'''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   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		@property
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								return self.get_dummy_input()






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								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		,						__A=True		,						__A=False		,						__A=False		,						__A=False		,						) ->     List[Any]:
								_lowerCAmelCase										=4
								_lowerCAmelCase										=32
								_lowerCAmelCase										=(32, 32)

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

								if include_temb:
														_lowerCAmelCase										=128
														_lowerCAmelCase										=randn_tensor((batch_size, temb_channels)		,						generator=__A		,						device=__A				)

								if include_res_hidden_states_tuple:
														_lowerCAmelCase										=torch.manual_seed(1				)
														_lowerCAmelCase										=(randn_tensor(__A		,						generator=__A		,						device=__A				),)

								if include_encoder_hidden_states:
														_lowerCAmelCase										=floats_tensor((batch_size, 32, 32)				).to(__A				)

								if include_skip_sample:
														_lowerCAmelCase										=randn_tensor(((batch_size, 3) + sizes)		,						generator=__A		,						device=__A				)

								return dummy_input






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										={
								    'in_channels': 32,
								    'out_channels': 32,
								    'temb_channels': 128,
								}
								if self.block_type == "up":
														_lowerCAmelCase										=32

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

								_lowerCAmelCase										=self.dummy_input
								return init_dict, inputs_dict






		def 		UpperCamelCase__       (   self		,						__A				) ->     Any:
								_lowerCAmelCase						,				_lowerCAmelCase										=self.prepare_init_args_and_inputs_for_common()
								_lowerCAmelCase										=self.block_class(**__A				)
								unet_block.to(__A				)
								unet_block.eval()

								with torch.no_grad():
														_lowerCAmelCase										=unet_block(**__A				)

								if isinstance(__A		,						__A				):
														_lowerCAmelCase										=output[0]

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

								_lowerCAmelCase										=output[0, -1, -3:, -3:]
								_lowerCAmelCase										=torch.tensor(__A				).to(__A				)
								assert torch_all_close(output_slice.flatten()		,						__A		,						atol=5E-3				)






		@unittest.skipIf(torch_device == 'mps'		,						'Training is not supported in mps'				)
		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								_lowerCAmelCase						,				_lowerCAmelCase										=self.prepare_init_args_and_inputs_for_common()
								_lowerCAmelCase										=self.block_class(**__A				)
								model.to(__A				)
								model.train()
								_lowerCAmelCase										=model(**__A				)

								if isinstance(__A		,						__A				):
														_lowerCAmelCase										=output[0]

								_lowerCAmelCase										=torch.device(__A				)
								_lowerCAmelCase										=randn_tensor(output.shape		,						device=__A				)
								_lowerCAmelCase										=torch.nn.functional.mse_loss(__A		,						__A				)
								loss.backward()






'''simple docstring'''

from __future__ import annotations

from typing import Any
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A				) ->     None:
								_lowerCAmelCase										=num_of_nodes
								_lowerCAmelCase										=[]
								_lowerCAmelCase										={}






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     None:
								self.m_edges.append([u_node, v_node, weight]				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     int:

								if self.m_component[u_node] == u_node:
														return u_node
								return self.find_component(self.m_component[u_node]				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     None:

								if self.m_component[u_node] != u_node:
														for k in self.m_component:
																				_lowerCAmelCase										=self.find_component(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     None:

								if component_size[u_node] <= component_size[v_node]:
														_lowerCAmelCase										=v_node
														component_size[v_node] += component_size[u_node]
														self.set_component(__A				)

								elif component_size[u_node] >= component_size[v_node]:
														_lowerCAmelCase										=self.find_component(__A				)
														component_size[u_node] += component_size[v_node]
														self.set_component(__A				)






		def 		UpperCamelCase__       (   self				) ->     None:
								_lowerCAmelCase										=[]
								_lowerCAmelCase										=0

								_lowerCAmelCase										=[-1] * self.m_num_of_nodes

								# A list of components (initialized to all of the nodes)
								for node in range(self.m_num_of_nodes				):
														self.m_component.update({node: node}				)
														component_size.append(1				)

								_lowerCAmelCase										=self.m_num_of_nodes

								while num_of_components > 1:
														for edge in self.m_edges:
																				_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=edge

																				_lowerCAmelCase										=self.m_component[u]
																				_lowerCAmelCase										=self.m_component[v]

																				if u_component != v_component:

																										for component in (u_component, v_component):
																																if (
																																    minimum_weight_edge[component] == -1
																																    or minimum_weight_edge[component][2] > w
																																):
																																						_lowerCAmelCase										=[u, v, w]

														for edge in minimum_weight_edge:
																				if isinstance(__A		,						__A				):
																										_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=edge

																										_lowerCAmelCase										=self.m_component[u]
																										_lowerCAmelCase										=self.m_component[v]

																										if u_component != v_component:
																																mst_weight += w
																																self.union(__A		,						__A		,						__A				)
																																print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''				)
																																num_of_components -= 1

														_lowerCAmelCase										=[-1] * self.m_num_of_nodes
								print(F'''The total weight of the minimal spanning tree is: {mst_weight}'''				)



def 							UpperCamelCase__			(			):


						'''simple docstring'''








if __name__ == "__main__":
							import doctest

							doctest.testmod()





