
"""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


_snake_case          =						logging.get_logger(__name__)  # pylint: disable=invalid-name
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



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







											"""simple docstring"""




											super().__init__()

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																		    F"""The configuration file of this scheduler: {scheduler} is outdated.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

											if hasattr(scheduler.config				,     'skip_prk_steps'  ) and scheduler.config.skip_prk_steps is False:
																		_lowerCAmelCase  	=			(
																		    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'				,     UpperCAmelCase_				,     standard_warn=UpperCAmelCase_  )
																		_lowerCAmelCase  	=			dict(scheduler.config  )
																		_lowerCAmelCase  	=			True
																		_lowerCAmelCase  	=			FrozenDict(UpperCAmelCase_  )

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


				def 				__lowerCamelCase	(					self	:			List[str]				,     UpperCAmelCase_	:			Optional[Union[str, int]] = "auto"  )		->  List[str]:







											"""simple docstring"""




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


				def 				__lowerCamelCase	(					self	:			Tuple  )		->  Optional[Any]:







											"""simple docstring"""




											self.enable_attention_slicing(UpperCAmelCase_  )


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







											"""simple docstring"""




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

											_lowerCAmelCase  	=			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(UpperCAmelCase_				,     UpperCAmelCase_  )


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







											"""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(UpperCAmelCase_				,     '_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	:			int				,     UpperCAmelCase_	:			Union[str, List[str]]				,     UpperCAmelCase_	:			Union[torch.FloatTensor, PIL.Image.Image]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			int = 512				,     UpperCAmelCase_	:			int = 512				,     UpperCAmelCase_	:			int = 50				,     UpperCAmelCase_	:			float = 7.5				,     UpperCAmelCase_	:			Optional[Union[str, List[str]]] = None				,     UpperCAmelCase_	:			Optional[int] = 1				,     UpperCAmelCase_	:			float = 0.0				,     UpperCAmelCase_	:			Optional[torch.Generator] = None				,     UpperCAmelCase_	:			Optional[torch.FloatTensor] = None				,     UpperCAmelCase_	:			Optional[str] = "pil"				,     UpperCAmelCase_	:			bool = True				,     UpperCAmelCase_	:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,     UpperCAmelCase_	:			int = 1				,     **UpperCAmelCase_	:			Optional[int]				,     )		->  List[Any]:







											"""simple docstring"""




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

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








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[0 for i in range(len(SCREAMING_SNAKE_CASE						)						)]

							# initialize interval's left pointer and right pointer
							_lowerCAmelCase	,		_lowerCAmelCase  	=			0, 0

							for i in range(1     ,  len(SCREAMING_SNAKE_CASE						)						):
														# case when current index is inside the interval
														if i <= right_pointer:
																					_lowerCAmelCase  	=			min(right_pointer - i + 1     ,  z_result[i - left_pointer]						)
																					_lowerCAmelCase  	=			min_edge

														while go_next(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
																					z_result[i] += 1

														# if new index's result gives us more right interval,
														# we've to update left_pointer and right_pointer
														if i + z_result[i] - 1 > right_pointer:
																					_lowerCAmelCase	,		_lowerCAmelCase  	=			i, i + z_result[i] - 1

							return z_result






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					list[int]     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							return i + z_result[i] < len(SCREAMING_SNAKE_CASE						) and s[z_result[i]] == s[i + z_result[i]]






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			0
							# concatenate 'pattern' and 'input_str' and call z_function
							# with concatenated string
							_lowerCAmelCase  	=			z_function(pattern + input_str						)

							for val in z_result:
														# if value is greater then length of the pattern string
														# that means this index is starting position of substring
														# which is equal to pattern string
														if val >= len(SCREAMING_SNAKE_CASE						):
																					answer += 1

							return answer


if __name__ == "__main__":
	import doctest

	doctest.testmod()






