



import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)






def 	__lowercase   ( a__							,   a__							,   a__   )				-> str:
							if isinstance(lowerCamelCase__							,   torch.Tensor   ):
														return image
							elif isinstance(lowerCamelCase__							,   PIL.Image.Image   ):
														__SCREAMING_SNAKE_CASE									=					[image]

							if isinstance(image[0]							,   PIL.Image.Image   ):
														__SCREAMING_SNAKE_CASE									=					[np.array(i.resize((w, h)							,   resample=PIL_INTERPOLATION['lanczos']   )   )[None, :] for i in image]
														__SCREAMING_SNAKE_CASE									=					np.concatenate(lowerCamelCase__							,   axis=0   )
														__SCREAMING_SNAKE_CASE									=					np.array(lowerCamelCase__   ).astype(np.floataa   ) / 2_55.0
														__SCREAMING_SNAKE_CASE									=					image.transpose(0							,   3							,   1							,   2   )
														__SCREAMING_SNAKE_CASE									=					2.0 * image - 1.0
														__SCREAMING_SNAKE_CASE									=					torch.from_numpy(lowerCamelCase__   )
							elif isinstance(image[0]							,   torch.Tensor   ):
														__SCREAMING_SNAKE_CASE									=					torch.cat(lowerCamelCase__							,   dim=0   )
							return image






def 	__lowercase   ( a__							,   a__							,   a__							,   a__=0.9995   )				-> Tuple:
							if not isinstance(lowerCamelCase__							,   np.ndarray   ):
														__SCREAMING_SNAKE_CASE									=					True
														__SCREAMING_SNAKE_CASE									=					va.device
														__SCREAMING_SNAKE_CASE									=					va.cpu().numpy()
														__SCREAMING_SNAKE_CASE									=					va.cpu().numpy()

							__SCREAMING_SNAKE_CASE									=					np.sum(va * va / (np.linalg.norm(lowerCamelCase__   ) * np.linalg.norm(lowerCamelCase__   ))   )
							if np.abs(lowerCamelCase__   ) > DOT_THRESHOLD:
														__SCREAMING_SNAKE_CASE									=					(1 - t) * va + t * va
							else:
														__SCREAMING_SNAKE_CASE									=					np.arccos(lowerCamelCase__   )
														__SCREAMING_SNAKE_CASE									=					np.sin(lowerCamelCase__   )
														__SCREAMING_SNAKE_CASE									=					theta_a * t
														__SCREAMING_SNAKE_CASE									=					np.sin(lowerCamelCase__   )
														__SCREAMING_SNAKE_CASE									=					np.sin(theta_a - theta_t   ) / sin_theta_a
														__SCREAMING_SNAKE_CASE									=					sin_theta_t / sin_theta_a
														__SCREAMING_SNAKE_CASE									=					sa * va + sa * va

							if inputs_are_torch:
														__SCREAMING_SNAKE_CASE									=					torch.from_numpy(lowerCamelCase__   ).to(lowerCamelCase__   )

							return va






def 	__lowercase   ( a__							,   a__   )				-> Dict:
							__SCREAMING_SNAKE_CASE									=					F.normalize(lowerCamelCase__							,   dim=-1   )
							__SCREAMING_SNAKE_CASE									=					F.normalize(lowerCamelCase__							,   dim=-1   )
							return (x - y).norm(dim=-1   ).div(2   ).arcsin().pow(2   ).mul(2   )






def 	__lowercase   ( a__							,   a__   )				-> Dict:
							for param in model.parameters():
														__SCREAMING_SNAKE_CASE									=					value

class    UpperCAmelCase_  (   snake_case_       ):

						'''simple docstring'''




						def __init__(    self      ,							_A      ,							_A      ,							_A      ,							_A      ,							_A      ,							_A      ,							_A      ,							_A=None      ,							_A=None      ,							_A=None      ,							):







													'''simple docstring'''

													super().__init__()
													self.register_modules(
													    vae=_A      ,							text_encoder=_A      ,							clip_model=_A      ,							tokenizer=_A      ,							unet=_A      ,							scheduler=_A      ,							feature_extractor=_A      ,							coca_model=_A      ,							coca_tokenizer=_A      ,							coca_transform=_A      ,							)
													__SCREAMING_SNAKE_CASE									=					(
													    feature_extractor.size
													    if isinstance(feature_extractor.size      ,							_A		)
													    else feature_extractor.size['shortest_edge']
													)
													__SCREAMING_SNAKE_CASE									=					transforms.Normalize(mean=feature_extractor.image_mean      ,							std=feature_extractor.image_std		)
													set_requires_grad(self.text_encoder      ,							_A		)
													set_requires_grad(self.clip_model      ,							_A		)






						def        _A	(    self      ,							_A = "auto"		):







													'''simple docstring'''

													if slice_size == "auto":
																				# half the attention head size is usually a good trade-off between
																				# speed and memory
																				__SCREAMING_SNAKE_CASE									=					self.unet.config.attention_head_dim // 2
													self.unet.set_attention_slice(_A		)






						def        _A	(    self		):







													'''simple docstring'''

													self.enable_attention_slicing(_A		)






						def        _A	(    self		):







													'''simple docstring'''

													set_requires_grad(self.vae      ,							_A		)






						def        _A	(    self		):







													'''simple docstring'''

													set_requires_grad(self.vae      ,							_A		)






						def        _A	(    self		):







													'''simple docstring'''

													set_requires_grad(self.unet      ,							_A		)






						def        _A	(    self		):







													'''simple docstring'''

													set_requires_grad(self.unet      ,							_A		)






						def        _A	(    self      ,							_A      ,							_A      ,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					min(int(num_inference_steps * strength		)      ,							_A		)

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

													return timesteps, num_inference_steps - t_start






						def        _A	(    self      ,							_A      ,							_A      ,							_A      ,							_A      ,							_A      ,							_A=None		):







													'''simple docstring'''

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

													__SCREAMING_SNAKE_CASE									=					image.to(device=_A      ,							dtype=_A		)

													if isinstance(_A      ,							_A		):
																				__SCREAMING_SNAKE_CASE									=					[
																				    self.vae.encode(image[i : i + 1]		).latent_dist.sample(generator[i]		) for i in range(_A		)
																				]
																				__SCREAMING_SNAKE_CASE									=					torch.cat(_A      ,							dim=0		)
													else:
																				__SCREAMING_SNAKE_CASE									=					self.vae.encode(_A		).latent_dist.sample(_A		)

													# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
													__SCREAMING_SNAKE_CASE									=					0.1_8_2_1_5 * init_latents
													__SCREAMING_SNAKE_CASE									=					init_latents.repeat_interleave(_A      ,							dim=0		)

													__SCREAMING_SNAKE_CASE									=					randn_tensor(init_latents.shape      ,							generator=_A      ,							device=_A      ,							dtype=_A		)

													# get latents
													__SCREAMING_SNAKE_CASE									=					self.scheduler.add_noise(_A      ,							_A      ,							_A		)
													__SCREAMING_SNAKE_CASE									=					init_latents

													return latents






						def        _A	(    self      ,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					self.coca_transform(_A		).unsqueeze(0		)
													with torch.no_grad(), torch.cuda.amp.autocast():
																				__SCREAMING_SNAKE_CASE									=					self.coca_model.generate(transformed_image.to(device=self.device      ,							dtype=self.coca_model.dtype		)		)
													__SCREAMING_SNAKE_CASE									=					self.coca_tokenizer.decode(generated[0].cpu().numpy()		)
													return generated.split('<end_of_text>'		)[0].replace('<start_of_text>'      ,							''		).rstrip(' .,'		)






						def        _A	(    self      ,							_A      ,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					self.feature_extractor.preprocess(_A		)
													__SCREAMING_SNAKE_CASE									=					torch.from_numpy(clip_image_input['pixel_values'][0]		).unsqueeze(0		).to(self.device		).half()
													__SCREAMING_SNAKE_CASE									=					self.clip_model.get_image_features(_A		)
													__SCREAMING_SNAKE_CASE									=					image_embeddings_clip / image_embeddings_clip.norm(p=2      ,							dim=-1      ,							keepdim=_A		)
													__SCREAMING_SNAKE_CASE									=					image_embeddings_clip.repeat_interleave(_A      ,							dim=0		)
													return image_embeddings_clip






						@torch.enable_grad()
						def        _A	(    self      ,							_A      ,							_A      ,							_A      ,							_A      ,							_A      ,							_A      ,							_A      ,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					latents.detach().requires_grad_()

													__SCREAMING_SNAKE_CASE									=					self.scheduler.scale_model_input(_A      ,							_A		)

													# predict the noise residual
													__SCREAMING_SNAKE_CASE									=					self.unet(_A      ,							_A      ,							encoder_hidden_states=_A		).sample

													if isinstance(self.scheduler      ,							(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)		):
																				__SCREAMING_SNAKE_CASE									=					self.scheduler.alphas_cumprod[timestep]
																				__SCREAMING_SNAKE_CASE									=					1 - alpha_prod_t
																				# compute predicted original sample from predicted noise also called
																				# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
																				__SCREAMING_SNAKE_CASE									=					(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

																				__SCREAMING_SNAKE_CASE									=					torch.sqrt(_A		)
																				__SCREAMING_SNAKE_CASE									=					pred_original_sample * (fac) + latents * (1 - fac)
													elif isinstance(self.scheduler      ,							_A		):
																				__SCREAMING_SNAKE_CASE									=					self.scheduler.sigmas[index]
																				__SCREAMING_SNAKE_CASE									=					latents - sigma * noise_pred
													else:
																				raise ValueError(f"""scheduler type {type(self.scheduler		)} not supported"""		)

													# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
													__SCREAMING_SNAKE_CASE									=					1 / 0.1_8_2_1_5 * sample
													__SCREAMING_SNAKE_CASE									=					self.vae.decode(_A		).sample
													__SCREAMING_SNAKE_CASE									=					(image / 2 + 0.5).clamp(0      ,							1		)

													__SCREAMING_SNAKE_CASE									=					transforms.Resize(self.feature_extractor_size		)(_A		)
													__SCREAMING_SNAKE_CASE									=					self.normalize(_A		).to(latents.dtype		)

													__SCREAMING_SNAKE_CASE									=					self.clip_model.get_image_features(_A		)
													__SCREAMING_SNAKE_CASE									=					image_embeddings_clip / image_embeddings_clip.norm(p=2      ,							dim=-1      ,							keepdim=_A		)

													__SCREAMING_SNAKE_CASE									=					spherical_dist_loss(_A      ,							_A		).mean() * clip_guidance_scale

													__SCREAMING_SNAKE_CASE									=					-torch.autograd.grad(_A      ,							_A		)[0]

													if isinstance(self.scheduler      ,							_A		):
																				__SCREAMING_SNAKE_CASE									=					latents.detach() + grads * (sigma**2)
																				__SCREAMING_SNAKE_CASE									=					noise_pred_original
													else:
																				__SCREAMING_SNAKE_CASE									=					noise_pred_original - torch.sqrt(_A		) * grads
													return noise_pred, latents






						@torch.no_grad()
						def __call__(    self      ,							_A      ,							_A      ,							_A = None      ,							_A = None      ,							_A = 512      ,							_A = 512      ,							_A = 0.6      ,							_A = 50      ,							_A = 7.5      ,							_A = 1      ,							_A = 0.0      ,							_A = 100      ,							_A = None      ,							_A = "pil"      ,							_A = True      ,							_A = 0.8      ,							_A = 0.1      ,							_A = 0.1      ,							):







													'''simple docstring'''

													if isinstance(_A      ,							_A		) and len(_A		) != batch_size:
																				raise ValueError(f"""You have passed {batch_size} batch_size, but only {len(_A		)} generators."""		)

													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 isinstance(_A      ,							torch.Generator		) and batch_size > 1:
																				__SCREAMING_SNAKE_CASE									=					[generator] + [None] * (batch_size - 1)

													__SCREAMING_SNAKE_CASE									=					[
													    ('model', self.coca_model is None),
													    ('tokenizer', self.coca_tokenizer is None),
													    ('transform', self.coca_transform is None),
													]
													__SCREAMING_SNAKE_CASE									=					[x[0] for x in coca_is_none if x[1]]
													__SCREAMING_SNAKE_CASE									=					', '.join(_A		)
													# generate prompts with coca model if prompt is None
													if content_prompt is None:
																				if len(_A		):
																											raise ValueError(
																											    f"""Content prompt is None and CoCa [{coca_is_none_str}] is None."""
																											    f"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""		)
																				__SCREAMING_SNAKE_CASE									=					self.get_image_description(_A		)
													if style_prompt is None:
																				if len(_A		):
																											raise ValueError(
																											    f"""Style prompt is None and CoCa [{coca_is_none_str}] is None."""
																											    f""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""		)
																				__SCREAMING_SNAKE_CASE									=					self.get_image_description(_A		)

													# get prompt text embeddings for content and style
													__SCREAMING_SNAKE_CASE									=					self.tokenizer(
													    _A      ,							padding='max_length'      ,							max_length=self.tokenizer.model_max_length      ,							truncation=_A      ,							return_tensors='pt'      ,							)
													__SCREAMING_SNAKE_CASE									=					self.text_encoder(content_text_input.input_ids.to(self.device		)		)[0]

													__SCREAMING_SNAKE_CASE									=					self.tokenizer(
													    _A      ,							padding='max_length'      ,							max_length=self.tokenizer.model_max_length      ,							truncation=_A      ,							return_tensors='pt'      ,							)
													__SCREAMING_SNAKE_CASE									=					self.text_encoder(style_text_input.input_ids.to(self.device		)		)[0]

													__SCREAMING_SNAKE_CASE									=					slerp(_A      ,							_A      ,							_A		)

													# duplicate text embeddings for each generation per prompt
													__SCREAMING_SNAKE_CASE									=					text_embeddings.repeat_interleave(_A      ,							dim=0		)

													# set timesteps
													__SCREAMING_SNAKE_CASE									=					'offset' in set(inspect.signature(self.scheduler.set_timesteps		).parameters.keys()		)
													__SCREAMING_SNAKE_CASE									=					{}
													if accepts_offset:
																				__SCREAMING_SNAKE_CASE									=					1

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

													__SCREAMING_SNAKE_CASE							,       __SCREAMING_SNAKE_CASE									=					self.get_timesteps(_A      ,							_A      ,							self.device		)
													__SCREAMING_SNAKE_CASE									=					timesteps[:1].repeat(_A		)

													# Preprocess image
													__SCREAMING_SNAKE_CASE									=					preprocess(_A      ,							_A      ,							_A		)
													__SCREAMING_SNAKE_CASE									=					self.prepare_latents(
													    _A      ,							_A      ,							_A      ,							text_embeddings.dtype      ,							self.device      ,							_A		)

													__SCREAMING_SNAKE_CASE									=					preprocess(_A      ,							_A      ,							_A		)
													__SCREAMING_SNAKE_CASE									=					self.prepare_latents(
													    _A      ,							_A      ,							_A      ,							text_embeddings.dtype      ,							self.device      ,							_A		)

													__SCREAMING_SNAKE_CASE									=					slerp(_A      ,							_A      ,							_A		)

													if clip_guidance_scale > 0:
																				__SCREAMING_SNAKE_CASE									=					self.get_clip_image_embeddings(_A      ,							_A		)
																				__SCREAMING_SNAKE_CASE									=					self.get_clip_image_embeddings(_A      ,							_A		)
																				__SCREAMING_SNAKE_CASE									=					slerp(
																				    _A      ,							_A      ,							_A		)

													# 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.
													__SCREAMING_SNAKE_CASE									=					guidance_scale > 1.0
													# get unconditional embeddings for classifier free guidance
													if do_classifier_free_guidance:
																				__SCREAMING_SNAKE_CASE									=					content_text_input.input_ids.shape[-1]
																				__SCREAMING_SNAKE_CASE									=					self.tokenizer(['']      ,							padding='max_length'      ,							max_length=_A      ,							return_tensors='pt'		)
																				__SCREAMING_SNAKE_CASE									=					self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]
																				# duplicate unconditional embeddings for each generation per prompt
																				__SCREAMING_SNAKE_CASE									=					uncond_embeddings.repeat_interleave(_A      ,							dim=0		)

																				# 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
																				__SCREAMING_SNAKE_CASE									=					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`.
													__SCREAMING_SNAKE_CASE									=					(batch_size, self.unet.config.in_channels, height // 8, width // 8)
													__SCREAMING_SNAKE_CASE									=					text_embeddings.dtype
													if latents is None:
																				if self.device.type == "mps":
																											# randn does not work reproducibly on mps
																											__SCREAMING_SNAKE_CASE									=					torch.randn(_A      ,							generator=_A      ,							device='cpu'      ,							dtype=_A		).to(
																											    self.device		)
																				else:
																											__SCREAMING_SNAKE_CASE									=					torch.randn(_A      ,							generator=_A      ,							device=self.device      ,							dtype=_A		)
													else:
																				if latents.shape != latents_shape:
																											raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""		)
																				__SCREAMING_SNAKE_CASE									=					latents.to(self.device		)

													# scale the initial noise by the standard deviation required by the scheduler
													__SCREAMING_SNAKE_CASE									=					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]
													__SCREAMING_SNAKE_CASE									=					'eta' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
													__SCREAMING_SNAKE_CASE									=					{}
													if accepts_eta:
																				__SCREAMING_SNAKE_CASE									=					eta

													# check if the scheduler accepts generator
													__SCREAMING_SNAKE_CASE									=					'generator' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
													if accepts_generator:
																				__SCREAMING_SNAKE_CASE									=					generator

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

																											# predict the noise residual
																											__SCREAMING_SNAKE_CASE									=					self.unet(_A      ,							_A      ,							encoder_hidden_states=_A		).sample

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

																											# perform clip guidance
																											if clip_guidance_scale > 0:
																																		__SCREAMING_SNAKE_CASE									=					(
																																		    text_embeddings.chunk(2		)[1] if do_classifier_free_guidance else text_embeddings
																																		)
																																		__SCREAMING_SNAKE_CASE							,       __SCREAMING_SNAKE_CASE									=					self.cond_fn(
																																		    _A      ,							_A      ,							_A      ,							_A      ,							_A      ,							_A      ,							_A      ,							)

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
													__SCREAMING_SNAKE_CASE									=					1 / 0.1_8_2_1_5 * latents
													__SCREAMING_SNAKE_CASE									=					self.vae.decode(_A		).sample

													__SCREAMING_SNAKE_CASE									=					(image / 2 + 0.5).clamp(0      ,							1		)
													__SCREAMING_SNAKE_CASE									=					image.cpu().permute(0      ,							2      ,							3      ,							1		).numpy()

													if output_type == "pil":
																				__SCREAMING_SNAKE_CASE									=					self.numpy_to_pil(_A		)

													if not return_dict:
																				return (image, None)

													return StableDiffusionPipelineOutput(images=_A      ,							nsfw_content_detected=_A		)








from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


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




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       List[Any]:
					super().__init__()

					if hasattr(scheduler.config						,  "steps_offset" ) and scheduler.config.steps_offset != 1:
									lowerCamelCase_						=			(
									    f'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
									    f' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
									    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
									    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
									    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
									    " file"
									)
									deprecate("steps_offset!=1"						,  "1.0.0"						,  lowercase						,  standard_warn=lowercase )
									lowerCamelCase_						=			dict(scheduler.config )
									lowerCamelCase_						=			1
									lowerCamelCase_						=			FrozenDict(lowercase )

					if hasattr(scheduler.config						,  "skip_prk_steps" ) and scheduler.config.skip_prk_steps is False:
									lowerCamelCase_						=			(
									    f'The configuration file of this scheduler: {scheduler} has not set the configuration'
									    " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
									    " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
									    " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
									    " Hub, it would be very nice if you could open a Pull request for the"
									    " `scheduler/scheduler_config.json` file"
									)
									deprecate("skip_prk_steps not set"						,  "1.0.0"						,  lowercase						,  standard_warn=lowercase )
									lowerCamelCase_						=			dict(scheduler.config )
									lowerCamelCase_						=			True
									lowerCamelCase_						=			FrozenDict(lowercase )

					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(
					    segmentation_model=lowercase						,  segmentation_processor=lowercase						,  vae=lowercase						,  text_encoder=lowercase						,  tokenizer=lowercase						,  unet=lowercase						,  scheduler=lowercase						,  safety_checker=lowercase						,  feature_extractor=lowercase						,  )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase = "auto" )				->       Tuple:
					if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									lowerCamelCase_						=			self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					self.enable_attention_slicing(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					if is_accelerate_available():
									from accelerate import cpu_offload
					else:
									raise ImportError("Please install accelerate via `pip install accelerate`" )

					lowerCamelCase_						=			torch.device("cuda" )

					for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
									if cpu_offloaded_model is not None:
													cpu_offload(lowercase						,  lowercase )



	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					if self.device != torch.device("meta" ) or not hasattr(self.unet						,  "_hf_hook" ):
									return self.device
					for module in self.unet.modules():
									if (
									    hasattr(lowercase						,  "_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()
	def __call__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase = 512						,  lowercase = 512						,  lowercase = 50						,  lowercase = 7.5						,  lowercase = None						,  lowercase = 1						,  lowercase = 0.0						,  lowercase = None						,  lowercase = None						,  lowercase = "pil"						,  lowercase = True						,  lowercase = None						,  lowercase = 1						,  **lowercase						,  )				->       int:
					lowerCamelCase_						=			self.segmentation_processor(
					    text=[text]						,  images=[image]						,  padding="max_length"						,  return_tensors="pt" ).to(self.device )
					lowerCamelCase_						=			self.segmentation_model(**lowercase )
					lowerCamelCase_						=			torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
					lowerCamelCase_						=			self.numpy_to_pil(lowercase )[0].resize(image.size )

					# Run inpainting pipeline with the generated mask
					lowerCamelCase_						=			StableDiffusionInpaintPipeline(
					    vae=self.vae						,  text_encoder=self.text_encoder						,  tokenizer=self.tokenizer						,  unet=self.unet						,  scheduler=self.scheduler						,  safety_checker=self.safety_checker						,  feature_extractor=self.feature_extractor						,  )
					return inpainting_pipeline(
					    prompt=lowercase						,  image=lowercase						,  mask_image=lowercase						,  height=lowercase						,  width=lowercase						,  num_inference_steps=lowercase						,  guidance_scale=lowercase						,  negative_prompt=lowercase						,  num_images_per_prompt=lowercase						,  eta=lowercase						,  generator=lowercase						,  latents=lowercase						,  output_type=lowercase						,  return_dict=lowercase						,  callback=lowercase						,  callback_steps=lowercase						,  )


