


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 							lowercase__(						tf.keras.layers.Layer			):






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = "longest"   ,			__SCREAMING_SNAKE_CASE = True   ,			__SCREAMING_SNAKE_CASE = 5_12   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = True   ,			__SCREAMING_SNAKE_CASE = True   ,			__SCREAMING_SNAKE_CASE = True   ,			**__SCREAMING_SNAKE_CASE   ,			)					->						Tuple:







         """simple docstring"""







         super().__init__()
         if use_fast_bert_tokenizer:
            UpperCamelCase__ :			Any 						=FastBertTokenizer(
                _A   ,			token_out_type=tf.intaa   ,			lower_case_nfd_strip_accents=_A   ,			**_A)
         else:
            UpperCamelCase__ :			int 						=tf.lookup.StaticVocabularyTable(
                tf.lookup.KeyValueTensorInitializer(
                    keys=_A   ,			key_dtype=tf.string   ,			values=tf.range(tf.size(_A   ,			out_type=tf.intaa)   ,			dtype=tf.intaa)   ,			value_dtype=tf.intaa   ,			)   ,			num_oov_buckets=1   ,			)
            UpperCamelCase__ :			Any 						=BertTokenizerLayer(
                _A   ,			token_out_type=tf.intaa   ,			lower_case=_A   ,			**_A)

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




      @classmethod
      def    UpperCAmelCase							(		cls   ,			__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE)					->						List[str]:  # noqa: F821







         """simple docstring"""







         UpperCamelCase__ :			Union[str, Any] 						=kwargs.pop("do_lower_case"   ,			_A)
         UpperCamelCase__ :			Tuple 						=tokenizer.do_lower_case if do_lower_case is None else do_lower_case
         UpperCamelCase__ :			List[str] 						=kwargs.pop("cls_token_id"   ,			_A)
         UpperCamelCase__ :			Any 						=tokenizer.cls_token_id if cls_token_id is None else cls_token_id
         UpperCamelCase__ :			Tuple 						=kwargs.pop("sep_token_id"   ,			_A)
         UpperCamelCase__ :			Tuple 						=tokenizer.sep_token_id if sep_token_id is None else sep_token_id
         UpperCamelCase__ :			List[str] 						=kwargs.pop("pad_token_id"   ,			_A)
         UpperCamelCase__ :			List[str] 						=tokenizer.pad_token_id if pad_token_id is None else pad_token_id

         UpperCamelCase__ :			Union[str, Any] 						=tokenizer.get_vocab()
         UpperCamelCase__ :			Optional[int] 						=sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]   ,			key=lambda __SCREAMING_SNAKE_CASE: x[1])
         UpperCamelCase__ :			Optional[int] 						=[entry[0] for entry in vocab]
         return cls(
             vocab_list=_A   ,			do_lower_case=_A   ,			cls_token_id=_A   ,			sep_token_id=_A   ,			pad_token_id=_A   ,			**_A   ,			)




      @classmethod
      def    UpperCAmelCase							(		cls   ,			__SCREAMING_SNAKE_CASE   ,			*__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE)					->						Tuple:







         """simple docstring"""







         try:
            UpperCamelCase__ :			Any 						=BertTokenizer.from_pretrained(_A   ,			*_A   ,			**_A)
         except:  # noqa: E722
            from .tokenization_bert_fast import BertTokenizerFast

            UpperCamelCase__ :			int 						=BertTokenizerFast.from_pretrained(_A   ,			*_A   ,			**_A)
         return cls.from_tokenizer(_A   ,			**_A)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						str:







         """simple docstring"""







         if self.do_lower_case:
            UpperCamelCase__ :			Optional[int] 						=case_fold_utfa(_A)
         UpperCamelCase__ :			Tuple 						=self.tf_tokenizer.tokenize(_A)
         return tokens.merge_dims(1   ,			-1)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=None   ,			)					->						Optional[int]:







         """simple docstring"""







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

         UpperCamelCase__ :			Tuple 						=pad_model_inputs(_A   ,			max_seq_length=_A   ,			pad_value=self.pad_token_id)
         UpperCamelCase__ :			str 						={'input_ids': input_ids}
         if return_attention_mask:
            UpperCamelCase__ :			Any 						=attention_mask
         if return_token_type_ids:
            UpperCamelCase__ :			Dict 						=pad_model_inputs(
                _A   ,			max_seq_length=_A   ,			pad_value=self.pad_token_id)
            UpperCamelCase__ :			Union[str, Any] 						=token_type_ids
         return output




      def    UpperCAmelCase							(		self)					->						Any:







         """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,
         }








def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	if not root or root not in binary_tree_mirror_dictionary:
		return
	snake_case_    ,snake_case_		:      Tuple           =						binary_tree_mirror_dictionary[root][:2]
	snake_case_		:      List[str]           =						[right_child, left_child]
	binary_tree_mirror_dict(__a ,			__a )
	binary_tree_mirror_dict(__a ,			__a )


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a = 1 ):
	if not binary_tree:
		raise ValueError('binary tree cannot be empty' )
	if root not in binary_tree:
		snake_case_		:      Dict           =						f"""root {root} is not present in the binary_tree"""
		raise ValueError(__a )
	snake_case_		:      Any           =						dict(__a )
	binary_tree_mirror_dict(__a ,			__a )
	return binary_tree_mirror_dictionary


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

