



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					(CMStochasticIterativeScheduler,)
			UpperCamelCase        =					1_0



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



						"""simple docstring"""






						_UpperCAmelCase			=       {
						    'num_train_timesteps': 2_01,
						    'sigma_min': 0.0_0_2,
						    'sigma_max': 8_0.0,
						}

						config.update(**A)
						return config



			def    _lowerCamelCase	(						self       :  Dict)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       10

						_UpperCAmelCase			=       self.get_scheduler_config()
						_UpperCAmelCase			=       self.scheduler_classes[0](**A)

						scheduler.set_timesteps(A)

						_UpperCAmelCase			=       scheduler.timesteps[0]
						_UpperCAmelCase			=       scheduler.timesteps[1]

						_UpperCAmelCase			=       self.dummy_sample
						_UpperCAmelCase			=       0.1 * sample

						_UpperCAmelCase			=       scheduler.step(A			,		A			,		A).prev_sample
						_UpperCAmelCase			=       scheduler.step(A			,		A			,		A).prev_sample

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



			def    _lowerCamelCase	(						self       :  int)       ->   Any:



						"""simple docstring"""






						for timesteps in [10, 50, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=A)



			def    _lowerCamelCase	(						self       :  int)       ->   Optional[int]:



						"""simple docstring"""






						for clip_denoised in [True, False]:
									self.check_over_configs(clip_denoised=A)



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.scheduler_classes[0]
						_UpperCAmelCase			=       self.get_scheduler_config()
						_UpperCAmelCase			=       scheduler_class(**A)

						_UpperCAmelCase			=       1
						scheduler.set_timesteps(A)
						_UpperCAmelCase			=       scheduler.timesteps

						_UpperCAmelCase			=       torch.manual_seed(0)

						_UpperCAmelCase			=       self.dummy_model()
						_UpperCAmelCase			=       self.dummy_sample_deter * scheduler.init_noise_sigma

						for i, t in enumerate(A):
									# 1. scale model input
									_UpperCAmelCase			=       scheduler.scale_model_input(A			,		A)

									# 2. predict noise residual
									_UpperCAmelCase			=       model(A			,		A)

									# 3. predict previous sample x_t-1
									_UpperCAmelCase			=       scheduler.step(A			,		A			,		A			,		generator=A).prev_sample

									_UpperCAmelCase			=       pred_prev_sample

						_UpperCAmelCase			=       torch.sum(torch.abs(A))
						_UpperCAmelCase			=       torch.mean(torch.abs(A))

						assert abs(result_sum.item() - 1_9_2.7_6_1_4) < 1E-2
						assert abs(result_mean.item() - 0.2_5_1_0) < 1E-3



			def    _lowerCamelCase	(						self       :  List[str])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.scheduler_classes[0]
						_UpperCAmelCase			=       self.get_scheduler_config()
						_UpperCAmelCase			=       scheduler_class(**A)

						_UpperCAmelCase			=       [1_06, 0]
						scheduler.set_timesteps(timesteps=A)
						_UpperCAmelCase			=       scheduler.timesteps

						_UpperCAmelCase			=       torch.manual_seed(0)

						_UpperCAmelCase			=       self.dummy_model()
						_UpperCAmelCase			=       self.dummy_sample_deter * scheduler.init_noise_sigma

						for t in timesteps:
									# 1. scale model input
									_UpperCAmelCase			=       scheduler.scale_model_input(A			,		A)

									# 2. predict noise residual
									_UpperCAmelCase			=       model(A			,		A)

									# 3. predict previous sample x_t-1
									_UpperCAmelCase			=       scheduler.step(A			,		A			,		A			,		generator=A).prev_sample

									_UpperCAmelCase			=       pred_prev_sample

						_UpperCAmelCase			=       torch.sum(torch.abs(A))
						_UpperCAmelCase			=       torch.mean(torch.abs(A))

						assert abs(result_sum.item() - 3_4_7.6_3_5_7) < 1E-2
						assert abs(result_mean.item() - 0.4_5_2_7) < 1E-3



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



						"""simple docstring"""






						_UpperCAmelCase			=       self.scheduler_classes[0]
						_UpperCAmelCase			=       self.get_scheduler_config()
						_UpperCAmelCase			=       scheduler_class(**A)

						_UpperCAmelCase			=       [39, 30, 12, 15, 0]

						with self.assertRaises(A			,		msg='`timesteps` must be in descending order.'):
									scheduler.set_timesteps(timesteps=A)



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



						"""simple docstring"""






						_UpperCAmelCase			=       self.scheduler_classes[0]
						_UpperCAmelCase			=       self.get_scheduler_config()
						_UpperCAmelCase			=       scheduler_class(**A)

						_UpperCAmelCase			=       [39, 30, 12, 1, 0]
						_UpperCAmelCase			=       len(A)

						with self.assertRaises(A			,		msg='Can only pass one of `num_inference_steps` or `timesteps`.'):
									scheduler.set_timesteps(num_inference_steps=A			,		timesteps=A)



			def    _lowerCamelCase	(						self       :  str)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.scheduler_classes[0]
						_UpperCAmelCase			=       self.get_scheduler_config()
						_UpperCAmelCase			=       scheduler_class(**A)

						_UpperCAmelCase			=       [scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    A			,		msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'			,		):
									scheduler.set_timesteps(timesteps=A)





UpperCAmelCase__       =	{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}





def 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       List[Any]   )    ->			list[str]:







			'''simple docstring'''
			_UpperCAmelCase			=       set()
			# keep track of all the paths to be checked
			_UpperCAmelCase			=       [[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						_UpperCAmelCase			=       queue.pop(0   )
						# get the last node from the path
						_UpperCAmelCase			=       path[-1]
						if node not in explored:
									_UpperCAmelCase			=       graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												_UpperCAmelCase			=       list(_UpperCAmelCase   )
												new_path.append(_UpperCAmelCase   )
												queue.append(_UpperCAmelCase   )
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(_UpperCAmelCase   )

    # in case there's no path between the 2 nodes
			return []





def 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[str]   )    ->			int:







			'''simple docstring'''
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			_UpperCAmelCase			=       [start]
			_UpperCAmelCase			=       set(_UpperCAmelCase   )
			# Keep tab on distances from `start` node.
			_UpperCAmelCase			=       {start: 0, target: -1}
			while queue:
						_UpperCAmelCase			=       queue.pop(0   )
						if node == target:
									_UpperCAmelCase			=       (
									    dist[node] if dist[target] == -1 else min(dist[target]							,       dist[node]   )
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(_UpperCAmelCase   )
												queue.append(_UpperCAmelCase   )
												_UpperCAmelCase			=       dist[node] + 1
			return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, "G", "D"))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, "G", "D"))  # returns 4
