


from __future__ import annotations

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


class 	a__						(    lowerCAmelCase_     ):

       @abstractmethod
       def __lt__( self   :  T   ,				lowerCamelCase_   :  T     ):
            pass

       @abstractmethod
       def __gt__( self   :  T   ,				lowerCamelCase_   :  T     ):
            pass

       @abstractmethod
       def __eq__( self   :  T   ,				lowerCamelCase_   :  object     ):
            pass

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


class 	a__						(    Generic[T]     ):

       def __init__( self   :  Tuple     ):
            a_     :  list[T]				      =     []
            a_     :  int				      =     0

       def __repr__( self   :  Optional[Any]     ):
            return str(self.h     )

       def     UpperCAmelCase( self   :  Dict   ,				lowerCamelCase_   :  int     ):
            if child_idx > 0:
                 return (child_idx - 1) // 2
            return None

       def     UpperCAmelCase( self   :  Any   ,				lowerCamelCase_   :  int     ):
            a_     :  Tuple				      =     2 * parent_idx + 1
            if left_child_index < self.heap_size:
                 return left_child_index
            return None

       def     UpperCAmelCase( self   :  List[Any]   ,				lowerCamelCase_   :  int     ):
            a_     :  List[Any]				      =     2 * parent_idx + 2
            if right_child_index < self.heap_size:
                 return right_child_index
            return None

       def     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  int     ):
            if index < self.heap_size:
                 a_     :  int				      =     index
                 a_     :  Union[str, Any]				      =     self.left_child_idx(lowerCamelCase_     )
                 a_     :  Optional[int]				      =     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_     :  str				      =     left_child
                 if right_child is not None and self.h[right_child] > self.h[violation]:
                      a_     :  List[Any]				      =     right_child
                 # if violation indeed exists
                 if violation != index:
                      # swap to fix the violation
                      a_      ,      a_     :  int				      =     self.h[index], self.h[violation]
                      # fix the subsequent violation recursively if any
                      self.max_heapify(lowerCamelCase_     )

       def     UpperCAmelCase( self   :  List[str]   ,				lowerCamelCase_   :  Iterable[T]     ):
            a_     :  Dict				      =     list(lowerCamelCase_     )
            a_     :  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(lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Tuple     ):
            if self.heap_size >= 2:
                 a_     :  int				      =     self.h[0]
                 a_     :  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     UpperCAmelCase( self   :  List[str]   ,				lowerCamelCase_   :  T     ):
            self.h.append(lowerCamelCase_     )
            a_     :  Optional[int]				      =     (self.heap_size - 1) // 2
            self.heap_size += 1
            while idx >= 0:
                 self.max_heapify(lowerCamelCase_     )
                 a_     :  Union[str, Any]				      =     (idx - 1) // 2

       def     UpperCAmelCase( self   :  int     ):
            a_     :  Union[str, Any]				      =     self.heap_size
            for j in range(size - 1   ,				0   ,				-1     ):
                 a_      ,      a_     :  str				      =     self.h[j], self.h[0]
                 self.heap_size -= 1
                 self.max_heapify(0     )
            a_     :  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}''')

              __lowerCamelCase   	=  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''')







from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.mpnet.modeling_tf_mpnet import (
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetModel,
  )







class  lowerCamelCase__  :





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase=1_3						,	UpperCAmelCase=7						,	UpperCAmelCase=True						,	UpperCAmelCase=True						,	UpperCAmelCase=False						,	UpperCAmelCase=True						,	UpperCAmelCase=9_9						,	UpperCAmelCase=6_4						,	UpperCAmelCase=2						,	UpperCAmelCase=4						,	UpperCAmelCase=6_4						,	UpperCAmelCase="gelu"						,	UpperCAmelCase=0.1						,	UpperCAmelCase=0.1						,	UpperCAmelCase=5_1_2						,	UpperCAmelCase=1_6						,	UpperCAmelCase=2						,	UpperCAmelCase=0.02						,	UpperCAmelCase=3						,	UpperCAmelCase=4						,	UpperCAmelCase=None						,	)	->  Optional[Any]:
      _lowercase				 =parent
      _lowercase				 =batch_size
      _lowercase				 =seq_length
      _lowercase				 =is_training
      _lowercase				 =use_input_mask
      _lowercase				 =use_token_type_ids
      _lowercase				 =use_labels
      _lowercase				 =vocab_size
      _lowercase				 =hidden_size
      _lowercase				 =num_hidden_layers
      _lowercase				 =num_attention_heads
      _lowercase				 =intermediate_size
      _lowercase				 =hidden_act
      _lowercase				 =hidden_dropout_prob
      _lowercase				 =attention_probs_dropout_prob
      _lowercase				 =max_position_embeddings
      _lowercase				 =type_vocab_size
      _lowercase				 =type_sequence_label_size
      _lowercase				 =initializer_range
      _lowercase				 =num_labels
      _lowercase				 =num_choices
      _lowercase				 =scope





     def 	__A   (self			)	->  Union[str, Any]:
      _lowercase				 =ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size			)

      _lowercase				 =None
      if self.use_input_mask:
       _lowercase				 =random_attention_mask([self.batch_size, self.seq_length]			)

      _lowercase				 =None
      _lowercase				 =None
      _lowercase				 =None
      if self.use_labels:
       _lowercase				 =ids_tensor([self.batch_size]						,	self.type_sequence_label_size			)
       _lowercase				 =ids_tensor([self.batch_size, self.seq_length]						,	self.num_labels			)
       _lowercase				 =ids_tensor([self.batch_size]						,	self.num_choices			)

      _lowercase				 =MPNetConfig(
          vocab_size=self.vocab_size						,	hidden_size=self.hidden_size						,	num_hidden_layers=self.num_hidden_layers						,	num_attention_heads=self.num_attention_heads						,	intermediate_size=self.intermediate_size						,	hidden_act=self.hidden_act						,	hidden_dropout_prob=self.hidden_dropout_prob						,	attention_probs_dropout_prob=self.attention_probs_dropout_prob						,	max_position_embeddings=self.max_position_embeddings						,	initializer_range=self.initializer_range						,	)
      return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Tuple:
      _lowercase				 =TFMPNetModel(config=UpperCAmelCase			)
      _lowercase				 ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      _lowercase				 =model(UpperCAmelCase			)
      _lowercase				 =[input_ids, input_mask]
      _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  int:
      _lowercase				 =TFMPNetForMaskedLM(config=UpperCAmelCase			)
      _lowercase				 ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Union[str, Any]:
      _lowercase				 =TFMPNetForQuestionAnswering(config=UpperCAmelCase			)
      _lowercase				 ={
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
      }
      _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(result.start_logits.shape						,	(self.batch_size, self.seq_length)			)
      self.parent.assertEqual(result.end_logits.shape						,	(self.batch_size, self.seq_length)			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Dict:
      _lowercase				 =self.num_labels
      _lowercase				 =TFMPNetForSequenceClassification(UpperCAmelCase			)
      _lowercase				 ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  List[Any]:
      _lowercase				 =self.num_choices
      _lowercase				 =TFMPNetForMultipleChoice(UpperCAmelCase			)
      _lowercase				 =tf.tile(tf.expand_dims(UpperCAmelCase						,	1			)						,	(1, self.num_choices, 1)			)
      _lowercase				 =tf.tile(tf.expand_dims(UpperCAmelCase						,	1			)						,	(1, self.num_choices, 1)			)
      _lowercase				 ={
          '''input_ids''': multiple_choice_inputs_ids,
          '''attention_mask''': multiple_choice_input_mask,
      }
      _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Dict:
      _lowercase				 =self.num_labels
      _lowercase				 =TFMPNetForTokenClassification(UpperCAmelCase			)
      _lowercase				 ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)			)





     def 	__A   (self			)	->  List[Any]:
      _lowercase				 =self.prepare_config_and_inputs()
      ((_lowercase) ,       (_lowercase) ,       (_lowercase) ,       (_lowercase) ,       (_lowercase) ,       (_lowercase))				 =config_and_inputs
      _lowercase				 ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict







@require_tf
class  lowerCamelCase__  (			lowerCAmelCase  ,				lowerCAmelCase  ,				unittest.TestCase):
     SCREAMING_SNAKE_CASE__      =     (
         (
             TFMPNetForMaskedLM,
             TFMPNetForMultipleChoice,
             TFMPNetForQuestionAnswering,
             TFMPNetForSequenceClassification,
             TFMPNetForTokenClassification,
             TFMPNetModel,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE__      =     (
         {
             '''feature-extraction''': TFMPNetModel,
             '''fill-mask''': TFMPNetForMaskedLM,
             '''question-answering''': TFMPNetForQuestionAnswering,
             '''text-classification''': TFMPNetForSequenceClassification,
             '''token-classification''': TFMPNetForTokenClassification,
             '''zero-shot''': TFMPNetForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     SCREAMING_SNAKE_CASE__      =     False
     SCREAMING_SNAKE_CASE__      =     False





     def 	__A   (self			)	->  Union[str, Any]:
      _lowercase				 =TFMPNetModelTester(self			)
      _lowercase				 =ConfigTester(self						,	config_class=UpperCAmelCase						,	hidden_size=3_7			)





     def 	__A   (self			)	->  Optional[Any]:
      self.config_tester.run_common_tests()





     def 	__A   (self			)	->  Tuple:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_model(*UpperCAmelCase			)





     def 	__A   (self			)	->  Tuple:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_masked_lm(*UpperCAmelCase			)





     def 	__A   (self			)	->  str:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_question_answering(*UpperCAmelCase			)





     def 	__A   (self			)	->  List[str]:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_sequence_classification(*UpperCAmelCase			)





     def 	__A   (self			)	->  Any:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_multiple_choice(*UpperCAmelCase			)





     def 	__A   (self			)	->  List[str]:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_token_classification(*UpperCAmelCase			)





     @slow
     def 	__A   (self			)	->  Tuple:
      for model_name in ["microsoft/mpnet-base"]:
       _lowercase				 =TFMPNetModel.from_pretrained(UpperCAmelCase			)
       self.assertIsNotNone(UpperCAmelCase			)







@require_tf
class  lowerCamelCase__  (			unittest.TestCase):





     @slow
     def 	__A   (self			)	->  Dict:
      _lowercase				 =TFMPNetModel.from_pretrained('''microsoft/mpnet-base'''			)
      _lowercase				 =tf.constant([[0, 1, 2, 3, 4, 5]]			)
      _lowercase				 =model(UpperCAmelCase			)[0]

      _lowercase				 =[1, 6, 7_6_8]
      self.assertEqual(output.shape						,	UpperCAmelCase			)

      _lowercase				 =tf.constant(
          [
              [
                  [-0.106_7172, 0.0821_6473, 0.002_4543],
                  [-0.0346_5879, 0.835_4118, -0.0325_2288],
                  [-0.0656_9476, -0.1242_4111, -0.049_4436],
              ]
          ]			)
      tf.debugging.assert_near(output[:, :3, :3]						,	UpperCAmelCase						,	atol=1e-4			)

