






import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


A_			:Optional[int]	    =		logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 							__A	(     _UpperCamelCase					):





				"""simple docstring"""







				UpperCamelCase__					: Tuple     					=4_2
				UpperCamelCase__					: Optional[int]     					=None






def 				A	(			a_				,a_=0.999				,a_="cosine"				,)	->		Optional[Any]:
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(a_   ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(a_   ):
															return math.exp(t * -12.0   )

					else:
										raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'   )

					__UpperCamelCase :   Optional[int]									=[]
					for i in range(__lowercase   ):
										__UpperCamelCase :   List[Any]									=i / num_diffusion_timesteps
										__UpperCamelCase :   Dict									=(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(__lowercase   ) / alpha_bar_fn(__lowercase   )				,__lowercase   )   )
					return torch.tensor(__lowercase				,dtype=torch.floataa   )




class 							__A	(     _UpperCamelCase ,	_UpperCamelCase					):





				"""simple docstring"""







				UpperCamelCase__					: str     					=[e.name for e in KarrasDiffusionSchedulers]
				UpperCamelCase__					: int     					=1


				@register_to_config
				def __init__(					self				,    lowerCamelCase__ = 1000				,    lowerCamelCase__ = 0.0_001				,    lowerCamelCase__ = 0.02				,    lowerCamelCase__ = "linear"				,    lowerCamelCase__ = None				,    lowerCamelCase__ = "epsilon"				,    lowerCamelCase__ = "linear"				,    lowerCamelCase__ = False				,    lowerCamelCase__ = "linspace"				,    lowerCamelCase__ = 0				,    ):




									"""simple docstring"""

									if trained_betas is not None:
														__UpperCamelCase :   int									=torch.tensor(a__				,    dtype=torch.floataa       )
									elif beta_schedule == "linear":
														__UpperCamelCase :   Tuple									=torch.linspace(a__				,    a__				,    a__				,    dtype=torch.floataa       )
									elif beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														__UpperCamelCase :   int									=(
														    torch.linspace(beta_start**0.5				,    beta_end**0.5				,    a__				,    dtype=torch.floataa       ) ** 2
														)
									elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														__UpperCamelCase :   Dict									=betas_for_alpha_bar(a__       )
									else:
														raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'       )

									__UpperCamelCase :   List[Any]									=1.0 - self.betas
									__UpperCamelCase :   Tuple									=torch.cumprod(self.alphas				,    dim=0       )

									__UpperCamelCase :   Optional[int]									=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
									__UpperCamelCase :   List[str]									=np.concatenate([sigmas[::-1], [0.0]]       ).astype(np.floataa       )
									__UpperCamelCase :   List[str]									=torch.from_numpy(a__       )

									# setable values
									__UpperCamelCase :   Union[str, Any]									=None
									__UpperCamelCase :   Union[str, Any]									=np.linspace(0				,    num_train_timesteps - 1				,    a__				,    dtype=a__       )[::-1].copy()
									__UpperCamelCase :   List[Any]									=torch.from_numpy(a__       )
									__UpperCamelCase :   Tuple									=False
									__UpperCamelCase :   int									=use_karras_sigmas


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									if self.config.timestep_spacing in ["linspace", "trailing"]:
														return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									if isinstance(a__				,    torch.Tensor       ):
														__UpperCamelCase :   Tuple									=timestep.to(self.timesteps.device       )
									__UpperCamelCase :   Optional[Any]									=(self.timesteps == timestep).nonzero().item()
									__UpperCamelCase :   Union[str, Any]									=self.sigmas[step_index]

									__UpperCamelCase :   Optional[int]									=sample / ((sigma**2 + 1) ** 0.5)

									__UpperCamelCase :   List[Any]									=True
									return sample


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__ = None       ):




									"""simple docstring"""

									__UpperCamelCase :   Dict									=num_inference_steps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
														__UpperCamelCase :   List[Any]									=np.linspace(0				,    self.config.num_train_timesteps - 1				,    a__				,    dtype=a__       )[
														    ::-1
														].copy()
									elif self.config.timestep_spacing == "leading":
														__UpperCamelCase :   List[str]									=self.config.num_train_timesteps // self.num_inference_steps
														# creates integer timesteps by multiplying by ratio
														# casting to int to avoid issues when num_inference_step is power of 3
														__UpperCamelCase :   Optional[Any]									=(np.arange(0				,    a__       ) * step_ratio).round()[::-1].copy().astype(a__       )
														timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
														__UpperCamelCase :   Optional[int]									=self.config.num_train_timesteps / self.num_inference_steps
														# creates integer timesteps by multiplying by ratio
														# casting to int to avoid issues when num_inference_step is power of 3
														__UpperCamelCase :   List[Any]									=(np.arange(self.config.num_train_timesteps				,    0				,    -step_ratio       )).round().copy().astype(a__       )
														timesteps -= 1
									else:
														raise ValueError(
														    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'       )

									__UpperCamelCase :   int									=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
									__UpperCamelCase :   Tuple									=np.log(a__       )

									if self.config.interpolation_type == "linear":
														__UpperCamelCase :   str									=np.interp(a__				,    np.arange(0				,    len(a__       )       )				,    a__       )
									elif self.config.interpolation_type == "log_linear":
														__UpperCamelCase :   List[str]									=torch.linspace(np.log(sigmas[-1]       )				,    np.log(sigmas[0]       )				,    num_inference_steps + 1       ).exp()
									else:
														raise ValueError(
														    f'{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'
														    ' \'linear\' or \'log_linear\''       )

									if self.use_karras_sigmas:
														__UpperCamelCase :   List[Any]									=self._convert_to_karras(in_sigmas=a__				,    num_inference_steps=self.num_inference_steps       )
														__UpperCamelCase :   str									=np.array([self._sigma_to_t(a__				,    a__       ) for sigma in sigmas]       )

									__UpperCamelCase :   Dict									=np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
									__UpperCamelCase :   Union[str, Any]									=torch.from_numpy(a__       ).to(device=a__       )
									if str(a__       ).startswith('mps'       ):
														# mps does not support float64
														__UpperCamelCase :   Tuple									=torch.from_numpy(a__       ).to(a__				,    dtype=torch.floataa       )
									else:
														__UpperCamelCase :   str									=torch.from_numpy(a__       ).to(device=a__       )


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   List[str]									=np.log(a__       )

									# get distribution
									__UpperCamelCase :   Any									=log_sigma - log_sigmas[:, np.newaxis]

									# get sigmas range
									__UpperCamelCase :   List[str]									=np.cumsum((dists >= 0)				,    axis=0       ).argmax(axis=0       ).clip(max=log_sigmas.shape[0] - 2       )
									__UpperCamelCase :   Dict									=low_idx + 1

									__UpperCamelCase :   Optional[Any]									=log_sigmas[low_idx]
									__UpperCamelCase :   Optional[Any]									=log_sigmas[high_idx]

									# interpolate sigmas
									__UpperCamelCase :   Any									=(low - log_sigma) / (low - high)
									__UpperCamelCase :   Union[str, Any]									=np.clip(a__				,    0				,    1       )

									# transform interpolation to time range
									__UpperCamelCase :   int									=(1 - w) * low_idx + w * high_idx
									__UpperCamelCase :   Optional[int]									=t.reshape(sigma.shape       )
									return t


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Union[str, Any]									=in_sigmas[-1].item()
									__UpperCamelCase :   List[Any]									=in_sigmas[0].item()

									__UpperCamelCase :   int									=7.0  # 7.0 is the value used in the paper
									__UpperCamelCase :   List[str]									=np.linspace(0				,    1				,    a__       )
									__UpperCamelCase :   Any									=sigma_min ** (1 / rho)
									__UpperCamelCase :   List[Any]									=sigma_max ** (1 / rho)
									__UpperCamelCase :   Tuple									=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__ = 0.0				,    lowerCamelCase__ = 0.0				,    lowerCamelCase__ = float('inf'       )				,    lowerCamelCase__ = 1.0				,    lowerCamelCase__ = None				,    lowerCamelCase__ = True				,    ):




									"""simple docstring"""

									if (
									    isinstance(a__				,    a__       )
									    or isinstance(a__				,    torch.IntTensor       )
									    or isinstance(a__				,    torch.LongTensor       )
									):
														raise ValueError(
														    (
														        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
														        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
														        ' one of the `scheduler.timesteps` as a timestep.'
														    )				,    )

									if not self.is_scale_input_called:
														logger.warning(
														    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
														    'See `StableDiffusionPipeline` for a usage example.'       )

									if isinstance(a__				,    torch.Tensor       ):
														__UpperCamelCase :   Any									=timestep.to(self.timesteps.device       )

									__UpperCamelCase :   Union[str, Any]									=(self.timesteps == timestep).nonzero().item()
									__UpperCamelCase :   List[str]									=self.sigmas[step_index]

									__UpperCamelCase :   int									=min(s_churn / (len(self.sigmas       ) - 1)				,    2**0.5 - 1       ) if s_tmin <= sigma <= s_tmax else 0.0

									__UpperCamelCase :   List[str]									=randn_tensor(
									    model_output.shape				,    dtype=model_output.dtype				,    device=model_output.device				,    generator=a__       )

									__UpperCamelCase :   int									=noise * s_noise
									__UpperCamelCase :   Tuple									=sigma * (gamma + 1)

									if gamma > 0:
														__UpperCamelCase :   Optional[int]									=sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									# NOTE: "original_sample" should not be an expected prediction_type but is left in for
									# backwards compatibility
									if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
														__UpperCamelCase :   Optional[Any]									=model_output
									elif self.config.prediction_type == "epsilon":
														__UpperCamelCase :   Tuple									=sample - sigma_hat * model_output
									elif self.config.prediction_type == "v_prediction":
														# * c_out + input * c_skip
														__UpperCamelCase :   int									=model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									else:
														raise ValueError(
														    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'       )

									# 2. Convert to an ODE derivative
									__UpperCamelCase :   str									=(sample - pred_original_sample) / sigma_hat

									__UpperCamelCase :   int									=self.sigmas[step_index + 1] - sigma_hat

									__UpperCamelCase :   Tuple									=sample + derivative * dt

									if not return_dict:
														return (prev_sample,)

									return EulerDiscreteSchedulerOutput(prev_sample=a__				,    pred_original_sample=a__       )


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    ):




									"""simple docstring"""

									__UpperCamelCase :   List[Any]									=self.sigmas.to(device=original_samples.device				,    dtype=original_samples.dtype       )
									if original_samples.device.type == "mps" and torch.is_floating_point(a__       ):
														# mps does not support float64
														__UpperCamelCase :   Optional[Any]									=self.timesteps.to(original_samples.device				,    dtype=torch.floataa       )
														__UpperCamelCase :   Any									=timesteps.to(original_samples.device				,    dtype=torch.floataa       )
									else:
														__UpperCamelCase :   Union[str, Any]									=self.timesteps.to(original_samples.device       )
														__UpperCamelCase :   str									=timesteps.to(original_samples.device       )

									__UpperCamelCase :   str									=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

									__UpperCamelCase :   Tuple									=sigmas[step_indices].flatten()
									while len(sigma.shape       ) < len(original_samples.shape       ):
														__UpperCamelCase :   str									=sigma.unsqueeze(-1       )

									__UpperCamelCase :   List[Any]									=original_samples + noise * sigma
									return noisy_samples







				def __len__(					self       ):




									"""simple docstring"""

									return self.config.num_train_timesteps





"""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


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






def   a__			(					self  ,      __lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase )						->						Tuple:
				_A       	=      image.to(device=__lowercase )
				_A       	=      image  # take image as image_embeddings
				_A       	=      image_embeddings.unsqueeze(1 )

				# duplicate image embeddings for each generation per prompt, using mps friendly method
				_A	,		_A	,		_A       	=      image_embeddings.shape
				_A       	=      image_embeddings.repeat(1  ,      __lowercase  ,      1 )
				_A       	=      image_embeddings.view(bs_embed * num_images_per_prompt  ,      __lowercase  ,      -1 )

				if do_classifier_free_guidance:
								_A       	=      torch.zeros_like(__lowercase )

								# 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
								_A       	=      torch.cat([uncond_embeddings, image_embeddings] )

				return image_embeddings




class snake_case   (	_UpperCamelCase):







		def __init__(       self   :							Dict     , a__   :							PriorTransformer     , a__   :							CLIPTokenizer     , a__   :							CLIPTextModelWithProjection     , a__   :							UnCLIPScheduler     , a__   :							Optional[dict] = None     , )							->    Optional[Any]:





						'''simple docstring'''







						super().__init__()

						_A       	=      {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

						_A       	=      decoder_pipe_kwargs.get("torch_dtype"     , a__		) or prior.dtype

						_A       	=      StableDiffusionImageVariationPipeline.from_pretrained(
						    "lambdalabs/sd-image-variations-diffusers"     , **a__		)

						# replace `_encode_image` method
						_A       	=      types.MethodType(_encode_image     , self.decoder_pipe		)

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







		def 			a_			(       self   :							Optional[Any]     , a__   :							str     , a__   :							Optional[Any]     , a__   :							int     , a__   :							Optional[int]     , a__   :							Optional[Union[CLIPTextModelOutput, Tuple]] = None     , a__   :							Optional[torch.Tensor] = None     , )							->    Optional[Any]:





						'''simple docstring'''







						if text_model_output is None:
										_A       	=      len(a__		) if isinstance(a__     , a__		) else 1
										# get prompt text embeddings
										_A       	=      self.tokenizer(
										    a__     , padding="max_length"     , max_length=self.tokenizer.model_max_length     , return_tensors="pt"     , )
										_A       	=      text_inputs.input_ids
										_A       	=      text_inputs.attention_mask.bool().to(a__		)

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
														_A       	=      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}"""		)
														_A       	=      text_input_ids[:, : self.tokenizer.model_max_length]

										_A       	=      self.text_encoder(text_input_ids.to(a__		)		)

										_A       	=      text_encoder_output.text_embeds
										_A       	=      text_encoder_output.last_hidden_state

						else:
										_A       	=      text_model_output[0].shape[0]
										_A	,		_A       	=      text_model_output[0], text_model_output[1]
										_A       	=      text_attention_mask

						_A       	=      text_embeddings.repeat_interleave(a__     , dim=0		)
						_A       	=      text_encoder_hidden_states.repeat_interleave(a__     , dim=0		)
						_A       	=      text_mask.repeat_interleave(a__     , dim=0		)

						if do_classifier_free_guidance:
										_A       	=      [""] * batch_size

										_A       	=      self.tokenizer(
										    a__     , padding="max_length"     , max_length=self.tokenizer.model_max_length     , truncation=a__     , return_tensors="pt"     , )
										_A       	=      uncond_input.attention_mask.bool().to(a__		)
										_A       	=      self.text_encoder(uncond_input.input_ids.to(a__		)		)

										_A       	=      uncond_embeddings_text_encoder_output.text_embeds
										_A       	=      uncond_embeddings_text_encoder_output.last_hidden_state

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

										_A       	=      uncond_embeddings.shape[1]
										_A       	=      uncond_embeddings.repeat(1     , a__		)
										_A       	=      uncond_embeddings.view(batch_size * num_images_per_prompt     , a__		)

										_A       	=      uncond_text_encoder_hidden_states.shape[1]
										_A       	=      uncond_text_encoder_hidden_states.repeat(1     , a__     , 1		)
										_A       	=      uncond_text_encoder_hidden_states.view(
										    batch_size * num_images_per_prompt     , a__     , -1		)
										_A       	=      uncond_text_mask.repeat_interleave(a__     , 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
										_A       	=      torch.cat([uncond_embeddings, text_embeddings]		)
										_A       	=      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]		)

										_A       	=      torch.cat([uncond_text_mask, text_mask]		)

						return text_embeddings, text_encoder_hidden_states, text_mask







		@property
		def 			a_			(       self   :							int		)							->    Tuple:





						'''simple docstring'''







						if self.device != torch.device("meta"		) or not hasattr(self.prior     , "_hf_hook"		):
										return self.device
						for module in self.prior.modules():
										if (
										    hasattr(a__     , "_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 			a_			(       self   :							str     , a__   :							int     , a__   :							str     , a__   :							List[str]     , a__   :							Dict     , a__   :							Optional[Any]     , a__   :							Dict		)							->    int:





						'''simple docstring'''







						if latents is None:
										_A       	=      randn_tensor(a__     , generator=a__     , device=a__     , dtype=a__		)
						else:
										if latents.shape != shape:
														raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""		)
										_A       	=      latents.to(a__		)

						_A       	=      latents * scheduler.init_noise_sigma
						return latents







		def 			a_			(       self   :							List[str]     , a__   :							Optional[Union[str, torch.device]] = None		)							->    Any:





						'''simple docstring'''







						self.decoder_pipe.to(a__		)
						super().to(a__		)







		@torch.no_grad()
		def __call__(       self   :							List[Any]     , a__   :							Optional[Union[str, List[str]]] = None     , a__   :							Optional[int] = None     , a__   :							Optional[int] = None     , a__   :							int = 1     , a__   :							int = 25     , a__   :							Optional[torch.Generator] = None     , a__   :							Optional[torch.FloatTensor] = None     , a__   :							Optional[Union[CLIPTextModelOutput, Tuple]] = None     , a__   :							Optional[torch.Tensor] = None     , a__   :							float = 4.0     , a__   :							float = 8.0     , a__   :							int = 50     , a__   :							Optional[int] = 1     , a__   :							float = 0.0     , a__   :							Optional[str] = "pil"     , a__   :							bool = True     , )							->    str:





						'''simple docstring'''







						if prompt is not None:
										if isinstance(a__     , a__		):
														_A       	=      1
										elif isinstance(a__     , a__		):
														_A       	=      len(a__		)
										else:
														raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(a__		)}"""		)
						else:
										_A       	=      text_model_output[0].shape[0]

						_A       	=      self._execution_device

						_A       	=      batch_size * num_images_per_prompt

						_A       	=      prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

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

						# prior

						self.prior_scheduler.set_timesteps(a__     , device=a__		)
						_A       	=      self.prior_scheduler.timesteps

						_A       	=      self.prior.config.embedding_dim

						_A       	=      self.prepare_latents(
						    (batch_size, embedding_dim)     , text_embeddings.dtype     , a__     , a__     , a__     , self.prior_scheduler     , )

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

										_A       	=      self.prior(
										    a__     , timestep=a__     , proj_embedding=a__     , encoder_hidden_states=a__     , attention_mask=a__     , ).predicted_image_embedding

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

										if i + 1 == prior_timesteps_tensor.shape[0]:
														_A       	=      None
										else:
														_A       	=      prior_timesteps_tensor[i + 1]

										_A       	=      self.prior_scheduler.step(
										    a__     , timestep=a__     , sample=a__     , generator=a__     , prev_timestep=a__     , ).prev_sample

						_A       	=      self.prior.post_process_latents(a__		)

						_A       	=      prior_latents

						_A       	=      self.decoder_pipe(
						    image=a__     , height=a__     , width=a__     , num_inference_steps=a__     , guidance_scale=a__     , generator=a__     , output_type=a__     , return_dict=a__     , num_images_per_prompt=a__     , eta=a__     , )
						return output