




import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=1    )	-> Optional[int]:
       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      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=0    )	-> Optional[int]:
       lowerCamelCase   :      Optional[Any]			 =					[]
       for old_item in old_list:
              lowerCamelCase   :      Dict			 =					old_item.replace("in_layers.0" ,"norm1"    )
              lowerCamelCase   :      int			 =					new_item.replace("in_layers.2" ,"conv1"    )

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

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

              lowerCamelCase   :      Optional[int]			 =					shave_segments(_SCREAMING_SNAKE_CASE ,n_shave_prefix_segments=_SCREAMING_SNAKE_CASE    )

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

       return mapping

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=0    )	-> Optional[Any]:
       lowerCamelCase   :      List[Any]			 =					[]
       for old_item in old_list:
              lowerCamelCase   :      int			 =					old_item

              lowerCamelCase   :      int			 =					new_item.replace("norm.weight" ,"group_norm.weight"    )
              lowerCamelCase   :      Optional[Any]			 =					new_item.replace("norm.bias" ,"group_norm.bias"    )

              lowerCamelCase   :      Union[str, Any]			 =					new_item.replace("proj_out.weight" ,"proj_attn.weight"    )
              lowerCamelCase   :      int			 =					new_item.replace("proj_out.bias" ,"proj_attn.bias"    )

              lowerCamelCase   :      List[Any]			 =					shave_segments(_SCREAMING_SNAKE_CASE ,n_shave_prefix_segments=_SCREAMING_SNAKE_CASE    )

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

       return mapping

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None    )	-> Dict:
       assert isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_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():
                     lowerCamelCase   :      Tuple			 =					old_checkpoint[path]
                     lowerCamelCase   :      Optional[int]			 =					old_tensor.shape[0] // 3

                     lowerCamelCase   :      Dict			 =					(-1, channels) if len(old_tensor.shape    ) == 3 else (-1)

                     lowerCamelCase   :      List[str]			 =					old_tensor.shape[0] // config["num_head_channels"] // 3

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

                     lowerCamelCase   :      int			 =					query.reshape(_SCREAMING_SNAKE_CASE    )
                     lowerCamelCase   :      Optional[int]			 =					key.reshape(_SCREAMING_SNAKE_CASE    )
                     lowerCamelCase   :      Optional[int]			 =					value.reshape(_SCREAMING_SNAKE_CASE    )

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

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

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> List[str]:
       lowerCamelCase   :      Union[str, Any]			 =					{}

       lowerCamelCase   :      str			 =					checkpoint["time_embed.0.weight"]
       lowerCamelCase   :      str			 =					checkpoint["time_embed.0.bias"]
       lowerCamelCase   :      Tuple			 =					checkpoint["time_embed.2.weight"]
       lowerCamelCase   :      Any			 =					checkpoint["time_embed.2.bias"]

       lowerCamelCase   :      Any			 =					checkpoint["input_blocks.0.0.weight"]
       lowerCamelCase   :      Tuple			 =					checkpoint["input_blocks.0.0.bias"]

       lowerCamelCase   :      Dict			 =					checkpoint["out.0.weight"]
       lowerCamelCase   :      Dict			 =					checkpoint["out.0.bias"]
       lowerCamelCase   :      Union[str, Any]			 =					checkpoint["out.2.weight"]
       lowerCamelCase   :      Optional[int]			 =					checkpoint["out.2.bias"]

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

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

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

       for i in range(1 ,_SCREAMING_SNAKE_CASE    ):
              lowerCamelCase   :      int			 =					(i - 1) // (config["num_res_blocks"] + 1)
              lowerCamelCase   :      int			 =					(i - 1) % (config["num_res_blocks"] + 1)

              lowerCamelCase   :      Any			 =					[key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
              lowerCamelCase   :      Optional[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:
                     lowerCamelCase   :      List[str]			 =					checkpoint[
                         f'''input_blocks.{i}.0.op.weight'''
                     ]
                     lowerCamelCase   :      Dict			 =					checkpoint[
                         f'''input_blocks.{i}.0.op.bias'''
                     ]
                     continue

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

              if len(_SCREAMING_SNAKE_CASE    ):
                     lowerCamelCase   :      List[str]			 =					renew_attention_paths(_SCREAMING_SNAKE_CASE    )
                     lowerCamelCase   :      Optional[int]			 =					{
                         "old": f'''input_blocks.{i}.1''',
                         "new": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
                     }
                     lowerCamelCase   :      Tuple			 =					{
                         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(
                         _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,additional_replacements=[meta_path] ,attention_paths_to_split=_SCREAMING_SNAKE_CASE ,config=_SCREAMING_SNAKE_CASE ,)

       lowerCamelCase   :      List[str]			 =					middle_blocks[0]
       lowerCamelCase   :      str			 =					middle_blocks[1]
       lowerCamelCase   :      Tuple			 =					middle_blocks[2]

       lowerCamelCase   :      int			 =					renew_resnet_paths(_SCREAMING_SNAKE_CASE    )
       assign_to_checkpoint(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,config=_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Tuple			 =					renew_resnet_paths(_SCREAMING_SNAKE_CASE    )
       assign_to_checkpoint(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,config=_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      List[str]			 =					renew_attention_paths(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, 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(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,attention_paths_to_split=_SCREAMING_SNAKE_CASE ,config=_SCREAMING_SNAKE_CASE    )

       for i in range(_SCREAMING_SNAKE_CASE    ):
              lowerCamelCase   :      List[str]			 =					i // (config["num_res_blocks"] + 1)
              lowerCamelCase   :      Optional[Any]			 =					i % (config["num_res_blocks"] + 1)
              lowerCamelCase   :      List[str]			 =					[shave_segments(_SCREAMING_SNAKE_CASE ,2    ) for name in output_blocks[i]]
              lowerCamelCase   :      Optional[int]			 =					{}

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

              if len(_SCREAMING_SNAKE_CASE    ) > 1:
                     lowerCamelCase   :      Optional[int]			 =					[key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
                     lowerCamelCase   :      Any			 =					[key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]

                     lowerCamelCase   :      Optional[int]			 =					renew_resnet_paths(_SCREAMING_SNAKE_CASE    )
                     lowerCamelCase   :      List[Any]			 =					renew_resnet_paths(_SCREAMING_SNAKE_CASE    )

                     lowerCamelCase   :      List[str]			 =					{"old": f'''output_blocks.{i}.0''', "new": f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
                     assign_to_checkpoint(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,additional_replacements=[meta_path] ,config=_SCREAMING_SNAKE_CASE    )

                     if ["conv.weight", "conv.bias"] in output_block_list.values():
                            lowerCamelCase   :      Optional[int]			 =					list(output_block_list.values()    ).index(["conv.weight", "conv.bias"]    )
                            lowerCamelCase   :      Dict			 =					checkpoint[
                                f'''output_blocks.{i}.{index}.conv.weight'''
                            ]
                            lowerCamelCase   :      List[Any]			 =					checkpoint[
                                f'''output_blocks.{i}.{index}.conv.bias'''
                            ]

                            # Clear attentions as they have been attributed above.
                            if len(_SCREAMING_SNAKE_CASE    ) == 2:
                                   lowerCamelCase   :      List[str]			 =					[]

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

                            lowerCamelCase   :      Any			 =					checkpoint[old_path]

       return new_checkpoint


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      List[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.')

       SCREAMING_SNAKE_CASE__       :      int  =   parser.parse_args()

       SCREAMING_SNAKE_CASE__       :      List[Any]  =   torch.load(args.checkpoint_path)

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

       SCREAMING_SNAKE_CASE__       :      str  =   convert_ldm_checkpoint(checkpoint, config)

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

       SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   UNetaDModel(**config)
       model.load_state_dict(converted_checkpoint)

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

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












import random

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> tuple:
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Any			 =					[], [], []
       for element in data:
              if element < pivot:
                     less.append(_SCREAMING_SNAKE_CASE    )
              elif element > pivot:
                     greater.append(_SCREAMING_SNAKE_CASE    )
              else:
                     equal.append(_SCREAMING_SNAKE_CASE    )
       return less, equal, greater


def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> str:
       # index = len(items) // 2 when trying to find the median
       #   (value of index when items is sorted)

       # invalid input
       if index >= len(_SCREAMING_SNAKE_CASE    ) or index < 0:
              return None

       lowerCamelCase   :      List[Any]			 =					items[random.randint(0 ,len(_SCREAMING_SNAKE_CASE    ) - 1    )]
       lowerCamelCase   :      Dict			 =					0
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					_partition(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					len(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      str			 =					len(_SCREAMING_SNAKE_CASE    )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       # must be in larger
       else:
              return quick_select(_SCREAMING_SNAKE_CASE ,index - (m + count)    )






