






'''simple docstring'''







class      __SCREAMING_SNAKE_CASE					:


  """simple docstring"""


  def __init__( self      ,  UpperCamelCase__  ):






         """simple docstring"""


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







  def       _a					( self      ,  UpperCamelCase__  ):






         """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      __SCREAMING_SNAKE_CASE					:


  """simple docstring"""


  def __init__( self      ,  UpperCamelCase__=None      ,  UpperCamelCase__=None      ,  UpperCamelCase__=0  ):






         """simple docstring"""


         a_	    = heap_size
         a_	    = bottom_root
         a_	    = min_node







  def       _a					( self      ,  UpperCamelCase__  ):






         """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(UpperCamelCase__  ) - 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      ,  UpperCamelCase__  ):






         """simple docstring"""


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

                # 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  ):






         """simple docstring"""


         return self.min_node.val







  def       _a					( self  ):






         """simple docstring"""


         return self.size == 0







  def       _a					( self  ):






         """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=UpperCamelCase__      ,  min_node=UpperCamelCase__      ,  heap_size=UpperCamelCase__  )

         # 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(UpperCamelCase__  )

         return min_value







  def       _a					( self  ):






         """simple docstring"""


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







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__=0  ):






         """simple docstring"""


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







  def __str__( self  ):






         """simple docstring"""


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

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


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

  doctest.testmod()












'''simple docstring'''







import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

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


if is_torch_available():
  import torch

  from transformers import BlipTextModel
  from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class      __SCREAMING_SNAKE_CASE					:


  """simple docstring"""


  def __init__( self      ,  UpperCamelCase__      ,  UpperCamelCase__=12      ,  UpperCamelCase__=7      ,  UpperCamelCase__=True      ,  UpperCamelCase__=True      ,  UpperCamelCase__=True      ,  UpperCamelCase__=99      ,  UpperCamelCase__=32      ,  UpperCamelCase__=32      ,  UpperCamelCase__=5      ,  UpperCamelCase__=4      ,  UpperCamelCase__=37      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=512      ,  UpperCamelCase__=0.02      ,  UpperCamelCase__=0      ,  UpperCamelCase__=None      ,  ):






         """simple docstring"""


         a_	    = parent
         a_	    = batch_size
         a_	    = seq_length
         a_	    = is_training
         a_	    = use_input_mask
         a_	    = use_labels
         a_	    = vocab_size
         a_	    = hidden_size
         a_	    = projection_dim
         a_	    = num_hidden_layers
         a_	    = num_attention_heads
         a_	    = intermediate_size
         a_	    = dropout
         a_	    = attention_dropout
         a_	    = max_position_embeddings
         a_	    = initializer_range
         a_	    = scope
         a_	    = bos_token_id







  def       _a					( self  ):






         """simple docstring"""


         a_	    = ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size  )

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

         if input_mask is not None:
                a_    ,  a_	    = input_mask.shape
                a_	    = np.random.randint(1      ,  seq_length - 1      ,  size=(batch_size,)  )
                for batch_idx, start_index in enumerate(UpperCamelCase__  ):
                       a_	    = 1
                       a_	    = 0

         a_	    = self.get_config()

         return config, input_ids, input_mask







  def       _a					( self  ):






         """simple docstring"""


         return BlipTextConfig(
             vocab_size=self.vocab_size      ,  hidden_size=self.hidden_size      ,  projection_dim=self.projection_dim      ,  num_hidden_layers=self.num_hidden_layers      ,  num_attention_heads=self.num_attention_heads      ,  intermediate_size=self.intermediate_size      ,  dropout=self.dropout      ,  attention_dropout=self.attention_dropout      ,  max_position_embeddings=self.max_position_embeddings      ,  initializer_range=self.initializer_range      ,  bos_token_id=self.bos_token_id      ,  )







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


         a_	    = BlipTextModel(config=UpperCamelCase__  )
         model.to(UpperCamelCase__  )
         model.eval()
         with torch.no_grad():
                a_	    = model(UpperCamelCase__      ,  attention_mask=UpperCamelCase__  )
                a_	    = model(UpperCamelCase__  )
         self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)  )
         self.parent.assertEqual(result.pooler_output.shape      ,  (self.batch_size, self.hidden_size)  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.prepare_config_and_inputs()
         a_    ,  a_    ,  a_	    = config_and_inputs
         a_	    = {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict





@require_torch
class      __SCREAMING_SNAKE_CASE					(__A       ,					unittest.TestCase					):


  """simple docstring"""


  _a     :   Dict			 =    (BlipTextModel,) if is_torch_available() else ()
  _a     :   str			 =    False
  _a     :   str			 =    False
  _a     :   str			 =    False







  def       _a					( self  ):






         """simple docstring"""


         a_	    = BlipTextModelTester(self  )
         a_	    = ConfigTester(self      ,  config_class=UpperCamelCase__      ,  hidden_size=37  )







  def       _a					( self  ):






         """simple docstring"""


         self.config_tester.run_common_tests()







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         pass







  def       _a					( self  ):






         """simple docstring"""


         pass







  @unittest.skip(reason='Blip does not use inputs_embeds'  )
  def       _a					( self  ):






         """simple docstring"""


         pass







  @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'  )
  def       _a					( self  ):






         """simple docstring"""


         pass







  @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'  )
  def       _a					( self  ):






         """simple docstring"""


         pass







  @slow
  def       _a					( self  ):






         """simple docstring"""


         for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                a_	    = BlipTextModel.from_pretrained(UpperCamelCase__  )
                self.assertIsNotNone(UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCamelCase__  )




