

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 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						(DPMSolverSDEScheduler,)
 UpperCAmelCase							    =						10


 def _UpperCamelCase	(						self		: int			,						**lowerCAmelCase__		: Any    )    ->      int:
   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(**lowerCAmelCase__    )
   return config


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Dict:
   for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
     self.check_over_configs(num_train_timesteps=lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Dict    )    ->      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=lowerCAmelCase__			,						beta_end=lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: List[str]    )    ->      Optional[int]:
   for schedule in ["linear", "scaled_linear"]:
     self.check_over_configs(beta_schedule=lowerCAmelCase__    )


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


 def _UpperCamelCase	(						self		: int    )    ->      Optional[int]:
   UpperCAmelCase					      =  self.scheduler_classes[0]
   UpperCAmelCase					      =  self.get_scheduler_config()
   UpperCAmelCase					      =  scheduler_class(**lowerCAmelCase__    )

   scheduler.set_timesteps(self.num_inference_steps    )

   UpperCAmelCase					      =  self.dummy_model()
   UpperCAmelCase					      =  self.dummy_sample_deter * scheduler.init_noise_sigma
   UpperCAmelCase					      =  sample.to(lowerCAmelCase__    )

   for i, t in enumerate(scheduler.timesteps    ):
     UpperCAmelCase					      =  scheduler.scale_model_input(lowerCAmelCase__			,						lowerCAmelCase__    )

     UpperCAmelCase					      =  model(lowerCAmelCase__			,						lowerCAmelCase__    )

     UpperCAmelCase					      =  scheduler.step(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )
     UpperCAmelCase					      =  output.prev_sample

   UpperCAmelCase					      =  torch.sum(torch.abs(lowerCAmelCase__    )    )
   UpperCAmelCase					      =  torch.mean(torch.abs(lowerCAmelCase__    )    )

   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 _UpperCamelCase	(						self		: Optional[Any]    )    ->      Optional[Any]:
   UpperCAmelCase					      =  self.scheduler_classes[0]
   UpperCAmelCase					      =  self.get_scheduler_config(prediction_type="v_prediction"    )
   UpperCAmelCase					      =  scheduler_class(**lowerCAmelCase__    )

   scheduler.set_timesteps(self.num_inference_steps    )

   UpperCAmelCase					      =  self.dummy_model()
   UpperCAmelCase					      =  self.dummy_sample_deter * scheduler.init_noise_sigma
   UpperCAmelCase					      =  sample.to(lowerCAmelCase__    )

   for i, t in enumerate(scheduler.timesteps    ):
     UpperCAmelCase					      =  scheduler.scale_model_input(lowerCAmelCase__			,						lowerCAmelCase__    )

     UpperCAmelCase					      =  model(lowerCAmelCase__			,						lowerCAmelCase__    )

     UpperCAmelCase					      =  scheduler.step(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )
     UpperCAmelCase					      =  output.prev_sample

   UpperCAmelCase					      =  torch.sum(torch.abs(lowerCAmelCase__    )    )
   UpperCAmelCase					      =  torch.mean(torch.abs(lowerCAmelCase__    )    )

   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 _UpperCamelCase	(						self		: int    )    ->      Optional[Any]:
   UpperCAmelCase					      =  self.scheduler_classes[0]
   UpperCAmelCase					      =  self.get_scheduler_config()
   UpperCAmelCase					      =  scheduler_class(**lowerCAmelCase__    )

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

   UpperCAmelCase					      =  self.dummy_model()
   UpperCAmelCase					      =  self.dummy_sample_deter.to(lowerCAmelCase__    ) * scheduler.init_noise_sigma

   for t in scheduler.timesteps:
     UpperCAmelCase					      =  scheduler.scale_model_input(lowerCAmelCase__			,						lowerCAmelCase__    )

     UpperCAmelCase					      =  model(lowerCAmelCase__			,						lowerCAmelCase__    )

     UpperCAmelCase					      =  scheduler.step(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )
     UpperCAmelCase					      =  output.prev_sample

   UpperCAmelCase					      =  torch.sum(torch.abs(lowerCAmelCase__    )    )
   UpperCAmelCase					      =  torch.mean(torch.abs(lowerCAmelCase__    )    )

   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 _UpperCamelCase	(						self		: Optional[Any]    )    ->      int:
   UpperCAmelCase					      =  self.scheduler_classes[0]
   UpperCAmelCase					      =  self.get_scheduler_config()
   UpperCAmelCase					      =  scheduler_class(**lowerCAmelCase__			,						use_karras_sigmas=lowerCAmelCase__    )

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

   UpperCAmelCase					      =  self.dummy_model()
   UpperCAmelCase					      =  self.dummy_sample_deter.to(lowerCAmelCase__    ) * scheduler.init_noise_sigma
   UpperCAmelCase					      =  sample.to(lowerCAmelCase__    )

   for t in scheduler.timesteps:
     UpperCAmelCase					      =  scheduler.scale_model_input(lowerCAmelCase__			,						lowerCAmelCase__    )

     UpperCAmelCase					      =  model(lowerCAmelCase__			,						lowerCAmelCase__    )

     UpperCAmelCase					      =  scheduler.step(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )
     UpperCAmelCase					      =  output.prev_sample

   UpperCAmelCase					      =  torch.sum(torch.abs(lowerCAmelCase__    )    )
   UpperCAmelCase					      =  torch.mean(torch.abs(lowerCAmelCase__    )    )

   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






import unittest

import numpy as np
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A = None   ,       ):
  UpperCAmelCase					      =  np.shape(__A			)
  UpperCAmelCase					      =  np.shape(__A			)
  UpperCAmelCase					      =  np.shape(__A			)

  if shape_a[0] != shape_b[0]:
    UpperCAmelCase					      =  (
        "Expected the same number of rows for A and B. "
        F"Instead found A of size {shape_a} and B of size {shape_b}"
    )
    raise ValueError(__A			)

  if shape_b[1] != shape_c[1]:
    UpperCAmelCase					      =  (
        "Expected the same number of columns for B and C. "
        F"Instead found B of size {shape_b} and C of size {shape_c}"
    )
    raise ValueError(__A			)

  UpperCAmelCase					      =  pseudo_inv
  if a_inv is None:
    try:
      UpperCAmelCase					      =  np.linalg.inv(__A			)
    except np.linalg.LinAlgError:
      raise ValueError(
          "Input matrix A is not invertible. Cannot compute Schur complement."			)

  return mat_c - mat_b.T @ a_inv @ mat_b






class 						__magic_name__    (       unittest.TestCase    ):


 def _UpperCamelCase	(						self		: List[str]    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1], [6, 3]]    )

   UpperCAmelCase					      =  schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )

   UpperCAmelCase					      =  np.block([[a, b], [b.T, c]]    )

   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )
   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )
   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )

   self.assertAlmostEqual(lowerCAmelCase__			,						det_a * det_s    )


 def _UpperCamelCase	(						self		: str    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1], [6, 3]]    )

   with self.assertRaises(lowerCAmelCase__    ):
     schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Dict    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1, 3], [6, 3, 5]]    )

   with self.assertRaises(lowerCAmelCase__    ):
     schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      unittest.main()



