


"""simple docstring"""



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class       __a	(   __snake_case       ):
				lowerCamelCase    :					Union[str, Any] 					=(LMSDiscreteScheduler,)
				lowerCamelCase    :					int 					=10







				def     lowerCamelCase_    (   self			,  **UpperCAmelCase      ):

							'''simple docstring'''






							lowerCAmelCase_			       = {
							    '''num_train_timesteps''': 1100,
							    '''beta_start''': 0.0_0_0_1,
							    '''beta_end''': 0.0_2,
							    '''beta_schedule''': '''linear''',
							}

							config.update(**UpperCAmelCase      )
							return config







				def     lowerCamelCase_    (   self      ):

							'''simple docstring'''






							for timesteps in [10, 50, 100, 1000]:
										self.check_over_configs(num_train_timesteps=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

							'''simple docstring'''






							for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]			,  [0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
										self.check_over_configs(beta_start=UpperCAmelCase			,  beta_end=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

							'''simple docstring'''






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







				def     lowerCamelCase_    (   self      ):

							'''simple docstring'''






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







				def     lowerCamelCase_    (   self      ):

							'''simple docstring'''






							for t in [0, 500, 800]:
										self.check_over_forward(time_step=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

							'''simple docstring'''






							lowerCAmelCase_			       = self.scheduler_classes[0]
							lowerCAmelCase_			       = self.get_scheduler_config()
							lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

							scheduler.set_timesteps(self.num_inference_steps      )

							lowerCAmelCase_			       = self.dummy_model()
							lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(scheduler.timesteps      ):
										lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

										lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

										lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
										lowerCAmelCase_			       = output.prev_sample

							lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
							lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

							assert abs(result_sum.item() - 1006.388      ) < 1E-2
							assert abs(result_mean.item() - 1.3_1      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

							'''simple docstring'''






							lowerCAmelCase_			       = self.scheduler_classes[0]
							lowerCAmelCase_			       = self.get_scheduler_config(prediction_type='''v_prediction'''      )
							lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

							scheduler.set_timesteps(self.num_inference_steps      )

							lowerCAmelCase_			       = self.dummy_model()
							lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(scheduler.timesteps      ):
										lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

										lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

										lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
										lowerCAmelCase_			       = output.prev_sample

							lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
							lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

							assert abs(result_sum.item() - 0.0_0_1_7      ) < 1E-2
							assert abs(result_mean.item() - 2.2_6_7_6E-0_6      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

							'''simple docstring'''






							lowerCAmelCase_			       = self.scheduler_classes[0]
							lowerCAmelCase_			       = self.get_scheduler_config()
							lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

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

							lowerCAmelCase_			       = self.dummy_model()
							lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							lowerCAmelCase_			       = sample.to(UpperCAmelCase      )

							for i, t in enumerate(scheduler.timesteps      ):
										lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

										lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

										lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
										lowerCAmelCase_			       = output.prev_sample

							lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
							lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

							assert abs(result_sum.item() - 1006.388      ) < 1E-2
							assert abs(result_mean.item() - 1.3_1      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

							'''simple docstring'''






							lowerCAmelCase_			       = self.scheduler_classes[0]
							lowerCAmelCase_			       = self.get_scheduler_config()
							lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase			,  use_karras_sigmas=UpperCAmelCase      )

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

							lowerCAmelCase_			       = self.dummy_model()
							lowerCAmelCase_			       = self.dummy_sample_deter.to(UpperCAmelCase      ) * scheduler.init_noise_sigma
							lowerCAmelCase_			       = sample.to(UpperCAmelCase      )

							for t in scheduler.timesteps:
										lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

										lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

										lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
										lowerCAmelCase_			       = output.prev_sample

							lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
							lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

							assert abs(result_sum.item() - 3812.9927      ) < 2E-2
							assert abs(result_mean.item() - 4.9_6_4_8      ) < 1E-3



"""simple docstring"""



from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available







@dataclass
class       __a	(   __snake_case       ):
				lowerCamelCase    :					int 					={
				    'sld_warmup_steps': 15,
				    'sld_guidance_scale': 20,
				    'sld_threshold': 0.0,
				    'sld_momentum_scale': 0.0,
				    'sld_mom_beta': 0.0,
				}
				lowerCamelCase    :					Optional[Any] 					={
				    'sld_warmup_steps': 10,
				    'sld_guidance_scale': 1000,
				    'sld_threshold': 0.01,
				    'sld_momentum_scale': 0.3,
				    'sld_mom_beta': 0.4,
				}
				lowerCamelCase    :					List[str] 					={
				    'sld_warmup_steps': 7,
				    'sld_guidance_scale': 2000,
				    'sld_threshold': 0.025,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}
				lowerCamelCase    :					Any 					={
				    'sld_warmup_steps': 0,
				    'sld_guidance_scale': 5000,
				    'sld_threshold': 1.0,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}







@dataclass
class       __a	(   __snake_case       ):
				lowerCamelCase    :					Union[List[PIL.Image.Image], np.ndarray]
				lowerCamelCase    :					Optional[List[bool]]
				lowerCamelCase    :					Optional[Union[List[PIL.Image.Image], np.ndarray]]
				lowerCamelCase    :					Optional[str]


if is_transformers_available() and is_torch_available():
		from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
		from .safety_checker import SafeStableDiffusionSafetyChecker