



"""simple docstring"""





from typing import List

from .keymap import KEYMAP, get_character
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str    ):


						'''simple docstring'''






						def decorator(__UpperCamelCase      :			str    ):
												snake_case_   : Dict						=			getattr(__UpperCamelCase					, """handle_key"""					, []    )
												handle += [key]
												setattr(__UpperCamelCase					, """handle_key"""					, __UpperCamelCase    )
												return func

						return decorator
def 	__lowerCAmelCase						(		*__UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						def decorator(__UpperCamelCase      :			Optional[int]    ):
												snake_case_   : Optional[int]						=			getattr(__UpperCamelCase					, """handle_key"""					, []    )
												handle += keys
												setattr(__UpperCamelCase					, """handle_key"""					, __UpperCamelCase    )
												return func

						return decorator



class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		def __new__(					cls							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : Any						=			super().__new__(cls							,    _lowercase							,    _lowercase							,    _lowercase   )
								if not hasattr(_lowercase							,    """key_handler"""   ):
														setattr(_lowercase							,    """key_handler"""							,    {}   )
								setattr(_lowercase							,    """handle_input"""							,    KeyHandler.handle_input   )

								for value in attrs.values():
														snake_case_   : List[str]						=			getattr(_lowercase							,    """handle_key"""							,    []   )
														for key in handled_keys:
																				snake_case_   : List[str]						=			value
								return new_cls





		@staticmethod
		def    UpperCAmelCase__							(					cls   ) ->						Any:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			get_character()
								if char != KEYMAP["undefined"]:
														snake_case_   : int						=			ord(_lowercase   )
								snake_case_   : Tuple						=			cls.key_handler.get(_lowercase   )
								if handler:
														snake_case_   : Union[str, Any]						=			char
														return handler(cls   )
								else:
														return None




def 	__lowerCAmelCase						(		cls      :			Optional[int]    ):


						'''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


__lowerCAmelCase       :					Optional[int]      					=					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       :					Optional[Any]      					=					parser.parse_args()


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

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

# to channels last
__lowerCAmelCase       :					Optional[Any]      					=					pipe.unet.to(memory_format=torch.channels_last)
__lowerCAmelCase       :					List[str]      					=					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       :					Dict      					=					pipe.safety_checker.to(memory_format=torch.channels_last)

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

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

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

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

