



"""simple docstring"""

from typing import List

from .keymap import KEYMAP, get_character
def  __snake_case							(	__A      :						str     )     ->       Optional[int]:






				'''simple docstring'''

				def decorator(__A      :						Optional[Any]     ):
								SCREAMING_SNAKE_CASE						:							Tuple							=     getattr(__A							,		'handle_key'							,		[]     )
								handle += [key]
								setattr(__A							,		'handle_key'							,		__A     )
								return func

				return decorator
def  __snake_case							(	*__A      :						List[str]     )     ->       Any:






				'''simple docstring'''

				def decorator(__A      :						str     ):
								SCREAMING_SNAKE_CASE						:							Optional[int]							=     getattr(__A							,		'handle_key'							,		[]     )
								handle += keys
								setattr(__A							,		'handle_key'							,		__A     )
								return func

				return decorator
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	def __new__(      cls				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     super().__new__(cls     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					if not hasattr(_SCREAMING_SNAKE_CASE     ,       'key_handler'    ):
									setattr(_SCREAMING_SNAKE_CASE     ,       'key_handler'     ,       {}    )
					setattr(_SCREAMING_SNAKE_CASE     ,       'handle_input'     ,       KeyHandler.handle_input    )

					for value in attrs.values():
									SCREAMING_SNAKE_CASE						:							Tuple							=     getattr(_SCREAMING_SNAKE_CASE     ,       'handle_key'     ,       []    )
									for key in handled_keys:
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     value
					return new_cls







	@staticmethod
	def 			_lowerCAmelCase     (      cls				:       Dict    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     get_character()
					if char != KEYMAP["undefined"]:
									SCREAMING_SNAKE_CASE						:							Tuple							=     ord(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     cls.key_handler.get(_SCREAMING_SNAKE_CASE    )
					if handler:
									SCREAMING_SNAKE_CASE						:							Any							=     char
									return handler(cls    )
					else:
									return None




def  __snake_case							(	cls      :						Dict     )     ->       List[Any]:






				'''simple docstring'''
				return KeyHandler(cls.__name__							,		cls.__bases__							,		cls.__dict__.copy()     )










"""simple docstring"""

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


A_						:    List[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_						:    Tuple				=      parser.parse_args()


A_						:    Optional[int]				=      'cpu'
A_						:    Dict				=      'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

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

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

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

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





