






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       (UniPCMultistepScheduler,)
				lowercase__											=       (("""num_inference_steps""", 25),)




				def 				lowercase__   (     self						:    Optional[Any],  **lowerCamelCase						:    Union[str, Any]  ):

								'''simple docstring'''
								lowercase__    =			{
								    '''num_train_timesteps''': 1_000,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''solver_order''': 2,
								    '''solver_type''': '''bh2''',
								}

								config.update(**lowerCamelCase  )
								return config




				def 				lowercase__   (     self						:    Optional[int],  lowerCamelCase						:    Dict=0,  **lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			dict(self.forward_default_kwargs  )
								lowercase__    =			kwargs.pop('''num_inference_steps''',  lowerCamelCase  )
								lowercase__    =			self.dummy_sample
								lowercase__    =			0.1 * sample
								lowercase__    =			[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
												lowercase__    =			self.get_scheduler_config(**lowerCamelCase  )
												lowercase__    =			scheduler_class(**lowerCamelCase  )
												scheduler.set_timesteps(lowerCamelCase  )
												# copy over dummy past residuals
												lowercase__    =			dummy_past_residuals[: scheduler.config.solver_order]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase  )
																lowercase__    =			scheduler_class.from_pretrained(lowerCamelCase  )
																new_scheduler.set_timesteps(lowerCamelCase  )
																# copy over dummy past residuals
																lowercase__    =			dummy_past_residuals[: new_scheduler.config.solver_order]

												lowercase__ ,						lowercase__    =			sample, sample
												for t in range(lowerCamelCase,  time_step + scheduler.config.solver_order + 1  ):
																lowercase__    =			scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  **lowerCamelCase  ).prev_sample
																lowercase__    =			new_scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  **lowerCamelCase  ).prev_sample

																assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    Dict=0,  **lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			dict(self.forward_default_kwargs  )
								lowercase__    =			kwargs.pop('''num_inference_steps''',  lowerCamelCase  )
								lowercase__    =			self.dummy_sample
								lowercase__    =			0.1 * sample
								lowercase__    =			[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
												lowercase__    =			self.get_scheduler_config()
												lowercase__    =			scheduler_class(**lowerCamelCase  )
												scheduler.set_timesteps(lowerCamelCase  )

												# copy over dummy past residuals (must be after setting timesteps)
												lowercase__    =			dummy_past_residuals[: scheduler.config.solver_order]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase  )
																lowercase__    =			scheduler_class.from_pretrained(lowerCamelCase  )
																# copy over dummy past residuals
																new_scheduler.set_timesteps(lowerCamelCase  )

																# copy over dummy past residual (must be after setting timesteps)
																lowercase__    =			dummy_past_residuals[: new_scheduler.config.solver_order]

												lowercase__    =			scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  **lowerCamelCase  ).prev_sample
												lowercase__    =			new_scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  **lowerCamelCase  ).prev_sample

												assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    int=None,  **lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								if scheduler is None:
												lowercase__    =			self.scheduler_classes[0]
												lowercase__    =			self.get_scheduler_config(**lowerCamelCase  )
												lowercase__    =			scheduler_class(**lowerCamelCase  )

								lowercase__    =			self.scheduler_classes[0]
								lowercase__    =			self.get_scheduler_config(**lowerCamelCase  )
								lowercase__    =			scheduler_class(**lowerCamelCase  )

								lowercase__    =			10
								lowercase__    =			self.dummy_model()
								lowercase__    =			self.dummy_sample_deter
								scheduler.set_timesteps(lowerCamelCase  )

								for i, t in enumerate(scheduler.timesteps  ):
												lowercase__    =			model(lowerCamelCase,  lowerCamelCase  )
												lowercase__    =			scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  ).prev_sample

								return sample




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			dict(self.forward_default_kwargs  )

								lowercase__    =			kwargs.pop('''num_inference_steps''',  lowerCamelCase  )

								for scheduler_class in self.scheduler_classes:
												lowercase__    =			self.get_scheduler_config()
												lowercase__    =			scheduler_class(**lowerCamelCase  )

												lowercase__    =			self.dummy_sample
												lowercase__    =			0.1 * sample

												if num_inference_steps is not None and hasattr(lowerCamelCase,  '''set_timesteps'''  ):
																scheduler.set_timesteps(lowerCamelCase  )
												elif num_inference_steps is not None and not hasattr(lowerCamelCase,  '''set_timesteps'''  ):
																lowercase__    =			num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												lowercase__    =			[residual + 0.2, residual + 0.15, residual + 0.10]
												lowercase__    =			dummy_past_residuals[: scheduler.config.solver_order]

												lowercase__    =			scheduler.timesteps[5]
												lowercase__    =			scheduler.timesteps[6]

												lowercase__    =			scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  **lowerCamelCase  ).prev_sample
												lowercase__    =			scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  **lowerCamelCase  ).prev_sample

												self.assertEqual(output_a.shape,  sample.shape  )
												self.assertEqual(output_a.shape,  output_a.shape  )




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								lowercase__    =			UniPCMultistepScheduler(**self.get_scheduler_config()  )
								lowercase__    =			self.full_loop(scheduler=lowerCamelCase  )
								lowercase__    =			torch.mean(torch.abs(lowerCamelCase  )  )

								assert abs(result_mean.item() - 0.2464  ) < 1E-3

								lowercase__    =			DPMSolverSinglestepScheduler.from_config(scheduler.config  )
								lowercase__    =			DEISMultistepScheduler.from_config(scheduler.config  )
								lowercase__    =			DPMSolverMultistepScheduler.from_config(scheduler.config  )
								lowercase__    =			UniPCMultistepScheduler.from_config(scheduler.config  )

								lowercase__    =			self.full_loop(scheduler=lowerCamelCase  )
								lowercase__    =			torch.mean(torch.abs(lowerCamelCase  )  )

								assert abs(result_mean.item() - 0.2464  ) < 1E-3




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								for timesteps in [25, 50, 100, 999, 1_000]:
												self.check_over_configs(num_train_timesteps=lowerCamelCase  )




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								self.check_over_configs(thresholding=lowerCamelCase  )
								for order in [1, 2, 3]:
												for solver_type in ["bh1", "bh2"]:
																for threshold in [0.5, 1.0, 2.0]:
																				for prediction_type in ["epsilon", "sample"]:
																								self.check_over_configs(
																								    thresholding=lowerCamelCase,  prediction_type=lowerCamelCase,  sample_max_value=lowerCamelCase,  solver_order=lowerCamelCase,  solver_type=lowerCamelCase,  )




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=lowerCamelCase  )




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								for solver_type in ["bh1", "bh2"]:
												for order in [1, 2, 3]:
																for prediction_type in ["epsilon", "sample"]:
																				self.check_over_configs(
																				    solver_order=lowerCamelCase,  solver_type=lowerCamelCase,  prediction_type=lowerCamelCase,  )
																				lowercase__    =			self.full_loop(
																				    solver_order=lowerCamelCase,  solver_type=lowerCamelCase,  prediction_type=lowerCamelCase,  )
																				assert not torch.isnan(lowerCamelCase  ).any(), "Samples have nan numbers"




				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								self.check_over_configs(lower_order_final=lowerCamelCase  )
								self.check_over_configs(lower_order_final=lowerCamelCase  )




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
												self.check_over_forward(num_inference_steps=lowerCamelCase,  time_step=0  )




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								lowercase__    =			self.full_loop()
								lowercase__    =			torch.mean(torch.abs(lowerCamelCase  )  )

								assert abs(result_mean.item() - 0.2464  ) < 1E-3




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								lowercase__    =			self.full_loop(prediction_type='''v_prediction'''  )
								lowercase__    =			torch.mean(torch.abs(lowerCamelCase  )  )

								assert abs(result_mean.item() - 0.1014  ) < 1E-3




				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								lowercase__    =			self.scheduler_classes[0]
								lowercase__    =			self.get_scheduler_config(thresholding=lowerCamelCase,  dynamic_thresholding_ratio=0  )
								lowercase__    =			scheduler_class(**lowerCamelCase  )

								lowercase__    =			10
								lowercase__    =			self.dummy_model()
								lowercase__    =			self.dummy_sample_deter.half()
								scheduler.set_timesteps(lowerCamelCase  )

								for i, t in enumerate(scheduler.timesteps  ):
												lowercase__    =			model(lowerCamelCase,  lowerCamelCase  )
												lowercase__    =			scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  ).prev_sample

								assert sample.dtype == torch.floataa




				def 				lowercase__   (     self						:    Tuple,  **lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								for scheduler_class in self.scheduler_classes:
												lowercase__    =			self.get_scheduler_config(**lowerCamelCase  )
												lowercase__    =			scheduler_class(**lowerCamelCase  )

												scheduler.set_timesteps(scheduler.config.num_train_timesteps  )
												assert len(scheduler.timesteps.unique()  ) == scheduler.num_inference_steps












from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			[]
				lowercase__    =			[]
				lowercase__    =			[]

				for rt in rc.restypes:
								lowercase__    =			rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
								restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]				)
								lowercase__    =			{name: i for i, name in enumerate(lowerCamelCase_				)}
								restype_atomaa_to_atomaa_list.append(
								    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]				)

								restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]				)

				# Add dummy mapping for restype 'UNK'
				restype_atomaa_to_atomaa_list.append([0] * 14				)
				restype_atomaa_to_atomaa_list.append([0] * 37				)
				restype_atomaa_mask_list.append([0.0] * 14				)

				lowercase__    =			torch.tensor(
				    lowerCamelCase_		,   dtype=torch.intaa		,   device=protein['''aatype'''].device		,   )
				lowercase__    =			torch.tensor(
				    lowerCamelCase_		,   dtype=torch.intaa		,   device=protein['''aatype'''].device		,   )
				lowercase__    =			torch.tensor(
				    lowerCamelCase_		,   dtype=torch.floataa		,   device=protein['''aatype'''].device		,   )
				lowercase__    =			protein['''aatype'''].to(torch.long				)

				# create the mapping for (residx, atom14) --> atom37, i.e. an array
				# with shape (num_res, 14) containing the atom37 indices for this protein
				lowercase__    =			restype_atomaa_to_atomaa[protein_aatype]
				lowercase__    =			restype_atomaa_mask[protein_aatype]

				lowercase__    =			residx_atomaa_mask
				lowercase__    =			residx_atomaa_to_atomaa.long()

				# create the gather indices for mapping back
				lowercase__    =			restype_atomaa_to_atomaa[protein_aatype]
				lowercase__    =			residx_atomaa_to_atomaa.long()

				# create the corresponding mask
				lowercase__    =			torch.zeros([21, 37]		,   dtype=torch.floataa		,   device=protein['''aatype'''].device				)
				for restype, restype_letter in enumerate(rc.restypes				):
								lowercase__    =			rc.restype_atoa[restype_letter]
								lowercase__    =			rc.residue_atoms[restype_name]
								for atom_name in atom_names:
												lowercase__    =			rc.atom_order[atom_name]
												lowercase__    =			1

				lowercase__    =			restype_atomaa_mask[protein_aatype]
				lowercase__    =			residx_atomaa_mask

				return protein






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			tree_map(lambda lowerCamelCase_				: torch.tensor(lowerCamelCase_		,   device=batch['''aatype'''].device				)		,   lowerCamelCase_		,   np.ndarray				)
				lowercase__    =			tensor_tree_map(lambda lowerCamelCase_				: np.array(lowerCamelCase_				)		,   make_atomaa_masks(lowerCamelCase_				)				)
				return out




