



'''simple docstring'''




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class 			UpperCamelCase_ (							__magic_name__			):
	lowercase          = (UnCLIPScheduler,)





	def  _lowercase(  self		,		**A    )			->			Optional[int]:
								UpperCAmelCase     :		int							     =		{
								    """num_train_timesteps""": 1000,
								    """variance_type""": """fixed_small_log""",
								    """clip_sample""": True,
								    """clip_sample_range""": 1.0,
								    """prediction_type""": """epsilon""",
								}

								config.update(**A    )
								return config





	def  _lowercase(  self    )			->			List[Any]:
								for timesteps in [1, 5, 100, 1000]:
															self.check_over_configs(num_train_timesteps=A    )





	def  _lowercase(  self    )			->			List[str]:
								for variance in ["fixed_small_log", "learned_range"]:
															self.check_over_configs(variance_type=A    )





	def  _lowercase(  self    )			->			Optional[int]:
								for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=A    )





	def  _lowercase(  self    )			->			str:
								for clip_sample_range in [1, 5, 10, 20]:
															self.check_over_configs(clip_sample_range=A    )





	def  _lowercase(  self    )			->			Tuple:
								for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(prediction_type=A    )





	def  _lowercase(  self    )			->			Tuple:
								for time_step in [0, 500, 999]:
															for prev_timestep in [None, 5, 100, 250, 500, 750]:
																						if prev_timestep is not None and prev_timestep >= time_step:
																													continue

																						self.check_over_forward(time_step=A		,		prev_timestep=A    )





	def  _lowercase(  self    )			->			Tuple:
								UpperCAmelCase     :		int							     =		self.scheduler_classes[0]
								UpperCAmelCase     :		Union[str, Any]							     =		self.get_scheduler_config(variance_type="""fixed_small_log"""    )
								UpperCAmelCase     :		Tuple							     =		scheduler_class(**A    )

								assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 1.0_000e-10    )    ) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(487    ) - 0.0_5_4_9_6_2_5    )    ) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(999    ) - 0.9_9_9_4_9_8_7    )    ) < 1e-5





	def  _lowercase(  self    )			->			Dict:
								UpperCAmelCase     :		Union[str, Any]							     =		self.scheduler_classes[0]
								UpperCAmelCase     :		Union[str, Any]							     =		self.get_scheduler_config(variance_type="""learned_range"""    )
								UpperCAmelCase     :		Union[str, Any]							     =		scheduler_class(**A    )

								UpperCAmelCase     :		Optional[Any]							     =		0.5

								assert scheduler._get_variance(1		,		predicted_variance=A    ) - -1_0.1_7_1_2_7_9_0 < 1e-5
								assert scheduler._get_variance(487		,		predicted_variance=A    ) - -5.7_9_9_8_0_5_2 < 1e-5
								assert scheduler._get_variance(999		,		predicted_variance=A    ) - -0.0_0_1_0_0_1_1 < 1e-5





	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Tuple							     =		self.scheduler_classes[0]
								UpperCAmelCase     :		Any							     =		self.get_scheduler_config()
								UpperCAmelCase     :		List[str]							     =		scheduler_class(**A    )

								UpperCAmelCase     :		Union[str, Any]							     =		scheduler.timesteps

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

								for i, t in enumerate(A    ):
															# 1. predict noise residual
															UpperCAmelCase     :		Optional[Any]							     =		model(A		,		A    )

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

															UpperCAmelCase     :		Any							     =		pred_prev_sample

								UpperCAmelCase     :		List[str]							     =		torch.sum(torch.abs(A    )    )
								UpperCAmelCase     :		int							     =		torch.mean(torch.abs(A    )    )

								assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5    ) < 1e-2
								assert abs(result_mean.item() - 0.3_2_8_4_7_4_3    ) < 1e-3





	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     :		str							     =		self.scheduler_classes[0]
								UpperCAmelCase     :		Optional[int]							     =		self.get_scheduler_config()
								UpperCAmelCase     :		int							     =		scheduler_class(**A    )

								scheduler.set_timesteps(25    )

								UpperCAmelCase     :		Any							     =		scheduler.timesteps

								UpperCAmelCase     :		Union[str, Any]							     =		self.dummy_model()
								UpperCAmelCase     :		Optional[int]							     =		self.dummy_sample_deter
								UpperCAmelCase     :		Tuple							     =		torch.manual_seed(0    )

								for i, t in enumerate(A    ):
															# 1. predict noise residual
															UpperCAmelCase     :		Tuple							     =		model(A		,		A    )

															if i + 1 == timesteps.shape[0]:
																						UpperCAmelCase     :		Dict							     =		None
															else:
																						UpperCAmelCase     :		List[str]							     =		timesteps[i + 1]

															# 2. predict previous mean of sample x_t-1
															UpperCAmelCase     :		Optional[int]							     =		scheduler.step(
															    A		,		A		,		A		,		prev_timestep=A		,		generator=A    ).prev_sample

															UpperCAmelCase     :		Union[str, Any]							     =		pred_prev_sample

								UpperCAmelCase     :		Union[str, Any]							     =		torch.sum(torch.abs(A    )    )
								UpperCAmelCase     :		Any							     =		torch.mean(torch.abs(A    )    )

								assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3    ) < 1e-2
								assert abs(result_mean.item() - 0.3_3_6_2_0_3_8    ) < 1e-3





	def  _lowercase(  self    )			->			List[str]:
								pass






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






'''simple docstring'''




from itertools import count



def       __lowerCamelCase  (					_lowercase = 5_0		)      ->			int:
							UpperCAmelCase     :		Any							     =		[1] * min_block_length

							for n in count(_lowercase		):
														fill_count_functions.append(1		)

														for block_length in range(_lowercase	,				n + 1		):
																					for block_start in range(n - block_length		):
																												fill_count_functions[n] += fill_count_functions[
																												    n - block_start - block_length - 1
																												]

																					fill_count_functions[n] += 1

														if fill_count_functions[n] > 1_0_0_0_0_0_0:
																					break

							return n


if __name__ == "__main__":
							print(F'''{solution() = }''')

