



class 						lowercase    :
						'''simple docstring'''


						def __init__(  self    :				Any       ,	snake_case    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				size
											SCREAMING_SNAKE_CASE							:     Any						=				[0] * size
											SCREAMING_SNAKE_CASE							:     str						=				[0] * size







						@staticmethod
						def       lowerCamelCase_				(  snake_case    :				int	):



											'''simple docstring'''


											return index | (index + 1)







						@staticmethod
						def       lowerCamelCase_				(  snake_case    :				int	):



											'''simple docstring'''


											return (index & (index + 1)) - 1







						def       lowerCamelCase_				(  self    :				Dict       ,	snake_case    :				int       ,	snake_case    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				value
											while index < self.size:
																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.get_prev(snake_case	) + 1
																if current_left_border == index:
																					SCREAMING_SNAKE_CASE							:     int						=				value
																else:
																					SCREAMING_SNAKE_CASE							:     Tuple						=				max(snake_case       ,	snake_case       ,	snake_case	)
																SCREAMING_SNAKE_CASE							:     List[Any]						=				self.get_next(snake_case	)







						def       lowerCamelCase_				(  self    :				Union[str, Any]       ,	snake_case    :				int       ,	snake_case    :				int	):



											'''simple docstring'''


											right -= 1  # Because of right is exclusive
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				0
											while left <= right:
																SCREAMING_SNAKE_CASE							:     List[Any]						=				self.get_prev(snake_case	)
																if left <= current_left:
																					SCREAMING_SNAKE_CASE							:     Dict						=				max(snake_case       ,	self.tree[right]	)
																					SCREAMING_SNAKE_CASE							:     Tuple						=				current_left
																else:
																					SCREAMING_SNAKE_CASE							:     List[Any]						=				max(snake_case       ,	self.arr[right]	)
																					right -= 1
											return result


if __name__ == "__main__":
			import doctest

			doctest.testmod()






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel

def   __snake_case    (       _lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			Union[str, Any]=1						)			->    Any:
   if n_shave_prefix_segments >= 0:
      return ".".join(path.split("."						)[n_shave_prefix_segments:]						)
   else:
      return ".".join(path.split("."						)[:n_shave_prefix_segments]						)

def   __snake_case    (       _lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			List[Any]=0						)			->    int:
   A_       :					Union[str, Any]        =      []
   for old_item in old_list:
      A_       :					Tuple        =      old_item.replace("in_layers.0"					,					"norm1"						)
      A_       :					str        =      new_item.replace("in_layers.2"					,					"conv1"						)

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

      A_       :					Union[str, Any]        =      new_item.replace("emb_layers.1"					,					"time_emb_proj"						)
      A_       :					Tuple        =      new_item.replace("skip_connection"					,					"conv_shortcut"						)

      A_       :					Any        =      shave_segments(_lowerCAmelCase					,					n_shave_prefix_segments=_lowerCAmelCase						)

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

   return mapping

def   __snake_case    (       _lowerCAmelCase			:			Union[str, Any]					,					_lowerCAmelCase			:			List[str]=0						)			->    Tuple:
   A_       :					Any        =      []
   for old_item in old_list:
      A_       :					int        =      old_item

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

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

      A_       :					List[Any]        =      shave_segments(_lowerCAmelCase					,					n_shave_prefix_segments=_lowerCAmelCase						)

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

   return mapping

def   __snake_case    (       _lowerCAmelCase			:			int					,					_lowerCAmelCase			:			str					,					_lowerCAmelCase			:			List[Any]					,					_lowerCAmelCase			:			Dict=None					,					_lowerCAmelCase			:			Union[str, Any]=None					,					_lowerCAmelCase			:			List[str]=None						)			->    Optional[Any]:
   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():
         A_       :					Any        =      old_checkpoint[path]
         A_       :					Tuple        =      old_tensor.shape[0] // 3

         A_       :					Union[str, Any]        =      (-1, channels) if len(old_tensor.shape						) == 3 else (-1)

         A_       :					Optional[Any]        =      old_tensor.shape[0] // config["num_head_channels"] // 3

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

         A_       :					Tuple        =      query.reshape(_lowerCAmelCase						)
         A_       :					List[Any]        =      key.reshape(_lowerCAmelCase						)
         A_       :					Union[str, Any]        =      value.reshape(_lowerCAmelCase						)

   for path in paths:
      A_       :					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
      A_       :					List[str]        =      new_path.replace("middle_block.0"					,					"mid_block.resnets.0"						)
      A_       :					int        =      new_path.replace("middle_block.1"					,					"mid_block.attentions.0"						)
      A_       :					Tuple        =      new_path.replace("middle_block.2"					,					"mid_block.resnets.1"						)

      if additional_replacements is not None:
         for replacement in additional_replacements:
            A_       :					Tuple        =      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:
         A_       :					Tuple        =      old_checkpoint[path["old"]][:, :, 0]
      else:
         A_       :					str        =      old_checkpoint[path["old"]]

def   __snake_case    (       _lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			List[Any]						)			->    Optional[int]:
   A_       :					Any        =      {}

   A_       :					Union[str, Any]        =      checkpoint["time_embed.0.weight"]
   A_       :					List[Any]        =      checkpoint["time_embed.0.bias"]
   A_       :					Any        =      checkpoint["time_embed.2.weight"]
   A_       :					Optional[Any]        =      checkpoint["time_embed.2.bias"]

   A_       :					List[str]        =      checkpoint["input_blocks.0.0.weight"]
   A_       :					Union[str, Any]        =      checkpoint["input_blocks.0.0.bias"]

   A_       :					str        =      checkpoint["out.0.weight"]
   A_       :					Optional[int]        =      checkpoint["out.0.bias"]
   A_       :					Optional[int]        =      checkpoint["out.2.weight"]
   A_       :					Optional[int]        =      checkpoint["out.2.bias"]

   # Retrieves the keys for the input blocks only
   A_       :					List[str]        =      len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "input_blocks" in layer}						)
   A_       :					Union[str, Any]        =      {
       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
   A_       :					Union[str, Any]        =      len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "middle_block" in layer}						)
   A_       :					Tuple        =      {
       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
   A_       :					Dict        =      len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "output_blocks" in layer}						)
   A_       :					Optional[Any]        =      {
       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						):
      A_       :					Union[str, Any]        =      (i - 1) // (config["num_res_blocks"] + 1)
      A_       :					Optional[int]        =      (i - 1) % (config["num_res_blocks"] + 1)

      A_       :					str        =      [key for key in input_blocks[i] if f"input_blocks.{i}.0" in key]
      A_       :					List[Any]        =      [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:
         A_       :					Tuple        =      checkpoint[
             f"input_blocks.{i}.0.op.weight"
         ]
         A_       :					List[Any]        =      checkpoint[
             f"input_blocks.{i}.0.op.bias"
         ]
         continue

      A_       :					List[Any]        =      renew_resnet_paths(_lowerCAmelCase						)
      A_       :					Any        =      {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
      A_       :					Any        =      {"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						):
         A_       :					Tuple        =      renew_attention_paths(_lowerCAmelCase						)
         A_       :					List[str]        =      {
             "old": f"input_blocks.{i}.1",
             "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}",
         }
         A_       :					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(
             _lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					additional_replacements=[meta_path]					,					attention_paths_to_split=_lowerCAmelCase					,					config=_lowerCAmelCase					,					)

   A_       :					List[Any]        =      middle_blocks[0]
   A_       :					Tuple        =      middle_blocks[1]
   A_       :					Dict        =      middle_blocks[2]

   A_       :					str        =      renew_resnet_paths(_lowerCAmelCase						)
   assign_to_checkpoint(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					config=_lowerCAmelCase						)

   A_       :					Tuple        =      renew_resnet_paths(_lowerCAmelCase						)
   assign_to_checkpoint(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					config=_lowerCAmelCase						)

   A_       :					Any        =      renew_attention_paths(_lowerCAmelCase						)
   A_       :					Optional[int]        =      {
       "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						):
      A_       :					Optional[int]        =      i // (config["num_res_blocks"] + 1)
      A_       :					Optional[Any]        =      i % (config["num_res_blocks"] + 1)
      A_       :					Optional[Any]        =      [shave_segments(_lowerCAmelCase					,					2						) for name in output_blocks[i]]
      A_       :					Union[str, Any]        =      {}

      for layer in output_block_layers:
         A_   ,    A_       :					str        =      layer.split("."						)[0], shave_segments(_lowerCAmelCase					,					1						)
         if layer_id in output_block_list:
            output_block_list[layer_id].append(_lowerCAmelCase						)
         else:
            A_       :					List[str]        =      [layer_name]

      if len(_lowerCAmelCase						) > 1:
         A_       :					Dict        =      [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
         A_       :					List[Any]        =      [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]

         A_       :					Optional[Any]        =      renew_resnet_paths(_lowerCAmelCase						)
         A_       :					Any        =      renew_resnet_paths(_lowerCAmelCase						)

         A_       :					Optional[Any]        =      {"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():
            A_       :					int        =      list(output_block_list.values()						).index(["conv.weight", "conv.bias"]						)
            A_       :					Union[str, Any]        =      checkpoint[
                f"output_blocks.{i}.{index}.conv.weight"
            ]
            A_       :					int        =      checkpoint[
                f"output_blocks.{i}.{index}.conv.bias"
            ]

            # Clear attentions as they have been attributed above.
            if len(_lowerCAmelCase						) == 2:
               A_       :					Union[str, Any]        =      []

         if len(_lowerCAmelCase						):
            A_       :					Dict        =      renew_attention_paths(_lowerCAmelCase						)
            A_       :					Union[str, Any]        =      {
                "old": f"output_blocks.{i}.1",
                "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
            }
            A_       :					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(
                _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:
         A_       :					Optional[int]        =      renew_resnet_paths(_lowerCAmelCase					,					n_shave_prefix_segments=1						)
         for path in resnet_0_paths:
            A_       :					List[Any]        =      ".".join(["output_blocks", str(_lowerCAmelCase						), path["old"]]						)
            A_       :					Optional[Any]        =      ".".join(["up_blocks", str(_lowerCAmelCase						), "resnets", str(_lowerCAmelCase						), path["new"]]						)

            A_       :					Optional[int]        =      checkpoint[old_path]

   return new_checkpoint


if __name__ == "__main__":
 _lowerCAmelCase				:   List[Any]						       =						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				:   Optional[Any]						       =						parser.parse_args()

 _lowerCAmelCase				:   Optional[Any]						       =						torch.load(args.checkpoint_path)

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

 _lowerCAmelCase				:   Tuple						       =						convert_ldm_checkpoint(checkpoint, config)

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

 _lowerCAmelCase				:   Optional[Any]						       =						UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  _lowerCAmelCase				:   List[str]						       =						DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
  _lowerCAmelCase				:   List[str]						       =						VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

  _lowerCAmelCase				:   Optional[Any]						       =						LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
  pipe.save_pretrained(args.dump_path)
 except:  # noqa: E722
  model.save_pretrained(args.dump_path)
