




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      _snake_case												=						(UnCLIPScheduler,)




      def      UpperCAmelCase				(				self    ,				**A     )		->    Any:
         snake_case				:    List[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(**A     )
         return config




      def      UpperCAmelCase				(				self     )		->    List[Any]:
         for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=A     )




      def      UpperCAmelCase				(				self     )		->    Optional[int]:
         for variance in ["fixed_small_log", "learned_range"]:
            self.check_over_configs(variance_type=A     )




      def      UpperCAmelCase				(				self     )		->    Tuple:
         for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=A     )




      def      UpperCAmelCase				(				self     )		->    str:
         for clip_sample_range in [1, 5, 1_0, 2_0]:
            self.check_over_configs(clip_sample_range=A     )




      def      UpperCAmelCase				(				self     )		->    str:
         for prediction_type in ["epsilon", "sample"]:
            self.check_over_configs(prediction_type=A     )




      def      UpperCAmelCase				(				self     )		->    Any:
         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=A    ,				prev_timestep=A     )




      def      UpperCAmelCase				(				self     )		->    List[Any]:
         snake_case				:    Union[str, Any]           =	self.scheduler_classes[0]
         snake_case				:    Tuple           =	self.get_scheduler_config(variance_type="""fixed_small_log"""     )
         snake_case				:    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_8_7     ) - 0.0_54_96_25     )     ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9     ) - 0.9_99_49_87     )     ) < 1e-5




      def      UpperCAmelCase				(				self     )		->    Optional[int]:
         snake_case				:    Optional[int]           =	self.scheduler_classes[0]
         snake_case				:    Tuple           =	self.get_scheduler_config(variance_type="""learned_range"""     )
         snake_case				:    Tuple           =	scheduler_class(**A     )

         snake_case				:    Any           =	0.5

         assert scheduler._get_variance(1    ,				predicted_variance=A     ) - -10.1_71_27_90 < 1e-5
         assert scheduler._get_variance(4_8_7    ,				predicted_variance=A     ) - -5.7_99_80_52 < 1e-5
         assert scheduler._get_variance(9_9_9    ,				predicted_variance=A     ) - -0.0_01_00_11 < 1e-5




      def      UpperCAmelCase				(				self     )		->    int:
         snake_case				:    List[Any]           =	self.scheduler_classes[0]
         snake_case				:    str           =	self.get_scheduler_config()
         snake_case				:    str           =	scheduler_class(**A     )

         snake_case				:    Any           =	scheduler.timesteps

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

         for i, t in enumerate(A     ):
            # 1. predict noise residual
            snake_case				:    List[str]           =	model(A    ,				A     )

            # 2. predict previous mean of sample x_t-1
            snake_case				:    Optional[Any]           =	scheduler.step(A    ,				A    ,				A    ,				generator=A     ).prev_sample

            snake_case				:    Tuple           =	pred_prev_sample

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

         assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5     ) < 1e-2
         assert abs(result_mean.item() - 0.3_28_47_43     ) < 1e-3




      def      UpperCAmelCase				(				self     )		->    List[str]:
         snake_case				:    Optional[int]           =	self.scheduler_classes[0]
         snake_case				:    Optional[Any]           =	self.get_scheduler_config()
         snake_case				:    List[Any]           =	scheduler_class(**A     )

         scheduler.set_timesteps(2_5     )

         snake_case				:    List[Any]           =	scheduler.timesteps

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

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

            if i + 1 == timesteps.shape[0]:
               snake_case				:    List[str]           =	None
            else:
               snake_case				:    List[Any]           =	timesteps[i + 1]

            # 2. predict previous mean of sample x_t-1
            snake_case				:    Dict           =	scheduler.step(
                A    ,				A    ,				A    ,				prev_timestep=A    ,				generator=A     ).prev_sample

            snake_case				:    Optional[int]           =	pred_prev_sample

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

         assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3     ) < 1e-2
         assert abs(result_mean.item() - 0.3_36_20_38     ) < 1e-3




      def      UpperCAmelCase				(				self     )		->    Tuple:
         pass




      def      UpperCAmelCase				(				self     )		->    Any:
         pass











import functools




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       int:

   # Validation
   if not isinstance(lowercase						,lowercase					) or not all(isinstance(lowercase						,lowercase					) for day in days					):
      raise ValueError("""The parameter days should be a list of integers"""					)

   if len(lowercase					) != 3 or not all(isinstance(lowercase						,lowercase					) for cost in costs					):
      raise ValueError("""The parameter costs should be a list of three integers"""					)

   if len(lowercase					) == 0:
      return 0

   if min(lowercase					) <= 0:
      raise ValueError("""All days elements should be greater than 0"""					)

   if max(lowercase					) >= 366:
      raise ValueError("""All days elements should be less than 366"""					)

   snake_case				:    List[str]           =	set(lowercase					)

   @functools.cache
   def dynamic_programming(lowercase					) -> int:
      if index > 365:
         return 0

      if index not in days_set:
         return dynamic_programming(index + 1					)

      return min(
          costs[0] + dynamic_programming(index + 1					)						,costs[1] + dynamic_programming(index + 7					)						,costs[2] + dynamic_programming(index + 30					)						,)

   return dynamic_programming(1					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





