


"""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


lowercase_			     =  logging.get_logger(__name__)  # pylint: disable=invalid-name
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def __init__(  self							, _a							, _a							, _a							, _a							, _a							, _a							, _a							, _a							, _a							, ):
				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						    f'''The configuration file of this scheduler: {scheduler} is outdated.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'''							, _a							, standard_warn=_a      )
						__a           =			dict(scheduler.config      )
						__a           =			1
						__a           =			FrozenDict(_a      )

				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'''							, _a							, standard_warn=_a      )
						__a           =			dict(scheduler.config      )
						__a           =			True
						__a           =			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 			__UpperCAmelCase       (  self							, _a = "auto"      ):
				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(_a      )

		def 			__UpperCAmelCase       (  self      ):
				self.enable_attention_slicing(_a      )

		def 			__UpperCAmelCase       (  self      ):
				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(_a							, _a      )

		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 			__UpperCAmelCase       (  self      ):
				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							, _a							, _a							, _a							, _a = 512							, _a = 512							, _a = 50							, _a = 7.5							, _a = None							, _a = 1							, _a = 0.0							, _a = None							, _a = None							, _a = "pil"							, _a = True							, _a = None							, _a = 1							, **_a							, ):
				__a           =			self.segmentation_processor(
				    text=[text]							, images=[image]							, padding='''max_length'''							, return_tensors='''pt'''      ).to(self.device      )
				__a           =			self.segmentation_model(**_a      )
				__a           =			torch.sigmoid(outputs.logits      ).cpu().detach().unsqueeze(-1      ).numpy()
				__a           =			self.numpy_to_pil(_a      )[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=_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							, )













from heapq import heappop, heappush

import numpy as np







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a					,						)      ->      tuple[float | int, list[tuple[int, int]]]:
					__A    ,				__A	:						int       =				grid.shape
					__A	:						Any       =				[-1, 1, 0, 0]
					__A	:						Optional[Any]       =				[0, 0, -1, 1]
					if allow_diagonal:
										dx += [-1, -1, 1, 1]
										dy += [-1, 1, -1, 1]

					__A    ,				__A	:						Optional[int]       =				[(0, source)], set()
					__A	:						Any       =				np.full((rows, cols)					,						np.inf  )
					__A	:						Any       =				0
					__A	:						Any       =				np.empty((rows, cols)					,						dtype=a  )
					__A	:						Optional[Any]       =				None

					while queue:
										((__A)    ,				(__A))	:						List[str]       =				heappop(a  )
										if (x, y) in visited:
															continue
										visited.add((x, y)  )

										if (x, y) == destination:
															__A	:						int       =				[]
															while (x, y) != source:
																				path.append((x, y)  )
																				__A    ,				__A	:						Optional[int]       =				predecessors[x, y]
															path.append(a  )  # add the source manually
															path.reverse()
															return matrix[destination], path

										for i in range(len(a  )  ):
															__A    ,				__A	:						Union[str, Any]       =				x + dx[i], y + dy[i]
															if 0 <= nx < rows and 0 <= ny < cols:
																				__A	:						Optional[int]       =				grid[nx][ny]
																				if next_node == 1 and matrix[nx, ny] > dist + 1:
																									heappush(a					,						(dist + 1, (nx, ny))  )
																									__A	:						List[Any]       =				dist + 1
																									__A	:						Union[str, Any]       =				(x, y)

					return np.inf, []


if __name__ == "__main__":
				import doctest

				doctest.testmod()




