

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       __snake_case						( __UpperCamelCase	: Dict[str, torch.Tensor]    ):



      """simple docstring"""

      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(__UpperCamelCase    )}
            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(
          __UpperCamelCase				,dtype=torch.intaa				,device=protein["aatype"].device				,)
      A_						  =				torch.tensor(
          __UpperCamelCase				,dtype=torch.intaa				,device=protein["aatype"].device				,)
      A_						  =				torch.tensor(
          __UpperCamelCase				,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       __snake_case						( __UpperCamelCase	: Dict[str, torch.Tensor]    ):



      """simple docstring"""

      A_						  =				tree_map(lambda __UpperCamelCase    : torch.tensor(__UpperCamelCase				,device=batch["aatype"].device    )				,__UpperCamelCase				,np.ndarray    )
      A_						  =				tensor_tree_map(lambda __UpperCamelCase    : np.array(__UpperCamelCase    )				,make_atomaa_masks(__UpperCamelCase    )    )
      return out
import logging
import os

from .state import PartialState
class     lowerCAmelCase_     (       logging.LoggerAdapter ):







							@staticmethod
							def  UpperCamelCase_						(   _A      :     Any		):
												_UpperCamelCase			     =     PartialState()
												return not main_process_only or (main_process_only and state.is_main_process)







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     Optional[Any] ,			_A      :     str ,			*_A      :     int ,			**_A      :     List[Any]		):
												if PartialState._shared_state == {}:
																	raise RuntimeError(
																	    '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''		)
												_UpperCamelCase			     =     kwargs.pop('''main_process_only''' ,			_A		)
												_UpperCamelCase			     =     kwargs.pop('''in_order''' ,			_A		)

												if self.isEnabledFor(_A		):
																	if self._should_log(_A		):
																						_UpperCamelCase     ,    _UpperCamelCase			     =     self.process(_A ,			_A		)
																						self.logger.log(_A ,			_A ,			*_A ,			**_A		)

																	elif in_order:
																						_UpperCamelCase			     =     PartialState()
																						for i in range(state.num_processes		):
																											if i == state.process_index:
																																_UpperCamelCase     ,    _UpperCamelCase			     =     self.process(_A ,			_A		)
																																self.logger.log(_A ,			_A ,			*_A ,			**_A		)
																											state.wait_for_everyone()




def 					_snake_case	(  __snake_case  ,     __snake_case = None						):
					if log_level is None:
										_UpperCamelCase			     =     os.environ.get('''ACCELERATE_LOG_LEVEL'''  ,     __snake_case						)
					_UpperCamelCase			     =     logging.getLogger(__snake_case						)
					if log_level is not None:
										logger.setLevel(log_level.upper()						)
										logger.root.setLevel(log_level.upper()						)
					return MultiProcessAdapter(__snake_case  ,     {}						)
