



"""simple docstring"""





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__=1   ):
      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__			(   lowerCAmelCase__			,    lowerCAmelCase__=0   ):
      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(lowerCAmelCase__			,    n_shave_prefix_segments=lowerCAmelCase__   )

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

      return mapping
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__=0   ):
      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(lowerCAmelCase__			,    n_shave_prefix_segments=lowerCAmelCase__   )

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

      return mapping
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=None			,    lowerCAmelCase__=None			,    lowerCAmelCase__=None   ):
      assert isinstance(lowerCAmelCase__			,    lowerCAmelCase__   ), "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(lowerCAmelCase__   )
                  UpperCAmelCase_    				=							key.reshape(lowerCAmelCase__   )
                  UpperCAmelCase_    				=							value.reshape(lowerCAmelCase__   )

      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      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      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(lowerCAmelCase__   )
      }

      # 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(lowerCAmelCase__   )
      }

      # 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(lowerCAmelCase__   )
      }

      for i in range(1			,    lowerCAmelCase__   ):
            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(lowerCAmelCase__   )
            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(
                lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    additional_replacements=[meta_path, resnet_op]			,    config=lowerCAmelCase__   )

            if len(lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							renew_attention_paths(lowerCAmelCase__   )
                  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(
                      lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    additional_replacements=[meta_path]			,    attention_paths_to_split=lowerCAmelCase__			,    config=lowerCAmelCase__			,    )

      UpperCAmelCase_    				=							middle_blocks[0]
      UpperCAmelCase_    				=							middle_blocks[1]
      UpperCAmelCase_    				=							middle_blocks[2]

      UpperCAmelCase_    				=							renew_resnet_paths(lowerCAmelCase__   )
      assign_to_checkpoint(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    config=lowerCAmelCase__   )

      UpperCAmelCase_    				=							renew_resnet_paths(lowerCAmelCase__   )
      assign_to_checkpoint(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    config=lowerCAmelCase__   )

      UpperCAmelCase_    				=							renew_attention_paths(lowerCAmelCase__   )
      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(
          lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    attention_paths_to_split=lowerCAmelCase__			,    config=lowerCAmelCase__   )

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

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

            if len(lowerCAmelCase__   ) > 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(lowerCAmelCase__   )
                  UpperCAmelCase_    				=							renew_resnet_paths(lowerCAmelCase__   )

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

                  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(lowerCAmelCase__   ) == 2:
                              UpperCAmelCase_    				=							[]

                  if len(lowerCAmelCase__   ):
                        UpperCAmelCase_    				=							renew_attention_paths(lowerCAmelCase__   )
                        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(
                            lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    additional_replacements=[meta_path]			,    attention_paths_to_split=to_split if any("qkv" in key for key in attentions   ) else None			,    config=lowerCAmelCase__			,    )
            else:
                  UpperCAmelCase_    				=							renew_resnet_paths(lowerCAmelCase__			,    n_shave_prefix_segments=1   )
                  for path in resnet_0_paths:
                        UpperCAmelCase_    				=							".".join(["output_blocks", str(lowerCAmelCase__   ), path["old"]]   )
                        UpperCAmelCase_    				=							".".join(["up_blocks", str(lowerCAmelCase__   ), "resnets", str(lowerCAmelCase__   ), path["new"]]   )

                        UpperCAmelCase_    				=							checkpoint[old_path]

      return new_checkpoint


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

  lowerCamelCase      =		parser.parse_args()

  lowerCamelCase      =		torch.load(args.checkpoint_path)

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

  lowerCamelCase      =		convert_ldm_checkpoint(checkpoint, config)

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

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

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

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









"""simple docstring"""





lowerCamelCase      =		{
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_186.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
    "britishthermalunit_it": 1_055.05_585,
    "footpound": 1.355_818,
}
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
            UpperCAmelCase_    				=							(
                f"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
                f"""Valid values are: {', '.join(lowerCAmelCase__   )}"""
            )
            raise ValueError(lowerCAmelCase__   )
      return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
  import doctest

  doctest.testmod()




