



import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel






def 					a(   A :					Optional[int]				,  A :					int=1 )	-> List[str]:
					"""simple docstring"""





					if n_shave_prefix_segments >= 0:
										return ".".join(path.split("." )[n_shave_prefix_segments:] )
					else:
										return ".".join(path.split("." )[:n_shave_prefix_segments] )






def 					a(   A :					int				,  A :					Any=0 )	-> List[str]:
					"""simple docstring"""





					a        =     []
					for old_item in old_list:
										a        =     old_item
										a        =     new_item.replace("block."				,  "resnets." )
										a        =     new_item.replace("conv_shorcut"				,  "conv1" )
										a        =     new_item.replace("in_shortcut"				,  "conv_shortcut" )
										a        =     new_item.replace("temb_proj"				,  "time_emb_proj" )

										a        =     shave_segments(A				,  n_shave_prefix_segments=A )

										mapping.append({"old": old_item, "new": new_item} )

					return mapping






def 					a(   A :					List[str]				,  A :					Tuple=0				,  A :					Dict=False )	-> Optional[int]:
					"""simple docstring"""





					a        =     []
					for old_item in old_list:
										a        =     old_item

										# In `model.mid`, the layer is called `attn`.
										if not in_mid:
															a        =     new_item.replace("attn"				,  "attentions" )
										a        =     new_item.replace(".k."				,  ".key." )
										a        =     new_item.replace(".v."				,  ".value." )
										a        =     new_item.replace(".q."				,  ".query." )

										a        =     new_item.replace("proj_out"				,  "proj_attn" )
										a        =     new_item.replace("norm"				,  "group_norm" )

										a        =     shave_segments(A				,  n_shave_prefix_segments=A )
										mapping.append({"old": old_item, "new": new_item} )

					return mapping






def 					a(   A :					Optional[Any]				,  A :					int				,  A :					Tuple				,  A :					int=None				,  A :					int=None				,  A :					Tuple=None )	-> Optional[Any]:
					"""simple docstring"""





					assert isinstance(A				,  A ), "Paths should be a list of dicts containing 'old' and 'new' keys."

					if attention_paths_to_split is not None:
										if config is None:
															raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'." )

										for path, path_map in attention_paths_to_split.items():
															a        =     old_checkpoint[path]
															a        =     old_tensor.shape[0] // 3

															a        =     (-1, channels) if len(old_tensor.shape ) == 3 else (-1)

															a        =     old_tensor.shape[0] // config.get("num_head_channels"				,  1 ) // 3

															a        =     old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
															a  ,       a  ,       a        =     old_tensor.split(channels // num_heads				,  dim=1 )

															a        =     query.reshape(A ).squeeze()
															a        =     key.reshape(A ).squeeze()
															a        =     value.reshape(A ).squeeze()

					for path in paths:
										a        =     path["new"]

										if attention_paths_to_split is not None and new_path in attention_paths_to_split:
															continue

										a        =     new_path.replace("down."				,  "down_blocks." )
										a        =     new_path.replace("up."				,  "up_blocks." )

										if additional_replacements is not None:
															for replacement in additional_replacements:
																				a        =     new_path.replace(replacement["old"]				,  replacement["new"] )

										if "attentions" in new_path:
															a        =     old_checkpoint[path["old"]].squeeze()
										else:
															a        =     old_checkpoint[path["old"]]






def 					a(   A :					Optional[Any]				,  A :					Dict )	-> Union[str, Any]:
					"""simple docstring"""





					a        =     {}

					a        =     checkpoint["temb.dense.0.weight"]
					a        =     checkpoint["temb.dense.0.bias"]
					a        =     checkpoint["temb.dense.1.weight"]
					a        =     checkpoint["temb.dense.1.bias"]

					a        =     checkpoint["norm_out.weight"]
					a        =     checkpoint["norm_out.bias"]

					a        =     checkpoint["conv_in.weight"]
					a        =     checkpoint["conv_in.bias"]
					a        =     checkpoint["conv_out.weight"]
					a        =     checkpoint["conv_out.bias"]

					a        =     len({".".join(layer.split("." )[:2] ) for layer in checkpoint if "down" in layer} )
					a        =     {
					    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(A )
					}

					a        =     len({".".join(layer.split("." )[:2] ) for layer in checkpoint if "up" in layer} )
					a        =     {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(A )}

					for i in range(A ):
										a        =     (i - 1) // (config["layers_per_block"] + 1)

										if any("downsample" in layer for layer in down_blocks[i] ):
															a        =     checkpoint[
															    f'''down.{i}.downsample.op.weight'''
															]
															a        =     checkpoint[f'''down.{i}.downsample.op.bias''']
										#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
										#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

										if any("block" in layer for layer in down_blocks[i] ):
															a        =     len(
															    {".".join(shave_segments(A				,  2 ).split("." )[:2] ) for layer in down_blocks[i] if "block" in layer} )
															a        =     {
															    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
															    for layer_id in range(A )
															}

															if num_blocks > 0:
																				for j in range(config["layers_per_block"] ):
																									a        =     renew_resnet_paths(blocks[j] )
																									assign_to_checkpoint(A				,  A				,  A )

										if any("attn" in layer for layer in down_blocks[i] ):
															a        =     len(
															    {".".join(shave_segments(A				,  2 ).split("." )[:2] ) for layer in down_blocks[i] if "attn" in layer} )
															a        =     {
															    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
															    for layer_id in range(A )
															}

															if num_attn > 0:
																				for j in range(config["layers_per_block"] ):
																									a        =     renew_attention_paths(attns[j] )
																									assign_to_checkpoint(A				,  A				,  A				,  config=A )

					a        =     [key for key in checkpoint if "mid.block_1" in key]
					a        =     [key for key in checkpoint if "mid.block_2" in key]
					a        =     [key for key in checkpoint if "mid.attn_1" in key]

					# Mid new 2
					a        =     renew_resnet_paths(A )
					assign_to_checkpoint(
					    A				,  A				,  A				,  additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]				,  )

					a        =     renew_resnet_paths(A )
					assign_to_checkpoint(
					    A				,  A				,  A				,  additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]				,  )

					a        =     renew_attention_paths(A				,  in_mid=A )
					assign_to_checkpoint(
					    A				,  A				,  A				,  additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]				,  )

					for i in range(A ):
										a        =     num_up_blocks - 1 - i

										if any("upsample" in layer for layer in up_blocks[i] ):
															a        =     checkpoint[
															    f'''up.{i}.upsample.conv.weight'''
															]
															a        =     checkpoint[f'''up.{i}.upsample.conv.bias''']

										if any("block" in layer for layer in up_blocks[i] ):
															a        =     len(
															    {".".join(shave_segments(A				,  2 ).split("." )[:2] ) for layer in up_blocks[i] if "block" in layer} )
															a        =     {
															    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(A )
															}

															if num_blocks > 0:
																				for j in range(config["layers_per_block"] + 1 ):
																									a        =     {"old": f'''up_blocks.{i}''', "new": f'''up_blocks.{block_id}'''}
																									a        =     renew_resnet_paths(blocks[j] )
																									assign_to_checkpoint(A				,  A				,  A				,  additional_replacements=[replace_indices] )

										if any("attn" in layer for layer in up_blocks[i] ):
															a        =     len(
															    {".".join(shave_segments(A				,  2 ).split("." )[:2] ) for layer in up_blocks[i] if "attn" in layer} )
															a        =     {
															    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(A )
															}

															if num_attn > 0:
																				for j in range(config["layers_per_block"] + 1 ):
																									a        =     {"old": f'''up_blocks.{i}''', "new": f'''up_blocks.{block_id}'''}
																									a        =     renew_attention_paths(attns[j] )
																									assign_to_checkpoint(A				,  A				,  A				,  additional_replacements=[replace_indices] )

					a        =     {k.replace("mid_new_2"				,  "mid_block" ): v for k, v in new_checkpoint.items()}
					return new_checkpoint






def 					a(   A :					Optional[int]				,  A :					Tuple )	-> Optional[Any]:
					"""simple docstring"""





					a        =     {}

					a        =     checkpoint["encoder.norm_out.weight"]
					a        =     checkpoint["encoder.norm_out.bias"]

					a        =     checkpoint["encoder.conv_in.weight"]
					a        =     checkpoint["encoder.conv_in.bias"]
					a        =     checkpoint["encoder.conv_out.weight"]
					a        =     checkpoint["encoder.conv_out.bias"]

					a        =     checkpoint["decoder.norm_out.weight"]
					a        =     checkpoint["decoder.norm_out.bias"]

					a        =     checkpoint["decoder.conv_in.weight"]
					a        =     checkpoint["decoder.conv_in.bias"]
					a        =     checkpoint["decoder.conv_out.weight"]
					a        =     checkpoint["decoder.conv_out.bias"]

					a        =     len({".".join(layer.split("." )[:3] ) for layer in checkpoint if "down" in layer} )
					a        =     {
					    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(A )
					}

					a        =     len({".".join(layer.split("." )[:3] ) for layer in checkpoint if "up" in layer} )
					a        =     {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(A )}

					for i in range(A ):
										a        =     (i - 1) // (config["layers_per_block"] + 1)

										if any("downsample" in layer for layer in down_blocks[i] ):
															a        =     checkpoint[
															    f'''encoder.down.{i}.downsample.conv.weight'''
															]
															a        =     checkpoint[
															    f'''encoder.down.{i}.downsample.conv.bias'''
															]

										if any("block" in layer for layer in down_blocks[i] ):
															a        =     len(
															    {".".join(shave_segments(A				,  3 ).split("." )[:3] ) for layer in down_blocks[i] if "block" in layer} )
															a        =     {
															    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
															    for layer_id in range(A )
															}

															if num_blocks > 0:
																				for j in range(config["layers_per_block"] ):
																									a        =     renew_resnet_paths(blocks[j] )
																									assign_to_checkpoint(A				,  A				,  A )

										if any("attn" in layer for layer in down_blocks[i] ):
															a        =     len(
															    {".".join(shave_segments(A				,  3 ).split("." )[:3] ) for layer in down_blocks[i] if "attn" in layer} )
															a        =     {
															    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
															    for layer_id in range(A )
															}

															if num_attn > 0:
																				for j in range(config["layers_per_block"] ):
																									a        =     renew_attention_paths(attns[j] )
																									assign_to_checkpoint(A				,  A				,  A				,  config=A )

					a        =     [key for key in checkpoint if "mid.block_1" in key]
					a        =     [key for key in checkpoint if "mid.block_2" in key]
					a        =     [key for key in checkpoint if "mid.attn_1" in key]

					# Mid new 2
					a        =     renew_resnet_paths(A )
					assign_to_checkpoint(
					    A				,  A				,  A				,  additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]				,  )

					a        =     renew_resnet_paths(A )
					assign_to_checkpoint(
					    A				,  A				,  A				,  additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]				,  )

					a        =     renew_attention_paths(A				,  in_mid=A )
					assign_to_checkpoint(
					    A				,  A				,  A				,  additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]				,  )

					for i in range(A ):
										a        =     num_up_blocks - 1 - i

										if any("upsample" in layer for layer in up_blocks[i] ):
															a        =     checkpoint[
															    f'''decoder.up.{i}.upsample.conv.weight'''
															]
															a        =     checkpoint[
															    f'''decoder.up.{i}.upsample.conv.bias'''
															]

										if any("block" in layer for layer in up_blocks[i] ):
															a        =     len(
															    {".".join(shave_segments(A				,  3 ).split("." )[:3] ) for layer in up_blocks[i] if "block" in layer} )
															a        =     {
															    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(A )
															}

															if num_blocks > 0:
																				for j in range(config["layers_per_block"] + 1 ):
																									a        =     {"old": f'''up_blocks.{i}''', "new": f'''up_blocks.{block_id}'''}
																									a        =     renew_resnet_paths(blocks[j] )
																									assign_to_checkpoint(A				,  A				,  A				,  additional_replacements=[replace_indices] )

										if any("attn" in layer for layer in up_blocks[i] ):
															a        =     len(
															    {".".join(shave_segments(A				,  3 ).split("." )[:3] ) for layer in up_blocks[i] if "attn" in layer} )
															a        =     {
															    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(A )
															}

															if num_attn > 0:
																				for j in range(config["layers_per_block"] + 1 ):
																									a        =     {"old": f'''up_blocks.{i}''', "new": f'''up_blocks.{block_id}'''}
																									a        =     renew_attention_paths(attns[j] )
																									assign_to_checkpoint(A				,  A				,  A				,  additional_replacements=[replace_indices] )

					a        =     {k.replace("mid_new_2"				,  "mid_block" ): v for k, v in new_checkpoint.items()}
					a        =     checkpoint["quant_conv.weight"]
					a        =     checkpoint["quant_conv.bias"]
					if "quantize.embedding.weight" in checkpoint:
										a        =     checkpoint["quantize.embedding.weight"]
					a        =     checkpoint["post_quant_conv.weight"]
					a        =     checkpoint["post_quant_conv.bias"]

					return new_checkpoint


if __name__ == "__main__":
				_lowercase:				str       =	argparse.ArgumentParser()

				parser.add_argument(
				    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
				)

				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The config json file corresponding to the architecture.",
				)

				parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

				_lowercase:				Any       =	parser.parse_args()
				_lowercase:				List[Any]       =	torch.load(args.checkpoint_path)

				with open(args.config_file) as f:
								_lowercase:				Dict       =	json.loads(f.read())

				# unet case
				_lowercase:				Dict       =	{key.split(".")[0] for key in checkpoint.keys()}
				if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
								_lowercase:				Optional[int]       =	convert_vq_autoenc_checkpoint(checkpoint, config)
				else:
								_lowercase:				Dict       =	convert_ddpm_checkpoint(checkpoint, config)

				if "ddpm" in config:
								del config["ddpm"]

				if config["_class_name"] == "VQModel":
								_lowercase:				Optional[int]       =	VQModel(**config)
								model.load_state_dict(converted_checkpoint)
								model.save_pretrained(args.dump_path)
				elif config["_class_name"] == "AutoencoderKL":
								_lowercase:				Any       =	AutoencoderKL(**config)
								model.load_state_dict(converted_checkpoint)
								model.save_pretrained(args.dump_path)
				else:
								_lowercase:				List[str]       =	UNetaDModel(**config)
								model.load_state_dict(converted_checkpoint)

								_lowercase:				Any       =	DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

								_lowercase:				Dict       =	DDPMPipeline(unet=model, scheduler=scheduler)
								pipe.save_pretrained(args.dump_path)





def 					a(   A :					Optional[Any]				,  A :					List[str] )	-> List[Any]:
					"""simple docstring"""





					print("\nVertex Distance" )
					for i in range(A ):
										if dist[i] != float("inf" ):
															print(A				,  "\t"				,  int(dist[i] )				,  end="\t" )
										else:
															print(A				,  "\t"				,  "INF"				,  end="\t" )
										print()






def 					a(   A :					str				,  A :					int				,  A :					int )	-> Optional[int]:
					"""simple docstring"""





					a        =     float("inf" )
					a        =     -1
					for i in range(A ):
										if (not vset[i]) and mdist[i] < min_val:
															a        =     i
															a        =     mdist[i]
					return min_ind






def 					a(   A :					List[str]				,  A :					Union[str, Any]				,  A :					Dict )	-> Tuple:
					"""simple docstring"""





					a        =     [float("inf" ) for _ in range(A )]
					a        =     [False for _ in range(A )]
					a        =     0.0

					for _ in range(v - 1 ):
										a        =     min_dist(A				,  A				,  A )
										a        =     True

										for i in range(A ):
															if (
															    (not vset[i])
															    and graph[u][i] != float("inf" )
															    and mdist[u] + graph[u][i] < mdist[i]
															):
																				a        =     mdist[u] + graph[u][i]

					print_dist(A				,  A )


if __name__ == "__main__":
				_lowercase:				Any       =	int(input("Enter number of vertices: ").strip())
				_lowercase:				Optional[Any]       =	int(input("Enter number of edges: ").strip())

				_lowercase:				List[str]       =	[[float("inf") for i in range(V)] for j in range(V)]

				for i in range(V):
								_lowercase:				List[Any]       =	0.0

				for i in range(E):
								print("\nEdge ", i + 1)
								_lowercase:				Tuple       =	int(input("Enter source:").strip())
								_lowercase:				Tuple       =	int(input("Enter destination:").strip())
								_lowercase:				int       =	float(input("Enter weight:").strip())
								_lowercase:				str       =	weight

				_lowercase:				Union[str, Any]       =	int(input("\nEnter shortest path source:").strip())
				dijkstra(graph, V, gsrc)
