



"""simple docstring"""
import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 							lowerCamelCase_(      A__			):





					'''simple docstring'''



					lowercase__      :				str        =       (EulerAncestralDiscreteScheduler,)
					lowercase__      :				Dict        =       10

					def 	snake_case__					(			self ,				**lowerCamelCase__      ):
									_lowerCamelCase					     = {
									    '''num_train_timesteps''': 1_1_0_0,
									    '''beta_start''': 0.0_0_0_1,
									    '''beta_end''': 0.0_2,
									    '''beta_schedule''': '''linear''',
									}

									config.update(**lowerCamelCase__      )
									return config

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

					def 	snake_case__					(			self      ):
									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=lowerCamelCase__ ,				beta_end=lowerCamelCase__      )

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

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

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.scheduler_classes[0]
									_lowerCamelCase					     = self.get_scheduler_config()
									_lowerCamelCase					     = scheduler_class(**lowerCamelCase__      )

									scheduler.set_timesteps(self.num_inference_steps      )

									_lowerCamelCase					     = torch.manual_seed(0      )

									_lowerCamelCase					     = self.dummy_model()
									_lowerCamelCase					     = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									_lowerCamelCase					     = sample.to(lowerCamelCase__      )

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

													_lowerCamelCase					     = model(lowerCamelCase__ ,				lowerCamelCase__      )

													_lowerCamelCase					     = scheduler.step(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				generator=lowerCamelCase__      )
													_lowerCamelCase					     = output.prev_sample

									_lowerCamelCase					     = torch.sum(torch.abs(lowerCamelCase__      )      )
									_lowerCamelCase					     = torch.mean(torch.abs(lowerCamelCase__      )      )

									assert abs(result_sum.item() - 1_5_2.3_1_9_2      ) < 1e-2
									assert abs(result_mean.item() - 0.1_9_8_3      ) < 1e-3

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.scheduler_classes[0]
									_lowerCamelCase					     = self.get_scheduler_config(prediction_type='''v_prediction'''      )
									_lowerCamelCase					     = scheduler_class(**lowerCamelCase__      )

									scheduler.set_timesteps(self.num_inference_steps      )

									_lowerCamelCase					     = torch.manual_seed(0      )

									_lowerCamelCase					     = self.dummy_model()
									_lowerCamelCase					     = self.dummy_sample_deter * scheduler.init_noise_sigma
									_lowerCamelCase					     = sample.to(lowerCamelCase__      )

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

													_lowerCamelCase					     = model(lowerCamelCase__ ,				lowerCamelCase__      )

													_lowerCamelCase					     = scheduler.step(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				generator=lowerCamelCase__      )
													_lowerCamelCase					     = output.prev_sample

									_lowerCamelCase					     = torch.sum(torch.abs(lowerCamelCase__      )      )
									_lowerCamelCase					     = torch.mean(torch.abs(lowerCamelCase__      )      )

									assert abs(result_sum.item() - 1_0_8.4_4_3_9      ) < 1e-2
									assert abs(result_mean.item() - 0.1_4_1_2      ) < 1e-3





					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.scheduler_classes[0]
									_lowerCamelCase					     = self.get_scheduler_config()
									_lowerCamelCase					     = scheduler_class(**lowerCamelCase__      )

									scheduler.set_timesteps(self.num_inference_steps ,				device=lowerCamelCase__      )
									_lowerCamelCase					     = torch.manual_seed(0      )

									_lowerCamelCase					     = self.dummy_model()
									_lowerCamelCase					     = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									_lowerCamelCase					     = sample.to(lowerCamelCase__      )

									for t in scheduler.timesteps:
													_lowerCamelCase					     = scheduler.scale_model_input(lowerCamelCase__ ,				lowerCamelCase__      )

													_lowerCamelCase					     = model(lowerCamelCase__ ,				lowerCamelCase__      )

													_lowerCamelCase					     = scheduler.step(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				generator=lowerCamelCase__      )
													_lowerCamelCase					     = output.prev_sample

									_lowerCamelCase					     = torch.sum(torch.abs(lowerCamelCase__      )      )
									_lowerCamelCase					     = torch.mean(torch.abs(lowerCamelCase__      )      )

									assert abs(result_sum.item() - 1_5_2.3_1_9_2      ) < 1e-2
									assert abs(result_mean.item() - 0.1_9_8_3      ) < 1e-3





from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims





