import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class 	lowerCamelCase       (UpperCAmelCase__       ):
  """simple docstring"""







  lowerCamelCase__  =    (UnCLIPScheduler,)







  def  __A  (				self			:							Union[str, Any]	,  **__magic_name__			:							Union[str, Any] )	->						Dict:
        SCREAMING_SNAKE_CASE_    =	{
            "num_train_timesteps": 1_000,
            "variance_type": "fixed_small_log",
            "clip_sample": True,
            "clip_sample_range": 1.0,
            "prediction_type": "epsilon",
        }

        config.update(**_UpperCAmelCase )
        return config







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


        for timesteps in [1, 5, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=_UpperCAmelCase )







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


        for variance in ["fixed_small_log", "learned_range"]:
              self.check_over_configs(variance_type=_UpperCAmelCase )







  def  __A  (				self			:							Tuple )	->						List[Any]:


        for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=_UpperCAmelCase )







  def  __A  (				self			:							Tuple )	->						Any:


        for clip_sample_range in [1, 5, 10, 20]:
              self.check_over_configs(clip_sample_range=_UpperCAmelCase )







  def  __A  (				self			:							str )	->						List[Any]:


        for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(prediction_type=_UpperCAmelCase )







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


        for time_step in [0, 500, 999]:
              for prev_timestep in [None, 5, 100, 250, 500, 750]:
                    if prev_timestep is not None and prev_timestep >= time_step:
                          continue

                    self.check_over_forward(time_step=_UpperCAmelCase	,  prev_timestep=_UpperCAmelCase )







  def  __A  (				self			:							Optional[Any] )	->						Tuple:
        SCREAMING_SNAKE_CASE_    =	self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_    =	self.get_scheduler_config(variance_type="fixed_small_log" )
        SCREAMING_SNAKE_CASE_    =	scheduler_class(**_UpperCAmelCase )

        assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0000e-10 ) ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.054_9625 ) ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.999_4987 ) ) < 1e-5







  def  __A  (				self			:							Union[str, Any] )	->						int:
        SCREAMING_SNAKE_CASE_    =	self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_    =	self.get_scheduler_config(variance_type="learned_range" )
        SCREAMING_SNAKE_CASE_    =	scheduler_class(**_UpperCAmelCase )

        SCREAMING_SNAKE_CASE_    =	0.5

        assert scheduler._get_variance(1	,  predicted_variance=_UpperCAmelCase ) - -10.171_2790 < 1e-5
        assert scheduler._get_variance(487	,  predicted_variance=_UpperCAmelCase ) - -5.799_8052 < 1e-5
        assert scheduler._get_variance(999	,  predicted_variance=_UpperCAmelCase ) - -0.001_0011 < 1e-5







  def  __A  (				self			:							str )	->						Optional[int]:
        SCREAMING_SNAKE_CASE_    =	self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_    =	self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_    =	scheduler_class(**_UpperCAmelCase )

        SCREAMING_SNAKE_CASE_    =	scheduler.timesteps

        SCREAMING_SNAKE_CASE_    =	self.dummy_model()
        SCREAMING_SNAKE_CASE_    =	self.dummy_sample_deter
        SCREAMING_SNAKE_CASE_    =	torch.manual_seed(0 )

        for i, t in enumerate(_UpperCAmelCase ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE_    =	model(_UpperCAmelCase	,  _UpperCAmelCase )

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE_    =	scheduler.step(_UpperCAmelCase	,  _UpperCAmelCase	,  _UpperCAmelCase	,  generator=_UpperCAmelCase ).prev_sample

              SCREAMING_SNAKE_CASE_    =	pred_prev_sample

        SCREAMING_SNAKE_CASE_    =	torch.sum(torch.abs(_UpperCAmelCase ) )
        SCREAMING_SNAKE_CASE_    =	torch.mean(torch.abs(_UpperCAmelCase ) )

        assert abs(result_sum.item() - 252.268_2495 ) < 1e-2
        assert abs(result_mean.item() - 0.328_4743 ) < 1e-3







  def  __A  (				self			:							str )	->						List[str]:
        SCREAMING_SNAKE_CASE_    =	self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_    =	self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_    =	scheduler_class(**_UpperCAmelCase )

        scheduler.set_timesteps(25 )

        SCREAMING_SNAKE_CASE_    =	scheduler.timesteps

        SCREAMING_SNAKE_CASE_    =	self.dummy_model()
        SCREAMING_SNAKE_CASE_    =	self.dummy_sample_deter
        SCREAMING_SNAKE_CASE_    =	torch.manual_seed(0 )

        for i, t in enumerate(_UpperCAmelCase ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE_    =	model(_UpperCAmelCase	,  _UpperCAmelCase )

              if i + 1 == timesteps.shape[0]:
                    SCREAMING_SNAKE_CASE_    =	None
              else:
                    SCREAMING_SNAKE_CASE_    =	timesteps[i + 1]

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE_    =	scheduler.step(
                  _UpperCAmelCase	,  _UpperCAmelCase	,  _UpperCAmelCase	,  prev_timestep=_UpperCAmelCase	,  generator=_UpperCAmelCase ).prev_sample

              SCREAMING_SNAKE_CASE_    =	pred_prev_sample

        SCREAMING_SNAKE_CASE_    =	torch.sum(torch.abs(_UpperCAmelCase ) )
        SCREAMING_SNAKE_CASE_    =	torch.mean(torch.abs(_UpperCAmelCase ) )

        assert abs(result_sum.item() - 258.204_4983 ) < 1e-2
        assert abs(result_mean.item() - 0.336_2038 ) < 1e-3







  def  __A  (				self			:							Optional[int] )	->						int:
        pass







  def  __A  (				self			:							List[str] )	->						str:
        pass






from math import log

from scipy.constants import Boltzmann, physical_constants

A			:						Any        =  3_0_0  # TEMPERATURE (unit = K)






def 					UpperCamelCase   (      __magic_name__	:				float       ,       __magic_name__	:				float       ,       __magic_name__	:				float       ,       )  ->    float:
		"""simple docstring"""




		if donor_conc <= 0:
				raise ValueError("""Donor concentration should be positive"""	)
		elif acceptor_conc <= 0:
				raise ValueError("""Acceptor concentration should be positive"""	)
		elif intrinsic_conc <= 0:
				raise ValueError("""Intrinsic concentration should be positive"""	)
		elif donor_conc <= intrinsic_conc:
				raise ValueError(
				    """Donor concentration should be greater than intrinsic concentration"""	)
		elif acceptor_conc <= intrinsic_conc:
				raise ValueError(
				    """Acceptor concentration should be greater than intrinsic concentration"""	)
		else:
				return (
				    Boltzmann
				    * T
				    * log((donor_conc * acceptor_conc) / intrinsic_conc**2	)
				    / physical_constants["electron volt"][0]
				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

