_          =		self.unpaired_tokenize(_lowercase     )
         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(_lowercase     )
             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						,		_lowercase     ))
         else:
             UpperCamelCase_          =		max_length

         UpperCamelCase_   ,    UpperCamelCase_          =		pad_model_inputs(_lowercase						,		max_seq_length=_lowercase						,		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(
                 _lowercase						,		max_seq_length=_lowercase						,		pad_value=self.pad_token_id     )
             UpperCamelCase_          =		token_type_ids
         return output






     def      UpperCAmelCase_							(							self     )->			Any:
         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,
         }



def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Tuple:



    """simple docstring"""






    if not root or root not in binary_tree_mirror_dictionary:
        return
    UpperCamelCase_   ,    UpperCamelCase_          =		binary_tree_mirror_dictionary[root][:2]
    UpperCamelCase_          =		[right_child, left_child]
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 1       )->		dict:



    """simple docstring"""






    if not binary_tree:
        raise ValueError("binary tree cannot be empty"       )
    if root not in binary_tree:
        UpperCamelCase_          =		f"root {root} is not present in the binary_tree"
        raise ValueError(SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		dict(SCREAMING_SNAKE_CASE_       )
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    return binary_tree_mirror_dictionary


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE						:Dict      =			{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
 print(F'''Binary tree: {binary_tree}''')
 SCREAMING_SNAKE_CASE						:str      =			binary_tree_mirror(binary_tree, 5)
 print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')
