





"""simple docstring"""




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 				_a		(   _lowerCAmelCase       ):
				UpperCamelCase			=					(EulerAncestralDiscreteScheduler,)
				UpperCamelCase			=					10






				def        snake_case			(				self	:					int,				**lowerCAmelCase__	:					Optional[int]							)			->   List[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      int						     =					{
										    '''num_train_timesteps''': 1_1_0_0,
										    '''beta_start''': 0.0_001,
										    '''beta_end''': 0.02,
										    '''beta_schedule''': '''linear''',
										}

										config.update(**lowerCAmelCase__							)
										return config






				def        snake_case			(				self	:					Optional[Any]							)			->   Tuple:




										'''simple docstring'''



										for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																self.check_over_configs(num_train_timesteps=lowerCAmelCase__							)






				def        snake_case			(				self	:					List[str]							)			->   Tuple:




										'''simple docstring'''



										for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001],				[0.0_002, 0.002, 0.02]							):
																self.check_over_configs(beta_start=lowerCAmelCase__,				beta_end=lowerCAmelCase__							)






				def        snake_case			(				self	:					Optional[Any]							)			->   Tuple:




										'''simple docstring'''



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






				def        snake_case			(				self	:					Dict							)			->   Tuple:




										'''simple docstring'''



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






				def        snake_case			(				self	:					Union[str, Any]							)			->   List[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      str						     =					self.scheduler_classes[0]
										_UpperCamelCase    :      List[Any]						     =					self.get_scheduler_config()
										_UpperCamelCase    :      List[Any]						     =					scheduler_class(**lowerCAmelCase__							)

										scheduler.set_timesteps(self.num_inference_steps							)

										_UpperCamelCase    :      Dict						     =					torch.manual_seed(0							)

										_UpperCamelCase    :      int						     =					self.dummy_model()
										_UpperCamelCase    :      Tuple						     =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										_UpperCamelCase    :      Union[str, Any]						     =					sample.to(lowerCAmelCase__							)

										for i, t in enumerate(scheduler.timesteps							):
																_UpperCamelCase    :      Optional[int]						     =					scheduler.scale_model_input(lowerCAmelCase__,				lowerCAmelCase__							)

																_UpperCamelCase    :      Optional[int]						     =					model(lowerCAmelCase__,				lowerCAmelCase__							)

																_UpperCamelCase    :      List[str]						     =					scheduler.step(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				generator=lowerCAmelCase__							)
																_UpperCamelCase    :      Dict						     =					output.prev_sample

										_UpperCamelCase    :      Optional[Any]						     =					torch.sum(torch.abs(lowerCAmelCase__							)							)
										_UpperCamelCase    :      Dict						     =					torch.mean(torch.abs(lowerCAmelCase__							)							)

										assert abs(result_sum.item() - 152.3_192							) < 1e-2
										assert abs(result_mean.item() - 0.1_983							) < 1e-3






				def        snake_case			(				self	:					Tuple							)			->   int:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					self.scheduler_classes[0]
										_UpperCamelCase    :      Dict						     =					self.get_scheduler_config(prediction_type='''v_prediction'''							)
										_UpperCamelCase    :      Optional[int]						     =					scheduler_class(**lowerCAmelCase__							)

										scheduler.set_timesteps(self.num_inference_steps							)

										_UpperCamelCase    :      Any						     =					torch.manual_seed(0							)

										_UpperCamelCase    :      Union[str, Any]						     =					self.dummy_model()
										_UpperCamelCase    :      Tuple						     =					self.dummy_sample_deter * scheduler.init_noise_sigma
										_UpperCamelCase    :      Optional[Any]						     =					sample.to(lowerCAmelCase__							)

										for i, t in enumerate(scheduler.timesteps							):
																_UpperCamelCase    :      List[str]						     =					scheduler.scale_model_input(lowerCAmelCase__,				lowerCAmelCase__							)

																_UpperCamelCase    :      List[Any]						     =					model(lowerCAmelCase__,				lowerCAmelCase__							)

																_UpperCamelCase    :      Union[str, Any]						     =					scheduler.step(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				generator=lowerCAmelCase__							)
																_UpperCamelCase    :      int						     =					output.prev_sample

										_UpperCamelCase    :      Optional[Any]						     =					torch.sum(torch.abs(lowerCAmelCase__							)							)
										_UpperCamelCase    :      str						     =					torch.mean(torch.abs(lowerCAmelCase__							)							)

										assert abs(result_sum.item() - 108.4_439							) < 1e-2
										assert abs(result_mean.item() - 0.1_412							) < 1e-3






				def        snake_case			(				self	:					Optional[Any]							)			->   Optional[int]:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					self.scheduler_classes[0]
										_UpperCamelCase    :      str						     =					self.get_scheduler_config()
										_UpperCamelCase    :      List[Any]						     =					scheduler_class(**lowerCAmelCase__							)

										scheduler.set_timesteps(self.num_inference_steps,				device=lowerCAmelCase__							)
										_UpperCamelCase    :      Optional[int]						     =					torch.manual_seed(0							)

										_UpperCamelCase    :      Union[str, Any]						     =					self.dummy_model()
										_UpperCamelCase    :      int						     =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										_UpperCamelCase    :      int						     =					sample.to(lowerCAmelCase__							)

										for t in scheduler.timesteps:
																_UpperCamelCase    :      Any						     =					scheduler.scale_model_input(lowerCAmelCase__,				lowerCAmelCase__							)

																_UpperCamelCase    :      List[str]						     =					model(lowerCAmelCase__,				lowerCAmelCase__							)

																_UpperCamelCase    :      Dict						     =					scheduler.step(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				generator=lowerCAmelCase__							)
																_UpperCamelCase    :      Union[str, Any]						     =					output.prev_sample

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

										assert abs(result_sum.item() - 152.3_192							) < 1e-2
										assert abs(result_mean.item() - 0.1_983							) < 1e-3










"""simple docstring"""




def  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     ):
						return len_board * len_board_column * row + column





def  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		):

						if board[row][column] != word[word_index]:
												return False

						if word_index == len(_lowercase     ) - 1:
												return True

						_UpperCamelCase    :      Union[str, Any]						     =					[(0, 1), (0, -1), (-1, 0), (1, 0)]
						_UpperCamelCase    :      List[str]						     =					len(_lowercase     )
						_UpperCamelCase    :      Optional[Any]						     =					len(board[0]     )
						for direction in traverts_directions:
												_UpperCamelCase    :      Union[str, Any]						     =					row + direction[0]
												_UpperCamelCase    :      List[Any]						     =					column + direction[1]
												if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
																		continue

												_UpperCamelCase    :      Optional[Any]						     =					get_point_key(_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     )
												if key in visited_points_set:
																		continue

												visited_points_set.add(_lowercase     )
												if exits_word(_lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		word_index + 1 ,		_lowercase     ):
																		return True

												visited_points_set.remove(_lowercase     )

						return False





def  a_       (							_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      Optional[Any]						     =					(
						    '''The board should be a non empty matrix of single chars strings.'''
						)

						_UpperCamelCase    :      int						     =					len(_lowercase     )
						if not isinstance(_lowercase ,		_lowercase     ) or len(_lowercase     ) == 0:
												raise ValueError(_lowercase     )

						for row in board:
												if not isinstance(_lowercase ,		_lowercase     ) or len(_lowercase     ) == 0:
																		raise ValueError(_lowercase     )

												for item in row:
																		if not isinstance(_lowercase ,		_lowercase     ) or len(_lowercase     ) != 1:
																								raise ValueError(_lowercase     )

    # Validate word
						if not isinstance(_lowercase ,		_lowercase     ) or len(_lowercase     ) == 0:
												raise ValueError(
												    '''The word parameter should be a string of length greater than 0.'''     )

						_UpperCamelCase    :      List[str]						     =					len(board[0]     )
						for i in range(_lowercase     ):
												for j in range(_lowercase     ):
																		if exits_word(
																		    _lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		0 ,		{get_point_key(_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     )}     ):
																								return True

						return False


if __name__ == "__main__":
						import doctest

						doctest.testmod()



