




"""simple docstring"""







import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     Optional[int]       =      (EulerDiscreteScheduler,)
				__UpperCAmelCase :     Tuple       =      10



				def       snake_case    (     self					:	Any    ,   **lowercase__					:	Dict						):
						__lowercase				:		List[str]												=				{
						    "num_train_timesteps": 1_1_0_0,
						    "beta_start": 0.0_0_0_1,
						    "beta_end": 0.0_2,
						    "beta_schedule": "linear",
						}

						config.update(**lowercase__						)
						return config



				def       snake_case    (     self					:	Optional[Any]						):
						for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
								self.check_over_configs(num_train_timesteps=lowercase__						)



				def       snake_case    (     self					:	Union[str, Any]						):
						for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]    ,   [0.0_0_0_2, 0.0_0_2, 0.0_2]						):
								self.check_over_configs(beta_start=lowercase__    ,   beta_end=lowercase__						)



				def       snake_case    (     self					:	Tuple						):
						for schedule in ["linear", "scaled_linear"]:
								self.check_over_configs(beta_schedule=lowercase__						)



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



				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		int												=				self.scheduler_classes[0]
						__lowercase				:		Any												=				self.get_scheduler_config()
						__lowercase				:		Tuple												=				scheduler_class(**lowercase__						)

						scheduler.set_timesteps(self.num_inference_steps						)

						__lowercase				:		int												=				torch.manual_seed(0						)

						__lowercase				:		str												=				self.dummy_model()
						__lowercase				:		str												=				self.dummy_sample_deter * scheduler.init_noise_sigma
						__lowercase				:		int												=				sample.to(lowercase__						)

						for i, t in enumerate(scheduler.timesteps						):
								__lowercase				:		List[Any]												=				scheduler.scale_model_input(lowercase__    ,   lowercase__						)

								__lowercase				:		Tuple												=				model(lowercase__    ,   lowercase__						)

								__lowercase				:		Tuple												=				scheduler.step(lowercase__    ,   lowercase__    ,   lowercase__    ,   generator=lowercase__						)
								__lowercase				:		List[str]												=				output.prev_sample

						__lowercase				:		Optional[int]												=				torch.sum(torch.abs(lowercase__						)						)
						__lowercase				:		int												=				torch.mean(torch.abs(lowercase__						)						)

						assert abs(result_sum.item() - 1_0.0_8_0_7						) < 1e-2
						assert abs(result_mean.item() - 0.0_1_3_1						) < 1e-3



				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		Union[str, Any]												=				self.scheduler_classes[0]
						__lowercase				:		int												=				self.get_scheduler_config(prediction_type="v_prediction"						)
						__lowercase				:		Tuple												=				scheduler_class(**lowercase__						)

						scheduler.set_timesteps(self.num_inference_steps						)

						__lowercase				:		List[str]												=				torch.manual_seed(0						)

						__lowercase				:		Any												=				self.dummy_model()
						__lowercase				:		Dict												=				self.dummy_sample_deter * scheduler.init_noise_sigma
						__lowercase				:		Dict												=				sample.to(lowercase__						)

						for i, t in enumerate(scheduler.timesteps						):
								__lowercase				:		Dict												=				scheduler.scale_model_input(lowercase__    ,   lowercase__						)

								__lowercase				:		List[str]												=				model(lowercase__    ,   lowercase__						)

								__lowercase				:		Any												=				scheduler.step(lowercase__    ,   lowercase__    ,   lowercase__    ,   generator=lowercase__						)
								__lowercase				:		List[str]												=				output.prev_sample

						__lowercase				:		Tuple												=				torch.sum(torch.abs(lowercase__						)						)
						__lowercase				:		Union[str, Any]												=				torch.mean(torch.abs(lowercase__						)						)

						assert abs(result_sum.item() - 0.0_0_0_2						) < 1e-2
						assert abs(result_mean.item() - 2.2676e-06						) < 1e-3



				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		List[Any]												=				self.scheduler_classes[0]
						__lowercase				:		Any												=				self.get_scheduler_config()
						__lowercase				:		List[Any]												=				scheduler_class(**lowercase__						)

						scheduler.set_timesteps(self.num_inference_steps    ,   device=lowercase__						)

						__lowercase				:		Dict												=				torch.manual_seed(0						)

						__lowercase				:		Union[str, Any]												=				self.dummy_model()
						__lowercase				:		List[str]												=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						__lowercase				:		Tuple												=				sample.to(lowercase__						)

						for t in scheduler.timesteps:
								__lowercase				:		Tuple												=				scheduler.scale_model_input(lowercase__    ,   lowercase__						)

								__lowercase				:		List[str]												=				model(lowercase__    ,   lowercase__						)

								__lowercase				:		Union[str, Any]												=				scheduler.step(lowercase__    ,   lowercase__    ,   lowercase__    ,   generator=lowercase__						)
								__lowercase				:		Optional[Any]												=				output.prev_sample

						__lowercase				:		int												=				torch.sum(torch.abs(lowercase__						)						)
						__lowercase				:		Any												=				torch.mean(torch.abs(lowercase__						)						)

						assert abs(result_sum.item() - 1_0.0_8_0_7						) < 1e-2
						assert abs(result_mean.item() - 0.0_1_3_1						) < 1e-3



				def       snake_case    (     self					:	Any						):
						__lowercase				:		Tuple												=				self.scheduler_classes[0]
						__lowercase				:		str												=				self.get_scheduler_config()
						__lowercase				:		Union[str, Any]												=				scheduler_class(**lowercase__    ,   use_karras_sigmas=lowercase__						)

						scheduler.set_timesteps(self.num_inference_steps    ,   device=lowercase__						)

						__lowercase				:		Union[str, Any]												=				torch.manual_seed(0						)

						__lowercase				:		List[Any]												=				self.dummy_model()
						__lowercase				:		Tuple												=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						__lowercase				:		str												=				sample.to(lowercase__						)

						for t in scheduler.timesteps:
								__lowercase				:		int												=				scheduler.scale_model_input(lowercase__    ,   lowercase__						)

								__lowercase				:		Union[str, Any]												=				model(lowercase__    ,   lowercase__						)

								__lowercase				:		Any												=				scheduler.step(lowercase__    ,   lowercase__    ,   lowercase__    ,   generator=lowercase__						)
								__lowercase				:		Optional[Any]												=				output.prev_sample

						__lowercase				:		int												=				torch.sum(torch.abs(lowercase__						)						)
						__lowercase				:		Union[str, Any]												=				torch.mean(torch.abs(lowercase__						)						)

						assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9						) < 1e-2
						assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3						) < 1e-3






"""simple docstring"""







from __future__ import annotations

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase = None				)			->list[list[str]]:






		"""simple docstring"""


		__lowercase				:		List[Any]												=				word_bank or []
		# create a table
		__lowercase				:		int												=				len(_lowerCamelCase				) + 1

		__lowercase				:		list[list[list[str]]]												=				[]
		for _ in range(_lowerCamelCase				):
				table.append([]				)
		# seed value
		__lowercase				:		Any												=				[[]]  # because empty string has empty combination

		# iterate through the indices
		for i in range(_lowerCamelCase				):
				# condition
				if table[i] != []:
						for word in word_bank:
								# slice condition
								if target[i : i + len(_lowerCamelCase				)] == 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(_lowerCamelCase				)] += new_combinations

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

		return table[len(_lowerCamelCase				)]


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'],
						    )
						)
