



"""simple docstring"""


import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


__A   :							Union[str, 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")
__A   :							Optional[Any]										=   parser.parse_args()


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

__A   :							Optional[Any]										=   "path-to-your-trained-model"
__A   :							str										=   StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
     __A   :							List[Any]										=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
__A   :							str										=   pipe.to(device)

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

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

# compute
__A   :							Union[str, Any]										=   666
__A   :							Any										=   torch.Generator(device).manual_seed(seed)
__A   :							str										=   {"generator": generator}
if args.steps is not None:
     __A   :							Any										=   args.steps

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

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








"""simple docstring"""


import functools







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						list[int]					,     _SCREAMING_SNAKE_CASE   :						list[int]				):






				'''simple docstring'''



				if not isinstance(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				) or not all(isinstance(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				) for day in days				):
								raise ValueError('''The parameter days should be a list of integers'''				)

				if len(_SCREAMING_SNAKE_CASE				) != 3 or not all(isinstance(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				) for cost in costs				):
								raise ValueError('''The parameter costs should be a list of three integers'''				)

				if len(_SCREAMING_SNAKE_CASE				) == 0:
								return 0

				if min(_SCREAMING_SNAKE_CASE				) <= 0:
								raise ValueError('''All days elements should be greater than 0'''				)

				if max(_SCREAMING_SNAKE_CASE				) >= 366:
								raise ValueError('''All days elements should be less than 366'''				)

				_UpperCAmelCase					=    set(_SCREAMING_SNAKE_CASE				)

				@functools.cache
				def dynamic_programming(_SCREAMING_SNAKE_CASE   :						int				) -> int:
								if index > 365:
												return 0

								if index not in days_set:
												return dynamic_programming(index + 1				)

								return min(
								    costs[0] + dynamic_programming(index + 1				)					,     costs[1] + dynamic_programming(index + 7				)					,     costs[2] + dynamic_programming(index + 30				)					,     )

				return dynamic_programming(1				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



