


'''simple docstring'''


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 		lowerCAmelCase__	(	__snake_case		):
			lowerCAmelCase       :		Tuple				      =   (DEISMultistepScheduler,)
			lowerCAmelCase       :		Dict				      =   (("""num_inference_steps""", 25),)




			def 							lowerCAmelCase__			(						self     :   str		,     **lowerCamelCase__     :   Union[str, Any]       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					{
					    "num_train_timesteps": 10_00,
					    "beta_start": 0.0_0_0_1,
					    "beta_end": 0.0_2,
					    "beta_schedule": "linear",
					    "solver_order": 2,
					}

					config.update(**UpperCamelCase__       )
					return config




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   Optional[Any]=0		,     **lowerCamelCase__     :   Any       )      ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					dict(self.forward_default_kwargs       )
					_UpperCAmelCase						:				Optional[Any]	    =					kwargs.pop("num_inference_steps"		,     UpperCamelCase__       )
					_UpperCAmelCase						:				str	    =					self.dummy_sample
					_UpperCAmelCase						:				Any	    =					0.1 * sample
					_UpperCAmelCase						:				Any	    =					[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

					for scheduler_class in self.scheduler_classes:
							_UpperCAmelCase						:				Optional[Any]	    =					self.get_scheduler_config(**UpperCamelCase__       )
							_UpperCAmelCase						:				Dict	    =					scheduler_class(**UpperCamelCase__       )
							scheduler.set_timesteps(UpperCamelCase__       )
							# copy over dummy past residuals
							_UpperCAmelCase						:				int	    =					dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(UpperCamelCase__       )
									_UpperCAmelCase						:				Optional[int]	    =					scheduler_class.from_pretrained(UpperCamelCase__       )
									new_scheduler.set_timesteps(UpperCamelCase__       )
									# copy over dummy past residuals
									_UpperCAmelCase						:				Tuple	    =					dummy_past_residuals[: new_scheduler.config.solver_order]

							_UpperCAmelCase						:				List[str]	    =					sample, sample
							for t in range(UpperCamelCase__		,     time_step + scheduler.config.solver_order + 1       ):
									_UpperCAmelCase						:				Optional[Any]	    =					scheduler.step(UpperCamelCase__		,     UpperCamelCase__		,     UpperCamelCase__		,     **UpperCamelCase__       ).prev_sample
									_UpperCAmelCase						:				Optional[Any]	    =					new_scheduler.step(UpperCamelCase__		,     UpperCamelCase__		,     UpperCamelCase__		,     **UpperCamelCase__       ).prev_sample

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




			def 							lowerCAmelCase__			(						self     :   List[str]       )      ->Dict:



					'''simple docstring'''







					pass




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Optional[int]=0		,     **lowerCamelCase__     :   List[Any]       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					dict(self.forward_default_kwargs       )
					_UpperCAmelCase						:				Tuple	    =					kwargs.pop("num_inference_steps"		,     UpperCamelCase__       )
					_UpperCAmelCase						:				str	    =					self.dummy_sample
					_UpperCAmelCase						:				Optional[Any]	    =					0.1 * sample
					_UpperCAmelCase						:				Optional[Any]	    =					[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

					for scheduler_class in self.scheduler_classes:
							_UpperCAmelCase						:				Union[str, Any]	    =					self.get_scheduler_config()
							_UpperCAmelCase						:				Optional[int]	    =					scheduler_class(**UpperCamelCase__       )
							scheduler.set_timesteps(UpperCamelCase__       )

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

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

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

							_UpperCAmelCase						:				List[str]	    =					scheduler.step(UpperCamelCase__		,     UpperCamelCase__		,     UpperCamelCase__		,     **UpperCamelCase__       ).prev_sample
							_UpperCAmelCase						:				Union[str, Any]	    =					new_scheduler.step(UpperCamelCase__		,     UpperCamelCase__		,     UpperCamelCase__		,     **UpperCamelCase__       ).prev_sample

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




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   str=None		,     **lowerCamelCase__     :   Tuple       )      ->List[str]:



					'''simple docstring'''







					if scheduler is None:
							_UpperCAmelCase						:				Tuple	    =					self.scheduler_classes[0]
							_UpperCAmelCase						:				Tuple	    =					self.get_scheduler_config(**UpperCamelCase__       )
							_UpperCAmelCase						:				Tuple	    =					scheduler_class(**UpperCamelCase__       )

					_UpperCAmelCase						:				Dict	    =					self.scheduler_classes[0]
					_UpperCAmelCase						:				List[Any]	    =					self.get_scheduler_config(**UpperCamelCase__       )
					_UpperCAmelCase						:				int	    =					scheduler_class(**UpperCamelCase__       )

					_UpperCAmelCase						:				Optional[Any]	    =					10
					_UpperCAmelCase						:				Optional[Any]	    =					self.dummy_model()
					_UpperCAmelCase						:				Optional[Any]	    =					self.dummy_sample_deter
					scheduler.set_timesteps(UpperCamelCase__       )

					for i, t in enumerate(scheduler.timesteps       ):
							_UpperCAmelCase						:				Any	    =					model(UpperCamelCase__		,     UpperCamelCase__       )
							_UpperCAmelCase						:				Union[str, Any]	    =					scheduler.step(UpperCamelCase__		,     UpperCamelCase__		,     UpperCamelCase__       ).prev_sample

					return sample




			def 							lowerCAmelCase__			(						self     :   List[str]       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					dict(self.forward_default_kwargs       )

					_UpperCAmelCase						:				Optional[Any]	    =					kwargs.pop("num_inference_steps"		,     UpperCamelCase__       )

					for scheduler_class in self.scheduler_classes:
							_UpperCAmelCase						:				Optional[Any]	    =					self.get_scheduler_config()
							_UpperCAmelCase						:				Union[str, Any]	    =					scheduler_class(**UpperCamelCase__       )

							_UpperCAmelCase						:				Optional[Any]	    =					self.dummy_sample
							_UpperCAmelCase						:				List[str]	    =					0.1 * sample

							if num_inference_steps is not None and hasattr(UpperCamelCase__		,     "set_timesteps"       ):
									scheduler.set_timesteps(UpperCamelCase__       )
							elif num_inference_steps is not None and not hasattr(UpperCamelCase__		,     "set_timesteps"       ):
									_UpperCAmelCase						:				List[Any]	    =					num_inference_steps

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

							_UpperCAmelCase						:				Any	    =					scheduler.timesteps[5]
							_UpperCAmelCase						:				str	    =					scheduler.timesteps[6]

							_UpperCAmelCase						:				Optional[Any]	    =					scheduler.step(UpperCamelCase__		,     UpperCamelCase__		,     UpperCamelCase__		,     **UpperCamelCase__       ).prev_sample
							_UpperCAmelCase						:				str	    =					scheduler.step(UpperCamelCase__		,     UpperCamelCase__		,     UpperCamelCase__		,     **UpperCamelCase__       ).prev_sample

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




			def 							lowerCAmelCase__			(						self     :   Any       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				Tuple	    =					DEISMultistepScheduler(**self.get_scheduler_config()       )
					_UpperCAmelCase						:				Dict	    =					self.full_loop(scheduler=UpperCamelCase__       )
					_UpperCAmelCase						:				List[str]	    =					torch.mean(torch.abs(UpperCamelCase__       )       )

					assert abs(result_mean.item() - 0.2_3_9_1_6       ) < 1E-3

					_UpperCAmelCase						:				List[Any]	    =					DPMSolverSinglestepScheduler.from_config(scheduler.config       )
					_UpperCAmelCase						:				Optional[Any]	    =					DPMSolverMultistepScheduler.from_config(scheduler.config       )
					_UpperCAmelCase						:				List[Any]	    =					UniPCMultistepScheduler.from_config(scheduler.config       )
					_UpperCAmelCase						:				str	    =					DEISMultistepScheduler.from_config(scheduler.config       )

					_UpperCAmelCase						:				Dict	    =					self.full_loop(scheduler=UpperCamelCase__       )
					_UpperCAmelCase						:				Optional[Any]	    =					torch.mean(torch.abs(UpperCamelCase__       )       )

					assert abs(result_mean.item() - 0.2_3_9_1_6       ) < 1E-3




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



					'''simple docstring'''







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




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



					'''simple docstring'''







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




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



					'''simple docstring'''







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




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



					'''simple docstring'''







					for algorithm_type in ["deis"]:
							for solver_type in ["logrho"]:
									for order in [1, 2, 3]:
											for prediction_type in ["epsilon", "sample"]:
													self.check_over_configs(
													    solver_order=UpperCamelCase__		,     solver_type=UpperCamelCase__		,     prediction_type=UpperCamelCase__		,     algorithm_type=UpperCamelCase__		,     )
													_UpperCAmelCase						:				List[Any]	    =					self.full_loop(
													    solver_order=UpperCamelCase__		,     solver_type=UpperCamelCase__		,     prediction_type=UpperCamelCase__		,     algorithm_type=UpperCamelCase__		,     )
													assert not torch.isnan(UpperCamelCase__       ).any(), "Samples have nan numbers"




			def 							lowerCAmelCase__			(						self     :   str       )      ->str:



					'''simple docstring'''







					self.check_over_configs(lower_order_final=UpperCamelCase__       )
					self.check_over_configs(lower_order_final=UpperCamelCase__       )




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



					'''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=UpperCamelCase__		,     time_step=0       )




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



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					self.full_loop()
					_UpperCAmelCase						:				List[str]	    =					torch.mean(torch.abs(UpperCamelCase__       )       )

					assert abs(result_mean.item() - 0.2_3_9_1_6       ) < 1E-3




			def 							lowerCAmelCase__			(						self     :   Dict       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					self.full_loop(prediction_type="v_prediction"       )
					_UpperCAmelCase						:				Optional[int]	    =					torch.mean(torch.abs(UpperCamelCase__       )       )

					assert abs(result_mean.item() - 0.0_9_1       ) < 1E-3






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



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					self.scheduler_classes[0]
					_UpperCAmelCase						:				int	    =					self.get_scheduler_config(thresholding=UpperCamelCase__		,     dynamic_thresholding_ratio=0       )
					_UpperCAmelCase						:				Dict	    =					scheduler_class(**UpperCamelCase__       )

					_UpperCAmelCase						:				List[str]	    =					10
					_UpperCAmelCase						:				str	    =					self.dummy_model()
					_UpperCAmelCase						:				int	    =					self.dummy_sample_deter.half()
					scheduler.set_timesteps(UpperCamelCase__       )

					for i, t in enumerate(scheduler.timesteps       ):
							_UpperCAmelCase						:				List[Any]	    =					model(UpperCamelCase__		,     UpperCamelCase__       )
							_UpperCAmelCase						:				Tuple	    =					scheduler.step(UpperCamelCase__		,     UpperCamelCase__		,     UpperCamelCase__       ).prev_sample

					assert sample.dtype == torch.floataa






"""simple docstring"""


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






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	list[str]:
		__lowerCAmelCase:       Tuple          =			set()
		# keep track of all the paths to be checked
		__lowerCAmelCase:       Optional[Any]          =			[[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
				__lowerCAmelCase:       str          =			queue.pop(0 )
				# get the last node from the path
				__lowerCAmelCase:       List[Any]          =			path[-1]
				if node not in explored:
						__lowerCAmelCase:       str          =			graph[node]
						# go through all neighbour nodes, construct a new path and
						# push it into the queue
						for neighbour in neighbours:
								__lowerCAmelCase:       Optional[int]          =			list(__SCREAMING_SNAKE_CASE )
								new_path.append(__SCREAMING_SNAKE_CASE )
								queue.append(__SCREAMING_SNAKE_CASE )
								# return path if neighbour is goal
								if neighbour == goal:
										return new_path

            # mark node as explored
						explored.add(__SCREAMING_SNAKE_CASE )

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






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	int:
		if not graph or start not in graph or target not in graph:
				return -1
		if start == target:
				return 0
		__lowerCAmelCase:       Dict          =			[start]
		__lowerCAmelCase:       Any          =			set(__SCREAMING_SNAKE_CASE )
		# Keep tab on distances from `start` node.
		__lowerCAmelCase:       int          =			{start: 0, target: -1}
		while queue:
				__lowerCAmelCase:       Optional[Any]          =			queue.pop(0 )
				if node == target:
						__lowerCAmelCase:       Dict          =			(
						    dist[node] if dist[target] == -1 else min(dist[target]					, dist[node] )
						)
				for adjacent in graph[node]:
						if adjacent not in visited:
								visited.add(__SCREAMING_SNAKE_CASE )
								queue.append(__SCREAMING_SNAKE_CASE )
								__lowerCAmelCase:       List[str]          =			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


