






from math import factorial




class 							_lowerCAmelCase:





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   List[Any]        =							real
         if isinstance(_lowerCamelCase    ,     _lowerCamelCase				):
           UpperCamelCase_:   Optional[int]        =							[1] * rank
         else:
           UpperCamelCase_:   List[str]        =							rank





       def __repr__(   self				):
         return (
             f'''{self.real}+'''
             f'''{'+'.join(str(_lowerCamelCase				)+'E'+str(n+1				)for n,dual in enumerate(self.duals				)				)}'''
         )





       def      _a       (   self				):
         UpperCamelCase_:   Union[str, Any]        =							self.duals.copy()
         while cur[-1] == 0:
           cur.pop(-1				)
         return Dual(self.real    ,     _lowerCamelCase				)





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

       a							:						Union[str, Any]										=__add__





       def __sub__(   self    ,     _lowerCamelCase				):
         return self + other * -1





       def __mul__(   self    ,     _lowerCamelCase				):
         if not isinstance(_lowerCamelCase    ,     _lowerCamelCase				):
           UpperCamelCase_:   Union[str, Any]        =							[]
           for i in self.duals:
             new_duals.append(i * other				)
           return Dual(self.real * other    ,     _lowerCamelCase				)
         UpperCamelCase_:   List[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    ,     _lowerCamelCase				)

       a							:						Optional[int]										=__mul__





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





       def __floordiv__(   self    ,     _lowerCamelCase				):
         if not isinstance(_lowerCamelCase    ,     _lowerCamelCase				):
           UpperCamelCase_:   Union[str, Any]        =							[]
           for i in self.duals:
             new_duals.append(i // other				)
           return Dual(self.real // other    ,     _lowerCamelCase				)
         raise ValueError





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







def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							int:
  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'    )
  UpperCamelCase_:   Optional[int]        =							Dual(UpperCAmelCase__      ,       1    )
  UpperCamelCase_:   Optional[Any]        =							func(UpperCAmelCase__    )
  if order == 0:
    return result.real
  return result.duals[order - 1] * factorial(UpperCAmelCase__    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   def     snake_case   (UpperCAmelCase__    )			->							Optional[Any]:
     return y**2 * y**4
   print(differentiate(f, 9, 2))


import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser()

			parser.add_argument(
			    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
			)
			parser.add_argument(
			    '''--original_config_file''',
			    type=str,
			    required=True,
			    help='''The YAML config file corresponding to the original architecture.''',
			)
			parser.add_argument(
			    '''--num_in_channels''',
			    default=None,
			    type=int,
			    help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
			)
			parser.add_argument(
			    '''--image_size''',
			    default=512,
			    type=int,
			    help=(
			        '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
			        ''' Base. Use 768 for Stable Diffusion v2.'''
			    ),
			)
			parser.add_argument(
			    '''--extract_ema''',
			    action='''store_true''',
			    help=(
			        '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
			        ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
			        ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
			    ),
			)
			parser.add_argument(
			    '''--upcast_attention''',
			    action='''store_true''',
			    help=(
			        '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
			        ''' diffusion 2.1.'''
			    ),
			)
			parser.add_argument(
			    '''--from_safetensors''',
			    action='''store_true''',
			    help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
			)
			parser.add_argument(
			    '''--to_safetensors''',
			    action='''store_true''',
			    help='''Whether to store pipeline in safetensors format or not.''',
			)
			parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
			parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')



			def   __UpperCAmelCase					(      __a :     Any      )     ->	List[Any]:

								"""simple docstring"""

								if string == "True":
													return True
								elif string == "False":
													return False
								else:
													raise ValueError(F"""could not parse string as bool {string}"""      )

			parser.add_argument(
			    '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
			)

			parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

			a__												=						parser.parse_args()

			a__												=						download_controlnet_from_original_ckpt(
			    checkpoint_path=args.checkpoint_path,
			    original_config_file=args.original_config_file,
			    image_size=args.image_size,
			    extract_ema=args.extract_ema,
			    num_in_channels=args.num_in_channels,
			    upcast_attention=args.upcast_attention,
			    from_safetensors=args.from_safetensors,
			    device=args.device,
			    use_linear_projection=args.use_linear_projection,
			    cross_attention_dim=args.cross_attention_dim,
			)







			controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



