code: 
  
  
  import argparse
  
  import torch
  
  from diffusers import UNetaDConditionModel
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: List[Any]	,	SCREAMING_SNAKE_CASE_				: Any	,	SCREAMING_SNAKE_CASE_				: Dict	,	SCREAMING_SNAKE_CASE_				: str=None	,	SCREAMING_SNAKE_CASE_				: Tuple=None	,	SCREAMING_SNAKE_CASE_				: str=None      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							assert isinstance(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      ), "Paths should be a list of dicts containing 'old' and 'new' keys."
  
  							# Splits the attention layers into three variables.
  							if attention_paths_to_split is not None:
  														for path, path_map in attention_paths_to_split.items():
  																					a_       :   Optional[Any]			=	old_checkpoint[path]
  																					a_       :   List[Any]			=	old_tensor.shape[0] // 3
  
  																					a_       :   Optional[int]			=	(-1, channels) if len(old_tensor.shape      ) == 3 else (-1)
  
  																					a_       :   List[Any]			=	old_tensor.shape[0] // config["""num_head_channels"""] // 3
  
  																					a_       :   str			=	old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]      )
  																					a_		,				a_		,				a_       :   int			=	old_tensor.split(channels // num_heads	,	dim=1      )
  
  																					a_       :   Optional[int]			=	query.reshape(SCREAMING_SNAKE_CASE_      )
  																					a_       :   Optional[int]			=	key.reshape(SCREAMING_SNAKE_CASE_      )
  																					a_       :   List[Any]			=	value.reshape(SCREAMING_SNAKE_CASE_      )
  
  							for path in paths:
  														a_       :   Union[str, Any]			=	path["""new"""]
  
  														# These have already been assigned
  														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
  																					continue
  
  														if additional_replacements is not None:
  																					for replacement in additional_replacements:
  																												a_       :   Any			=	new_path.replace(replacement["""old"""]	,	replacement["""new"""]      )
  
          # proj_attn.weight has to be converted from conv 1D to linear
  														a_       :   int			=	old_checkpoint[path["""old"""]]
  														a_       :   List[str]			=	["""proj_attn.weight"""]
  														a_       :   Dict			=	["""proj_out.weight""", """proj_in.weight"""]
  														if any(k in new_path for k in names      ):
  																					a_       :   List[Any]			=	weight[:, :, 0]
  														elif any(k in new_path for k in names_a      ) and len(weight.shape      ) > 2 and ".attentions." not in new_path:
  																					a_       :   Tuple			=	weight[:, :, 0]
  														else:
  																					a_       :   str			=	weight
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[Any]	,	SCREAMING_SNAKE_CASE_				: List[str]=0      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Optional[Any]			=	[]
  							for old_item in old_list:
  														a_       :   Tuple			=	old_item
  
  														#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
  														#         new_item = new_item.replace('norm.bias', 'group_norm.bias')
  
  														#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
  														#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
  
  														#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
  
  														mapping.append({"""old""": old_item, """new""": new_item}      )
  
  							return mapping
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[int]	,	SCREAMING_SNAKE_CASE_				: List[str]=1      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							if n_shave_prefix_segments >= 0:
  														return ".".join(path.split("""."""      )[n_shave_prefix_segments:]      )
  							else:
  														return ".".join(path.split("""."""      )[:n_shave_prefix_segments]      )
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: str	,	SCREAMING_SNAKE_CASE_				: Optional[int]=0      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Optional[Any]			=	[]
  							for old_item in old_list:
  														mapping.append({"""old""": old_item, """new""": old_item}      )
  
  							return mapping
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: List[Any]	,	SCREAMING_SNAKE_CASE_				: Optional[int]=0      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Optional[int]			=	[]
  							for old_item in old_list:
  														a_       :   int			=	old_item.replace("""in_layers.0"""	,	"""norm1"""      )
  														a_       :   Union[str, Any]			=	new_item.replace("""in_layers.2"""	,	"""conv1"""      )
  
  														a_       :   Optional[int]			=	new_item.replace("""out_layers.0"""	,	"""norm2"""      )
  														a_       :   Dict			=	new_item.replace("""out_layers.3"""	,	"""conv2"""      )
  
  														a_       :   Any			=	new_item.replace("""emb_layers.1"""	,	"""time_emb_proj"""      )
  														a_       :   Union[str, Any]			=	new_item.replace("""skip_connection"""	,	"""conv_shortcut"""      )
  
  														a_       :   Union[str, Any]			=	shave_segments(SCREAMING_SNAKE_CASE_	,	n_shave_prefix_segments=SCREAMING_SNAKE_CASE_      )
  
  														if "temopral_conv" not in old_item:
  																					mapping.append({"""old""": old_item, """new""": new_item}      )
  
  							return mapping
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: int	,	SCREAMING_SNAKE_CASE_				: List[str]	,	SCREAMING_SNAKE_CASE_				: str=None	,	SCREAMING_SNAKE_CASE_				: int=False      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Union[str, Any]			=	{}
  							a_       :   Optional[int]			=	list(checkpoint.keys()      )
  
  							a_       :   Dict			=	"""model.diffusion_model."""
  
  							# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
  							if sum(k.startswith("""model_ema"""      ) for k in keys      ) > 1_00 and extract_ema:
  														print(F"""Checkpoint {path} has both EMA and non-EMA weights."""      )
  														print(
  														    """In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"""
  														    """ weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."""      )
  														for key in keys:
  																					if key.startswith("""model.diffusion_model"""      ):
  																												a_       :   Optional[int]			=	"""model_ema.""" + """""".join(key.split("""."""      )[1:]      )
  																												a_       :   Any			=	checkpoint.pop(SCREAMING_SNAKE_CASE_      )
  							else:
  														if sum(k.startswith("""model_ema"""      ) for k in keys      ) > 1_00:
  																					print(
  																					    """In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"""
  																					    """ weights (usually better for inference), please make sure to add the `--extract_ema` flag."""      )
  
  														for key in keys:
  																					a_       :   str			=	checkpoint.pop(SCREAMING_SNAKE_CASE_      )
  
  							a_       :   Any			=	{}
  
  							a_       :   Dict			=	unet_state_dict["""time_embed.0.weight"""]
  							a_       :   Dict			=	unet_state_dict["""time_embed.0.bias"""]
  							a_       :   Optional[Any]			=	unet_state_dict["""time_embed.2.weight"""]
  							a_       :   Optional[int]			=	unet_state_dict["""time_embed.2.bias"""]
  
  							if config["class_embed_type"] is None:
  														# No parameters to port
  														...
  							elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
  														a_       :   str			=	unet_state_dict["""label_emb.0.0.weight"""]
  														a_       :   Union[str, Any]			=	unet_state_dict["""label_emb.0.0.bias"""]
  														a_       :   Dict			=	unet_state_dict["""label_emb.0.2.weight"""]
  														a_       :   List[str]			=	unet_state_dict["""label_emb.0.2.bias"""]
  							else:
  														raise NotImplementedError(F"""Not implemented `class_embed_type`: {config['class_embed_type']}"""      )
  
  							a_       :   Optional[int]			=	unet_state_dict["""input_blocks.0.0.weight"""]
  							a_       :   str			=	unet_state_dict["""input_blocks.0.0.bias"""]
  
  							a_       :   Optional[Any]			=	[v for v in unet_state_dict if v.startswith("""input_blocks.0.1"""      )]
  							a_       :   Optional[int]			=	renew_attention_paths(SCREAMING_SNAKE_CASE_      )
  							a_       :   str			=	{"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
  							assign_to_checkpoint(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )
  
  							a_       :   str			=	unet_state_dict["""out.0.weight"""]
  							a_       :   Any			=	unet_state_dict["""out.0.bias"""]
  							a_       :   Optional[int]			=	unet_state_dict["""out.2.weight"""]
  							a_       :   Tuple			=	unet_state_dict["""out.2.bias"""]
  
  							# Retrieves the keys for the input blocks only
  							a_       :   str			=	len({""".""".join(layer.split("""."""      )[:2]      ) for layer in unet_state_dict if """input_blocks""" in layer}      )
  							a_       :   Union[str, Any]			=	{
  							    layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
  							    for layer_id in range(SCREAMING_SNAKE_CASE_      )
  							}
  
  							# Retrieves the keys for the middle blocks only
  							a_       :   Tuple			=	len({""".""".join(layer.split("""."""      )[:2]      ) for layer in unet_state_dict if """middle_block""" in layer}      )
  							a_       :   List[Any]			=	{
  							    layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
  							    for layer_id in range(SCREAMING_SNAKE_CASE_      )
  							}
  
  							# Retrieves the keys for the output blocks only
  							a_       :   Optional[int]			=	len({""".""".join(layer.split("""."""      )[:2]      ) for layer in unet_state_dict if """output_blocks""" in layer}      )
  							a_       :   str			=	{
  							    layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
  							    for layer_id in range(SCREAMING_SNAKE_CASE_      )
  							}
  
  							for i in range(1	,	SCREAMING_SNAKE_CASE_      ):
  														a_       :   Dict			=	(i - 1) // (config["""layers_per_block"""] + 1)
  														a_       :   Optional[int]			=	(i - 1) % (config["""layers_per_block"""] + 1)
  
  														a_       :   int			=	[
  														    key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key and F"""input_blocks.{i}.0.op""" not in key
  														]
  														a_       :   List[str]			=	[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
  														a_       :   List[str]			=	[key for key in input_blocks[i] if F"""input_blocks.{i}.2""" in key]
  
  														if F"""input_blocks.{i}.op.weight""" in unet_state_dict:
  																					a_       :   str			=	unet_state_dict.pop(
  																					    F"""input_blocks.{i}.op.weight"""      )
  																					a_       :   Tuple			=	unet_state_dict.pop(
  																					    F"""input_blocks.{i}.op.bias"""      )
  
  														a_       :   Any			=	renew_resnet_paths(SCREAMING_SNAKE_CASE_      )
  														a_       :   Optional[Any]			=	{"""old""": F"""input_blocks.{i}.0""", """new""": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
  														assign_to_checkpoint(
  														    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )
  
  														a_       :   List[str]			=	[key for key in resnets if """temopral_conv""" in key]
  														a_       :   List[Any]			=	renew_temp_conv_paths(SCREAMING_SNAKE_CASE_      )
  														a_       :   str			=	{
  														    """old""": F"""input_blocks.{i}.0.temopral_conv""",
  														    """new""": F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
  														}
  														assign_to_checkpoint(
  														    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )
  
  														if len(SCREAMING_SNAKE_CASE_      ):
  																					a_       :   Optional[Any]			=	renew_attention_paths(SCREAMING_SNAKE_CASE_      )
  																					a_       :   Optional[Any]			=	{"""old""": F"""input_blocks.{i}.1""", """new""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
  																					assign_to_checkpoint(
  																					    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )
  
  														if len(SCREAMING_SNAKE_CASE_      ):
  																					a_       :   str			=	renew_attention_paths(SCREAMING_SNAKE_CASE_      )
  																					a_       :   Optional[Any]			=	{
  																					    """old""": F"""input_blocks.{i}.2""",
  																					    """new""": F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
  																					}
  																					assign_to_checkpoint(
  																					    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )
  
  							a_       :   List[str]			=	middle_blocks[0]
  							a_       :   Tuple			=	[key for key in resnet_a if """temopral_conv""" in key]
  							a_       :   Dict			=	middle_blocks[1]
  							a_       :   Dict			=	middle_blocks[2]
  							a_       :   Optional[Any]			=	middle_blocks[3]
  							a_       :   int			=	[key for key in resnet_a if """temopral_conv""" in key]
  
  							a_       :   int			=	renew_resnet_paths(SCREAMING_SNAKE_CASE_      )
  							a_       :   Union[str, Any]			=	{"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
  							assign_to_checkpoint(
  							    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	config=SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]      )
  
  							a_       :   List[Any]			=	renew_temp_conv_paths(SCREAMING_SNAKE_CASE_      )
  							a_       :   Optional[Any]			=	{"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
  							assign_to_checkpoint(
  							    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	config=SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]      )
  
  							a_       :   Optional[Any]			=	renew_resnet_paths(SCREAMING_SNAKE_CASE_      )
  							a_       :   Dict			=	{"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
  							assign_to_checkpoint(
  							    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	config=SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]      )
  
  							a_       :   List[Any]			=	renew_temp_conv_paths(SCREAMING_SNAKE_CASE_      )
  							a_       :   List[str]			=	{"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
  							assign_to_checkpoint(
  							    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	config=SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]      )
  
  							a_       :   Optional[int]			=	renew_attention_paths(SCREAMING_SNAKE_CASE_      )
  							a_       :   Union[str, Any]			=	{"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
  							assign_to_checkpoint(
  							    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )
  
  							a_       :   List[str]			=	renew_attention_paths(SCREAMING_SNAKE_CASE_      )
  							a_       :   Optional[int]			=	{"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
  							assign_to_checkpoint(
  							    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )
  
  							for i in range(SCREAMING_SNAKE_CASE_      ):
  														a_       :   Optional[Any]			=	i // (config["""layers_per_block"""] + 1)
  														a_       :   Optional[Any]			=	i % (config["""layers_per_block"""] + 1)
  														a_       :   str			=	[shave_segments(SCREAMING_SNAKE_CASE_	,	2      ) for name in output_blocks[i]]
  														a_       :   Optional[int]			=	{}
  
  														for layer in output_block_layers:
  																					a_		,				a_       :   List[str]			=	layer.split("""."""      )[0], shave_segments(SCREAMING_SNAKE_CASE_	,	1      )
  																					if layer_id in output_block_list:
  																												output_block_list[layer_id].append(SCREAMING_SNAKE_CASE_      )
  																					else:
  																												a_       :   Union[str, Any]			=	[layer_name]
  
  														if len(SCREAMING_SNAKE_CASE_      ) > 1:
  																					a_       :   Any			=	[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
  																					a_       :   Dict			=	[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
  																					a_       :   Optional[Any]			=	[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]
  
  																					a_       :   Dict			=	renew_resnet_paths(SCREAMING_SNAKE_CASE_      )
  																					a_       :   Optional[int]			=	renew_resnet_paths(SCREAMING_SNAKE_CASE_      )
  
  																					a_       :   Tuple			=	{"""old""": F"""output_blocks.{i}.0""", """new""": F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
  																					assign_to_checkpoint(
  																					    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )
  
  																					a_       :   Optional[int]			=	[key for key in resnets if """temopral_conv""" in key]
  																					a_       :   Any			=	renew_temp_conv_paths(SCREAMING_SNAKE_CASE_      )
  																					a_       :   Dict			=	{
  																					    """old""": F"""output_blocks.{i}.0.temopral_conv""",
  																					    """new""": F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
  																					}
  																					assign_to_checkpoint(
  																					    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )
  
  																					a_       :   Tuple			=	{k: sorted(SCREAMING_SNAKE_CASE_      ) for k, v in output_block_list.items()}
  																					if ["conv.bias", "conv.weight"] in output_block_list.values():
  																												a_       :   int			=	list(output_block_list.values()      ).index(["""conv.bias""", """conv.weight"""]      )
  																												a_       :   Optional[int]			=	unet_state_dict[
  																												    F"""output_blocks.{i}.{index}.conv.weight"""
  																												]
  																												a_       :   List[str]			=	unet_state_dict[
  																												    F"""output_blocks.{i}.{index}.conv.bias"""
  																												]
  
  																												# Clear attentions as they have been attributed above.
  																												if len(SCREAMING_SNAKE_CASE_      ) == 2:
  																																			a_       :   Tuple			=	[]
  
  																					if len(SCREAMING_SNAKE_CASE_      ):
  																												a_       :   Optional[Any]			=	renew_attention_paths(SCREAMING_SNAKE_CASE_      )
  																												a_       :   Optional[Any]			=	{
  																												    """old""": F"""output_blocks.{i}.1""",
  																												    """new""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
  																												}
  																												assign_to_checkpoint(
  																												    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )
  
  																					if len(SCREAMING_SNAKE_CASE_      ):
  																												a_       :   Optional[Any]			=	renew_attention_paths(SCREAMING_SNAKE_CASE_      )
  																												a_       :   List[str]			=	{
  																												    """old""": F"""output_blocks.{i}.2""",
  																												    """new""": F"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
  																												}
  																												assign_to_checkpoint(
  																												    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	additional_replacements=[meta_path]	,	config=SCREAMING_SNAKE_CASE_      )
  														else:
  																					a_       :   List[Any]			=	renew_resnet_paths(SCREAMING_SNAKE_CASE_	,	n_shave_prefix_segments=1      )
  																					for path in resnet_0_paths:
  																												a_       :   Tuple			=	""".""".join(["""output_blocks""", str(SCREAMING_SNAKE_CASE_      ), path["""old"""]]      )
  																												a_       :   Union[str, Any]			=	""".""".join(["""up_blocks""", str(SCREAMING_SNAKE_CASE_      ), """resnets""", str(SCREAMING_SNAKE_CASE_      ), path["""new"""]]      )
  																												a_       :   Dict			=	unet_state_dict[old_path]
  
  																					a_       :   str			=	[l for l in output_block_layers if """temopral_conv""" in l]
  																					for path in temopral_conv_paths:
  																												a_       :   Any			=	path.split("""temopral_conv."""      )[-1]
  																												a_       :   Tuple			=	""".""".join(["""output_blocks""", str(SCREAMING_SNAKE_CASE_      ), str(SCREAMING_SNAKE_CASE_      ), """temopral_conv""", pruned_path]      )
  																												a_       :   Tuple			=	""".""".join(["""up_blocks""", str(SCREAMING_SNAKE_CASE_      ), """temp_convs""", str(SCREAMING_SNAKE_CASE_      ), pruned_path]      )
  																												a_       :   Tuple			=	unet_state_dict[old_path]
  
  							return new_checkpoint
  
  
  if __name__ == "__main__":
  	SCREAMING_SNAKE_CASE : Any          =    argparse.ArgumentParser()
  
  	parser.add_argument(
  	    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
  	)
  	parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
  	SCREAMING_SNAKE_CASE : int          =    parser.parse_args()
  
  	SCREAMING_SNAKE_CASE : Optional[int]          =    torch.load(args.checkpoint_path, map_location="cpu")
  	SCREAMING_SNAKE_CASE : Tuple          =    UNetaDConditionModel()
  
  	SCREAMING_SNAKE_CASE : int          =    convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)
  
  	SCREAMING_SNAKE_CASE : Dict          =    set(unet.state_dict().keys()) - set(converted_ckpt.keys())
  	SCREAMING_SNAKE_CASE : List[Any]          =    set(converted_ckpt.keys()) - set(unet.state_dict().keys())
  
  	assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"
  
  	# load state_dict
  	unet.load_state_dict(converted_ckpt)
  
  	unet.save_pretrained(args.dump_path)
  
  	# -- finish converting the unet --
  
  
  
style_context: 
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  SCREAMING_SNAKE_CASE : Dict          =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE : str          =    {
      "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
      "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
      "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
      "apple/deeplabv3-mobilevit-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
      ),
      "apple/deeplabv3-mobilevit-x-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
      ),
      "apple/deeplabv3-mobilevit-xx-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
      ),
      # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
  }
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							List[str]         = """mobilevit"""
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_=3				,    UpperCamelCase_=256				,    UpperCamelCase_=2				,    UpperCamelCase_=[144, 192, 240]				,    UpperCamelCase_=[16, 32, 64, 96, 128, 160, 640]				,    UpperCamelCase_=4				,    UpperCamelCase_=2.0				,    UpperCamelCase_=4.0				,    UpperCamelCase_="silu"				,    UpperCamelCase_=3				,    UpperCamelCase_=32				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.0				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.02				,    UpperCamelCase_=1e-5				,    UpperCamelCase_=True				,    UpperCamelCase_=256				,    UpperCamelCase_=[6, 12, 18]				,    UpperCamelCase_=0.1				,    UpperCamelCase_=255				,    **UpperCamelCase_				,    )		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								super().__init__(**UpperCamelCase_							)
  
  								a_       :   Optional[int]			=	num_channels
  								a_       :   Optional[Any]			=	image_size
  								a_       :   int			=	patch_size
  								a_       :   List[str]			=	hidden_sizes
  								a_       :   Optional[int]			=	neck_hidden_sizes
  								a_       :   List[Any]			=	num_attention_heads
  								a_       :   Union[str, Any]			=	mlp_ratio
  								a_       :   List[str]			=	expand_ratio
  								a_       :   str			=	hidden_act
  								a_       :   Union[str, Any]			=	conv_kernel_size
  								a_       :   Optional[Any]			=	output_stride
  								a_       :   Union[str, Any]			=	hidden_dropout_prob
  								a_       :   List[Any]			=	attention_probs_dropout_prob
  								a_       :   List[Any]			=	classifier_dropout_prob
  								a_       :   Union[str, Any]			=	initializer_range
  								a_       :   List[Any]			=	layer_norm_eps
  								a_       :   str			=	qkv_bias
  
  								# decode head attributes for semantic segmentation
  								a_       :   Optional[Any]			=	aspp_out_channels
  								a_       :   Any			=	atrous_rates
  								a_       :   Union[str, Any]			=	aspp_dropout_prob
  								a_       :   Any			=	semantic_loss_ignore_index
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							Optional[int]         = version.parse("""1.11"""       )
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]							)
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if self.task == "image-classification":
  															return OrderedDict([("""logits""", {0: """batch"""})]							)
  								else:
  															return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]							)
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			float:
  								"""simple docstring"""
  
  
  
  
  
  
  								return 1e-4
  
  
  