

"""simple docstring"""

import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__=1      ) ->	Optional[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 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__=0      ) ->	Union[str, Any]:
 lowerCAmelCase__      :							str    =							[]
 for old_item in old_list:
  lowerCAmelCase__      :							Union[str, Any]    =							old_item
  lowerCAmelCase__      :							Optional[int]    =							new_item.replace("block."				,				"resnets."      )
  lowerCAmelCase__      :							int    =							new_item.replace("conv_shorcut"				,				"conv1"      )
  lowerCAmelCase__      :							str    =							new_item.replace("in_shortcut"				,				"conv_shortcut"      )
  lowerCAmelCase__      :							Dict    =							new_item.replace("temb_proj"				,				"time_emb_proj"      )

  lowerCAmelCase__      :							str    =							shave_segments(lowercase__				,				n_shave_prefix_segments=lowercase__      )

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

 return mapping



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__=0				,				lowercase__=False      ) ->	Tuple:
 lowerCAmelCase__      :							Optional[Any]    =							[]
 for old_item in old_list:
  lowerCAmelCase__      :							Union[str, Any]    =							old_item

  # In `model.mid`, the layer is called `attn`.
  if not in_mid:
   lowerCAmelCase__      :							Optional[int]    =							new_item.replace("attn"				,				"attentions"      )
  lowerCAmelCase__      :							Optional[int]    =							new_item.replace(".k."				,				".key."      )
  lowerCAmelCase__      :							str    =							new_item.replace(".v."				,				".value."      )
  lowerCAmelCase__      :							Tuple    =							new_item.replace(".q."				,				".query."      )

  lowerCAmelCase__      :							Optional[int]    =							new_item.replace("proj_out"				,				"proj_attn"      )
  lowerCAmelCase__      :							List[str]    =							new_item.replace("norm"				,				"group_norm"      )

  lowerCAmelCase__      :							str    =							shave_segments(lowercase__				,				n_shave_prefix_segments=lowercase__      )
  mapping.append({"old": old_item, "new": new_item}      )

 return mapping



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__				,				lowercase__=None				,				lowercase__=None				,				lowercase__=None      ) ->	List[Any]:
 assert isinstance(lowercase__				,				lowercase__      ), "Paths should be a list of dicts containing 'old' and 'new' keys."

 if attention_paths_to_split is not None:
  if config is None:
   raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'."      )

  for path, path_map in attention_paths_to_split.items():
   lowerCAmelCase__      :							Dict    =							old_checkpoint[path]
   lowerCAmelCase__      :							List[str]    =							old_tensor.shape[0] // 3

   lowerCAmelCase__      :							List[Any]    =							(-1, channels) if len(old_tensor.shape      ) == 3 else (-1)

   lowerCAmelCase__      :							Tuple    =							old_tensor.shape[0] // config.get("num_head_channels"				,				1      ) // 3

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

   lowerCAmelCase__      :							Union[str, Any]    =							query.reshape(lowercase__      ).squeeze()
   lowerCAmelCase__      :							List[Any]    =							key.reshape(lowercase__      ).squeeze()
   lowerCAmelCase__      :							List[Any]    =							value.reshape(lowercase__      ).squeeze()

 for path in paths:
  lowerCAmelCase__      :							List[Any]    =							path["new"]

  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

  lowerCAmelCase__      :							int    =							new_path.replace("down."				,				"down_blocks."      )
  lowerCAmelCase__      :							Union[str, Any]    =							new_path.replace("up."				,				"up_blocks."      )

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

  if "attentions" in new_path:
   lowerCAmelCase__      :							str    =							old_checkpoint[path["old"]].squeeze()
  else:
   lowerCAmelCase__      :							Tuple    =							old_checkpoint[path["old"]]



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	List[str]:
 lowerCAmelCase__      :							Tuple    =							{}

 lowerCAmelCase__      :							int    =							checkpoint["temb.dense.0.weight"]
 lowerCAmelCase__      :							List[str]    =							checkpoint["temb.dense.0.bias"]
 lowerCAmelCase__      :							Optional[Any]    =							checkpoint["temb.dense.1.weight"]
 lowerCAmelCase__      :							Optional[Any]    =							checkpoint["temb.dense.1.bias"]

 lowerCAmelCase__      :							int    =							checkpoint["norm_out.weight"]
 lowerCAmelCase__      :							Tuple    =							checkpoint["norm_out.bias"]

 lowerCAmelCase__      :							Union[str, Any]    =							checkpoint["conv_in.weight"]
 lowerCAmelCase__      :							List[str]    =							checkpoint["conv_in.bias"]
 lowerCAmelCase__      :							Union[str, Any]    =							checkpoint["conv_out.weight"]
 lowerCAmelCase__      :							List[Any]    =							checkpoint["conv_out.bias"]

 lowerCAmelCase__      :							Optional[int]    =							len({".".join(layer.split("."      )[:2]      ) for layer in checkpoint if "down" in layer}      )
 lowerCAmelCase__      :							Union[str, Any]    =							{
     layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(lowercase__      )
 }

 lowerCAmelCase__      :							str    =							len({".".join(layer.split("."      )[:2]      ) for layer in checkpoint if "up" in layer}      )
 lowerCAmelCase__      :							Any    =							{layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(lowercase__      )}

 for i in range(lowercase__      ):
  lowerCAmelCase__      :							int    =							(i - 1) // (config["layers_per_block"] + 1)

  if any("downsample" in layer for layer in down_blocks[i]      ):
   lowerCAmelCase__      :							int    =							checkpoint[
       F"""down.{i}.downsample.op.weight"""
   ]
   lowerCAmelCase__      :							Union[str, Any]    =							checkpoint[F"""down.{i}.downsample.op.bias"""]
  #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
  #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

  if any("block" in layer for layer in down_blocks[i]      ):
   lowerCAmelCase__      :							List[str]    =							len(
       {".".join(shave_segments(lowercase__				,				2      ).split("."      )[:2]      ) for layer in down_blocks[i] if "block" in layer}      )
   lowerCAmelCase__      :							Dict    =							{
       layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
       for layer_id in range(lowercase__      )
   }

   if num_blocks > 0:
    for j in range(config["layers_per_block"]      ):
     lowerCAmelCase__      :							Optional[Any]    =							renew_resnet_paths(blocks[j]      )
     assign_to_checkpoint(lowercase__				,				lowercase__				,				lowercase__      )

  if any("attn" in layer for layer in down_blocks[i]      ):
   lowerCAmelCase__      :							Optional[Any]    =							len(
       {".".join(shave_segments(lowercase__				,				2      ).split("."      )[:2]      ) for layer in down_blocks[i] if "attn" in layer}      )
   lowerCAmelCase__      :							Tuple    =							{
       layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
       for layer_id in range(lowercase__      )
   }

   if num_attn > 0:
    for j in range(config["layers_per_block"]      ):
     lowerCAmelCase__      :							Any    =							renew_attention_paths(attns[j]      )
     assign_to_checkpoint(lowercase__				,				lowercase__				,				lowercase__				,				config=lowercase__      )

 lowerCAmelCase__      :							Optional[Any]    =							[key for key in checkpoint if "mid.block_1" in key]
 lowerCAmelCase__      :							Tuple    =							[key for key in checkpoint if "mid.block_2" in key]
 lowerCAmelCase__      :							int    =							[key for key in checkpoint if "mid.attn_1" in key]

 # Mid new 2
 lowerCAmelCase__      :							Optional[Any]    =							renew_resnet_paths(lowercase__      )
 assign_to_checkpoint(
     lowercase__				,				lowercase__				,				lowercase__				,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]				,				)

 lowerCAmelCase__      :							List[str]    =							renew_resnet_paths(lowercase__      )
 assign_to_checkpoint(
     lowercase__				,				lowercase__				,				lowercase__				,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]				,				)

 lowerCAmelCase__      :							Optional[Any]    =							renew_attention_paths(lowercase__				,				in_mid=lowercase__      )
 assign_to_checkpoint(
     lowercase__				,				lowercase__				,				lowercase__				,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]				,				)

 for i in range(lowercase__      ):
  lowerCAmelCase__      :							Union[str, Any]    =							num_up_blocks - 1 - i

  if any("upsample" in layer for layer in up_blocks[i]      ):
   lowerCAmelCase__      :							Optional[int]    =							checkpoint[
       F"""up.{i}.upsample.conv.weight"""
   ]
   lowerCAmelCase__      :							Dict    =							checkpoint[F"""up.{i}.upsample.conv.bias"""]

  if any("block" in layer for layer in up_blocks[i]      ):
   lowerCAmelCase__      :							Any    =							len(
       {".".join(shave_segments(lowercase__				,				2      ).split("."      )[:2]      ) for layer in up_blocks[i] if "block" in layer}      )
   lowerCAmelCase__      :							Tuple    =							{
       layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(lowercase__      )
   }

   if num_blocks > 0:
    for j in range(config["layers_per_block"] + 1      ):
     lowerCAmelCase__      :							str    =							{"old": F"""up_blocks.{i}""", "new": F"""up_blocks.{block_id}"""}
     lowerCAmelCase__      :							Dict    =							renew_resnet_paths(blocks[j]      )
     assign_to_checkpoint(lowercase__				,				lowercase__				,				lowercase__				,				additional_replacements=[replace_indices]      )

  if any("attn" in layer for layer in up_blocks[i]      ):
   lowerCAmelCase__      :							Optional[Any]    =							len(
       {".".join(shave_segments(lowercase__				,				2      ).split("."      )[:2]      ) for layer in up_blocks[i] if "attn" in layer}      )
   lowerCAmelCase__      :							Union[str, Any]    =							{
       layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(lowercase__      )
   }

   if num_attn > 0:
    for j in range(config["layers_per_block"] + 1      ):
     lowerCAmelCase__      :							Tuple    =							{"old": F"""up_blocks.{i}""", "new": F"""up_blocks.{block_id}"""}
     lowerCAmelCase__      :							Dict    =							renew_attention_paths(attns[j]      )
     assign_to_checkpoint(lowercase__				,				lowercase__				,				lowercase__				,				additional_replacements=[replace_indices]      )

 lowerCAmelCase__      :							str    =							{k.replace("mid_new_2"				,				"mid_block"      ): v for k, v in new_checkpoint.items()}
 return new_checkpoint



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	List[str]:
 lowerCAmelCase__      :							Optional[Any]    =							{}

 lowerCAmelCase__      :							Optional[int]    =							checkpoint["encoder.norm_out.weight"]
 lowerCAmelCase__      :							Any    =							checkpoint["encoder.norm_out.bias"]

 lowerCAmelCase__      :							int    =							checkpoint["encoder.conv_in.weight"]
 lowerCAmelCase__      :							Optional[int]    =							checkpoint["encoder.conv_in.bias"]
 lowerCAmelCase__      :							int    =							checkpoint["encoder.conv_out.weight"]
 lowerCAmelCase__      :							Dict    =							checkpoint["encoder.conv_out.bias"]

 lowerCAmelCase__      :							List[Any]    =							checkpoint["decoder.norm_out.weight"]
 lowerCAmelCase__      :							Union[str, Any]    =							checkpoint["decoder.norm_out.bias"]

 lowerCAmelCase__      :							int    =							checkpoint["decoder.conv_in.weight"]
 lowerCAmelCase__      :							Optional[int]    =							checkpoint["decoder.conv_in.bias"]
 lowerCAmelCase__      :							List[str]    =							checkpoint["decoder.conv_out.weight"]
 lowerCAmelCase__      :							Any    =							checkpoint["decoder.conv_out.bias"]

 lowerCAmelCase__      :							Dict    =							len({".".join(layer.split("."      )[:3]      ) for layer in checkpoint if "down" in layer}      )
 lowerCAmelCase__      :							Any    =							{
     layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(lowercase__      )
 }

 lowerCAmelCase__      :							int    =							len({".".join(layer.split("."      )[:3]      ) for layer in checkpoint if "up" in layer}      )
 lowerCAmelCase__      :							Optional[Any]    =							{layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(lowercase__      )}

 for i in range(lowercase__      ):
  lowerCAmelCase__      :							int    =							(i - 1) // (config["layers_per_block"] + 1)

  if any("downsample" in layer for layer in down_blocks[i]      ):
   lowerCAmelCase__      :							Dict    =							checkpoint[
       F"""encoder.down.{i}.downsample.conv.weight"""
   ]
   lowerCAmelCase__      :							List[Any]    =							checkpoint[
       F"""encoder.down.{i}.downsample.conv.bias"""
   ]

  if any("block" in layer for layer in down_blocks[i]      ):
   lowerCAmelCase__      :							int    =							len(
       {".".join(shave_segments(lowercase__				,				3      ).split("."      )[:3]      ) for layer in down_blocks[i] if "block" in layer}      )
   lowerCAmelCase__      :							Tuple    =							{
       layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
       for layer_id in range(lowercase__      )
   }

   if num_blocks > 0:
    for j in range(config["layers_per_block"]      ):
     lowerCAmelCase__      :							List[str]    =							renew_resnet_paths(blocks[j]      )
     assign_to_checkpoint(lowercase__				,				lowercase__				,				lowercase__      )

  if any("attn" in layer for layer in down_blocks[i]      ):
   lowerCAmelCase__      :							List[Any]    =							len(
       {".".join(shave_segments(lowercase__				,				3      ).split("."      )[:3]      ) for layer in down_blocks[i] if "attn" in layer}      )
   lowerCAmelCase__      :							Union[str, Any]    =							{
       layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
       for layer_id in range(lowercase__      )
   }

   if num_attn > 0:
    for j in range(config["layers_per_block"]      ):
     lowerCAmelCase__      :							str    =							renew_attention_paths(attns[j]      )
     assign_to_checkpoint(lowercase__				,				lowercase__				,				lowercase__				,				config=lowercase__      )

 lowerCAmelCase__      :							Optional[int]    =							[key for key in checkpoint if "mid.block_1" in key]
 lowerCAmelCase__      :							Optional[int]    =							[key for key in checkpoint if "mid.block_2" in key]
 lowerCAmelCase__      :							List[Any]    =							[key for key in checkpoint if "mid.attn_1" in key]

 # Mid new 2
 lowerCAmelCase__      :							int    =							renew_resnet_paths(lowercase__      )
 assign_to_checkpoint(
     lowercase__				,				lowercase__				,				lowercase__				,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]				,				)

 lowerCAmelCase__      :							List[Any]    =							renew_resnet_paths(lowercase__      )
 assign_to_checkpoint(
     lowercase__				,				lowercase__				,				lowercase__				,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]				,				)

 lowerCAmelCase__      :							Optional[int]    =							renew_attention_paths(lowercase__				,				in_mid=lowercase__      )
 assign_to_checkpoint(
     lowercase__				,				lowercase__				,				lowercase__				,				additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]				,				)

 for i in range(lowercase__      ):
  lowerCAmelCase__      :							Dict    =							num_up_blocks - 1 - i

  if any("upsample" in layer for layer in up_blocks[i]      ):
   lowerCAmelCase__      :							Union[str, Any]    =							checkpoint[
       F"""decoder.up.{i}.upsample.conv.weight"""
   ]
   lowerCAmelCase__      :							Any    =							checkpoint[
       F"""decoder.up.{i}.upsample.conv.bias"""
   ]

  if any("block" in layer for layer in up_blocks[i]      ):
   lowerCAmelCase__      :							Dict    =							len(
       {".".join(shave_segments(lowercase__				,				3      ).split("."      )[:3]      ) for layer in up_blocks[i] if "block" in layer}      )
   lowerCAmelCase__      :							Union[str, Any]    =							{
       layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(lowercase__      )
   }

   if num_blocks > 0:
    for j in range(config["layers_per_block"] + 1      ):
     lowerCAmelCase__      :							Dict    =							{"old": F"""up_blocks.{i}""", "new": F"""up_blocks.{block_id}"""}
     lowerCAmelCase__      :							Union[str, Any]    =							renew_resnet_paths(blocks[j]      )
     assign_to_checkpoint(lowercase__				,				lowercase__				,				lowercase__				,				additional_replacements=[replace_indices]      )

  if any("attn" in layer for layer in up_blocks[i]      ):
   lowerCAmelCase__      :							Tuple    =							len(
       {".".join(shave_segments(lowercase__				,				3      ).split("."      )[:3]      ) for layer in up_blocks[i] if "attn" in layer}      )
   lowerCAmelCase__      :							int    =							{
       layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(lowercase__      )
   }

   if num_attn > 0:
    for j in range(config["layers_per_block"] + 1      ):
     lowerCAmelCase__      :							Tuple    =							{"old": F"""up_blocks.{i}""", "new": F"""up_blocks.{block_id}"""}
     lowerCAmelCase__      :							Any    =							renew_attention_paths(attns[j]      )
     assign_to_checkpoint(lowercase__				,				lowercase__				,				lowercase__				,				additional_replacements=[replace_indices]      )

 lowerCAmelCase__      :							Dict    =							{k.replace("mid_new_2"				,				"mid_block"      ): v for k, v in new_checkpoint.items()}
 lowerCAmelCase__      :							Optional[Any]    =							checkpoint["quant_conv.weight"]
 lowerCAmelCase__      :							List[str]    =							checkpoint["quant_conv.bias"]
 if "quantize.embedding.weight" in checkpoint:
  lowerCAmelCase__      :							Dict    =							checkpoint["quantize.embedding.weight"]
 lowerCAmelCase__      :							int    =							checkpoint["post_quant_conv.weight"]
 lowerCAmelCase__      :							List[Any]    =							checkpoint["post_quant_conv.bias"]

 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())

    # unet case
    _UpperCamelCase		    =						{key.split(""".""")[0] for key in checkpoint.keys()}
    if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
        _UpperCamelCase		    =						convert_vq_autoenc_checkpoint(checkpoint, config)
    else:
        _UpperCamelCase		    =						convert_ddpm_checkpoint(checkpoint, config)

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

    if config["_class_name"] == "VQModel":
        _UpperCamelCase		    =						VQModel(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
    elif config["_class_name"] == "AutoencoderKL":
        _UpperCamelCase		    =						AutoencoderKL(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
    else:
        _UpperCamelCase		    =						UNetaDModel(**config)
        model.load_state_dict(converted_checkpoint)

        _UpperCamelCase		    =						DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

        _UpperCamelCase		    =						DDPMPipeline(unet=model, scheduler=scheduler)
        pipe.save_pretrained(args.dump_path)









"""simple docstring"""

# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	List[str]:
 lowerCAmelCase__      :							Tuple    =							Queue()
 lowerCAmelCase__      :							Optional[int]    =							[False] * len(lowercase__      )
 lowerCAmelCase__      :							Dict    =							[-1] * len(lowercase__      )

 def bfs():
  while not queue.empty():
   lowerCAmelCase__      :							Optional[Any]    =							queue.get()
   lowerCAmelCase__      :							Union[str, Any]    =							True

   for neighbour in graph[u]:
    if neighbour == u:
     return False

    if color[neighbour] == -1:
     lowerCAmelCase__      :							Any    =							1 - color[u]
     queue.put(lowercase__      )

    elif color[neighbour] == color[u]:
     return False

  return True

 for i in range(len(lowercase__      )      ):
  if not visited[i]:
   queue.put(lowercase__      )
   lowerCAmelCase__      :							int    =							0
   if bfs() is False:
    return False

 return True


if __name__ == "__main__":
    # Adjacency List of graph
    print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))






