
'''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)








'''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)






