

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						(DDPMScheduler,)





			def 		__magic_name__    (		self					,  **__snake_case					)   -> Optional[int]:




						'''simple docstring'''
						__a        ={
						    'num_train_timesteps': 1000,
						    'beta_start': 0.0001,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						    'variance_type': 'fixed_small',
						    'clip_sample': True,
						}

						config.update(**__snake_case					)
						return config





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						for timesteps in [1, 5, 100, 1000]:
									self.check_over_configs(num_train_timesteps=__snake_case					)





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]					,  [0.002, 0.02, 0.2, 2]					):
									self.check_over_configs(beta_start=__snake_case					,  beta_end=__snake_case					)





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=__snake_case					)





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						for variance in ["fixed_small", "fixed_large", "other"]:
									self.check_over_configs(variance_type=__snake_case					)





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=__snake_case					)





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						self.check_over_configs(thresholding=__snake_case					)
						for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(
												    thresholding=__snake_case					,  prediction_type=__snake_case					,  sample_max_value=__snake_case					,  )





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




						'''simple docstring'''
						for prediction_type in ["epsilon", "sample", "v_prediction"]:
									self.check_over_configs(prediction_type=__snake_case					)





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						for t in [0, 500, 999]:
									self.check_over_forward(time_step=__snake_case					)





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        =self.scheduler_classes[0]
						__a        =self.get_scheduler_config()
						__a        =scheduler_class(**__snake_case					)

						assert torch.sum(torch.abs(scheduler._get_variance(0					) - 0.0					)					) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.0_0979					)					) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.02					)					) < 1e-5





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        =self.scheduler_classes[0]
						__a        =self.get_scheduler_config()
						__a        =scheduler_class(**__snake_case					)

						__a        =len(__snake_case					)

						__a        =self.dummy_model()
						__a        =self.dummy_sample_deter
						__a        =torch.manual_seed(0					)

						for t in reversed(range(__snake_case					)					):
									# 1. predict noise residual
									__a        =model(__snake_case					,  __snake_case					)

									# 2. predict previous mean of sample x_t-1
									__a        =scheduler.step(__snake_case					,  __snake_case					,  __snake_case					,  generator=__snake_case					).prev_sample

									# if t > 0:
									#     noise = self.dummy_sample_deter
									#     variance = scheduler.get_variance(t) ** (0.5) * noise
									#
									# sample = pred_prev_sample + variance
									__a        =pred_prev_sample

						__a        =torch.sum(torch.abs(__snake_case					)					)
						__a        =torch.mean(torch.abs(__snake_case					)					)

						assert abs(result_sum.item() - 258.9606					) < 1e-2
						assert abs(result_mean.item() - 0.3372					) < 1e-3





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




						'''simple docstring'''
						__a        =self.scheduler_classes[0]
						__a        =self.get_scheduler_config(prediction_type='v_prediction'					)
						__a        =scheduler_class(**__snake_case					)

						__a        =len(__snake_case					)

						__a        =self.dummy_model()
						__a        =self.dummy_sample_deter
						__a        =torch.manual_seed(0					)

						for t in reversed(range(__snake_case					)					):
									# 1. predict noise residual
									__a        =model(__snake_case					,  __snake_case					)

									# 2. predict previous mean of sample x_t-1
									__a        =scheduler.step(__snake_case					,  __snake_case					,  __snake_case					,  generator=__snake_case					).prev_sample

									# if t > 0:
									#     noise = self.dummy_sample_deter
									#     variance = scheduler.get_variance(t) ** (0.5) * noise
									#
									# sample = pred_prev_sample + variance
									__a        =pred_prev_sample

						__a        =torch.sum(torch.abs(__snake_case					)					)
						__a        =torch.mean(torch.abs(__snake_case					)					)

						assert abs(result_sum.item() - 202.0296					) < 1e-2
						assert abs(result_mean.item() - 0.2631					) < 1e-3





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =self.scheduler_classes[0]
						__a        =self.get_scheduler_config()
						__a        =scheduler_class(**__snake_case					)

						__a        =[100, 87, 50, 1, 0]

						scheduler.set_timesteps(timesteps=__snake_case					)

						__a        =scheduler.timesteps

						for i, timestep in enumerate(__snake_case					):
									if i == len(__snake_case					) - 1:
												__a        =-1
									else:
												__a        =timesteps[i + 1]

									__a        =scheduler.previous_timestep(__snake_case					)
									__a        =prev_t.item()

									self.assertEqual(__snake_case					,  __snake_case					)





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




						'''simple docstring'''
						__a        =self.scheduler_classes[0]
						__a        =self.get_scheduler_config()
						__a        =scheduler_class(**__snake_case					)

						__a        =[100, 87, 50, 51, 0]

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





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__a        =self.scheduler_classes[0]
						__a        =self.get_scheduler_config()
						__a        =scheduler_class(**__snake_case					)

						__a        =[100, 87, 50, 1, 0]
						__a        =len(__snake_case					)

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





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =self.scheduler_classes[0]
						__a        =self.get_scheduler_config()
						__a        =scheduler_class(**__snake_case					)

						__a        =[scheduler.config.num_train_timesteps]

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









# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def        UpperCamelCase_(							_snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =[False] * len(_snake_case	)
			__a        =[-1] * len(_snake_case	)

			def dfs(_snake_case   :  Dict       ,     _snake_case   :  Any	):
						__a        =True
						__a        =c
						for u in graph[v]:
									if not visited[u]:
												dfs(_snake_case       ,     1 - c	)

			for i in range(len(_snake_case	)	):
						if not visited[i]:
									dfs(_snake_case       ,     0	)

			for i in range(len(_snake_case	)	):
						for j in graph[i]:
									if color[i] == color[j]:
												return False

			return True


# Adjacency list of graph
_lowerCAmelCase					:			int  				=			{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}


print(check_bipartite_dfs(graph))






