






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


__lowerCamelCase :      Optional[int]										=				logging.get_logger(__name__)  # pylint: disable=invalid-name



class 							SCREAMING_SNAKE_CASE__						(					UpperCamelCase_						):





					"""simple docstring"""






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

						if hasattr(scheduler.config							,		"steps_offset"	) and scheduler.config.steps_offset != 1:
							snake_case__			:				Dict       =  (
							    f'''The configuration file of this scheduler: {scheduler} is outdated.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"							,		__A							,		standard_warn=__A	)
							snake_case__			:				Union[str, Any]       =  dict(scheduler.config	)
							snake_case__			:				Tuple       =  1
							snake_case__			:				List[str]       =  FrozenDict(__A	)

						if hasattr(scheduler.config							,		"skip_prk_steps"	) and scheduler.config.skip_prk_steps is False:
							snake_case__			:				Optional[int]       =  (
							    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"							,		__A							,		standard_warn=__A	)
							snake_case__			:				Tuple       =  dict(scheduler.config	)
							snake_case__			:				List[str]       =  True
							snake_case__			:				List[str]       =  FrozenDict(__A	)

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



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  Optional[Union[str, int]] = "auto"	):
						if slice_size == "auto":
							# half the attention head size is usually a good trade-off between
							# speed and memory
							snake_case__			:				Tuple       =  self.unet.config.attention_head_dim // 2
						self.unet.set_attention_slice(__A	)



					def 	_lowercase					( self					:  int	):
						self.enable_attention_slicing(__A	)



					def 	_lowercase					( self					:  Union[str, Any]	):
						if is_accelerate_available():
							from accelerate import cpu_offload
						else:
							raise ImportError("Please install accelerate via `pip install accelerate`"	)

						snake_case__			:				Dict       =  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(__A							,		__A	)



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

						# Run inpainting pipeline with the generated mask
						snake_case__			:				Optional[Any]       =  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=__A							,		image=__A							,		mask_image=__A							,		height=__A							,		width=__A							,		num_inference_steps=__A							,		guidance_scale=__A							,		negative_prompt=__A							,		num_images_per_prompt=__A							,		eta=__A							,		generator=__A							,		latents=__A							,		output_type=__A							,		return_dict=__A							,		callback=__A							,		callback_steps=__A							,		)











def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float(moles / volume       ) * nfactor       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((moles * 0.08_21 * temperature) / (volume)       )       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((moles * 0.08_21 * temperature) / (pressure)       )       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((pressure * volume) / (0.08_21 * moles)       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



