



'''simple docstring'''



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 							UpperCAmelCase__	(     UpperCAmelCase_):
 __SCREAMING_SNAKE_CASE          =					(DPMSolverSDEScheduler,)
 __SCREAMING_SNAKE_CASE          =					1_0



 def  __lowerCamelCase					(	self		,						**lowercase      )	->				Optional[Any]:
  __UpperCamelCase					   =	{
      """num_train_timesteps""": 1_1_0_0,
      """beta_start""": 0.0_001,
      """beta_end""": 0.02,
      """beta_schedule""": """linear""",
      """noise_sampler_seed""": 0,
  }

  config.update(**__SCREAMING_SNAKE_CASE      )
  return config



 def  __lowerCamelCase					(	self      )	->				Union[str, Any]:




  for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
   self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE      )



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




  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=__SCREAMING_SNAKE_CASE		,						beta_end=__SCREAMING_SNAKE_CASE      )



 def  __lowerCamelCase					(	self      )	->				Any:




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



 def  __lowerCamelCase					(	self      )	->				int:




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



 def  __lowerCamelCase					(	self      )	->				Optional[Any]:
  __UpperCamelCase					   =	self.scheduler_classes[0]
  __UpperCamelCase					   =	self.get_scheduler_config()
  __UpperCamelCase					   =	scheduler_class(**__SCREAMING_SNAKE_CASE      )

  scheduler.set_timesteps(self.num_inference_steps      )

  __UpperCamelCase					   =	self.dummy_model()
  __UpperCamelCase					   =	self.dummy_sample_deter * scheduler.init_noise_sigma
  __UpperCamelCase					   =	sample.to(__SCREAMING_SNAKE_CASE      )

  for i, t in enumerate(scheduler.timesteps      ):
   __UpperCamelCase					   =	scheduler.scale_model_input(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE      )

   __UpperCamelCase					   =	model(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE      )

   __UpperCamelCase					   =	scheduler.step(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE      )
   __UpperCamelCase					   =	output.prev_sample

  __UpperCamelCase					   =	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE      )      )
  __UpperCamelCase					   =	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE      )      )

  if torch_device in ["mps"]:
   assert abs(result_sum.item() - 167.47_821_044_921_875      ) < 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_352_111_816_406      ) < 1E-2
   assert abs(result_mean.item() - 0.22_342_906_892_299_652      ) < 1E-3
  else:
   assert abs(result_sum.item() - 162.52_383_422_851_562      ) < 1E-2
   assert abs(result_mean.item() - 0.211_619_570_851_326      ) < 1E-3



 def  __lowerCamelCase					(	self      )	->				List[str]:
  __UpperCamelCase					   =	self.scheduler_classes[0]
  __UpperCamelCase					   =	self.get_scheduler_config(prediction_type="""v_prediction"""      )
  __UpperCamelCase					   =	scheduler_class(**__SCREAMING_SNAKE_CASE      )

  scheduler.set_timesteps(self.num_inference_steps      )

  __UpperCamelCase					   =	self.dummy_model()
  __UpperCamelCase					   =	self.dummy_sample_deter * scheduler.init_noise_sigma
  __UpperCamelCase					   =	sample.to(__SCREAMING_SNAKE_CASE      )

  for i, t in enumerate(scheduler.timesteps      ):
   __UpperCamelCase					   =	scheduler.scale_model_input(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE      )

   __UpperCamelCase					   =	model(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE      )

   __UpperCamelCase					   =	scheduler.step(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE      )
   __UpperCamelCase					   =	output.prev_sample

  __UpperCamelCase					   =	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE      )      )
  __UpperCamelCase					   =	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE      )      )

  if torch_device in ["mps"]:
   assert abs(result_sum.item() - 124.77_149_200_439_453      ) < 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_663_360_595_703      ) < 1E-2
   assert abs(result_mean.item() - 0.16_688_326_001_167_297      ) < 1E-3
  else:
   assert abs(result_sum.item() - 119.8_487_548_828_125      ) < 1E-2
   assert abs(result_mean.item() - 0.1_560_530_662_536_621      ) < 1E-3



 def  __lowerCamelCase					(	self      )	->				Union[str, Any]:
  __UpperCamelCase					   =	self.scheduler_classes[0]
  __UpperCamelCase					   =	self.get_scheduler_config()
  __UpperCamelCase					   =	scheduler_class(**__SCREAMING_SNAKE_CASE      )

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

  __UpperCamelCase					   =	self.dummy_model()
  __UpperCamelCase					   =	self.dummy_sample_deter.to(__SCREAMING_SNAKE_CASE      ) * scheduler.init_noise_sigma

  for t in scheduler.timesteps:
   __UpperCamelCase					   =	scheduler.scale_model_input(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE      )

   __UpperCamelCase					   =	model(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE      )

   __UpperCamelCase					   =	scheduler.step(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE      )
   __UpperCamelCase					   =	output.prev_sample

  __UpperCamelCase					   =	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE      )      )
  __UpperCamelCase					   =	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE      )      )

  if torch_device in ["mps"]:
   assert abs(result_sum.item() - 167.46_957_397_460_938      ) < 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_353_637_695_312      ) < 1E-2
   assert abs(result_mean.item() - 0.22_342_908_382_415_771      ) < 1E-3
  else:
   assert abs(result_sum.item() - 162.52_383_422_851_562      ) < 1E-2
   assert abs(result_mean.item() - 0.211_619_570_851_326      ) < 1E-3




 def  __lowerCamelCase					(	self      )	->				List[Any]:
  __UpperCamelCase					   =	self.scheduler_classes[0]
  __UpperCamelCase					   =	self.get_scheduler_config()
  __UpperCamelCase					   =	scheduler_class(**__SCREAMING_SNAKE_CASE		,						use_karras_sigmas=__SCREAMING_SNAKE_CASE      )

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

  __UpperCamelCase					   =	self.dummy_model()
  __UpperCamelCase					   =	self.dummy_sample_deter.to(__SCREAMING_SNAKE_CASE      ) * scheduler.init_noise_sigma
  __UpperCamelCase					   =	sample.to(__SCREAMING_SNAKE_CASE      )

  for t in scheduler.timesteps:
   __UpperCamelCase					   =	scheduler.scale_model_input(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE      )

   __UpperCamelCase					   =	model(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE      )

   __UpperCamelCase					   =	scheduler.step(__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE		,						__SCREAMING_SNAKE_CASE      )
   __UpperCamelCase					   =	output.prev_sample

  __UpperCamelCase					   =	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE      )      )
  __UpperCamelCase					   =	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE      )      )

  if torch_device in ["mps"]:
   assert abs(result_sum.item() - 176.66_974_135_742_188      ) < 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_653_564_453_125      ) < 1E-2
   assert abs(result_mean.item() - 0.23_003_872_730_981_811      ) < 1E-2
  else:
   assert abs(result_sum.item() - 170.3_135_223_388_672      ) < 1E-2
   assert abs(result_mean.item() - 0.23_003_872_730_981_811      ) < 1E-2








'''simple docstring'''



UpperCAmelCase					:			Tuple       =	range(2, 2_0 + 1)
UpperCAmelCase					:			int       =	[1_0**k for k in range(ks[-1] + 1)]
UpperCAmelCase					:			dict[int, dict[int, list[list[int]]]]       =	{}







def   a__			(    a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = sum(a_i[j] for j in range(a__ ,       len(a__		)		)		)
     __SCREAMING_SNAKE_CASE      = sum(a_i[j] * base[j] for j in range(min(len(a__		) ,       a__		)		)		)

     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = 0, 0
     __SCREAMING_SNAKE_CASE      = n - i

     __SCREAMING_SNAKE_CASE      = memo.get(a__		)

     if sub_memo is not None:
          __SCREAMING_SNAKE_CASE      = sub_memo.get(a__		)

          if jumps is not None and len(a__		) > 0:
               # find and make the largest jump without going over
               __SCREAMING_SNAKE_CASE      = -1
               for _k in range(len(a__		) - 1 ,       -1 ,       -1		):
                    if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
                         __SCREAMING_SNAKE_CASE      = _k
                         break

               if max_jump >= 0:
                    __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = jumps[max_jump]
                    # since the difference between jumps is cached, add c
                    __SCREAMING_SNAKE_CASE      = diff + c
                    for j in range(min(a__ ,       len(a__		)		)		):
                         __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = divmod(a__ ,       10		)
                    if new_c > 0:
                         add(a__ ,       a__ ,       a__		)

          else:
               __SCREAMING_SNAKE_CASE      = []
     else:
          __SCREAMING_SNAKE_CASE      = {c: []}
          __SCREAMING_SNAKE_CASE      = sub_memo

     if dn >= max_dn or c + diff >= base[k]:
          return diff, dn

     if k > ks[0]:
          while True:
               # keep doing smaller jumps
               __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = next_term(a__ ,       k - 1 ,       i + dn ,       a__		)
               diff += _diff
               dn += terms_jumped

               if dn >= max_dn or c + diff >= base[k]:
                    break
     else:
          # would be too small a jump, just compute sequential terms instead
          __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = compute(a__ ,       a__ ,       i + dn ,       a__		)
          diff += _diff
          dn += terms_jumped

     __SCREAMING_SNAKE_CASE      = sub_memo[c]

     # keep jumps sorted by # of terms skipped
     __SCREAMING_SNAKE_CASE      = 0
     while j < len(a__		):
          if jumps[j][1] > dn:
               break
          j += 1

     # cache the jump for this value digitsum(b) and c
     sub_memo[c].insert(a__ ,       (diff, dn, k)		)
     return (diff, dn)







def   a__			(    a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     if i >= n:
          return 0, i
     if k > len(a__		):
          a_i.extend([0 for _ in range(k - len(a__		)		)]		)

     # note: a_i -> b * 10^k + c
     # ds_b -> digitsum(b)
     # ds_c -> digitsum(c)
     __SCREAMING_SNAKE_CASE      = i
     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = 0, 0, 0
     for j in range(len(a__		)		):
          if j >= k:
               ds_b += a_i[j]
          else:
               ds_c += a_i[j]

     while i < n:
          i += 1
          __SCREAMING_SNAKE_CASE      = ds_c + ds_b
          diff += addend
          __SCREAMING_SNAKE_CASE      = 0
          for j in range(a__		):
               __SCREAMING_SNAKE_CASE      = a_i[j] + addend
               __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = divmod(a__ ,       10		)

               ds_c += a_i[j]

          if addend > 0:
               break

     if addend > 0:
          add(a__ ,       a__ ,       a__		)
     return diff, i - start_i







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     for j in range(a__ ,       len(a__		)		):
          __SCREAMING_SNAKE_CASE      = digits[j] + addend
          if s >= 10:
               __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = divmod(a__ ,       10		)
               __SCREAMING_SNAKE_CASE      = addend // 10 + quotient
          else:
               __SCREAMING_SNAKE_CASE      = s
               __SCREAMING_SNAKE_CASE      = addend // 10

          if addend == 0:
               break

     while addend > 0:
          __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = divmod(a__ ,       10		)
          digits.append(a__		)







def   a__			(    a__ = 10**15		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = [1]
     __SCREAMING_SNAKE_CASE      = 1
     __SCREAMING_SNAKE_CASE      = 0
     while True:
          __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = next_term(a__ ,       20 ,       i + dn ,       a__		)
          dn += terms_jumped
          if dn == n - i:
               break

     __SCREAMING_SNAKE_CASE      = 0
     for j in range(len(a__		)		):
          a_n += digits[j] * 10**j
     return a_n


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






