


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)








def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any]       ):
							return [
							    {
							        0: [1, 2],
							        1: [0, 2],
							        2: [0, 1, 3, 5],
							        3: [2, 4],
							        4: [3],
							        5: [2, 6, 8],
							        6: [5, 7],
							        7: [6, 8],
							        8: [5, 7],
							    },
							    {
							        0: [6],
							        1: [9],
							        2: [4, 5],
							        3: [4],
							        4: [2, 3],
							        5: [2],
							        6: [0, 7],
							        7: [6],
							        8: [],
							        9: [1],
							    },
							    {
							        0: [4],
							        1: [6],
							        2: [],
							        3: [5, 6, 7],
							        4: [0, 6],
							        5: [3, 8, 9],
							        6: [1, 3, 4, 7],
							        7: [3, 6, 8, 9],
							        8: [5, 7],
							        9: [5, 7],
							    },
							    {
							        0: [1, 3],
							        1: [0, 2, 4],
							        2: [1, 3, 4],
							        3: [0, 2, 4],
							        4: [1, 2, 3],
							    },
							][index]






def 							__lowercase       (			__lowerCAmelCase							:    dict[int, list[int]]       ):
							a__					=					0
							a__					=					len(__lowerCAmelCase       )  # No of vertices in graph
							a__					=					[0] * n
							a__					=					[False] * n

							def dfs(__lowerCAmelCase							:    int ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    Tuple       ):
														a__					=					True
														a__					=					id_
														id_ += 1
														for to in graph[at]:
																					if to == parent:
																												pass
																					elif not visited[to]:
																												dfs(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   id_       )
																												a__					=					min(low[at] ,   low[to]       )
																												if id_ <= low[to]:
																																			bridges.append((at, to) if at < to else (to, at)       )
																					else:
																												# This edge is a back edge and cannot be a bridge
																												a__					=					min(low[at] ,   low[to]       )

							a__					=					[]
							for i in range(__lowerCAmelCase       ):
														if not visited[i]:
																					dfs(__lowerCAmelCase ,   -1 ,   __lowerCAmelCase ,   id_       )
							return bridges


if __name__ == "__main__":
						import doctest

						doctest.testmod()




