






'''simple docstring'''





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class  _lowercase   (   _A						):
      _a							:						str										=							(UnCLIPScheduler,)
      def 						lowercase__					(     self				,			**a   ):
       snake_case__  :    Optional[Any]							       ={
           """num_train_timesteps""": 1_0_0_0,
           """variance_type""": """fixed_small_log""",
           """clip_sample""": True,
           """clip_sample_range""": 1.0,
           """prediction_type""": """epsilon""",
       }

       config.update(**_UpperCAmelCase   )
       return config
      def 						lowercase__					(     self   ):




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




       for variance in ["fixed_small_log", "learned_range"]:
        self.check_over_configs(variance_type=_UpperCAmelCase   )
      def 						lowercase__					(     self   ):




       for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=_UpperCAmelCase   )
      def 						lowercase__					(     self   ):




       for clip_sample_range in [1, 5, 1_0, 2_0]:
        self.check_over_configs(clip_sample_range=_UpperCAmelCase   )
      def 						lowercase__					(     self   ):




       for prediction_type in ["epsilon", "sample"]:
        self.check_over_configs(prediction_type=_UpperCAmelCase   )
      def 						lowercase__					(     self   ):




       for time_step in [0, 5_0_0, 9_9_9]:
        for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
         if prev_timestep is not None and prev_timestep >= time_step:
          continue

         self.check_over_forward(time_step=_UpperCAmelCase				,			prev_timestep=_UpperCAmelCase   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =self.scheduler_classes[0]
       snake_case__  :    Any							       =self.get_scheduler_config(variance_type="""fixed_small_log"""   )
       snake_case__  :    Dict							       =scheduler_class(**_UpperCAmelCase   )

       assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.00_00e-10   )   ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_8_7   ) - 0.0549625   )   ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_9_9   ) - 0.9994987   )   ) < 1e-5
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =self.scheduler_classes[0]
       snake_case__  :    int							       =self.get_scheduler_config(variance_type="""learned_range"""   )
       snake_case__  :    Optional[Any]							       =scheduler_class(**_UpperCAmelCase   )

       snake_case__  :    Optional[Any]							       =0.5

       assert scheduler._get_variance(1				,			predicted_variance=_UpperCAmelCase   ) - -10.1712790 < 1e-5
       assert scheduler._get_variance(4_8_7				,			predicted_variance=_UpperCAmelCase   ) - -5.7998052 < 1e-5
       assert scheduler._get_variance(9_9_9				,			predicted_variance=_UpperCAmelCase   ) - -0.0010011 < 1e-5
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =self.scheduler_classes[0]
       snake_case__  :    Optional[Any]							       =self.get_scheduler_config()
       snake_case__  :    Union[str, Any]							       =scheduler_class(**_UpperCAmelCase   )

       snake_case__  :    List[Any]							       =scheduler.timesteps

       snake_case__  :    str							       =self.dummy_model()
       snake_case__  :    Optional[int]							       =self.dummy_sample_deter
       snake_case__  :    str							       =torch.manual_seed(0   )

       for i, t in enumerate(_UpperCAmelCase   ):
        # 1. predict noise residual
        snake_case__  :    Union[str, Any]							       =model(_UpperCAmelCase				,			_UpperCAmelCase   )

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

        snake_case__  :    Optional[Any]							       =pred_prev_sample

       snake_case__  :    List[str]							       =torch.sum(torch.abs(_UpperCAmelCase   )   )
       snake_case__  :    Union[str, Any]							       =torch.mean(torch.abs(_UpperCAmelCase   )   )

       assert abs(result_sum.item() - 252.2682495   ) < 1e-2
       assert abs(result_mean.item() - 0.3284743   ) < 1e-3
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =self.scheduler_classes[0]
       snake_case__  :    Any							       =self.get_scheduler_config()
       snake_case__  :    Any							       =scheduler_class(**_UpperCAmelCase   )

       scheduler.set_timesteps(2_5   )

       snake_case__  :    List[str]							       =scheduler.timesteps

       snake_case__  :    int							       =self.dummy_model()
       snake_case__  :    int							       =self.dummy_sample_deter
       snake_case__  :    Dict							       =torch.manual_seed(0   )

       for i, t in enumerate(_UpperCAmelCase   ):
        # 1. predict noise residual
        snake_case__  :    int							       =model(_UpperCAmelCase				,			_UpperCAmelCase   )

        if i + 1 == timesteps.shape[0]:
         snake_case__  :    Optional[int]							       =None
        else:
         snake_case__  :    int							       =timesteps[i + 1]

        # 2. predict previous mean of sample x_t-1
        snake_case__  :    str							       =scheduler.step(
            _UpperCAmelCase				,			_UpperCAmelCase				,			_UpperCAmelCase				,			prev_timestep=_UpperCAmelCase				,			generator=_UpperCAmelCase   ).prev_sample

        snake_case__  :    Union[str, Any]							       =pred_prev_sample

       snake_case__  :    List[Any]							       =torch.sum(torch.abs(_UpperCAmelCase   )   )
       snake_case__  :    Union[str, Any]							       =torch.mean(torch.abs(_UpperCAmelCase   )   )

       assert abs(result_sum.item() - 258.2044983   ) < 1e-2
       assert abs(result_mean.item() - 0.3362038   ) < 1e-3
      def 						lowercase__					(     self   ):
       pass



      def 						lowercase__					(     self   ):
       pass













__lowerCamelCase	:		dict[str, float]										=   {
    "km/h": 1.0,
    "m/s": 3.6,
    "mph": 1.60_93_44,
    "knot": 1.8_52,
}

__lowerCamelCase	:		dict[str, float]										=   {
    "km/h": 1.0,
    "m/s": 0.2_77_77_77_78,
    "mph": 0.6_21_37_11_92,
    "knot": 0.5_39_95_68_03,
}







def 			A__							(      _a  :					float		,  _a  :					str		,  _a  :					str	):





 '''simple docstring'''







 if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
  snake_case__  :    Tuple							       =(
      f"Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"
      f"Valid values are: {', '.join(_a	)}"
  )
  raise ValueError(_a	)
 return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to]		,  3	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





