


"""simple docstring"""





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 		snake_case_(       a__			):
     __UpperCamelCase  =					(DDIMParallelScheduler,)
     __UpperCamelCase  =					(('''eta''', 0.0), ('''num_inference_steps''', 50))






     def      lowerCamelCase__     (	self  :					str					,				**UpperCamelCase_  :					Dict				):
           lowerCAmelCase					:	Tuple       =   {
               '''num_train_timesteps''': 1_0_0_0,
               '''beta_start''': 0.0_001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''clip_sample''': True,
           }

           config.update(**_SCREAMING_SNAKE_CASE				)
           return config






     def      lowerCamelCase__     (	self  :					Optional[int]					,				**UpperCamelCase_  :					Union[str, Any]				):
           lowerCAmelCase					:	Dict       =   self.scheduler_classes[0]
           lowerCAmelCase					:	Any       =   self.get_scheduler_config(**_SCREAMING_SNAKE_CASE				)
           lowerCAmelCase					:	Optional[int]       =   scheduler_class(**_SCREAMING_SNAKE_CASE				)

           lowerCAmelCase,      lowerCAmelCase					:	Any       =   1_0, 0.0

           lowerCAmelCase					:	int       =   self.dummy_model()
           lowerCAmelCase					:	int       =   self.dummy_sample_deter

           scheduler.set_timesteps(_SCREAMING_SNAKE_CASE				)

           for t in scheduler.timesteps:
                 lowerCAmelCase					:	List[Any]       =   model(_SCREAMING_SNAKE_CASE					,				_SCREAMING_SNAKE_CASE				)
                 lowerCAmelCase					:	Union[str, Any]       =   scheduler.step(_SCREAMING_SNAKE_CASE					,				_SCREAMING_SNAKE_CASE					,				_SCREAMING_SNAKE_CASE					,				_SCREAMING_SNAKE_CASE				).prev_sample

           return sample






     def      lowerCamelCase__     (	self  :					List[Any]				):






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






     def      lowerCamelCase__     (	self  :					Tuple				):






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

           lowerCAmelCase					:	Any       =   self.scheduler_classes[0]
           lowerCAmelCase					:	str       =   self.get_scheduler_config(steps_offset=1				)
           lowerCAmelCase					:	Tuple       =   scheduler_class(**_SCREAMING_SNAKE_CASE				)
           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      lowerCamelCase__     (	self  :					Any				):






           for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]					,				[0.002, 0.02, 0.2, 2]				):
                 self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE					,				beta_end=_SCREAMING_SNAKE_CASE				)






     def      lowerCamelCase__     (	self  :					str				):






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






     def      lowerCamelCase__     (	self  :					Any				):






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






     def      lowerCamelCase__     (	self  :					List[Any]				):






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






     def      lowerCamelCase__     (	self  :					Any				):






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






     def      lowerCamelCase__     (	self  :					int				):






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






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






     def      lowerCamelCase__     (	self  :					List[Any]				):






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






     def      lowerCamelCase__     (	self  :					Tuple				):






           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=_SCREAMING_SNAKE_CASE					,				num_inference_steps=_SCREAMING_SNAKE_CASE				)






     def      lowerCamelCase__     (	self  :					int				):






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






     def      lowerCamelCase__     (	self  :					Optional[int]				):
           lowerCAmelCase					:	Dict       =   self.scheduler_classes[0]
           lowerCAmelCase					:	Union[str, Any]       =   self.get_scheduler_config()
           lowerCAmelCase					:	List[str]       =   scheduler_class(**_SCREAMING_SNAKE_CASE				)

           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				) - 0.14_771				)				) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(9_8_0					,				9_6_0				) - 0.32_460				)				) < 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				) - 0.00_979				)				) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(9_9_9					,				9_9_8				) - 0.02				)				) < 1E-5






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	Dict       =   self.scheduler_classes[0]
           lowerCAmelCase					:	Any       =   self.get_scheduler_config()
           lowerCAmelCase					:	int       =   scheduler_class(**_SCREAMING_SNAKE_CASE				)

           lowerCAmelCase,      lowerCAmelCase					:	Any       =   1_0, 0.0
           scheduler.set_timesteps(_SCREAMING_SNAKE_CASE				)

           lowerCAmelCase					:	Optional[int]       =   self.dummy_model()
           lowerCAmelCase					:	Optional[int]       =   self.dummy_sample_deter
           lowerCAmelCase					:	List[Any]       =   self.dummy_sample_deter + 0.1
           lowerCAmelCase					:	str       =   self.dummy_sample_deter - 0.1

           lowerCAmelCase					:	str       =   samplea.shape[0]
           lowerCAmelCase					:	Union[str, Any]       =   torch.stack([samplea, samplea, samplea]					,				dim=0				)
           lowerCAmelCase					:	Any       =   torch.arange(_SCREAMING_SNAKE_CASE				)[0:3, None].repeat(1					,				_SCREAMING_SNAKE_CASE				)

           lowerCAmelCase					:	Optional[int]       =   model(samples.flatten(0					,				1				)					,				timesteps.flatten(0					,				1				)				)
           lowerCAmelCase					:	Dict       =   scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE					,				timesteps.flatten(0					,				1				)					,				samples.flatten(0					,				1				)					,				_SCREAMING_SNAKE_CASE				)

           lowerCAmelCase					:	int       =   torch.sum(torch.abs(_SCREAMING_SNAKE_CASE				)				)
           lowerCAmelCase					:	Tuple       =   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE				)				)

           assert abs(result_sum.item() - 1_1_4_7.7_9_0_4				) < 1E-2
           assert abs(result_mean.item() - 0.4_982				) < 1E-3






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	List[str]       =   self.full_loop()

           lowerCAmelCase					:	Tuple       =   torch.sum(torch.abs(_SCREAMING_SNAKE_CASE				)				)
           lowerCAmelCase					:	Optional[Any]       =   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE				)				)

           assert abs(result_sum.item() - 1_7_2.0_0_6_7				) < 1E-2
           assert abs(result_mean.item() - 0.223_967				) < 1E-3






     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	Union[str, Any]       =   self.full_loop(prediction_type='''v_prediction'''				)

           lowerCAmelCase					:	Dict       =   torch.sum(torch.abs(_SCREAMING_SNAKE_CASE				)				)
           lowerCAmelCase					:	Optional[int]       =   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE				)				)

           assert abs(result_sum.item() - 5_2.5_3_0_2				) < 1E-2
           assert abs(result_mean.item() - 0.0_684				) < 1E-3






     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	Optional[Any]       =   self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE					,				beta_start=0.01				)
           lowerCAmelCase					:	int       =   torch.sum(torch.abs(_SCREAMING_SNAKE_CASE				)				)
           lowerCAmelCase					:	str       =   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE				)				)

           assert abs(result_sum.item() - 1_4_9.8_2_9_5				) < 1E-2
           assert abs(result_mean.item() - 0.1_951				) < 1E-3






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	Optional[int]       =   self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE					,				beta_start=0.01				)
           lowerCAmelCase					:	Optional[int]       =   torch.sum(torch.abs(_SCREAMING_SNAKE_CASE				)				)
           lowerCAmelCase					:	List[str]       =   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE				)				)

           assert abs(result_sum.item() - 1_4_9.0_7_8_4				) < 1E-2
           assert abs(result_mean.item() - 0.1_941				) < 1E-3






"""simple docstring"""





# using dfs for finding eulerian path traversal


def      _snake_case						(						_snake_case				:			Optional[Any] ,   _snake_case				:			List[Any] ,   _snake_case				:			str ,   _snake_case				:			List[Any]=None			):
      lowerCAmelCase					:	Any       =   (path or []) + [u]
      for v in graph[u]:
            if visited_edge[u][v] is False:
                  lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   True, True
                  lowerCAmelCase					:	int       =   dfs(_snake_case ,   _snake_case ,   _snake_case ,   _snake_case			)
      return path


def      _snake_case						(						_snake_case				:			Optional[int] ,   _snake_case				:			Dict			):
      lowerCAmelCase					:	Tuple       =   0
      lowerCAmelCase					:	Optional[Any]       =   -1
      for i in range(_snake_case			):
            if i not in graph.keys():
                  continue
            if len(graph[i]			) % 2 == 1:
                  odd_degree_nodes += 1
                  lowerCAmelCase					:	Optional[Any]       =   i
      if odd_degree_nodes == 0:
            return 1, odd_node
      if odd_degree_nodes == 2:
            return 2, odd_node
      return 3, odd_node


def      _snake_case						(						_snake_case				:			Tuple ,   _snake_case				:			List[Any]			):
      lowerCAmelCase					:	Any       =   [[False for _ in range(max_node + 1			)] for _ in range(max_node + 1			)]
      lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   check_circuit_or_path(_snake_case ,   _snake_case			)
      if check == 3:
            print('''graph is not Eulerian'''			)
            print('''no path'''			)
            return
      lowerCAmelCase					:	Dict       =   1
      if check == 2:
            lowerCAmelCase					:	int       =   odd_node
            print('''graph has a Euler path'''			)
      if check == 1:
            print('''graph has a Euler cycle'''			)
      lowerCAmelCase					:	List[str]       =   dfs(_snake_case ,   _snake_case ,   _snake_case			)
      print(_snake_case			)


def      _snake_case						(						):
      lowerCAmelCase					:	Optional[Any]       =   {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
      lowerCAmelCase					:	Union[str, Any]       =   {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
      lowerCAmelCase					:	List[Any]       =   {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
      lowerCAmelCase					:	Optional[Any]       =   {1: [2, 3], 2: [1, 3], 3: [1, 2]}
      lowerCAmelCase					:	Any       =   {
          1: [],
          2: []
          # all degree is zero
      }
      lowerCAmelCase					:	List[str]       =   10
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)


if __name__ == "__main__":
 main()


