


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 			lowerCAmelCase__(     tf.keras.layers.Layer    ):






    '''simple docstring'''





    def __init__(						self					: str							, __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					: Optional[Any]							, ):





       '''simple docstring'''
       super().__init__()
       if use_fast_bert_tokenizer:
          UpperCAmelCase_				:   str							= FastBertTokenizer(
              __snake_case							, token_out_type=tf.intaa							, lower_case_nfd_strip_accents=__snake_case							, **__snake_case						)
       else:
          UpperCAmelCase_				:   Dict							= 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_				:   Any							= vocab_list
       UpperCAmelCase_				:   str							= do_lower_case
       UpperCAmelCase_				:   Dict							= cls_token_id or vocab_list.index('''[CLS]'''						)
       UpperCAmelCase_				:   str							= sep_token_id or vocab_list.index('''[SEP]'''						)
       UpperCAmelCase_				:   str							= pad_token_id or vocab_list.index('''[PAD]'''						)
       UpperCAmelCase_				:   Any							= ShrinkLongestTrimmer(max_length - 3							, axis=1						)  # Allow room for special tokens
       UpperCAmelCase_				:   Any							= max_length
       UpperCAmelCase_				:   str							= padding
       UpperCAmelCase_				:   Tuple							= truncation
       UpperCAmelCase_				:   Optional[int]							= pad_to_multiple_of
       UpperCAmelCase_				:   Any							= return_token_type_ids
       UpperCAmelCase_				:   Dict							= return_attention_mask





    @classmethod
    def        _lowerCamelCase			(						cls					: str							, __snake_case					: "PreTrainedTokenizerBase"							, **__snake_case					: List[str]						):  # noqa: F821





       '''simple docstring'''
       UpperCAmelCase_				:   List[Any]							= kwargs.pop('''do_lower_case'''							, __snake_case						)
       UpperCAmelCase_				:   Optional[int]							= tokenizer.do_lower_case if do_lower_case is None else do_lower_case
       UpperCAmelCase_				:   List[str]							= kwargs.pop('''cls_token_id'''							, __snake_case						)
       UpperCAmelCase_				:   List[str]							= tokenizer.cls_token_id if cls_token_id is None else cls_token_id
       UpperCAmelCase_				:   Union[str, Any]							= kwargs.pop('''sep_token_id'''							, __snake_case						)
       UpperCAmelCase_				:   Union[str, Any]							= tokenizer.sep_token_id if sep_token_id is None else sep_token_id
       UpperCAmelCase_				:   List[str]							= kwargs.pop('''pad_token_id'''							, __snake_case						)
       UpperCAmelCase_				:   List[str]							= tokenizer.pad_token_id if pad_token_id is None else pad_token_id

       UpperCAmelCase_				:   List[str]							= tokenizer.get_vocab()
       UpperCAmelCase_				:   Union[str, Any]							= sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]							, key=lambda __snake_case						: x[1]						)
       UpperCAmelCase_				:   Dict							= [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        _lowerCamelCase			(						cls					: Tuple							, __snake_case					: Union[str, os.PathLike]							, *__snake_case					: Union[str, Any]							, **__snake_case					: int						):





       '''simple docstring'''
       try:
          UpperCAmelCase_				:   str							= BertTokenizer.from_pretrained(__snake_case							, *__snake_case							, **__snake_case						)
       except:  # noqa: E722
          from .tokenization_bert_fast import BertTokenizerFast

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





    def        _lowerCamelCase			(						self					: int							, __snake_case					: Optional[Any]						):





       '''simple docstring'''
       if self.do_lower_case:
          UpperCAmelCase_				:   List[Any]							= case_fold_utfa(__snake_case						)
       UpperCAmelCase_				:   Tuple							= self.tf_tokenizer.tokenize(__snake_case						)
       return tokens.merge_dims(1							, -1						)





    def        _lowerCamelCase			(						self					: int							, __snake_case					: int							, __snake_case					: int=None							, __snake_case					: Optional[Any]=None							, __snake_case					: Union[str, Any]=None							, __snake_case					: Union[str, Any]=None							, __snake_case					: Dict=None							, __snake_case					: Dict=None							, __snake_case					: List[str]=None							, ):





       '''simple docstring'''
       if padding is None:
          UpperCAmelCase_				:   List[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_				:   int							= self.truncation
       if pad_to_multiple_of is None:
          UpperCAmelCase_				:   int							= self.pad_to_multiple_of
       if return_token_type_ids is None:
          UpperCAmelCase_				:   Dict							= self.return_token_type_ids
       if return_attention_mask is None:
          UpperCAmelCase_				:   Tuple							= 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_				:   Tuple							= 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_				:   Dict							= text[:, 0], text[:, 1]
       UpperCAmelCase_				:   Optional[int]							= self.unpaired_tokenize(__snake_case						)
       if text_pair is None:  # Unpaired text
          if truncation:
             UpperCAmelCase_				:   Any							= text[:, : max_length - 2]  # Allow room for special tokens
          UpperCAmelCase_		,   UpperCAmelCase_				:   Any							= combine_segments(
              (text,)							, start_of_sequence_id=self.cls_token_id							, end_of_segment_id=self.sep_token_id						)
       else:  # Paired text
          UpperCAmelCase_				:   str							= self.unpaired_tokenize(__snake_case						)
          if truncation:
             UpperCAmelCase_		,   UpperCAmelCase_				:   Any							= self.paired_trimmer.trim([text, text_pair]						)
          UpperCAmelCase_		,   UpperCAmelCase_				:   str							= 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_				:   Optional[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_				:   List[Any]							= pad_to_multiple_of * (-tf.math.floordiv(-pad_length							, __snake_case						))
       else:
          UpperCAmelCase_				:   Dict							= max_length

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





    def        _lowerCamelCase			(						self					: Union[str, 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,
       }



from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar





class 			lowerCAmelCase__(     snake_case__    ):






    '''simple docstring'''





    @abstractmethod
    def __lt__(						self					: T							, __snake_case					: T						):





       '''simple docstring'''
       pass





    @abstractmethod
    def __gt__(						self					: T							, __snake_case					: T						):





       '''simple docstring'''
       pass





    @abstractmethod
    def __eq__(						self					: T							, __snake_case					: object						):





       '''simple docstring'''
       pass





__UpperCamelCase					:	Dict          =				TypeVar('T', bound=Comparable)





class 			lowerCAmelCase__(     Generic[T]    ):






    '''simple docstring'''





    def __init__(						self					: Tuple						):





       '''simple docstring'''
       UpperCAmelCase_				:   list[T]							= []
       UpperCAmelCase_				:   int							= 0





    def __repr__(						self					: Any						):





       '''simple docstring'''
       return str(self.h						)





    def        _lowerCamelCase			(						self					: str							, __snake_case					: int						):





       '''simple docstring'''
       if child_idx > 0:
          return (child_idx - 1) // 2
       return None





    def        _lowerCamelCase			(						self					: str							, __snake_case					: int						):





       '''simple docstring'''
       UpperCAmelCase_				:   Dict							= 2 * parent_idx + 1
       if left_child_index < self.heap_size:
          return left_child_index
       return None





    def        _lowerCamelCase			(						self					: int							, __snake_case					: int						):





       '''simple docstring'''
       UpperCAmelCase_				:   Union[str, Any]							= 2 * parent_idx + 2
       if right_child_index < self.heap_size:
          return right_child_index
       return None





    def        _lowerCamelCase			(						self					: Union[str, Any]							, __snake_case					: int						):





       '''simple docstring'''
       if index < self.heap_size:
          UpperCAmelCase_				:   int							= index
          UpperCAmelCase_				:   List[str]							= self.left_child_idx(__snake_case						)
          UpperCAmelCase_				:   List[str]							= self.right_child_idx(__snake_case						)
          # check which child is larger than its parent
          if left_child is not None and self.h[left_child] > self.h[violation]:
             UpperCAmelCase_				:   List[str]							= left_child
          if right_child is not None and self.h[right_child] > self.h[violation]:
             UpperCAmelCase_				:   List[str]							= right_child
          # if violation indeed exists
          if violation != index:
             # swap to fix the violation
             UpperCAmelCase_		,   UpperCAmelCase_				:   List[Any]							= self.h[index], self.h[violation]
             # fix the subsequent violation recursively if any
             self.max_heapify(__snake_case						)





    def        _lowerCamelCase			(						self					: Optional[Any]							, __snake_case					: Iterable[T]						):





       '''simple docstring'''
       UpperCAmelCase_				:   Union[str, Any]							= list(__snake_case						)
       UpperCAmelCase_				:   int							= len(self.h						)
       if self.heap_size > 1:
          # max_heapify from right to left but exclude leaves (last level)
          for i in range(self.heap_size // 2 - 1							, -1							, -1						):
             self.max_heapify(__snake_case						)





    def        _lowerCamelCase			(						self					: Tuple						):





       '''simple docstring'''
       if self.heap_size >= 2:
          UpperCAmelCase_				:   List[str]							= self.h[0]
          UpperCAmelCase_				:   Dict							= self.h.pop(-1						)
          self.heap_size -= 1
          self.max_heapify(0						)
          return me
       elif self.heap_size == 1:
          self.heap_size -= 1
          return self.h.pop(-1						)
       else:
          raise Exception('''Empty heap'''						)





    def        _lowerCamelCase			(						self					: List[str]							, __snake_case					: T						):





       '''simple docstring'''
       self.h.append(__snake_case						)
       UpperCAmelCase_				:   Tuple							= (self.heap_size - 1) // 2
       self.heap_size += 1
       while idx >= 0:
          self.max_heapify(__snake_case						)
          UpperCAmelCase_				:   int							= (idx - 1) // 2





    def        _lowerCamelCase			(						self					: List[str]						):





       '''simple docstring'''
       UpperCAmelCase_				:   Tuple							= self.heap_size
       for j in range(size - 1							, 0							, -1						):
          UpperCAmelCase_		,   UpperCAmelCase_				:   Any							= self.h[j], self.h[0]
          self.heap_size -= 1
          self.max_heapify(0						)
       UpperCAmelCase_				:   Union[str, Any]							= size


if __name__ == "__main__":
       import doctest

       # run doc test
       doctest.testmod()

       # demo
       for unsorted in [
           [0],
           [2],
           [3, 5],
           [5, 3],
           [5, 5],
           [0, 0, 0, 0],
           [1, 1, 1, 1],
           [2, 2, 3, 5],
           [0, 2, 2, 3, 5],
           [2, 5, 3, 0, 2, 3, 0, 3],
           [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
           [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
           [-45, -2, -5],
       ]:
              print(F'unsorted array: {unsorted}')

              __UpperCamelCase					:	Heap[int]          =				Heap()
              heap.build_max_heap(unsorted)
              print(F'after build heap: {heap}')

              print(F'max value: {heap.extract_max()}')
              print(F'after max value removed: {heap}')

              heap.insert(100)
              print(F'after new value 100 inserted: {heap}')

              heap.heap_sort()
              print(F'heap-sorted array: {heap}\n')