
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  (DDPMScheduler,)







	def      _lowerCamelCase       (      self	:Optional[Any]  ,     **a	:List[Any]		)	->				Optional[int]:
								__UpperCamelCase			:     Tuple         =      {
								    "num_train_timesteps": 1_0_0_0,
								    "beta_start": 0.0001,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								    "variance_type": "fixed_small",
								    "clip_sample": True,
								}

								config.update(**a		)
								return config







	def      _lowerCamelCase       (      self	:Any		)	->				List[str]:
								for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=a		)







	def      _lowerCamelCase       (      self	:Optional[int]		)	->				Optional[int]:
								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=a  ,     beta_end=a		)







	def      _lowerCamelCase       (      self	:Tuple		)	->				Tuple:
								for schedule in ["linear", "squaredcos_cap_v2"]:
															self.check_over_configs(beta_schedule=a		)







	def      _lowerCamelCase       (      self	:List[Any]		)	->				List[str]:
								for variance in ["fixed_small", "fixed_large", "other"]:
															self.check_over_configs(variance_type=a		)







	def      _lowerCamelCase       (      self	:int		)	->				str:
								for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=a		)







	def      _lowerCamelCase       (      self	:Optional[int]		)	->				Tuple:
								self.check_over_configs(thresholding=a		)
								for threshold in [0.5, 1.0, 2.0]:
															for prediction_type in ["epsilon", "sample", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=a  ,     prediction_type=a  ,     sample_max_value=a  ,     )







	def      _lowerCamelCase       (      self	:Any		)	->				int:
								for prediction_type in ["epsilon", "sample", "v_prediction"]:
															self.check_over_configs(prediction_type=a		)







	def      _lowerCamelCase       (      self	:List[Any]		)	->				Union[str, Any]:
								for t in [0, 5_0_0, 9_9_9]:
															self.check_over_forward(time_step=a		)







	def      _lowerCamelCase       (      self	:Tuple		)	->				Dict:
								__UpperCamelCase			:     Union[str, Any]         =      self.scheduler_classes[0]
								__UpperCamelCase			:     Any         =      self.get_scheduler_config()
								__UpperCamelCase			:     str         =      scheduler_class(**a		)

								assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_8_7		) - 0.00979		)		) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_9_9		) - 0.02		)		) < 1E-5







	def      _lowerCamelCase       (      self	:List[str]		)	->				List[Any]:
								__UpperCamelCase			:     Union[str, Any]         =      self.scheduler_classes[0]
								__UpperCamelCase			:     Union[str, Any]         =      self.get_scheduler_config()
								__UpperCamelCase			:     str         =      scheduler_class(**a		)

								__UpperCamelCase			:     Optional[int]         =      len(a		)

								__UpperCamelCase			:     Optional[Any]         =      self.dummy_model()
								__UpperCamelCase			:     List[str]         =      self.dummy_sample_deter
								__UpperCamelCase			:     Any         =      torch.manual_seed(0		)

								for t in reversed(range(a		)		):
															# 1. predict noise residual
															__UpperCamelCase			:     Tuple         =      model(a  ,     a		)

															# 2. predict previous mean of sample x_t-1
															__UpperCamelCase			:     int         =      scheduler.step(a  ,     a  ,     a  ,     generator=a		).prev_sample

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

								__UpperCamelCase			:     Optional[Any]         =      torch.sum(torch.abs(a		)		)
								__UpperCamelCase			:     int         =      torch.mean(torch.abs(a		)		)

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







	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				Any:
								__UpperCamelCase			:     Tuple         =      self.scheduler_classes[0]
								__UpperCamelCase			:     str         =      self.get_scheduler_config(prediction_type="v_prediction"		)
								__UpperCamelCase			:     Optional[int]         =      scheduler_class(**a		)

								__UpperCamelCase			:     List[str]         =      len(a		)

								__UpperCamelCase			:     Dict         =      self.dummy_model()
								__UpperCamelCase			:     Union[str, Any]         =      self.dummy_sample_deter
								__UpperCamelCase			:     List[Any]         =      torch.manual_seed(0		)

								for t in reversed(range(a		)		):
															# 1. predict noise residual
															__UpperCamelCase			:     Tuple         =      model(a  ,     a		)

															# 2. predict previous mean of sample x_t-1
															__UpperCamelCase			:     List[Any]         =      scheduler.step(a  ,     a  ,     a  ,     generator=a		).prev_sample

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

								__UpperCamelCase			:     List[str]         =      torch.sum(torch.abs(a		)		)
								__UpperCamelCase			:     Optional[int]         =      torch.mean(torch.abs(a		)		)

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







	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				Any:
								__UpperCamelCase			:     List[Any]         =      self.scheduler_classes[0]
								__UpperCamelCase			:     int         =      self.get_scheduler_config()
								__UpperCamelCase			:     Union[str, Any]         =      scheduler_class(**a		)

								__UpperCamelCase			:     Any         =      [1_0_0, 8_7, 5_0, 1, 0]

								scheduler.set_timesteps(timesteps=a		)

								__UpperCamelCase			:     Dict         =      scheduler.timesteps

								for i, timestep in enumerate(a		):
															if i == len(a		) - 1:
																						__UpperCamelCase			:     Tuple         =      -1
															else:
																						__UpperCamelCase			:     Any         =      timesteps[i + 1]

															__UpperCamelCase			:     Optional[Any]         =      scheduler.previous_timestep(a		)
															__UpperCamelCase			:     Union[str, Any]         =      prev_t.item()

															self.assertEqual(a  ,     a		)







	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				Any:
								__UpperCamelCase			:     Union[str, Any]         =      self.scheduler_classes[0]
								__UpperCamelCase			:     Any         =      self.get_scheduler_config()
								__UpperCamelCase			:     Optional[Any]         =      scheduler_class(**a		)

								__UpperCamelCase			:     Dict         =      [1_0_0, 8_7, 5_0, 5_1, 0]

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







	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				int:
								__UpperCamelCase			:     Tuple         =      self.scheduler_classes[0]
								__UpperCamelCase			:     int         =      self.get_scheduler_config()
								__UpperCamelCase			:     List[Any]         =      scheduler_class(**a		)

								__UpperCamelCase			:     Union[str, Any]         =      [1_0_0, 8_7, 5_0, 1, 0]
								__UpperCamelCase			:     Optional[Any]         =      len(a		)

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







	def      _lowerCamelCase       (      self	:List[Any]		)	->				Optional[Any]:
								__UpperCamelCase			:     str         =      self.scheduler_classes[0]
								__UpperCamelCase			:     Optional[Any]         =      self.get_scheduler_config()
								__UpperCamelCase			:     List[str]         =      scheduler_class(**a		)

								__UpperCamelCase			:     Optional[Any]         =      [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		)

def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int = 200)      ->					int:






							'''simple docstring'''





							__UpperCamelCase			:     Any         =      [1, 2, 5, 10, 20, 50, 100, 200]
							__UpperCamelCase			:     Any         =      [0] * (pence + 1)
							__UpperCamelCase			:     Union[str, Any]         =      1  # base case: 1 way to make 0 pence

							for coin in coins:
														for i in range(_lowerCamelCase		,						pence + 1		,						1):
																					number_of_ways[i] += number_of_ways[i - coin]
							return number_of_ways[pence]


if __name__ == "__main__":
		assert solution(200) == 73682