
"""simple docstring"""





from __future__ import annotations

import os
from collections.abc import Mapping

UpperCamelCase_       :    int						      =			tuple[int, int]



class __lowerCAmelCase					:




			"""simple docstring"""







			def __init__(					self    :       int							, _snake_case    :       set[int]							, _snake_case    :       Mapping[EdgeT, int]						)   ->			None:







								"""simple docstring"""
								A_								=    vertices
								A_								=    {
								    (min(_snake_case						), max(_snake_case						)): weight for edge, weight in edges.items()
								}




			def   lowerCamelCase__  (					self    :       Optional[Any]							, _snake_case    :       EdgeT							, _snake_case    :       int						)   ->			None:







								"""simple docstring"""
								self.vertices.add(edge[0]						)
								self.vertices.add(edge[1]						)
								A_								=    weight






			def   lowerCamelCase__  (					self    :       Any						)   ->			Graph:







								"""simple docstring"""
								A_								=    Graph({min(self.vertices						)}							, {}						)
								A_								=    42
								A_								=    42
								A_								=    42
								A_								=    42

								while len(subgraph.vertices						) < len(self.vertices						):
													A_								=    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:
																												A_								=    edge
																												A_								=    weight

													subgraph.add_edge(_snake_case							, _snake_case						)

								return subgraph





def 				A_  (__a = "p107_network.txt"					):

					'''simple docstring'''





					A_								=    os.path.abspath(os.path.dirname(__a					)					)
					A_								=    os.path.join(__a					, __a					)
					A_								=    {}
					A_								=    42
					A_								=    42
					A_								=    42

					with open(__a					) as f:
										A_								=    f.read().strip().split("\n"					)

					A_								=    [line.split(","					) for line in data]

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

					A_								=    Graph(set(range(len(__a					)					)					)					, __a					)

					A_								=    graph.prims_algorithm()

					A_								=    sum(graph.edges.values()					)
					A_								=    sum(subgraph.edges.values()					)

					return initial_total - optimal_total


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






"""simple docstring"""





import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort



class __lowerCAmelCase					(       _lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







			pass





@nightly
@require_onnxruntime
@require_torch_gpu
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			@property
			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Dict:







								"""simple docstring"""
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)




			@property
			def   lowerCamelCase__  (					self    :       List[str]						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    ort.SessionOptions()
								A_								=    False
								return options




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







								"""simple docstring"""
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
								A_								=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, revision="onnx"							, safety_checker=_snake_case							, feature_extractor=_snake_case							, provider=self.gpu_provider							, sess_options=self.gpu_options							, )
								pipe.set_progress_bar_config(disable=_snake_case						)

								A_								=    "A red cat sitting on a park bench"

								A_								=    np.random.RandomState(0						)
								A_								=    pipe(
								    prompt=_snake_case							, image=_snake_case							, mask_image=_snake_case							, guidance_scale=7.5							, num_inference_steps=10							, generator=_snake_case							, output_type="np"							, )
								A_								=    output.images
								A_								=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								A_								=    np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]						)

								assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3






			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Tuple:







								"""simple docstring"""
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
								A_								=    LMSDiscreteScheduler.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, subfolder="scheduler"							, revision="onnx"						)
								A_								=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, revision="onnx"							, scheduler=_snake_case							, safety_checker=_snake_case							, feature_extractor=_snake_case							, provider=self.gpu_provider							, sess_options=self.gpu_options							, )
								pipe.set_progress_bar_config(disable=_snake_case						)

								A_								=    "A red cat sitting on a park bench"

								A_								=    np.random.RandomState(0						)
								A_								=    pipe(
								    prompt=_snake_case							, image=_snake_case							, mask_image=_snake_case							, guidance_scale=7.5							, num_inference_steps=20							, generator=_snake_case							, output_type="np"							, )
								A_								=    output.images
								A_								=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								A_								=    np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]						)

								assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3




