




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


_lowerCamelCase      			=						False



class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     pass



@nightly
@require_torch_gpu
class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''    )
         # remove text_unet
         pipe.remove_unused_weights()
         pipe.to(__snake_case    )
         pipe.set_progress_bar_config(disable=__snake_case    )

         UpperCAmelCase_										=					'''A painting of a squirrel eating a burger '''
         UpperCAmelCase_										=					torch.manual_seed(0    )
         UpperCAmelCase_										=					pipe(
             prompt=__snake_case ,   generator=__snake_case ,   guidance_scale=7.5 ,   num_inference_steps=2 ,   output_type='''numpy'''    ).images

         with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(__snake_case    )
             UpperCAmelCase_										=					VersatileDiffusionTextToImagePipeline.from_pretrained(__snake_case    )
         pipe.to(__snake_case    )
         pipe.set_progress_bar_config(disable=__snake_case    )

         UpperCAmelCase_										=					generator.manual_seed(0    )
         UpperCAmelCase_										=					pipe(
             prompt=__snake_case ,   generator=__snake_case ,   guidance_scale=7.5 ,   num_inference_steps=2 ,   output_type='''numpy'''    ).images

         assert np.abs(image - new_image    ).sum() < 1E-5, "Models don't have the same forward pass"




     def   lowerCamelCase_       (     self       :      str    ):
         UpperCAmelCase_										=					VersatileDiffusionTextToImagePipeline.from_pretrained(
             '''shi-labs/versatile-diffusion''' ,   torch_dtype=torch.floataa    )
         pipe.to(__snake_case    )
         pipe.set_progress_bar_config(disable=__snake_case    )

         UpperCAmelCase_										=					'''A painting of a squirrel eating a burger '''
         UpperCAmelCase_										=					torch.manual_seed(0    )
         UpperCAmelCase_										=					pipe(
             prompt=__snake_case ,   generator=__snake_case ,   guidance_scale=7.5 ,   num_inference_steps=50 ,   output_type='''numpy'''    ).images

         UpperCAmelCase_										=					image[0, 2_53:2_56, 2_53:2_56, -1]

         assert image.shape == (1, 5_12, 5_12, 3)
         UpperCAmelCase_										=					np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2











from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCamelCase      			=						tuple[int, int]



class       a					:

     '''simple docstring'''





     def __init__(     self       :      str ,   __snake_case       :      set[int] ,   __snake_case       :      Mapping[EdgeT, int]    ):
         UpperCAmelCase_										=					vertices
         UpperCAmelCase_										=					{
             (min(__snake_case    ), max(__snake_case    )): weight for edge, weight in edges.items()
         }
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      EdgeT ,   __snake_case       :      int    ):
         self.vertices.add(edge[0]    )
         self.vertices.add(edge[1]    )
         UpperCAmelCase_										=					weight




     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					Graph({min(self.vertices    )} ,   {}    )
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42

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

             subgraph.add_edge(__snake_case ,   __snake_case    )

         return subgraph




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str = "p107_network.txt"				)  ->							int:
    UpperCAmelCase_										=					os.path.abspath(os.path.dirname(__UpperCamelCase				)				)
    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					__UpperCamelCase				)
    UpperCAmelCase_										=					{}
    UpperCAmelCase_										=					42
    UpperCAmelCase_										=					42
    UpperCAmelCase_										=					42

    with open(__UpperCamelCase				) as f:
        UpperCAmelCase_										=					f.read().strip().split('''\n'''				)

    UpperCAmelCase_										=					[line.split(''','''				) for line in data]

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

    UpperCAmelCase_										=					Graph(set(range(len(__UpperCamelCase				)				)				)	,					__UpperCamelCase				)

    UpperCAmelCase_										=					graph.prims_algorithm()

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

    return initial_total - optimal_total


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





