




"""simple docstring"""

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 			_snake_case					(				__snake_case							):

				'''simple docstring'''




				UpperCamelCase__		  =(DDIMParallelScheduler,)
				UpperCamelCase__		  =(("""eta""", 0.0), ("""num_inference_steps""", 50))





				def 					snake_case_			(				self   :	Union[str, Any]       ,				**snake_case   :	Optional[int]  ):
								UpperCAmelCase_       :Union[str, Any]         =   {
								    "num_train_timesteps": 1_000,
								    "beta_start": 0.0_001,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								    "clip_sample": True,
								}

								config.update(**snake_case  )
								return config





				def 					snake_case_			(				self   :	List[str]       ,				**snake_case   :	str  ):
								UpperCAmelCase_       :Union[str, Any]         =   self.scheduler_classes[0]
								UpperCAmelCase_       :List[Any]         =   self.get_scheduler_config(**snake_case  )
								UpperCAmelCase_       :int         =   scheduler_class(**snake_case  )

								UpperCAmelCase_       :str         =   10, 0.0

								UpperCAmelCase_       :List[Any]         =   self.dummy_model()
								UpperCAmelCase_       :Optional[int]         =   self.dummy_sample_deter

								scheduler.set_timesteps(snake_case  )

								for t in scheduler.timesteps:
												UpperCAmelCase_       :List[str]         =   model(snake_case       ,				snake_case  )
												UpperCAmelCase_       :Union[str, Any]         =   scheduler.step(snake_case       ,				snake_case       ,				snake_case       ,				snake_case  ).prev_sample

								return sample





				def 					snake_case_			(				self   :	Any  ):
								for timesteps in [100, 500, 1_000]:
												self.check_over_configs(num_train_timesteps=snake_case  )





				def 					snake_case_			(				self   :	Union[str, Any]  ):
								for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=snake_case  )

								UpperCAmelCase_       :int         =   self.scheduler_classes[0]
								UpperCAmelCase_       :Optional[Any]         =   self.get_scheduler_config(steps_offset=1  )
								UpperCAmelCase_       :int         =   scheduler_class(**snake_case  )
								scheduler.set_timesteps(5  )
								assert torch.equal(scheduler.timesteps       ,				torch.LongTensor([801, 601, 401, 201, 1]  )  )





				def 					snake_case_			(				self   :	Dict  ):
								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=snake_case       ,				beta_end=snake_case  )





				def 					snake_case_			(				self   :	str  ):
								for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=snake_case  )





				def 					snake_case_			(				self   :	Any  ):
								for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=snake_case  )





				def 					snake_case_			(				self   :	List[str]  ):
								for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=snake_case  )





				def 					snake_case_			(				self   :	str  ):
								for timestep_spacing in ["trailing", "leading"]:
												self.check_over_configs(timestep_spacing=snake_case  )





				def 					snake_case_			(				self   :	Union[str, Any]  ):
								for rescale_betas_zero_snr in [True, False]:
												self.check_over_configs(rescale_betas_zero_snr=snake_case  )





				def 					snake_case_			(				self   :	Optional[Any]  ):
								self.check_over_configs(thresholding=snake_case  )
								for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(
																    thresholding=snake_case       ,				prediction_type=snake_case       ,				sample_max_value=snake_case       ,				)





				def 					snake_case_			(				self   :	List[str]  ):
								for t in [1, 10, 49]:
												self.check_over_forward(time_step=snake_case  )





				def 					snake_case_			(				self   :	Optional[int]  ):
								for t, num_inference_steps in zip([1, 10, 50]       ,				[10, 50, 500]  ):
												self.check_over_forward(time_step=snake_case       ,				num_inference_steps=snake_case  )





				def 					snake_case_			(				self   :	List[Any]  ):
								for t, eta in zip([1, 10, 49]       ,				[0.0, 0.5, 1.0]  ):
												self.check_over_forward(time_step=snake_case       ,				eta=snake_case  )





				def 					snake_case_			(				self   :	Any  ):
								UpperCAmelCase_       :List[str]         =   self.scheduler_classes[0]
								UpperCAmelCase_       :List[str]         =   self.get_scheduler_config()
								UpperCAmelCase_       :int         =   scheduler_class(**snake_case  )

								assert torch.sum(torch.abs(scheduler._get_variance(0       ,				0  ) - 0.0  )  ) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(420       ,				400  ) - 0.14_771  )  ) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(980       ,				960  ) - 0.32_460  )  ) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(0       ,				0  ) - 0.0  )  ) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(487       ,				486  ) - 0.00_979  )  ) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(999       ,				998  ) - 0.02  )  ) < 1e-5





				def 					snake_case_			(				self   :	Tuple  ):
								UpperCAmelCase_       :Dict         =   self.scheduler_classes[0]
								UpperCAmelCase_       :Tuple         =   self.get_scheduler_config()
								UpperCAmelCase_       :List[str]         =   scheduler_class(**snake_case  )

								UpperCAmelCase_       :Dict         =   10, 0.0
								scheduler.set_timesteps(snake_case  )

								UpperCAmelCase_       :str         =   self.dummy_model()
								UpperCAmelCase_       :Union[str, Any]         =   self.dummy_sample_deter
								UpperCAmelCase_       :Any         =   self.dummy_sample_deter + 0.1
								UpperCAmelCase_       :Tuple         =   self.dummy_sample_deter - 0.1

								UpperCAmelCase_       :Optional[int]         =   samplea.shape[0]
								UpperCAmelCase_       :List[str]         =   torch.stack([samplea, samplea, samplea]       ,				dim=0  )
								UpperCAmelCase_       :List[str]         =   torch.arange(snake_case  )[0:3, None].repeat(1       ,				snake_case  )

								UpperCAmelCase_       :Tuple         =   model(samples.flatten(0       ,				1  )       ,				timesteps.flatten(0       ,				1  )  )
								UpperCAmelCase_       :str         =   scheduler.batch_step_no_noise(snake_case       ,				timesteps.flatten(0       ,				1  )       ,				samples.flatten(0       ,				1  )       ,				snake_case  )

								UpperCAmelCase_       :List[str]         =   torch.sum(torch.abs(snake_case  )  )
								UpperCAmelCase_       :Dict         =   torch.mean(torch.abs(snake_case  )  )

								assert abs(result_sum.item() - 1_147.7_904  ) < 1e-2
								assert abs(result_mean.item() - 0.4_982  ) < 1e-3





				def 					snake_case_			(				self   :	List[Any]  ):
								UpperCAmelCase_       :Optional[int]         =   self.full_loop()

								UpperCAmelCase_       :Optional[int]         =   torch.sum(torch.abs(snake_case  )  )
								UpperCAmelCase_       :Dict         =   torch.mean(torch.abs(snake_case  )  )

								assert abs(result_sum.item() - 172.0_067  ) < 1e-2
								assert abs(result_mean.item() - 0.223_967  ) < 1e-3





				def 					snake_case_			(				self   :	Any  ):
								UpperCAmelCase_       :Optional[Any]         =   self.full_loop(prediction_type='''v_prediction'''  )

								UpperCAmelCase_       :List[Any]         =   torch.sum(torch.abs(snake_case  )  )
								UpperCAmelCase_       :Union[str, Any]         =   torch.mean(torch.abs(snake_case  )  )

								assert abs(result_sum.item() - 52.5_302  ) < 1e-2
								assert abs(result_mean.item() - 0.0_684  ) < 1e-3





				def 					snake_case_			(				self   :	Union[str, Any]  ):
								# We specify different beta, so that the first alpha is 0.99
								UpperCAmelCase_       :Union[str, Any]         =   self.full_loop(set_alpha_to_one=snake_case       ,				beta_start=0.01  )
								UpperCAmelCase_       :int         =   torch.sum(torch.abs(snake_case  )  )
								UpperCAmelCase_       :Optional[Any]         =   torch.mean(torch.abs(snake_case  )  )

								assert abs(result_sum.item() - 149.8_295  ) < 1e-2
								assert abs(result_mean.item() - 0.1_951  ) < 1e-3





				def 					snake_case_			(				self   :	Optional[int]  ):
								# We specify different beta, so that the first alpha is 0.99
								UpperCAmelCase_       :List[Any]         =   self.full_loop(set_alpha_to_one=snake_case       ,				beta_start=0.01  )
								UpperCAmelCase_       :Any         =   torch.sum(torch.abs(snake_case  )  )
								UpperCAmelCase_       :Union[str, Any]         =   torch.mean(torch.abs(snake_case  )  )

								assert abs(result_sum.item() - 149.0_784  ) < 1e-2
								assert abs(result_mean.item() - 0.1_941  ) < 1e-3




from __future__ import annotations



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   list[str] | None = None  )   ->			list[list[str]]:






						'''simple docstring'''

						_lowercase       :	Dict           =   word_bank or []
						# create a table
						_lowercase       :	int           =   len(_UpperCAmelCase  ) + 1

						_lowercase       :	list[list[list[str]]]           =   []
						for _ in range(_UpperCAmelCase  ):
												table.append([]  )
						# seed value
						_lowercase       :	Optional[int]           =   [[]]  # because empty string has empty combination

						# iterate through the indices
						for i in range(_UpperCAmelCase  ):
												# condition
												if table[i] != []:
																		for word in word_bank:
																								# slice condition
																								if target[i : i + len(_UpperCAmelCase  )] == word:
																														_lowercase       :	list[list[str]]           =   [
																														    [word, *way] for way in table[i]
																														]
																														# adds the word to every combination the current position holds
																														# now,push that combination to the table[i+len(word)]
																														table[i + len(_UpperCAmelCase  )] += new_combinations

    # combinations are in reverse order so reverse for better output
						for combination in table[len(_UpperCAmelCase  )]:
												combination.reverse()

						return table[len(_UpperCAmelCase  )]


if __name__ == "__main__":
							print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
							print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
							print(
							    all_construct(
							        """hexagonosaurus""",
							        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
							    )
							)




