
import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class     lowerCamelCase__							(       UpperCamelCase_):




	'''simple docstring'''




	_A			=  (EulerDiscreteScheduler,)
	_A			=  1_0







	def      _lowerCamelCase       (      self	:Optional[Any]  ,     **a	:List[str]		)	->				Any:
								__UpperCamelCase			:     Dict         =      {
								    'num_train_timesteps': 1_1_0_0,
								    'beta_start': 0.0001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								}

								config.update(**__a		)
								return config







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






								for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=__a		)







	def      _lowerCamelCase       (      self	:List[Any]		)	->				Union[str, Any]:






								for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]  ,     [0.0002, 0.002, 0.02]		):
															self.check_over_configs(beta_start=__a  ,     beta_end=__a		)







	def      _lowerCamelCase       (      self	:List[Any]		)	->				Union[str, Any]:






								for schedule in ["linear", "scaled_linear"]:
															self.check_over_configs(beta_schedule=__a		)







	def      _lowerCamelCase       (      self	:List[str]		)	->				List[str]:






								for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=__a		)







	def      _lowerCamelCase       (      self	:Any		)	->				Dict:
								__UpperCamelCase			:     Optional[Any]         =      self.scheduler_classes[0]
								__UpperCamelCase			:     Optional[Any]         =      self.get_scheduler_config()
								__UpperCamelCase			:     Any         =      scheduler_class(**__a		)

								scheduler.set_timesteps(self.num_inference_steps		)

								__UpperCamelCase			:     Union[str, Any]         =      torch.manual_seed(0		)

								__UpperCamelCase			:     int         =      self.dummy_model()
								__UpperCamelCase			:     Optional[Any]         =      self.dummy_sample_deter * scheduler.init_noise_sigma
								__UpperCamelCase			:     Optional[int]         =      sample.to(__a		)

								for i, t in enumerate(scheduler.timesteps		):
															__UpperCamelCase			:     Dict         =      scheduler.scale_model_input(__a  ,     __a		)

															__UpperCamelCase			:     Union[str, Any]         =      model(__a  ,     __a		)

															__UpperCamelCase			:     str         =      scheduler.step(__a  ,     __a  ,     __a  ,     generator=__a		)
															__UpperCamelCase			:     Union[str, Any]         =      output.prev_sample

								__UpperCamelCase			:     Optional[int]         =      torch.sum(torch.abs(__a		)		)
								__UpperCamelCase			:     List[Any]         =      torch.mean(torch.abs(__a		)		)

								assert abs(result_sum.item() - 10.0807		) < 1E-2
								assert abs(result_mean.item() - 0.0131		) < 1E-3







	def      _lowerCamelCase       (      self	:int		)	->				int:
								__UpperCamelCase			:     Tuple         =      self.scheduler_classes[0]
								__UpperCamelCase			:     Optional[int]         =      self.get_scheduler_config(prediction_type="v_prediction"		)
								__UpperCamelCase			:     List[Any]         =      scheduler_class(**__a		)

								scheduler.set_timesteps(self.num_inference_steps		)

								__UpperCamelCase			:     Tuple         =      torch.manual_seed(0		)

								__UpperCamelCase			:     List[str]         =      self.dummy_model()
								__UpperCamelCase			:     Tuple         =      self.dummy_sample_deter * scheduler.init_noise_sigma
								__UpperCamelCase			:     Dict         =      sample.to(__a		)

								for i, t in enumerate(scheduler.timesteps		):
															__UpperCamelCase			:     int         =      scheduler.scale_model_input(__a  ,     __a		)

															__UpperCamelCase			:     Union[str, Any]         =      model(__a  ,     __a		)

															__UpperCamelCase			:     Tuple         =      scheduler.step(__a  ,     __a  ,     __a  ,     generator=__a		)
															__UpperCamelCase			:     Optional[int]         =      output.prev_sample

								__UpperCamelCase			:     Tuple         =      torch.sum(torch.abs(__a		)		)
								__UpperCamelCase			:     str         =      torch.mean(torch.abs(__a		)		)

								assert abs(result_sum.item() - 0.0002		) < 1E-2
								assert abs(result_mean.item() - 2.2_6_7_6E-0_6		) < 1E-3







	def      _lowerCamelCase       (      self	:Any		)	->				Any:
								__UpperCamelCase			:     List[str]         =      self.scheduler_classes[0]
								__UpperCamelCase			:     Dict         =      self.get_scheduler_config()
								__UpperCamelCase			:     str         =      scheduler_class(**__a		)

								scheduler.set_timesteps(self.num_inference_steps  ,     device=__a		)

								__UpperCamelCase			:     List[str]         =      torch.manual_seed(0		)

								__UpperCamelCase			:     Union[str, Any]         =      self.dummy_model()
								__UpperCamelCase			:     Optional[int]         =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								__UpperCamelCase			:     Any         =      sample.to(__a		)

								for t in scheduler.timesteps:
															__UpperCamelCase			:     Optional[int]         =      scheduler.scale_model_input(__a  ,     __a		)

															__UpperCamelCase			:     Optional[int]         =      model(__a  ,     __a		)

															__UpperCamelCase			:     List[str]         =      scheduler.step(__a  ,     __a  ,     __a  ,     generator=__a		)
															__UpperCamelCase			:     int         =      output.prev_sample

								__UpperCamelCase			:     List[Any]         =      torch.sum(torch.abs(__a		)		)
								__UpperCamelCase			:     List[Any]         =      torch.mean(torch.abs(__a		)		)

								assert abs(result_sum.item() - 10.0807		) < 1E-2
								assert abs(result_mean.item() - 0.0131		) < 1E-3







	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				List[str]:
								__UpperCamelCase			:     Tuple         =      self.scheduler_classes[0]
								__UpperCamelCase			:     Tuple         =      self.get_scheduler_config()
								__UpperCamelCase			:     str         =      scheduler_class(**__a  ,     use_karras_sigmas=__a		)

								scheduler.set_timesteps(self.num_inference_steps  ,     device=__a		)

								__UpperCamelCase			:     str         =      torch.manual_seed(0		)

								__UpperCamelCase			:     int         =      self.dummy_model()
								__UpperCamelCase			:     Optional[int]         =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								__UpperCamelCase			:     Optional[int]         =      sample.to(__a		)

								for t in scheduler.timesteps:
															__UpperCamelCase			:     Tuple         =      scheduler.scale_model_input(__a  ,     __a		)

															__UpperCamelCase			:     Tuple         =      model(__a  ,     __a		)

															__UpperCamelCase			:     Union[str, Any]         =      scheduler.step(__a  ,     __a  ,     __a  ,     generator=__a		)
															__UpperCamelCase			:     Optional[int]         =      output.prev_sample

								__UpperCamelCase			:     str         =      torch.sum(torch.abs(__a		)		)
								__UpperCamelCase			:     Dict         =      torch.mean(torch.abs(__a		)		)

								assert abs(result_sum.item() - 124.52299499511719		) < 1E-2
								assert abs(result_mean.item() - 0.16213932633399963		) < 1E-3

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase						:      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')
lowercase						:      Tuple									= parser.parse_args()


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

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

# to channels last
lowercase						:      int									= pipe.unet.to(memory_format=torch.channels_last)
lowercase						:      int									= pipe.vae.to(memory_format=torch.channels_last)
lowercase						:      Dict									= pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
		lowercase						:      List[str]									= pipe.safety_checker.to(memory_format=torch.channels_last)

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

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

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

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