



"""simple docstring"""






import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					a__        =     (HeunDiscreteScheduler,)
					a__        =     10







					def 		_a (      self    ,       **a   ):




										"""simple docstring"""





										snake_case_						:Tuple								=						{
										    "num_train_timesteps": 11_00,
										    "beta_start": 0.0001,
										    "beta_end": 0.02,
										    "beta_schedule": "linear",
										}

										config.update(**a   )
										return config







					def 		_a (      self   ):




										"""simple docstring"""





										for timesteps in [10, 50, 1_00, 10_00]:
															self.check_over_configs(num_train_timesteps=a   )







					def 		_a (      self   ):




										"""simple docstring"""





										for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]    ,       [0.0002, 0.002, 0.02]   ):
															self.check_over_configs(beta_start=a    ,       beta_end=a   )







					def 		_a (      self   ):




										"""simple docstring"""





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







					def 		_a (      self   ):




										"""simple docstring"""





										for clip_sample_range in [1.0, 2.0, 3.0]:
															self.check_over_configs(clip_sample_range=a    ,       clip_sample=a   )







					def 		_a (      self   ):




										"""simple docstring"""





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







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Dict								=						self.scheduler_classes[0]
										snake_case_						:str								=						self.get_scheduler_config()
										snake_case_						:Dict								=						scheduler_class(**a   )

										scheduler.set_timesteps(self.num_inference_steps   )

										snake_case_						:List[Any]								=						self.dummy_model()
										snake_case_						:Union[str, Any]								=						self.dummy_sample_deter * scheduler.init_noise_sigma
										snake_case_						:str								=						sample.to(a   )

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

															snake_case_						:str								=						model(a    ,       a   )

															snake_case_						:str								=						scheduler.step(a    ,       a    ,       a   )
															snake_case_						:List[str]								=						output.prev_sample

										snake_case_						:List[Any]								=						torch.sum(torch.abs(a   )   )
										snake_case_						:List[str]								=						torch.mean(torch.abs(a   )   )

										if torch_device in ["cpu", "mps"]:
															assert abs(result_sum.item() - 0.1233   ) < 1e-2
															assert abs(result_mean.item() - 0.0002   ) < 1e-3
										else:
															# CUDA
															assert abs(result_sum.item() - 0.1233   ) < 1e-2
															assert abs(result_mean.item() - 0.0002   ) < 1e-3







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:List[str]								=						self.scheduler_classes[0]
										snake_case_						:Any								=						self.get_scheduler_config(prediction_type="v_prediction"   )
										snake_case_						:str								=						scheduler_class(**a   )

										scheduler.set_timesteps(self.num_inference_steps   )

										snake_case_						:Any								=						self.dummy_model()
										snake_case_						:Union[str, Any]								=						self.dummy_sample_deter * scheduler.init_noise_sigma
										snake_case_						:str								=						sample.to(a   )

										for i, t in enumerate(scheduler.timesteps   ):
															snake_case_						:Union[str, Any]								=						scheduler.scale_model_input(a    ,       a   )

															snake_case_						:Any								=						model(a    ,       a   )

															snake_case_						:str								=						scheduler.step(a    ,       a    ,       a   )
															snake_case_						:Optional[Any]								=						output.prev_sample

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

										if torch_device in ["cpu", "mps"]:
															assert abs(result_sum.item() - 4.6_9_3_4e-0_7   ) < 1e-2
															assert abs(result_mean.item() - 6.1_1_1_2e-1_0   ) < 1e-3
										else:
															# CUDA
															assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7   ) < 1e-2
															assert abs(result_mean.item() - 0.0002   ) < 1e-3







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:str								=						self.scheduler_classes[0]
										snake_case_						:Tuple								=						self.get_scheduler_config()
										snake_case_						:int								=						scheduler_class(**a   )

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

										snake_case_						:List[str]								=						self.dummy_model()
										snake_case_						:Optional[Any]								=						self.dummy_sample_deter.to(a   ) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
															snake_case_						:Optional[int]								=						scheduler.scale_model_input(a    ,       a   )

															snake_case_						:Dict								=						model(a    ,       a   )

															snake_case_						:List[str]								=						scheduler.step(a    ,       a    ,       a   )
															snake_case_						:List[str]								=						output.prev_sample

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

										if str(a   ).startswith("cpu"   ):
															# The following sum varies between 148 and 156 on mps. Why?
															assert abs(result_sum.item() - 0.1233   ) < 1e-2
															assert abs(result_mean.item() - 0.0002   ) < 1e-3
										elif str(a   ).startswith("mps"   ):
															# Larger tolerance on mps
															assert abs(result_mean.item() - 0.0002   ) < 1e-2
										else:
															# CUDA
															assert abs(result_sum.item() - 0.1233   ) < 1e-2
															assert abs(result_mean.item() - 0.0002   ) < 1e-3







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Any								=						self.scheduler_classes[0]
										snake_case_						:Union[str, Any]								=						self.get_scheduler_config()
										snake_case_						:Any								=						scheduler_class(**a    ,       use_karras_sigmas=a   )

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

										snake_case_						:Dict								=						self.dummy_model()
										snake_case_						:Optional[Any]								=						self.dummy_sample_deter.to(a   ) * scheduler.init_noise_sigma
										snake_case_						:Union[str, Any]								=						sample.to(a   )

										for t in scheduler.timesteps:
															snake_case_						:Optional[int]								=						scheduler.scale_model_input(a    ,       a   )

															snake_case_						:List[Any]								=						model(a    ,       a   )

															snake_case_						:List[Any]								=						scheduler.step(a    ,       a    ,       a   )
															snake_case_						:Any								=						output.prev_sample

										snake_case_						:Optional[Any]								=						torch.sum(torch.abs(a   )   )
										snake_case_						:Tuple								=						torch.mean(torch.abs(a   )   )

										assert abs(result_sum.item() - 0.00015   ) < 1e-2
										assert abs(result_mean.item() - 1.9_8_6_9_5_5_4_5_3_5_0_3_4_6_9_5e-0_7   ) < 1e-2









"""simple docstring"""






def      A      (			_A,     _A,     _A,     _A,     _A,     _A,     ):




					"""simple docstring"""
					snake_case_						:List[Any]								=						list(_A							)
					snake_case_						:int								=						list(_A							)
					snake_case_						:List[Any]								=						len(_A							)
					snake_case_						:List[Any]								=						len(_A							)

					snake_case_						:Union[str, Any]								=						[
					    [0 for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
					]
					snake_case_						:int								=						[
					    ["0" for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
					]

					for i in range(1,     len_source_seq + 1							):
										snake_case_						:List[str]								=						i * delete_cost
										snake_case_						:str								=						F'''D{source_seq[i - 1]:c}'''

					for i in range(1,     len_destination_seq + 1							):
										snake_case_						:int								=						i * insert_cost
										snake_case_						:Dict								=						F'''I{destination_seq[i - 1]:c}'''

					for i in range(1,     len_source_seq + 1							):
										for j in range(1,     len_destination_seq + 1							):
															if source_seq[i - 1] == destination_seq[j - 1]:
																				snake_case_						:List[Any]								=						costs[i - 1][j - 1] + copy_cost
																				snake_case_						:str								=						F'''C{source_seq[i - 1]:c}'''
															else:
																				snake_case_						:Any								=						costs[i - 1][j - 1] + replace_cost
																				snake_case_						:Any								=						F'''R{source_seq[i - 1]:c}''' + str(destination_seq[j - 1]							)

															if costs[i - 1][j] + delete_cost < costs[i][j]:
																				snake_case_						:str								=						costs[i - 1][j] + delete_cost
																				snake_case_						:Tuple								=						F'''D{source_seq[i - 1]:c}'''

															if costs[i][j - 1] + insert_cost < costs[i][j]:
																				snake_case_						:int								=						costs[i][j - 1] + insert_cost
																				snake_case_						:int								=						F'''I{destination_seq[j - 1]:c}'''

					return costs, ops
def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					if i == 0 and j == 0:
										return []
					else:
										if ops[i][j][0] == "C" or ops[i][j][0] == "R":
															snake_case_						:Optional[Any]								=						assemble_transformation(_A,     i - 1,     j - 1							)
															seq.append(ops[i][j]							)
															return seq
										elif ops[i][j][0] == "D":
															snake_case_						:Union[str, Any]								=						assemble_transformation(_A,     i - 1,     _A							)
															seq.append(ops[i][j]							)
															return seq
										else:
															snake_case_						:Optional[Any]								=						assemble_transformation(_A,     _A,     j - 1							)
															seq.append(ops[i][j]							)
															return seq


if __name__ == "__main__":
				__UpperCAmelCase					,	__UpperCAmelCase      :      List[Any]   					=					compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

				__UpperCAmelCase      :      List[Any]   					=					len(operations)
				__UpperCAmelCase      :      Dict   					=					len(operations[0])
				__UpperCAmelCase      :      List[str]   					=					assemble_transformation(operations, m - 1, n - 1)

				__UpperCAmelCase      :      Any   					=					list('Python')
				__UpperCAmelCase      :      Tuple   					=					0
				__UpperCAmelCase      :      Optional[int]   					=					0

				with open('min_cost.txt', 'w') as file:
								for op in sequence:
												print(''.join(string))

												if op[0] == "C":
																file.write('%-16s' % 'Copy %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost -= 1
												elif op[0] == "R":
																__UpperCAmelCase      :      Any   					=					op[2]

																file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
																file.write('\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 1
												elif op[0] == "D":
																string.pop(i)

																file.write('%-16s' % 'Delete %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 2
												else:
																string.insert(i, op[1])

																file.write('%-16s' % 'Insert %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 2

												i += 1

								print(''.join(string))
								print('Cost: ', cost)

								file.write('\r\nMinimum cost: ' + str(cost))

