
"""simple docstring"""


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 					A_			(  A__     ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_			   =     (LMSDiscreteScheduler,)
							SCREAMING_SNAKE_CASE_			   =     10





							def    UpperCAmelCase__							(						self						:Any  ,							**lowerCamelCase_						:Optional[Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  Optional[int]  ={
										    'num_train_timesteps': 1_100,
										    'beta_start': 0.00_01,
										    'beta_end': 0.02,
										    'beta_schedule': 'linear',
										}

										config.update(**lowerCamelCase_ )
										return config





							def    UpperCAmelCase__							(						self						:Optional[Any] ):





										"""simple docstring"""

										for timesteps in [10, 50, 100, 1_000]:
													self.check_over_configs(num_train_timesteps=lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Dict ):





										"""simple docstring"""

										for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]  ,							[0.00_02, 0.0_02, 0.02] ):
													self.check_over_configs(beta_start=lowerCamelCase_  ,							beta_end=lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

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





							def    UpperCAmelCase__							(						self						:List[str] ):





										"""simple docstring"""

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





							def    UpperCAmelCase__							(						self						:List[str] ):





										"""simple docstring"""

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





							def    UpperCAmelCase__							(						self						:List[str] ):





										"""simple docstring"""

										lowerCamelCase__			:  str  =self.scheduler_classes[0]
										lowerCamelCase__			:  int  =self.get_scheduler_config()
										lowerCamelCase__			:  Tuple  =scheduler_class(**lowerCamelCase_ )

										scheduler.set_timesteps(self.num_inference_steps )

										lowerCamelCase__			:  int  =self.dummy_model()
										lowerCamelCase__			:  Optional[int]  =self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(scheduler.timesteps ):
													lowerCamelCase__			:  Union[str, Any]  =scheduler.scale_model_input(lowerCamelCase_  ,							lowerCamelCase_ )

													lowerCamelCase__			:  Any  =model(lowerCamelCase_  ,							lowerCamelCase_ )

													lowerCamelCase__			:  Union[str, Any]  =scheduler.step(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_ )
													lowerCamelCase__			:  List[Any]  =output.prev_sample

										lowerCamelCase__			:  Dict  =torch.sum(torch.abs(lowerCamelCase_ ) )
										lowerCamelCase__			:  int  =torch.mean(torch.abs(lowerCamelCase_ ) )

										assert abs(result_sum.item() - 10_06.3_88 ) < 1e-2
										assert abs(result_mean.item() - 1.31 ) < 1e-3





							def    UpperCAmelCase__							(						self						:Dict ):





										"""simple docstring"""

										lowerCamelCase__			:  Dict  =self.scheduler_classes[0]
										lowerCamelCase__			:  Any  =self.get_scheduler_config(prediction_type='v_prediction' )
										lowerCamelCase__			:  List[Any]  =scheduler_class(**lowerCamelCase_ )

										scheduler.set_timesteps(self.num_inference_steps )

										lowerCamelCase__			:  Any  =self.dummy_model()
										lowerCamelCase__			:  Tuple  =self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(scheduler.timesteps ):
													lowerCamelCase__			:  List[str]  =scheduler.scale_model_input(lowerCamelCase_  ,							lowerCamelCase_ )

													lowerCamelCase__			:  Optional[int]  =model(lowerCamelCase_  ,							lowerCamelCase_ )

													lowerCamelCase__			:  str  =scheduler.step(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_ )
													lowerCamelCase__			:  Any  =output.prev_sample

										lowerCamelCase__			:  Any  =torch.sum(torch.abs(lowerCamelCase_ ) )
										lowerCamelCase__			:  Optional[int]  =torch.mean(torch.abs(lowerCamelCase_ ) )

										assert abs(result_sum.item() - 0.00_17 ) < 1e-2
										assert abs(result_mean.item() - 2.2676e-06 ) < 1e-3





							def    UpperCAmelCase__							(						self						:Optional[int] ):





										"""simple docstring"""

										lowerCamelCase__			:  Union[str, Any]  =self.scheduler_classes[0]
										lowerCamelCase__			:  Any  =self.get_scheduler_config()
										lowerCamelCase__			:  Optional[int]  =scheduler_class(**lowerCamelCase_ )

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

										lowerCamelCase__			:  Dict  =self.dummy_model()
										lowerCamelCase__			:  List[Any]  =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										lowerCamelCase__			:  Union[str, Any]  =sample.to(lowerCamelCase_ )

										for i, t in enumerate(scheduler.timesteps ):
													lowerCamelCase__			:  int  =scheduler.scale_model_input(lowerCamelCase_  ,							lowerCamelCase_ )

													lowerCamelCase__			:  str  =model(lowerCamelCase_  ,							lowerCamelCase_ )

													lowerCamelCase__			:  List[Any]  =scheduler.step(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_ )
													lowerCamelCase__			:  List[str]  =output.prev_sample

										lowerCamelCase__			:  Optional[int]  =torch.sum(torch.abs(lowerCamelCase_ ) )
										lowerCamelCase__			:  int  =torch.mean(torch.abs(lowerCamelCase_ ) )

										assert abs(result_sum.item() - 10_06.3_88 ) < 1e-2
										assert abs(result_mean.item() - 1.31 ) < 1e-3





							def    UpperCAmelCase__							(						self						:str ):





										"""simple docstring"""

										lowerCamelCase__			:  Any  =self.scheduler_classes[0]
										lowerCamelCase__			:  Dict  =self.get_scheduler_config()
										lowerCamelCase__			:  Optional[int]  =scheduler_class(**lowerCamelCase_  ,							use_karras_sigmas=lowerCamelCase_ )

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

										lowerCamelCase__			:  Union[str, Any]  =self.dummy_model()
										lowerCamelCase__			:  str  =self.dummy_sample_deter.to(lowerCamelCase_ ) * scheduler.init_noise_sigma
										lowerCamelCase__			:  Any  =sample.to(lowerCamelCase_ )

										for t in scheduler.timesteps:
													lowerCamelCase__			:  List[str]  =scheduler.scale_model_input(lowerCamelCase_  ,							lowerCamelCase_ )

													lowerCamelCase__			:  Union[str, Any]  =model(lowerCamelCase_  ,							lowerCamelCase_ )

													lowerCamelCase__			:  Union[str, Any]  =scheduler.step(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_ )
													lowerCamelCase__			:  List[Any]  =output.prev_sample

										lowerCamelCase__			:  List[str]  =torch.sum(torch.abs(lowerCamelCase_ ) )
										lowerCamelCase__			:  Union[str, Any]  =torch.mean(torch.abs(lowerCamelCase_ ) )

										assert abs(result_sum.item() - 38_12.99_27 ) < 2e-2
										assert abs(result_mean.item() - 4.96_48 ) < 1e-3

"""simple docstring"""


from sklearn.neural_network import MLPClassifier

lowerCAmelCase			=		[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCAmelCase			=		[0, 1, 0, 0]


lowerCAmelCase			=		MLPClassifier(
    solver="""lbfgs""", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


lowerCAmelCase			=		[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
lowerCAmelCase			=		clf.predict(test)






def      lowerCAmelCase_     (	snake_case_	:    Tuple    )     ->List[Any]:
			return list(snake_case_    )


if __name__ == "__main__":
							import doctest

							doctest.testmod()