import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class _lowercase       (							__lowerCamelCase    ):





							"""simple docstring"""




							lowerCAmelCase__							=       (DDPMParallelScheduler,)



							def      _UpperCAmelCase							(     self  , **UpperCAmelCase	):







														'''simple docstring'''






														_lowercase					=						{
														    """num_train_timesteps""": 1000,
														    """beta_start""": 0.0_001,
														    """beta_end""": 0.02,
														    """beta_schedule""": """linear""",
														    """variance_type""": """fixed_small""",
														    """clip_sample""": True,
														}

														config.update(**UpperCAmelCase	)
														return config



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														for timesteps in [1, 5, 100, 1000]:
																					self.check_over_configs(num_train_timesteps=UpperCAmelCase	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]  , [0.002, 0.02, 0.2, 2]	):
																					self.check_over_configs(beta_start=UpperCAmelCase  , beta_end=UpperCAmelCase	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														for schedule in ["linear", "squaredcos_cap_v2"]:
																					self.check_over_configs(beta_schedule=UpperCAmelCase	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														for variance in ["fixed_small", "fixed_large", "other"]:
																					self.check_over_configs(variance_type=UpperCAmelCase	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														for clip_sample in [True, False]:
																					self.check_over_configs(clip_sample=UpperCAmelCase	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






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



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






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



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														for t in [0, 500, 999]:
																					self.check_over_forward(time_step=UpperCAmelCase	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						self.scheduler_classes[0]
														_lowercase					=						self.get_scheduler_config()
														_lowercase					=						scheduler_class(**UpperCAmelCase	)

														assert torch.sum(torch.abs(scheduler._get_variance(0	) - 0.0	)	) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(487	) - 0.00_979	)	) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(999	) - 0.02	)	) < 1e-5



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						self.scheduler_classes[0]
														_lowercase					=						self.get_scheduler_config()
														_lowercase					=						scheduler_class(**UpperCAmelCase	)

														_lowercase					=						len(UpperCAmelCase	)

														_lowercase					=						self.dummy_model()
														_lowercase					=						self.dummy_sample_deter
														_lowercase					=						self.dummy_sample_deter + 0.1
														_lowercase					=						self.dummy_sample_deter - 0.1

														_lowercase					=						samplea.shape[0]
														_lowercase					=						torch.stack([samplea, samplea, samplea]  , dim=0	)
														_lowercase					=						torch.arange(UpperCAmelCase	)[0:3, None].repeat(1  , UpperCAmelCase	)

														_lowercase					=						model(samples.flatten(0  , 1	)  , timesteps.flatten(0  , 1	)	)
														_lowercase					=						scheduler.batch_step_no_noise(UpperCAmelCase  , timesteps.flatten(0  , 1	)  , samples.flatten(0  , 1	)	)

														_lowercase					=						torch.sum(torch.abs(UpperCAmelCase	)	)
														_lowercase					=						torch.mean(torch.abs(UpperCAmelCase	)	)

														assert abs(result_sum.item() - 1153.1833	) < 1e-2
														assert abs(result_mean.item() - 0.5_005	) < 1e-3



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						self.scheduler_classes[0]
														_lowercase					=						self.get_scheduler_config()
														_lowercase					=						scheduler_class(**UpperCAmelCase	)

														_lowercase					=						len(UpperCAmelCase	)

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

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

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

																					_lowercase					=						pred_prev_sample

														_lowercase					=						torch.sum(torch.abs(UpperCAmelCase	)	)
														_lowercase					=						torch.mean(torch.abs(UpperCAmelCase	)	)

														assert abs(result_sum.item() - 258.9606	) < 1e-2
														assert abs(result_mean.item() - 0.3_372	) < 1e-3



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						self.scheduler_classes[0]
														_lowercase					=						self.get_scheduler_config(prediction_type="""v_prediction"""	)
														_lowercase					=						scheduler_class(**UpperCAmelCase	)

														_lowercase					=						len(UpperCAmelCase	)

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

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

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

																					_lowercase					=						pred_prev_sample

														_lowercase					=						torch.sum(torch.abs(UpperCAmelCase	)	)
														_lowercase					=						torch.mean(torch.abs(UpperCAmelCase	)	)

														assert abs(result_sum.item() - 202.0296	) < 1e-2
														assert abs(result_mean.item() - 0.2_631	) < 1e-3



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						self.scheduler_classes[0]
														_lowercase					=						self.get_scheduler_config()
														_lowercase					=						scheduler_class(**UpperCAmelCase	)

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

														scheduler.set_timesteps(timesteps=UpperCAmelCase	)

														_lowercase					=						scheduler.timesteps

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

																					_lowercase					=						scheduler.previous_timestep(UpperCAmelCase	)
																					_lowercase					=						prev_t.item()

																					self.assertEqual(UpperCAmelCase  , UpperCAmelCase	)



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						self.scheduler_classes[0]
														_lowercase					=						self.get_scheduler_config()
														_lowercase					=						scheduler_class(**UpperCAmelCase	)

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

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



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						self.scheduler_classes[0]
														_lowercase					=						self.get_scheduler_config()
														_lowercase					=						scheduler_class(**UpperCAmelCase	)

														_lowercase					=						[100, 87, 50, 1, 0]
														_lowercase					=						len(UpperCAmelCase	)

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



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						self.scheduler_classes[0]
														_lowercase					=						self.get_scheduler_config()
														_lowercase					=						scheduler_class(**UpperCAmelCase	)

														_lowercase					=						[scheduler.config.num_train_timesteps]

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




def    a      ( a	)     ->List[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= len(a	)
			while cur > 1:
						# Find the maximum number in arr
						SCREAMING_SNAKE_CASE     						= arr.index(max(arr[0:cur]	)	)
						# Reverse from 0 to mi
						SCREAMING_SNAKE_CASE     						= arr[mi::-1] + arr[mi + 1 : len(a	)]
						# Reverse whole list
						SCREAMING_SNAKE_CASE     						= arr[cur - 1 :: -1] + arr[cur : len(a	)]
						cur -= 1
			return arr


if __name__ == "__main__":
							__lowerCAmelCase					   =       input('Enter numbers separated by a comma:\n').strip()
							__lowerCAmelCase					   =       [int(item) for item in user_input.split(',')]
							print(pancake_sort(unsorted))