


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







class 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :Optional[int]      ,__snake_case       :CLIPSegForImageSegmentation      ,__snake_case       :CLIPSegProcessor      ,__snake_case       :AutoencoderKL      ,__snake_case       :CLIPTextModel      ,__snake_case       :CLIPTokenizer      ,__snake_case       :UNetaDConditionModel      ,__snake_case       :Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]      ,__snake_case       :StableDiffusionSafetyChecker      ,__snake_case       :CLIPImageProcessor      ,) ->   Tuple:
												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																			    F'The configuration file of this scheduler: {scheduler} is outdated.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'      ,__snake_case      ,standard_warn=__snake_case				)
																			a__					=					dict(scheduler.config				)
																			a__					=					1
																			a__					=					FrozenDict(__snake_case				)

												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'      ,__snake_case      ,standard_warn=__snake_case				)
																			a__					=					dict(scheduler.config				)
																			a__					=					True
																			a__					=					FrozenDict(__snake_case				)

												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=__snake_case      ,segmentation_processor=__snake_case      ,vae=__snake_case      ,text_encoder=__snake_case      ,tokenizer=__snake_case      ,unet=__snake_case      ,scheduler=__snake_case      ,safety_checker=__snake_case      ,feature_extractor=__snake_case      ,)
					def 			lowerCamelCase__(				self       :int      ,__snake_case       :Optional[Union[str, int]] = "auto"				) ->   Union[str, Any]:
												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(__snake_case				)
					def 			lowerCamelCase__(				self       :str				) ->   Dict:
												self.enable_attention_slicing(__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Union[str, 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(__snake_case      ,__snake_case				)
					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 			lowerCamelCase__(				self       :str				) ->   str:
												if self.device != torch.device('meta'				) or not hasattr(self.unet      ,'_hf_hook'				):
																			return self.device
												for module in self.unet.modules():
																			if (
																			    hasattr(__snake_case      ,'_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]      ,__snake_case       :Union[str, List[str]]      ,__snake_case       :Union[torch.FloatTensor, PIL.Image.Image]      ,__snake_case       :str      ,__snake_case       :int = 5_12      ,__snake_case       :int = 5_12      ,__snake_case       :int = 50      ,__snake_case       :float = 7.5      ,__snake_case       :Optional[Union[str, List[str]]] = None      ,__snake_case       :Optional[int] = 1      ,__snake_case       :float = 0.0      ,__snake_case       :Optional[torch.Generator] = None      ,__snake_case       :Optional[torch.FloatTensor] = None      ,__snake_case       :Optional[str] = "pil"      ,__snake_case       :bool = True      ,__snake_case       :Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,__snake_case       :int = 1      ,**__snake_case       :Tuple      ,) ->   List[str]:
												a__					=					self.segmentation_processor(
												    text=[text]      ,images=[image]      ,padding='max_length'      ,return_tensors='pt'				).to(self.device				)
												a__					=					self.segmentation_model(**__snake_case				)
												a__					=					torch.sigmoid(outputs.logits				).cpu().detach().unsqueeze(-1				).numpy()
												a__					=					self.numpy_to_pil(__snake_case				)[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=__snake_case      ,image=__snake_case      ,mask_image=__snake_case      ,height=__snake_case      ,width=__snake_case      ,num_inference_steps=__snake_case      ,guidance_scale=__snake_case      ,negative_prompt=__snake_case      ,num_images_per_prompt=__snake_case      ,eta=__snake_case      ,generator=__snake_case      ,latents=__snake_case      ,output_type=__snake_case      ,return_dict=__snake_case      ,callback=__snake_case      ,callback_steps=__snake_case      ,)








from __future__ import annotations







class 	snake_case_       :
					def __init__(				self       :List[str]      ,__snake_case       :int				) ->   None:
												a__					=					data
												a__					=					None
												a__					=					None






def 							__lowercase       (			__lowerCAmelCase							:    Node | None       ):  # In Order traversal of the tree
							if tree:
														display(tree.left       )
														print(tree.data       )
														display(tree.right       )






def 							__lowercase       (			__lowerCAmelCase							:    Node | None       ):
							return 1 + max(depth_of_tree(tree.left       ) ,   depth_of_tree(tree.right       )       ) if tree else 0






def 							__lowercase       (			__lowerCAmelCase							:    Node       ):
							if not tree:
														return True
							if tree.left and tree.right:
														return is_full_binary_tree(tree.left       ) and is_full_binary_tree(tree.right       )
							else:
														return not tree.left and not tree.right






def 							__lowercase       (			):  # Main function for testing.
							a__					=					Node(1       )
							a__					=					Node(2       )
							a__					=					Node(3       )
							a__					=					Node(4       )
							a__					=					Node(5       )
							a__					=					Node(6       )
							a__					=					Node(7       )
							a__					=					Node(8       )
							a__					=					Node(9       )

							print(is_full_binary_tree(__lowerCAmelCase       )       )
							print(depth_of_tree(__lowerCAmelCase       )       )
							print('Tree is: '       )
							display(__lowerCAmelCase       )


if __name__ == "__main__":
						main()




