




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 				UpperCamelCase_    ( _UpperCAmelCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      (KDPMaDiscreteScheduler,)
    UpperCAmelCase__							  =      10


    def        SCREAMING_SNAKE_CASE							(			self      :	int					, **UpperCAmelCase__      :	Any)						->Union[str, Any]:





           '''simple docstring'''

           A__       =    {
               '''num_train_timesteps''': 1_100,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
           }

           config.update(**A_)
           return config


    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->Dict:





           '''simple docstring'''

           for timesteps in [10, 50, 100, 1_000]:
                  self.check_over_configs(num_train_timesteps=A_)


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->List[str]:





           '''simple docstring'''

           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        SCREAMING_SNAKE_CASE							(			self      :	str)						->str:





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->List[Any]:





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->int:





           '''simple docstring'''

           A__       =    self.scheduler_classes[0]
           A__       =    self.get_scheduler_config(prediction_type='''v_prediction''')
           A__       =    scheduler_class(**A_)

           scheduler.set_timesteps(self.num_inference_steps)

           A__       =    self.dummy_model()
           A__       =    self.dummy_sample_deter * scheduler.init_noise_sigma
           A__       =    sample.to(A_)

           for i, t in enumerate(scheduler.timesteps):
                  A__       =    scheduler.scale_model_input(A_					, A_)

                  A__       =    model(A_					, A_)

                  A__       =    scheduler.step(A_					, A_					, A_)
                  A__       =    output.prev_sample

           A__       =    torch.sum(torch.abs(A_))
           A__       =    torch.mean(torch.abs(A_))

           if torch_device in ["cpu", "mps"]:
                  assert abs(result_sum.item() - 4.69_34e-07) < 1e-2
                  assert abs(result_mean.item() - 6.11_12e-10) < 1e-3
           else:
                  # CUDA
                  assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07) < 1e-2
                  assert abs(result_mean.item() - 0.0002) < 1e-3


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





           '''simple docstring'''

           if torch_device == "mps":
                  return
           A__       =    self.scheduler_classes[0]
           A__       =    self.get_scheduler_config()
           A__       =    scheduler_class(**A_)

           scheduler.set_timesteps(self.num_inference_steps)

           A__       =    self.dummy_model()
           A__       =    self.dummy_sample_deter * scheduler.init_noise_sigma
           A__       =    sample.to(A_)

           for i, t in enumerate(scheduler.timesteps):
                  A__       =    scheduler.scale_model_input(A_					, A_)

                  A__       =    model(A_					, A_)

                  A__       =    scheduler.step(A_					, A_					, A_)
                  A__       =    output.prev_sample

           A__       =    torch.sum(torch.abs(A_))
           A__       =    torch.mean(torch.abs(A_))

           if torch_device in ["cpu", "mps"]:
                  assert abs(result_sum.item() - 20.4125) < 1e-2
                  assert abs(result_mean.item() - 0.0266) < 1e-3
           else:
                  # CUDA
                  assert abs(result_sum.item() - 20.4125) < 1e-2
                  assert abs(result_mean.item() - 0.0266) < 1e-3





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





           '''simple docstring'''

           if torch_device == "mps":
                  return
           A__       =    self.scheduler_classes[0]
           A__       =    self.get_scheduler_config()
           A__       =    scheduler_class(**A_)

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

           A__       =    self.dummy_model()
           A__       =    self.dummy_sample_deter.to(A_) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                  A__       =    scheduler.scale_model_input(A_					, A_)

                  A__       =    model(A_					, A_)

                  A__       =    scheduler.step(A_					, A_					, A_)
                  A__       =    output.prev_sample

           A__       =    torch.sum(torch.abs(A_))
           A__       =    torch.mean(torch.abs(A_))

           if str(A_).startswith('''cpu'''):
                  # The following sum varies between 148 and 156 on mps. Why?
                  assert abs(result_sum.item() - 20.4125) < 1e-2
                  assert abs(result_mean.item() - 0.0266) < 1e-3
           else:
                  # CUDA
                  assert abs(result_sum.item() - 20.4125) < 1e-2
                  assert abs(result_mean.item() - 0.0266) < 1e-3





def 		_lowercase (     lowercase__ = 2_0_0						):
      __lowerCAmelCase					:						Union[str, Any]					=					[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
      __lowerCAmelCase					:						Dict					=					[0] * (pence + 1)
      __lowerCAmelCase					:						Optional[int]					=					1  # base case: 1 way to make 0 pence

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


if __name__ == "__main__":
 assert solution(200) == 7_3682


