


class       _lowercase			:


       """simple docstring"""




       def __init__(       self							:      Optional[int] ,				__lowerCamelCase							:      Optional[int]	):


             '''simple docstring'''



             lowerCamelCase__		:    Dict   =  val
             # Number of nodes in left subtree
             lowerCamelCase__		:    Optional[int]   =  0
             lowerCamelCase__		:    Optional[Any]   =  None
             lowerCamelCase__		:    Union[str, Any]   =  None
             lowerCamelCase__		:    Optional[int]   =  None





       def 						lowerCAmelCase					(       self							:      Union[str, Any] ,				__lowerCamelCase							:      int	):


             '''simple docstring'''



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

             if self.val < other.val:
                   lowerCamelCase__		:    List[str]   =  self.right
                   lowerCamelCase__		:    Optional[Any]   =  None
                   if self.right:
                         lowerCamelCase__		:    Tuple   =  other
                   lowerCamelCase__		:    Union[str, Any]   =  other
                   lowerCamelCase__		:    List[str]   =  self.left_tree_size * 2 + 1
                   return self
             else:
                   lowerCamelCase__		:    str   =  other.right
                   lowerCamelCase__		:    Optional[Any]   =  None
                   if other.right:
                         lowerCamelCase__		:    Dict   =  self
                   lowerCamelCase__		:    Optional[int]   =  self
                   lowerCamelCase__		:    Dict   =  other.left_tree_size * 2 + 1
                   return other







class       _lowercase			:


       """simple docstring"""




       def __init__(       self							:      Union[str, Any] ,				__lowerCamelCase							:      List[Any]=None ,				__lowerCamelCase							:      List[str]=None ,				__lowerCamelCase							:      Optional[int]=0	):


             '''simple docstring'''



             lowerCamelCase__		:    Union[str, Any]   =  heap_size
             lowerCamelCase__		:    Any   =  bottom_root
             lowerCamelCase__		:    Optional[int]   =  min_node





       def 						lowerCAmelCase					(       self							:      Tuple ,				__lowerCamelCase							:      int	):


             '''simple docstring'''



             if other.size == 0:
                   return None
             if self.size == 0:
                   lowerCamelCase__		:    Any   =  other.size
                   lowerCamelCase__		:    Optional[Any]   =  other.bottom_root
                   lowerCamelCase__		:    Tuple   =  other.min_node
                   return None
             # Update size
             lowerCamelCase__		:    List[str]   =  self.size + other.size

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

             # Order roots by left_subtree_size
             lowerCamelCase__		:    Union[str, Any]   =  []
             lowerCamelCase__   ,       lowerCamelCase__		:    Tuple   =  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)	)
                         lowerCamelCase__		:    Optional[Any]   =  i.parent
                   else:
                         combined_roots_list.append((j, False)	)
                         lowerCamelCase__		:    str   =  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]:
                         lowerCamelCase__		:    int   =  combined_roots_list[i + 1][0]
                         lowerCamelCase__		:    str   =  combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
             lowerCamelCase__		:    List[Any]   =  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
                         lowerCamelCase__		:    List[Any]   =  i.left
                         lowerCamelCase__		:    Dict   =  i.parent.parent

                         # Merging trees
                         lowerCamelCase__		:    Tuple   =  i.merge_trees(i.parent	)

                         # Updating links
                         lowerCamelCase__		:    Any   =  previous_node
                         lowerCamelCase__		:    Optional[Any]   =  next_node
                         if previous_node:
                               lowerCamelCase__		:    int   =  i
                         if next_node:
                               lowerCamelCase__		:    Any   =  i
                   else:
                         lowerCamelCase__		:    Any   =  i.parent
        # Updating self.bottom_root
             while i.left:
                   lowerCamelCase__		:    Union[str, Any]   =  i.left
             lowerCamelCase__		:    Union[str, Any]   =  i

             # Update other
             lowerCamelCase__		:    Optional[int]   =  self.size
             lowerCamelCase__		:    List[str]   =  self.bottom_root
             lowerCamelCase__		:    Optional[Any]   =  self.min_node

             # Return the merged heap
             return self





       def 						lowerCAmelCase					(       self							:      Tuple ,				__lowerCamelCase							:      List[Any]	):


             '''simple docstring'''



             if self.size == 0:
                   lowerCamelCase__		:    int   =  Node(__UpperCamelCase	)
                   lowerCamelCase__		:    List[Any]   =  1
                   lowerCamelCase__		:    Optional[Any]   =  self.bottom_root
             else:
                   # Create new node
                   lowerCamelCase__		:    int   =  Node(__UpperCamelCase	)

                   # Update size
                   self.size += 1

                   # update min_node
                   if val < self.min_node.val:
                         lowerCamelCase__		:    List[str]   =  new_node
                   # Put new_node as a bottom_root in heap
                   lowerCamelCase__		:    int   =  new_node
                   lowerCamelCase__		:    Optional[Any]   =  self.bottom_root
                   lowerCamelCase__		:    Dict   =  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
                         lowerCamelCase__		:    Dict   =  self.bottom_root.parent.parent

                         # Merge
                         lowerCamelCase__		:    List[Any]   =  self.bottom_root.merge_trees(self.bottom_root.parent	)

                         # Update Links
                         lowerCamelCase__		:    Optional[int]   =  next_node
                         lowerCamelCase__		:    Tuple   =  None
                         if next_node:
                               lowerCamelCase__		:    str   =  self.bottom_root





       def 						lowerCAmelCase					(       self							:      List[str]	):


             '''simple docstring'''



             return self.min_node.val





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             return self.size == 0





       def 						lowerCAmelCase					(       self							:      str	):


             '''simple docstring'''



             lowerCamelCase__		:    int   =  self.min_node.val

             # Last element in heap corner case
             if self.size == 1:
                   # Update size
                   lowerCamelCase__		:    Optional[Any]   =  0

                   # Update bottom root
                   lowerCamelCase__		:    List[Any]   =  None

                   # Update min_node
                   lowerCamelCase__		:    Any   =  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
                   lowerCamelCase__		:    Optional[Any]   =  self.bottom_root.parent
                   lowerCamelCase__		:    Optional[int]   =  None

                   # Update min_node
                   lowerCamelCase__		:    List[str]   =  self.bottom_root
                   lowerCamelCase__		:    List[Any]   =  self.bottom_root.parent
                   while i:
                         if i.val < self.min_node.val:
                               lowerCamelCase__		:    Any   =  i
                         lowerCamelCase__		:    Union[str, Any]   =  i.parent
                   return min_value
             # General case
             # Find the BinomialHeap of the right subtree of min_node
             lowerCamelCase__		:    Dict   =  self.min_node.right
             lowerCamelCase__		:    List[Any]   =  None
             lowerCamelCase__		:    Union[str, Any]   =  bottom_of_new
             lowerCamelCase__		:    Optional[int]   =  1

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

             # Update size
             lowerCamelCase__		:    List[Any]   =  self.size - 1 - size_of_new

             # Neighbour nodes
             lowerCamelCase__		:    List[Any]   =  self.min_node.left
             lowerCamelCase__		:    Any   =  self.min_node.parent

             # Initialize new bottom_root and min_node
             lowerCamelCase__		:    Tuple   =  previous_node or next_node
             lowerCamelCase__		:    Tuple   =  next_node

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

                   # Update bottom_root and search for min_node below
                   lowerCamelCase__		:    int   =  previous_node
                   lowerCamelCase__		:    int   =  previous_node
                   while self.bottom_root.left:
                         lowerCamelCase__		:    Optional[int]   =  self.bottom_root.left
                         if self.bottom_root.val < self.min_node.val:
                               lowerCamelCase__		:    Tuple   =  self.bottom_root
             if next_node:
                   lowerCamelCase__		:    Optional[Any]   =  previous_node

                   # Search for new min_node above min_node
                   lowerCamelCase__		:    List[str]   =  next_node
                   while i:
                         if i.val < self.min_node.val:
                               lowerCamelCase__		:    Union[str, Any]   =  i
                         lowerCamelCase__		:    int   =  i.parent
        # Merge heaps
             self.merge_heaps(__UpperCamelCase	)

             return min_value





       def 						lowerCAmelCase					(       self							:      str	):


             '''simple docstring'''



             lowerCamelCase__		:    Tuple   =  self.bottom_root
             while top_root.parent:
                   lowerCamelCase__		:    int   =  top_root.parent
             # preorder
             lowerCamelCase__		:    Any   =  []
             self.__traversal(__UpperCamelCase ,				__UpperCamelCase	)
             return heap_pre_order





       def 						lowerCAmelCase					(       self							:      Dict ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      List[str] ,				__lowerCamelCase							:      Tuple=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							:      int	):


             '''simple docstring'''



             if self.is_empty():
                   return ""
             lowerCamelCase__		:    Optional[int]   =  self.pre_order()

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


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

    doctest.testmod()






from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


A				:   str		= logging.get_logger(__name__)  # pylint: disable=invalid-name







class       _lowercase			(							lowercase__):


       """simple docstring"""




       A__ 						=      ["safety_checker", "feature_extractor"]





       def __init__(       self							:      Optional[Any] ,				__lowerCamelCase							:      AutoencoderKL ,				__lowerCamelCase							:      CLIPTextModel ,				__lowerCamelCase							:      CLIPTokenizer ,				__lowerCamelCase							:      UNetaDConditionModel ,				__lowerCamelCase							:      Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,				__lowerCamelCase							:      StableDiffusionSafetyChecker ,				__lowerCamelCase							:      CLIPImageProcessor ,				__lowerCamelCase							:      bool = True ,				):


             '''simple docstring'''



             super().__init__()
             if hasattr(scheduler.config ,				"steps_offset"	) and scheduler.config.steps_offset != 1:
                   lowerCamelCase__		:    List[str]   =  (
                       f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                       f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                       "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                       " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                       " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
                       " file"
                   )
                   deprecate("steps_offset!=1" ,				"1.0.0" ,				__lowerCamelCase ,				standard_warn=__lowerCamelCase	)
                   lowerCamelCase__		:    List[str]   =  dict(scheduler.config	)
                   lowerCamelCase__		:    Optional[Any]   =  1
                   lowerCamelCase__		:    Union[str, Any]   =  FrozenDict(__lowerCamelCase	)

             self.register_modules(
                 vae=__lowerCamelCase ,				text_encoder=__lowerCamelCase ,				tokenizer=__lowerCamelCase ,				unet=__lowerCamelCase ,				scheduler=__lowerCamelCase ,				safety_checker=__lowerCamelCase ,				feature_extractor=__lowerCamelCase ,				)
             self.register_to_config(requires_safety_checker=__lowerCamelCase	)





       @property
       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             return {k: getattr(self ,				__lowerCamelCase	) for k in self.config.keys() if not k.startswith("_"	)}





       def 						lowerCAmelCase					(       self							:      List[Any] ,				__lowerCamelCase							:      Optional[Union[str, int]] = "auto"	):


             '''simple docstring'''



             if slice_size == "auto":
                   # half the attention head size is usually a good trade-off between
                   # speed and memory
                   lowerCamelCase__		:    Tuple   =  self.unet.config.attention_head_dim // 2
             self.unet.set_attention_slice(__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Optional[Any]	):


             '''simple docstring'''



             self.enable_attention_slicing(__lowerCamelCase	)





       @torch.no_grad()
       def 						lowerCAmelCase					(       self							:      Any ,				__lowerCamelCase							:      Union[str, List[str]] ,				__lowerCamelCase							:      Union[torch.FloatTensor, PIL.Image.Image] ,				__lowerCamelCase							:      Union[torch.FloatTensor, PIL.Image.Image] ,				__lowerCamelCase							:      float = 0.8 ,				__lowerCamelCase							:      Optional[int] = 50 ,				__lowerCamelCase							:      Optional[float] = 7.5 ,				__lowerCamelCase							:      Optional[Union[str, List[str]]] = None ,				__lowerCamelCase							:      Optional[int] = 1 ,				__lowerCamelCase							:      Optional[float] = 0.0 ,				__lowerCamelCase							:      Optional[torch.Generator] = None ,				__lowerCamelCase							:      Optional[str] = "pil" ,				__lowerCamelCase							:      bool = True ,				__lowerCamelCase							:      Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,				__lowerCamelCase							:      int = 1 ,				):


             '''simple docstring'''



             return StableDiffusionInpaintPipelineLegacy(**self.components	)(
                 prompt=__lowerCamelCase ,				image=__lowerCamelCase ,				mask_image=__lowerCamelCase ,				strength=__lowerCamelCase ,				num_inference_steps=__lowerCamelCase ,				guidance_scale=__lowerCamelCase ,				negative_prompt=__lowerCamelCase ,				num_images_per_prompt=__lowerCamelCase ,				eta=__lowerCamelCase ,				generator=__lowerCamelCase ,				output_type=__lowerCamelCase ,				return_dict=__lowerCamelCase ,				callback=__lowerCamelCase ,				)





       @torch.no_grad()
       def 						lowerCAmelCase					(       self							:      Optional[int] ,				__lowerCamelCase							:      Union[str, List[str]] ,				__lowerCamelCase							:      Union[torch.FloatTensor, PIL.Image.Image] ,				__lowerCamelCase							:      float = 0.8 ,				__lowerCamelCase							:      Optional[int] = 50 ,				__lowerCamelCase							:      Optional[float] = 7.5 ,				__lowerCamelCase							:      Optional[Union[str, List[str]]] = None ,				__lowerCamelCase							:      Optional[int] = 1 ,				__lowerCamelCase							:      Optional[float] = 0.0 ,				__lowerCamelCase							:      Optional[torch.Generator] = None ,				__lowerCamelCase							:      Optional[str] = "pil" ,				__lowerCamelCase							:      bool = True ,				__lowerCamelCase							:      Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,				__lowerCamelCase							:      int = 1 ,				**__lowerCamelCase							:      List[str] ,				):


             '''simple docstring'''



             return StableDiffusionImgaImgPipeline(**self.components	)(
                 prompt=__lowerCamelCase ,				image=__lowerCamelCase ,				strength=__lowerCamelCase ,				num_inference_steps=__lowerCamelCase ,				guidance_scale=__lowerCamelCase ,				negative_prompt=__lowerCamelCase ,				num_images_per_prompt=__lowerCamelCase ,				eta=__lowerCamelCase ,				generator=__lowerCamelCase ,				output_type=__lowerCamelCase ,				return_dict=__lowerCamelCase ,				callback=__lowerCamelCase ,				callback_steps=__lowerCamelCase ,				)





       @torch.no_grad()
       def 						lowerCAmelCase					(       self							:      List[Any] ,				__lowerCamelCase							:      Union[str, List[str]] ,				__lowerCamelCase							:      int = 512 ,				__lowerCamelCase							:      int = 512 ,				__lowerCamelCase							:      int = 50 ,				__lowerCamelCase							:      float = 7.5 ,				__lowerCamelCase							:      Optional[Union[str, List[str]]] = None ,				__lowerCamelCase							:      Optional[int] = 1 ,				__lowerCamelCase							:      float = 0.0 ,				__lowerCamelCase							:      Optional[torch.Generator] = None ,				__lowerCamelCase							:      Optional[torch.FloatTensor] = None ,				__lowerCamelCase							:      Optional[str] = "pil" ,				__lowerCamelCase							:      bool = True ,				__lowerCamelCase							:      Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,				__lowerCamelCase							:      int = 1 ,				):


             '''simple docstring'''



             return StableDiffusionPipeline(**self.components	)(
                 prompt=__lowerCamelCase ,				height=__lowerCamelCase ,				width=__lowerCamelCase ,				num_inference_steps=__lowerCamelCase ,				guidance_scale=__lowerCamelCase ,				negative_prompt=__lowerCamelCase ,				num_images_per_prompt=__lowerCamelCase ,				eta=__lowerCamelCase ,				generator=__lowerCamelCase ,				latents=__lowerCamelCase ,				output_type=__lowerCamelCase ,				return_dict=__lowerCamelCase ,				callback=__lowerCamelCase ,				callback_steps=__lowerCamelCase ,				)


