





"""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


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






class 					_UpperCAmelCase   (	lowercase_ ):


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

												if hasattr(scheduler.config     ,       "steps_offset"		) and scheduler.config.steps_offset != 1:
																			A		=  (
																			    f"The configuration file of this scheduler: {scheduler} is outdated.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		)
																			A		=  dict(scheduler.config		)
																			A		=  1
																			A		=  FrozenDict(__UpperCamelCase		)

												if hasattr(scheduler.config     ,       "skip_prk_steps"		) and scheduler.config.skip_prk_steps is False:
																			A		=  (
																			    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		)
																			A		=  dict(scheduler.config		)
																			A		=  True
																			A		=  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 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:Optional[Union[str, int]] = "auto"		):
												if slice_size == "auto":
																			# half the attention head size is usually a good trade-off between
																			# speed and memory
																			A		=  self.unet.config.attention_head_dim // 2
												self.unet.set_attention_slice(__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Optional[int]		):
												self.enable_attention_slicing(__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[Any]		):
												if is_accelerate_available():
																			from accelerate import cpu_offload
												else:
																			raise ImportError("Please install accelerate via `pip install accelerate`"		)

												A		=  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 	lowerCamelCase       (					self			:Optional[int]		):
												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_12     ,       __UpperCamelCase			:int = 5_12     ,       __UpperCamelCase			:int = 50     ,       __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			:Dict     ,       ):
												A		=  self.segmentation_processor(
												    text=[text]     ,       images=[image]     ,       padding="max_length"     ,       return_tensors="pt"		).to(self.device		)
												A		=  self.segmentation_model(**__UpperCamelCase		)
												A		=  torch.sigmoid(outputs.logits		).cpu().detach().unsqueeze(-1		).numpy()
												A		=  self.numpy_to_pil(__UpperCamelCase		)[0].resize(image.size		)

												# Run inpainting pipeline with the generated mask
												A		=  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"""
from typing import List, Optional, Union

import torch

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,
)


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

_snake_case     :		List[Any]       						= '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> 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"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase=8       ):
							A		=  height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							A		=  width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor






class 					_UpperCAmelCase   (	lowercase_ ):


					def __init__(					self			:Any     ,       __UpperCamelCase			:UNetaDConditionModel     ,       __UpperCamelCase			:DDPMScheduler     ,       __UpperCamelCase			:VQModel     ,       ):
												super().__init__()

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


					def 	lowerCamelCase       (					self			:Union[str, Any]     ,       __UpperCamelCase			:Tuple     ,       __UpperCamelCase			:Dict     ,       __UpperCamelCase			:Dict     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:List[str]		):
												if latents is None:
																			A		=  randn_tensor(__UpperCamelCase     ,       generator=__UpperCamelCase     ,       device=__UpperCamelCase     ,       dtype=__UpperCamelCase		)
												else:
																			if latents.shape != shape:
																										raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"		)
																			A		=  latents.to(__UpperCamelCase		)

												A		=  latents * scheduler.init_noise_sigma
												return latents


					def 	lowerCamelCase       (					self			:Tuple     ,       __UpperCamelCase			:Any=0		):
												if is_accelerate_available():
																			from accelerate import cpu_offload
												else:
																			raise ImportError("Please install accelerate via `pip install accelerate`"		)

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

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


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:int=0		):
												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."		)

												A		=  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)

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

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


					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 	lowerCamelCase       (					self			:str		):
												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			:List[Any]     ,       __UpperCamelCase			:Union[torch.FloatTensor, List[torch.FloatTensor]]     ,       __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			:int = 1     ,       __UpperCamelCase			:Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,       __UpperCamelCase			:Optional[torch.FloatTensor] = None     ,       __UpperCamelCase			:Optional[str] = "pil"     ,       __UpperCamelCase			:bool = True     ,       ):
												A		=  self._execution_device

												A		=  guidance_scale > 1.0

												if isinstance(__UpperCamelCase     ,       __UpperCamelCase		):
																			A		=  torch.cat(__UpperCamelCase     ,       dim=0		)
												if isinstance(__UpperCamelCase     ,       __UpperCamelCase		):
																			A		=  torch.cat(__UpperCamelCase     ,       dim=0		)
												if isinstance(__UpperCamelCase     ,       __UpperCamelCase		):
																			A		=  torch.cat(__UpperCamelCase     ,       dim=0		)

												A		=  image_embeds.shape[0] * num_images_per_prompt

												if do_classifier_free_guidance:
																			A		=  image_embeds.repeat_interleave(__UpperCamelCase     ,       dim=0		)
																			A		=  negative_image_embeds.repeat_interleave(__UpperCamelCase     ,       dim=0		)
																			A		=  hint.repeat_interleave(__UpperCamelCase     ,       dim=0		)

												A		=  torch.cat([negative_image_embeds, image_embeds]     ,       dim=0		).to(dtype=self.unet.dtype     ,       device=__UpperCamelCase		)
												A		=  torch.cat([hint, hint]     ,       dim=0		).to(dtype=self.unet.dtype     ,       device=__UpperCamelCase		)
												self.scheduler.set_timesteps(__UpperCamelCase     ,       device=__UpperCamelCase		)
												A		=  self.scheduler.timesteps

												A		=  self.movq.config.latent_channels

												A,	A		=  downscale_height_and_width(__UpperCamelCase     ,       __UpperCamelCase     ,       self.movq_scale_factor		)

												# create initial latent
												A		=  self.prepare_latents(
												    (batch_size, num_channels_latents, height, width)     ,       image_embeds.dtype     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       self.scheduler     ,       )

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

																			A		=  {"image_embeds": image_embeds, "hint": hint}
																			A		=  self.unet(
																			    sample=__UpperCamelCase     ,       timestep=__UpperCamelCase     ,       encoder_hidden_states=__UpperCamelCase     ,       added_cond_kwargs=__UpperCamelCase     ,       return_dict=__UpperCamelCase     ,       )[0]

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

																			# compute the previous noisy sample x_t -> x_t-1
																			A		=  self.scheduler.step(
																			    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       generator=__UpperCamelCase     ,       )[0]
												# post-processing
												A		=  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"]:
																			A		=  image * 0.5 + 0.5
																			A		=  image.clamp(0     ,       1		)
																			A		=  image.cpu().permute(0     ,       2     ,       3     ,       1		).float().numpy()

												if output_type == "pil":
																			A		=  self.numpy_to_pil(__UpperCamelCase		)

												if not return_dict:
																			return (image,)

												return ImagePipelineOutput(images=__UpperCamelCase		)


