






import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
 lowerCamelCase	:							Union[str, Any]				=argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
 )
 # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
 parser.add_argument(
     '''--original_config_file''',
     default=None,
     type=str,
     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(
     '''--scheduler_type''',
     default='''pndm''',
     type=str,
     help='''Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']''',
 )
 parser.add_argument(
     '''--pipeline_type''',
     default=None,
     type=str,
     help=(
         '''The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''''
         '''. If `None` pipeline will be automatically inferred.'''
     ),
 )
 parser.add_argument(
     '''--image_size''',
     default=None,
     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(
     '''--prediction_type''',
     default=None,
     type=str,
     help=(
         '''The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'''
         ''' Diffusion v2 Base. Use \'v_prediction\' 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.)''')
 parser.add_argument(
     '''--stable_unclip''',
     type=str,
     default=None,
     required=False,
     help='''Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.''',
 )
 parser.add_argument(
     '''--stable_unclip_prior''',
     type=str,
     default=None,
     required=False,
     help='''Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.''',
 )
 parser.add_argument(
     '''--clip_stats_path''',
     type=str,
     help='''Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.''',
     required=False,
 )
 parser.add_argument(
     '''--controlnet''', action='''store_true''', default=None, help='''Set flag if this is a controlnet checkpoint.'''
 )
 parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
 parser.add_argument(
     '''--vae_path''',
     type=str,
     default=None,
     required=False,
     help='''Set to a path, hub id to an already converted vae to not convert it again.''',
 )
 lowerCamelCase	:							Union[str, Any]				=parser.parse_args()

 lowerCamelCase	:							Any				=download_from_original_stable_diffusion_ckpt(
     checkpoint_path=args.checkpoint_path,
     original_config_file=args.original_config_file,
     image_size=args.image_size,
     prediction_type=args.prediction_type,
     model_type=args.pipeline_type,
     extract_ema=args.extract_ema,
     scheduler_type=args.scheduler_type,
     num_in_channels=args.num_in_channels,
     upcast_attention=args.upcast_attention,
     from_safetensors=args.from_safetensors,
     device=args.device,
     stable_unclip=args.stable_unclip,
     stable_unclip_prior=args.stable_unclip_prior,
     clip_stats_path=args.clip_stats_path,
     controlnet=args.controlnet,
     vae_path=args.vae_path,
 )

 if args.half:
  pipe.to(torch_dtype=torch.floataa)

 if args.controlnet:
  # only save the controlnet model
  pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
 else:
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)







from math import factorial





class 			__a  :







 def __init__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  real
      if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   Union[str, Any]       				=  [1] * rank
      else:
           UpperCamelCase__			:   int       				=  rank







 def __repr__(		self					:   Tuple						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self.duals.copy()
      while cur[-1] == 0:
           cur.pop(-1						)
      return Dual(self.real							,   SCREAMING_SNAKE_CASE						)







 def __add__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           return Dual(self.real + other							,   self.duals						)
      UpperCamelCase__			:   Optional[int]       				=  self.duals.copy()
      UpperCamelCase__			:   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						))						)
      UpperCamelCase__			:   Optional[int]       				=  []
      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						)

 _lowerCAmelCase       :       Dict        = __add__







 def __sub__(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      return self + other * -1







 def __mul__(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






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

 _lowerCAmelCase       :       Union[str, Any]        = __mul__







 def __truediv__(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Any						):


      '''simple docstring'''






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







 def __floordiv__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






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







 def __pow__(		self					:   str							,   SCREAMING_SNAKE_CASE					:   int						):


      '''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
      UpperCamelCase__			:   str       				=  self
      for _ in range(n - 1						):
           x *= self
      return x







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


if __name__ == "__main__":
 import doctest

 doctest.testmod()







 def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	List[Any]:
      return y**2 * y**4




 print(differentiate(f, 9, 2))