code: 
  '''simple docstring'''
  
  
  
  import glob
  import os
  from typing import Dict, List, Union
  
  import torch
  
  from diffusers.utils import is_safetensors_available
  
  
  if is_safetensors_available():
  				import safetensors.torch
  
  from huggingface_hub import snapshot_download
  
  from diffusers import DiffusionPipeline, __version__
  from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
  from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			def __init__(					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									self.register_to_config()
  									super().__init__()
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if dicta == dicta:
  															return True
  									else:
  															lowercase__ ,						lowercase__							 =       self._remove_meta_keys(lowerCamelCase__       )
  															lowercase__ ,						lowercase__							 =       self._remove_meta_keys(lowerCamelCase__       )
  															if configa == configa:
  																					print(F'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'''       )
  																					return True
  									return False
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       []
  									lowercase__							 =       config_dict.copy()
  									for key in config_dict.keys():
  															if key.startswith("""_"""       ):
  																					temp_dict.pop(lowerCamelCase__       )
  																					meta_keys.append(lowerCamelCase__       )
  									return (temp_dict, meta_keys)
  
  
  
  			@torch.no_grad()
  			def A__ (					self       ,     lowerCamelCase__       ,     **lowerCamelCase__       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       kwargs.pop("""cache_dir"""       ,     lowerCamelCase__       )
  									lowercase__							 =       kwargs.pop("""resume_download"""       ,     lowerCamelCase__       )
  									lowercase__							 =       kwargs.pop("""force_download"""       ,     lowerCamelCase__       )
  									lowercase__							 =       kwargs.pop("""proxies"""       ,     lowerCamelCase__       )
  									lowercase__							 =       kwargs.pop("""local_files_only"""       ,     lowerCamelCase__       )
  									lowercase__							 =       kwargs.pop("""use_auth_token"""       ,     lowerCamelCase__       )
  									lowercase__							 =       kwargs.pop("""revision"""       ,     lowerCamelCase__       )
  									lowercase__							 =       kwargs.pop("""torch_dtype"""       ,     lowerCamelCase__       )
  									lowercase__							 =       kwargs.pop("""device_map"""       ,     lowerCamelCase__       )
  
  									lowercase__							 =       kwargs.pop("""alpha"""       ,     0.5       )
  									lowercase__							 =       kwargs.pop("""interp"""       ,     lowerCamelCase__       )
  
  									print("""Received list"""       ,     lowerCamelCase__       )
  									print(F'''Combining with alpha={alpha}, interpolation mode={interp}'''       )
  
  									lowercase__							 =       len(lowerCamelCase__       )
  									# Ignore result from model_index_json comparision of the two checkpoints
  									lowercase__							 =       kwargs.pop("""force"""       ,     lowerCamelCase__       )
  
  									# If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
  									if checkpoint_count > 3 or checkpoint_count < 2:
  															raise ValueError(
  															    """Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being"""
  															    """ passed."""       )
  
  									print("""Received the right number of checkpoints"""       )
  									# chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
  									# chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None
  
  									# Validate that the checkpoints can be merged
  									# Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
  									lowercase__							 =       []
  									for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
  															lowercase__							 =       DiffusionPipeline.load_config(
  															    lowerCamelCase__       ,     cache_dir=lowerCamelCase__       ,     resume_download=lowerCamelCase__       ,     force_download=lowerCamelCase__       ,     proxies=lowerCamelCase__       ,     local_files_only=lowerCamelCase__       ,     use_auth_token=lowerCamelCase__       ,     revision=lowerCamelCase__       ,     )
  															config_dicts.append(lowerCamelCase__       )
  
  									lowercase__							 =       True
  									for idx in range(1       ,     len(lowerCamelCase__       )       ):
  															comparison_result &= self._compare_model_configs(config_dicts[idx - 1]       ,     config_dicts[idx]       )
  															if not force and comparison_result is False:
  																					raise ValueError("""Incompatible checkpoints. Please check model_index.json for the models."""       )
  																					print(config_dicts[0]       ,     config_dicts[1]       )
  									print("""Compatible model_index.json files found"""       )
  									# Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
  									lowercase__							 =       []
  									for pretrained_model_name_or_path, config_dict in zip(lowerCamelCase__       ,     lowerCamelCase__       ):
  															lowercase__							 =       [k for k in config_dict.keys() if not k.startswith("""_"""       )]
  															lowercase__							 =       [os.path.join(lowerCamelCase__       ,     """*"""       ) for k in folder_names]
  															allow_patterns += [
  															    WEIGHTS_NAME,
  															    SCHEDULER_CONFIG_NAME,
  															    CONFIG_NAME,
  															    ONNX_WEIGHTS_NAME,
  															    DiffusionPipeline.config_name,
  															]
  															lowercase__							 =       config_dict.get("""_class_name"""       )
  															lowercase__							 =       {"""diffusers""": __version__, """pipeline_class""": requested_pipeline_class}
  
  															lowercase__							 =       (
  															    pretrained_model_name_or_path
  															    if os.path.isdir(lowerCamelCase__       )
  															    else snapshot_download(
  															        lowerCamelCase__       ,     cache_dir=lowerCamelCase__       ,     resume_download=lowerCamelCase__       ,     proxies=lowerCamelCase__       ,     local_files_only=lowerCamelCase__       ,     use_auth_token=lowerCamelCase__       ,     revision=lowerCamelCase__       ,     allow_patterns=lowerCamelCase__       ,     user_agent=lowerCamelCase__       ,     )
  															)
  															print("""Cached Folder"""       ,     lowerCamelCase__       )
  															cached_folders.append(lowerCamelCase__       )
  
  									# Step 3:-
  									# Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
  									lowercase__							 =       DiffusionPipeline.from_pretrained(
  									    cached_folders[0]       ,     torch_dtype=lowerCamelCase__       ,     device_map=lowerCamelCase__       )
  									final_pipe.to(self.device       )
  
  									lowercase__							 =       None
  									if len(lowerCamelCase__       ) > 2:
  															lowercase__							 =       os.path.join(cached_folders[2]       )
  
  									if interp == "sigmoid":
  															lowercase__							 =       CheckpointMergerPipeline.sigmoid
  									elif interp == "inv_sigmoid":
  															lowercase__							 =       CheckpointMergerPipeline.inv_sigmoid
  									elif interp == "add_diff":
  															lowercase__							 =       CheckpointMergerPipeline.add_difference
  									else:
  															lowercase__							 =       CheckpointMergerPipeline.weighted_sum
  
  									# Find each module's state dict.
  									for attr in final_pipe.config.keys():
  															if not attr.startswith("""_"""       ):
  																					lowercase__							 =       os.path.join(cached_folders[1]       ,     lowerCamelCase__       )
  																					if os.path.exists(lowerCamelCase__       ):
  																											lowercase__							 =       [
  																											    *glob.glob(os.path.join(lowerCamelCase__       ,     """*.safetensors"""       )       ),
  																											    *glob.glob(os.path.join(lowerCamelCase__       ,     """*.bin"""       )       ),
  																											]
  																											lowercase__							 =       files[0] if len(lowerCamelCase__       ) > 0 else None
  																					if len(lowerCamelCase__       ) < 3:
  																											lowercase__							 =       None
  																					else:
  																											lowercase__							 =       os.path.join(cached_folders[2]       ,     lowerCamelCase__       )
  																											if os.path.exists(lowerCamelCase__       ):
  																																	lowercase__							 =       [
  																																	    *glob.glob(os.path.join(lowerCamelCase__       ,     """*.safetensors"""       )       ),
  																																	    *glob.glob(os.path.join(lowerCamelCase__       ,     """*.bin"""       )       ),
  																																	]
  																																	lowercase__							 =       files[0] if len(lowerCamelCase__       ) > 0 else None
                  # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                  # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
  																					if checkpoint_path_a is None and checkpoint_path_a is None:
  																											print(F'''Skipping {attr}: not present in 2nd or 3d model'''       )
  																											continue
  																					try:
  																											lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  																											if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):  # ignore requires_safety_checker boolean
  																																	continue
  																											lowercase__							 =       getattr(lowerCamelCase__       ,     """state_dict"""       )
  																											lowercase__							 =       theta_a()
  
  																											lowercase__							 =       getattr(lowerCamelCase__       ,     """load_state_dict"""       )
  																											lowercase__							 =       (
  																											    safetensors.torch.load_file(lowerCamelCase__       )
  																											    if (is_safetensors_available() and checkpoint_path_a.endswith(""".safetensors"""       ))
  																											    else torch.load(lowerCamelCase__       ,     map_location="""cpu"""       )
  																											)
  																											lowercase__							 =       None
  																											if checkpoint_path_a:
  																																	lowercase__							 =       (
  																																	    safetensors.torch.load_file(lowerCamelCase__       )
  																																	    if (is_safetensors_available() and checkpoint_path_a.endswith(""".safetensors"""       ))
  																																	    else torch.load(lowerCamelCase__       ,     map_location="""cpu"""       )
  																																	)
  
  																											if not theta_a.keys() == theta_a.keys():
  																																	print(F'''Skipping {attr}: key mismatch'''       )
  																																	continue
  																											if theta_a and not theta_a.keys() == theta_a.keys():
  																																	print(F'''Skipping {attr}:y mismatch'''       )
  																					except Exception as e:
  																											print(F'''Skipping {attr} do to an unexpected error: {str(lowerCamelCase__       )}'''       )
  																											continue
  																					print(F'''MERGING {attr}'''       )
  
  																					for key in theta_a.keys():
  																											if theta_a:
  																																	lowercase__							 =       theta_func(theta_a[key]       ,     theta_a[key]       ,     theta_a[key]       ,     lowerCamelCase__       )
  																											else:
  																																	lowercase__							 =       theta_func(theta_a[key]       ,     theta_a[key]       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  																					del theta_a
  																					del theta_a
  																					update_theta_a(lowerCamelCase__       )
  
  																					del theta_a
  									return final_pipe
  
  
  
  			@staticmethod
  			def A__ (					lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return ((1 - alpha) * thetaa) + (alpha * thetaa)
  
  
  
  			@staticmethod
  			def A__ (					lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       alpha * alpha * (3 - (2 * alpha))
  									return thetaa + ((thetaa - thetaa) * alpha)
  
  
  
  			@staticmethod
  			def A__ (					lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									import math
  
  									lowercase__							 =       0.5 - math.sin(math.asin(1.0 - 2.0 * alpha       ) / 3.0       )
  									return thetaa + ((thetaa - thetaa) * alpha)
  
  
  
  			@staticmethod
  			def A__ (					lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return thetaa + (thetaa - thetaa) * (1.0 - alpha)
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__=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		(							lowercase__		,		lowercase__=0	):
  						lowercase__							 =       []
  						for old_item in old_list:
  												lowercase__							 =       old_item
  												lowercase__							 =       new_item.replace("""block."""		,		"""resnets."""	)
  												lowercase__							 =       new_item.replace("""conv_shorcut"""		,		"""conv1"""	)
  												lowercase__							 =       new_item.replace("""in_shortcut"""		,		"""conv_shortcut"""	)
  												lowercase__							 =       new_item.replace("""temb_proj"""		,		"""time_emb_proj"""	)
  
  												lowercase__							 =       shave_segments(lowercase__		,		n_shave_prefix_segments=lowercase__	)
  
  												mapping.append({"""old""": old_item, """new""": new_item}	)
  
  						return mapping
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__=0		,		lowercase__=False	):
  						lowercase__							 =       []
  						for old_item in old_list:
  												lowercase__							 =       old_item
  
  												# In `model.mid`, the layer is called `attn`.
  												if not in_mid:
  																		lowercase__							 =       new_item.replace("""attn"""		,		"""attentions"""	)
  												lowercase__							 =       new_item.replace(""".k."""		,		""".key."""	)
  												lowercase__							 =       new_item.replace(""".v."""		,		""".value."""	)
  												lowercase__							 =       new_item.replace(""".q."""		,		""".query."""	)
  
  												lowercase__							 =       new_item.replace("""proj_out"""		,		"""proj_attn"""	)
  												lowercase__							 =       new_item.replace("""norm"""		,		"""group_norm"""	)
  
  												lowercase__							 =       shave_segments(lowercase__		,		n_shave_prefix_segments=lowercase__	)
  												mapping.append({"""old""": old_item, """new""": new_item}	)
  
  						return mapping
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__=None		,		lowercase__=None		,		lowercase__=None	):
  						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():
  																		lowercase__							 =       old_checkpoint[path]
  																		lowercase__							 =       old_tensor.shape[0] // 3
  
  																		lowercase__							 =       (-1, channels) if len(old_tensor.shape	) == 3 else (-1)
  
  																		lowercase__							 =       old_tensor.shape[0] // config.get("""num_head_channels"""		,		1	) // 3
  
  																		lowercase__							 =       old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]	)
  																		lowercase__ ,						lowercase__ ,						lowercase__							 =       old_tensor.split(channels // num_heads		,		dim=1	)
  
  																		lowercase__							 =       query.reshape(lowercase__	).squeeze()
  																		lowercase__							 =       key.reshape(lowercase__	).squeeze()
  																		lowercase__							 =       value.reshape(lowercase__	).squeeze()
  
  						for path in paths:
  												lowercase__							 =       path["""new"""]
  
  												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
  																		continue
  
  												lowercase__							 =       new_path.replace("""down."""		,		"""down_blocks."""	)
  												lowercase__							 =       new_path.replace("""up."""		,		"""up_blocks."""	)
  
  												if additional_replacements is not None:
  																		for replacement in additional_replacements:
  																								lowercase__							 =       new_path.replace(replacement["""old"""]		,		replacement["""new"""]	)
  
  												if "attentions" in new_path:
  																		lowercase__							 =       old_checkpoint[path["""old"""]].squeeze()
  												else:
  																		lowercase__							 =       old_checkpoint[path["""old"""]]
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       {}
  
  						lowercase__							 =       checkpoint["""temb.dense.0.weight"""]
  						lowercase__							 =       checkpoint["""temb.dense.0.bias"""]
  						lowercase__							 =       checkpoint["""temb.dense.1.weight"""]
  						lowercase__							 =       checkpoint["""temb.dense.1.bias"""]
  
  						lowercase__							 =       checkpoint["""norm_out.weight"""]
  						lowercase__							 =       checkpoint["""norm_out.bias"""]
  
  						lowercase__							 =       checkpoint["""conv_in.weight"""]
  						lowercase__							 =       checkpoint["""conv_in.bias"""]
  						lowercase__							 =       checkpoint["""conv_out.weight"""]
  						lowercase__							 =       checkpoint["""conv_out.bias"""]
  
  						lowercase__							 =       len({""".""".join(layer.split("""."""	)[:2]	) for layer in checkpoint if """down""" in layer}	)
  						lowercase__							 =       {
  						    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(lowercase__	)
  						}
  
  						lowercase__							 =       len({""".""".join(layer.split("""."""	)[:2]	) for layer in checkpoint if """up""" in layer}	)
  						lowercase__							 =       {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__	):
  												lowercase__							 =       (i - 1) // (config["""layers_per_block"""] + 1)
  
  												if any("""downsample""" in layer for layer in down_blocks[i]	):
  																		lowercase__							 =       checkpoint[
  																		    f'''down.{i}.downsample.op.weight'''
  																		]
  																		lowercase__							 =       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]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		2	).split("""."""	)[:2]	) for layer in down_blocks[i] if """block""" in layer}	)
  																		lowercase__							 =       {
  																		    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"""]	):
  																														lowercase__							 =       renew_resnet_paths(blocks[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__	)
  
  												if any("""attn""" in layer for layer in down_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		2	).split("""."""	)[:2]	) for layer in down_blocks[i] if """attn""" in layer}	)
  																		lowercase__							 =       {
  																		    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"""]	):
  																														lowercase__							 =       renew_attention_paths(attns[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__		,		config=lowercase__	)
  
  						lowercase__							 =       [key for key in checkpoint if """mid.block_1""" in key]
  						lowercase__							 =       [key for key in checkpoint if """mid.block_2""" in key]
  						lowercase__							 =       [key for key in checkpoint if """mid.attn_1""" in key]
  
  						# Mid new 2
  						lowercase__							 =       renew_resnet_paths(lowercase__	)
  						assign_to_checkpoint(
  						    lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]		,		)
  
  						lowercase__							 =       renew_resnet_paths(lowercase__	)
  						assign_to_checkpoint(
  						    lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]		,		)
  
  						lowercase__							 =       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__	):
  												lowercase__							 =       num_up_blocks - 1 - i
  
  												if any("""upsample""" in layer for layer in up_blocks[i]	):
  																		lowercase__							 =       checkpoint[
  																		    f'''up.{i}.upsample.conv.weight'''
  																		]
  																		lowercase__							 =       checkpoint[f'''up.{i}.upsample.conv.bias''']
  
  												if any("""block""" in layer for layer in up_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		2	).split("""."""	)[:2]	) for layer in up_blocks[i] if """block""" in layer}	)
  																		lowercase__							 =       {
  																		    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	):
  																														lowercase__							 =       {"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
  																														lowercase__							 =       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]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		2	).split("""."""	)[:2]	) for layer in up_blocks[i] if """attn""" in layer}	)
  																		lowercase__							 =       {
  																		    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	):
  																														lowercase__							 =       {"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
  																														lowercase__							 =       renew_attention_paths(attns[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[replace_indices]	)
  
  						lowercase__							 =       {k.replace("""mid_new_2"""		,		"""mid_block"""	): v for k, v in new_checkpoint.items()}
  						return new_checkpoint
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       {}
  
  						lowercase__							 =       checkpoint["""encoder.norm_out.weight"""]
  						lowercase__							 =       checkpoint["""encoder.norm_out.bias"""]
  
  						lowercase__							 =       checkpoint["""encoder.conv_in.weight"""]
  						lowercase__							 =       checkpoint["""encoder.conv_in.bias"""]
  						lowercase__							 =       checkpoint["""encoder.conv_out.weight"""]
  						lowercase__							 =       checkpoint["""encoder.conv_out.bias"""]
  
  						lowercase__							 =       checkpoint["""decoder.norm_out.weight"""]
  						lowercase__							 =       checkpoint["""decoder.norm_out.bias"""]
  
  						lowercase__							 =       checkpoint["""decoder.conv_in.weight"""]
  						lowercase__							 =       checkpoint["""decoder.conv_in.bias"""]
  						lowercase__							 =       checkpoint["""decoder.conv_out.weight"""]
  						lowercase__							 =       checkpoint["""decoder.conv_out.bias"""]
  
  						lowercase__							 =       len({""".""".join(layer.split("""."""	)[:3]	) for layer in checkpoint if """down""" in layer}	)
  						lowercase__							 =       {
  						    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(lowercase__	)
  						}
  
  						lowercase__							 =       len({""".""".join(layer.split("""."""	)[:3]	) for layer in checkpoint if """up""" in layer}	)
  						lowercase__							 =       {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__	):
  												lowercase__							 =       (i - 1) // (config["""layers_per_block"""] + 1)
  
  												if any("""downsample""" in layer for layer in down_blocks[i]	):
  																		lowercase__							 =       checkpoint[
  																		    f'''encoder.down.{i}.downsample.conv.weight'''
  																		]
  																		lowercase__							 =       checkpoint[
  																		    f'''encoder.down.{i}.downsample.conv.bias'''
  																		]
  
  												if any("""block""" in layer for layer in down_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		3	).split("""."""	)[:3]	) for layer in down_blocks[i] if """block""" in layer}	)
  																		lowercase__							 =       {
  																		    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"""]	):
  																														lowercase__							 =       renew_resnet_paths(blocks[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__	)
  
  												if any("""attn""" in layer for layer in down_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		3	).split("""."""	)[:3]	) for layer in down_blocks[i] if """attn""" in layer}	)
  																		lowercase__							 =       {
  																		    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"""]	):
  																														lowercase__							 =       renew_attention_paths(attns[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__		,		config=lowercase__	)
  
  						lowercase__							 =       [key for key in checkpoint if """mid.block_1""" in key]
  						lowercase__							 =       [key for key in checkpoint if """mid.block_2""" in key]
  						lowercase__							 =       [key for key in checkpoint if """mid.attn_1""" in key]
  
  						# Mid new 2
  						lowercase__							 =       renew_resnet_paths(lowercase__	)
  						assign_to_checkpoint(
  						    lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]		,		)
  
  						lowercase__							 =       renew_resnet_paths(lowercase__	)
  						assign_to_checkpoint(
  						    lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]		,		)
  
  						lowercase__							 =       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__	):
  												lowercase__							 =       num_up_blocks - 1 - i
  
  												if any("""upsample""" in layer for layer in up_blocks[i]	):
  																		lowercase__							 =       checkpoint[
  																		    f'''decoder.up.{i}.upsample.conv.weight'''
  																		]
  																		lowercase__							 =       checkpoint[
  																		    f'''decoder.up.{i}.upsample.conv.bias'''
  																		]
  
  												if any("""block""" in layer for layer in up_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		3	).split("""."""	)[:3]	) for layer in up_blocks[i] if """block""" in layer}	)
  																		lowercase__							 =       {
  																		    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	):
  																														lowercase__							 =       {"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
  																														lowercase__							 =       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]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		3	).split("""."""	)[:3]	) for layer in up_blocks[i] if """attn""" in layer}	)
  																		lowercase__							 =       {
  																		    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	):
  																														lowercase__							 =       {"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
  																														lowercase__							 =       renew_attention_paths(attns[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[replace_indices]	)
  
  						lowercase__							 =       {k.replace("""mid_new_2"""		,		"""mid_block"""	): v for k, v in new_checkpoint.items()}
  						lowercase__							 =       checkpoint["""quant_conv.weight"""]
  						lowercase__							 =       checkpoint["""quant_conv.bias"""]
  						if "quantize.embedding.weight" in checkpoint:
  												lowercase__							 =       checkpoint["""quantize.embedding.weight"""]
  						lowercase__							 =       checkpoint["""post_quant_conv.weight"""]
  						lowercase__							 =       checkpoint["""post_quant_conv.bias"""]
  
  						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())
  
  				# unet case
  				__A          =    {key.split(".")[0] for key in checkpoint.keys()}
  				if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
  								__A          =    convert_vq_autoenc_checkpoint(checkpoint, config)
  				else:
  								__A          =    convert_ddpm_checkpoint(checkpoint, config)
  
  				if "ddpm" in config:
  								del config["ddpm"]
  
  				if config["_class_name"] == "VQModel":
  								__A          =    VQModel(**config)
  								model.load_state_dict(converted_checkpoint)
  								model.save_pretrained(args.dump_path)
  				elif config["_class_name"] == "AutoencoderKL":
  								__A          =    AutoencoderKL(**config)
  								model.load_state_dict(converted_checkpoint)
  								model.save_pretrained(args.dump_path)
  				else:
  								__A          =    UNetaDModel(**config)
  								model.load_state_dict(converted_checkpoint)
  
  								__A          =    DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
  
  								__A          =    DDPMPipeline(unet=model, scheduler=scheduler)
  								pipe.save_pretrained(args.dump_path)
  
  
  
  
  
  
  