
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      UpperCamelCase   (       __lowerCamelCase	:					Dict[str, torch.Tensor]       ):
       snake_case			:  Optional[int]     							= []
       snake_case			:  Any     							= []
       snake_case			:  Any     							= []

       for rt in rc.restypes:
              snake_case			:  Dict     							= 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			:  Optional[Any]     							= {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       )

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

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

       # create the gather indices for mapping back
       snake_case			:  Optional[Any]     							= restype_atomaa_to_atomaa[protein_aatype]
       snake_case			:  str     							= residx_atomaa_to_atomaa.long()

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

       snake_case			:  Union[str, Any]     							= restype_atomaa_mask[protein_aatype]
       snake_case			:  List[Any]     							= residx_atomaa_mask

       return protein




def      UpperCamelCase   (       __lowerCamelCase	:					Dict[str, torch.Tensor]       ):
       snake_case			:  Dict     							= tree_map(lambda __lowerCamelCase       : torch.tensor(__lowerCamelCase							,   device=batch["aatype"].device       )							,   __lowerCamelCase							,   np.ndarray       )
       snake_case			:  List[Any]     							= tensor_tree_map(lambda __lowerCamelCase       : np.array(__lowerCamelCase       )							,   make_atomaa_masks(__lowerCamelCase       )       )
       return out







__lowerCamelCase				=			"""Tobias Carryer"""

from time import time






class     UpperCAmelCase						:



   def __init__(self						:   Dict       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   str       ,		snake_case__						:   Optional[int]=int(time()					)					)						->      List[Any]:  # noqa: B008


          '''simple docstring'''


          snake_case			:  Dict     							= multiplier
          snake_case			:  Dict     							= increment
          snake_case			:  Union[str, Any]     							= modulo
          snake_case			:  Tuple     							= seed



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      Any:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= (self.multiplier * self.seed + self.increment) % self.modulo
          return self.seed


if __name__ == "__main__":
   # Show the LCG in action.
   __lowerCamelCase				=			LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
   while True:
      print(lcg.next_number())





