





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 							SCREAMING_SNAKE_CASE  (__SCREAMING_SNAKE_CASE ):
   _UpperCamelCase       :						Optional[Any]					   =			(UnCLIPScheduler,)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							int     ,	**a     :							Optional[Any]			)->  Optional[int]:





     """simple docstring"""







     lowercase__           =    {
         "num_train_timesteps": 1_000,
         "variance_type": "fixed_small_log",
         "clip_sample": True,
         "clip_sample_range": 1.0,
         "prediction_type": "epsilon",
     }

     config.update(**_SCREAMING_SNAKE_CASE			)
     return config


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





     """simple docstring"""







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


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Any			)->  Optional[Any]:





     """simple docstring"""







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


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Dict			)->  int:





     """simple docstring"""







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


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





     """simple docstring"""







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


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





     """simple docstring"""







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


   def 			SCREAMING_SNAKE_CASE_				(       self     :							int			)->  List[Any]:





     """simple docstring"""







     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=_SCREAMING_SNAKE_CASE     ,	prev_timestep=_SCREAMING_SNAKE_CASE			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Optional[int]			)->  List[Any]:





     """simple docstring"""







     lowercase__           =    self.scheduler_classes[0]
     lowercase__           =    self.get_scheduler_config(variance_type='fixed_small_log'			)
     lowercase__           =    scheduler_class(**_SCREAMING_SNAKE_CASE			)

     assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0_0_0_0E-1_0			)			) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0549625			)			) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9994987			)			) < 1E-5


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





     """simple docstring"""







     lowercase__           =    self.scheduler_classes[0]
     lowercase__           =    self.get_scheduler_config(variance_type='learned_range'			)
     lowercase__           =    scheduler_class(**_SCREAMING_SNAKE_CASE			)

     lowercase__           =    0.5

     assert scheduler._get_variance(1     ,	predicted_variance=_SCREAMING_SNAKE_CASE			) - -10.1712790 < 1E-5
     assert scheduler._get_variance(487     ,	predicted_variance=_SCREAMING_SNAKE_CASE			) - -5.7998052 < 1E-5
     assert scheduler._get_variance(999     ,	predicted_variance=_SCREAMING_SNAKE_CASE			) - -0.0010011 < 1E-5


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Optional[Any]			)->  Any:





     """simple docstring"""







     lowercase__           =    self.scheduler_classes[0]
     lowercase__           =    self.get_scheduler_config()
     lowercase__           =    scheduler_class(**_SCREAMING_SNAKE_CASE			)

     lowercase__           =    scheduler.timesteps

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

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

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

       lowercase__           =    pred_prev_sample

     lowercase__           =    torch.sum(torch.abs(_SCREAMING_SNAKE_CASE			)			)
     lowercase__           =    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE			)			)

     assert abs(result_sum.item() - 252.2682495			) < 1E-2
     assert abs(result_mean.item() - 0.3284743			) < 1E-3


   def 			SCREAMING_SNAKE_CASE_				(       self     :							int			)->  Optional[Any]:





     """simple docstring"""







     lowercase__           =    self.scheduler_classes[0]
     lowercase__           =    self.get_scheduler_config()
     lowercase__           =    scheduler_class(**_SCREAMING_SNAKE_CASE			)

     scheduler.set_timesteps(25			)

     lowercase__           =    scheduler.timesteps

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

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

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

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

       lowercase__           =    pred_prev_sample

     lowercase__           =    torch.sum(torch.abs(_SCREAMING_SNAKE_CASE			)			)
     lowercase__           =    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE			)			)

     assert abs(result_sum.item() - 258.2044983			) < 1E-2
     assert abs(result_mean.item() - 0.3362038			) < 1E-3


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Any			)->  Tuple:





     """simple docstring"""







     pass


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Dict			)->  Tuple:





     """simple docstring"""







     pass







"""simple docstring"""






from math import factorial



class      A__  :




	'''simple docstring'''





	def __init__(	self:						str			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						Any)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			real
				if isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    Optional[int]				=			[1] * rank
				else:
							__lowerCAmelCase :    Optional[Any]				=			rank

	def __repr__(	self:						Optional[int])							->			List[str]:

				"""simple docstring"""

				return (
				    F"""{self.real}+"""
				    F"""{'+'.join(str(_SCREAMING_SNAKE_CASE)+'E'+str(n+1)for n,dual in enumerate(self.duals))}"""
				)

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

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self.duals.copy()
				while cur[-1] == 0:
							cur.pop(-1)
				return Dual(self.real			,     _SCREAMING_SNAKE_CASE)

	def __add__(	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			Union[str, Any]:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							return Dual(self.real + other			,     self.duals)
				__lowerCAmelCase :    int				=			self.duals.copy()
				__lowerCAmelCase :    List[Any]				=			other.duals.copy()
				if len(_SCREAMING_SNAKE_CASE) > len(_SCREAMING_SNAKE_CASE):
							o_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE) - len(_SCREAMING_SNAKE_CASE)))
				elif len(_SCREAMING_SNAKE_CASE) < len(_SCREAMING_SNAKE_CASE):
							s_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE) - len(_SCREAMING_SNAKE_CASE)))
				__lowerCAmelCase :    Tuple				=			[]
				for i in range(len(_SCREAMING_SNAKE_CASE)):
							new_duals.append(s_dual[i] + o_dual[i])
				return Dual(self.real + other.real			,     _SCREAMING_SNAKE_CASE)

	SCREAMING_SNAKE_CASE        =      __add__

	def __sub__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						str)							->			Optional[Any]:

				"""simple docstring"""

				return self + other * -1

	def __mul__(	self:						str			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			Optional[int]:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    List[Any]				=			[]
							for i in self.duals:
										new_duals.append(i * other)
							return Dual(self.real * other			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[str]				=			[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			,     _SCREAMING_SNAKE_CASE)

	SCREAMING_SNAKE_CASE        =      __mul__

	def __truediv__(	self:						Any			,     _SCREAMING_SNAKE_CASE:						str)							->			Dict:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    List[str]				=			[]
							for i in self.duals:
										new_duals.append(i / other)
							return Dual(self.real / other			,     _SCREAMING_SNAKE_CASE)
				raise ValueError

	def __floordiv__(	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			str:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    str				=			[]
							for i in self.duals:
										new_duals.append(i // other)
							return Dual(self.real // other			,     _SCREAMING_SNAKE_CASE)
				raise ValueError







	def __pow__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			List[Any]:

				"""simple docstring"""

				if n < 0 or isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							raise ValueError("power must be a positive integer")
				if n == 0:
							return 1
				if n == 1:
							return self
				__lowerCAmelCase :    List[Any]				=			self
				for _ in range(n - 1):
							x *= self
				return x






def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Any:
			if not callable(__snake_case      ):
						raise ValueError("differentiate() requires a function as input for func"      )
			if not isinstance(__snake_case   ,(float, int)      ):
						raise ValueError("differentiate() requires a float as input for position"      )
			if not isinstance(__snake_case   ,__snake_case      ):
						raise ValueError("differentiate() requires an int as input for order"      )
			__lowerCAmelCase :    Any				=			Dual(__snake_case   ,1      )
			__lowerCAmelCase :    Tuple				=			func(__snake_case      )
			if order == 0:
						return result.real
			return result.duals[order - 1] * factorial(__snake_case      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()


	def      _lowercase							(    __snake_case      )		->  Tuple:
				return y**2 * y**4





	print(differentiate(f, 9, 2))