


"""simple docstring"""


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			(UniPCMultistepScheduler,)
		__UpperCamelCase           =			(('num_inference_steps', 25),)







		def  a_ (					self      :       Union[str, Any]    ,   **a__      :       Dict		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			{
						    "num_train_timesteps": 10_00,
						    "beta_start": 0.0_0_0_1,
						    "beta_end": 0.0_2,
						    "beta_schedule": "linear",
						    "solver_order": 2,
						    "solver_type": "bh2",
						}

						config.update(**a__		)
						return config







		def  a_ (					self      :       Tuple    ,   a__      :       int=0    ,   **a__      :       List[Any]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			dict(self.forward_default_kwargs		)
						_A					  =			kwargs.pop("num_inference_steps"    ,   a__		)
						_A					  =			self.dummy_sample
						_A					  =			0.1 * sample
						_A					  =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
										_A					  =			self.get_scheduler_config(**a__		)
										_A					  =			scheduler_class(**a__		)
										scheduler.set_timesteps(a__		)
										# copy over dummy past residuals
										_A					  =			dummy_past_residuals[: scheduler.config.solver_order]

										with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(a__		)
														_A					  =			scheduler_class.from_pretrained(a__		)
														new_scheduler.set_timesteps(a__		)
														# copy over dummy past residuals
														_A					  =			dummy_past_residuals[: new_scheduler.config.solver_order]

										_A  ,		_A					  =			sample, sample
										for t in range(a__    ,   time_step + scheduler.config.solver_order + 1		):
														_A					  =			scheduler.step(a__    ,   a__    ,   a__    ,   **a__		).prev_sample
														_A					  =			new_scheduler.step(a__    ,   a__    ,   a__    ,   **a__		).prev_sample

														assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"







		def  a_ (					self      :       Tuple    ,   a__      :       Any=0    ,   **a__      :       Dict		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			dict(self.forward_default_kwargs		)
						_A					  =			kwargs.pop("num_inference_steps"    ,   a__		)
						_A					  =			self.dummy_sample
						_A					  =			0.1 * sample
						_A					  =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
										_A					  =			self.get_scheduler_config()
										_A					  =			scheduler_class(**a__		)
										scheduler.set_timesteps(a__		)

										# copy over dummy past residuals (must be after setting timesteps)
										_A					  =			dummy_past_residuals[: scheduler.config.solver_order]

										with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(a__		)
														_A					  =			scheduler_class.from_pretrained(a__		)
														# copy over dummy past residuals
														new_scheduler.set_timesteps(a__		)

														# copy over dummy past residual (must be after setting timesteps)
														_A					  =			dummy_past_residuals[: new_scheduler.config.solver_order]

										_A					  =			scheduler.step(a__    ,   a__    ,   a__    ,   **a__		).prev_sample
										_A					  =			new_scheduler.step(a__    ,   a__    ,   a__    ,   **a__		).prev_sample

										assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"







		def  a_ (					self      :       Optional[Any]    ,   a__      :       Dict=None    ,   **a__      :       str		)       ->						int:





						'''simple docstring'''







						if scheduler is None:
										_A					  =			self.scheduler_classes[0]
										_A					  =			self.get_scheduler_config(**a__		)
										_A					  =			scheduler_class(**a__		)

						_A					  =			self.scheduler_classes[0]
						_A					  =			self.get_scheduler_config(**a__		)
						_A					  =			scheduler_class(**a__		)

						_A					  =			10
						_A					  =			self.dummy_model()
						_A					  =			self.dummy_sample_deter
						scheduler.set_timesteps(a__		)

						for i, t in enumerate(scheduler.timesteps		):
										_A					  =			model(a__    ,   a__		)
										_A					  =			scheduler.step(a__    ,   a__    ,   a__		).prev_sample

						return sample







		def  a_ (					self      :       str		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			dict(self.forward_default_kwargs		)

						_A					  =			kwargs.pop("num_inference_steps"    ,   a__		)

						for scheduler_class in self.scheduler_classes:
										_A					  =			self.get_scheduler_config()
										_A					  =			scheduler_class(**a__		)

										_A					  =			self.dummy_sample
										_A					  =			0.1 * sample

										if num_inference_steps is not None and hasattr(a__    ,   "set_timesteps"		):
														scheduler.set_timesteps(a__		)
										elif num_inference_steps is not None and not hasattr(a__    ,   "set_timesteps"		):
														_A					  =			num_inference_steps

										# copy over dummy past residuals (must be done after set_timesteps)
										_A					  =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
										_A					  =			dummy_past_residuals[: scheduler.config.solver_order]

										_A					  =			scheduler.timesteps[5]
										_A					  =			scheduler.timesteps[6]

										_A					  =			scheduler.step(a__    ,   a__    ,   a__    ,   **a__		).prev_sample
										_A					  =			scheduler.step(a__    ,   a__    ,   a__    ,   **a__		).prev_sample

										self.assertEqual(output_a.shape    ,   sample.shape		)
										self.assertEqual(output_a.shape    ,   output_a.shape		)







		def  a_ (					self      :       Union[str, Any]		)       ->						int:





						'''simple docstring'''







						_A					  =			UniPCMultistepScheduler(**self.get_scheduler_config()		)
						_A					  =			self.full_loop(scheduler=a__		)
						_A					  =			torch.mean(torch.abs(a__		)		)

						assert abs(result_mean.item() - 0.2_4_6_4		) < 1E-3

						_A					  =			DPMSolverSinglestepScheduler.from_config(scheduler.config		)
						_A					  =			DEISMultistepScheduler.from_config(scheduler.config		)
						_A					  =			DPMSolverMultistepScheduler.from_config(scheduler.config		)
						_A					  =			UniPCMultistepScheduler.from_config(scheduler.config		)

						_A					  =			self.full_loop(scheduler=a__		)
						_A					  =			torch.mean(torch.abs(a__		)		)

						assert abs(result_mean.item() - 0.2_4_6_4		) < 1E-3







		def  a_ (					self      :       Union[str, Any]		)       ->						Any:





						'''simple docstring'''







						for timesteps in [25, 50, 1_00, 9_99, 10_00]:
										self.check_over_configs(num_train_timesteps=a__		)







		def  a_ (					self      :       int		)       ->						Tuple:





						'''simple docstring'''







						self.check_over_configs(thresholding=a__		)
						for order in [1, 2, 3]:
										for solver_type in ["bh1", "bh2"]:
														for threshold in [0.5, 1.0, 2.0]:
																		for prediction_type in ["epsilon", "sample"]:
																						self.check_over_configs(
																						    thresholding=a__    ,   prediction_type=a__    ,   sample_max_value=a__    ,   solver_order=a__    ,   solver_type=a__    ,   )







		def  a_ (					self      :       Dict		)       ->						List[Any]:





						'''simple docstring'''







						for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=a__		)







		def  a_ (					self      :       Dict		)       ->						Optional[int]:





						'''simple docstring'''







						for solver_type in ["bh1", "bh2"]:
										for order in [1, 2, 3]:
														for prediction_type in ["epsilon", "sample"]:
																		self.check_over_configs(
																		    solver_order=a__    ,   solver_type=a__    ,   prediction_type=a__    ,   )
																		_A					  =			self.full_loop(
																		    solver_order=a__    ,   solver_type=a__    ,   prediction_type=a__    ,   )
																		assert not torch.isnan(a__		).any(), "Samples have nan numbers"







		def  a_ (					self      :       str		)       ->						Tuple:





						'''simple docstring'''







						self.check_over_configs(lower_order_final=a__		)
						self.check_over_configs(lower_order_final=a__		)







		def  a_ (					self      :       Optional[Any]		)       ->						Any:





						'''simple docstring'''







						for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
										self.check_over_forward(num_inference_steps=a__    ,   time_step=0		)







		def  a_ (					self      :       Tuple		)       ->						List[Any]:





						'''simple docstring'''







						_A					  =			self.full_loop()
						_A					  =			torch.mean(torch.abs(a__		)		)

						assert abs(result_mean.item() - 0.2_4_6_4		) < 1E-3







		def  a_ (					self      :       Optional[int]		)       ->						List[Any]:





						'''simple docstring'''







						_A					  =			self.full_loop(prediction_type="v_prediction"		)
						_A					  =			torch.mean(torch.abs(a__		)		)

						assert abs(result_mean.item() - 0.1_0_1_4		) < 1E-3







		def  a_ (					self      :       Optional[int]		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			self.scheduler_classes[0]
						_A					  =			self.get_scheduler_config(thresholding=a__    ,   dynamic_thresholding_ratio=0		)
						_A					  =			scheduler_class(**a__		)

						_A					  =			10
						_A					  =			self.dummy_model()
						_A					  =			self.dummy_sample_deter.half()
						scheduler.set_timesteps(a__		)

						for i, t in enumerate(scheduler.timesteps		):
										_A					  =			model(a__    ,   a__		)
										_A					  =			scheduler.step(a__    ,   a__    ,   a__		).prev_sample

						assert sample.dtype == torch.floataa







		def  a_ (					self      :       Optional[int]    ,   **a__      :       Tuple		)       ->						Union[str, Any]:





						'''simple docstring'''







						for scheduler_class in self.scheduler_classes:
										_A					  =			self.get_scheduler_config(**a__		)
										_A					  =			scheduler_class(**a__		)

										scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
										assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps



"""simple docstring"""


class snake_case      :







		def __init__(					self      :       Optional[int]    ,   a__      :       List[Any]    ,   a__      :       List[str]    ,   a__      :       Tuple		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			None
						_A					  =			None
						_A					  =			graph

						self._normalize_graph(a__    ,   a__		)
						_A					  =			len(a__		)
						_A					  =			None







		def  a_ (					self      :       str    ,   a__      :       List[str]    ,   a__      :       List[Any]		)       ->						Dict:





						'''simple docstring'''







						if sources is int:
										_A					  =			[sources]
						if sinks is int:
										_A					  =			[sinks]

						if len(a__		) == 0 or len(a__		) == 0:
										return

						_A					  =			sources[0]
						_A					  =			sinks[0]

						# make fake vertex if there are more
						# than one source or sink
						if len(a__		) > 1 or len(a__		) > 1:
										_A					  =			0
										for i in sources:
														max_input_flow += sum(self.graph[i]		)

										_A					  =			len(self.graph		) + 1
										for room in self.graph:
														room.insert(0    ,   0		)
										self.graph.insert(0    ,   [0] * size		)
										for i in sources:
														_A					  =			max_input_flow
										_A					  =			0

										_A					  =			len(self.graph		) + 1
										for room in self.graph:
														room.append(0		)
										self.graph.append([0] * size		)
										for i in sinks:
														_A					  =			max_input_flow
										_A					  =			size - 1







		def  a_ (					self      :       Optional[int]		)       ->						List[Any]:





						'''simple docstring'''







						if self.maximum_flow_algorithm is None:
										raise Exception("You need to set maximum flow algorithm before."		)
						if self.source_index is None or self.sink_index is None:
										return 0

						self.maximum_flow_algorithm.execute()
						return self.maximum_flow_algorithm.getMaximumFlow()







		def  a_ (					self      :       List[Any]    ,   a__      :       Optional[Any]		)       ->						str:





						'''simple docstring'''







						_A					  =			algorithm(self		)





class snake_case      :







		def __init__(					self      :       List[str]    ,   a__      :       List[str]		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			flow_network
						_A					  =			flow_network.verticesCount
						_A					  =			flow_network.sourceIndex
						_A					  =			flow_network.sinkIndex
						# it's just a reference, so you shouldn't change
						# it in your algorithms, use deep copy before doing that
						_A					  =			flow_network.graph
						_A					  =			False







		def  a_ (					self      :       Optional[Any]		)       ->						List[Any]:





						'''simple docstring'''







						if not self.executed:
										self._algorithm()
										_A					  =			True







		def  a_ (					self      :       Any		)       ->						int:





						'''simple docstring'''







						pass





class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       Optional[Any]    ,   a__      :       Dict		)       ->						List[str]:





						'''simple docstring'''







						super().__init__(a__		)
						# use this to save your result
						_A					  =			-1







		def  a_ (					self      :       Any		)       ->						List[str]:





						'''simple docstring'''







						if not self.executed:
										raise Exception("You should execute algorithm before using its result!"		)

						return self.maximum_flow





class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       Union[str, Any]    ,   a__      :       Union[str, Any]		)       ->						Dict:





						'''simple docstring'''







						super().__init__(a__		)

						_A					  =			[[0] * self.verticies_count for i in range(self.verticies_count		)]

						_A					  =			[0] * self.verticies_count
						_A					  =			[0] * self.verticies_count







		def  a_ (					self      :       Any		)       ->						Dict:





						'''simple docstring'''







						_A					  =			self.verticies_count

						# push some substance to graph
						for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]		):
										self.preflow[self.source_index][nextvertex_index] += bandwidth
										self.preflow[nextvertex_index][self.source_index] -= bandwidth
										self.excesses[nextvertex_index] += bandwidth

						# Relabel-to-front selection rule
						_A					  =			[
						    i
						    for i in range(self.verticies_count		)
						    if i != self.source_index and i != self.sink_index
						]

						# move through list
						_A					  =			0
						while i < len(a__		):
										_A					  =			vertices_list[i]
										_A					  =			self.heights[vertex_index]
										self.process_vertex(a__		)
										if self.heights[vertex_index] > previous_height:
														# if it was relabeled, swap elements
														# and start from 0 index
														vertices_list.insert(0    ,   vertices_list.pop(a__		)		)
														_A					  =			0
										else:
														i += 1

						_A					  =			sum(self.preflow[self.source_index]		)







		def  a_ (					self      :       Dict    ,   a__      :       Any		)       ->						Optional[int]:





						'''simple docstring'''







						while self.excesses[vertex_index] > 0:
										for neighbour_index in range(self.verticies_count		):
														# if it's neighbour and current vertex is higher
														if (
														    self.graph[vertex_index][neighbour_index]
														    - self.preflow[vertex_index][neighbour_index]
														    > 0
														    and self.heights[vertex_index] > self.heights[neighbour_index]
														):
																		self.push(a__    ,   a__		)

										self.relabel(a__		)







		def  a_ (					self      :       str    ,   a__      :       Optional[int]    ,   a__      :       List[Any]		)       ->						Optional[int]:





						'''simple docstring'''







						_A					  =			min(
						    self.excesses[from_index]    ,   self.graph[from_index][to_index] - self.preflow[from_index][to_index]    ,   )
						self.preflow[from_index][to_index] += preflow_delta
						self.preflow[to_index][from_index] -= preflow_delta
						self.excesses[from_index] -= preflow_delta
						self.excesses[to_index] += preflow_delta







		def  a_ (					self      :       Any    ,   a__      :       Dict		)       ->						Any:





						'''simple docstring'''







						_A					  =			None
						for to_index in range(self.verticies_count		):
										if (
										    self.graph[vertex_index][to_index]
										    - self.preflow[vertex_index][to_index]
										    > 0
										) and (min_height is None or self.heights[to_index] < min_height):
														_A					  =			self.heights[to_index]

						if min_height is not None:
										_A					  =			min_height + 1


if __name__ == "__main__":
		a_       							=    [0]
		a_       							=    [3]
		# graph = [
		#     [0, 0, 4, 6, 0, 0],
		#     [0, 0, 5, 2, 0, 0],
		#     [0, 0, 0, 0, 4, 4],
		#     [0, 0, 0, 0, 6, 6],
		#     [0, 0, 0, 0, 0, 0],
		#     [0, 0, 0, 0, 0, 0],
		# ]
		a_       							=    [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

		# prepare our network
		a_       							=    FlowNetwork(graph, entrances, exits)
		# set algorithm
		flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
		# and calculate
		a_       							=    flow_network.find_maximum_flow()

		print(f'''maximum flow is {maximum_flow}''')