





import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput



class     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			1






  @register_to_config
  def __init__(    self			:		str ,  lowerCAmelCase_			:		int = 1_0_0_0 ,  lowerCAmelCase_			:		Optional[Union[np.ndarray, List[float]]] = None					)							->						int:
     # set `betas`, `alphas`, `timesteps`
     self.set_timesteps(lowerCAmelCase_					)

     # standard deviation of the initial noise distribution
     __lowerCAmelCase							     =     1.0

     # For now we only support F-PNDM, i.e. the runge-kutta method
     # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
     # mainly at formula (9), (12), (13) and the Algorithm 2.
     __lowerCAmelCase							     =     4

     # running values
     __lowerCAmelCase							     =     []






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Union[str, torch.device] = None					)							->						Optional[int]:
     __lowerCAmelCase							     =     num_inference_steps
     __lowerCAmelCase							     =     torch.linspace(1 ,  0 ,  num_inference_steps + 1					)[:-1]
     __lowerCAmelCase							     =     torch.cat([steps, torch.tensor([0.0]					)]					)

     if self.config.trained_betas is not None:
        __lowerCAmelCase							     =     torch.tensor(self.config.trained_betas ,  dtype=torch.floataa					)
     else:
        __lowerCAmelCase							     =     torch.sin(steps * math.pi / 2					) ** 2

     __lowerCAmelCase							     =     (1.0 - self.betas**2) ** 0.5

     __lowerCAmelCase							     =     (torch.atana(self.betas ,  self.alphas					) / math.pi * 2)[:-1]
     __lowerCAmelCase							     =     timesteps.to(lowerCAmelCase_					)

     __lowerCAmelCase							     =     []






  def     lowercase		(    self			:		Optional[int] ,  lowerCAmelCase_			:		torch.FloatTensor ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		torch.FloatTensor ,  lowerCAmelCase_			:		bool = True ,  )							->						Union[SchedulerOutput, Tuple]:
     if self.num_inference_steps is None:
        raise ValueError(
            'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'					)

     __lowerCAmelCase							     =     (self.timesteps == timestep).nonzero().item()
     __lowerCAmelCase							     =     timestep_index + 1

     __lowerCAmelCase							     =     sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
     self.ets.append(lowerCAmelCase_					)

     if len(self.ets					) == 1:
        __lowerCAmelCase							     =     self.ets[-1]
     elif len(self.ets					) == 2:
        __lowerCAmelCase							     =     (3 * self.ets[-1] - self.ets[-2]) / 2
     elif len(self.ets					) == 3:
        __lowerCAmelCase							     =     (2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
     else:
        __lowerCAmelCase							     =     (1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

     __lowerCAmelCase							     =     self._get_prev_sample(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)

     if not return_dict:
        return (prev_sample,)

     return SchedulerOutput(prev_sample=lowerCAmelCase_					)






  def     lowercase		(    self			:		str ,  lowerCAmelCase_			:		torch.FloatTensor ,  *lowerCAmelCase_			:		str ,  **lowerCAmelCase_			:		Union[str, Any]					)							->						torch.FloatTensor:
     return sample






  def     lowercase		(    self			:		Optional[int] ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Union[str, Any] ,  lowerCAmelCase_			:		int					)							->						Tuple:
     __lowerCAmelCase							     =     self.alphas[timestep_index]
     __lowerCAmelCase							     =     self.betas[timestep_index]

     __lowerCAmelCase							     =     self.alphas[prev_timestep_index]
     __lowerCAmelCase							     =     self.betas[prev_timestep_index]

     __lowerCAmelCase							     =     (sample - sigma * ets) / max(lowerCAmelCase_ ,  1e-8					)
     __lowerCAmelCase							     =     next_alpha * pred + ets * next_sigma

     return prev_sample






  def __len__(    self			:		int					)							->						List[Any]:
     return self.config.num_train_timesteps








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_				:      Dict[str, torch.Tensor]     ):
   __lowerCAmelCase							     =     []
   __lowerCAmelCase							     =     []
   __lowerCAmelCase							     =     []

   for rt in rc.restypes:
      __lowerCAmelCase							     =     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]     )
      __lowerCAmelCase							     =     {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     )

   __lowerCAmelCase							     =     torch.tensor(
       lowerCAmelCase_,     dtype=torch.intaa,     device=protein['aatype'].device,     )
   __lowerCAmelCase							     =     torch.tensor(
       lowerCAmelCase_,     dtype=torch.intaa,     device=protein['aatype'].device,     )
   __lowerCAmelCase							     =     torch.tensor(
       lowerCAmelCase_,     dtype=torch.floataa,     device=protein['aatype'].device,     )
   __lowerCAmelCase							     =     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
   __lowerCAmelCase							     =     restype_atomaa_to_atomaa[protein_aatype]
   __lowerCAmelCase							     =     restype_atomaa_mask[protein_aatype]

   __lowerCAmelCase							     =     residx_atomaa_mask
   __lowerCAmelCase							     =     residx_atomaa_to_atomaa.long()

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

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

   __lowerCAmelCase							     =     restype_atomaa_mask[protein_aatype]
   __lowerCAmelCase							     =     residx_atomaa_mask

   return protein




def   a_   (			lowerCAmelCase_				:      Dict[str, torch.Tensor]     ):
   __lowerCAmelCase							     =     tree_map(lambda lowerCAmelCase_     : torch.tensor(lowerCAmelCase_,     device=batch['aatype'].device     ),     lowerCAmelCase_,     np.ndarray     )
   __lowerCAmelCase							     =     tensor_tree_map(lambda lowerCAmelCase_     : np.array(lowerCAmelCase_     ),     make_atomaa_masks(lowerCAmelCase_     )     )
   return out

