


"""simple docstring"""





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
	snake_case__		:     int    				=				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.''',
	)
	snake_case__		:     List[str]    				=				parser.parse_args()

	snake_case__		:     Tuple    				=				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)






"""simple docstring"""





class 		snake_case_:






     def __init__(	self  :					Union[str, Any]					,				UpperCamelCase_  :					str				):
           lowerCAmelCase					:	Dict       =   val
           lowerCAmelCase					:	str       =   None
           lowerCAmelCase					:	Dict       =   None






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					Dict				):
           if self.val:
                 if val < self.val:
                       if self.left is None:
                             lowerCAmelCase					:	int       =   Node(UpperCamelCase_				)
                       else:
                             self.left.insert(UpperCamelCase_				)
                 elif val > self.val:
                       if self.right is None:
                             lowerCAmelCase					:	Any       =   Node(UpperCamelCase_				)
                       else:
                             self.right.insert(UpperCamelCase_				)
           else:
                 lowerCAmelCase					:	Optional[Any]       =   val



def      _snake_case						(						_snake_case				:			Tuple ,   _snake_case				:			str			):
      # Recursive traversal
      if root:
            inorder(root.left ,   _snake_case			)
            res.append(root.val			)
            inorder(root.right ,   _snake_case			)


def      _snake_case						(						_snake_case				:			Optional[Any]			):
      # Build BST
      if len(_snake_case			) == 0:
            return arr
      lowerCAmelCase					:	Optional[Any]       =   Node(arr[0]			)
      for i in range(1 ,   len(_snake_case			)			):
            root.insert(arr[i]			)
      # Traverse BST in order.
      lowerCAmelCase					:	Optional[int]       =   []
      inorder(_snake_case ,   _snake_case			)
      return res


if __name__ == "__main__":
 print(tree_sort([10, 1, 3, 2, 9, 14, 13]))


