



class 		_lowercase		:

   def __init__(   self      :							Any				, __lowerCAmelCase      :							List[Any]				)							->					Dict:


    """simple docstring"""






    a							=		val
    # Number of nodes in left subtree
    a							=		0
    a							=		None
    a							=		None
    a							=		None




   def 		A				(   self      :							Any				, __lowerCAmelCase      :							List[Any]				)							->					str:


    """simple docstring"""






    assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

    if self.val < other.val:
     a							=		self.right
     a							=		None
     if self.right:
      a							=		other
     a							=		other
     a							=		self.left_tree_size * 2 + 1
     return self
    else:
     a							=		other.right
     a							=		None
     if other.right:
      a							=		self
     a							=		self
     a							=		other.left_tree_size * 2 + 1
     return other



class 		_lowercase		:

   def __init__(   self      :							str				, __lowerCAmelCase      :							str=None				, __lowerCAmelCase      :							Union[str, Any]=None				, __lowerCAmelCase      :							List[Any]=0				)							->					List[Any]:


    """simple docstring"""






    a							=		heap_size
    a							=		bottom_root
    a							=		min_node

   def 		A				(   self      :							int				, __lowerCAmelCase      :							Any				)							->					str:


    """simple docstring"""






    if other.size == 0:
     return None
    if self.size == 0:
     a							=		other.size
     a							=		other.bottom_root
     a							=		other.min_node
     return None
    # Update size
    a							=		self.size + other.size

    # Update min.node
    if self.min_node.val > other.min_node.val:
     a							=		other.min_node
    # Merge

    # Order roots by left_subtree_size
    a							=		[]
    a		,	a							=		self.bottom_root, other.bottom_root
    while i or j:
     if i and ((not j) or i.left_tree_size < j.left_tree_size):
      combined_roots_list.append((i, True)				)
      a							=		i.parent
     else:
      combined_roots_list.append((j, False)				)
      a							=		j.parent
        # Insert links between them
    for i in range(len(__lowerCAmelCase				) - 1				):
     if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
      a							=		combined_roots_list[i + 1][0]
      a							=		combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
    a							=		combined_roots_list[0][0]
    while i.parent:
     if (
         (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
     ) or (
         i.left_tree_size == i.parent.left_tree_size
         and i.left_tree_size != i.parent.parent.left_tree_size
     ):
      # Neighbouring Nodes
      a							=		i.left
      a							=		i.parent.parent

      # Merging trees
      a							=		i.merge_trees(i.parent				)

      # Updating links
      a							=		previous_node
      a							=		next_node
      if previous_node:
       a							=		i
      if next_node:
       a							=		i
     else:
      a							=		i.parent
        # Updating self.bottom_root
    while i.left:
     a							=		i.left
    a							=		i

    # Update other
    a							=		self.size
    a							=		self.bottom_root
    a							=		self.min_node

    # Return the merged heap
    return self

   def 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							List[str]				)							->					Optional[int]:


    """simple docstring"""






    if self.size == 0:
     a							=		Node(__lowerCAmelCase				)
     a							=		1
     a							=		self.bottom_root
    else:
     # Create new node
     a							=		Node(__lowerCAmelCase				)

     # Update size
     self.size += 1

     # update min_node
     if val < self.min_node.val:
      a							=		new_node
     # Put new_node as a bottom_root in heap
     a							=		new_node
     a							=		self.bottom_root
     a							=		new_node

     # Consecutively merge roots with same left_tree_size
     while (
         self.bottom_root.parent
         and self.bottom_root.left_tree_size
         == self.bottom_root.parent.left_tree_size
     ):
      # Next node
      a							=		self.bottom_root.parent.parent

      # Merge
      a							=		self.bottom_root.merge_trees(self.bottom_root.parent				)

      # Update Links
      a							=		next_node
      a							=		None
      if next_node:
       a							=		self.bottom_root

   def 		A				(   self      :							List[str]				)							->					str:


    """simple docstring"""






    return self.min_node.val

   def 		A				(   self      :							List[Any]				)							->					List[Any]:


    """simple docstring"""






    return self.size == 0

   def 		A				(   self      :							List[Any]				)							->					Any:


    """simple docstring"""






    a							=		self.min_node.val

    # Last element in heap corner case
    if self.size == 1:
     # Update size
     a							=		0

     # Update bottom root
     a							=		None

     # Update min_node
     a							=		None

     return min_value
    # No right subtree corner case
    # The structure of the tree implies that this should be the bottom root
    # and there is at least one other root
    if self.min_node.right is None:
     # Update size
     self.size -= 1

     # Update bottom root
     a							=		self.bottom_root.parent
     a							=		None

     # Update min_node
     a							=		self.bottom_root
     a							=		self.bottom_root.parent
     while i:
      if i.val < self.min_node.val:
       a							=		i
      a							=		i.parent
     return min_value
    # General case
    # Find the BinomialHeap of the right subtree of min_node
    a							=		self.min_node.right
    a							=		None
    a							=		bottom_of_new
    a							=		1

    # Size, min_node and bottom_root
    while bottom_of_new.left:
     a							=		size_of_new * 2 + 1
     a							=		bottom_of_new.left
     if bottom_of_new.val < min_of_new.val:
      a							=		bottom_of_new
        # Corner case of single root on top left path
    if (not self.min_node.left) and (not self.min_node.parent):
     a							=		size_of_new
     a							=		bottom_of_new
     a							=		min_of_new
     # print("Single root, multiple nodes case")
     return min_value
    # Remaining cases
    # Construct heap of right subtree
    a							=		BinomialHeap(
        bottom_root=__lowerCAmelCase				, min_node=__lowerCAmelCase				, heap_size=__lowerCAmelCase				)

    # Update size
    a							=		self.size - 1 - size_of_new

    # Neighbour nodes
    a							=		self.min_node.left
    a							=		self.min_node.parent

    # Initialize new bottom_root and min_node
    a							=		previous_node or next_node
    a							=		next_node

    # Update links of previous_node and search below for new min_node and
    # bottom_root
    if previous_node:
     a							=		next_node

     # Update bottom_root and search for min_node below
     a							=		previous_node
     a							=		previous_node
     while self.bottom_root.left:
      a							=		self.bottom_root.left
      if self.bottom_root.val < self.min_node.val:
       a							=		self.bottom_root
    if next_node:
     a							=		previous_node

     # Search for new min_node above min_node
     a							=		next_node
     while i:
      if i.val < self.min_node.val:
       a							=		i
      a							=		i.parent
        # Merge heaps
    self.merge_heaps(__lowerCAmelCase				)

    return min_value

   def 		A				(   self      :							Optional[int]				)							->					Optional[Any]:


    """simple docstring"""






    a							=		self.bottom_root
    while top_root.parent:
     a							=		top_root.parent
    # preorder
    a							=		[]
    self.__traversal(__lowerCAmelCase				, __lowerCAmelCase				)
    return heap_pre_order

   def 		A				(   self      :							List[Any]				, __lowerCAmelCase      :							Any				, __lowerCAmelCase      :							Any				, __lowerCAmelCase      :							List[str]=0				)							->					Tuple:


    """simple docstring"""






    if curr_node:
     preorder.append((curr_node.val, level)				)
     self.__traversal(curr_node.left				, __lowerCAmelCase				, level + 1				)
     self.__traversal(curr_node.right				, __lowerCAmelCase				, level + 1				)
    else:
     preorder.append(("#", level)				)




   def __str__(   self      :							Optional[int]				)							->					int:


    """simple docstring"""






    if self.is_empty():
     return ""
    a							=		self.pre_order()

    return "\n".join(("-" * level + str(__lowerCAmelCase				)) for value, level in preorder_heap				)


# Unit Tests
if __name__ == "__main__":
     import doctest

     doctest.testmod()





"""simple docstring"""





import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     ErnieMForInformationExtraction,
     ErnieMForMultipleChoice,
     ErnieMForQuestionAnswering,
     ErnieMForSequenceClassification,
     ErnieMForTokenClassification,
     ErnieMModel,
 )
 from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST



class 		snake_case_:






     def __init__(						self	:       Optional[int]       ,					UpperCamelCase_	:       Dict       ,					UpperCamelCase_	:       Optional[int]=1_3       ,					UpperCamelCase_	:       Dict=7       ,					UpperCamelCase_	:       Dict=True       ,					UpperCamelCase_	:       str=True       ,					UpperCamelCase_	:       int=True       ,					UpperCamelCase_	:       Optional[Any]=9_9       ,					UpperCamelCase_	:       Optional[int]=3_2       ,					UpperCamelCase_	:       Any=5       ,					UpperCamelCase_	:       Any=4       ,					UpperCamelCase_	:       Tuple=3_7       ,					UpperCamelCase_	:       Tuple="gelu"       ,					UpperCamelCase_	:       Union[str, Any]=0.1       ,					UpperCamelCase_	:       List[Any]=0.1       ,					UpperCamelCase_	:       int=5_1_2       ,					UpperCamelCase_	:       int=1_6       ,					UpperCamelCase_	:       List[Any]=2       ,					UpperCamelCase_	:       str=0.02       ,					UpperCamelCase_	:       int=3       ,					UpperCamelCase_	:       str=4       ,					UpperCamelCase_	:       Union[str, Any]=None       ,					):
           lowerCAmelCase       :						List[str]							= parent
           lowerCAmelCase       :						str							= batch_size
           lowerCAmelCase       :						Dict							= seq_length
           lowerCAmelCase       :						Dict							= is_training
           lowerCAmelCase       :						Tuple							= use_input_mask
           lowerCAmelCase       :						str							= use_labels
           lowerCAmelCase       :						Optional[Any]							= vocab_size
           lowerCAmelCase       :						Optional[Any]							= hidden_size
           lowerCAmelCase       :						Optional[int]							= num_hidden_layers
           lowerCAmelCase       :						List[Any]							= num_attention_heads
           lowerCAmelCase       :						Tuple							= intermediate_size
           lowerCAmelCase       :						Tuple							= hidden_act
           lowerCAmelCase       :						Optional[int]							= hidden_dropout_prob
           lowerCAmelCase       :						str							= attention_probs_dropout_prob
           lowerCAmelCase       :						str							= max_position_embeddings
           lowerCAmelCase       :						List[str]							= type_vocab_size
           lowerCAmelCase       :						str							= type_sequence_label_size
           lowerCAmelCase       :						int							= initializer_range
           lowerCAmelCase       :						Optional[Any]							= num_labels
           lowerCAmelCase       :						Any							= num_choices
           lowerCAmelCase       :						str							= scope






     def   lowerCamelCase__  (						self	:       Dict					):
           lowerCAmelCase       :						str							= ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size					)

           lowerCAmelCase       :						List[Any]							= None
           if self.use_input_mask:
                 lowerCAmelCase       :						str							= random_attention_mask([self.batch_size, self.seq_length]					)

           lowerCAmelCase       :						List[Any]							= None
           lowerCAmelCase       :						str							= None
           lowerCAmelCase       :						str							= None
           if self.use_labels:
                 lowerCAmelCase       :						Tuple							= ids_tensor([self.batch_size]       ,					self.type_sequence_label_size					)
                 lowerCAmelCase       :						Dict							= ids_tensor([self.batch_size, self.seq_length]       ,					self.num_labels					)
                 lowerCAmelCase       :						List[Any]							= ids_tensor([self.batch_size]       ,					self.num_choices					)

           lowerCAmelCase       :						Any							= self.get_config()

           return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






     def   lowerCamelCase__  (						self	:       str					):
           lowerCAmelCase       :						Tuple							= ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size					)

           lowerCAmelCase       :						Optional[Any]							= None
           if self.use_input_mask:
                 lowerCAmelCase       :						Union[str, Any]							= random_attention_mask([self.batch_size, self.seq_length]					)
           lowerCAmelCase       :						Dict							= self.get_config()

           return config, input_ids, input_mask






     def   lowerCamelCase__  (						self	:       Union[str, Any]					):
           return ErnieMConfig(
               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       ,					type_vocab_size=self.type_vocab_size       ,					initializer_range=self.initializer_range       ,					)






     def   lowerCamelCase__  (						self	:       Union[str, Any]       ,					UpperCamelCase_	:       Tuple       ,					UpperCamelCase_	:       Union[str, Any]       ,					UpperCamelCase_	:       Optional[Any]       ,					UpperCamelCase_	:       Dict       ,					UpperCamelCase_	:       Union[str, Any]       ,					UpperCamelCase_	:       Optional[Any]					):
           lowerCAmelCase       :						List[str]							= ErnieMModel(config=UpperCamelCase_					)
           model.to(UpperCamelCase_					)
           model.eval()
           lowerCAmelCase       :						List[Any]							= model(UpperCamelCase_       ,					return_dict=UpperCamelCase_					)
           self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, self.seq_length, self.hidden_size)					)






     def   lowerCamelCase__  (						self	:       Union[str, Any]       ,					UpperCamelCase_	:       Union[str, Any]       ,					UpperCamelCase_	:       Optional[Any]       ,					UpperCamelCase_	:       int       ,					UpperCamelCase_	:       List[Any]       ,					UpperCamelCase_	:       List[Any]       ,					UpperCamelCase_	:       Optional[Any]					):
           lowerCAmelCase       :						Optional[int]							= ErnieMForQuestionAnswering(config=UpperCamelCase_					)
           model.to(UpperCamelCase_					)
           model.eval()
           lowerCAmelCase       :						List[Any]							= model(
               UpperCamelCase_       ,					attention_mask=UpperCamelCase_       ,					start_positions=UpperCamelCase_       ,					end_positions=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   lowerCamelCase__  (						self	:       Dict       ,					UpperCamelCase_	:       Optional[int]       ,					UpperCamelCase_	:       Any       ,					UpperCamelCase_	:       List[str]       ,					UpperCamelCase_	:       Dict       ,					UpperCamelCase_	:       Optional[Any]       ,					UpperCamelCase_	:       Union[str, Any]					):
           lowerCAmelCase       :						Tuple							= ErnieMForInformationExtraction(config=UpperCamelCase_					)
           model.to(UpperCamelCase_					)
           model.eval()
           lowerCAmelCase       :						Tuple							= torch.ones_like(UpperCamelCase_       ,					dtype=torch.floataa					)
           lowerCAmelCase       :						str							= model(
               UpperCamelCase_       ,					attention_mask=UpperCamelCase_       ,					start_positions=UpperCamelCase_       ,					end_positions=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   lowerCamelCase__  (						self	:       Optional[int]       ,					UpperCamelCase_	:       Optional[int]       ,					UpperCamelCase_	:       Dict       ,					UpperCamelCase_	:       int       ,					UpperCamelCase_	:       int       ,					UpperCamelCase_	:       Union[str, Any]       ,					UpperCamelCase_	:       Any					):
           lowerCAmelCase       :						Union[str, Any]							= self.num_labels
           lowerCAmelCase       :						Tuple							= ErnieMForSequenceClassification(UpperCamelCase_					)
           model.to(UpperCamelCase_					)
           model.eval()
           lowerCAmelCase       :						Union[str, Any]							= model(UpperCamelCase_       ,					attention_mask=UpperCamelCase_       ,					labels=UpperCamelCase_					)
           self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)					)






     def   lowerCamelCase__  (						self	:       Optional[int]       ,					UpperCamelCase_	:       Any       ,					UpperCamelCase_	:       List[str]       ,					UpperCamelCase_	:       int       ,					UpperCamelCase_	:       Dict       ,					UpperCamelCase_	:       str       ,					UpperCamelCase_	:       List[str]					):
           lowerCAmelCase       :						Union[str, Any]							= self.num_labels
           lowerCAmelCase       :						List[Any]							= ErnieMForTokenClassification(config=UpperCamelCase_					)
           model.to(UpperCamelCase_					)
           model.eval()
           input_ids.to(UpperCamelCase_					)
           input_mask.to(UpperCamelCase_					)
           token_labels.to(UpperCamelCase_					)

           lowerCAmelCase       :						Optional[Any]							= model(UpperCamelCase_       ,					attention_mask=UpperCamelCase_       ,					labels=UpperCamelCase_					)

           self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.num_labels)					)






     def   lowerCamelCase__  (						self	:       Any       ,					UpperCamelCase_	:       str       ,					UpperCamelCase_	:       Optional[int]       ,					UpperCamelCase_	:       Tuple       ,					UpperCamelCase_	:       Dict       ,					UpperCamelCase_	:       Dict       ,					UpperCamelCase_	:       str					):
           lowerCAmelCase       :						Union[str, Any]							= self.num_choices
           lowerCAmelCase       :						Any							= ErnieMForMultipleChoice(config=UpperCamelCase_					)
           model.to(UpperCamelCase_					)
           model.eval()
           lowerCAmelCase       :						Any							= input_ids.unsqueeze(1					).expand(-1       ,					self.num_choices       ,					-1					).contiguous()
           lowerCAmelCase       :						int							= input_mask.unsqueeze(1					).expand(-1       ,					self.num_choices       ,					-1					).contiguous()
           lowerCAmelCase       :						Any							= model(
               UpperCamelCase_       ,					attention_mask=UpperCamelCase_       ,					labels=UpperCamelCase_       ,					)
           self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_choices)					)






     def   lowerCamelCase__  (						self	:       Tuple					):
           lowerCAmelCase       :						List[Any]							= self.prepare_config_and_inputs()
           (
               (
               lowerCAmelCase
           ),   (
               lowerCAmelCase
           ),   (
               lowerCAmelCase
           ),   (
               lowerCAmelCase
           ),   (
               lowerCAmelCase
           ),   (
               lowerCAmelCase
           ),   
           )       :						str							= config_and_inputs
           lowerCAmelCase       :						Dict							= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
           return config, inputs_dict



@require_torch
class 		snake_case_(							a__				,       a__				,       unittest.TestCase      ):
     __UpperCamelCase	=       (
         (
             ErnieMModel,
             ErnieMForMultipleChoice,
             ErnieMForQuestionAnswering,
             ErnieMForSequenceClassification,
             ErnieMForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     __UpperCamelCase	=       ()
     __UpperCamelCase	=       (
         {
             '''feature-extraction''': ErnieMModel,
             '''question-answering''': ErnieMForQuestionAnswering,
             '''text-classification''': ErnieMForSequenceClassification,
             '''token-classification''': ErnieMForTokenClassification,
             '''zero-shot''': ErnieMForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     __UpperCamelCase	=       False






     def   lowerCamelCase__  (						self	:       Optional[Any]       ,					UpperCamelCase_	:       str       ,					UpperCamelCase_	:       List[Any]       ,					UpperCamelCase_	:       Tuple       ,					UpperCamelCase_	:       int       ,					UpperCamelCase_	:       Any					):
           if pipeline_test_casse_name == "QAPipelineTests":
                 return True

           return False






     def   lowerCamelCase__  (						self	:       Dict					):
           lowerCAmelCase       :						str							= ErnieMModelTester(self					)
           lowerCAmelCase       :						Optional[Any]							= ConfigTester(self       ,					config_class=UpperCamelCase_       ,					hidden_size=3_7					)






     def   lowerCamelCase__  (						self	:       Tuple					):
           self.config_tester.run_common_tests()






     def   lowerCamelCase__  (						self	:       List[str]					):
           lowerCAmelCase       :						str							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       Union[str, Any]					):
           lowerCAmelCase       :						Optional[Any]							= self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                 lowerCAmelCase       :						Union[str, Any]							= type
                 self.model_tester.create_and_check_model(*UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       Tuple					):
           lowerCAmelCase       :						Dict							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       Dict					):
           lowerCAmelCase       :						int							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       Optional[int]					):
           lowerCAmelCase       :						Tuple							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       Tuple					):
           lowerCAmelCase       :						List[str]							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_information_extraction(*UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       List[Any]					):
           lowerCAmelCase       :						str							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_					)






     @slow
     def   lowerCamelCase__  (						self	:       List[str]					):
           for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCAmelCase       :						int							= ErnieMModel.from_pretrained(UpperCamelCase_					)
                 self.assertIsNotNone(UpperCamelCase_					)



@require_torch
class 		snake_case_(							unittest.TestCase      ):






     @slow
     def   lowerCamelCase__  (						self	:       Dict					):
           lowerCAmelCase       :						Optional[int]							= ErnieMModel.from_pretrained('''susnato/ernie-m-base_pytorch'''					)
           model.eval()
           lowerCAmelCase       :						Optional[int]							= torch.tensor([[0, 1, 2, 3, 4, 5]]					)
           lowerCAmelCase       :						str							= model(UpperCamelCase_					)[0]

           # TODO Replace vocab size
           lowerCAmelCase       :						Any							= 7_6_8

           lowerCAmelCase       :						int							= torch.Size((1, 6, hidden_size)					)
           self.assertEqual(output.shape       ,					UpperCamelCase_					)

           lowerCAmelCase       :						str							= torch.tensor(
               [[[-0.0_012, 0.1_245, -0.0_214], [-0.0_742, 0.0_244, -0.0_771], [-0.0_333, 0.1_164, -0.1_554]]]					)

           self.assertTrue(torch.allclose(output[:, :3, :3]       ,					UpperCamelCase_       ,					atol=1E-3					)					)


