

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       SCREAMING_SNAKE_CASE__					(SCREAMING_SNAKE_CASE_     ):
    __lowerCamelCase						:       str														=     (DPMSolverSDEScheduler,)
    __lowerCamelCase						:       Optional[int]														=     10





    def       snake_case_				(      self						,   **a):
         lowercase__    :   Optional[int]      =       {
             """num_train_timesteps""": 1100,
             """beta_start""": 0.0_001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
             """noise_sampler_seed""": 0,
         }

         config.update(**a__)
         return config





    def       snake_case_				(      self):
         for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=a__)





    def       snake_case_				(      self):
         for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]						,   [0.0_002, 0.002, 0.02]):
              self.check_over_configs(beta_start=a__						,   beta_end=a__)





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





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





    def       snake_case_				(      self):
         lowercase__    :   Tuple      =       self.scheduler_classes[0]
         lowercase__    :   Any      =       self.get_scheduler_config()
         lowercase__    :   Tuple      =       scheduler_class(**a__)

         scheduler.set_timesteps(self.num_inference_steps)

         lowercase__    :   int      =       self.dummy_model()
         lowercase__    :   Union[str, Any]      =       self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase__    :   int      =       sample.to(a__)

         for i, t in enumerate(scheduler.timesteps):
              lowercase__    :   int      =       scheduler.scale_model_input(a__						,   a__)

              lowercase__    :   Optional[int]      =       model(a__						,   a__)

              lowercase__    :   List[Any]      =       scheduler.step(a__						,   a__						,   a__)
              lowercase__    :   Dict      =       output.prev_sample

         lowercase__    :   Dict      =       torch.sum(torch.abs(a__))
         lowercase__    :   Optional[Any]      =       torch.mean(torch.abs(a__))

         if torch_device in ["mps"]:
              assert abs(result_sum.item() - 167.47_8210_4492_1875) < 1e-2
              assert abs(result_mean.item() - 0.2_178_705_964_565_277) < 1e-3
         elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 171.59_3521_1181_6406) < 1e-2
              assert abs(result_mean.item() - 0.22_342_906_892_299_652) < 1e-3
         else:
              assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
              assert abs(result_mean.item() - 0.211_619_570_851_326) < 1e-3





    def       snake_case_				(      self):
         lowercase__    :   Dict      =       self.scheduler_classes[0]
         lowercase__    :   str      =       self.get_scheduler_config(prediction_type='v_prediction')
         lowercase__    :   Union[str, Any]      =       scheduler_class(**a__)

         scheduler.set_timesteps(self.num_inference_steps)

         lowercase__    :   Any      =       self.dummy_model()
         lowercase__    :   List[str]      =       self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase__    :   List[Any]      =       sample.to(a__)

         for i, t in enumerate(scheduler.timesteps):
              lowercase__    :   int      =       scheduler.scale_model_input(a__						,   a__)

              lowercase__    :   int      =       model(a__						,   a__)

              lowercase__    :   List[str]      =       scheduler.step(a__						,   a__						,   a__)
              lowercase__    :   Optional[int]      =       output.prev_sample

         lowercase__    :   Any      =       torch.sum(torch.abs(a__))
         lowercase__    :   str      =       torch.mean(torch.abs(a__))

         if torch_device in ["mps"]:
              assert abs(result_sum.item() - 124.77_1492_0043_9453) < 1e-2
              assert abs(result_mean.item() - 0.16_226_289_014_816_284) < 1e-3
         elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 128.1_6633_6059_5703) < 1e-2
              assert abs(result_mean.item() - 0.16_688_326_001_167_297) < 1e-3
         else:
              assert abs(result_sum.item() - 119.8_4875_4882_8125) < 1e-2
              assert abs(result_mean.item() - 0.1_560_530_662_536_621) < 1e-3





    def       snake_case_				(      self):
         lowercase__    :   Union[str, Any]      =       self.scheduler_classes[0]
         lowercase__    :   Optional[Any]      =       self.get_scheduler_config()
         lowercase__    :   str      =       scheduler_class(**a__)

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

         lowercase__    :   Tuple      =       self.dummy_model()
         lowercase__    :   Optional[Any]      =       self.dummy_sample_deter.to(a__) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
              lowercase__    :   Union[str, Any]      =       scheduler.scale_model_input(a__						,   a__)

              lowercase__    :   Tuple      =       model(a__						,   a__)

              lowercase__    :   Optional[int]      =       scheduler.step(a__						,   a__						,   a__)
              lowercase__    :   List[str]      =       output.prev_sample

         lowercase__    :   int      =       torch.sum(torch.abs(a__))
         lowercase__    :   List[str]      =       torch.mean(torch.abs(a__))

         if torch_device in ["mps"]:
              assert abs(result_sum.item() - 167.46_9573_9746_0938) < 1e-2
              assert abs(result_mean.item() - 0.21_805_934_607_982_635) < 1e-3
         elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 171.59_3536_3769_5312) < 1e-2
              assert abs(result_mean.item() - 0.22_342_908_382_415_771) < 1e-3
         else:
              assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
              assert abs(result_mean.item() - 0.211_619_570_851_326) < 1e-3





    def       snake_case_				(      self):
         lowercase__    :   Any      =       self.scheduler_classes[0]
         lowercase__    :   Dict      =       self.get_scheduler_config()
         lowercase__    :   Optional[int]      =       scheduler_class(**a__						,   use_karras_sigmas=a__)

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

         lowercase__    :   Any      =       self.dummy_model()
         lowercase__    :   int      =       self.dummy_sample_deter.to(a__) * scheduler.init_noise_sigma
         lowercase__    :   int      =       sample.to(a__)

         for t in scheduler.timesteps:
              lowercase__    :   Optional[Any]      =       scheduler.scale_model_input(a__						,   a__)

              lowercase__    :   Optional[int]      =       model(a__						,   a__)

              lowercase__    :   Union[str, Any]      =       scheduler.step(a__						,   a__						,   a__)
              lowercase__    :   Tuple      =       output.prev_sample

         lowercase__    :   List[Any]      =       torch.sum(torch.abs(a__))
         lowercase__    :   Dict      =       torch.mean(torch.abs(a__))

         if torch_device in ["mps"]:
              assert abs(result_sum.item() - 176.66_9741_3574_2188) < 1e-2
              assert abs(result_mean.item() - 0.23_003_872_730_981_811) < 1e-2
         elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 177.63_6535_6445_3125) < 1e-2
              assert abs(result_mean.item() - 0.23_003_872_730_981_811) < 1e-2
         else:
              assert abs(result_sum.item() - 170.3_1352_2338_8672) < 1e-2
              assert abs(result_mean.item() - 0.23_003_872_730_981_811) < 1e-2


"""simple docstring"""




def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int = 50	)       ->      int:
  _lowerCAmelCase						:       int  			=     [1] * (length + 1)

  for row_length in range(3			,length + 1	):
    for block_length in range(3			,row_length + 1	):
      for block_start in range(row_length - block_length	):
        ways_number[row_length] += ways_number[
            row_length - block_start - block_length - 1
        ]

      ways_number[row_length] += 1

  return ways_number[length]


if __name__ == "__main__":
     print(F"""{solution() = }""")




