




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

				'''simple docstring'''




				def __init__(							self      :  Union[str, Any]					,							snake_case      :  List					,							snake_case      :  bool					,							snake_case      :  int = None					,							snake_case      :  int = None					,							snake_case      :  int = None					,							snake_case      :  str = "longest"					,							snake_case      :  bool = True					,							snake_case      :  int = 512					,							snake_case      :  int = None					,							snake_case      :  bool = True					,							snake_case      :  bool = True					,							snake_case      :  bool = True					,							**snake_case      :  List[str]					,							):
								super().__init__()
								if use_fast_bert_tokenizer:
												UpperCAmelCase_					:Union[str, Any]      			=      FastBertTokenizer(
												    snake_case					,							token_out_type=tf.intaa					,							lower_case_nfd_strip_accents=snake_case					,							**snake_case     )
								else:
												UpperCAmelCase_					: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					,							)
												UpperCAmelCase_					:List[Any]      			=      BertTokenizerLayer(
												    snake_case					,							token_out_type=tf.intaa					,							lower_case=snake_case					,							**snake_case     )

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





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

								UpperCAmelCase_					:Optional[Any]      			=      tokenizer.get_vocab()
								UpperCAmelCase_					:Tuple      			=      sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]					,							key=lambda snake_case     : x[1]     )
								UpperCAmelCase_					:int      			=      [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 				snake_case_ (							cls      :  Any					,							snake_case      :  Union[str, os.PathLike]					,							*snake_case      :  int					,							**snake_case      :  str     ):
								try:
												UpperCAmelCase_					:Optional[int]      			=      BertTokenizer.from_pretrained(snake_case					,							*snake_case					,							**snake_case     )
								except:  # noqa: E722
												from .tokenization_bert_fast import BertTokenizerFast

												UpperCAmelCase_					:List[Any]      			=      BertTokenizerFast.from_pretrained(snake_case					,							*snake_case					,							**snake_case     )
								return cls.from_tokenizer(snake_case					,							**snake_case     )





				def 				snake_case_ (							self      :  Dict					,							snake_case      :  Tuple     ):
								if self.do_lower_case:
												UpperCAmelCase_					:int      			=      case_fold_utfa(snake_case     )
								UpperCAmelCase_					:int      			=      self.tf_tokenizer.tokenize(snake_case     )
								return tokens.merge_dims(1					,							-1     )





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

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





				def 				snake_case_ (							self      :  List[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,
								}








"""simple docstring"""

# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint



class  _snake_case				:

				'''simple docstring'''




				def __init__(							self      :  str					,							snake_case      :  Optional[Any]					,							snake_case      :  str     ):
								UpperCAmelCase_					:Union[str, Any]      			=      key
								UpperCAmelCase_					:str      			=      freq





				def __str__(							self      :  Optional[Any]     ):
								return f'Node(key={self.key}, freq={self.freq})'



def 				a (						__snake_case     :				int,    __snake_case     :				Optional[int],    __snake_case     :				Tuple,    __snake_case     :				Any,    __snake_case     :				Dict,    __snake_case     :				List[str]  ):



				'''simple docstring'''





				if i > j or i < 0 or j > len(__snake_case  ) - 1:
								return

				UpperCAmelCase_					:List[str]      			=      root[i][j]
				if parent == -1:  # root does not have a parent
								print(f'{key[node]} is the root of the binary search tree.'  )
				elif is_left:
								print(f'{key[node]} is the left child of key {parent}.'  )
				else:
								print(f'{key[node]} is the right child of key {parent}.'  )

				print_binary_search_tree(__snake_case,    __snake_case,    __snake_case,    node - 1,    key[node],    __snake_case  )
				print_binary_search_tree(__snake_case,    __snake_case,    node + 1,    __snake_case,    key[node],    __snake_case  )



def 				a (						__snake_case     :				Optional[Any]  ):



				'''simple docstring'''





				nodes.sort(key=lambda __snake_case  : node.key  )

				UpperCAmelCase_					:List[str]      			=      len(__snake_case  )

				UpperCAmelCase_					:Any      			=      [nodes[i].key for i in range(__snake_case  )]
				UpperCAmelCase_					:int      			=      [nodes[i].freq for i in range(__snake_case  )]

				# This 2D array stores the overall tree cost (which's as minimized as possible);
				# for a single key, cost is equal to frequency of the key.
				UpperCAmelCase_					:Dict      			=      [[freqs[i] if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]
				# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
				# array
				UpperCAmelCase_					:Optional[Any]      			=      [[freqs[i] if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]
				# stores tree roots that will be used later for constructing binary search tree
				UpperCAmelCase_					:int      			=      [[i if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]

				for interval_length in range(2,    n + 1  ):
								for i in range(n - interval_length + 1  ):
												UpperCAmelCase_					:Union[str, Any]      			=      i + interval_length - 1

												UpperCAmelCase_					:Optional[int]      			=      sys.maxsize  # set the value to "infinity"
												UpperCAmelCase_					:List[Any]      			=      total[i][j - 1] + freqs[j]

												# Apply Knuth's optimization
												# Loop without optimization: for r in range(i, j + 1):
												for r in range(root[i][j - 1],    root[i + 1][j] + 1  ):  # r is a temporal root
																UpperCAmelCase_					:Any      			=      dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
																UpperCAmelCase_					:Optional[int]      			=      dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
																UpperCAmelCase_					:Union[str, Any]      			=      left + total[i][j] + right

																if dp[i][j] > cost:
																				UpperCAmelCase_					:Optional[Any]      			=      cost
																				UpperCAmelCase_					:Optional[int]      			=      r

				print('''Binary search tree nodes:'''  )
				for node in nodes:
								print(__snake_case  )

				print(f'\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.'  )
				print_binary_search_tree(__snake_case,    __snake_case,    0,    n - 1,    -1,    __snake_case  )



def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Union[str, Any]      			=      [Node(__snake_case,    randint(1,    50  )  ) for i in range(10,    0,    -1  )]
				find_optimal_binary_search_tree(__snake_case  )


if __name__ == "__main__":
						main()


