





"""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 					__UpperCAmelCase							:
				@property
				def       UpperCAmelCase      (		self    :			Tuple    )	->	List[Any]:



							'''simple docstring'''



							return self.get_dummy_input()
				@property
				def       UpperCAmelCase      (		self    :			Any    )	->	Dict:



							'''simple docstring'''



							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    :			Union[str, Any]	, a_    :			List[str]=True	, a_    :			Any=False	, a_    :			Any=False	, a_    :			List[Any]=False	, )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	str		 =			4
							a__  :	List[str]		 =			32
							a__  :	Union[str, Any]		 =			(32, 32)

							a__  :	Tuple		 =			torch.manual_seed(0    )
							a__  :	List[Any]		 =			torch.device(a_    )
							a__  :	Dict		 =			(batch_size, num_channels) + sizes
							a__  :	Tuple		 =			randn_tensor(a_	, generator=a_	, device=a_    )
							a__  :	Dict		 =			{"hidden_states": hidden_states}

							if include_temb:
										a__  :	Any		 =			1_28
										a__  :	int		 =			randn_tensor((batch_size, temb_channels)	, generator=a_	, device=a_    )

							if include_res_hidden_states_tuple:
										a__  :	Dict		 =			torch.manual_seed(1    )
										a__  :	Dict		 =			(randn_tensor(a_	, generator=a_	, device=a_    ),)

							if include_encoder_hidden_states:
										a__  :	Dict		 =			floats_tensor((batch_size, 32, 32)    ).to(a_    )

							if include_skip_sample:
										a__  :	Tuple		 =			randn_tensor(((batch_size, 3) + sizes)	, generator=a_	, device=a_    )

							return dummy_input
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	int:



							'''simple docstring'''



							a__  :	Any		 =			{
							    "in_channels": 32,
							    "out_channels": 32,
							    "temb_channels": 1_28,
							}
							if self.block_type == "up":
										a__  :	int		 =			32

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

							a__  :	List[str]		 =			self.dummy_input
							return init_dict, inputs_dict
				def       UpperCAmelCase      (		self    :			Dict	, a_    :			Union[str, Any]    )	->	int:



							'''simple docstring'''



							a__ ,	a__  :	Optional[int]		 =			self.prepare_init_args_and_inputs_for_common()
							a__  :	int		 =			self.block_class(**a_    )
							unet_block.to(a_    )
							unet_block.eval()

							with torch.no_grad():
										a__  :	Any		 =			unet_block(**a_    )

							if isinstance(a_	, a_    ):
										a__  :	str		 =			output[0]

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

							a__  :	str		 =			output[0, -1, -3:, -3:]
							a__  :	Optional[int]		 =			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    :			List[Any]    )	->	Tuple:



							'''simple docstring'''



							a__ ,	a__  :	Dict		 =			self.prepare_init_args_and_inputs_for_common()
							a__  :	Optional[Any]		 =			self.block_class(**a_    )
							model.to(a_    )
							model.train()
							a__  :	Optional[int]		 =			model(**a_    )

							if isinstance(a_	, a_    ):
										a__  :	Optional[Any]		 =			output[0]

							a__  :	Optional[Any]		 =			torch.device(a_    )
							a__  :	Union[str, Any]		 =			randn_tensor(output.shape	, device=a_    )
							a__  :	Any		 =			torch.nn.functional.mse_loss(a_	, a_    )
							loss.backward()






"""simple docstring"""






__UpperCAmelCase						=			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]






def      lowercase__ (					lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Tuple			)    ->			Dict:



			'''simple docstring'''
			# Return True if there is node that has not iterated.
			a__  :	int		 =			[False] * len(lowerCAmelCase__			)
			a__  :	Optional[Any]		 =			[s]
			a__  :	Optional[int]		 =			True

			while queue:
						a__  :	Any		 =			queue.pop(0			)
						for ind in range(len(graph[u]			)			):
									if visited[ind] is False and graph[u][ind] > 0:
												queue.append(lowerCAmelCase__			)
												a__  :	Any		 =			True
												a__  :	Union[str, Any]		 =			u

			return visited[t]






def      lowercase__ (					lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Optional[Any]			)    ->			Any:



			'''simple docstring'''
			a__  :	Any		 =			[-1] * (len(lowerCAmelCase__			))
			a__  :	Any		 =			0
			a__  :	List[str]		 =			[]
			a__  :	Tuple		 =			[i[:] for i in graph]  # Record original cut, copy.
			while bfs(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			):
						a__  :	Any		 =			float("Inf"			)
						a__  :	Optional[int]		 =			sink

						while s != source:
									# Find the minimum value in select path
									a__  :	Union[str, Any]		 =			min(lowerCAmelCase__							,			graph[parent[s]][s]			)
									a__  :	List[str]		 =			parent[s]

						max_flow += path_flow
						a__  :	List[str]		 =			sink

						while v != source:
									a__  :	int		 =			parent[v]
									graph[u][v] -= path_flow
									graph[v][u] += path_flow
									a__  :	List[str]		 =			parent[v]

			for i in range(len(lowerCAmelCase__			)			):
						for j in range(len(graph[0]			)			):
									if graph[i][j] == 0 and temp[i][j] > 0:
												res.append((i, j)			)

			return res


if __name__ == "__main__":
		print(mincut(test_graph, source=0, sink=5))