






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       ( a					:Dict[str, torch.Tensor]					)				->     Dict[str, torch.Tensor]:
 a						    = []
 a						    = []
 a						    = []

 for rt in rc.restypes:
  a						    = 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]					)
  a						    = {name: i for i, name in enumerate(a					)}
  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					)

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

 a						    = residx_atomaa_mask
 a						    = residx_atomaa_to_atomaa.long()

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

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

 a						    = restype_atomaa_mask[protein_aatype]
 a						    = residx_atomaa_mask

 return protein

def 		_a       ( a					:Dict[str, torch.Tensor]					)				->     Dict[str, np.ndarray]:
 a						    = tree_map(lambda a					: torch.tensor(a      ,				device=batch['''aatype'''].device					)      ,				a      ,				np.ndarray					)
 a						    = tensor_tree_map(lambda a					: np.array(a					)      ,				make_atomaa_masks(a					)					)
 return out










# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")






class 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		Optional[int]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		bool = True					,   __UpperCAmelCase :		bool = False    )       ->Tuple:




       """simple docstring"""


       a						    = scheduler
       a						    = optimizers if isinstance(__UpperCAmelCase					,   (list, tuple)    ) else [optimizers]
       a						    = split_batches
       a						    = step_with_optimizer
       a						    = GradientState()







      def  __lowerCAmelCase   ( self :		Tuple					,   *__UpperCAmelCase :		List[Any]					,   **__UpperCAmelCase :		Optional[Any]    )       ->int:




       """simple docstring"""


       if not self.step_with_optimizer:
        # No link between scheduler and optimizer -> just step
        self.scheduler.step(*__UpperCAmelCase					,   **__UpperCAmelCase    )
        return

       # Otherwise, first make sure the optimizer was stepped.
       if not self.gradient_state.sync_gradients:
        if self.gradient_state.adjust_scheduler:
         self.scheduler._step_count += 1
        return

       for opt in self.optimizers:
        if opt.step_was_skipped:
         return
       if self.split_batches:
        # Split batches -> the training dataloader batch size is not changed so one step per training step
        self.scheduler.step(*__UpperCAmelCase					,   **__UpperCAmelCase    )
       else:
        # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
        # num_processes steps per training step
        a						    = AcceleratorState().num_processes
        for _ in range(__UpperCAmelCase    ):
         # Special case when using OneCycle and `drop_last` was not used
         if hasattr(self.scheduler					,   '''total_steps'''    ):
          if self.scheduler._step_count <= self.scheduler.total_steps:
           self.scheduler.step(*__UpperCAmelCase					,   **__UpperCAmelCase    )
         else:
          self.scheduler.step(*__UpperCAmelCase					,   **__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->List[str]:




       """simple docstring"""


       return self.scheduler.get_last_lr()







      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Any:




       """simple docstring"""


       return self.scheduler.state_dict()







      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		Any    )       ->Tuple:




       """simple docstring"""


       self.scheduler.load_state_dict(__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Dict    )       ->Optional[int]:




       """simple docstring"""


       return self.scheduler.get_lr()







      def  __lowerCAmelCase   ( self :		int					,   *__UpperCAmelCase :		List[str]					,   **__UpperCAmelCase :		Optional[Any]    )       ->List[str]:




       """simple docstring"""


       return self.scheduler.print_lr(*__UpperCAmelCase					,   **__UpperCAmelCase    )


