




class __snake_case     :







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

        """simple docstring"""


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







   def lowerCamelCase		( self						:		Dict   ,		_snake_case						:		str):

        """simple docstring"""


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

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







class __snake_case     :







   def __init__( self						:		Any   ,		_snake_case						:		int=None   ,		_snake_case						:		str=None   ,		_snake_case						:		Dict=0):

        """simple docstring"""


        UpperCAmelCase_		=				heap_size
        UpperCAmelCase_		=				bottom_root
        UpperCAmelCase_		=				min_node







   def lowerCamelCase		( self						:		Any   ,		_snake_case						:		Any):

        """simple docstring"""


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

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

        # Order roots by left_subtree_size
        UpperCAmelCase_		=				[]
        UpperCAmelCase_	,			UpperCAmelCase_		=				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))
                  UpperCAmelCase_		=				i.parent
             else:
                  combined_roots_list.append((j, False))
                  UpperCAmelCase_		=				j.parent
        # Insert links between them
        for i in range(len(_snake_case) - 1):
             if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
                  UpperCAmelCase_		=				combined_roots_list[i + 1][0]
                  UpperCAmelCase_		=				combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
        UpperCAmelCase_		=				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
                  UpperCAmelCase_		=				i.left
                  UpperCAmelCase_		=				i.parent.parent

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

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

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

        # Return the merged heap
        return self







   def lowerCamelCase		( self						:		List[Any]   ,		_snake_case						:		int):

        """simple docstring"""


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

             # Update size
             self.size += 1

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

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

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







   def lowerCamelCase		( self						:		Tuple):

        """simple docstring"""


        return self.min_node.val







   def lowerCamelCase		( self						:		List[Any]):

        """simple docstring"""


        return self.size == 0







   def lowerCamelCase		( self						:		Tuple):

        """simple docstring"""


        UpperCAmelCase_		=				self.min_node.val

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

             # Update bottom root
             UpperCAmelCase_		=				None

             # Update min_node
             UpperCAmelCase_		=				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
             UpperCAmelCase_		=				self.bottom_root.parent
             UpperCAmelCase_		=				None

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

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

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

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

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

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

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

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

        return min_value







   def lowerCamelCase		( self						:		str):

        """simple docstring"""


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







   def lowerCamelCase		( self						:		List[Any]   ,		_snake_case						:		List[str]   ,		_snake_case						:		Optional[Any]   ,		_snake_case						:		List[Any]=0):

        """simple docstring"""


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







   def __str__( self						:		Tuple):

        """simple docstring"""


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

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


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

    doctest.testmod()












import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class __snake_case     (     a					,    unittest.TestCase						):
   UpperCAmelCase__    :       str		=    PaintByExamplePipeline
   UpperCAmelCase__    :       Tuple		=    IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   UpperCAmelCase__    :       Any		=    IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   UpperCAmelCase__    :       Union[str, Any]		=    frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_		=				UNetaDConditionModel(
            block_out_channels=(32, 64)   ,		layers_per_block=2   ,		sample_size=32   ,		in_channels=9   ,		out_channels=4   ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,		cross_attention_dim=32   ,		)
        UpperCAmelCase_		=				PNDMScheduler(skip_prk_steps=_snake_case)
        torch.manual_seed(0)
        UpperCAmelCase_		=				AutoencoderKL(
            block_out_channels=[32, 64]   ,		in_channels=3   ,		out_channels=3   ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,		latent_channels=4   ,		)
        torch.manual_seed(0)
        UpperCAmelCase_		=				CLIPVisionConfig(
            hidden_size=32   ,		projection_dim=32   ,		intermediate_size=37   ,		layer_norm_eps=1e-05   ,		num_attention_heads=4   ,		num_hidden_layers=5   ,		image_size=32   ,		patch_size=4   ,		)
        UpperCAmelCase_		=				PaintByExampleImageEncoder(_snake_case   ,		proj_size=32)
        UpperCAmelCase_		=				CLIPImageProcessor(crop_size=32   ,		size=32)

        UpperCAmelCase_		=				{
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''image_encoder''': image_encoder,
            '''safety_checker''': None,
            '''feature_extractor''': feature_extractor,
        }
        return components







   def lowerCamelCase		( self						:		Optional[Any]   ,		_snake_case						:		List[str]):

        """simple docstring"""


        UpperCAmelCase_		=				np.array(image.convert('''RGB'''))
        UpperCAmelCase_		=				image[None].transpose(0   ,		3   ,		1   ,		2)
        UpperCAmelCase_		=				torch.from_numpy(_snake_case).to(dtype=torch.floataa) / 1_2_7.5 - 1.0
        return image







   def lowerCamelCase		( self						:		Tuple   ,		_snake_case						:		Dict="cpu"   ,		_snake_case						:		int=0):

        """simple docstring"""


        UpperCAmelCase_		=				floats_tensor((1, 3, 32, 32)   ,		rng=random.Random(_snake_case)).to(_snake_case)
        UpperCAmelCase_		=				image.cpu().permute(0   ,		2   ,		3   ,		1)[0]
        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''').resize((64, 64))
        UpperCAmelCase_		=				Image.fromarray(np.uinta(image + 4)).convert('''RGB''').resize((64, 64))
        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''').resize((32, 32))

        if str(_snake_case).startswith('''mps'''):
             UpperCAmelCase_		=				torch.manual_seed(_snake_case)
        else:
             UpperCAmelCase_		=				torch.Generator(device=_snake_case).manual_seed(_snake_case)
        UpperCAmelCase_		=				{
            '''example_image''': example_image,
            '''image''': init_image,
            '''mask_image''': mask_image,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }
        return inputs







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        UpperCAmelCase_		=				self.get_dummy_components()

        # make sure here that pndm scheduler skips prk
        UpperCAmelCase_		=				PaintByExamplePipeline(**_snake_case)
        UpperCAmelCase_		=				pipe.to('''cpu''')
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_		=				self.get_dummy_inputs()
        UpperCAmelCase_		=				pipe(**_snake_case)
        UpperCAmelCase_		=				output.images

        UpperCAmelCase_		=				image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        UpperCAmelCase_		=				np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_		=				'''cpu'''
        UpperCAmelCase_		=				self.get_dummy_inputs()
        inputs.pop('''mask_image''')
        UpperCAmelCase_		=				self.convert_to_pt(inputs.pop('''image'''))
        UpperCAmelCase_		=				image.clamp(0   ,		1) / 2

        # make sure here that pndm scheduler skips prk
        UpperCAmelCase_		=				PaintByExamplePipeline(**self.get_dummy_components())
        UpperCAmelCase_		=				pipe.to(_snake_case)
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_		=				pipe(image=_snake_case   ,		mask_image=mask_image[:, 0]   ,		**_snake_case)
        UpperCAmelCase_		=				output.images

        UpperCAmelCase_		=				image.cpu().permute(0   ,		2   ,		3   ,		1)[0]
        UpperCAmelCase_		=				mask_image.cpu().permute(0   ,		2   ,		3   ,		1)[0]

        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''')
        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''')

        UpperCAmelCase_		=				pipe(**self.get_dummy_inputs())
        UpperCAmelCase_		=				output.images

        assert out_a.shape == (1, 64, 64, 3)
        assert np.abs(out_a.flatten() - out_a.flatten()).max() < 5e-2







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        super().test_inference_batch_single_identical(expected_max_diff=3e-3)







@slow
@require_torch_gpu
class __snake_case     (     unittest.TestCase						):







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

        """simple docstring"""


        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







   def lowerCamelCase		( self						:		Tuple):

        """simple docstring"""


        UpperCAmelCase_		=				load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/dog_in_bucket.png''')
        UpperCAmelCase_		=				load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/mask.png''')
        UpperCAmelCase_		=				load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/panda.jpg''')

        UpperCAmelCase_		=				PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example''')
        UpperCAmelCase_		=				pipe.to(_snake_case)
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_		=				torch.manual_seed(321)
        UpperCAmelCase_		=				pipe(
            image=_snake_case   ,		mask_image=_snake_case   ,		example_image=_snake_case   ,		generator=_snake_case   ,		guidance_scale=5.0   ,		num_inference_steps=50   ,		output_type='''np'''   ,		)

        UpperCAmelCase_		=				output.images

        UpperCAmelCase_		=				image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_		=				np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2






