code: 
  '''simple docstring'''
  
  
  
  import os
  import warnings
  from pathlib import Path
  from typing import Optional, Union
  
  from .dynamic_module_utils import custom_object_save
  from .tokenization_utils_base import PreTrainedTokenizerBase
  from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging
  
  
  __A          =    logging.get_logger(__name__)
  
  # Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
  __A          =    direct_transformers_import(Path(__file__).parent)
  
  
  __A          =    {
      "AutoTokenizer": "PreTrainedTokenizerBase",
      "AutoFeatureExtractor": "FeatureExtractionMixin",
      "AutoImageProcessor": "ImageProcessingMixin",
  }
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							Optional[int]       		=  ["""feature_extractor""", """tokenizer"""]
  			# Names need to be attr_class for attr in attributes
  			lowerCamelCase							:							Optional[Any]       		=  None
  			lowerCamelCase							:							str       		=  None
  			lowerCamelCase							:							Dict       		=  None
  
  
  
  			def __init__(					self       ,     *lowerCamelCase__       ,     **lowerCamelCase__       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									for key in kwargs:
  															if key not in self.attributes:
  																					raise TypeError(F'''Unexpected keyword argument {key}.'''       )
  									for arg, attribute_name in zip(lowerCamelCase__       ,     self.attributes       ):
  															if attribute_name in kwargs:
  																					raise TypeError(F'''Got multiple values for argument {attribute_name}.'''       )
  															else:
  																					lowercase__							 =       arg
  
  									if len(lowerCamelCase__       ) != len(self.attributes       ):
  															raise ValueError(
  															    F'''This processor requires {len(self.attributes       )} arguments: {', '.join(self.attributes       )}. Got '''
  															    F'''{len(lowerCamelCase__       )} arguments instead.'''       )
  
  									# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
  									for attribute_name, arg in kwargs.items():
  															lowercase__							 =       getattr(self       ,     F'''{attribute_name}_class'''       )
  															# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
  															lowercase__							 =       AUTO_TO_BASE_CLASS_MAPPING.get(lowerCamelCase__       ,     lowerCamelCase__       )
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       tuple(getattr(lowerCamelCase__       ,     lowerCamelCase__       ) for n in class_name if n is not None       )
  															else:
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  
  															if not isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					raise ValueError(
  																					    F'''Received a {type(lowerCamelCase__       ).__name__} for argument {attribute_name}, but a {class_name} was expected.'''       )
  
  															setattr(self       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  
  
  			def __repr__(					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       [F'''- {name}: {repr(getattr(self       ,     lowerCamelCase__       )       )}''' for name in self.attributes]
  									lowercase__							 =       """\n""".join(lowerCamelCase__       )
  									return F'''{self.__class__.__name__}:\n{attributes_repr}'''
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = False       ,     **lowerCamelCase__       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									os.makedirs(lowerCamelCase__       ,     exist_ok=lowerCamelCase__       )
  
  									if push_to_hub:
  															lowercase__							 =       kwargs.pop("""commit_message"""       ,     lowerCamelCase__       )
  															lowercase__							 =       kwargs.pop("""repo_id"""       ,     save_directory.split(os.path.sep       )[-1]       )
  															lowercase__							 =       self._create_repo(lowerCamelCase__       ,     **lowerCamelCase__       )
  															lowercase__							 =       self._get_files_timestamps(lowerCamelCase__       )
  									# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
  									# loaded from the Hub.
  									if self._auto_class is not None:
  															lowercase__							 =       [getattr(self       ,     lowerCamelCase__       ) for attribute_name in self.attributes]
  															lowercase__							 =       [(a.init_kwargs if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ) else a) for a in attrs]
  															custom_object_save(self       ,     lowerCamelCase__       ,     config=lowerCamelCase__       )
  
  									for attribute_name in self.attributes:
  															lowercase__							 =       getattr(self       ,     lowerCamelCase__       )
  															# Include the processor class in the attribute config so this processor can then be reloaded with the
  															# `AutoProcessor` API.
  															if hasattr(lowerCamelCase__       ,     """_set_processor_class"""       ):
  																					attribute._set_processor_class(self.__class__.__name__       )
  															attribute.save_pretrained(lowerCamelCase__       )
  
  									if self._auto_class is not None:
  															# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
  															for attribute_name in self.attributes:
  																					lowercase__							 =       getattr(self       ,     lowerCamelCase__       )
  																					if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																											del attribute.init_kwargs["auto_map"]
  
  									if push_to_hub:
  															self._upload_modified_files(
  															    lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     commit_message=lowerCamelCase__       ,     token=kwargs.get("""use_auth_token"""       )       ,     )
  
  
  
  			@classmethod
  			def A__ (					cls       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = "main"       ,     **lowerCamelCase__       ,     )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       cache_dir
  									lowercase__							 =       force_download
  									lowercase__							 =       local_files_only
  									lowercase__							 =       revision
  
  									lowercase__							 =       kwargs.pop("""use_auth_token"""       ,     lowerCamelCase__       )
  									if use_auth_token is not None:
  															warnings.warn(
  															    """The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers."""       ,     lowerCamelCase__       )
  															if token is not None:
  																					raise ValueError(
  																					    """`token` and `use_auth_token` are both specified. Please set only the argument `token`."""       )
  															lowercase__							 =       use_auth_token
  
  									if token is not None:
  															# change to `token` in a follow-up PR
  															lowercase__							 =       token
  
  									lowercase__							 =       cls._get_arguments_from_pretrained(lowerCamelCase__       ,     **lowerCamelCase__       )
  									return cls(*lowerCamelCase__       )
  
  
  
  			@classmethod
  			def A__ (					cls       ,     lowerCamelCase__="AutoProcessor"       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if not isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  															lowercase__							 =       auto_class.__name__
  
  									import transformers.models.auto as auto_module
  
  									if not hasattr(lowerCamelCase__       ,     lowerCamelCase__       ):
  															raise ValueError(F'''{auto_class} is not a valid auto class.'''       )
  
  									lowercase__							 =       auto_class
  
  
  
  			@classmethod
  			def A__ (					cls       ,     lowerCamelCase__       ,     **lowerCamelCase__       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       []
  									for attribute_name in cls.attributes:
  															lowercase__							 =       getattr(cls       ,     F'''{attribute_name}_class'''       )
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       tuple(getattr(lowerCamelCase__       ,     lowerCamelCase__       ) if n is not None else None for n in class_name       )
  																					lowercase__							 =       kwargs.get("""use_fast"""       ,     lowerCamelCase__       )
  																					if use_fast and classes[1] is not None:
  																											lowercase__							 =       classes[1]
  																					else:
  																											lowercase__							 =       classes[0]
  															else:
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  
  															args.append(attribute_class.from_pretrained(lowerCamelCase__       ,     **lowerCamelCase__       )       )
  									return args
  
  
  
  			@property
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       getattr(self       ,     self.attributes[0]       )
  									return getattr(lowerCamelCase__       ,     """model_input_names"""       ,     lowerCamelCase__       )
  
  
  
  
  __A          =    copy_func(ProcessorMixin.push_to_hub)
  if ProcessorMixin.push_to_hub.__doc__ is not None:
  	__A          =    ProcessorMixin.push_to_hub.__doc__.format(
  	    object="processor", object_class="AutoProcessor", object_files="processor files"
  	)
  
  
  
  
  
  
  
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)
  
  
  
  
  
  
  