





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  List[str]			   =   (UnCLIPScheduler,)







       def 						__lowerCamelCase   (				self					:     List[str]		, **A					:     Optional[int]      )       ->Optional[int]:
          lowerCamelCase__       :						Any        =							{
              '''num_train_timesteps''': 1_0_0_0,
              '''variance_type''': '''fixed_small_log''',
              '''clip_sample''': True,
              '''clip_sample_range''': 1.0,
              '''prediction_type''': '''epsilon''',
          }

          config.update(**A      )
          return config







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Optional[int]:
          for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=A      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->List[Any]:
          for variance in ["fixed_small_log", "learned_range"]:
             self.check_over_configs(variance_type=A      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Dict:
          for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=A      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->List[str]:
          for clip_sample_range in [1, 5, 1_0, 2_0]:
             self.check_over_configs(clip_sample_range=A      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Tuple:
          for prediction_type in ["epsilon", "sample"]:
             self.check_over_configs(prediction_type=A      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Any:
          for time_step in [0, 5_0_0, 9_9_9]:
             for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
                if prev_timestep is not None and prev_timestep >= time_step:
                   continue

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







       def 						__lowerCamelCase   (				self					:     int      )       ->Dict:
          lowerCamelCase__       :						Any        =							self.scheduler_classes[0]
          lowerCamelCase__       :						str        =							self.get_scheduler_config(variance_type='''fixed_small_log'''      )
          lowerCamelCase__       :						Dict        =							scheduler_class(**A      )

          assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 1.0_000e-10      )      ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_8_7      ) - 0.0_54_96_25      )      ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_9_9      ) - 0.9_99_49_87      )      ) < 1e-5







       def 						__lowerCamelCase   (				self					:     int      )       ->Union[str, Any]:
          lowerCamelCase__       :						Optional[int]        =							self.scheduler_classes[0]
          lowerCamelCase__       :						Dict        =							self.get_scheduler_config(variance_type='''learned_range'''      )
          lowerCamelCase__       :						Dict        =							scheduler_class(**A      )

          lowerCamelCase__       :						List[str]        =							0.5

          assert scheduler._get_variance(1		, predicted_variance=A      ) - -10.1_71_27_90 < 1e-5
          assert scheduler._get_variance(4_8_7		, predicted_variance=A      ) - -5.7_99_80_52 < 1e-5
          assert scheduler._get_variance(9_9_9		, predicted_variance=A      ) - -0.0_01_00_11 < 1e-5







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Any:
          lowerCamelCase__       :						Dict        =							self.scheduler_classes[0]
          lowerCamelCase__       :						List[Any]        =							self.get_scheduler_config()
          lowerCamelCase__       :						Union[str, Any]        =							scheduler_class(**A      )

          lowerCamelCase__       :						Tuple        =							scheduler.timesteps

          lowerCamelCase__       :						Any        =							self.dummy_model()
          lowerCamelCase__       :						Union[str, Any]        =							self.dummy_sample_deter
          lowerCamelCase__       :						List[Any]        =							torch.manual_seed(0      )

          for i, t in enumerate(A      ):
             # 1. predict noise residual
             lowerCamelCase__       :						Tuple        =							model(A		, A      )

             # 2. predict previous mean of sample x_t-1
             lowerCamelCase__       :						Tuple        =							scheduler.step(A		, A		, A		, generator=A      ).prev_sample

             lowerCamelCase__       :						List[str]        =							pred_prev_sample

          lowerCamelCase__       :						Union[str, Any]        =							torch.sum(torch.abs(A      )      )
          lowerCamelCase__       :						Union[str, Any]        =							torch.mean(torch.abs(A      )      )

          assert abs(result_sum.item() - 2_52.2_68_24_95      ) < 1e-2
          assert abs(result_mean.item() - 0.3_28_47_43      ) < 1e-3







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Any:
          lowerCamelCase__       :						Optional[Any]        =							self.scheduler_classes[0]
          lowerCamelCase__       :						Optional[Any]        =							self.get_scheduler_config()
          lowerCamelCase__       :						int        =							scheduler_class(**A      )

          scheduler.set_timesteps(2_5      )

          lowerCamelCase__       :						str        =							scheduler.timesteps

          lowerCamelCase__       :						Any        =							self.dummy_model()
          lowerCamelCase__       :						str        =							self.dummy_sample_deter
          lowerCamelCase__       :						List[Any]        =							torch.manual_seed(0      )

          for i, t in enumerate(A      ):
             # 1. predict noise residual
             lowerCamelCase__       :						Optional[Any]        =							model(A		, A      )

             if i + 1 == timesteps.shape[0]:
                lowerCamelCase__       :						List[str]        =							None
             else:
                lowerCamelCase__       :						Optional[int]        =							timesteps[i + 1]

             # 2. predict previous mean of sample x_t-1
             lowerCamelCase__       :						Tuple        =							scheduler.step(
                 A		, A		, A		, prev_timestep=A		, generator=A      ).prev_sample

             lowerCamelCase__       :						Dict        =							pred_prev_sample

          lowerCamelCase__       :						str        =							torch.sum(torch.abs(A      )      )
          lowerCamelCase__       :						List[Any]        =							torch.mean(torch.abs(A      )      )

          assert abs(result_sum.item() - 2_58.2_04_49_83      ) < 1e-2
          assert abs(result_mean.item() - 0.3_36_20_38      ) < 1e-3







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







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










from math import factorial


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     List[Any]		, A					:     Dict		, A					:     Any      )       ->Optional[Any]:
          lowerCamelCase__       :						Tuple        =							real
          if isinstance(A		, A      ):
             lowerCamelCase__       :						Optional[int]        =							[1] * rank
          else:
             lowerCamelCase__       :						List[Any]        =							rank







       def __repr__(				self					:     Tuple      )       ->str:
          return (
              F"{self.real}+"
              F"{'+'.join(str(A      )+'E'+str(n+1      )for n,dual in enumerate(self.duals      )      )}"
          )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->List[Any]:
          lowerCamelCase__       :						Tuple        =							self.duals.copy()
          while cur[-1] == 0:
             cur.pop(-1      )
          return Dual(self.real		, A      )







       def __add__(				self					:     Union[str, Any]		, A					:     int      )       ->str:
          if not isinstance(A		, A      ):
             return Dual(self.real + other		, self.duals      )
          lowerCamelCase__       :						int        =							self.duals.copy()
          lowerCamelCase__       :						int        =							other.duals.copy()
          if len(A      ) > len(A      ):
             o_dual.extend([1] * (len(A      ) - len(A      ))      )
          elif len(A      ) < len(A      ):
             s_dual.extend([1] * (len(A      ) - len(A      ))      )
          lowerCamelCase__       :						Optional[Any]        =							[]
          for i in range(len(A      )      ):
             new_duals.append(s_dual[i] + o_dual[i]      )
          return Dual(self.real + other.real		, A      )

       _UpperCAmelCase				:  List[Any]			   =   __add__







       def __sub__(				self					:     Any		, A					:     Dict      )       ->int:
          return self + other * -1







       def __mul__(				self					:     Optional[Any]		, A					:     List[Any]      )       ->Union[str, Any]:
          if not isinstance(A		, A      ):
             lowerCamelCase__       :						Optional[int]        =							[]
             for i in self.duals:
                new_duals.append(i * other      )
             return Dual(self.real * other		, A      )
          lowerCamelCase__       :						Tuple        =							[0] * (len(self.duals      ) + len(other.duals      ) + 1)
          for i, item in enumerate(self.duals      ):
             for j, jtem in enumerate(other.duals      ):
                new_duals[i + j + 1] += item * jtem
          for k in range(len(self.duals      )      ):
             new_duals[k] += self.duals[k] * other.real
          for index in range(len(other.duals      )      ):
             new_duals[index] += other.duals[index] * self.real
          return Dual(self.real * other.real		, A      )

       _UpperCAmelCase				:  Optional[Any]			   =   __mul__







       def __truediv__(				self					:     int		, A					:     List[Any]      )       ->Dict:
          if not isinstance(A		, A      ):
             lowerCamelCase__       :						Tuple        =							[]
             for i in self.duals:
                new_duals.append(i / other      )
             return Dual(self.real / other		, A      )
          raise ValueError







       def __floordiv__(				self					:     Dict		, A					:     Union[str, Any]      )       ->Union[str, Any]:
          if not isinstance(A		, A      ):
             lowerCamelCase__       :						Tuple        =							[]
             for i in self.duals:
                new_duals.append(i // other      )
             return Dual(self.real // other		, A      )
          raise ValueError







       def __pow__(				self					:     Any		, A					:     List[Any]      )       ->Tuple:
          if n < 0 or isinstance(A		, A      ):
             raise ValueError('''power must be a positive integer'''      )
          if n == 0:
             return 1
          if n == 1:
             return self
          lowerCamelCase__       :						Union[str, Any]        =							self
          for _ in range(n - 1      ):
             x *= self
          return x



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   if not callable(UpperCAmelCase					):
      raise ValueError('''differentiate() requires a function as input for func'''					)
   if not isinstance(UpperCAmelCase      ,  (float, int)					):
      raise ValueError('''differentiate() requires a float as input for position'''					)
   if not isinstance(UpperCAmelCase      ,  UpperCAmelCase					):
      raise ValueError('''differentiate() requires an int as input for order'''					)
   lowerCamelCase__       :						List[str]        =							Dual(UpperCAmelCase      ,  1					)
   lowerCamelCase__       :						Any        =							func(UpperCAmelCase					)
   if order == 0:
      return result.real
   return result.duals[order - 1] * factorial(UpperCAmelCase					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()



       def 	_a						(   UpperCAmelCase					)    ->    int:







          """simple docstring"""
          return y**2 * y**4




       print(differentiate(f, 9, 2))



