import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest




class  __magic_name__  (		lowercase__    ):





   """simple docstring"""




   lowerCAmelCase      :    Any  			=   (CMStochasticIterativeScheduler,)
   lowerCAmelCase      :    List[str]  			=   1_0



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  **_lowercase					:  Optional[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= {
        '''num_train_timesteps''': 201,
        '''sigma_min''': 0.002,
        '''sigma_max''': 80.0,
    }

    config.update(**UpperCAmelCase__							)
    return config



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    _UpperCamelCase:     str							= 10

    _UpperCamelCase:     Dict							= self.get_scheduler_config()
    _UpperCamelCase:     Union[str, Any]							= self.scheduler_classes[0](**UpperCAmelCase__							)

    scheduler.set_timesteps(UpperCAmelCase__							)

    _UpperCamelCase:     Optional[int]							= scheduler.timesteps[0]
    _UpperCamelCase:     List[Any]							= scheduler.timesteps[1]

    _UpperCamelCase:     List[Any]							= self.dummy_sample
    _UpperCamelCase:     Tuple							= 0.1 * sample

    _UpperCamelCase:     Union[str, Any]							= scheduler.step(UpperCAmelCase__		,  UpperCAmelCase__		,  UpperCAmelCase__							).prev_sample
    _UpperCamelCase:     Optional[int]							= scheduler.step(UpperCAmelCase__		,  UpperCAmelCase__		,  UpperCAmelCase__							).prev_sample

    self.assertEqual(output_a.shape		,  sample.shape							)
    self.assertEqual(output_a.shape		,  output_a.shape							)



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    for clip_denoised in [True, False]:
     self.check_over_configs(clip_denoised=UpperCAmelCase__							)



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Any							= self.scheduler_classes[0]
    _UpperCamelCase:     Dict							= self.get_scheduler_config()
    _UpperCamelCase:     Optional[Any]							= scheduler_class(**UpperCAmelCase__							)

    _UpperCamelCase:     Optional[int]							= 1
    scheduler.set_timesteps(UpperCAmelCase__							)
    _UpperCamelCase:     Optional[int]							= scheduler.timesteps

    _UpperCamelCase:     Optional[int]							= torch.manual_seed(0							)

    _UpperCamelCase:     Union[str, Any]							= self.dummy_model()
    _UpperCamelCase:     Any							= self.dummy_sample_deter * scheduler.init_noise_sigma

    for i, t in enumerate(UpperCAmelCase__							):
     # 1. scale model input
     _UpperCamelCase:     Tuple							= scheduler.scale_model_input(UpperCAmelCase__		,  UpperCAmelCase__							)

     # 2. predict noise residual
     _UpperCamelCase:     Optional[Any]							= model(UpperCAmelCase__		,  UpperCAmelCase__							)

     # 3. predict previous sample x_t-1
     _UpperCamelCase:     Tuple							= scheduler.step(UpperCAmelCase__		,  UpperCAmelCase__		,  UpperCAmelCase__		,  generator=UpperCAmelCase__							).prev_sample

     _UpperCamelCase:     List[Any]							= pred_prev_sample

    _UpperCamelCase:     int							= torch.sum(torch.abs(UpperCAmelCase__							)							)
    _UpperCamelCase:     List[str]							= torch.mean(torch.abs(UpperCAmelCase__							)							)

    assert abs(result_sum.item() - 192.7_614							) < 1E-2
    assert abs(result_mean.item() - 0.2510							) < 1E-3



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     str							= self.scheduler_classes[0]
    _UpperCamelCase:     Optional[Any]							= self.get_scheduler_config()
    _UpperCamelCase:     str							= scheduler_class(**UpperCAmelCase__							)

    _UpperCamelCase:     Union[str, Any]							= [106, 0]
    scheduler.set_timesteps(timesteps=UpperCAmelCase__							)
    _UpperCamelCase:     Any							= scheduler.timesteps

    _UpperCamelCase:     Dict							= torch.manual_seed(0							)

    _UpperCamelCase:     Union[str, Any]							= self.dummy_model()
    _UpperCamelCase:     Tuple							= self.dummy_sample_deter * scheduler.init_noise_sigma

    for t in timesteps:
     # 1. scale model input
     _UpperCamelCase:     Optional[Any]							= scheduler.scale_model_input(UpperCAmelCase__		,  UpperCAmelCase__							)

     # 2. predict noise residual
     _UpperCamelCase:     str							= model(UpperCAmelCase__		,  UpperCAmelCase__							)

     # 3. predict previous sample x_t-1
     _UpperCamelCase:     Tuple							= scheduler.step(UpperCAmelCase__		,  UpperCAmelCase__		,  UpperCAmelCase__		,  generator=UpperCAmelCase__							).prev_sample

     _UpperCamelCase:     Dict							= pred_prev_sample

    _UpperCamelCase:     str							= torch.sum(torch.abs(UpperCAmelCase__							)							)
    _UpperCamelCase:     int							= torch.mean(torch.abs(UpperCAmelCase__							)							)

    assert abs(result_sum.item() - 347.6_357							) < 1E-2
    assert abs(result_mean.item() - 0.4527							) < 1E-3



   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= self.scheduler_classes[0]
    _UpperCamelCase:     Dict							= self.get_scheduler_config()
    _UpperCamelCase:     str							= scheduler_class(**UpperCAmelCase__							)

    _UpperCamelCase:     Any							= [39, 30, 12, 15, 0]

    with self.assertRaises(UpperCAmelCase__		,  msg='''`timesteps` must be in descending order.'''							):
     scheduler.set_timesteps(timesteps=UpperCAmelCase__							)



   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= self.scheduler_classes[0]
    _UpperCamelCase:     Dict							= self.get_scheduler_config()
    _UpperCamelCase:     Dict							= scheduler_class(**UpperCAmelCase__							)

    _UpperCamelCase:     Optional[Any]							= [39, 30, 12, 1, 0]
    _UpperCamelCase:     str							= len(UpperCAmelCase__							)

    with self.assertRaises(UpperCAmelCase__		,  msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''							):
     scheduler.set_timesteps(num_inference_steps=UpperCAmelCase__		,  timesteps=UpperCAmelCase__							)



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= self.scheduler_classes[0]
    _UpperCamelCase:     Union[str, Any]							= self.get_scheduler_config()
    _UpperCamelCase:     Optional[Any]							= scheduler_class(**UpperCAmelCase__							)

    _UpperCamelCase:     Optional[Any]							= [scheduler.config.num_train_timesteps]

    with self.assertRaises(
        UpperCAmelCase__		,  msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''		,  ):
     scheduler.set_timesteps(timesteps=UpperCAmelCase__							)
from __future__ import annotations

UpperCAmelCase_			    =							1.6_0_2_1E-1_9  # units = C




def       lowerCAmelCase_     (		lowercase:							float  ,					lowercase:							float  ,					lowercase:							float  ,					)    ->				tuple[str, float]:







 '''simple docstring'''
 if (conductivity, electron_conc, mobility).count(0		) != 1:
  raise ValueError('''You cannot supply more or less than 2 values'''		)
 elif conductivity < 0:
  raise ValueError('''Conductivity cannot be negative'''		)
 elif electron_conc < 0:
  raise ValueError('''Electron concentration cannot be negative'''		)
 elif mobility < 0:
  raise ValueError('''mobility cannot be negative'''		)
 elif conductivity == 0:
  return (
      "conductivity",
      mobility * electron_conc * ELECTRON_CHARGE,
  )
 elif electron_conc == 0:
  return (
      "electron_conc",
      conductivity / (mobility * ELECTRON_CHARGE),
  )
 else:
  return (
      "mobility",
      conductivity / (electron_conc * ELECTRON_CHARGE),
  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()