


"""simple docstring"""







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




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	def __init__( self			:				List[Any]			,			UpperCAmelCase__			:				CLIPSegForImageSegmentation			,			UpperCAmelCase__			:				CLIPSegProcessor			,			UpperCAmelCase__			:				AutoencoderKL			,			UpperCAmelCase__			:				CLIPTextModel			,			UpperCAmelCase__			:				CLIPTokenizer			,			UpperCAmelCase__			:				UNetaDConditionModel			,			UpperCAmelCase__			:				Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,			UpperCAmelCase__			:				StableDiffusionSafetyChecker			,			UpperCAmelCase__			:				CLIPImageProcessor			,			)       ->	List[Any]:
						super().__init__()

						if hasattr(scheduler.config			,			"steps_offset"   ) and scheduler.config.steps_offset != 1:
											__SCREAMING_SNAKE_CASE						   =				(
											    F"""The configuration file of this scheduler: {scheduler} is outdated.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"			,			UpperCAmelCase__			,			standard_warn=UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				dict(scheduler.config   )
											__SCREAMING_SNAKE_CASE						   =				1
											__SCREAMING_SNAKE_CASE						   =				FrozenDict(UpperCAmelCase__   )

						if hasattr(scheduler.config			,			"skip_prk_steps"   ) and scheduler.config.skip_prk_steps is False:
											__SCREAMING_SNAKE_CASE						   =				(
											    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"			,			UpperCAmelCase__			,			standard_warn=UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				dict(scheduler.config   )
											__SCREAMING_SNAKE_CASE						   =				True
											__SCREAMING_SNAKE_CASE						   =				FrozenDict(UpperCAmelCase__   )

						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=UpperCAmelCase__			,			segmentation_processor=UpperCAmelCase__			,			vae=UpperCAmelCase__			,			text_encoder=UpperCAmelCase__			,			tokenizer=UpperCAmelCase__			,			unet=UpperCAmelCase__			,			scheduler=UpperCAmelCase__			,			safety_checker=UpperCAmelCase__			,			feature_extractor=UpperCAmelCase__			,			)




	def   UpperCAmelCase_      ( self			:				Optional[Any]			,			UpperCAmelCase__			:				Optional[Union[str, int]] = "auto"   )       ->	List[str]:
						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(UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	List[Any]:
						self.enable_attention_slicing(UpperCAmelCase__   )




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

						__SCREAMING_SNAKE_CASE						   =				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(UpperCAmelCase__			,			UpperCAmelCase__   )




	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	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(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()
	def __call__( self			:				List[Any]			,			UpperCAmelCase__			:				Union[str, List[str]]			,			UpperCAmelCase__			:				Union[torch.FloatTensor, PIL.Image.Image]			,			UpperCAmelCase__			:				str			,			UpperCAmelCase__			:				int = 5_1_2			,			UpperCAmelCase__			:				int = 5_1_2			,			UpperCAmelCase__			:				int = 5_0			,			UpperCAmelCase__			:				float = 7.5			,			UpperCAmelCase__			:				Optional[Union[str, List[str]]] = None			,			UpperCAmelCase__			:				Optional[int] = 1			,			UpperCAmelCase__			:				float = 0.0			,			UpperCAmelCase__			:				Optional[torch.Generator] = None			,			UpperCAmelCase__			:				Optional[torch.FloatTensor] = None			,			UpperCAmelCase__			:				Optional[str] = "pil"			,			UpperCAmelCase__			:				bool = True			,			UpperCAmelCase__			:				Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,			UpperCAmelCase__			:				int = 1			,			**UpperCAmelCase__			:				Optional[Any]			,			)       ->	Any:
						__SCREAMING_SNAKE_CASE						   =				self.segmentation_processor(
						    text=[text]			,			images=[image]			,			padding="max_length"			,			return_tensors="pt"   ).to(self.device   )
						__SCREAMING_SNAKE_CASE						   =				self.segmentation_model(**UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				torch.sigmoid(outputs.logits   ).cpu().detach().unsqueeze(-1   ).numpy()
						__SCREAMING_SNAKE_CASE						   =				self.numpy_to_pil(UpperCAmelCase__   )[0].resize(image.size   )

						# Run inpainting pipeline with the generated mask
						__SCREAMING_SNAKE_CASE						   =				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=UpperCAmelCase__			,			image=UpperCAmelCase__			,			mask_image=UpperCAmelCase__			,			height=UpperCAmelCase__			,			width=UpperCAmelCase__			,			num_inference_steps=UpperCAmelCase__			,			guidance_scale=UpperCAmelCase__			,			negative_prompt=UpperCAmelCase__			,			num_images_per_prompt=UpperCAmelCase__			,			eta=UpperCAmelCase__			,			generator=UpperCAmelCase__			,			latents=UpperCAmelCase__			,			output_type=UpperCAmelCase__			,			return_dict=UpperCAmelCase__			,			callback=UpperCAmelCase__			,			callback_steps=UpperCAmelCase__			,			)




"""simple docstring"""







import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


a__				:							Tuple						=     False




class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	pass




@slow
@require_torch_gpu
class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				int   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
						pipe.to(UpperCAmelCase__   )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )
						__SCREAMING_SNAKE_CASE						   =				torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				pipe(
						    image=UpperCAmelCase__			,			generator=UpperCAmelCase__			,			guidance_scale=7.5			,			num_inference_steps=5_0			,			output_type="numpy"			,			).images

						__SCREAMING_SNAKE_CASE						   =				image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__SCREAMING_SNAKE_CASE						   =				np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]   )

						assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
