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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						torch.FloatTensor
							lowerCAmelCase__		:						torch.FloatTensor






def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				List[Any]   ,							SCREAMING_SNAKE_CASE	:				Optional[int]=0.999   ,							SCREAMING_SNAKE_CASE	:				str="cosine"   ,							)					->						List[str]:
				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(SCREAMING_SNAKE_CASE	:				Union[str, Any]      ):
												return math.exp(t * -12.0      )

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

				__lowercase								= []
				for i in range(SCREAMING_SNAKE_CASE      ):
								__lowercase								= i / num_diffusion_timesteps
								__lowercase								= (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE      ) / alpha_bar_fn(SCREAMING_SNAKE_CASE      )   ,							SCREAMING_SNAKE_CASE      )      )
				return torch.tensor(SCREAMING_SNAKE_CASE   ,							dtype=torch.floataa      )
class      A__       (						lowerCAmelCase__   ,						lowerCAmelCase__    ):
							lowerCAmelCase__		:						List[str]         =							1





							@register_to_config
							def __init__(			self  :       Optional[Any]					,			_UpperCAmelCase  :       int = 10_00					,			_UpperCAmelCase  :       float = 0.0_001					,			_UpperCAmelCase  :       float = 0.02					,			_UpperCAmelCase  :       str = "linear"					,			_UpperCAmelCase  :       float = 0.0					,			_UpperCAmelCase  :       Optional[np.ndarray] = None					,			_UpperCAmelCase  :       bool = True					,			)						->		Tuple:



											"""simple docstring"""


											if trained_betas is not None:
															__lowercase								= torch.from_numpy(_UpperCAmelCase    )
											elif beta_schedule == "linear":
															__lowercase								= torch.linspace(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			dtype=torch.floataa    )
											elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															__lowercase								= (
															    torch.linspace(beta_start**0.5					,			beta_end**0.5					,			_UpperCAmelCase					,			dtype=torch.floataa    ) ** 2
															)
											elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															__lowercase								= betas_for_alpha_bar(_UpperCAmelCase    )
											elif beta_schedule == "sigmoid":
															# GeoDiff sigmoid schedule
															__lowercase								= torch.linspace(-6					,			6					,			_UpperCAmelCase    )
															__lowercase								= torch.sigmoid(_UpperCAmelCase    ) * (beta_end - beta_start) + beta_start
											else:
															raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""    )

											__lowercase								= 1.0 - self.betas
											__lowercase								= torch.cumprod(self.alphas					,			dim=0    )
											__lowercase								= torch.tensor(1.0    )

											__lowercase								= torch.tensor(1.0    )

											# standard deviation of the initial noise distribution
											__lowercase								= 1.0

											# setable values
											__lowercase								= None
											__lowercase								= torch.from_numpy(np.arange(0					,			_UpperCAmelCase    )[::-1].copy()    )

											__lowercase								= eta





							def       a__			(			self  :       int					,			_UpperCAmelCase  :       torch.FloatTensor					,			_UpperCAmelCase  :       Optional[int] = None    )						->		torch.FloatTensor:



											"""simple docstring"""


											return sample





							def       a__			(			self  :       int					,			_UpperCAmelCase  :       int					,			_UpperCAmelCase  :       int = 10					,			_UpperCAmelCase  :       int = 10					,			_UpperCAmelCase  :       Union[str, torch.device] = None					,			)						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= min(self.config.num_train_timesteps					,			_UpperCAmelCase    )
											__lowercase								= num_inference_steps

											__lowercase								= []

											__lowercase								= {}
											for j in range(0					,			num_inference_steps - jump_length					,			_UpperCAmelCase    ):
															__lowercase								= jump_n_sample - 1

											__lowercase								= num_inference_steps
											while t >= 1:
															__lowercase								= t - 1
															timesteps.append(_UpperCAmelCase    )

															if jumps.get(_UpperCAmelCase					,			0    ) > 0:
																			__lowercase								= jumps[t] - 1
																			for _ in range(_UpperCAmelCase    ):
																							__lowercase								= t + 1
																							timesteps.append(_UpperCAmelCase    )

											__lowercase								= np.array(_UpperCAmelCase    ) * (self.config.num_train_timesteps // self.num_inference_steps)
											__lowercase								= torch.from_numpy(_UpperCAmelCase    ).to(_UpperCAmelCase    )





							def       a__			(			self  :       str					,			_UpperCAmelCase  :       List[Any]    )						->		Union[str, Any]:



											"""simple docstring"""


											__lowercase								= t - self.config.num_train_timesteps // self.num_inference_steps

											__lowercase								= self.alphas_cumprod[t]
											__lowercase								= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
											__lowercase								= 1 - alpha_prod_t
											__lowercase								= 1 - alpha_prod_t_prev

											# For t > 0, compute predicted variance βt (see formula (6) and (7) from
											# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
											# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
											# variance to pred_sample
											# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
											# without eta.
											# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
											__lowercase								= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

											return variance





							def       a__			(			self  :       List[Any]					,			_UpperCAmelCase  :       torch.FloatTensor					,			_UpperCAmelCase  :       int					,			_UpperCAmelCase  :       torch.FloatTensor					,			_UpperCAmelCase  :       torch.FloatTensor					,			_UpperCAmelCase  :       torch.FloatTensor					,			_UpperCAmelCase  :       Optional[torch.Generator] = None					,			_UpperCAmelCase  :       bool = True					,			)						->		Union[RePaintSchedulerOutput, Tuple]:



											"""simple docstring"""


											__lowercase								= timestep
											__lowercase								= timestep - self.config.num_train_timesteps // self.num_inference_steps

											# 1. compute alphas, betas
											__lowercase								= self.alphas_cumprod[t]
											__lowercase								= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
											__lowercase								= 1 - alpha_prod_t

											# 2. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
											__lowercase								= (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
															__lowercase								= torch.clamp(_UpperCAmelCase					,			-1					,			1    )

											# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
											# substitute formula (7) in the algorithm coming from DDPM paper
											# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
											# DDIM schedule gives the same results as DDPM with eta = 1.0
											# Noise is being reused in 7. and 8., but no impact on quality has
											# been observed.

											# 5. Add noise
											__lowercase								= model_output.device
											__lowercase								= randn_tensor(model_output.shape					,			generator=_UpperCAmelCase					,			device=_UpperCAmelCase					,			dtype=model_output.dtype    )
											__lowercase								= self.eta * self._get_variance(_UpperCAmelCase    ) ** 0.5

											__lowercase								= 0
											if t > 0 and self.eta > 0:
															__lowercase								= std_dev_t * noise

											# 6. compute "direction pointing to x_t" of formula (12)
											# from https://arxiv.org/pdf/2010.02502.pdf
											__lowercase								= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

											# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											__lowercase								= alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

											# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
											__lowercase								= (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

											# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
											__lowercase								= mask * prev_known_part + (1.0 - mask) * prev_unknown_part

											if not return_dict:
															return (
															    pred_prev_sample,
															    pred_original_sample,
															)

											return RePaintSchedulerOutput(prev_sample=_UpperCAmelCase					,			pred_original_sample=_UpperCAmelCase    )





							def       a__			(			self  :       Optional[int]					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       Dict=None    )						->		List[Any]:



											"""simple docstring"""


											__lowercase								= self.config.num_train_timesteps // self.num_inference_steps

											for i in range(_UpperCAmelCase    ):
															__lowercase								= self.betas[timestep + i]
															if sample.device.type == "mps":
																			# randn does not work reproducibly on mps
																			__lowercase								= randn_tensor(sample.shape					,			dtype=sample.dtype					,			generator=_UpperCAmelCase    )
																			__lowercase								= noise.to(sample.device    )
															else:
																			__lowercase								= randn_tensor(sample.shape					,			generator=_UpperCAmelCase					,			device=sample.device					,			dtype=sample.dtype    )

															# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
															__lowercase								= (1 - beta) ** 0.5 * sample + beta**0.5 * noise

											return sample





							def       a__			(			self  :       Tuple					,			_UpperCAmelCase  :       torch.FloatTensor					,			_UpperCAmelCase  :       torch.FloatTensor					,			_UpperCAmelCase  :       torch.IntTensor					,			)						->		torch.FloatTensor:



											"""simple docstring"""


											raise NotImplementedError('Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'    )





							def __len__(			self  :       Tuple    )						->		Union[str, Any]:



											"""simple docstring"""


											return self.config.num_train_timesteps







from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

SCREAMING_SNAKE_CASE__							=    """
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")

        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				List[Any]   ,							SCREAMING_SNAKE_CASE	:				Dict   ,							SCREAMING_SNAKE_CASE	:				Any=8      )					->						Any:
				__lowercase								= height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				__lowercase								= width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				List[Any]   ,							SCREAMING_SNAKE_CASE	:				Any=512   ,							SCREAMING_SNAKE_CASE	:				List[str]=512      )					->						str:
				__lowercase								= pil_image.resize((w, h)   ,							resample=Image.BICUBIC   ,							reducing_gap=1      )
				__lowercase								= np.array(pil_image.convert('RGB'      )      )
				__lowercase								= arr.astype(np.floataa      ) / 127.5 - 1
				__lowercase								= np.transpose(SCREAMING_SNAKE_CASE   ,							[2, 0, 1]      )
				__lowercase								= torch.from_numpy(SCREAMING_SNAKE_CASE      ).unsqueeze(0      )
				return image
class      A__       (						lowerCAmelCase__    ):





							def __init__(			self  :       str					,			_UpperCAmelCase  :       UNetaDConditionModel					,			_UpperCAmelCase  :       DDPMScheduler					,			_UpperCAmelCase  :       VQModel					,			)						->		Optional[Any]:



											"""simple docstring"""


											super().__init__()

											self.register_modules(
											    unet=_UpperCAmelCase					,			scheduler=_UpperCAmelCase					,			movq=_UpperCAmelCase					,			)
											__lowercase								= 2 ** (len(self.movq.config.block_out_channels    ) - 1)





							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       Dict					,			_UpperCAmelCase  :       int					,			_UpperCAmelCase  :       Dict    )						->		List[str]:



											"""simple docstring"""


											__lowercase								= min(int(num_inference_steps * strength    )					,			_UpperCAmelCase    )

											__lowercase								= max(num_inference_steps - init_timestep					,			0    )
											__lowercase								= self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start





							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       int					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       Any					,			_UpperCAmelCase  :       Optional[int]					,			_UpperCAmelCase  :       Optional[Any]					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       List[Any]=None    )						->		Optional[Any]:



											"""simple docstring"""


											if not isinstance(_UpperCAmelCase					,			(torch.Tensor, PIL.Image.Image, list)    ):
															raise ValueError(
															    f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_UpperCAmelCase    )}"""    )

											__lowercase								= image.to(device=_UpperCAmelCase					,			dtype=_UpperCAmelCase    )

											__lowercase								= batch_size * num_images_per_prompt

											if image.shape[1] == 4:
															__lowercase								= image

											else:
															if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ) and len(_UpperCAmelCase    ) != batch_size:
																			raise ValueError(
																			    f"""You have passed a list of generators of length {len(_UpperCAmelCase    )}, but requested an effective batch"""
																			    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""    )

															elif isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
																			__lowercase								= [
																			    self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(_UpperCAmelCase    )
																			]
																			__lowercase								= torch.cat(_UpperCAmelCase					,			dim=0    )
															else:
																			__lowercase								= self.movq.encode(_UpperCAmelCase    ).latent_dist.sample(_UpperCAmelCase    )

															__lowercase								= self.movq.config.scaling_factor * init_latents

											__lowercase								= torch.cat([init_latents]					,			dim=0    )

											__lowercase								= init_latents.shape
											__lowercase								= randn_tensor(_UpperCAmelCase					,			generator=_UpperCAmelCase					,			device=_UpperCAmelCase					,			dtype=_UpperCAmelCase    )

											# get latents
											__lowercase								= self.scheduler.add_noise(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )

											__lowercase								= init_latents

											return latents





							def       a__			(			self  :       List[Any]					,			_UpperCAmelCase  :       Dict=0    )						->		List[str]:



											"""simple docstring"""


											if is_accelerate_available():
															from accelerate import cpu_offload
											else:
															raise ImportError('Please install accelerate via `pip install accelerate`'    )

											__lowercase								= torch.device(f"""cuda:{gpu_id}"""    )

											__lowercase								= [
											    self.unet,
											    self.movq,
											]
											for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																			cpu_offload(_UpperCAmelCase					,			_UpperCAmelCase    )





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



											"""simple docstring"""


											if is_accelerate_available() and is_accelerate_version('>='					,			'0.17.0.dev0'    ):
															from accelerate import cpu_offload_with_hook
											else:
															raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'    )

											__lowercase								= torch.device(f"""cuda:{gpu_id}"""    )

											if self.device.type != "cpu":
															self.to('cpu'					,			silence_dtype_warnings=_UpperCAmelCase    )
															torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

											__lowercase								= None
											for cpu_offloaded_model in [self.unet, self.movq]:
															__lowercase		, __lowercase								= cpu_offload_with_hook(_UpperCAmelCase					,			_UpperCAmelCase					,			prev_module_hook=_UpperCAmelCase    )

											# We'll offload the last model manually.
											__lowercase								= hook





							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def       a__			(			self  :       int    )						->		Optional[int]:



											"""simple docstring"""


											if not hasattr(self.unet					,			'_hf_hook'    ):
															return self.device
											for module in self.unet.modules():
															if (
															    hasattr(_UpperCAmelCase					,			'_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





							@torch.no_grad()
							@replace_example_docstring(_UpperCAmelCase    )
							def __call__(			self  :       Optional[Any]					,			_UpperCAmelCase  :       Union[torch.FloatTensor, List[torch.FloatTensor]]					,			_UpperCAmelCase  :       Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]					,			_UpperCAmelCase  :       Union[torch.FloatTensor, List[torch.FloatTensor]]					,			_UpperCAmelCase  :       torch.FloatTensor					,			_UpperCAmelCase  :       int = 5_12					,			_UpperCAmelCase  :       int = 5_12					,			_UpperCAmelCase  :       int = 1_00					,			_UpperCAmelCase  :       float = 4.0					,			_UpperCAmelCase  :       float = 0.3					,			_UpperCAmelCase  :       int = 1					,			_UpperCAmelCase  :       Optional[Union[torch.Generator, List[torch.Generator]]] = None					,			_UpperCAmelCase  :       Optional[str] = "pil"					,			_UpperCAmelCase  :       bool = True					,			)						->		Union[str, Any]:



											"""simple docstring"""


											__lowercase								= self._execution_device

											__lowercase								= guidance_scale > 1.0

											if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= torch.cat(_UpperCAmelCase					,			dim=0    )
											if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= torch.cat(_UpperCAmelCase					,			dim=0    )
											if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= torch.cat(_UpperCAmelCase					,			dim=0    )

											__lowercase								= image_embeds.shape[0]

											if do_classifier_free_guidance:
															__lowercase								= image_embeds.repeat_interleave(_UpperCAmelCase					,			dim=0    )
															__lowercase								= negative_image_embeds.repeat_interleave(_UpperCAmelCase					,			dim=0    )
															__lowercase								= hint.repeat_interleave(_UpperCAmelCase					,			dim=0    )

											__lowercase								= torch.cat([negative_image_embeds, image_embeds]					,			dim=0    ).to(dtype=self.unet.dtype					,			device=_UpperCAmelCase    )
											__lowercase								= torch.cat([hint, hint]					,			dim=0    ).to(dtype=self.unet.dtype					,			device=_UpperCAmelCase    )

											if not isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= [image]
											if not all(isinstance(_UpperCAmelCase					,			(PIL.Image.Image, torch.Tensor)    ) for i in image    ):
															raise ValueError(
															    f"""Input is in incorrect format: {[type(_UpperCAmelCase    ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""    )

											__lowercase								= torch.cat([prepare_image(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    ) for i in image]					,			dim=0    )
											__lowercase								= image.to(dtype=image_embeds.dtype					,			device=_UpperCAmelCase    )

											__lowercase								= self.movq.encode(_UpperCAmelCase    )['latents']
											__lowercase								= latents.repeat_interleave(_UpperCAmelCase					,			dim=0    )
											self.scheduler.set_timesteps(_UpperCAmelCase					,			device=_UpperCAmelCase    )
											__lowercase		, __lowercase								= self.get_timesteps(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )
											__lowercase								= timesteps[:1].repeat(batch_size * num_images_per_prompt    )
											__lowercase		, __lowercase								= downscale_height_and_width(_UpperCAmelCase					,			_UpperCAmelCase					,			self.movq_scale_factor    )
											__lowercase								= self.prepare_latents(
											    _UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			image_embeds.dtype					,			_UpperCAmelCase					,			_UpperCAmelCase    )
											for i, t in enumerate(self.progress_bar(_UpperCAmelCase    )    ):
															# expand the latents if we are doing classifier free guidance
															__lowercase								= torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

															__lowercase								= {'image_embeds': image_embeds, 'hint': hint}
															__lowercase								= self.unet(
															    sample=_UpperCAmelCase					,			timestep=_UpperCAmelCase					,			encoder_hidden_states=_UpperCAmelCase					,			added_cond_kwargs=_UpperCAmelCase					,			return_dict=_UpperCAmelCase					,			)[0]

															if do_classifier_free_guidance:
																			__lowercase		, __lowercase								= noise_pred.split(latents.shape[1]					,			dim=1    )
																			__lowercase		, __lowercase								= noise_pred.chunk(2    )
																			__lowercase		, __lowercase								= variance_pred.chunk(2    )
																			__lowercase								= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																			__lowercase								= torch.cat([noise_pred, variance_pred_text]					,			dim=1    )

															if not (
															    hasattr(self.scheduler.config					,			'variance_type'    )
															    and self.scheduler.config.variance_type in ["learned", "learned_range"]
															):
																			__lowercase		, __lowercase								= noise_pred.split(latents.shape[1]					,			dim=1    )

															# compute the previous noisy sample x_t -> x_t-1

															__lowercase								= self.scheduler.step(
															    _UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			generator=_UpperCAmelCase					,			)[0]

											# post-processing
											__lowercase								= self.movq.decode(_UpperCAmelCase					,			force_not_quantize=_UpperCAmelCase    )['sample']

											if output_type not in ["pt", "np", "pil"]:
															raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""    )

											if output_type in ["np", "pil"]:
															__lowercase								= image * 0.5 + 0.5
															__lowercase								= image.clamp(0					,			1    )
															__lowercase								= image.cpu().permute(0					,			2					,			3					,			1    ).float().numpy()

											if output_type == "pil":
															__lowercase								= self.numpy_to_pil(_UpperCAmelCase    )

											if not return_dict:
															return (image,)

											return ImagePipelineOutput(images=_UpperCAmelCase    )





