
from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__lowerCamelCase							=       TypeVar("""T""")


def 			UpperCamelCase					(    __lowerCamelCase     : int    ):
       return (position - 1) // 2


def 			UpperCamelCase					(    __lowerCamelCase     : int    ):
       return (2 * position) + 1


def 			UpperCamelCase					(    __lowerCamelCase     : int    ):
       return (2 * position) + 2






class   UpperCAmelCase		(						Generic[T] ):



   def __init__(self		:					Optional[Any] )		->	None:


          '''simple docstring'''


          snake_case       :   list[tuple[T, int]]							=      []
          snake_case       :   dict[T, int]							=      {}
          snake_case       :   int							=      0



   def __len__(self		:					int )		->	int:


          '''simple docstring'''


          return self.elements



   def __repr__(self		:					Tuple )		->	str:


          '''simple docstring'''


          return str(self.heap )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	bool:


          '''simple docstring'''


          return self.elements == 0



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					T    ,						snake_case__		:					int )		->	None:


          '''simple docstring'''


          self.heap.append((elem, weight) )
          snake_case       :   Dict							=      self.elements
          self.elements += 1
          self._bubble_up(snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	T:


          '''simple docstring'''


          if self.elements > 1:
                 self._swap_nodes(0    ,						self.elements - 1 )
          snake_case				,					snake_case       :   Dict							=      self.heap.pop()
          del self.position_map[elem]
          self.elements -= 1
          if self.elements > 0:
                 snake_case				,					snake_case       :   List[Any]							=      self.heap[0]
                 self._bubble_down(snake_case__ )
          return elem



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					T    ,						snake_case__		:					int )		->	None:


          '''simple docstring'''


          snake_case       :   int							=      self.position_map[elem]
          snake_case       :   Tuple							=      (elem, weight)
          if position > 0:
                 snake_case       :   str							=      get_parent_position(snake_case__ )
                 snake_case				,					snake_case       :   List[Any]							=      self.heap[parent_position]
                 if parent_weight > weight:
                        self._bubble_up(snake_case__ )
                 else:
                        self._bubble_down(snake_case__ )
          else:
                 self._bubble_down(snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					T )		->	None:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      self.position_map[elem]
          if curr_pos == 0:
                 return None
          snake_case       :   List[str]							=      get_parent_position(snake_case__ )
          snake_case				,					snake_case       :   str							=      self.heap[curr_pos]
          snake_case				,					snake_case       :   Any							=      self.heap[parent_position]
          if parent_weight > weight:
                 self._swap_nodes(snake_case__    ,						snake_case__ )
                 return self._bubble_up(snake_case__ )
          return None



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					T )		->	None:


          '''simple docstring'''


          snake_case       :   Dict							=      self.position_map[elem]
          snake_case				,					snake_case       :   Any							=      self.heap[curr_pos]
          snake_case       :   List[str]							=      get_child_left_position(snake_case__ )
          snake_case       :   Tuple							=      get_child_right_position(snake_case__ )
          if child_left_position < self.elements and child_right_position < self.elements:
                 snake_case				,					snake_case       :   List[Any]							=      self.heap[child_left_position]
                 snake_case				,					snake_case       :   Union[str, Any]							=      self.heap[child_right_position]
                 if child_right_weight < child_left_weight and child_right_weight < weight:
                        self._swap_nodes(snake_case__    ,						snake_case__ )
                        return self._bubble_down(snake_case__ )
          if child_left_position < self.elements:
                 snake_case				,					snake_case       :   List[Any]							=      self.heap[child_left_position]
                 if child_left_weight < weight:
                        self._swap_nodes(snake_case__    ,						snake_case__ )
                        return self._bubble_down(snake_case__ )
          else:
                 return None
          if child_right_position < self.elements:
                 snake_case				,					snake_case       :   Optional[Any]							=      self.heap[child_right_position]
                 if child_right_weight < weight:
                        self._swap_nodes(snake_case__    ,						snake_case__ )
                        return self._bubble_down(snake_case__ )
          return None



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					int    ,						snake_case__		:					int )		->	None:


          '''simple docstring'''


          snake_case       :   int							=      self.heap[nodea_pos][0]
          snake_case       :   List[str]							=      self.heap[nodea_pos][0]
          snake_case				,					snake_case       :   List[str]							=      (
              self.heap[nodea_pos],
              self.heap[nodea_pos],
          )
          snake_case       :   Dict							=      nodea_pos
          snake_case       :   Optional[Any]							=      nodea_pos






class   UpperCAmelCase		(						Generic[T] ):



   def __init__(self		:					Optional[int] )		->	None:


          '''simple docstring'''


          snake_case       :   dict[T, dict[T, int]]							=      {}
          snake_case       :   int							=      0



   def __repr__(self		:					Any )		->	str:


          '''simple docstring'''


          return str(self.connections )



   def __len__(self		:					Tuple )		->	int:


          '''simple docstring'''


          return self.nodes



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					T )		->	None:


          '''simple docstring'''


          if node not in self.connections:
                 snake_case       :   Union[str, Any]							=      {}
                 self.nodes += 1



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					T    ,						snake_case__		:					T    ,						snake_case__		:					int )		->	None:


          '''simple docstring'''


          self.add_node(snake_case__ )
          self.add_node(snake_case__ )
          snake_case       :   List[str]							=      weight
          snake_case       :   List[Any]							=      weight



def 			UpperCamelCase					(    __lowerCamelCase     : GraphUndirectedWeighted[T]				,					):
       snake_case       :   dict[T, int]							=      {node: maxsize for node in graph.connections}
       snake_case       :   dict[T, T | None]							=      {node: None for node in graph.connections}

       snake_case       :   MinPriorityQueue[T]							=      MinPriorityQueue()
       for node, weight in dist.items():
              priority_queue.push(__lowerCamelCase				,					__lowerCamelCase    )

       if priority_queue.is_empty():
              return dist, parent

       # initialization
       snake_case       :   int							=      priority_queue.extract_min()
       snake_case       :   str							=      0
       for neighbour in graph.connections[node]:
              if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                     snake_case       :   Optional[Any]							=      dist[node] + graph.connections[node][neighbour]
                     priority_queue.update_key(__lowerCamelCase				,					dist[neighbour]    )
                     snake_case       :   int							=      node

    # running prim's algorithm
       while not priority_queue.is_empty():
              snake_case       :   Dict							=      priority_queue.extract_min()
              for neighbour in graph.connections[node]:
                     if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                            snake_case       :   Dict							=      dist[node] + graph.connections[node][neighbour]
                            priority_queue.update_key(__lowerCamelCase				,					dist[neighbour]    )
                            snake_case       :   List[Any]							=      node
       return dist, parent







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       :   List[str]							=      []
       snake_case       :   Optional[int]							=      []
       snake_case       :   Any							=      []

       for rt in rc.restypes:
              snake_case       :   List[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       :   str							=      {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       :   List[Any]							=      torch.tensor(
           __lowerCamelCase				,					dtype=torch.intaa				,					device=protein["aatype"].device				,					)
       snake_case       :   int							=      torch.tensor(
           __lowerCamelCase				,					dtype=torch.floataa				,					device=protein["aatype"].device				,					)
       snake_case       :   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       :   List[Any]							=      restype_atomaa_to_atomaa[protein_aatype]
       snake_case       :   str							=      restype_atomaa_mask[protein_aatype]

       snake_case       :   str							=      residx_atomaa_mask
       snake_case       :   Any							=      residx_atomaa_to_atomaa.long()

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

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

       snake_case       :   List[Any]							=      restype_atomaa_mask[protein_aatype]
       snake_case       :   int							=      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[str]							=      tensor_tree_map(lambda __lowerCamelCase    : np.array(__lowerCamelCase    )				,					make_atomaa_masks(__lowerCamelCase    )    )
       return out





