
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,
)


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

SCREAMING_SNAKE_CASE							:Dict							 =				'\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 						UpperCAmelCase			(	a_		,   a_		,   a_=8						)		->	Union[str, Any]:







				"""simple docstring"""



				__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      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(       self					: Optional[int]							,A					: UNetaDConditionModel							,A					: DDPMScheduler							,A					: VQModel							,):
					super().__init__()

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






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

					__A   			= latents * scheduler.init_noise_sigma
					return latents






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: List[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(A							,A )






	def 				UpperCamelCase_  (       self					: str							,A					: Optional[Any]=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=A )
									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(A							,A							,prev_module_hook=A )

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






	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					if not hasattr(self.unet							,"_hf_hook" ):
									return self.device
					for module in self.unet.modules():
									if (
									    hasattr(A							,"_hf_hook" )
									    and hasattr(module._hf_hook							,"execution_device" )
									    and module._hf_hook.execution_device is not None
									):
													return torch.device(module._hf_hook.execution_device )
					return self.device







	@torch.no_grad()
	@replace_example_docstring(A )
	def __call__(       self					: Union[str, Any]							,A					: Union[torch.FloatTensor, List[torch.FloatTensor]]							,A					: Union[torch.FloatTensor, List[torch.FloatTensor]]							,A					: torch.FloatTensor							,A					: int = 5_12							,A					: int = 5_12							,A					: int = 1_00							,A					: float = 4.0							,A					: int = 1							,A					: Optional[Union[torch.Generator, List[torch.Generator]]] = None							,A					: Optional[torch.FloatTensor] = None							,A					: Optional[str] = "pil"							,A					: bool = True							,):
					__A   			= self._execution_device

					__A   			= guidance_scale > 1.0

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

					__A   			= image_embeds.shape[0] * num_images_per_prompt

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

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

					__A   			= self.movq.config.latent_channels

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

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

					for i, t in enumerate(self.progress_bar(A ) ):
									# 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=A							,timestep=A							,encoder_hidden_states=A							,added_cond_kwargs=A							,return_dict=A							,)[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(
									    A							,A							,A							,generator=A							,)[0]
					# post-processing
					__A   			= self.movq.decode(A							,force_not_quantize=A )["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(A )

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=A )








"""simple docstring"""
import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)



