


import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
						snake_case							:	Tuple					      =				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=5_12,
						    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 							__lowercase       (			__lowerCAmelCase							:    Tuple       ):
													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)

						snake_case							:	Optional[Any]					      =				parser.parse_args()

						snake_case							:	Any					      =				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)








from __future__ import annotations







class 	snake_case_       :
					def __init__(				self       :List[str]      ,__snake_case       :int				) ->   None:
												a__					=					data
												a__					=					None
												a__					=					None






def 							__lowercase       (			__lowerCAmelCase							:    Node | None       ):  # In Order traversal of the tree
							if tree:
														display(tree.left       )
														print(tree.data       )
														display(tree.right       )






def 							__lowercase       (			__lowerCAmelCase							:    Node | None       ):
							return 1 + max(depth_of_tree(tree.left       ) ,   depth_of_tree(tree.right       )       ) if tree else 0






def 							__lowercase       (			__lowerCAmelCase							:    Node       ):
							if not tree:
														return True
							if tree.left and tree.right:
														return is_full_binary_tree(tree.left       ) and is_full_binary_tree(tree.right       )
							else:
														return not tree.left and not tree.right






def 							__lowercase       (			):  # Main function for testing.
							a__					=					Node(1       )
							a__					=					Node(2       )
							a__					=					Node(3       )
							a__					=					Node(4       )
							a__					=					Node(5       )
							a__					=					Node(6       )
							a__					=					Node(7       )
							a__					=					Node(8       )
							a__					=					Node(9       )

							print(is_full_binary_tree(__lowerCAmelCase       )       )
							print(depth_of_tree(__lowerCAmelCase       )       )
							print('Tree is: '       )
							display(__lowerCAmelCase       )


if __name__ == "__main__":
						main()




