


'''simple docstring'''






import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class 	a       :
 @property
 def 							__UpperCAmelCase						(	self  )   ->							int:
      return self.get_dummy_input()
 @property
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      if self.block_type == "down":
           return (4, 32, 16, 16)
      elif self.block_type == "mid":
           return (4, 32, 32, 32)
      elif self.block_type == "up":
           return (4, 32, 64, 64)

      raise ValueError(f'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__=True ,			__magic_name__=False ,			__magic_name__=False ,			__magic_name__=False ,			)   ->							Any:
      _a      =							4
      _a      =							32
      _a      =							(32, 32)

      _a      =							torch.manual_seed(0  )
      _a      =							torch.device(__magic_name__  )
      _a      =							(batch_size, num_channels) + sizes
      _a      =							randn_tensor(__magic_name__ ,			generator=__magic_name__ ,			device=__magic_name__  )
      _a      =							{'hidden_states': hidden_states}

      if include_temb:
           _a      =							1_28
           _a      =							randn_tensor((batch_size, temb_channels) ,			generator=__magic_name__ ,			device=__magic_name__  )

      if include_res_hidden_states_tuple:
           _a      =							torch.manual_seed(1  )
           _a      =							(randn_tensor(__magic_name__ ,			generator=__magic_name__ ,			device=__magic_name__  ),)

      if include_encoder_hidden_states:
           _a      =							floats_tensor((batch_size, 32, 32)  ).to(__magic_name__  )

      if include_skip_sample:
           _a      =							randn_tensor(((batch_size, 3) + sizes) ,			generator=__magic_name__ ,			device=__magic_name__  )

      return dummy_input
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							{
          'in_channels': 32,
          'out_channels': 32,
          'temb_channels': 1_28,
      }
      if self.block_type == "up":
           _a      =							32

      if self.block_type == "mid":
           init_dict.pop('out_channels'  )

      _a      =							self.dummy_input
      return init_dict, inputs_dict
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Tuple:
      _a ,				_a      =							self.prepare_init_args_and_inputs_for_common()
      _a      =							self.block_class(**__magic_name__  )
      unet_block.to(__magic_name__  )
      unet_block.eval()

      with torch.no_grad():
           _a      =							unet_block(**__magic_name__  )

      if isinstance(__magic_name__ ,			__magic_name__  ):
           _a      =							output[0]

      self.assertEqual(output.shape ,			self.output_shape  )

      _a      =							output[0, -1, -3:, -3:]
      _a      =							torch.tensor(__magic_name__  ).to(__magic_name__  )
      assert torch_all_close(output_slice.flatten() ,			__magic_name__ ,			atol=5e-3  )




 @unittest.skipIf(torch_device == 'mps' ,			'Training is not supported in mps'  )
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a ,				_a      =							self.prepare_init_args_and_inputs_for_common()
      _a      =							self.block_class(**__magic_name__  )
      model.to(__magic_name__  )
      model.train()
      _a      =							model(**__magic_name__  )

      if isinstance(__magic_name__ ,			__magic_name__  ):
           _a      =							output[0]

      _a      =							torch.device(__magic_name__  )
      _a      =							randn_tensor(output.shape ,			device=__magic_name__  )
      _a      =							torch.nn.functional.mse_loss(__magic_name__ ,			__magic_name__  )
      loss.backward()





'''simple docstring'''






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

a_   :      Optional[Any]				    =      TypeVar("T")






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (position - 1) // 2






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (2 * position) + 1






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     return (2 * position) + 2




class 	a       (       Generic[T]    ):
 def __init__(	self  )   ->							None:
      _a      =							[]
      _a      =							{}
      _a      =							0
 def __len__(	self  )   ->							int:
      return self.elements
 def __repr__(	self  )   ->							str:
      return str(self.heap  )
 def 							__UpperCAmelCase						(	self  )   ->							bool:
      # Check if the priority queue is empty
      return self.elements == 0
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Add an element with given priority to the queue
      self.heap.append((elem, weight)  )
      _a      =							self.elements
      self.elements += 1
      self._bubble_up(__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							T:
      # Remove and return the element with lowest weight (highest priority)
      if self.elements > 1:
           self._swap_nodes(0 ,			self.elements - 1  )
      _a ,				_a      =							self.heap.pop()
      del self.position_map[elem]
      self.elements -= 1
      if self.elements > 0:
           _a ,				_a      =							self.heap[0]
           self._bubble_down(__magic_name__  )
      return elem
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Update the weight of the given key
      _a      =							self.position_map[elem]
      _a      =							(elem, weight)
      if position > 0:
           _a      =							get_parent_position(__magic_name__  )
           _a ,				_a      =							self.heap[parent_position]
           if parent_weight > weight:
                self._bubble_up(__magic_name__  )
           else:
                self._bubble_down(__magic_name__  )
      else:
           self._bubble_down(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Place a node at the proper position (upward movement) [to be used internally
      # only]
      _a      =							self.position_map[elem]
      if curr_pos == 0:
           return None
      _a      =							get_parent_position(__magic_name__  )
      _a ,				_a      =							self.heap[curr_pos]
      _a ,				_a      =							self.heap[parent_position]
      if parent_weight > weight:
           self._swap_nodes(__magic_name__ ,			__magic_name__  )
           return self._bubble_up(__magic_name__  )
      return None
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Place a node at the proper position (downward movement) [to be used
      # internally only]
      _a      =							self.position_map[elem]
      _a ,				_a      =							self.heap[curr_pos]
      _a      =							get_child_left_position(__magic_name__  )
      _a      =							get_child_right_position(__magic_name__  )
      if child_left_position < self.elements and child_right_position < self.elements:
           _a ,				_a      =							self.heap[child_left_position]
           _a ,				_a      =							self.heap[child_right_position]
           if child_right_weight < child_left_weight and child_right_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      if child_left_position < self.elements:
           _a ,				_a      =							self.heap[child_left_position]
           if child_left_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      else:
           return None
      if child_right_position < self.elements:
           _a ,				_a      =							self.heap[child_right_position]
           if child_right_weight < weight:
                self._swap_nodes(__magic_name__ ,			__magic_name__  )
                return self._bubble_down(__magic_name__  )
      return None




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Swap the nodes at the given positions
      _a      =							self.heap[nodea_pos][0]
      _a      =							self.heap[nodea_pos][0]
      _a ,				_a      =							(
          self.heap[nodea_pos],
          self.heap[nodea_pos],
      )
      _a      =							nodea_pos
      _a      =							nodea_pos





class 	a       (       Generic[T]    ):
 def __init__(	self  )   ->							None:
      _a      =							{}
      _a      =							0
 def __repr__(	self  )   ->							str:
      return str(self.connections  )
 def __len__(	self  )   ->							int:
      return self.nodes
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							None:
      # Add a node in the graph if it is not in the graph
      if node not in self.connections:
           _a      =							{}
           self.nodes += 1




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							None:
      # Add an edge between 2 nodes in the graph
      self.add_node(__magic_name__  )
      self.add_node(__magic_name__  )
      _a      =							weight
      _a      =							weight






def 		_A			(lowerCAmelCase__					:GraphUndirectedWeighted[T]			,  )				->					tuple[dict[T, int], dict[T, T | None]]:




     '''simple docstring'''



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

     _a      =							MinPriorityQueue()
     for node, weight in dist.items():
          priority_queue.push(lowerCAmelCase__			,  lowerCAmelCase__						)

     if priority_queue.is_empty():
          return dist, parent

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

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

