

'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def       A_     (       snake_case					,				snake_case=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_     (       snake_case					,				snake_case=0       ):
  SCREAMING_SNAKE_CASE:Dict  					=    []
  for old_item in old_list:
    SCREAMING_SNAKE_CASE:Any  					=    old_item.replace("in_layers.0"					,				"norm1"       )
    SCREAMING_SNAKE_CASE:Union[str, Any]  					=    new_item.replace("in_layers.2"					,				"conv1"       )

    SCREAMING_SNAKE_CASE:List[str]  					=    new_item.replace("out_layers.0"					,				"norm2"       )
    SCREAMING_SNAKE_CASE:Optional[Any]  					=    new_item.replace("out_layers.3"					,				"conv2"       )

    SCREAMING_SNAKE_CASE:int  					=    new_item.replace("emb_layers.1"					,				"time_emb_proj"       )
    SCREAMING_SNAKE_CASE:List[Any]  					=    new_item.replace("skip_connection"					,				"conv_shortcut"       )

    SCREAMING_SNAKE_CASE:Optional[Any]  					=    shave_segments(__snake_case					,				n_shave_prefix_segments=__snake_case       )

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

  return mapping





def       A_     (       snake_case					,				snake_case=0       ):
  SCREAMING_SNAKE_CASE:Optional[int]  					=    []
  for old_item in old_list:
    SCREAMING_SNAKE_CASE:Dict  					=    old_item

    SCREAMING_SNAKE_CASE:Any  					=    new_item.replace("norm.weight"					,				"group_norm.weight"       )
    SCREAMING_SNAKE_CASE:Any  					=    new_item.replace("norm.bias"					,				"group_norm.bias"       )

    SCREAMING_SNAKE_CASE:int  					=    new_item.replace("proj_out.weight"					,				"proj_attn.weight"       )
    SCREAMING_SNAKE_CASE:List[Any]  					=    new_item.replace("proj_out.bias"					,				"proj_attn.bias"       )

    SCREAMING_SNAKE_CASE:Dict  					=    shave_segments(__snake_case					,				n_shave_prefix_segments=__snake_case       )

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

  return mapping





def       A_     (       snake_case					,				snake_case					,				snake_case					,				snake_case=None					,				snake_case=None					,				snake_case=None       ):
  assert isinstance(__snake_case					,				__snake_case       ), "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():
      SCREAMING_SNAKE_CASE:Any  					=    old_checkpoint[path]
      SCREAMING_SNAKE_CASE:List[str]  					=    old_tensor.shape[0] // 3

      SCREAMING_SNAKE_CASE:str  					=    (-1, channels) if len(old_tensor.shape       ) == 3 else (-1)

      SCREAMING_SNAKE_CASE:Dict  					=    old_tensor.shape[0] // config["num_head_channels"] // 3

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

      SCREAMING_SNAKE_CASE:Optional[Any]  					=    query.reshape(__snake_case       )
      SCREAMING_SNAKE_CASE:Optional[Any]  					=    key.reshape(__snake_case       )
      SCREAMING_SNAKE_CASE:Any  					=    value.reshape(__snake_case       )

  for path in paths:
    SCREAMING_SNAKE_CASE:Any  					=    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
    SCREAMING_SNAKE_CASE:Optional[int]  					=    new_path.replace("middle_block.0"					,				"mid_block.resnets.0"       )
    SCREAMING_SNAKE_CASE:Union[str, Any]  					=    new_path.replace("middle_block.1"					,				"mid_block.attentions.0"       )
    SCREAMING_SNAKE_CASE:Dict  					=    new_path.replace("middle_block.2"					,				"mid_block.resnets.1"       )

    if additional_replacements is not None:
      for replacement in additional_replacements:
        SCREAMING_SNAKE_CASE:Union[str, Any]  					=    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:
      SCREAMING_SNAKE_CASE:List[Any]  					=    old_checkpoint[path["old"]][:, :, 0]
    else:
      SCREAMING_SNAKE_CASE:int  					=    old_checkpoint[path["old"]]





def       A_     (       snake_case					,				snake_case       ):
  SCREAMING_SNAKE_CASE:Any  					=    {}

  SCREAMING_SNAKE_CASE:Any  					=    checkpoint["time_embed.0.weight"]
  SCREAMING_SNAKE_CASE:Dict  					=    checkpoint["time_embed.0.bias"]
  SCREAMING_SNAKE_CASE:Union[str, Any]  					=    checkpoint["time_embed.2.weight"]
  SCREAMING_SNAKE_CASE:int  					=    checkpoint["time_embed.2.bias"]

  SCREAMING_SNAKE_CASE:Dict  					=    checkpoint["input_blocks.0.0.weight"]
  SCREAMING_SNAKE_CASE:List[str]  					=    checkpoint["input_blocks.0.0.bias"]

  SCREAMING_SNAKE_CASE:Optional[Any]  					=    checkpoint["out.0.weight"]
  SCREAMING_SNAKE_CASE:int  					=    checkpoint["out.0.bias"]
  SCREAMING_SNAKE_CASE:Optional[Any]  					=    checkpoint["out.2.weight"]
  SCREAMING_SNAKE_CASE:List[str]  					=    checkpoint["out.2.bias"]

  # Retrieves the keys for the input blocks only
  SCREAMING_SNAKE_CASE:str  					=    len({".".join(layer.split("."       )[:2]       ) for layer in checkpoint if "input_blocks" in layer}       )
  SCREAMING_SNAKE_CASE:List[str]  					=    {
      layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
      for layer_id in range(__snake_case       )
  }

  # Retrieves the keys for the middle blocks only
  SCREAMING_SNAKE_CASE:Dict  					=    len({".".join(layer.split("."       )[:2]       ) for layer in checkpoint if "middle_block" in layer}       )
  SCREAMING_SNAKE_CASE:Optional[int]  					=    {
      layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
      for layer_id in range(__snake_case       )
  }

  # Retrieves the keys for the output blocks only
  SCREAMING_SNAKE_CASE:List[Any]  					=    len({".".join(layer.split("."       )[:2]       ) for layer in checkpoint if "output_blocks" in layer}       )
  SCREAMING_SNAKE_CASE:Optional[int]  					=    {
      layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
      for layer_id in range(__snake_case       )
  }

  for i in range(1					,				__snake_case       ):
    SCREAMING_SNAKE_CASE:Optional[Any]  					=    (i - 1) // (config["num_res_blocks"] + 1)
    SCREAMING_SNAKE_CASE:Optional[int]  					=    (i - 1) % (config["num_res_blocks"] + 1)

    SCREAMING_SNAKE_CASE:Dict  					=    [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
    SCREAMING_SNAKE_CASE:Dict  					=    [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:
      SCREAMING_SNAKE_CASE:Optional[int]  					=    checkpoint[
          F'''input_blocks.{i}.0.op.weight'''
      ]
      SCREAMING_SNAKE_CASE:Any  					=    checkpoint[
          F'''input_blocks.{i}.0.op.bias'''
      ]
      continue

    SCREAMING_SNAKE_CASE:Any  					=    renew_resnet_paths(__snake_case       )
    SCREAMING_SNAKE_CASE:Optional[int]  					=    {"old": F'''input_blocks.{i}.0''', "new": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
    SCREAMING_SNAKE_CASE:Optional[int]  					=    {"old": "resnets.2.op", "new": "downsamplers.0.op"}
    assign_to_checkpoint(
        __snake_case					,				__snake_case					,				__snake_case					,				additional_replacements=[meta_path, resnet_op]					,				config=__snake_case       )

    if len(__snake_case       ):
      SCREAMING_SNAKE_CASE:str  					=    renew_attention_paths(__snake_case       )
      SCREAMING_SNAKE_CASE:Optional[Any]  					=    {
          "old": F'''input_blocks.{i}.1''',
          "new": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
      }
      SCREAMING_SNAKE_CASE:Optional[Any]  					=    {
          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(
          __snake_case					,				__snake_case					,				__snake_case					,				additional_replacements=[meta_path]					,				attention_paths_to_split=__snake_case					,				config=__snake_case					,				)

  SCREAMING_SNAKE_CASE:str  					=    middle_blocks[0]
  SCREAMING_SNAKE_CASE:Union[str, Any]  					=    middle_blocks[1]
  SCREAMING_SNAKE_CASE:str  					=    middle_blocks[2]

  SCREAMING_SNAKE_CASE:List[str]  					=    renew_resnet_paths(__snake_case       )
  assign_to_checkpoint(__snake_case					,				__snake_case					,				__snake_case					,				config=__snake_case       )

  SCREAMING_SNAKE_CASE:Optional[int]  					=    renew_resnet_paths(__snake_case       )
  assign_to_checkpoint(__snake_case					,				__snake_case					,				__snake_case					,				config=__snake_case       )

  SCREAMING_SNAKE_CASE:Dict  					=    renew_attention_paths(__snake_case       )
  SCREAMING_SNAKE_CASE:Any  					=    {
      "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(
      __snake_case					,				__snake_case					,				__snake_case					,				attention_paths_to_split=__snake_case					,				config=__snake_case       )

  for i in range(__snake_case       ):
    SCREAMING_SNAKE_CASE:Optional[int]  					=    i // (config["num_res_blocks"] + 1)
    SCREAMING_SNAKE_CASE:Optional[Any]  					=    i % (config["num_res_blocks"] + 1)
    SCREAMING_SNAKE_CASE:Optional[int]  					=    [shave_segments(__snake_case					,				2       ) for name in output_blocks[i]]
    SCREAMING_SNAKE_CASE:Dict  					=    {}

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

    if len(__snake_case       ) > 1:
      SCREAMING_SNAKE_CASE:Tuple  					=    [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
      SCREAMING_SNAKE_CASE:str  					=    [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

      SCREAMING_SNAKE_CASE:Tuple  					=    renew_resnet_paths(__snake_case       )
      SCREAMING_SNAKE_CASE:Any  					=    renew_resnet_paths(__snake_case       )

      SCREAMING_SNAKE_CASE:Union[str, Any]  					=    {"old": F'''output_blocks.{i}.0''', "new": F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
      assign_to_checkpoint(__snake_case					,				__snake_case					,				__snake_case					,				additional_replacements=[meta_path]					,				config=__snake_case       )

      if ["conv.weight", "conv.bias"] in output_block_list.values():
        SCREAMING_SNAKE_CASE:List[str]  					=    list(output_block_list.values()       ).index(["conv.weight", "conv.bias"]       )
        SCREAMING_SNAKE_CASE:List[str]  					=    checkpoint[
            F'''output_blocks.{i}.{index}.conv.weight'''
        ]
        SCREAMING_SNAKE_CASE:List[str]  					=    checkpoint[
            F'''output_blocks.{i}.{index}.conv.bias'''
        ]

        # Clear attentions as they have been attributed above.
        if len(__snake_case       ) == 2:
          SCREAMING_SNAKE_CASE:Optional[Any]  					=    []

      if len(__snake_case       ):
        SCREAMING_SNAKE_CASE:List[Any]  					=    renew_attention_paths(__snake_case       )
        SCREAMING_SNAKE_CASE:Dict  					=    {
            "old": F'''output_blocks.{i}.1''',
            "new": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
        }
        SCREAMING_SNAKE_CASE:Any  					=    {
            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(
            __snake_case					,				__snake_case					,				__snake_case					,				additional_replacements=[meta_path]					,				attention_paths_to_split=to_split if any("qkv" in key for key in attentions       ) else None					,				config=__snake_case					,				)
    else:
      SCREAMING_SNAKE_CASE:List[Any]  					=    renew_resnet_paths(__snake_case					,				n_shave_prefix_segments=1       )
      for path in resnet_0_paths:
        SCREAMING_SNAKE_CASE:int  					=    ".".join(["output_blocks", str(__snake_case       ), path["old"]]       )
        SCREAMING_SNAKE_CASE:List[Any]  					=    ".".join(["up_blocks", str(__snake_case       ), "resnets", str(__snake_case       ), path["new"]]       )

        SCREAMING_SNAKE_CASE:int  					=    checkpoint[old_path]

  return new_checkpoint


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

    A_				  =							parser.parse_args()

    A_				  =							torch.load(args.checkpoint_path)

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

    A_				  =							convert_ldm_checkpoint(checkpoint, config)

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

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

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

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








"""simple docstring"""




def   __magic_name__	(      __snake_case				:       int     , __snake_case				:       int     , __snake_case				:       int  ) ->				float:
       lowercase    :				List[Any]       		=							(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
       # formula for sum of series
       return total


def   __magic_name__	(      ) ->				int:
       print(sum_of_series(1     , 1     , 10  )  )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


