import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest

@require_torchsde
class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  (DPMSolverSDEScheduler,)
  lowercase_								=  10




  def 						__lowercase(   self  :						Any  ,			**a_  :						List[Any]					)->							Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		{
           'num_train_timesteps': 1100,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
           'noise_sampler_seed': 0,
       }

       config.update(**a_					)
       return config




  def 						__lowercase(   self  :						int					)->							str:




       """simple docstring"""
       for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=a_					)




  def 						__lowercase(   self  :						Any					)->							Tuple:




       """simple docstring"""
       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]  ,			[0.0002, 0.002, 0.02]					):
            self.check_over_configs(beta_start=a_  ,			beta_end=a_					)




  def 						__lowercase(   self  :						List[Any]					)->							Tuple:




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




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




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




  def 						__lowercase(   self  :						Union[str, Any]					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__					:					str      =		self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		scheduler_class(**a_					)

       scheduler.set_timesteps(self.num_inference_steps					)

       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.dummy_model()
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.dummy_sample_deter * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		sample.to(a_					)

       for i, t in enumerate(scheduler.timesteps					):
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		scheduler.scale_model_input(a_  ,			a_					)

            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		model(a_  ,			a_					)

            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		scheduler.step(a_  ,			a_  ,			a_					)
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		output.prev_sample

       SCREAMING_SNAKE_CASE__					:					Dict      =		torch.sum(torch.abs(a_					)					)
       SCREAMING_SNAKE_CASE__					:					Any      =		torch.mean(torch.abs(a_					)					)

       if torch_device in ["mps"]:
            assert abs(result_sum.item() - 167.47_8210_4492_1875					) < 1e-2
            assert abs(result_mean.item() - 0.2178_7059_6456_5277					) < 1e-3
       elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 171.59_3521_1181_6406					) < 1e-2
            assert abs(result_mean.item() - 0.2_2342_9068_9229_9652					) < 1e-3
       else:
            assert abs(result_sum.item() - 162.52_3834_2285_1562					) < 1e-2
            assert abs(result_mean.item() - 0.211_6195_7085_1326					) < 1e-3




  def 						__lowercase(   self  :						Optional[int]					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__					:					Any      =		self.get_scheduler_config(prediction_type='v_prediction'					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		scheduler_class(**a_					)

       scheduler.set_timesteps(self.num_inference_steps					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.dummy_model()
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.dummy_sample_deter * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		sample.to(a_					)

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

            SCREAMING_SNAKE_CASE__					:					Any      =		model(a_  ,			a_					)

            SCREAMING_SNAKE_CASE__					:					List[str]      =		scheduler.step(a_  ,			a_  ,			a_					)
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		output.prev_sample

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		torch.sum(torch.abs(a_					)					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.mean(torch.abs(a_					)					)

       if torch_device in ["mps"]:
            assert abs(result_sum.item() - 124.77_1492_0043_9453					) < 1e-2
            assert abs(result_mean.item() - 0.1_6226_2890_1481_6284					) < 1e-3
       elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 128.1_6633_6059_5703					) < 1e-2
            assert abs(result_mean.item() - 0.1_6688_3260_0116_7297					) < 1e-3
       else:
            assert abs(result_sum.item() - 119.8_4875_4882_8125					) < 1e-2
            assert abs(result_mean.item() - 0.1560_5306_6253_6621					) < 1e-3




  def 						__lowercase(   self  :						str					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__					:					int      =		scheduler_class(**a_					)

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

       SCREAMING_SNAKE_CASE__					:					str      =		self.dummy_model()
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.dummy_sample_deter.to(a_					) * scheduler.init_noise_sigma

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

            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		model(a_  ,			a_					)

            SCREAMING_SNAKE_CASE__					:					Any      =		scheduler.step(a_  ,			a_  ,			a_					)
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		output.prev_sample

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.sum(torch.abs(a_					)					)
       SCREAMING_SNAKE_CASE__					:					Any      =		torch.mean(torch.abs(a_					)					)

       if torch_device in ["mps"]:
            assert abs(result_sum.item() - 167.46_9573_9746_0938					) < 1e-2
            assert abs(result_mean.item() - 0.2_1805_9346_0798_2635					) < 1e-3
       elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 171.59_3536_3769_5312					) < 1e-2
            assert abs(result_mean.item() - 0.2_2342_9083_8241_5771					) < 1e-3
       else:
            assert abs(result_sum.item() - 162.52_3834_2285_1562					) < 1e-2
            assert abs(result_mean.item() - 0.211_6195_7085_1326					) < 1e-3




  def 						__lowercase(   self  :						str					)->							Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__					:					Any      =		self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		scheduler_class(**a_  ,			use_karras_sigmas=a_					)

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

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.dummy_model()
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.dummy_sample_deter.to(a_					) * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		sample.to(a_					)

       for t in scheduler.timesteps:
            SCREAMING_SNAKE_CASE__					:					Tuple      =		scheduler.scale_model_input(a_  ,			a_					)

            SCREAMING_SNAKE_CASE__					:					str      =		model(a_  ,			a_					)

            SCREAMING_SNAKE_CASE__					:					List[Any]      =		scheduler.step(a_  ,			a_  ,			a_					)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		output.prev_sample

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.sum(torch.abs(a_					)					)
       SCREAMING_SNAKE_CASE__					:					str      =		torch.mean(torch.abs(a_					)					)

       if torch_device in ["mps"]:
            assert abs(result_sum.item() - 176.66_9741_3574_2188					) < 1e-2
            assert abs(result_mean.item() - 0.2_3003_8727_3098_1811					) < 1e-2
       elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 177.63_6535_6445_3125					) < 1e-2
            assert abs(result_mean.item() - 0.2_3003_8727_3098_1811					) < 1e-2
       else:
            assert abs(result_sum.item() - 170.3_1352_2338_8672					) < 1e-2
            assert abs(result_mean.item() - 0.2_3003_8727_3098_1811					) < 1e-2





'''simple docstring'''
def 		A_(     A						:		list[int]):
							UpperCamelCase   			=     []
							if len(A) == 1:
														return [nums.copy()]
							for _ in range(len(A)):
														UpperCamelCase   			=     nums.pop(0)
														UpperCamelCase   			=     permute(A)
														for perm in permutations:
																					perm.append(A)
														result.extend(A)
														nums.append(A)
							return result







def 		A_(     A						:		str):

							def backtrack(A						:		str):
														if start == len(A) - 1:
																					output.append(nums[:])
														else:
																					for i in range(A		,     len(A)):
																												UpperCamelCase			,	UpperCamelCase   			=     nums[i], nums[start]
																												backtrack(start + 1)
																												UpperCamelCase			,	UpperCamelCase   			=     nums[i], nums[start]  # backtrack

							UpperCamelCase   			=     []
							backtrack(0)
							return output


if __name__ == "__main__":
						import doctest

						# use res to print the data in permute2 function
						lowerCAmelCase					:    Dict           = permutea([1, 2, 3])
						print(res)
						doctest.testmod()




