






"""simple docstring"""


from __future__ import annotations

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


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

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

     '''simple docstring'''
     pass


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

     '''simple docstring'''
     pass






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

     '''simple docstring'''
     pass





lowercase_								=	TypeVar('T', bound=Comparable)


class    snake_case						(				Generic[T]		):


   '''simple docstring'''

   def __init__(      self					:	Union[str, Any]    ):

     '''simple docstring'''
     __A		     =					[]
     __A		     =					0


   def __repr__(      self					:	Dict    ):

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str],			_lowerCamelCase					:	int    ):

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


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

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	int    ):

     '''simple docstring'''
     __A		     =					2 * parent_idx + 2
     if right_child_index < self.heap_size:
       return right_child_index
     return None


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	int    ):

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Iterable[T]    ):

     '''simple docstring'''
     __A		     =					list(_lowerCamelCase    )
     __A		     =					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(_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     if self.heap_size >= 2:
       __A		     =					self.h[0]
       __A		     =					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 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any],			_lowerCamelCase					:	T    ):

     '''simple docstring'''
     self.h.append(_lowerCamelCase    )
     __A		     =					(self.heap_size - 1) // 2
     self.heap_size += 1
     while idx >= 0:
       self.max_heapify(_lowerCamelCase    )
       __A		     =					(idx - 1) // 2






   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     __A		     =					self.heap_size
     for j in range(size - 1,			0,			-1    ):
       __A     ,       __A		     =					self.h[j], self.h[0]
       self.heap_size -= 1
       self.max_heapify(0    )
     __A		     =					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}''')

              lowercase_								=	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''')












"""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					:	List[str],			_lowerCamelCase					:	List,			_lowerCamelCase					:	bool,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	str = "longest",			_lowerCamelCase					:	bool = True,			_lowerCamelCase					:	int = 5_12,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	bool = True,			_lowerCamelCase					:	bool = True,			_lowerCamelCase					:	bool = True,			**_lowerCamelCase					:	int,			):

     '''simple docstring'''
     super().__init__()
     if use_fast_bert_tokenizer:
       __A		     =					FastBertTokenizer(
           _lowerCamelCase,			token_out_type=tf.intaa,			lower_case_nfd_strip_accents=_lowerCamelCase,			**_lowerCamelCase    )
     else:
       __A		     =					tf.lookup.StaticVocabularyTable(
           tf.lookup.KeyValueTensorInitializer(
               keys=_lowerCamelCase,			key_dtype=tf.string,			values=tf.range(tf.size(_lowerCamelCase,			out_type=tf.intaa    ),			dtype=tf.intaa    ),			value_dtype=tf.intaa,			),			num_oov_buckets=1,			)
       __A		     =					BertTokenizerLayer(
           _lowerCamelCase,			token_out_type=tf.intaa,			lower_case=_lowerCamelCase,			**_lowerCamelCase    )

     __A		     =					vocab_list
     __A		     =					do_lower_case
     __A		     =					cls_token_id or vocab_list.index('''[CLS]'''    )
     __A		     =					sep_token_id or vocab_list.index('''[SEP]'''    )
     __A		     =					pad_token_id or vocab_list.index('''[PAD]'''    )
     __A		     =					ShrinkLongestTrimmer(max_length - 3,			axis=1    )  # Allow room for special tokens
     __A		     =					max_length
     __A		     =					padding
     __A		     =					truncation
     __A		     =					pad_to_multiple_of
     __A		     =					return_token_type_ids
     __A		     =					return_attention_mask


   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	int,			_lowerCamelCase					:	"PreTrainedTokenizerBase",			**_lowerCamelCase					:	List[Any]    ):  # noqa: F821

     '''simple docstring'''
     __A		     =					kwargs.pop('''do_lower_case''',			_lowerCamelCase    )
     __A		     =					tokenizer.do_lower_case if do_lower_case is None else do_lower_case
     __A		     =					kwargs.pop('''cls_token_id''',			_lowerCamelCase    )
     __A		     =					tokenizer.cls_token_id if cls_token_id is None else cls_token_id
     __A		     =					kwargs.pop('''sep_token_id''',			_lowerCamelCase    )
     __A		     =					tokenizer.sep_token_id if sep_token_id is None else sep_token_id
     __A		     =					kwargs.pop('''pad_token_id''',			_lowerCamelCase    )
     __A		     =					tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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


   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	Optional[Any],			_lowerCamelCase					:	Union[str, os.PathLike],			*_lowerCamelCase					:	Any,			**_lowerCamelCase					:	List[str]    ):

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

       __A		     =					BertTokenizerFast.from_pretrained(_lowerCamelCase,			*_lowerCamelCase,			**_lowerCamelCase    )
     return cls.from_tokenizer(_lowerCamelCase,			**_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any],			_lowerCamelCase					:	Tuple    ):

     '''simple docstring'''
     if self.do_lower_case:
       __A		     =					case_fold_utfa(_lowerCamelCase    )
     __A		     =					self.tf_tokenizer.tokenize(_lowerCamelCase    )
     return tokens.merge_dims(1,			-1    )


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

     '''simple docstring'''
     if padding is None:
       __A		     =					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:
       __A		     =					self.max_length
     if truncation is None:
       __A		     =					self.truncation
     if pad_to_multiple_of is None:
       __A		     =					self.pad_to_multiple_of
     if return_token_type_ids is None:
       __A		     =					self.return_token_type_ids
     if return_attention_mask is None:
       __A		     =					self.return_attention_mask
     if not isinstance(_lowerCamelCase,			tf.Tensor    ):
       __A		     =					tf.convert_to_tensor(_lowerCamelCase    )
     if text_pair is not None and not isinstance(_lowerCamelCase,			tf.Tensor    ):
       __A		     =					tf.convert_to_tensor(_lowerCamelCase    )
     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:
       __A     ,       __A		     =					text[:, 0], text[:, 1]
     __A		     =					self.unpaired_tokenize(_lowerCamelCase    )
     if text_pair is None:  # Unpaired text
       if truncation:
         __A		     =					text[:, : max_length - 2]  # Allow room for special tokens
       __A     ,       __A		     =					combine_segments(
           (text,),			start_of_sequence_id=self.cls_token_id,			end_of_segment_id=self.sep_token_id    )
     else:  # Paired text
       __A		     =					self.unpaired_tokenize(_lowerCamelCase    )
       if truncation:
         __A     ,       __A		     =					self.paired_trimmer.trim([text, text_pair]    )
       __A     ,       __A		     =					combine_segments(
           (text, text_pair),			start_of_sequence_id=self.cls_token_id,			end_of_segment_id=self.sep_token_id    )
     if padding == "longest":
       __A		     =					input_ids.bounding_shape(axis=1    )
       if pad_to_multiple_of is not None:
         # No ceiling division in tensorflow, so we negate floordiv instead
         __A		     =					pad_to_multiple_of * (-tf.math.floordiv(-pad_length,			_lowerCamelCase    ))
     else:
       __A		     =					max_length

     __A     ,       __A		     =					pad_model_inputs(_lowerCamelCase,			max_seq_length=_lowerCamelCase,			pad_value=self.pad_token_id    )
     __A		     =					{'''input_ids''': input_ids}
     if return_attention_mask:
       __A		     =					attention_mask
     if return_token_type_ids:
       __A     ,       __A		     =					pad_model_inputs(
           _lowerCamelCase,			max_seq_length=_lowerCamelCase,			pad_value=self.pad_token_id    )
       __A		     =					token_type_ids
     return output






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[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,
     }




