


'''simple docstring'''

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase__				     =		argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true", help="Enable DPMSolver or not")
parser.add_argument("--steps", default=None, type=int, help="Num inference steps")
lowercase__				     =		parser.parse_args()


lowercase__				     =		"cpu"
lowercase__				     =		"a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"

lowercase__				     =		"path-to-your-trained-model"
lowercase__				     =		StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
   lowercase__				     =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase__				     =		pipe.to(device)

# to channels last
lowercase__				     =		pipe.unet.to(memory_format=torch.channels_last)
lowercase__				     =		pipe.vae.to(memory_format=torch.channels_last)
lowercase__				     =		pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
   lowercase__				     =		pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
lowercase__				     =		torch.randn(2, 4, 64, 64)
lowercase__				     =		torch.rand(1) * 999
lowercase__				     =		torch.randn(2, 77, 768)
lowercase__				     =		(sample, timestep, encoder_hidden_status)
try:
   lowercase__				     =		ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
   lowercase__				     =		ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__				     =		ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__				     =		ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
   lowercase__				     =		ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase__				     =		666
lowercase__				     =		torch.Generator(device).manual_seed(seed)
lowercase__				     =		{"generator": generator}
if args.steps is not None:
   lowercase__				     =		args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
   lowercase__				     =		pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("generated.png")





'''simple docstring'''

from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst






def __UpperCamelCase   (			)      ->     Tuple:







     '''simple docstring'''






     _a			,			_a						=		9, 14  # noqa: F841
     _a						=		[
         [0, 1, 4],
         [0, 7, 8],
         [1, 2, 8],
         [7, 8, 7],
         [7, 6, 1],
         [2, 8, 2],
         [8, 6, 6],
         [2, 3, 7],
         [2, 5, 4],
         [6, 5, 2],
         [3, 5, 14],
         [3, 4, 9],
         [5, 4, 10],
         [1, 7, 11],
     ]

     _a						=		defaultdict(__lowerCamelCase				)
     for nodea, nodea, cost in edges:
          adjancency[nodea].append([nodea, cost]				)
          adjancency[nodea].append([nodea, cost]				)

     _a						=		mst(__lowerCamelCase				)

     _a						=		[
         [7, 6, 1],
         [2, 8, 2],
         [6, 5, 2],
         [0, 1, 4],
         [2, 5, 4],
         [2, 3, 7],
         [0, 7, 8],
         [3, 4, 9],
     ]

     for answer in expected:
          _a						=		tuple(answer[:2]				)
          _a						=		tuple(edge[::-1]				)
          assert edge in result or reverse in result

