

class 						__magic_name__      :

   '''simple docstring'''




   def __init__(    self,   lowercase_	)							->   List[str]:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Tuple:






     """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 						__magic_name__      :

   '''simple docstring'''




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






     """simple docstring"""






     a__  =heap_size
     a__  =bottom_root
     a__  =min_node



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   List[Any]:






     """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(lowercase_	) - 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 			_UpperCAmelCase			(    self,   lowercase_	)							->   Optional[Any]:






     """simple docstring"""






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

       # 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 			_UpperCAmelCase			(    self	)							->   str:






     """simple docstring"""






     return self.min_node.val



   def 			_UpperCAmelCase			(    self	)							->   Tuple:






     """simple docstring"""






     return self.size == 0



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






     """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=lowercase_,   min_node=lowercase_,   heap_size=lowercase_	)

     # 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(lowercase_	)

     return min_value



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






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_=0	)							->   List[str]:






     """simple docstring"""






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



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






     """simple docstring"""






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

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


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

       doctest.testmod()



from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = 0.93,   lowercase_ = True,   )							->   Any:






     """simple docstring"""






     a__  =mixing_coeff
     a__  =leapfrog_steps

     super().__init__()
     self.register_modules(
         vae=lowercase_,   text_encoder=lowercase_,   tokenizer=lowercase_,   unet=lowercase_,   scheduler=lowercase_,   )

     a__  =2 ** (len(self.vae.config.block_out_channels	) - 1)
     a__  =VaeImageProcessor(vae_scale_factor=self.vae_scale_factor	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = None,   lowercase_ = False	)							->   int:






     """simple docstring"""






     a__  =self.tokenizer(
         lowercase_,   padding='''max_length''',   max_length=self.tokenizer.model_max_length,   truncation=lowercase_,   return_tensors='''pt''',   )

     a__  =self.text_encoder(text_inputs.input_ids.to(self.device	)	).last_hidden_state

     a__  =prompt_embeds.to(dtype=self.text_encoder.dtype,   device=self.device	)

     if do_classifier_free_guidance:
       a__  ='''''' if negative_prompt is None else negative_prompt

       a__  =self.tokenizer(
           lowercase_,   padding='''max_length''',   max_length=self.tokenizer.model_max_length,   truncation=lowercase_,   return_tensors='''pt''',   )

       a__  =self.text_encoder(uncond_input.input_ids.to(self.device	)	).last_hidden_state

       a__  =torch.cat([negative_prompt_embeds, prompt_embeds]	)

     return prompt_embeds



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Optional[Any]:






     """simple docstring"""






     a__  =self.mixing_coeff * x + (1 - self.mixing_coeff) * y
     a__  =self.mixing_coeff * y + (1 - self.mixing_coeff) * x

     return [x, y]



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Dict:






     """simple docstring"""






     a__  =(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
     a__  =(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

     return [x, y]



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =int(lowercase_	)

     a__  =self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

     return alpha_prod, 1 - alpha_prod



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   )							->   Any:






     """simple docstring"""






     a__  =timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

     a__,				a__  =self._get_alpha_and_beta(lowercase_	)
     a__,				a__  =self._get_alpha_and_beta(lowercase_	)

     a__  =(alpha_prod_t_prev / alpha_prod_t) ** 0.5
     a__  =-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

     a__  =(base - b_t * model_output) / a_t

     return model_input, next_model_input.to(base.dtype	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   )							->   Any:






     """simple docstring"""






     a__  =timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

     a__,				a__  =self._get_alpha_and_beta(lowercase_	)
     a__,				a__  =self._get_alpha_and_beta(lowercase_	)

     a__  =(alpha_prod_t_prev / alpha_prod_t) ** 0.5
     a__  =-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
     a__  =a_t * base + b_t * model_output

     return model_input, next_model_input.to(base.dtype	)



   @torch.no_grad()
   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   int:






     """simple docstring"""






     a__  =1 / self.vae.config.scaling_factor * latents
     a__  =self.vae.decode(lowercase_	).sample
     a__  =(image / 2 + 0.5).clamp(0,   1	)
     return image



   @torch.no_grad()
   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = None,   )							->   str:






     """simple docstring"""






     a__  =guidance_scale > 1.0

     a__  =image.to(device=self.device,   dtype=text_embeds.dtype	)
     a__  =self.vae.encode(lowercase_	).latent_dist.sample(lowercase_	)

     a__  =self.vae.config.scaling_factor * latent

     a__  =[latent.clone(), latent.clone()]

     for i, t in tqdm(enumerate(lowercase_	),   total=len(lowercase_	)	):
       a__  =self.noise_mixing_layer(x=coupled_latents[0],   y=coupled_latents[1]	)

       # j - model_input index, k - base index
       for j in range(2	):
         a__  =j ^ 1

         if self.leapfrog_steps:
           if i % 2 == 0:
             a__,				a__  =j, k

         a__  =coupled_latents[j]
         a__  =coupled_latents[k]

         a__  =torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

         a__  =self.unet(lowercase_,   lowercase_,   encoder_hidden_states=lowercase_	).sample

         if do_classifier_free_guidance:
           a__,				a__  =noise_pred.chunk(2	)
           a__  =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

         a__,				a__  =self.noise_step(
             base=lowercase_,   model_input=lowercase_,   model_output=lowercase_,   timestep=lowercase_,   )

         a__  =model_input

     return coupled_latents



   @torch.no_grad()
   def __call__(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = 3.0,   lowercase_ = 50,   lowercase_ = 0.8,   lowercase_ = None,   lowercase_ = None,   lowercase_ = "pil",   )							->   List[Any]:






     """simple docstring"""






     a__  =guidance_scale > 1.0

     a__  =self.image_processor.preprocess(lowercase_	)

     a__  =self._encode_prompt(lowercase_,   lowercase_,   lowercase_	)
     a__  =self._encode_prompt(lowercase_,   lowercase_,   lowercase_	)

     self.scheduler.set_timesteps(lowercase_,   self.device	)

     a__  =num_inference_steps - int(num_inference_steps * strength	)
     a__  =self.scheduler.timesteps[t_limit:]
     a__  =fwd_timesteps.flip(0	)

     a__  =self.prepare_latents(lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)

     for i, t in tqdm(enumerate(lowercase_	),   total=len(lowercase_	)	):
       # j - model_input index, k - base index
       for k in range(2	):
         a__  =k ^ 1

         if self.leapfrog_steps:
           if i % 2 == 1:
             a__,				a__  =j, k

         a__  =coupled_latents[j]
         a__  =coupled_latents[k]

         a__  =torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

         a__  =self.unet(lowercase_,   lowercase_,   encoder_hidden_states=lowercase_	).sample

         if do_classifier_free_guidance:
           a__,				a__  =noise_pred.chunk(2	)
           a__  =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

         a__,				a__  =self.denoise_step(
             base=lowercase_,   model_input=lowercase_,   model_output=lowercase_,   timestep=lowercase_,   )

         a__  =model_input

       a__  =self.denoise_mixing_layer(x=coupled_latents[0],   y=coupled_latents[1]	)

     # either one is fine
     a__  =coupled_latents[0]

     if output_type not in ["latent", "pt", "np", "pil"]:
       a__  =(
           F"""the output_type {output_type} is outdated. Please make sure to set it to one of these instead: """
           '''`pil`, `np`, `pt`, `latent`'''
       )
       deprecate('''Unsupported output_type''',   '''1.0.0''',   lowercase_,   standard_warn=lowercase_	)
       a__  ='''np'''

     if output_type == "latent":
       a__  =final_latent
     else:
       a__  =self.decode_latents(lowercase_	)
       a__  =self.image_processor.postprocess(lowercase_,   output_type=lowercase_	)

     return image
