





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = (UniPCMultistepScheduler,)
   lowerCAmelCase__        = (("""num_inference_steps""", 25),)




   def _lowerCAmelCase      (self	:Optional[int]    ,   **_UpperCamelCase	:Optional[int] )->					Optional[Any]:
         __A          =				{
             '''num_train_timesteps''': 1000,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
             '''solver_order''': 2,
             '''solver_type''': '''bh2''',
         }

         config.update(**_UpperCamelCase )
         return config




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:Union[str, Any]=0    ,   **_UpperCamelCase	:Optional[Any] )->					Optional[int]:
         __A          =				dict(self.forward_default_kwargs )
         __A          =				kwargs.pop('''num_inference_steps'''    ,   _UpperCamelCase )
         __A          =				self.dummy_sample
         __A          =				0.1 * sample
         __A          =				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
               __A          =				self.get_scheduler_config(**_UpperCamelCase )
               __A          =				scheduler_class(**_UpperCamelCase )
               scheduler.set_timesteps(_UpperCamelCase )
               # copy over dummy past residuals
               __A          =				dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                     scheduler.save_config(_UpperCamelCase )
                     __A          =				scheduler_class.from_pretrained(_UpperCamelCase )
                     new_scheduler.set_timesteps(_UpperCamelCase )
                     # copy over dummy past residuals
                     __A          =				dummy_past_residuals[: new_scheduler.config.solver_order]

               __A   ,						__A          =				sample, sample
               for t in range(_UpperCamelCase    ,   time_step + scheduler.config.solver_order + 1 ):
                     __A          =				scheduler.step(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   **_UpperCamelCase ).prev_sample
                     __A          =				new_scheduler.step(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   **_UpperCamelCase ).prev_sample

                     assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"




   def _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:Union[str, Any]=0    ,   **_UpperCamelCase	:List[str] )->					Tuple:
         __A          =				dict(self.forward_default_kwargs )
         __A          =				kwargs.pop('''num_inference_steps'''    ,   _UpperCamelCase )
         __A          =				self.dummy_sample
         __A          =				0.1 * sample
         __A          =				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
               __A          =				self.get_scheduler_config()
               __A          =				scheduler_class(**_UpperCamelCase )
               scheduler.set_timesteps(_UpperCamelCase )

               # copy over dummy past residuals (must be after setting timesteps)
               __A          =				dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                     scheduler.save_config(_UpperCamelCase )
                     __A          =				scheduler_class.from_pretrained(_UpperCamelCase )
                     # copy over dummy past residuals
                     new_scheduler.set_timesteps(_UpperCamelCase )

                     # copy over dummy past residual (must be after setting timesteps)
                     __A          =				dummy_past_residuals[: new_scheduler.config.solver_order]

               __A          =				scheduler.step(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   **_UpperCamelCase ).prev_sample
               __A          =				new_scheduler.step(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   **_UpperCamelCase ).prev_sample

               assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"




   def _lowerCAmelCase      (self	:Union[str, Any]    ,   _UpperCamelCase	:Dict=None    ,   **_UpperCamelCase	:str )->					List[Any]:
         if scheduler is None:
               __A          =				self.scheduler_classes[0]
               __A          =				self.get_scheduler_config(**_UpperCamelCase )
               __A          =				scheduler_class(**_UpperCamelCase )

         __A          =				self.scheduler_classes[0]
         __A          =				self.get_scheduler_config(**_UpperCamelCase )
         __A          =				scheduler_class(**_UpperCamelCase )

         __A          =				10
         __A          =				self.dummy_model()
         __A          =				self.dummy_sample_deter
         scheduler.set_timesteps(_UpperCamelCase )

         for i, t in enumerate(scheduler.timesteps ):
               __A          =				model(_UpperCamelCase    ,   _UpperCamelCase )
               __A          =				scheduler.step(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase ).prev_sample

         return sample




   def _lowerCAmelCase      (self	:Optional[Any] )->					Optional[Any]:
         __A          =				dict(self.forward_default_kwargs )

         __A          =				kwargs.pop('''num_inference_steps'''    ,   _UpperCamelCase )

         for scheduler_class in self.scheduler_classes:
               __A          =				self.get_scheduler_config()
               __A          =				scheduler_class(**_UpperCamelCase )

               __A          =				self.dummy_sample
               __A          =				0.1 * sample

               if num_inference_steps is not None and hasattr(_UpperCamelCase    ,   '''set_timesteps''' ):
                     scheduler.set_timesteps(_UpperCamelCase )
               elif num_inference_steps is not None and not hasattr(_UpperCamelCase    ,   '''set_timesteps''' ):
                     __A          =				num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               __A          =				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
               __A          =				dummy_past_residuals[: scheduler.config.solver_order]

               __A          =				scheduler.timesteps[5]
               __A          =				scheduler.timesteps[6]

               __A          =				scheduler.step(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   **_UpperCamelCase ).prev_sample
               __A          =				scheduler.step(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   **_UpperCamelCase ).prev_sample

               self.assertEqual(output_a.shape    ,   sample.shape )
               self.assertEqual(output_a.shape    ,   output_a.shape )




   def _lowerCAmelCase      (self	:int )->					int:
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         __A          =				UniPCMultistepScheduler(**self.get_scheduler_config() )
         __A          =				self.full_loop(scheduler=_UpperCamelCase )
         __A          =				torch.mean(torch.abs(_UpperCamelCase ) )

         assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3

         __A          =				DPMSolverSinglestepScheduler.from_config(scheduler.config )
         __A          =				DEISMultistepScheduler.from_config(scheduler.config )
         __A          =				DPMSolverMultistepScheduler.from_config(scheduler.config )
         __A          =				UniPCMultistepScheduler.from_config(scheduler.config )

         __A          =				self.full_loop(scheduler=_UpperCamelCase )
         __A          =				torch.mean(torch.abs(_UpperCamelCase ) )

         assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3




   def _lowerCAmelCase      (self	:Optional[Any] )->					List[str]:
         for timesteps in [25, 50, 100, 999, 1000]:
               self.check_over_configs(num_train_timesteps=_UpperCamelCase )




   def _lowerCAmelCase      (self	:List[Any] )->					Any:
         self.check_over_configs(thresholding=_UpperCamelCase )
         for order in [1, 2, 3]:
               for solver_type in ["bh1", "bh2"]:
                     for threshold in [0.5, 1.0, 2.0]:
                           for prediction_type in ["epsilon", "sample"]:
                                 self.check_over_configs(
                                     thresholding=_UpperCamelCase    ,   prediction_type=_UpperCamelCase    ,   sample_max_value=_UpperCamelCase    ,   solver_order=_UpperCamelCase    ,   solver_type=_UpperCamelCase    ,   )




   def _lowerCAmelCase      (self	:int )->					List[Any]:
         for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=_UpperCamelCase )




   def _lowerCAmelCase      (self	:Dict )->					Tuple:
         for solver_type in ["bh1", "bh2"]:
               for order in [1, 2, 3]:
                     for prediction_type in ["epsilon", "sample"]:
                           self.check_over_configs(
                               solver_order=_UpperCamelCase    ,   solver_type=_UpperCamelCase    ,   prediction_type=_UpperCamelCase    ,   )
                           __A          =				self.full_loop(
                               solver_order=_UpperCamelCase    ,   solver_type=_UpperCamelCase    ,   prediction_type=_UpperCamelCase    ,   )
                           assert not torch.isnan(_UpperCamelCase ).any(), "Samples have nan numbers"




   def _lowerCAmelCase      (self	:Union[str, Any] )->					List[Any]:
         self.check_over_configs(lower_order_final=_UpperCamelCase )
         self.check_over_configs(lower_order_final=_UpperCamelCase )




   def _lowerCAmelCase      (self	:Any )->					Optional[int]:
         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
               self.check_over_forward(num_inference_steps=_UpperCamelCase    ,   time_step=0 )




   def _lowerCAmelCase      (self	:Dict )->					Any:
         __A          =				self.full_loop()
         __A          =				torch.mean(torch.abs(_UpperCamelCase ) )

         assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3




   def _lowerCAmelCase      (self	:List[Any] )->					Tuple:
         __A          =				self.full_loop(prediction_type='''v_prediction''' )
         __A          =				torch.mean(torch.abs(_UpperCamelCase ) )

         assert abs(result_mean.item() - 0.1_0_1_4 ) < 1e-3




   def _lowerCAmelCase      (self	:int )->					Optional[Any]:
         __A          =				self.scheduler_classes[0]
         __A          =				self.get_scheduler_config(thresholding=_UpperCamelCase    ,   dynamic_thresholding_ratio=0 )
         __A          =				scheduler_class(**_UpperCamelCase )

         __A          =				10
         __A          =				self.dummy_model()
         __A          =				self.dummy_sample_deter.half()
         scheduler.set_timesteps(_UpperCamelCase )

         for i, t in enumerate(scheduler.timesteps ):
               __A          =				model(_UpperCamelCase    ,   _UpperCamelCase )
               __A          =				scheduler.step(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase ).prev_sample

         assert sample.dtype == torch.floataa






   def _lowerCAmelCase      (self	:int    ,   **_UpperCamelCase	:Any )->					Union[str, Any]:
         for scheduler_class in self.scheduler_classes:
               __A          =				self.get_scheduler_config(**_UpperCamelCase )
               __A          =				scheduler_class(**_UpperCamelCase )

               scheduler.set_timesteps(scheduler.config.num_train_timesteps )
               assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps












def _a				(	lowerCamelCase:   int = 2_00 )	->						int:







      '''simple docstring'''






      __A          =				[1, 2, 5, 10, 20, 50, 1_00, 2_00]
      __A          =				[0] * (pence + 1)
      __A          =				1  # base case: 1 way to make 0 pence

      for coin in coins:
            for i in range(lowerCamelCase					,				pence + 1					,				1 ):
                  number_of_ways[i] += number_of_ways[i - coin]
      return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73682





