"""simple docstring"""







import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest



class 			_lowerCamelCase      (__lowerCamelCase  ):
				_snake_case  =	(DDIMScheduler,)
				_snake_case  =	(("eta", 0.0), ("num_inference_steps", 50))







				def 				__UpperCAmelCase       (			self     :							List[Any]      ,       **lowerCamelCase_     :							Union[str, Any]    ):

					"""simple docstring"""






					_lowercase			:     List[str]      					=				{
					    'num_train_timesteps': 1_0_0_0,
					    'beta_start': 0.0001,
					    'beta_end': 0.02,
					    'beta_schedule': 'linear',
					    'clip_sample': True,
					}

					config.update(**lowerCamelCase_    )
					return config







				def 				__UpperCAmelCase       (			self     :							Union[str, Any]      ,       **lowerCamelCase_     :							List[str]    ):

					"""simple docstring"""






					_lowercase			:     Dict      					=				self.scheduler_classes[0]
					_lowercase			:     Optional[int]      					=				self.get_scheduler_config(**lowerCamelCase_    )
					_lowercase			:     Tuple      					=				scheduler_class(**lowerCamelCase_    )

					_lowercase					,   _lowercase			:     List[Any]      					=				1_0, 0.0

					_lowercase			:     str      					=				self.dummy_model()
					_lowercase			:     str      					=				self.dummy_sample_deter

					scheduler.set_timesteps(lowerCamelCase_    )

					for t in scheduler.timesteps:
						_lowercase			:     Optional[int]      					=				model(lowerCamelCase_      ,       lowerCamelCase_    )
						_lowercase			:     Optional[Any]      					=				scheduler.step(lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_    ).prev_sample

					return sample







				def 				__UpperCAmelCase       (			self     :							Tuple    ):

					"""simple docstring"""






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







				def 				__UpperCAmelCase       (			self     :							Optional[int]    ):

					"""simple docstring"""






					for steps_offset in [0, 1]:
						self.check_over_configs(steps_offset=lowerCamelCase_    )

					_lowercase			:     Tuple      					=				self.scheduler_classes[0]
					_lowercase			:     Tuple      					=				self.get_scheduler_config(steps_offset=1    )
					_lowercase			:     List[str]      					=				scheduler_class(**lowerCamelCase_    )
					scheduler.set_timesteps(5    )
					assert torch.equal(scheduler.timesteps      ,       torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]    )    )







				def 				__UpperCAmelCase       (			self     :							List[str]    ):

					"""simple docstring"""






					for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]      ,       [0.002, 0.02, 0.2, 2]    ):
						self.check_over_configs(beta_start=lowerCamelCase_      ,       beta_end=lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Any    ):

					"""simple docstring"""






					for schedule in ["linear", "squaredcos_cap_v2"]:
						self.check_over_configs(beta_schedule=lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






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







				def 				__UpperCAmelCase       (			self     :							Dict    ):

					"""simple docstring"""






					for clip_sample in [True, False]:
						self.check_over_configs(clip_sample=lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							str    ):

					"""simple docstring"""






					for timestep_spacing in ["trailing", "leading"]:
						self.check_over_configs(timestep_spacing=lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






					for rescale_betas_zero_snr in [True, False]:
						self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






					self.check_over_configs(thresholding=lowerCamelCase_    )
					for threshold in [0.5, 1.0, 2.0]:
						for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(
							    thresholding=lowerCamelCase_      ,       prediction_type=lowerCamelCase_      ,       sample_max_value=lowerCamelCase_      ,       )







				def 				__UpperCAmelCase       (			self     :							str    ):

					"""simple docstring"""






					for t in [1, 1_0, 4_9]:
						self.check_over_forward(time_step=lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Optional[int]    ):

					"""simple docstring"""






					for t, num_inference_steps in zip([1, 1_0, 5_0]      ,       [1_0, 5_0, 5_0_0]    ):
						self.check_over_forward(time_step=lowerCamelCase_      ,       num_inference_steps=lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






					for t, eta in zip([1, 1_0, 4_9]      ,       [0.0, 0.5, 1.0]    ):
						self.check_over_forward(time_step=lowerCamelCase_      ,       eta=lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Optional[Any]    ):

					"""simple docstring"""






					_lowercase			:     Dict      					=				self.scheduler_classes[0]
					_lowercase			:     Dict      					=				self.get_scheduler_config()
					_lowercase			:     Union[str, Any]      					=				scheduler_class(**lowerCamelCase_    )

					assert torch.sum(torch.abs(scheduler._get_variance(0      ,       0    ) - 0.0    )    ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_2_0      ,       4_0_0    ) - 0.1_4771    )    ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_8_0      ,       9_6_0    ) - 0.3_2460    )    ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(0      ,       0    ) - 0.0    )    ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_8_7      ,       4_8_6    ) - 0.0_0979    )    ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_9_9      ,       9_9_8    ) - 0.02    )    ) < 1E-5







				def 				__UpperCAmelCase       (			self     :							Union[str, Any]    ):

					"""simple docstring"""






					_lowercase			:     Union[str, Any]      					=				self.full_loop()

					_lowercase			:     str      					=				torch.sum(torch.abs(lowerCamelCase_    )    )
					_lowercase			:     Optional[Any]      					=				torch.mean(torch.abs(lowerCamelCase_    )    )

					assert abs(result_sum.item() - 172.0067    ) < 1E-2
					assert abs(result_mean.item() - 0.22_3967    ) < 1E-3







				def 				__UpperCAmelCase       (			self     :							Optional[int]    ):

					"""simple docstring"""






					_lowercase			:     int      					=				self.full_loop(prediction_type='v_prediction'    )

					_lowercase			:     Optional[Any]      					=				torch.sum(torch.abs(lowerCamelCase_    )    )
					_lowercase			:     int      					=				torch.mean(torch.abs(lowerCamelCase_    )    )

					assert abs(result_sum.item() - 52.5302    ) < 1E-2
					assert abs(result_mean.item() - 0.0684    ) < 1E-3







				def 				__UpperCAmelCase       (			self     :							Tuple    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				self.full_loop(set_alpha_to_one=lowerCamelCase_      ,       beta_start=0.01    )
					_lowercase			:     Optional[Any]      					=				torch.sum(torch.abs(lowerCamelCase_    )    )
					_lowercase			:     Union[str, Any]      					=				torch.mean(torch.abs(lowerCamelCase_    )    )

					assert abs(result_sum.item() - 149.8295    ) < 1E-2
					assert abs(result_mean.item() - 0.1951    ) < 1E-3







				def 				__UpperCAmelCase       (			self     :							List[str]    ):

					"""simple docstring"""






					_lowercase			:     int      					=				self.full_loop(set_alpha_to_one=lowerCamelCase_      ,       beta_start=0.01    )
					_lowercase			:     Tuple      					=				torch.sum(torch.abs(lowerCamelCase_    )    )
					_lowercase			:     Dict      					=				torch.mean(torch.abs(lowerCamelCase_    )    )

					assert abs(result_sum.item() - 149.0784    ) < 1E-2
					assert abs(result_mean.item() - 0.1941    ) < 1E-3




"""simple docstring"""







def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





	if len(__UpperCAmelCase      ) > 1:
		_lowercase			:     Dict      					=				len(__UpperCAmelCase      ) // 2  # Finds the middle of the array
		_lowercase			:     List[str]      					=				arr[
		    :middle_length
		]  # Creates an array of the elements in the first half.
		_lowercase			:     Optional[int]      					=				arr[
		    middle_length:
		]  # Creates an array of the elements in the second half.
		_lowercase			:     Optional[Any]      					=				len(__UpperCAmelCase      )
		_lowercase			:     Optional[int]      					=				len(__UpperCAmelCase      )
		merge(__UpperCAmelCase      )  # Starts sorting the left.
		merge(__UpperCAmelCase      )  # Starts sorting the right
		_lowercase			:     Dict      					=				0  # Left Counter
		_lowercase			:     List[str]      					=				0  # Right Counter
		_lowercase			:     Optional[Any]      					=				0  # Position Counter
		while (
		    left_index < left_size and right_index < right_size
		):  # Runs until the lowers size of the left and right are sorted.
			if left_array[left_index] < right_array[right_index]:
				_lowercase			:     Optional[int]      					=				left_array[left_index]
				left_index += 1
			else:
				_lowercase			:     int      					=				right_array[right_index]
				right_index += 1
			index += 1
		while (
		    left_index < left_size
		):  # Adds the left over elements in the left half of the array
			_lowercase			:     Any      					=				left_array[left_index]
			left_index += 1
			index += 1
		while (
		    right_index < right_size
		):  # Adds the left over elements in the right half of the array
			_lowercase			:     str      					=				right_array[right_index]
			right_index += 1
			index += 1
	return arr


if __name__ == "__main__":
						import doctest

						doctest.testmod()



