

"""simple docstring"""






import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows





def 	lowercase      (   __UpperCamelCase						) ->      int:  # picklable for multiprocessing
 return i + 1





@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def 	lowercase      (   ) ->      Optional[int]:
 with parallel_backend('''spark'''						):
  assert ParallelBackendConfig.backend_name == "spark"

 __magic_name__     =      [1, 2, 3]
 with pytest.raises(lowerCamelCase_						):
  with parallel_backend('''unsupported backend'''						):
   map_nested(lowerCamelCase_      ,   lowerCamelCase_      ,   num_proc=2						)

 with pytest.raises(lowerCamelCase_						):
  with parallel_backend('''unsupported backend'''						):
   map_nested(lowerCamelCase_      ,   lowerCamelCase_      ,   num_proc=-1						)





@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('''num_proc'''      ,   [2, -1]						)
def 	lowercase      (   __UpperCamelCase						) ->      Union[str, Any]:
 __magic_name__     =      [1, 2]
 __magic_name__     =      {'''a''': 1, '''b''': 2}
 __magic_name__     =      {'''a''': [1, 2], '''b''': [3, 4]}
 __magic_name__     =      {'''a''': {'''1''': 1}, '''b''': 2}
 __magic_name__     =      {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
 __magic_name__     =      [2, 3]
 __magic_name__     =      {'''a''': 2, '''b''': 3}
 __magic_name__     =      {'''a''': [2, 3], '''b''': [4, 5]}
 __magic_name__     =      {'''a''': {'''1''': 2}, '''b''': 3}
 __magic_name__     =      {'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}

 with parallel_backend('''spark'''						):
  assert map_nested(lowerCamelCase_      ,   lowerCamelCase_      ,   num_proc=lowerCamelCase_						) == expected_map_nested_sa
  assert map_nested(lowerCamelCase_      ,   lowerCamelCase_      ,   num_proc=lowerCamelCase_						) == expected_map_nested_sa
  assert map_nested(lowerCamelCase_      ,   lowerCamelCase_      ,   num_proc=lowerCamelCase_						) == expected_map_nested_sa
  assert map_nested(lowerCamelCase_      ,   lowerCamelCase_      ,   num_proc=lowerCamelCase_						) == expected_map_nested_sa
  assert map_nested(lowerCamelCase_      ,   lowerCamelCase_      ,   num_proc=lowerCamelCase_						) == expected_map_nested_sa




"""simple docstring"""






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       (EulerDiscreteScheduler,)
     _lowerCamelCase     =       10







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      __magic_name__     =      {
          '''num_train_timesteps''': 1100,
          '''beta_start''': 0.0_0_0_1,
          '''beta_end''': 0.0_2,
          '''beta_schedule''': '''linear''',
      }

      config.update(**UpperCamelCase_       )
      return config







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







     def 	lowerCAmelCase__    (      self       ):
      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1] ,						[0.0_0_0_2, 0.0_0_2, 0.0_2]       ):
       self.check_over_configs(beta_start=UpperCamelCase_ ,						beta_end=UpperCamelCase_       )







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







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







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps       )

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for i, t in enumerate(scheduler.timesteps       ):
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 1_0.0_8_0_7       ) < 1E-2
      assert abs(result_mean.item() - 0.0_1_3_1       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config(prediction_type='''v_prediction'''       )
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps       )

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for i, t in enumerate(scheduler.timesteps       ):
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 0.0_0_0_2       ) < 1E-2
      assert abs(result_mean.item() - 2.2_6_7_6E-0_6       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

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

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for t in scheduler.timesteps:
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 1_0.0_8_0_7       ) < 1E-2
      assert abs(result_mean.item() - 0.0_1_3_1       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**UpperCamelCase_ ,						use_karras_sigmas=UpperCamelCase_       )

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

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for t in scheduler.timesteps:
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9       ) < 1E-2
      assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3       ) < 1E-3

