



from abc import ABC, abstractmethod
from typing import List, Optional
class    _UpperCAmelCase      ( __a			):

				"""simple docstring"""




				def __init__(  self		:					Dict						):

								'''simple docstring'''
								self.test()




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    0
								lowercase__     				=    False
								while not completed:
												if counter == 1:
																self.reset()
												lowercase__     				=    self.advance()
												if not self.does_advance(a_						):
																raise Exception(
																    '''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'''						)

												lowercase__     				=    self.update(a_						)
												counter += 1

												if counter > 10_000:
																raise Exception('''update() does not fulfill the constraint.'''						)

								if self.remaining() != 0:
												raise Exception('''Custom Constraint is not defined correctly.'''						)




				@abstractmethod
				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								raise NotImplementedError(
								    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""						)




				@abstractmethod
				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					int						):

								'''simple docstring'''
								raise NotImplementedError(
								    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""						)




				@abstractmethod
				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					int						):

								'''simple docstring'''
								raise NotImplementedError(
								    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""						)




				@abstractmethod
				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								raise NotImplementedError(
								    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""						)




				@abstractmethod
				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								raise NotImplementedError(
								    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""						)




				@abstractmethod
				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					Tuple=False						):

								'''simple docstring'''
								raise NotImplementedError(
								    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""						)



class    _UpperCAmelCase      ( __a			):

				"""simple docstring"""




				def __init__(  self		:					Tuple,						lowerCamelCase		:					List[int]						):

								'''simple docstring'''
								super(a_,						self						).__init__()

								if not isinstance(a_,						a_						) or len(a_						) == 0:
												raise ValueError(F"""`token_ids` has to be a non-empty list, but is {token_ids}."""						)
								if any((not isinstance(a_,						a_						) or token_id < 0) for token_id in token_ids						):
												raise ValueError(F"""Each list in `token_ids` has to be a list of positive integers, but is {token_ids}."""						)

								lowercase__     				=    token_ids

								lowercase__     				=    len(self.token_ids						)
								lowercase__     				=    -1  # the index of the currently fulfilled step
								lowercase__     				=    False




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''



								if self.completed:
												return None
								return self.token_ids[self.fulfilled_idx + 1]




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

								'''simple docstring'''



								if not isinstance(a_,						a_						):
												raise ValueError(F"""`token_id` has to be an `int`, but is {token_id} of type {type(a_						)}"""						)

								if self.completed:
												return False

								return token_id == self.token_ids[self.fulfilled_idx + 1]




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					int						):

								'''simple docstring'''



								if not isinstance(a_,						a_						):
												raise ValueError(F"""`token_id` has to be an `int`, but is {token_id} of type {type(a_						)}"""						)

								lowercase__     				=    False
								lowercase__     				=    False
								lowercase__     				=    False

								if self.does_advance(a_						):
												self.fulfilled_idx += 1
												lowercase__     				=    True
												if self.fulfilled_idx == (self.seqlen - 1):
																lowercase__     				=    True
												lowercase__     				=    completed
								else:
												# failed to make progress.
												lowercase__     				=    True
												self.reset()
								return stepped, completed, reset




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

								'''simple docstring'''
								lowercase__     				=    False
								lowercase__     				=    0




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								return self.seqlen - (self.fulfilled_idx + 1)




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

								'''simple docstring'''
								lowercase__     				=    PhrasalConstraint(self.token_ids						)

								if stateful:
												lowercase__     				=    self.seqlen
												lowercase__     				=    self.fulfilled_idx
												lowercase__     				=    self.completed

								return new_constraint



class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					int,						lowerCamelCase		:					List[List[int]],						lowerCamelCase		:					int=True						):

								'''simple docstring'''
								lowercase__     				=    max([len(a_						) for one in nested_token_ids]						)

								lowercase__     				=    {}
								for token_ids in nested_token_ids:
												lowercase__     				=    root
												for tidx, token_id in enumerate(a_						):
																if token_id not in level:
																				lowercase__     				=    {}

																lowercase__     				=    level[token_id]

								if no_subsets and self.has_subsets(a_,						a_						):
												raise ValueError(
												    '''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
												    F""" {nested_token_ids}."""						)

								lowercase__     				=    root




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.trie

								for current_token in current_seq:
												lowercase__     				=    start[current_token]

								lowercase__     				=    list(start.keys()						)

								return next_tokens




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

								'''simple docstring'''
								lowercase__     				=    self.next_tokens(a_						)

								return len(a_						) == 0




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    list(root.values()						)
								if len(a_						) == 0:
												return 1
								else:
												return sum([self.count_leaves(a_						) for nn in next_nodes]						)




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

								'''simple docstring'''
								lowercase__     				=    self.count_leaves(a_						)
								return len(a_						) != leaf_count



class    _UpperCAmelCase      ( __a			):

				"""simple docstring"""




				def __init__(  self		:					Tuple,						lowerCamelCase		:					List[List[int]]						):

								'''simple docstring'''
								super(a_,						self						).__init__()

								if not isinstance(a_,						a_						) or len(a_						) == 0:
												raise ValueError(F"""`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}."""						)
								if any(not isinstance(a_,						a_						) for token_ids in nested_token_ids						):
												raise ValueError(F"""`nested_token_ids` has to be a list of lists, but is {nested_token_ids}."""						)
								if any(
								    any((not isinstance(a_,						a_						) or token_id < 0) for token_id in token_ids						)
								    for token_ids in nested_token_ids						):
												raise ValueError(
												    F"""Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}."""						)

								lowercase__     				=    DisjunctiveTrie(a_						)
								lowercase__     				=    nested_token_ids

								lowercase__     				=    self.trie.max_height
								lowercase__     				=    []
								lowercase__     				=    False




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    self.trie.next_tokens(self.current_seq						)

								if len(a_						) == 0:
												return None
								else:
												return token_list




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					int						):

								'''simple docstring'''



								if not isinstance(a_,						a_						):
												raise ValueError(F"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(a_						)}"""						)

								lowercase__     				=    self.trie.next_tokens(self.current_seq						)

								return token_id in next_tokens




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					int						):

								'''simple docstring'''



								if not isinstance(a_,						a_						):
												raise ValueError(F"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(a_						)}"""						)

								lowercase__     				=    False
								lowercase__     				=    False
								lowercase__     				=    False

								if self.does_advance(a_						):
												self.current_seq.append(a_						)
												lowercase__     				=    True
								else:
												lowercase__     				=    True
												self.reset()

								lowercase__     				=    self.trie.reached_leaf(self.current_seq						)
								lowercase__     				=    completed

								return stepped, completed, reset




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    False
								lowercase__     				=    []




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''



								if self.completed:
												# since this can be completed without reaching max height
												return 0
								else:
												return self.seqlen - len(self.current_seq						)




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					List[Any]=False						):

								'''simple docstring'''
								lowercase__     				=    DisjunctiveConstraint(self.token_ids						)

								if stateful:
												lowercase__     				=    self.seqlen
												lowercase__     				=    self.current_seq
												lowercase__     				=    self.completed

								return new_constraint



class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					Any,						lowerCamelCase		:					List[Constraint]						):

								'''simple docstring'''
								lowercase__     				=    constraints

								# max # of steps required to fulfill a given constraint
								lowercase__     				=    max([c.seqlen for c in constraints]						)
								lowercase__     				=    len(a_						)
								lowercase__     				=    False

								self.init_state()




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    []
								lowercase__     				=    None
								lowercase__     				=    [constraint.copy(stateful=a_						) for constraint in self.constraints]




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

								'''simple docstring'''
								lowercase__     				=    0
								if self.inprogress_constraint:
												# extra points for having a constraint mid-fulfilled
												add += self.max_seqlen - self.inprogress_constraint.remaining()

								return (len(self.complete_constraints						) * self.max_seqlen) + add




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    []
								if self.inprogress_constraint is None:
												for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
																lowercase__     				=    constraint.advance()
																if isinstance(a_,						a_						):
																				token_list.append(a_						)
																elif isinstance(a_,						a_						):
																				token_list.extend(a_						)
								else:
												lowercase__     				=    self.inprogress_constraint.advance()
												if isinstance(a_,						a_						):
																token_list.append(a_						)
												elif isinstance(a_,						a_						):
																token_list.extend(a_						)

								if len(a_						) == 0:
												return None
								else:
												return token_list




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					Optional[List[int]]						):

								'''simple docstring'''
								self.init_state()

								if token_ids is not None:
												for token in token_ids:
																# completes or steps **one** constraint
																lowercase__     				=    self.add(a_						)

																# the entire list of constraints are fulfilled
																if self.completed:
																				break




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

								'''simple docstring'''



								if not isinstance(a_,						a_						):
												raise ValueError(F"""`token_id` should be an `int`, but is `{token_id}`."""						)

								lowercase__     				=    False, False

								if self.completed:
												lowercase__     				=    True
												lowercase__     				=    False
												return complete, stepped

								if self.inprogress_constraint is not None:
												# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
												# job, simply update the state

												lowercase__     				=    self.inprogress_constraint.update(a_						)
												if reset:
																# 1. If the next token breaks the progress, then we must restart.
																#     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

																#     But that doesn't mean we self.init_state(), since we only reset the state for this particular
																#     constraint, not the full list of constraints.

																self.pending_constraints.append(self.inprogress_constraint.copy(stateful=a_						)						)
																lowercase__     				=    None

												if complete:
																# 2. If the next token completes the constraint, move it to completed list, set
																#     inprogress to None. If there are no pending constraints either, then this full list of constraints
																#     is complete.

																self.complete_constraints.append(self.inprogress_constraint						)
																lowercase__     				=    None

																if len(self.pending_constraints						) == 0:
																				# we're done!
																				lowercase__     				=    True

								else:
												# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
												# of constraints?

												for cidx, pending_constraint in enumerate(self.pending_constraints						):
																if pending_constraint.does_advance(a_						):
																				lowercase__     				=    pending_constraint.update(a_						)

																				if not stepped:
																								raise Exception(
																								    '''`constraint.update(token_id)` is not yielding incremental progress, '''
																								    '''even though `constraint.does_advance(token_id)` is true.'''						)

																				if complete:
																								self.complete_constraints.append(a_						)
																								lowercase__     				=    None

																				if not complete and stepped:
																								lowercase__     				=    pending_constraint

																				if complete or stepped:
																								# If we made any progress at all, then it's at least not a "pending constraint".

																								lowercase__     				=    (
																								    self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
																								)

																								if len(self.pending_constraints						) == 0 and self.inprogress_constraint is None:
																												# If there's no longer any pending after this and no inprogress either, then we must be
																												# complete.

																												lowercase__     				=    True

																								break  # prevent accidentally stepping through multiple constraints with just one token.

								return complete, stepped




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					int=True						):

								'''simple docstring'''
								lowercase__     				=    ConstraintListState(self.constraints						)  # we actually never though self.constraints objects
								# throughout this process. So it's at initialization state.

								if stateful:
												lowercase__     				=    [
												    constraint.copy(stateful=a_						) for constraint in self.complete_constraints
												]
												if self.inprogress_constraint is not None:
																lowercase__     				=    self.inprogress_constraint.copy(stateful=a_						)
												lowercase__     				=    [constraint.copy() for constraint in self.pending_constraints]

								return new_state












import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


A__			:    Any         =				logging.get_logger(__name__)  # pylint: disable=invalid-name
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					WhisperForConditionalGeneration,						lowerCamelCase		:					WhisperProcessor,						lowerCamelCase		:					AutoencoderKL,						lowerCamelCase		:					CLIPTextModel,						lowerCamelCase		:					CLIPTokenizer,						lowerCamelCase		:					UNetaDConditionModel,						lowerCamelCase		:					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],						lowerCamelCase		:					StableDiffusionSafetyChecker,						lowerCamelCase		:					CLIPImageProcessor,						):

								'''simple docstring'''
								super().__init__()

								if safety_checker is None:
												logger.warning(
												    F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
												    ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
												    ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
												    ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
												    ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
												    ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''						)

								self.register_modules(
								    speech_model=lowerCamelCase,						speech_processor=lowerCamelCase,						vae=lowerCamelCase,						text_encoder=lowerCamelCase,						tokenizer=lowerCamelCase,						unet=lowerCamelCase,						scheduler=lowerCamelCase,						feature_extractor=lowerCamelCase,						)




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					Optional[Union[str, int]] = "auto"						):

								'''simple docstring'''
								if slice_size == "auto":
												lowercase__     				=    self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								self.enable_attention_slicing(lowerCamelCase						)




				@torch.no_grad()
				def __call__(  self		:					Any,						lowerCamelCase		:					Optional[Any],						lowerCamelCase		:					Optional[Any]=16_000,						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,						**lowerCamelCase		:					Optional[Any],						):

								'''simple docstring'''
								lowercase__     				=    self.speech_processor.feature_extractor(
								    lowerCamelCase,						return_tensors='''pt''',						sampling_rate=lowerCamelCase						).input_features.to(self.device						)
								lowercase__     				=    self.speech_model.generate(lowerCamelCase,						max_length=480_000						)

								lowercase__     				=    self.speech_processor.tokenizer.batch_decode(lowerCamelCase,						skip_special_tokens=lowerCamelCase,						normalize=lowerCamelCase						)[
								    0
								]

								if isinstance(lowerCamelCase,						lowerCamelCase						):
												lowercase__     				=    1
								elif isinstance(lowerCamelCase,						lowerCamelCase						):
												lowercase__     				=    len(lowerCamelCase						)
								else:
												raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase						)}"""						)

								if height % 8 != 0 or width % 8 != 0:
												raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""						)

								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(lowerCamelCase,						lowerCamelCase						) or callback_steps <= 0)
								):
												raise ValueError(
												    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
												    F""" {type(lowerCamelCase						)}."""						)

								# get prompt text embeddings
								lowercase__     				=    self.tokenizer(
								    lowerCamelCase,						padding='''max_length''',						max_length=self.tokenizer.model_max_length,						return_tensors='''pt''',						)
								lowercase__     				=    text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
												lowercase__     				=    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}"""						)
												lowercase__     				=    text_input_ids[:, : self.tokenizer.model_max_length]
								lowercase__     				=    self.text_encoder(text_input_ids.to(self.device						)						)[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								lowercase__				,		lowercase__				,		lowercase__     				=    text_embeddings.shape
								lowercase__     				=    text_embeddings.repeat(1,						lowerCamelCase,						1						)
								lowercase__     				=    text_embeddings.view(bs_embed * num_images_per_prompt,						lowerCamelCase,						-1						)

								# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
								# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
								# corresponds to doing no classifier free guidance.
								lowercase__     				=    guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
												lowercase__     				=    42
												if negative_prompt is None:
																lowercase__     				=    [''''''] * batch_size
												elif type(lowerCamelCase						) is not type(lowerCamelCase						):
																raise TypeError(
																    F"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase						)} !="""
																    F""" {type(lowerCamelCase						)}."""						)
												elif isinstance(lowerCamelCase,						lowerCamelCase						):
																lowercase__     				=    [negative_prompt]
												elif batch_size != len(lowerCamelCase						):
																raise ValueError(
																    F"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase						)}, but `prompt`:"""
																    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																    ''' the batch size of `prompt`.'''						)
												else:
																lowercase__     				=    negative_prompt

												lowercase__     				=    text_input_ids.shape[-1]
												lowercase__     				=    self.tokenizer(
												    lowerCamelCase,						padding='''max_length''',						max_length=lowerCamelCase,						truncation=lowerCamelCase,						return_tensors='''pt''',						)
												lowercase__     				=    self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												lowercase__     				=    uncond_embeddings.shape[1]
												lowercase__     				=    uncond_embeddings.repeat(1,						lowerCamelCase,						1						)
												lowercase__     				=    uncond_embeddings.view(batch_size * num_images_per_prompt,						lowerCamelCase,						-1						)

												# 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
												lowercase__     				=    torch.cat([uncond_embeddings, text_embeddings]						)

								# get the initial random noise unless the user supplied it

								# Unlike in other pipelines, latents need to be generated in the target device
								# for 1-to-1 results reproducibility with the CompVis implementation.
								# However this currently doesn't work in `mps`.
								lowercase__     				=    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								lowercase__     				=    text_embeddings.dtype
								if latents is None:
												if self.device.type == "mps":
																# randn does not exist on mps
																lowercase__     				=    torch.randn(lowerCamelCase,						generator=lowerCamelCase,						device='''cpu''',						dtype=lowerCamelCase						).to(
																    self.device						)
												else:
																lowercase__     				=    torch.randn(lowerCamelCase,						generator=lowerCamelCase,						device=self.device,						dtype=lowerCamelCase						)
								else:
												if latents.shape != latents_shape:
																raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""						)
												lowercase__     				=    latents.to(self.device						)

								# set timesteps
								self.scheduler.set_timesteps(lowerCamelCase						)

								# Some schedulers like PNDM have timesteps as arrays
								# It's more optimized to move all timesteps to correct device beforehand
								lowercase__     				=    self.scheduler.timesteps.to(self.device						)

								# scale the initial noise by the standard deviation required by the scheduler
								lowercase__     				=    latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								lowercase__     				=    '''eta''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
								lowercase__     				=    {}
								if accepts_eta:
												lowercase__     				=    eta

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

												# predict the noise residual
												lowercase__     				=    self.unet(lowerCamelCase,						lowerCamelCase,						encoder_hidden_states=lowerCamelCase						).sample

												# perform guidance
												if do_classifier_free_guidance:
																lowercase__				,		lowercase__     				=    noise_pred.chunk(2						)
																lowercase__     				=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

												# compute the previous noisy sample x_t -> x_t-1
												lowercase__     				=    self.scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase						).prev_sample

												# call the callback, if provided
												if callback is not None and i % callback_steps == 0:
																callback(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)

								lowercase__     				=    1 / 0.18215 * latents
								lowercase__     				=    self.vae.decode(lowerCamelCase						).sample

								lowercase__     				=    (image / 2 + 0.5).clamp(0,						1						)

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								lowercase__     				=    image.cpu().permute(0,						2,						3,						1						).float().numpy()

								if output_type == "pil":
												lowercase__     				=    self.numpy_to_pil(lowerCamelCase						)

								if not return_dict:
												return image

								return StableDiffusionPipelineOutput(images=lowerCamelCase,						nsfw_content_detected=lowerCamelCase						)




