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

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				List[str]   , _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   , )							->       Optional[int]:
									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													    F"""The configuration file of this scheduler: {scheduler} is outdated.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"   , _snake_case   , standard_warn=_snake_case		)
													SCREAMING_SNAKE_CASE__		  =				dict(scheduler.config		)
													SCREAMING_SNAKE_CASE__		  =				1
													SCREAMING_SNAKE_CASE__		  =				FrozenDict(_snake_case		)

									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"   , _snake_case   , standard_warn=_snake_case		)
													SCREAMING_SNAKE_CASE__		  =				dict(scheduler.config		)
													SCREAMING_SNAKE_CASE__		  =				True
													SCREAMING_SNAKE_CASE__		  =				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"		)							->       Optional[Any]:
									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(_snake_case		)



					def 			lowerCAmelCase_					(						self			:				int		)							->       Union[str, Any]:
									self.enable_attention_slicing(_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       List[str]:
									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(_snake_case   , _snake_case		)



					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 			lowerCAmelCase_					(						self			:				int		)							->       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(_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			:				Tuple   , _snake_case			:				Union[str, List[str]]   , _snake_case			:				Union[torch.FloatTensor, PIL.Image.Image]   , _snake_case			:				str   , _snake_case			:				int = 512   , _snake_case			:				int = 512   , _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			:				int   , )							->       List[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(**_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				torch.sigmoid(outputs.logits		).cpu().detach().unsqueeze(-1		).numpy()
									SCREAMING_SNAKE_CASE__		  =				self.numpy_to_pil(_snake_case		)[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=_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   , )





"""simple docstring"""






def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   List[str]:
				if not head:
								return True
				# split the list to two parts
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				head.next, head
				while fast and fast.next:
								SCREAMING_SNAKE_CASE__		  =				fast.next.next
								SCREAMING_SNAKE_CASE__		  =				slow.next
				SCREAMING_SNAKE_CASE__		  =				slow.next
				SCREAMING_SNAKE_CASE__		  =				None  # Don't forget here! But forget still works!
				# reverse the second part
				SCREAMING_SNAKE_CASE__		  =				None
				while second:
								SCREAMING_SNAKE_CASE__		  =				second.next
								SCREAMING_SNAKE_CASE__		  =				node
								SCREAMING_SNAKE_CASE__		  =				second
								SCREAMING_SNAKE_CASE__		  =				nxt
				# compare two parts
				# second part has the same or one less node
				while node:
								if node.val != head.val:
												return False
								SCREAMING_SNAKE_CASE__		  =				node.next
								SCREAMING_SNAKE_CASE__		  =				head.next
				return True




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   str:
				if not head or not head.next:
								return True

				# 1. Get the midpoint (slow)
				SCREAMING_SNAKE_CASE__		  =				SCREAMING_SNAKE_CASE__		  =				SCREAMING_SNAKE_CASE__		  =				head
				while fast and fast.next:
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				fast.next.next, slow.next

				# 2. Push the second half into the stack
				SCREAMING_SNAKE_CASE__		  =				[slow.val]
				while slow.next:
								SCREAMING_SNAKE_CASE__		  =				slow.next
								stack.append(slow.val       )

				# 3. Comparison
				while stack:
								if stack.pop() != cur.val:
												return False
								SCREAMING_SNAKE_CASE__		  =				cur.next

				return True




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   str:
				if not head or not head.next:
								return True
				SCREAMING_SNAKE_CASE__		  =				{}
				SCREAMING_SNAKE_CASE__		  =				0
				while head:
								if head.val in d:
												d[head.val].append(__UpperCAmelCase       )
								else:
												SCREAMING_SNAKE_CASE__		  =				[pos]
								SCREAMING_SNAKE_CASE__		  =				head.next
								pos += 1
				SCREAMING_SNAKE_CASE__		  =				pos - 1
				SCREAMING_SNAKE_CASE__		  =				0
				for v in d.values():
								if len(__UpperCAmelCase       ) % 2 != 0:
												middle += 1
								else:
												SCREAMING_SNAKE_CASE__		  =				0
												for i in range(0  ,					len(__UpperCAmelCase       )       ):
																if v[i] + v[len(__UpperCAmelCase       ) - 1 - step] != checksum:
																				return False
																step += 1
								if middle > 1:
												return False
				return True




