"""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        __SCREAMING_SNAKE_CASE     (   tf.keras.layers.Layer							):







				def __init__(    self    :List[str]      ,__UpperCAmelCase    :List      ,__UpperCAmelCase    :bool      ,__UpperCAmelCase    :int = None      ,__UpperCAmelCase    :int = None      ,__UpperCAmelCase    :int = None      ,__UpperCAmelCase    :str = "longest"      ,__UpperCAmelCase    :bool = True      ,__UpperCAmelCase    :int = 5_12      ,__UpperCAmelCase    :int = None      ,__UpperCAmelCase    :bool = True      ,__UpperCAmelCase    :bool = True      ,__UpperCAmelCase    :bool = True      ,**__UpperCAmelCase    :Optional[Any]      ,)      ->					List[str]:




									"""simple docstring"""



									super().__init__()
									if use_fast_bert_tokenizer:
														lowerCamelCase__		:   Tuple  				=					FastBertTokenizer(
														    __UpperCAmelCase      ,token_out_type=tf.intaa      ,lower_case_nfd_strip_accents=__UpperCAmelCase      ,**__UpperCAmelCase  )
									else:
														lowerCamelCase__		:   Optional[int]  				=					tf.lookup.StaticVocabularyTable(
														    tf.lookup.KeyValueTensorInitializer(
														        keys=__UpperCAmelCase      ,key_dtype=tf.string      ,values=tf.range(tf.size(__UpperCAmelCase      ,out_type=tf.intaa  )      ,dtype=tf.intaa  )      ,value_dtype=tf.intaa      ,)      ,num_oov_buckets=1      ,)
														lowerCamelCase__		:   List[Any]  				=					BertTokenizerLayer(
														    __UpperCAmelCase      ,token_out_type=tf.intaa      ,lower_case=__UpperCAmelCase      ,**__UpperCAmelCase  )

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







				@classmethod
				def   lowercase_		(    cls    :str      ,__UpperCAmelCase    :"PreTrainedTokenizerBase"      ,**__UpperCAmelCase    :Dict  )      ->					int:  # noqa: F821




									"""simple docstring"""



									lowerCamelCase__		:   Tuple  				=					kwargs.pop('''do_lower_case'''      ,__UpperCAmelCase  )
									lowerCamelCase__		:   Any  				=					tokenizer.do_lower_case if do_lower_case is None else do_lower_case
									lowerCamelCase__		:   Union[str, Any]  				=					kwargs.pop('''cls_token_id'''      ,__UpperCAmelCase  )
									lowerCamelCase__		:   int  				=					tokenizer.cls_token_id if cls_token_id is None else cls_token_id
									lowerCamelCase__		:   Tuple  				=					kwargs.pop('''sep_token_id'''      ,__UpperCAmelCase  )
									lowerCamelCase__		:   Union[str, Any]  				=					tokenizer.sep_token_id if sep_token_id is None else sep_token_id
									lowerCamelCase__		:   Union[str, Any]  				=					kwargs.pop('''pad_token_id'''      ,__UpperCAmelCase  )
									lowerCamelCase__		:   str  				=					tokenizer.pad_token_id if pad_token_id is None else pad_token_id

									lowerCamelCase__		:   List[Any]  				=					tokenizer.get_vocab()
									lowerCamelCase__		:   str  				=					sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]      ,key=lambda __UpperCAmelCase  : x[1]  )
									lowerCamelCase__		:   str  				=					[entry[0] for entry in vocab]
									return cls(
									    vocab_list=__UpperCAmelCase      ,do_lower_case=__UpperCAmelCase      ,cls_token_id=__UpperCAmelCase      ,sep_token_id=__UpperCAmelCase      ,pad_token_id=__UpperCAmelCase      ,**__UpperCAmelCase      ,)







				@classmethod
				def   lowercase_		(    cls    :Any      ,__UpperCAmelCase    :Union[str, os.PathLike]      ,*__UpperCAmelCase    :Optional[int]      ,**__UpperCAmelCase    :int  )      ->					Union[str, Any]:




									"""simple docstring"""



									try:
														lowerCamelCase__		:   List[Any]  				=					BertTokenizer.from_pretrained(__UpperCAmelCase      ,*__UpperCAmelCase      ,**__UpperCAmelCase  )
									except:  # noqa: E722
														from .tokenization_bert_fast import BertTokenizerFast

														lowerCamelCase__		:   Tuple  				=					BertTokenizerFast.from_pretrained(__UpperCAmelCase      ,*__UpperCAmelCase      ,**__UpperCAmelCase  )
									return cls.from_tokenizer(__UpperCAmelCase      ,**__UpperCAmelCase  )







				def   lowercase_		(    self    :str      ,__UpperCAmelCase    :Optional[int]  )      ->					int:




									"""simple docstring"""



									if self.do_lower_case:
														lowerCamelCase__		:   List[str]  				=					case_fold_utfa(__UpperCAmelCase  )
									lowerCamelCase__		:   List[str]  				=					self.tf_tokenizer.tokenize(__UpperCAmelCase  )
									return tokens.merge_dims(1      ,-1  )







				def   lowercase_		(    self    :Tuple      ,__UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :Union[str, Any]=None      ,__UpperCAmelCase    :List[str]=None      ,__UpperCAmelCase    :Tuple=None      ,__UpperCAmelCase    :Union[str, Any]=None      ,__UpperCAmelCase    :str=None      ,__UpperCAmelCase    :Dict=None      ,__UpperCAmelCase    :List[str]=None      ,)      ->					List[str]:




									"""simple docstring"""



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

									lowerCamelCase__				,						lowerCamelCase__		:   Tuple  				=					pad_model_inputs(__UpperCAmelCase      ,max_seq_length=__UpperCAmelCase      ,pad_value=self.pad_token_id  )
									lowerCamelCase__		:   Any  				=					{'''input_ids''': input_ids}
									if return_attention_mask:
														lowerCamelCase__		:   Optional[int]  				=					attention_mask
									if return_token_type_ids:
														lowerCamelCase__				,						lowerCamelCase__		:   int  				=					pad_model_inputs(
														    __UpperCAmelCase      ,max_seq_length=__UpperCAmelCase      ,pad_value=self.pad_token_id  )
														lowerCamelCase__		:   int  				=					token_type_ids
									return output







				def   lowercase_		(    self    :Any  )      ->					str:




									"""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,
									}







"""simple docstring"""




def 				__a       (			_lowercase							, _lowercase						):







					"""simple docstring"""





					if not root or root not in binary_tree_mirror_dictionary:
										return
					lowerCamelCase__				,						lowerCamelCase__		:   Tuple  				=					binary_tree_mirror_dictionary[root][:2]
					lowerCamelCase__		:   Optional[Any]  				=					[right_child, left_child]
					binary_tree_mirror_dict(_lowercase							, _lowercase						)
					binary_tree_mirror_dict(_lowercase							, _lowercase						)



def 				__a       (			_lowercase							, _lowercase = 1						):







					"""simple docstring"""





					if not binary_tree:
										raise ValueError('''binary tree cannot be empty'''						)
					if root not in binary_tree:
										lowerCamelCase__		:   Dict  				=					f"""root {root} is not present in the binary_tree"""
										raise ValueError(_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					dict(_lowercase						)
					binary_tree_mirror_dict(_lowercase							, _lowercase						)
					return binary_tree_mirror_dictionary


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






