


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  (DPMSolverSinglestepScheduler,)
							__UpperCAmelCase   :							str					  =  (("num_inference_steps", 25),)

							def    __snake_case (							self	:  Dict   ,     **lowerCamelCase	:  Any    )     ->   str:
														__snake_case		:      Union[str, Any]										=       {
														    "num_train_timesteps": 1000,
														    "beta_start": 0.00_01,
														    "beta_end": 0.02,
														    "beta_schedule": "linear",
														    "solver_order": 2,
														    "prediction_type": "epsilon",
														    "thresholding": False,
														    "sample_max_value": 1.0,
														    "algorithm_type": "dpmsolver++",
														    "solver_type": "midpoint",
														    "lambda_min_clipped": -float("inf"    ),
														    "variance_type": None,
														}

														config.update(**lowerCamelCase    )
														return config

							def    __snake_case (							self	:  Tuple   ,     lowerCamelCase	:  Union[str, Any]=0   ,     **lowerCamelCase	:  Optional[int]    )     ->   Optional[Any]:
														__snake_case		:      List[Any]										=       dict(self.forward_default_kwargs    )
														__snake_case		:      Any										=       kwargs.pop("num_inference_steps"   ,     lowerCamelCase    )
														__snake_case		:      List[str]										=       self.dummy_sample
														__snake_case		:      Union[str, Any]										=       0.1 * sample
														__snake_case		:      Dict										=       [residual + 0.2, residual + 0.15, residual + 0.10]

														for scheduler_class in self.scheduler_classes:
																					__snake_case		:      Union[str, Any]										=       self.get_scheduler_config(**lowerCamelCase    )
																					__snake_case		:      Tuple										=       scheduler_class(**lowerCamelCase    )
																					scheduler.set_timesteps(lowerCamelCase    )
																					# copy over dummy past residuals
																					__snake_case		:      Tuple										=       dummy_past_residuals[: scheduler.config.solver_order]

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

																					__snake_case			,      __snake_case		:      Any										=       sample, sample
																					for t in range(lowerCamelCase   ,     time_step + scheduler.config.solver_order + 1    ):
																												__snake_case		:      List[str]										=       scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     **lowerCamelCase    ).prev_sample
																												__snake_case		:      Optional[Any]										=       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    __snake_case (							self	:  List[str]    )     ->   Dict:
														pass

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  List[Any]=0   ,     **lowerCamelCase	:  Optional[int]    )     ->   int:
														__snake_case		:      Dict										=       dict(self.forward_default_kwargs    )
														__snake_case		:      Optional[Any]										=       kwargs.pop("num_inference_steps"   ,     lowerCamelCase    )
														__snake_case		:      List[Any]										=       self.dummy_sample
														__snake_case		:      List[Any]										=       0.1 * sample
														__snake_case		:      Tuple										=       [residual + 0.2, residual + 0.15, residual + 0.10]

														for scheduler_class in self.scheduler_classes:
																					__snake_case		:      Any										=       self.get_scheduler_config()
																					__snake_case		:      Union[str, Any]										=       scheduler_class(**lowerCamelCase    )
																					scheduler.set_timesteps(lowerCamelCase    )

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

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(lowerCamelCase    )
																												__snake_case		:      List[str]										=       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)
																												__snake_case		:      Any										=       dummy_past_residuals[: new_scheduler.config.solver_order]

																					__snake_case		:      Optional[Any]										=       scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     **lowerCamelCase    ).prev_sample
																					__snake_case		:      int										=       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    __snake_case (							self	:  Tuple   ,     lowerCamelCase	:  Dict=None   ,     **lowerCamelCase	:  str    )     ->   Dict:
														if scheduler is None:
																					__snake_case		:      List[Any]										=       self.scheduler_classes[0]
																					__snake_case		:      List[str]										=       self.get_scheduler_config(**lowerCamelCase    )
																					__snake_case		:      List[Any]										=       scheduler_class(**lowerCamelCase    )

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

														__snake_case		:      Optional[int]										=       10
														__snake_case		:      List[Any]										=       self.dummy_model()
														__snake_case		:      Optional[Any]										=       self.dummy_sample_deter
														scheduler.set_timesteps(lowerCamelCase    )

														for i, t in enumerate(scheduler.timesteps    ):
																					__snake_case		:      Union[str, Any]										=       model(lowerCamelCase   ,     lowerCamelCase    )
																					__snake_case		:      List[Any]										=       scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    ).prev_sample

														return sample

							def    __snake_case (							self	:  str    )     ->   int:
														__snake_case		:      Union[str, Any]										=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()    )
														__snake_case		:      Union[str, Any]										=       50
														__snake_case		:      Optional[Any]										=       self.dummy_model()
														__snake_case		:      Optional[Any]										=       self.dummy_sample_deter
														scheduler.set_timesteps(lowerCamelCase    )

														# make sure that the first t is uneven
														for i, t in enumerate(scheduler.timesteps[3:]    ):
																					__snake_case		:      List[Any]										=       model(lowerCamelCase   ,     lowerCamelCase    )
																					__snake_case		:      str										=       scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    ).prev_sample

														__snake_case		:      Union[str, Any]										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_mean.item() - 0.25_74    ) < 1E-3

							def    __snake_case (							self	:  str    )     ->   List[str]:
														for timesteps in [25, 50, 100, 999, 1000]:
																					self.check_over_configs(num_train_timesteps=lowerCamelCase    )

							def    __snake_case (							self	:  Optional[int]    )     ->   int:
														# make sure that iterating over schedulers with same config names gives same results
														# for defaults
														__snake_case		:      List[Any]										=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()    )
														__snake_case		:      List[Any]										=       self.full_loop(scheduler=lowerCamelCase    )
														__snake_case		:      Any										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_mean.item() - 0.27_91    ) < 1E-3

														__snake_case		:      Union[str, Any]										=       DEISMultistepScheduler.from_config(scheduler.config    )
														__snake_case		:      Optional[Any]										=       DPMSolverMultistepScheduler.from_config(scheduler.config    )
														__snake_case		:      Tuple										=       UniPCMultistepScheduler.from_config(scheduler.config    )
														__snake_case		:      int										=       DPMSolverSinglestepScheduler.from_config(scheduler.config    )

														__snake_case		:      str										=       self.full_loop(scheduler=lowerCamelCase    )
														__snake_case		:      Tuple										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_mean.item() - 0.27_91    ) < 1E-3

							def    __snake_case (							self	:  List[str]    )     ->   Any:
														self.check_over_configs(thresholding=lowerCamelCase    )
														for order in [1, 2, 3]:
																					for solver_type in ["midpoint", "heun"]:
																												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   ,     algorithm_type="dpmsolver++"   ,     solver_order=lowerCamelCase   ,     solver_type=lowerCamelCase   ,     )

							def    __snake_case (							self	:  int    )     ->   Tuple:
														for prediction_type in ["epsilon", "v_prediction"]:
																					self.check_over_configs(prediction_type=lowerCamelCase    )

							def    __snake_case (							self	:  Any    )     ->   Dict:
														for algorithm_type in ["dpmsolver", "dpmsolver++"]:
																					for solver_type in ["midpoint", "heun"]:
																												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   ,     algorithm_type=lowerCamelCase   ,     )
																																										__snake_case		:      Any										=       self.full_loop(
																																										    solver_order=lowerCamelCase   ,     solver_type=lowerCamelCase   ,     prediction_type=lowerCamelCase   ,     algorithm_type=lowerCamelCase   ,     )
																																										assert not torch.isnan(lowerCamelCase    ).any(), "Samples have nan numbers"

							def    __snake_case (							self	:  Tuple    )     ->   str:
														self.check_over_configs(lower_order_final=lowerCamelCase    )
														self.check_over_configs(lower_order_final=lowerCamelCase    )

							def    __snake_case (							self	:  Tuple    )     ->   Optional[Any]:
														self.check_over_configs(lambda_min_clipped=-float("inf"    )    )
														self.check_over_configs(lambda_min_clipped=-5.1    )

							def    __snake_case (							self	:  int    )     ->   Dict:
														self.check_over_configs(variance_type=lowerCamelCase    )
														self.check_over_configs(variance_type="learned_range"    )

							def    __snake_case (							self	:  Optional[int]    )     ->   Union[str, Any]:
														for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
																					self.check_over_forward(num_inference_steps=lowerCamelCase   ,     time_step=0    )

							def    __snake_case (							self	:  List[str]    )     ->   List[str]:
														__snake_case		:      Union[str, Any]										=       self.full_loop()
														__snake_case		:      Optional[Any]										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_mean.item() - 0.27_91    ) < 1E-3

							def    __snake_case (							self	:  int    )     ->   List[str]:
														__snake_case		:      Any										=       self.full_loop(use_karras_sigmas=lowerCamelCase    )
														__snake_case		:      List[Any]										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_mean.item() - 0.22_48    ) < 1E-3

							def    __snake_case (							self	:  str    )     ->   Any:
														__snake_case		:      int										=       self.full_loop(prediction_type="v_prediction"    )
														__snake_case		:      Dict										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_mean.item() - 0.14_53    ) < 1E-3

							def    __snake_case (							self	:  Any    )     ->   Dict:
														__snake_case		:      str										=       self.full_loop(prediction_type="v_prediction"   ,     use_karras_sigmas=lowerCamelCase    )
														__snake_case		:      str										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_mean.item() - 0.06_49    ) < 1E-3






							def    __snake_case (							self	:  int    )     ->   Optional[Any]:
														__snake_case		:      int										=       self.scheduler_classes[0]
														__snake_case		:      Any										=       self.get_scheduler_config(thresholding=lowerCamelCase   ,     dynamic_thresholding_ratio=0    )
														__snake_case		:      List[Any]										=       scheduler_class(**lowerCamelCase    )

														__snake_case		:      str										=       10
														__snake_case		:      Optional[Any]										=       self.dummy_model()
														__snake_case		:      Optional[Any]										=       self.dummy_sample_deter.half()
														scheduler.set_timesteps(lowerCamelCase    )

														for i, t in enumerate(scheduler.timesteps    ):
																					__snake_case		:      List[Any]										=       model(lowerCamelCase   ,     lowerCamelCase    )
																					__snake_case		:      Optional[Any]										=       scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    ).prev_sample

														assert sample.dtype == torch.floataa





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     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Any										=       []
							__snake_case		:      Optional[Any]										=       []
							__snake_case		:      List[Any]										=       []

							for rt in rc.restypes:
														__snake_case		:      Any										=       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]     )
														__snake_case		:      Tuple										=       {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] * 1_4     )
							restype_atomaa_to_atomaa_list.append([0] * 3_7     )
							restype_atomaa_mask_list.append([0.0] * 1_4     )

							__snake_case		:      int										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.intaa							,     device=protein["aatype"].device							,     )
							__snake_case		:      List[str]										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.intaa							,     device=protein["aatype"].device							,     )
							__snake_case		:      Optional[Any]										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.floataa							,     device=protein["aatype"].device							,     )
							__snake_case		:      Optional[int]										=       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
							__snake_case		:      Optional[Any]										=       restype_atomaa_to_atomaa[protein_aatype]
							__snake_case		:      Tuple										=       restype_atomaa_mask[protein_aatype]

							__snake_case		:      Optional[Any]										=       residx_atomaa_mask
							__snake_case		:      Union[str, Any]										=       residx_atomaa_to_atomaa.long()

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

							# create the corresponding mask
							__snake_case		:      List[str]										=       torch.zeros([2_1, 3_7]							,     dtype=torch.floataa							,     device=protein["aatype"].device     )
							for restype, restype_letter in enumerate(rc.restypes     ):
														__snake_case		:      List[str]										=       rc.restype_atoa[restype_letter]
														__snake_case		:      List[Any]										=       rc.residue_atoms[restype_name]
														for atom_name in atom_names:
																					__snake_case		:      Union[str, Any]										=       rc.atom_order[atom_name]
																					__snake_case		:      str										=       1

							__snake_case		:      List[str]										=       restype_atomaa_mask[protein_aatype]
							__snake_case		:      List[str]										=       residx_atomaa_mask

							return protein







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      str										=       tree_map(lambda __lowerCamelCase     : torch.tensor(__lowerCamelCase							,     device=batch["aatype"].device     )							,     __lowerCamelCase							,     np.ndarray     )
							__snake_case		:      str										=       tensor_tree_map(lambda __lowerCamelCase     : np.array(__lowerCamelCase     )							,     make_atomaa_masks(__lowerCamelCase     )     )
							return out

