




"""simple docstring"""


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class _lowerCamelCase				(     UpperCamelCase__						):
   _lowerCamelCase				:List[Any]	      =	(UnCLIPScheduler,)






   def        _lowerCAmelCase    (		self   :		Optional[int]    ,	**UpperCamelCase   :		Union[str, Any]    ) ->						Any:

         """simple docstring"""
         lowerCAmelCase__			: Dict  					=       {
             """num_train_timesteps""": 10_00,
             """variance_type""": """fixed_small_log""",
             """clip_sample""": True,
             """clip_sample_range""": 1.0,
             """prediction_type""": """epsilon""",
         }

         config.update(**_a    )
         return config






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

         """simple docstring"""
         for timesteps in [1, 5, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=_a    )






   def        _lowerCAmelCase    (		self   :		str    ) ->						str:

         """simple docstring"""
         for variance in ["fixed_small_log", "learned_range"]:
               self.check_over_configs(variance_type=_a    )






   def        _lowerCAmelCase    (		self   :		str    ) ->						List[str]:

         """simple docstring"""
         for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=_a    )






   def        _lowerCAmelCase    (		self   :		List[Any]    ) ->						Optional[Any]:

         """simple docstring"""
         for clip_sample_range in [1, 5, 10, 20]:
               self.check_over_configs(clip_sample_range=_a    )






   def        _lowerCAmelCase    (		self   :		Any    ) ->						str:

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






   def        _lowerCAmelCase    (		self   :		Union[str, Any]    ) ->						Tuple:

         """simple docstring"""
         for time_step in [0, 5_00, 9_99]:
               for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
                     if prev_timestep is not None and prev_timestep >= time_step:
                           continue

                     self.check_over_forward(time_step=_a    ,	prev_timestep=_a    )






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

         """simple docstring"""
         lowerCAmelCase__			: str  					=       self.scheduler_classes[0]
         lowerCAmelCase__			: str  					=       self.get_scheduler_config(variance_type="""fixed_small_log"""    )
         lowerCAmelCase__			: Optional[Any]  					=       scheduler_class(**_a    )

         assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 1.0_0_0_0E-1_0    )    ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_87    ) - 0.054_9625    )    ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_99    ) - 0.999_4987    )    ) < 1E-5






   def        _lowerCAmelCase    (		self   :		int    ) ->						int:

         """simple docstring"""
         lowerCAmelCase__			: Dict  					=       self.scheduler_classes[0]
         lowerCAmelCase__			: List[str]  					=       self.get_scheduler_config(variance_type="""learned_range"""    )
         lowerCAmelCase__			: Optional[Any]  					=       scheduler_class(**_a    )

         lowerCAmelCase__			: str  					=       0.5

         assert scheduler._get_variance(1    ,	predicted_variance=_a    ) - -10.171_2790 < 1E-5
         assert scheduler._get_variance(4_87    ,	predicted_variance=_a    ) - -5.799_8052 < 1E-5
         assert scheduler._get_variance(9_99    ,	predicted_variance=_a    ) - -0.001_0011 < 1E-5






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

         """simple docstring"""
         lowerCAmelCase__			: Any  					=       self.scheduler_classes[0]
         lowerCAmelCase__			: Tuple  					=       self.get_scheduler_config()
         lowerCAmelCase__			: Optional[Any]  					=       scheduler_class(**_a    )

         lowerCAmelCase__			: Optional[int]  					=       scheduler.timesteps

         lowerCAmelCase__			: Union[str, Any]  					=       self.dummy_model()
         lowerCAmelCase__			: Any  					=       self.dummy_sample_deter
         lowerCAmelCase__			: List[str]  					=       torch.manual_seed(0    )

         for i, t in enumerate(_a    ):
               # 1. predict noise residual
               lowerCAmelCase__			: Dict  					=       model(_a    ,	_a    )

               # 2. predict previous mean of sample x_t-1
               lowerCAmelCase__			: List[Any]  					=       scheduler.step(_a    ,	_a    ,	_a    ,	generator=_a    ).prev_sample

               lowerCAmelCase__			: Any  					=       pred_prev_sample

         lowerCAmelCase__			: int  					=       torch.sum(torch.abs(_a    )    )
         lowerCAmelCase__			: Optional[int]  					=       torch.mean(torch.abs(_a    )    )

         assert abs(result_sum.item() - 252.268_2495    ) < 1E-2
         assert abs(result_mean.item() - 0.328_4743    ) < 1E-3






   def        _lowerCAmelCase    (		self   :		List[str]    ) ->						Any:

         """simple docstring"""
         lowerCAmelCase__			: Dict  					=       self.scheduler_classes[0]
         lowerCAmelCase__			: Optional[int]  					=       self.get_scheduler_config()
         lowerCAmelCase__			: int  					=       scheduler_class(**_a    )

         scheduler.set_timesteps(25    )

         lowerCAmelCase__			: List[str]  					=       scheduler.timesteps

         lowerCAmelCase__			: str  					=       self.dummy_model()
         lowerCAmelCase__			: Union[str, Any]  					=       self.dummy_sample_deter
         lowerCAmelCase__			: str  					=       torch.manual_seed(0    )

         for i, t in enumerate(_a    ):
               # 1. predict noise residual
               lowerCAmelCase__			: List[Any]  					=       model(_a    ,	_a    )

               if i + 1 == timesteps.shape[0]:
                     lowerCAmelCase__			: Dict  					=       None
               else:
                     lowerCAmelCase__			: Optional[Any]  					=       timesteps[i + 1]

               # 2. predict previous mean of sample x_t-1
               lowerCAmelCase__			: Any  					=       scheduler.step(
                   _a    ,	_a    ,	_a    ,	prev_timestep=_a    ,	generator=_a    ).prev_sample

               lowerCAmelCase__			: int  					=       pred_prev_sample

         lowerCAmelCase__			: Optional[int]  					=       torch.sum(torch.abs(_a    )    )
         lowerCAmelCase__			: int  					=       torch.mean(torch.abs(_a    )    )

         assert abs(result_sum.item() - 258.204_4983    ) < 1E-2
         assert abs(result_mean.item() - 0.336_2038    ) < 1E-3






   def        _lowerCAmelCase    (		self   :		int    ) ->						Dict:

         """simple docstring"""
         pass






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

         """simple docstring"""
         pass











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 						lowercase							(   UpperCamelCase__		):
     _a							 =						(DPMSolverSDEScheduler,)
     _a							 =						1_0

     def 					a__	(  self		,     **_a				)						->							Optional[Any]:
           _A							:     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 					a__	(  self				)						->							Tuple:
           for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           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				)						->							Any:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           _A							:     Any			=						self.scheduler_classes[0]
           _A							:     List[str]			=						self.get_scheduler_config()
           _A							:     Optional[Any]			=						scheduler_class(**_a				)

           scheduler.set_timesteps(self.num_inference_steps				)

           _A							:     Dict			=						self.dummy_model()
           _A							:     Any			=						self.dummy_sample_deter * scheduler.init_noise_sigma
           _A							:     Dict			=						sample.to(_a				)

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

                 _A							:     str			=						model(_a		,     _a				)

                 _A							:     List[Any]			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Optional[int]			=						output.prev_sample

           _A							:     Dict			=						torch.sum(torch.abs(_a				)				)
           _A							:     Dict			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 167.47821044921875				) < 1e-2
                 assert abs(result_mean.item() - 0.2178705964565277				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 171.59352111816406				) < 1e-2
                 assert abs(result_mean.item() - 0.22342906892299652				) < 1e-3
           else:
                 assert abs(result_sum.item() - 162.52383422851562				) < 1e-2
                 assert abs(result_mean.item() - 0.211619570851326				) < 1e-3

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Dict			=						self.scheduler_classes[0]
           _A							:     Optional[int]			=						self.get_scheduler_config(prediction_type="""v_prediction"""				)
           _A							:     Optional[Any]			=						scheduler_class(**_a				)

           scheduler.set_timesteps(self.num_inference_steps				)

           _A							:     Tuple			=						self.dummy_model()
           _A							:     int			=						self.dummy_sample_deter * scheduler.init_noise_sigma
           _A							:     Tuple			=						sample.to(_a				)

           for i, t in enumerate(scheduler.timesteps				):
                 _A							:     int			=						scheduler.scale_model_input(_a		,     _a				)

                 _A							:     Tuple			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Optional[int]			=						output.prev_sample

           _A							:     Optional[Any]			=						torch.sum(torch.abs(_a				)				)
           _A							:     List[Any]			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 124.77149200439453				) < 1e-2
                 assert abs(result_mean.item() - 0.16226289014816284				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 128.1663360595703				) < 1e-2
                 assert abs(result_mean.item() - 0.16688326001167297				) < 1e-3
           else:
                 assert abs(result_sum.item() - 119.8487548828125				) < 1e-2
                 assert abs(result_mean.item() - 0.1560530662536621				) < 1e-3

     def 					a__	(  self				)						->							List[str]:
           _A							:     Union[str, Any]			=						self.scheduler_classes[0]
           _A							:     List[Any]			=						self.get_scheduler_config()
           _A							:     List[str]			=						scheduler_class(**_a				)

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

           _A							:     Union[str, Any]			=						self.dummy_model()
           _A							:     Optional[Any]			=						self.dummy_sample_deter.to(_a				) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 _A							:     int			=						scheduler.scale_model_input(_a		,     _a				)

                 _A							:     List[Any]			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Dict			=						output.prev_sample

           _A							:     str			=						torch.sum(torch.abs(_a				)				)
           _A							:     str			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 167.46957397460938				) < 1e-2
                 assert abs(result_mean.item() - 0.21805934607982635				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 171.59353637695312				) < 1e-2
                 assert abs(result_mean.item() - 0.22342908382415771				) < 1e-3
           else:
                 assert abs(result_sum.item() - 162.52383422851562				) < 1e-2
                 assert abs(result_mean.item() - 0.211619570851326				) < 1e-3



     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     List[Any]			=						self.scheduler_classes[0]
           _A							:     Optional[Any]			=						self.get_scheduler_config()
           _A							:     int			=						scheduler_class(**_a		,     use_karras_sigmas=_a				)

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

           _A							:     Optional[Any]			=						self.dummy_model()
           _A							:     Dict			=						self.dummy_sample_deter.to(_a				) * scheduler.init_noise_sigma
           _A							:     str			=						sample.to(_a				)

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

                 _A							:     List[Any]			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     List[str]			=						output.prev_sample

           _A							:     str			=						torch.sum(torch.abs(_a				)				)
           _A							:     List[str]			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 176.66974135742188				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 177.63653564453125				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2
           else:
                 assert abs(result_sum.item() - 170.3135223388672				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2

