





def    A__(		__lowerCAmelCase			):

				if len(_lowerCAmelCase			) <= 1:
								return [tuple(_lowerCAmelCase			)]

				_snake_case			:      Optional[Any]	    = []

				def generate(__lowerCAmelCase						,      __lowerCAmelCase			):
								_snake_case			:      List[Any]	    = [0] * n
								res.append(tuple(_lowerCAmelCase			)			)

								_snake_case			:      Tuple	    = 0
								while i < n:
												if c[i] < i:
																if i % 2 == 0:
																				_snake_case			:      Optional[Any]	    = arr[i], arr[0]
																else:
																				_snake_case			:      List[str]	    = arr[i], arr[c[i]]
																res.append(tuple(_lowerCAmelCase			)			)
																c[i] += 1
																_snake_case			:      List[str]	    = 0
												else:
																_snake_case			:      str	    = 0
																i += 1

				generate(len(_lowerCAmelCase			)						,      _lowerCAmelCase			)
				return res


if __name__ == "__main__":
		lowercase_		:					str      						=    input('''Enter numbers separated by a comma:\n''').strip()
		lowercase_		:					List[Any]      						=    [int(item) for item in user_input.split(''',''')]
		print(heaps(arr))













import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_lowerCAmelCase				:   Any						       =						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''')
_lowerCAmelCase				:   Tuple						       =						parser.parse_args()


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

_lowerCAmelCase				:   Union[str, Any]						       =						'''path-to-your-trained-model'''
_lowerCAmelCase				:   Tuple						       =						StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
 _lowerCAmelCase				:   List[str]						       =						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_lowerCAmelCase				:   List[Any]						       =						pipe.to(device)

# to channels last
_lowerCAmelCase				:   Any						       =						pipe.unet.to(memory_format=torch.channels_last)
_lowerCAmelCase				:   int						       =						pipe.vae.to(memory_format=torch.channels_last)
_lowerCAmelCase				:   Optional[Any]						       =						pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
 _lowerCAmelCase				:   Optional[int]						       =						pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
_lowerCAmelCase				:   Tuple						       =						torch.randn(2, 4, 64, 64)
_lowerCAmelCase				:   List[str]						       =						torch.rand(1) * 999
_lowerCAmelCase				:   List[Any]						       =						torch.randn(2, 77, 768)
_lowerCAmelCase				:   int						       =						(sample, timestep, encoder_hidden_status)
try:
 _lowerCAmelCase				:   Tuple						       =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
 _lowerCAmelCase				:   Optional[Any]						       =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_lowerCAmelCase				:   Dict						       =						ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_lowerCAmelCase				:   Dict						       =						ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
 _lowerCAmelCase				:   Tuple						       =						ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_lowerCAmelCase				:   Dict						       =						666
_lowerCAmelCase				:   Tuple						       =						torch.Generator(device).manual_seed(seed)
_lowerCAmelCase				:   Tuple						       =						{'''generator''': generator}
if args.steps is not None:
 _lowerCAmelCase				:   Tuple						       =						args.steps

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

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