





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







class 			lowerCamelCase					(						__UpperCAmelCase  ):


					def __init__(					self	:  str       ,     __snake_case	:  Dict       ,     __snake_case	:  int       ,     __snake_case	:  Dict       ,     __snake_case	:  Tuple       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Tuple       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  Tuple       ,     __snake_case	:  int       ,     ):





											'''simple docstring'''





											super().__init__()

											if hasattr(scheduler.config       ,     'steps_offset'       ) and scheduler.config.steps_offset != 1:
																	_snake_case:   Optional[Any]  					=		(
																	    f'''The configuration file of this scheduler: {scheduler} is outdated.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__       )
																	_snake_case:   List[Any]  					=		dict(scheduler.config       )
																	_snake_case:   Optional[Any]  					=		1
																	_snake_case:   Optional[Any]  					=		FrozenDict(UpperCamelCase__       )

											if hasattr(scheduler.config       ,     'skip_prk_steps'       ) and scheduler.config.skip_prk_steps is False:
																	_snake_case:   Any  					=		(
																	    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__       )
																	_snake_case:   List[str]  					=		dict(scheduler.config       )
																	_snake_case:   Tuple  					=		True
																	_snake_case:   List[Any]  					=		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    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     __snake_case	:  Optional[int] = "auto"       ):





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											self.enable_attention_slicing(UpperCamelCase__       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





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

											_snake_case:   Union[str, Any]  					=		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    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, 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	:  Dict       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  List[Any] = 5_12       ,     __snake_case	:  Optional[int] = 5_12       ,     __snake_case	:  List[Any] = 50       ,     __snake_case	:  Optional[Any] = 7.5       ,     __snake_case	:  Dict = None       ,     __snake_case	:  List[Any] = 1       ,     __snake_case	:  Optional[int] = 0.0       ,     __snake_case	:  Union[str, Any] = None       ,     __snake_case	:  Union[str, Any] = None       ,     __snake_case	:  Any = "pil"       ,     __snake_case	:  Optional[Any] = True       ,     __snake_case	:  Any = None       ,     __snake_case	:  Any = 1       ,     **__snake_case	:  Union[str, Any]       ,     ):





											'''simple docstring'''





											_snake_case:   int  					=		self.segmentation_processor(
											    text=[text]       ,     images=[image]       ,     padding='max_length'       ,     return_tensors='pt'       ).to(self.device       )
											_snake_case:   List[str]  					=		self.segmentation_model(**UpperCamelCase__       )
											_snake_case:   Tuple  					=		torch.sigmoid(outputs.logits       ).cpu().detach().unsqueeze(-1       ).numpy()
											_snake_case:   Optional[int]  					=		self.numpy_to_pil(UpperCamelCase__       )[0].resize(image.size       )

											# Run inpainting pipeline with the generated mask
											_snake_case:   int  					=		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'''






from __future__ import annotations

import os
from collections.abc import Mapping

A   : Optional[Any]             =			tuple[int, int]







class 			lowerCamelCase					:


					def __init__(					self	:  Tuple       ,     __snake_case	:  set[int]       ,     __snake_case	:  Mapping[EdgeT, int]       ):





											'''simple docstring'''





											_snake_case:   set[int]  					=		vertices
											_snake_case:   dict[EdgeT, int]  					=		{
											    (min(__snake_case       ), max(__snake_case       )): weight for edge, weight in edges.items()
											}


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  EdgeT       ,     __snake_case	:  int       ):





											'''simple docstring'''





											self.vertices.add(edge[0]       )
											self.vertices.add(edge[1]       )
											_snake_case:   Dict  					=		weight




					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   Graph  					=		Graph({min(self.vertices       )}       ,     {}       )
											_snake_case:   EdgeT
											_snake_case:   int
											_snake_case:   EdgeT
											_snake_case:   int

											while len(subgraph.vertices       ) < len(self.vertices       ):
																	_snake_case:   List[str]  					=		max(self.edges.values()       ) + 1
																	for edge, weight in self.edges.items():
																							if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
																													if weight < min_weight:
																																			_snake_case:   Optional[Any]  					=		edge
																																			_snake_case:   Optional[int]  					=		weight

																	subgraph.add_edge(__snake_case       ,     __snake_case       )

											return subgraph





def       lowercase_      (		lowercase__ = "p107_network.txt"						)   ->int:
						_snake_case:   str  					=		os.path.abspath(os.path.dirname(lowercase__						)						)
						_snake_case:   str  					=		os.path.join(lowercase__				,  lowercase__						)
						_snake_case:   dict[EdgeT, int]  					=		{}
						_snake_case:   list[str]
						_snake_case:   int
						_snake_case:   int

						with open(lowercase__						) as f:
												_snake_case:   Tuple  					=		f.read().strip().split('\n'						)

						_snake_case:   Tuple  					=		[line.split(','						) for line in data]

						for edgea in range(1				,  len(lowercase__						)						):
												for edgea in range(lowercase__						):
																		if adjaceny_matrix[edgea][edgea] != "-":
																								_snake_case:   int  					=		int(adjaceny_matrix[edgea][edgea]						)

						_snake_case:   Graph  					=		Graph(set(range(len(lowercase__						)						)						)				,  lowercase__						)

						_snake_case:   Graph  					=		graph.prims_algorithm()

						_snake_case:   int  					=		sum(graph.edges.values()						)
						_snake_case:   int  					=		sum(subgraph.edges.values()						)

						return initial_total - optimal_total


if __name__ == "__main__":
		print(F'{solution() = }')





