


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






class      UpperCamelCase(	_a ):


					def __init__(    self						:		Dict  ,     SCREAMING_SNAKE_CASE						:		CLIPSegForImageSegmentation  ,     SCREAMING_SNAKE_CASE						:		CLIPSegProcessor  ,     SCREAMING_SNAKE_CASE						:		AutoencoderKL  ,     SCREAMING_SNAKE_CASE						:		CLIPTextModel  ,     SCREAMING_SNAKE_CASE						:		CLIPTokenizer  ,     SCREAMING_SNAKE_CASE						:		UNetaDConditionModel  ,     SCREAMING_SNAKE_CASE						:		Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]  ,     SCREAMING_SNAKE_CASE						:		StableDiffusionSafetyChecker  ,     SCREAMING_SNAKE_CASE						:		CLIPImageProcessor  ,     )      ->						Optional[Any]:




											'''simple docstring'''

											super().__init__()

											if hasattr(scheduler.config  ,     "steps_offset"    ) and scheduler.config.steps_offset != 1:
																	__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																	    f'''The configuration file of this scheduler: {scheduler} is outdated.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"  ,     SCREAMING_SNAKE_CASE  ,     standard_warn=SCREAMING_SNAKE_CASE    )
																	__snake_case				=		dict(scheduler.config    )
																	__snake_case				=		1
																	__snake_case				=		FrozenDict(SCREAMING_SNAKE_CASE    )

											if hasattr(scheduler.config  ,     "skip_prk_steps"    ) and scheduler.config.skip_prk_steps is False:
																	__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"  ,     SCREAMING_SNAKE_CASE  ,     standard_warn=SCREAMING_SNAKE_CASE    )
																	__snake_case				=		dict(scheduler.config    )
																	__snake_case				=		True
																	__snake_case				=		FrozenDict(SCREAMING_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=SCREAMING_SNAKE_CASE  ,     segmentation_processor=SCREAMING_SNAKE_CASE  ,     vae=SCREAMING_SNAKE_CASE  ,     text_encoder=SCREAMING_SNAKE_CASE  ,     tokenizer=SCREAMING_SNAKE_CASE  ,     unet=SCREAMING_SNAKE_CASE  ,     scheduler=SCREAMING_SNAKE_CASE  ,     safety_checker=SCREAMING_SNAKE_CASE  ,     feature_extractor=SCREAMING_SNAKE_CASE  ,     )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		Optional[Union[str, int]] = "auto"    )      ->						Dict:




											'''simple docstring'''

											if slice_size == "auto":
																	# half the attention head size is usually a good trade-off between
																	# speed and memory
																	__snake_case				=		self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Optional[Any]:




											'''simple docstring'''

											self.enable_attention_slicing(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						Optional[int]:




											'''simple docstring'''

											if is_accelerate_available():
																	from accelerate import cpu_offload
											else:
																	raise ImportError("Please install accelerate via `pip install accelerate`"    )

											__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(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )


					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Tuple:




											'''simple docstring'''

											if self.device != torch.device("meta"    ) or not hasattr(self.unet  ,     "_hf_hook"    ):
																	return self.device
											for module in self.unet.modules():
																	if (
																	    hasattr(SCREAMING_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						:		Dict  ,     SCREAMING_SNAKE_CASE						:		Union[str, List[str]]  ,     SCREAMING_SNAKE_CASE						:		Union[torch.FloatTensor, PIL.Image.Image]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		int = 5_1_2  ,     SCREAMING_SNAKE_CASE						:		int = 5_1_2  ,     SCREAMING_SNAKE_CASE						:		int = 5_0  ,     SCREAMING_SNAKE_CASE						:		float = 7.5  ,     SCREAMING_SNAKE_CASE						:		Optional[Union[str, List[str]]] = None  ,     SCREAMING_SNAKE_CASE						:		Optional[int] = 1  ,     SCREAMING_SNAKE_CASE						:		float = 0.0  ,     SCREAMING_SNAKE_CASE						:		Optional[torch.Generator] = None  ,     SCREAMING_SNAKE_CASE						:		Optional[torch.FloatTensor] = None  ,     SCREAMING_SNAKE_CASE						:		Optional[str] = "pil"  ,     SCREAMING_SNAKE_CASE						:		bool = True  ,     SCREAMING_SNAKE_CASE						:		Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,     SCREAMING_SNAKE_CASE						:		int = 1  ,     **SCREAMING_SNAKE_CASE						:		Union[str, Any]  ,     )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.segmentation_processor(
											    text=[text]  ,     images=[image]  ,     padding="max_length"  ,     return_tensors="pt"    ).to(self.device    )
											__snake_case				=		self.segmentation_model(**SCREAMING_SNAKE_CASE    )
											__snake_case				=		torch.sigmoid(outputs.logits    ).cpu().detach().unsqueeze(-1    ).numpy()
											__snake_case				=		self.numpy_to_pil(SCREAMING_SNAKE_CASE    )[0].resize(image.size    )

											# Run inpainting pipeline with the generated mask
											__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=SCREAMING_SNAKE_CASE  ,     image=SCREAMING_SNAKE_CASE  ,     mask_image=SCREAMING_SNAKE_CASE  ,     height=SCREAMING_SNAKE_CASE  ,     width=SCREAMING_SNAKE_CASE  ,     num_inference_steps=SCREAMING_SNAKE_CASE  ,     guidance_scale=SCREAMING_SNAKE_CASE  ,     negative_prompt=SCREAMING_SNAKE_CASE  ,     num_images_per_prompt=SCREAMING_SNAKE_CASE  ,     eta=SCREAMING_SNAKE_CASE  ,     generator=SCREAMING_SNAKE_CASE  ,     latents=SCREAMING_SNAKE_CASE  ,     output_type=SCREAMING_SNAKE_CASE  ,     return_dict=SCREAMING_SNAKE_CASE  ,     callback=SCREAMING_SNAKE_CASE  ,     callback_steps=SCREAMING_SNAKE_CASE  ,     )









def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						int:


						'''simple docstring'''






						assert column_title.isupper()
						__snake_case				=		0
						__snake_case				=		len(_lowerCAmelCase			) - 1
						__snake_case				=		0

						while index >= 0:
												__snake_case				=		(ord(column_title[index]			) - 64) * pow(26   ,						_lowerCAmelCase			)
												answer += value
												power += 1
												index -= 1

						return answer


if __name__ == "__main__":
	from doctest import testmod

	testmod()





