




"""simple docstring"""


import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


A_						=				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''')
A_						=				parser.parse_args()


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

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

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

# optimize with ipex
A_						=				torch.randn(2, 4, 64, 64)
A_						=				torch.rand(1) * 9_99
A_						=				torch.randn(2, 77, 7_68)
A_						=				(sample, timestep, encoder_hidden_status)
try:
     A_						=				ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
     A_						=				ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
A_						=				ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
A_						=				ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
     A_						=				ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
A_						=				6_66
A_						=				torch.Generator(device).manual_seed(seed)
A_						=				{'''generator''': generator}
if args.steps is not None:
     A_						=				args.steps

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

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




"""simple docstring"""





class     _UpperCAmelCase:



		def __init__(					self)       ->       Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  {}



		def 						UpperCAmelCase							(					self)       ->       None:






							'''simple docstring'''
							print(self.vertex)
							for i in self.vertex:
												print(__a				,		''' -> '''				,		''' -> '''.join([str(__a) for j in self.vertex[i]]))



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       None:






							'''simple docstring'''
							# check if vertex is already present,
							if from_vertex in self.vertex:
												self.vertex[from_vertex].append(__a)
							else:
												# else make a new vertex
												_UpperCamelCase							 =  [to_vertex]



		def 						UpperCAmelCase							(					self)       ->       None:






							'''simple docstring'''
							# visited array for storing already visited nodes
							_UpperCamelCase							 =  [False] * len(self.vertex)

							# call the recursive helper function
							for i in range(len(self.vertex)):
												if not visited[i]:
																	self.dfs_recursive(__a				,		__a)




		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       None:






							'''simple docstring'''
							# mark start vertex as visited
							_UpperCamelCase							 =  True

							print(__a				,		end=''' ''')

							# Recur for all the vertices that are adjacent to this node
							for i in self.vertex:
												if not visited[i]:
																	self.dfs_recursive(__a				,		__a)


if __name__ == "__main__":
				_a														=		Graph()
				g.add_edge(0, 1)
				g.add_edge(0, 2)
				g.add_edge(1, 2)
				g.add_edge(2, 0)
				g.add_edge(2, 3)
				g.add_edge(3, 3)

				g.print_graph()
				print("""DFS:""")
				g.dfs()

				# OUTPUT:
				# 0  ->  1 -> 2
				# 1  ->  2
				# 2  ->  0 -> 3
				# 3  ->  3
				# DFS:
				#  0 1 2 3






