






"""simple docstring"""



from math import factorial






class 							__SCREAMING_SNAKE_CASE						:


       def __init__(			self      :						int			,    snake_case      :						Tuple			,    snake_case      :						Tuple							):
             '''simple docstring'''





             A__						:  int		  =  real
             if isinstance(snake_case			,    snake_case							):
                   A__						:  Optional[int]		  =  [1] * rank
             else:
                   A__						:  Tuple		  =  rank


       def __repr__(			self      :						Any							):
             '''simple docstring'''





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


       def 						_UpperCamelCase       (			self      :						str							):
             '''simple docstring'''





             A__						:  Union[str, Any]		  =  self.duals.copy()
             while cur[-1] == 0:
                   cur.pop(-1							)
             return Dual(self.real			,    snake_case							)


       def __add__(			self      :						Any			,    snake_case      :						int							):
             '''simple docstring'''





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

       snake_case_     =							__add__


       def __sub__(			self      :						Dict			,    snake_case      :						Tuple							):
             '''simple docstring'''





             return self + other * -1


       def __mul__(			self      :						int			,    snake_case      :						str							):
             '''simple docstring'''





             if not isinstance(snake_case			,    snake_case							):
                   A__						:  Dict		  =  []
                   for i in self.duals:
                         new_duals.append(i * other							)
                   return Dual(self.real * other			,    snake_case							)
             A__						:  Any		  =  [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			,    snake_case							)

       snake_case_     =							__mul__


       def __truediv__(			self      :						Dict			,    snake_case      :						Tuple							):
             '''simple docstring'''





             if not isinstance(snake_case			,    snake_case							):
                   A__						:  Optional[int]		  =  []
                   for i in self.duals:
                         new_duals.append(i / other							)
                   return Dual(self.real / other			,    snake_case							)
             raise ValueError


       def __floordiv__(			self      :						Union[str, Any]			,    snake_case      :						str							):
             '''simple docstring'''





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


       def __pow__(			self      :						str			,    snake_case      :						Tuple							):
             '''simple docstring'''





             if n < 0 or isinstance(snake_case			,    snake_case							):
                   raise ValueError("""power must be a positive integer"""							)
             if n == 0:
                   return 1
             if n == 1:
                   return self
             A__						:  int		  =  self
             for _ in range(n - 1							):
                   x *= self
             return x




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Any,						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					int      )		->Tuple:
      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"""      )
      A__						:  Union[str, Any]		  =  Dual(UpperCAmelCase__,						1      )
      A__						:  Optional[int]		  =  func(UpperCAmelCase__      )
      if order == 0:
            return result.real
      return result.duals[order - 1] * factorial(UpperCAmelCase__      )


if __name__ == "__main__":
      import doctest

      doctest.testmod()




      def      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict      )		->Any:
            return y**2 * y**4

      print(differentiate(f, 9, 2))














"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


							@property
							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													torch.manual_seed(0							)
													A__						:  Optional[Any]		  =  UNetaDModel(
													    block_out_channels=(32, 64)			,    layers_per_block=2			,    sample_size=32			,    in_channels=3			,    out_channels=3			,    down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")			,    up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")			,    )
													return model


							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  List[Any]		  =  self.dummy_uncond_unet
													A__						:  Dict		  =  ScoreSdeVeScheduler()

													A__						:  str		  =  ScoreSdeVePipeline(unet=snake_case			,    scheduler=snake_case							)
													sde_ve.to(snake_case							)
													sde_ve.set_progress_bar_config(disable=snake_case							)

													A__						:  Optional[int]		  =  torch.manual_seed(0							)
													A__						:  Any		  =  sde_ve(num_inference_steps=2			,    output_type="""numpy"""			,    generator=snake_case							).images

													A__						:  List[str]		  =  torch.manual_seed(0							)
													A__						:  int		  =  sde_ve(num_inference_steps=2			,    output_type="""numpy"""			,    generator=snake_case			,    return_dict=snake_case							)[
													    0
													]

													A__						:  int		  =  image[0, -3:, -3:, -1]
													A__						:  Optional[Any]		  =  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													A__						:  Optional[Any]		  =  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

													assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2






@slow
@require_torch
class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  """google/ncsnpp-church-256"""
													A__						:  Tuple		  =  UNetaDModel.from_pretrained(snake_case							)

													A__						:  int		  =  ScoreSdeVeScheduler.from_pretrained(snake_case							)

													A__						:  List[Any]		  =  ScoreSdeVePipeline(unet=snake_case			,    scheduler=snake_case							)
													sde_ve.to(snake_case							)
													sde_ve.set_progress_bar_config(disable=snake_case							)

													A__						:  Dict		  =  torch.manual_seed(0							)
													A__						:  Optional[int]		  =  sde_ve(num_inference_steps=10			,    output_type="""numpy"""			,    generator=snake_case							).images

													A__						:  Optional[int]		  =  image[0, -3:, -3:, -1]

													assert image.shape == (1, 256, 256, 3)

													A__						:  Dict		  =  np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]							)

													assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2






