




class  __lowercase    :


				'''simple docstring'''


				def __init__(				self				:       int ,SCREAMING_SNAKE_CASE_				:       Optional[int] ):
										snake_case__    :   Optional[Any]							     =   val
										# Number of nodes in left subtree
										snake_case__    :   Any							     =   0
										snake_case__    :   str							     =   None
										snake_case__    :   Optional[Any]							     =   None
										snake_case__    :   int							     =   None





				def  _lowerCamelCase   (				self				:       str ,SCREAMING_SNAKE_CASE_				:       Any ):
										assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

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







class  __lowercase    :


				'''simple docstring'''


				def __init__(				self				:       Dict ,SCREAMING_SNAKE_CASE_				:       str=None ,SCREAMING_SNAKE_CASE_				:       List[str]=None ,SCREAMING_SNAKE_CASE_				:       List[Any]=0 ):
										snake_case__    :   Union[str, Any]							     =   heap_size
										snake_case__    :   List[str]							     =   bottom_root
										snake_case__    :   str							     =   min_node

				def  _lowerCamelCase   (				self				:       Any ,SCREAMING_SNAKE_CASE_				:       Any ):

										# Empty heaps corner cases
										if other.size == 0:
																return None
										if self.size == 0:
																snake_case__    :   Union[str, Any]							     =   other.size
																snake_case__    :   int							     =   other.bottom_root
																snake_case__    :   List[Any]							     =   other.min_node
																return None
										# Update size
										snake_case__    :   str							     =   self.size + other.size

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

										# Order roots by left_subtree_size
										snake_case__    :   List[Any]							     =   []
										snake_case__    :   List[str]							     =   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) )
																						snake_case__    :   Dict							     =   i.parent
																else:
																						combined_roots_list.append((j, False) )
																						snake_case__    :   List[Any]							     =   j.parent
        # Insert links between them
										for i in range(len(SCREAMING_SNAKE_CASE_ ) - 1 ):
																if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
																						snake_case__    :   Optional[int]							     =   combined_roots_list[i + 1][0]
																						snake_case__    :   Any							     =   combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
										snake_case__    :   str							     =   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
																						snake_case__    :   Dict							     =   i.left
																						snake_case__    :   Union[str, Any]							     =   i.parent.parent

																						# Merging trees
																						snake_case__    :   int							     =   i.merge_trees(i.parent )

																						# Updating links
																						snake_case__    :   Optional[Any]							     =   previous_node
																						snake_case__    :   str							     =   next_node
																						if previous_node:
																												snake_case__    :   int							     =   i
																						if next_node:
																												snake_case__    :   int							     =   i
																else:
																						snake_case__    :   List[Any]							     =   i.parent
        # Updating self.bottom_root
										while i.left:
																snake_case__    :   Any							     =   i.left
										snake_case__    :   Dict							     =   i

										# Update other
										snake_case__    :   Any							     =   self.size
										snake_case__    :   List[str]							     =   self.bottom_root
										snake_case__    :   str							     =   self.min_node

										# Return the merged heap
										return self

				def  _lowerCamelCase   (				self				:       int ,SCREAMING_SNAKE_CASE_				:       List[str] ):
										if self.size == 0:
																snake_case__    :   int							     =   Node(SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Tuple							     =   1
																snake_case__    :   str							     =   self.bottom_root
										else:
																# Create new node
																snake_case__    :   Optional[int]							     =   Node(SCREAMING_SNAKE_CASE_ )

																# Update size
																self.size += 1

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

																						# Merge
																						snake_case__    :   Optional[Any]							     =   self.bottom_root.merge_trees(self.bottom_root.parent )

																						# Update Links
																						snake_case__    :   Any							     =   next_node
																						snake_case__    :   Dict							     =   None
																						if next_node:
																												snake_case__    :   Tuple							     =   self.bottom_root

				def  _lowerCamelCase   (				self				:       Optional[Any] ):
										return self.min_node.val

				def  _lowerCamelCase   (				self				:       Dict ):
										return self.size == 0

				def  _lowerCamelCase   (				self				:       Any ):
										snake_case__    :   Optional[int]							     =   self.min_node.val

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

																# Update bottom root
																snake_case__    :   Tuple							     =   None

																# Update min_node
																snake_case__    :   List[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
																snake_case__    :   List[Any]							     =   self.bottom_root.parent
																snake_case__    :   Union[str, Any]							     =   None

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

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

										# Update size
										snake_case__    :   Tuple							     =   self.size - 1 - size_of_new

										# Neighbour nodes
										snake_case__    :   int							     =   self.min_node.left
										snake_case__    :   Optional[Any]							     =   self.min_node.parent

										# Initialize new bottom_root and min_node
										snake_case__    :   Optional[Any]							     =   previous_node or next_node
										snake_case__    :   int							     =   next_node

										# Update links of previous_node and search below for new min_node and
										# bottom_root
										if previous_node:
																snake_case__    :   Optional[Any]							     =   next_node

																# Update bottom_root and search for min_node below
																snake_case__    :   str							     =   previous_node
																snake_case__    :   Union[str, Any]							     =   previous_node
																while self.bottom_root.left:
																						snake_case__    :   Dict							     =   self.bottom_root.left
																						if self.bottom_root.val < self.min_node.val:
																												snake_case__    :   Dict							     =   self.bottom_root
										if next_node:
																snake_case__    :   List[str]							     =   previous_node

																# Search for new min_node above min_node
																snake_case__    :   Optional[Any]							     =   next_node
																while i:
																						if i.val < self.min_node.val:
																												snake_case__    :   Optional[Any]							     =   i
																						snake_case__    :   Any							     =   i.parent
        # Merge heaps
										self.merge_heaps(SCREAMING_SNAKE_CASE_ )

										return min_value

				def  _lowerCamelCase   (				self				:       Optional[int] ):
										snake_case__    :   List[Any]							     =   self.bottom_root
										while top_root.parent:
																snake_case__    :   str							     =   top_root.parent
										# preorder
										snake_case__    :   Tuple							     =   []
										self.__traversal(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
										return heap_pre_order

				def  _lowerCamelCase   (				self				:       int ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Dict ,SCREAMING_SNAKE_CASE_				:       Dict=0 ):
										if curr_node:
																preorder.append((curr_node.val, level) )
																self.__traversal(curr_node.left ,SCREAMING_SNAKE_CASE_ ,level + 1 )
																self.__traversal(curr_node.right ,SCREAMING_SNAKE_CASE_ ,level + 1 )
										else:
																preorder.append(('''#''', level) )





				def __str__(				self				:       int ):
										if self.is_empty():
																return ""
										snake_case__    :   List[Any]							     =   self.pre_order()

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


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

							doctest.testmod()








"""simple docstring"""





import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


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

def 						lowerCAmelCase							(	self ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Optional[Any]							     =   image.to(device=lowerCamelCase						)
						snake_case__    :   List[Any]							     =   image  # take image as image_embeddings
						snake_case__    :   Tuple							     =   image_embeddings.unsqueeze(1						)

						# duplicate image embeddings for each generation per prompt, using mps friendly method
						snake_case__     ,		snake_case__     ,		snake_case__    :   int							     =   image_embeddings.shape
						snake_case__    :   List[Any]							     =   image_embeddings.repeat(1 ,     lowerCamelCase ,     1						)
						snake_case__    :   int							     =   image_embeddings.view(bs_embed * num_images_per_prompt ,     lowerCamelCase ,     -1						)

						if do_classifier_free_guidance:
												snake_case__    :   Tuple							     =   torch.zeros_like(lowerCamelCase						)

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												snake_case__    :   Any							     =   torch.cat([uncond_embeddings, image_embeddings]						)

						return image_embeddings

class  __lowercase    (     snake_case_		):


				'''simple docstring'''


				def __init__(				self				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       PriorTransformer ,SCREAMING_SNAKE_CASE_				:       CLIPTokenizer ,SCREAMING_SNAKE_CASE_				:       CLIPTextModelWithProjection ,SCREAMING_SNAKE_CASE_				:       UnCLIPScheduler ,SCREAMING_SNAKE_CASE_				:       Optional[dict] = None ,):
										super().__init__()

										snake_case__    :   Optional[Any]							     =   {'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

										snake_case__    :   List[Any]							     =   decoder_pipe_kwargs.get('''torch_dtype''' ,SCREAMING_SNAKE_CASE_ ) or prior.dtype

										snake_case__    :   str							     =   StableDiffusionImageVariationPipeline.from_pretrained(
										    '''lambdalabs/sd-image-variations-diffusers''' ,**SCREAMING_SNAKE_CASE_ )

										# replace `_encode_image` method
										snake_case__    :   Tuple							     =   types.MethodType(_encode_image ,self.decoder_pipe )

										self.register_modules(
										    prior=SCREAMING_SNAKE_CASE_ ,tokenizer=SCREAMING_SNAKE_CASE_ ,text_encoder=SCREAMING_SNAKE_CASE_ ,prior_scheduler=SCREAMING_SNAKE_CASE_ ,)

				def  _lowerCamelCase   (				self				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       Dict ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       List[str] ,SCREAMING_SNAKE_CASE_				:       Optional[Union[CLIPTextModelOutput, Tuple]] = None ,SCREAMING_SNAKE_CASE_				:       Optional[torch.Tensor] = None ,):
										if text_model_output is None:
																snake_case__    :   str							     =   len(SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) else 1
																# get prompt text embeddings
																snake_case__    :   Dict							     =   self.tokenizer(
																    SCREAMING_SNAKE_CASE_ ,padding='''max_length''' ,max_length=self.tokenizer.model_max_length ,return_tensors='''pt''' ,)
																snake_case__    :   List[Any]							     =   text_inputs.input_ids
																snake_case__    :   List[str]							     =   text_inputs.attention_mask.bool().to(SCREAMING_SNAKE_CASE_ )

																if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																						snake_case__    :   Optional[int]							     =   self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
																						logger.warning(
																						    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																						    F''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
																						snake_case__    :   str							     =   text_input_ids[:, : self.tokenizer.model_max_length]

																snake_case__    :   Tuple							     =   self.text_encoder(text_input_ids.to(SCREAMING_SNAKE_CASE_ ) )

																snake_case__    :   Any							     =   text_encoder_output.text_embeds
																snake_case__    :   List[Any]							     =   text_encoder_output.last_hidden_state

										else:
																snake_case__    :   List[Any]							     =   text_model_output[0].shape[0]
																snake_case__     ,		snake_case__    :   List[Any]							     =   text_model_output[0], text_model_output[1]
																snake_case__    :   Tuple							     =   text_attention_mask

										snake_case__    :   Tuple							     =   text_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE_ ,dim=0 )
										snake_case__    :   Optional[int]							     =   text_encoder_hidden_states.repeat_interleave(SCREAMING_SNAKE_CASE_ ,dim=0 )
										snake_case__    :   Optional[int]							     =   text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_ ,dim=0 )

										if do_classifier_free_guidance:
																snake_case__    :   Optional[Any]							     =   [''''''] * batch_size

																snake_case__    :   str							     =   self.tokenizer(
																    SCREAMING_SNAKE_CASE_ ,padding='''max_length''' ,max_length=self.tokenizer.model_max_length ,truncation=SCREAMING_SNAKE_CASE_ ,return_tensors='''pt''' ,)
																snake_case__    :   Union[str, Any]							     =   uncond_input.attention_mask.bool().to(SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Tuple							     =   self.text_encoder(uncond_input.input_ids.to(SCREAMING_SNAKE_CASE_ ) )

																snake_case__    :   Optional[Any]							     =   uncond_embeddings_text_encoder_output.text_embeds
																snake_case__    :   Optional[int]							     =   uncond_embeddings_text_encoder_output.last_hidden_state

																# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

																snake_case__    :   Tuple							     =   uncond_embeddings.shape[1]
																snake_case__    :   int							     =   uncond_embeddings.repeat(1 ,SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Optional[int]							     =   uncond_embeddings.view(batch_size * num_images_per_prompt ,SCREAMING_SNAKE_CASE_ )

																snake_case__    :   Tuple							     =   uncond_text_encoder_hidden_states.shape[1]
																snake_case__    :   int							     =   uncond_text_encoder_hidden_states.repeat(1 ,SCREAMING_SNAKE_CASE_ ,1 )
																snake_case__    :   Optional[Any]							     =   uncond_text_encoder_hidden_states.view(
																    batch_size * num_images_per_prompt ,SCREAMING_SNAKE_CASE_ ,-1 )
																snake_case__    :   int							     =   uncond_text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_ ,dim=0 )

																# done duplicates

																# For classifier free guidance, we need to do two forward passes.
																# Here we concatenate the unconditional and text embeddings into a single batch
																# to avoid doing two forward passes
																snake_case__    :   Dict							     =   torch.cat([uncond_embeddings, text_embeddings] )
																snake_case__    :   Dict							     =   torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )

																snake_case__    :   Optional[int]							     =   torch.cat([uncond_text_mask, text_mask] )

										return text_embeddings, text_encoder_hidden_states, text_mask

				@property
				def  _lowerCamelCase   (				self				:       Dict ):
										if self.device != torch.device('''meta''' ) or not hasattr(self.prior ,'''_hf_hook''' ):
																return self.device
										for module in self.prior.modules():
																if (
																    hasattr(SCREAMING_SNAKE_CASE_ ,'''_hf_hook''' )
																    and hasattr(module._hf_hook ,'''execution_device''' )
																    and module._hf_hook.execution_device is not None
																):
																						return torch.device(module._hf_hook.execution_device )
										return self.device

				def  _lowerCamelCase   (				self				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Dict ):
										if latents is None:
																snake_case__    :   str							     =   randn_tensor(SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,device=SCREAMING_SNAKE_CASE_ ,dtype=SCREAMING_SNAKE_CASE_ )
										else:
																if latents.shape != shape:
																						raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
																snake_case__    :   Any							     =   latents.to(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   Optional[Any]							     =   latents * scheduler.init_noise_sigma
										return latents

				def  _lowerCamelCase   (				self				:       str ,SCREAMING_SNAKE_CASE_				:       Optional[Union[str, torch.device]] = None ):
										self.decoder_pipe.to(SCREAMING_SNAKE_CASE_ )
										super().to(SCREAMING_SNAKE_CASE_ )





				@torch.no_grad()
				def __call__(				self				:       str ,SCREAMING_SNAKE_CASE_				:       Optional[Union[str, List[str]]] = None ,SCREAMING_SNAKE_CASE_				:       Optional[int] = None ,SCREAMING_SNAKE_CASE_				:       Optional[int] = None ,SCREAMING_SNAKE_CASE_				:       int = 1 ,SCREAMING_SNAKE_CASE_				:       int = 2_5 ,SCREAMING_SNAKE_CASE_				:       Optional[torch.Generator] = None ,SCREAMING_SNAKE_CASE_				:       Optional[torch.FloatTensor] = None ,SCREAMING_SNAKE_CASE_				:       Optional[Union[CLIPTextModelOutput, Tuple]] = None ,SCREAMING_SNAKE_CASE_				:       Optional[torch.Tensor] = None ,SCREAMING_SNAKE_CASE_				:       float = 4.0 ,SCREAMING_SNAKE_CASE_				:       float = 8.0 ,SCREAMING_SNAKE_CASE_				:       int = 5_0 ,SCREAMING_SNAKE_CASE_				:       Optional[int] = 1 ,SCREAMING_SNAKE_CASE_				:       float = 0.0 ,SCREAMING_SNAKE_CASE_				:       Optional[str] = "pil" ,SCREAMING_SNAKE_CASE_				:       bool = True ,):
										if prompt is not None:
																if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
																						snake_case__    :   Dict							     =   1
																elif isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
																						snake_case__    :   List[Any]							     =   len(SCREAMING_SNAKE_CASE_ )
																else:
																						raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_ )}''' )
										else:
																snake_case__    :   Tuple							     =   text_model_output[0].shape[0]

										snake_case__    :   List[str]							     =   self._execution_device

										snake_case__    :   str							     =   batch_size * num_images_per_prompt

										snake_case__    :   List[str]							     =   prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

										snake_case__     ,		snake_case__     ,		snake_case__    :   Tuple							     =   self._encode_prompt(
										    SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )

										# prior

										self.prior_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ ,device=SCREAMING_SNAKE_CASE_ )
										snake_case__    :   str							     =   self.prior_scheduler.timesteps

										snake_case__    :   str							     =   self.prior.config.embedding_dim

										snake_case__    :   Optional[int]							     =   self.prepare_latents(
										    (batch_size, embedding_dim) ,text_embeddings.dtype ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,self.prior_scheduler ,)

										for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_ ) ):
																# expand the latents if we are doing classifier free guidance
																snake_case__    :   Tuple							     =   torch.cat([prior_latents] * 2 ) if do_classifier_free_guidance else prior_latents

																snake_case__    :   Optional[int]							     =   self.prior(
																    SCREAMING_SNAKE_CASE_ ,timestep=SCREAMING_SNAKE_CASE_ ,proj_embedding=SCREAMING_SNAKE_CASE_ ,encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,attention_mask=SCREAMING_SNAKE_CASE_ ,).predicted_image_embedding

																if do_classifier_free_guidance:
																						snake_case__     ,		snake_case__    :   Dict							     =   predicted_image_embedding.chunk(2 )
																						snake_case__    :   Any							     =   predicted_image_embedding_uncond + prior_guidance_scale * (
																						    predicted_image_embedding_text - predicted_image_embedding_uncond
																						)

																if i + 1 == prior_timesteps_tensor.shape[0]:
																						snake_case__    :   str							     =   None
																else:
																						snake_case__    :   Tuple							     =   prior_timesteps_tensor[i + 1]

																snake_case__    :   List[Any]							     =   self.prior_scheduler.step(
																    SCREAMING_SNAKE_CASE_ ,timestep=SCREAMING_SNAKE_CASE_ ,sample=SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,prev_timestep=SCREAMING_SNAKE_CASE_ ,).prev_sample

										snake_case__    :   Union[str, Any]							     =   self.prior.post_process_latents(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   Any							     =   prior_latents

										snake_case__    :   Dict							     =   self.decoder_pipe(
										    image=SCREAMING_SNAKE_CASE_ ,height=SCREAMING_SNAKE_CASE_ ,width=SCREAMING_SNAKE_CASE_ ,num_inference_steps=SCREAMING_SNAKE_CASE_ ,guidance_scale=SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,output_type=SCREAMING_SNAKE_CASE_ ,return_dict=SCREAMING_SNAKE_CASE_ ,num_images_per_prompt=SCREAMING_SNAKE_CASE_ ,eta=SCREAMING_SNAKE_CASE_ ,)
										return output


