


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class  _a							(    _lowercase):
	_a  :  Dict				   =							(KDPMaDiscreteScheduler,)
	_a  :  List[str]				   =							10




	def 							UpperCAmelCase__( self						:		List[Any]		, **_SCREAMING_SNAKE_CASE						:		List[Any]		)->	Union[str, Any]:
			lowerCAmelCase__       :      int   					= {
			    '''num_train_timesteps''': 1100,
			    '''beta_start''': 0.0001,
			    '''beta_end''': 0.02,
			    '''beta_schedule''': '''linear''',
			}

			config.update(**lowercase_		)
			return config




	def 							UpperCAmelCase__( self						:		str		)->	str:







			for timesteps in [10, 50, 100, 1000]:
					self.check_over_configs(num_train_timesteps=lowercase_		)




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







			for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]		, [0.0002, 0.002, 0.02]		):
					self.check_over_configs(beta_start=lowercase_		, beta_end=lowercase_		)




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







			for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=lowercase_		)




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







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




	def 							UpperCAmelCase__( self						:		str		)->	str:
			lowerCAmelCase__       :      Dict   					= self.scheduler_classes[0]
			lowerCAmelCase__       :      Union[str, Any]   					= self.get_scheduler_config(prediction_type='''v_prediction'''		)
			lowerCAmelCase__       :      Union[str, Any]   					= scheduler_class(**lowercase_		)

			scheduler.set_timesteps(self.num_inference_steps		)

			lowerCAmelCase__       :      List[str]   					= self.dummy_model()
			lowerCAmelCase__       :      Optional[int]   					= self.dummy_sample_deter * scheduler.init_noise_sigma
			lowerCAmelCase__       :      Any   					= sample.to(lowercase_		)

			for i, t in enumerate(scheduler.timesteps		):
					lowerCAmelCase__       :      int   					= scheduler.scale_model_input(lowercase_		, lowercase_		)

					lowerCAmelCase__       :      Tuple   					= model(lowercase_		, lowercase_		)

					lowerCAmelCase__       :      Union[str, Any]   					= scheduler.step(lowercase_		, lowercase_		, lowercase_		)
					lowerCAmelCase__       :      List[str]   					= output.prev_sample

			lowerCAmelCase__       :      List[str]   					= torch.sum(torch.abs(lowercase_		)		)
			lowerCAmelCase__       :      Any   					= torch.mean(torch.abs(lowercase_		)		)

			if torch_device in ["cpu", "mps"]:
					assert abs(result_sum.item() - 4.6_934E-07		) < 1E-2
					assert abs(result_mean.item() - 6.1_112E-10		) < 1E-3
			else:
					# CUDA
					assert abs(result_sum.item() - 4.693_428_650_170_972E-07		) < 1E-2
					assert abs(result_mean.item() - 0.0002		) < 1E-3




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







			if torch_device == "mps":
					return
			lowerCAmelCase__       :      Optional[Any]   					= self.scheduler_classes[0]
			lowerCAmelCase__       :      List[Any]   					= self.get_scheduler_config()
			lowerCAmelCase__       :      Any   					= scheduler_class(**lowercase_		)

			scheduler.set_timesteps(self.num_inference_steps		)

			lowerCAmelCase__       :      str   					= self.dummy_model()
			lowerCAmelCase__       :      Tuple   					= self.dummy_sample_deter * scheduler.init_noise_sigma
			lowerCAmelCase__       :      Optional[int]   					= sample.to(lowercase_		)

			for i, t in enumerate(scheduler.timesteps		):
					lowerCAmelCase__       :      Tuple   					= scheduler.scale_model_input(lowercase_		, lowercase_		)

					lowerCAmelCase__       :      List[Any]   					= model(lowercase_		, lowercase_		)

					lowerCAmelCase__       :      str   					= scheduler.step(lowercase_		, lowercase_		, lowercase_		)
					lowerCAmelCase__       :      Dict   					= output.prev_sample

			lowerCAmelCase__       :      Union[str, Any]   					= torch.sum(torch.abs(lowercase_		)		)
			lowerCAmelCase__       :      str   					= torch.mean(torch.abs(lowercase_		)		)

			if torch_device in ["cpu", "mps"]:
					assert abs(result_sum.item() - 20.4125		) < 1E-2
					assert abs(result_mean.item() - 0.0266		) < 1E-3
			else:
					# CUDA
					assert abs(result_sum.item() - 20.4125		) < 1E-2
					assert abs(result_mean.item() - 0.0266		) < 1E-3




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







			if torch_device == "mps":
					return
			lowerCAmelCase__       :      Union[str, Any]   					= self.scheduler_classes[0]
			lowerCAmelCase__       :      List[Any]   					= self.get_scheduler_config()
			lowerCAmelCase__       :      List[str]   					= scheduler_class(**lowercase_		)

			scheduler.set_timesteps(self.num_inference_steps		, device=lowercase_		)

			lowerCAmelCase__       :      List[str]   					= self.dummy_model()
			lowerCAmelCase__       :      Any   					= self.dummy_sample_deter.to(lowercase_		) * scheduler.init_noise_sigma

			for t in scheduler.timesteps:
					lowerCAmelCase__       :      Tuple   					= scheduler.scale_model_input(lowercase_		, lowercase_		)

					lowerCAmelCase__       :      int   					= model(lowercase_		, lowercase_		)

					lowerCAmelCase__       :      str   					= scheduler.step(lowercase_		, lowercase_		, lowercase_		)
					lowerCAmelCase__       :      str   					= output.prev_sample

			lowerCAmelCase__       :      int   					= torch.sum(torch.abs(lowercase_		)		)
			lowerCAmelCase__       :      Optional[int]   					= torch.mean(torch.abs(lowercase_		)		)

			if str(lowercase_		).startswith('''cpu'''		):
					# The following sum varies between 148 and 156 on mps. Why?
					assert abs(result_sum.item() - 20.4125		) < 1E-2
					assert abs(result_mean.item() - 0.0266		) < 1E-3
			else:
					# CUDA
					assert abs(result_sum.item() - 20.4125		) < 1E-2
					assert abs(result_mean.item() - 0.0266		) < 1E-3



# using dfs for finding eulerian path traversal






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[Any]   					= (path or []) + [u]
		for v in graph[u]:
				if visited_edge[u][v] is False:
						lowerCAmelCase__					,     lowerCAmelCase__       :      List[Any]   					= True, True
						lowerCAmelCase__       :      Any   					= dfs(_a     ,       _a     ,       _a     ,       _a	)
		return path






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Union[str, Any]   					= 0
		lowerCAmelCase__       :      str   					= -1
		for i in range(_a	):
				if i not in graph.keys():
						continue
				if len(graph[i]	) % 2 == 1:
						odd_degree_nodes += 1
						lowerCAmelCase__       :      Tuple   					= i
		if odd_degree_nodes == 0:
				return 1, odd_node
		if odd_degree_nodes == 2:
				return 2, odd_node
		return 3, odd_node






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[Any]   					= [[False for _ in range(max_node + 1	)] for _ in range(max_node + 1	)]
		lowerCAmelCase__					,     lowerCAmelCase__       :      Optional[int]   					= check_circuit_or_path(_a     ,       _a	)
		if check == 3:
				print('''graph is not Eulerian'''	)
				print('''no path'''	)
				return
		lowerCAmelCase__       :      Optional[int]   					= 1
		if check == 2:
				lowerCAmelCase__       :      Any   					= odd_node
				print('''graph has a Euler path'''	)
		if check == 1:
				print('''graph has a Euler cycle'''	)
		lowerCAmelCase__       :      Optional[int]   					= dfs(_a     ,       _a     ,       _a	)
		print(_a	)






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
		lowerCAmelCase__       :      Tuple   					= {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
		lowerCAmelCase__       :      str   					= {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
		lowerCAmelCase__       :      List[str]   					= {1: [2, 3], 2: [1, 3], 3: [1, 2]}
		lowerCAmelCase__       :      List[Any]   					= {
		    1: [],
		    2: []
		    # all degree is zero
		}
		lowerCAmelCase__       :      Optional[Any]   					= 10
		check_euler(_a     ,       _a	)
		check_euler(_a     ,       _a	)
		check_euler(_a     ,       _a	)
		check_euler(_a     ,       _a	)
		check_euler(_a     ,       _a	)


if __name__ == "__main__":
		main()




