





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def     _UpperCamelCase     (a__   :Union[str, Any]   ,			a__   :Optional[Any]=1       ):






							"""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     _UpperCamelCase     (a__   :Dict   ,			a__   :int=0       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[]
							for old_item in old_list:
														UpperCamelCase__				     =			old_item.replace("""in_layers.0"""   ,			"""norm1"""       )
														UpperCamelCase__				     =			new_item.replace("""in_layers.2"""   ,			"""conv1"""       )

														UpperCamelCase__				     =			new_item.replace("""out_layers.0"""   ,			"""norm2"""       )
														UpperCamelCase__				     =			new_item.replace("""out_layers.3"""   ,			"""conv2"""       )

														UpperCamelCase__				     =			new_item.replace("""emb_layers.1"""   ,			"""time_emb_proj"""       )
														UpperCamelCase__				     =			new_item.replace("""skip_connection"""   ,			"""conv_shortcut"""       )

														UpperCamelCase__				     =			shave_segments(a__   ,			n_shave_prefix_segments=a__       )

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

							return mapping





def     _UpperCamelCase     (a__   :str   ,			a__   :Union[str, Any]=0       ):






							"""simple docstring"""





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

														UpperCamelCase__				     =			new_item.replace("""norm.weight"""   ,			"""group_norm.weight"""       )
														UpperCamelCase__				     =			new_item.replace("""norm.bias"""   ,			"""group_norm.bias"""       )

														UpperCamelCase__				     =			new_item.replace("""proj_out.weight"""   ,			"""proj_attn.weight"""       )
														UpperCamelCase__				     =			new_item.replace("""proj_out.bias"""   ,			"""proj_attn.bias"""       )

														UpperCamelCase__				     =			shave_segments(a__   ,			n_shave_prefix_segments=a__       )

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

							return mapping





def     _UpperCamelCase     (a__   :Union[str, Any]   ,			a__   :List[str]   ,			a__   :Optional[Any]   ,			a__   :Union[str, Any]=None   ,			a__   :List[Any]=None   ,			a__   :List[Any]=None       ):






							"""simple docstring"""





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

							# Splits the attention layers into three variables.
							if attention_paths_to_split is not None:
														for path, path_map in attention_paths_to_split.items():
																					UpperCamelCase__				     =			old_checkpoint[path]
																					UpperCamelCase__				     =			old_tensor.shape[0] // 3

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

																					UpperCamelCase__				     =			old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

																					UpperCamelCase__				     =			query.reshape(a__       )
																					UpperCamelCase__				     =			key.reshape(a__       )
																					UpperCamelCase__				     =			value.reshape(a__       )

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

														# These have already been assigned
														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

														# Global renaming happens here
														UpperCamelCase__				     =			new_path.replace("""middle_block.0"""   ,			"""mid_block.resnets.0"""       )
														UpperCamelCase__				     =			new_path.replace("""middle_block.1"""   ,			"""mid_block.attentions.0"""       )
														UpperCamelCase__				     =			new_path.replace("""middle_block.2"""   ,			"""mid_block.resnets.1"""       )

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

        # proj_attn.weight has to be converted from conv 1D to linear
														if "proj_attn.weight" in new_path:
																					UpperCamelCase__				     =			old_checkpoint[path["""old"""]][:, :, 0]
														else:
																					UpperCamelCase__				     =			old_checkpoint[path["""old"""]]





def     _UpperCamelCase     (a__   :Optional[Any]   ,			a__   :Tuple       ):






							"""simple docstring"""





							UpperCamelCase__				     =			{}

							UpperCamelCase__				     =			checkpoint["""time_embed.0.weight"""]
							UpperCamelCase__				     =			checkpoint["""time_embed.0.bias"""]
							UpperCamelCase__				     =			checkpoint["""time_embed.2.weight"""]
							UpperCamelCase__				     =			checkpoint["""time_embed.2.bias"""]

							UpperCamelCase__				     =			checkpoint["""input_blocks.0.0.weight"""]
							UpperCamelCase__				     =			checkpoint["""input_blocks.0.0.bias"""]

							UpperCamelCase__				     =			checkpoint["""out.0.weight"""]
							UpperCamelCase__				     =			checkpoint["""out.0.bias"""]
							UpperCamelCase__				     =			checkpoint["""out.2.weight"""]
							UpperCamelCase__				     =			checkpoint["""out.2.bias"""]

							# Retrieves the keys for the input blocks only
							UpperCamelCase__				     =			len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """input_blocks""" in layer}       )
							UpperCamelCase__				     =			{
							    layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
							    for layer_id in range(a__       )
							}

							# Retrieves the keys for the middle blocks only
							UpperCamelCase__				     =			len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """middle_block""" in layer}       )
							UpperCamelCase__				     =			{
							    layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
							    for layer_id in range(a__       )
							}

							# Retrieves the keys for the output blocks only
							UpperCamelCase__				     =			len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """output_blocks""" in layer}       )
							UpperCamelCase__				     =			{
							    layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
							    for layer_id in range(a__       )
							}

							for i in range(1   ,			a__       ):
														UpperCamelCase__				     =			(i - 1) // (config["""num_res_blocks"""] + 1)
														UpperCamelCase__				     =			(i - 1) % (config["""num_res_blocks"""] + 1)

														UpperCamelCase__				     =			[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
														UpperCamelCase__				     =			[key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]

														if f"""input_blocks.{i}.0.op.weight""" in checkpoint:
																					UpperCamelCase__				     =			checkpoint[
																					    f"""input_blocks.{i}.0.op.weight"""
																					]
																					UpperCamelCase__				     =			checkpoint[
																					    f"""input_blocks.{i}.0.op.bias"""
																					]
																					continue

														UpperCamelCase__				     =			renew_resnet_paths(a__       )
														UpperCamelCase__				     =			{"""old""": f"""input_blocks.{i}.0""", """new""": f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
														UpperCamelCase__				     =			{"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
														assign_to_checkpoint(
														    a__   ,			a__   ,			a__   ,			additional_replacements=[meta_path, resnet_op]   ,			config=a__       )

														if len(a__       ):
																					UpperCamelCase__				     =			renew_attention_paths(a__       )
																					UpperCamelCase__				     =			{
																					    """old""": f"""input_blocks.{i}.1""",
																					    """new""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
																					}
																					UpperCamelCase__				     =			{
																					    f"""input_blocks.{i}.1.qkv.bias""": {
																					        """key""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
																					        """query""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
																					        """value""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
																					    },
																					    f"""input_blocks.{i}.1.qkv.weight""": {
																					        """key""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
																					        """query""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
																					        """value""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
																					    },
																					}
																					assign_to_checkpoint(
																					    a__   ,			a__   ,			a__   ,			additional_replacements=[meta_path]   ,			attention_paths_to_split=a__   ,			config=a__   ,			)

							UpperCamelCase__				     =			middle_blocks[0]
							UpperCamelCase__				     =			middle_blocks[1]
							UpperCamelCase__				     =			middle_blocks[2]

							UpperCamelCase__				     =			renew_resnet_paths(a__       )
							assign_to_checkpoint(a__   ,			a__   ,			a__   ,			config=a__       )

							UpperCamelCase__				     =			renew_resnet_paths(a__       )
							assign_to_checkpoint(a__   ,			a__   ,			a__   ,			config=a__       )

							UpperCamelCase__				     =			renew_attention_paths(a__       )
							UpperCamelCase__				     =			{
							    """middle_block.1.qkv.bias""": {
							        """key""": """mid_block.attentions.0.key.bias""",
							        """query""": """mid_block.attentions.0.query.bias""",
							        """value""": """mid_block.attentions.0.value.bias""",
							    },
							    """middle_block.1.qkv.weight""": {
							        """key""": """mid_block.attentions.0.key.weight""",
							        """query""": """mid_block.attentions.0.query.weight""",
							        """value""": """mid_block.attentions.0.value.weight""",
							    },
							}
							assign_to_checkpoint(
							    a__   ,			a__   ,			a__   ,			attention_paths_to_split=a__   ,			config=a__       )

							for i in range(a__       ):
														UpperCamelCase__				     =			i // (config["""num_res_blocks"""] + 1)
														UpperCamelCase__				     =			i % (config["""num_res_blocks"""] + 1)
														UpperCamelCase__				     =			[shave_segments(a__   ,			2       ) for name in output_blocks[i]]
														UpperCamelCase__				     =			{}

														for layer in output_block_layers:
																					UpperCamelCase__     ,     UpperCamelCase__				     =			layer.split("""."""       )[0], shave_segments(a__   ,			1       )
																					if layer_id in output_block_list:
																												output_block_list[layer_id].append(a__       )
																					else:
																												UpperCamelCase__				     =			[layer_name]

														if len(a__       ) > 1:
																					UpperCamelCase__				     =			[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
																					UpperCamelCase__				     =			[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

																					UpperCamelCase__				     =			renew_resnet_paths(a__       )
																					UpperCamelCase__				     =			renew_resnet_paths(a__       )

																					UpperCamelCase__				     =			{"""old""": f"""output_blocks.{i}.0""", """new""": f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
																					assign_to_checkpoint(a__   ,			a__   ,			a__   ,			additional_replacements=[meta_path]   ,			config=a__       )

																					if ["conv.weight", "conv.bias"] in output_block_list.values():
																												UpperCamelCase__				     =			list(output_block_list.values()       ).index(["""conv.weight""", """conv.bias"""]       )
																												UpperCamelCase__				     =			checkpoint[
																												    f"""output_blocks.{i}.{index}.conv.weight"""
																												]
																												UpperCamelCase__				     =			checkpoint[
																												    f"""output_blocks.{i}.{index}.conv.bias"""
																												]

																												# Clear attentions as they have been attributed above.
																												if len(a__       ) == 2:
																																			UpperCamelCase__				     =			[]

																					if len(a__       ):
																												UpperCamelCase__				     =			renew_attention_paths(a__       )
																												UpperCamelCase__				     =			{
																												    """old""": f"""output_blocks.{i}.1""",
																												    """new""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																												}
																												UpperCamelCase__				     =			{
																												    f"""output_blocks.{i}.1.qkv.bias""": {
																												        """key""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
																												        """query""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
																												        """value""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
																												    },
																												    f"""output_blocks.{i}.1.qkv.weight""": {
																												        """key""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
																												        """query""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
																												        """value""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
																												    },
																												}
																												assign_to_checkpoint(
																												    a__   ,			a__   ,			a__   ,			additional_replacements=[meta_path]   ,			attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions       ) else None   ,			config=a__   ,			)
														else:
																					UpperCamelCase__				     =			renew_resnet_paths(a__   ,			n_shave_prefix_segments=1       )
																					for path in resnet_0_paths:
																												UpperCamelCase__				     =			""".""".join(["""output_blocks""", str(a__       ), path["""old"""]]       )
																												UpperCamelCase__				     =			""".""".join(["""up_blocks""", str(a__       ), """resnets""", str(a__       ), path["""new"""]]       )

																												UpperCamelCase__				     =			checkpoint[old_path]

							return new_checkpoint


if __name__ == "__main__":
					UpperCamelCase__											= 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.")

					UpperCamelCase__											= parser.parse_args()

					UpperCamelCase__											= torch.load(args.checkpoint_path)

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

					UpperCamelCase__											= convert_ldm_checkpoint(checkpoint, config)

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

					UpperCamelCase__											= UNetaDModel(**config)
					model.load_state_dict(converted_checkpoint)

					try:
										UpperCamelCase__											= DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
										UpperCamelCase__											= VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

										UpperCamelCase__											= LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
										pipe.save_pretrained(args.dump_path)
					except:  # noqa: E722
										model.save_pretrained(args.dump_path)











from argparse import ArgumentParser

from .env import EnvironmentCommand





def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			ArgumentParser("""Diffusers CLI tool"""   ,			usage="""diffusers-cli <command> [<args>]"""       )
							UpperCamelCase__				     =			parser.add_subparsers(help="""diffusers-cli command helpers"""       )

							# Register commands
							EnvironmentCommand.register_subcommand(a__       )

							# Let's go
							UpperCamelCase__				     =			parser.parse_args()

							if not hasattr(a__   ,			"""func"""       ):
														parser.print_help()
														exit(1       )

							# Run
							UpperCamelCase__				     =			args.func(a__       )
							service.run()


if __name__ == "__main__":
					main()




