

'''simple docstring'''






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCAmelCase__											=	TypeVar('''T''')


def 		_A     (    A__      ):






 """simple docstring"""



 return (position - 1) // 2


def 		_A     (    A__      ):






 """simple docstring"""



 return (2 * position) + 1


def 		_A     (    A__      ):






 """simple docstring"""



 return (2 * position) + 2






class 			lowercase_	(Generic[T]      ):




  """simple docstring"""




  def __init__(      self   :     Union[str, Any]			):
   __lowercase							  = []
   __lowercase							  = {}
   __lowercase							  = 0






  def __len__(      self   :     Optional[Any]			):
   return self.elements






  def __repr__(      self   :     Any			):
   return str(self.heap			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   # Check if the priority queue is empty
   return self.elements == 0






  def 					SCREAMING_SNAKE_CASE      (      self   :     str					,lowercase__   :     T					,lowercase__   :     int			):
   # Add an element with given priority to the queue
   self.heap.append((elem, weight)			)
   __lowercase							  = self.elements
   self.elements += 1
   self._bubble_up(lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   # Remove and return the element with lowest weight (highest priority)
   if self.elements > 1:
    self._swap_nodes(0					,self.elements - 1			)
   __lowercase  ,	__lowercase							  = self.heap.pop()
   del self.position_map[elem]
   self.elements -= 1
   if self.elements > 0:
    __lowercase  ,	__lowercase							  = self.heap[0]
    self._bubble_down(lowercase__			)
   return elem






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]					,lowercase__   :     T					,lowercase__   :     int			):
   # Update the weight of the given key
   __lowercase							  = self.position_map[elem]
   __lowercase							  = (elem, weight)
   if position > 0:
    __lowercase							  = get_parent_position(lowercase__			)
    __lowercase  ,	__lowercase							  = self.heap[parent_position]
    if parent_weight > weight:
     self._bubble_up(lowercase__			)
    else:
     self._bubble_down(lowercase__			)
   else:
    self._bubble_down(lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,lowercase__   :     T			):
   # Place a node at the proper position (upward movement) [to be used internally
   # only]
   __lowercase							  = self.position_map[elem]
   if curr_pos == 0:
    return None
   __lowercase							  = get_parent_position(lowercase__			)
   __lowercase  ,	__lowercase							  = self.heap[curr_pos]
   __lowercase  ,	__lowercase							  = self.heap[parent_position]
   if parent_weight > weight:
    self._swap_nodes(lowercase__					,lowercase__			)
    return self._bubble_up(lowercase__			)
   return None






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     T			):
   # Place a node at the proper position (downward movement) [to be used
   # internally only]
   __lowercase							  = self.position_map[elem]
   __lowercase  ,	__lowercase							  = self.heap[curr_pos]
   __lowercase							  = get_child_left_position(lowercase__			)
   __lowercase							  = get_child_right_position(lowercase__			)
   if child_left_position < self.elements and child_right_position < self.elements:
    __lowercase  ,	__lowercase							  = self.heap[child_left_position]
    __lowercase  ,	__lowercase							  = self.heap[child_right_position]
    if child_right_weight < child_left_weight and child_right_weight < weight:
     self._swap_nodes(lowercase__					,lowercase__			)
     return self._bubble_down(lowercase__			)
   if child_left_position < self.elements:
    __lowercase  ,	__lowercase							  = self.heap[child_left_position]
    if child_left_weight < weight:
     self._swap_nodes(lowercase__					,lowercase__			)
     return self._bubble_down(lowercase__			)
   else:
    return None
   if child_right_position < self.elements:
    __lowercase  ,	__lowercase							  = self.heap[child_right_position]
    if child_right_weight < weight:
     self._swap_nodes(lowercase__					,lowercase__			)
     return self._bubble_down(lowercase__			)
   return None






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,lowercase__   :     int					,lowercase__   :     int			):
   # Swap the nodes at the given positions
   __lowercase							  = self.heap[nodea_pos][0]
   __lowercase							  = self.heap[nodea_pos][0]
   __lowercase  ,	__lowercase							  = (
       self.heap[nodea_pos],
       self.heap[nodea_pos],
   )
   __lowercase							  = nodea_pos
   __lowercase							  = nodea_pos






class 			lowercase_	(Generic[T]      ):




  """simple docstring"""




  def __init__(      self   :     Any			):
   __lowercase							  = {}
   __lowercase							  = 0






  def __repr__(      self   :     Any			):
   return str(self.connections			)






  def __len__(      self   :     Any			):
   return self.nodes






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,lowercase__   :     T			):
   # Add a node in the graph if it is not in the graph
   if node not in self.connections:
    __lowercase							  = {}
    self.nodes += 1






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]					,lowercase__   :     T					,lowercase__   :     T					,lowercase__   :     int			):
   # Add an edge between 2 nodes in the graph
   self.add_node(lowercase__			)
   self.add_node(lowercase__			)
   __lowercase							  = weight
   __lowercase							  = weight


def 		_A     (    A__     ,				):






 """simple docstring"""



 __lowercase							  = {node: maxsize for node in graph.connections}
 __lowercase							  = {node: None for node in graph.connections}

 __lowercase							  = MinPriorityQueue()
 for node, weight in dist.items():
  priority_queue.push(A__     ,				A__      )

 if priority_queue.is_empty():
  return dist, parent

 # initialization
 __lowercase							  = priority_queue.extract_min()
 __lowercase							  = 0
 for neighbour in graph.connections[node]:
  if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
   __lowercase							  = dist[node] + graph.connections[node][neighbour]
   priority_queue.update_key(A__     ,				dist[neighbour]      )
   __lowercase							  = node

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





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class       lowerCamelCase__   (       nn.Module):





     def __init__(self		,     UpperCAmelCase = 1_6		,     UpperCAmelCase = 8_8		,     UpperCAmelCase = None		,     UpperCAmelCase = 1		,     UpperCAmelCase = 0.0		,     UpperCAmelCase = 3_2		,     UpperCAmelCase = None		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = "geglu"		,     UpperCAmelCase = None		,     )     ->   Any:
      super().__init__()
      _lowercase							      =nn.ModuleList(
          [
              TransformeraDModel(
                  num_attention_heads=UpperCAmelCase		,     attention_head_dim=UpperCAmelCase		,     in_channels=UpperCAmelCase		,     num_layers=UpperCAmelCase		,     dropout=UpperCAmelCase		,     norm_num_groups=UpperCAmelCase		,     cross_attention_dim=UpperCAmelCase		,     attention_bias=UpperCAmelCase		,     sample_size=UpperCAmelCase		,     num_vector_embeds=UpperCAmelCase		,     activation_fn=UpperCAmelCase		,     num_embeds_ada_norm=UpperCAmelCase		,     )
              for _ in range(2       )
          ]       )

      # Variables that can be set by a pipeline:

      # The ratio of transformer1 to transformer2's output states to be combined during inference
      _lowercase							      =0.5

      # The shape of `encoder_hidden_states` is expected to be
      # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
      _lowercase							      =[7_7, 2_5_7]

      # Which transformer to use to encode which condition.
      # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
      _lowercase							      =[1, 0]





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase = True		,     )     ->   str:
      _lowercase							      =hidden_states

      _lowercase							      =[]
      _lowercase							      =0
      # attention_mask is not used yet
      for i in range(2       ):
       # for each of the two transformers, pass the corresponding condition tokens
       _lowercase							      =encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
       _lowercase							      =self.transformer_index_for_condition[i]
       _lowercase							      =self.transformers[transformer_index](
           UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     timestep=UpperCAmelCase		,     cross_attention_kwargs=UpperCAmelCase		,     return_dict=UpperCAmelCase		,     )[0]
       encoded_states.append(encoded_state - input_states       )
       tokens_start += self.condition_lengths[i]

      _lowercase							      =encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
      _lowercase							      =output_states + input_states

      if not return_dict:
       return (output_states,)

      return TransformeraDModelOutput(sample=UpperCAmelCase       )

