




"""simple docstring"""




import argparse

import torch

from diffusers import UNetaDConditionModel

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case       ,_snake_case=None       ,_snake_case=None       ,_snake_case=None						):
							assert isinstance(__UpperCamelCase       ,__UpperCamelCase						), "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():
																					SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       old_checkpoint[path]
																					SCREAMING_SNAKE_CASE__						:	List[Any]									=       old_tensor.shape[0] // 3

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

																					SCREAMING_SNAKE_CASE__						:	Dict									=       old_tensor.shape[0] // config["""num_head_channels"""] // 3

																					SCREAMING_SNAKE_CASE__						:	str									=       old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
																					SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Optional[int]									=       old_tensor.split(channels // num_heads       ,dim=1						)

																					SCREAMING_SNAKE_CASE__						:	Dict									=       query.reshape(__UpperCamelCase						)
																					SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       key.reshape(__UpperCamelCase						)
																					SCREAMING_SNAKE_CASE__						:	Dict									=       value.reshape(__UpperCamelCase						)

							for path in paths:
														SCREAMING_SNAKE_CASE__						:	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:
																												SCREAMING_SNAKE_CASE__						:	List[Any]									=       new_path.replace(replacement["""old"""]       ,replacement["""new"""]						)

        # proj_attn.weight has to be converted from conv 1D to linear
														SCREAMING_SNAKE_CASE__						:	Any									=       old_checkpoint[path["""old"""]]
														SCREAMING_SNAKE_CASE__						:	str									=       ["""proj_attn.weight"""]
														SCREAMING_SNAKE_CASE__						:	List[str]									=       ["""proj_out.weight""", """proj_in.weight"""]
														if any(k in new_path for k in names						):
																					SCREAMING_SNAKE_CASE__						:	Optional[int]									=       weight[:, :, 0]
														elif any(k in new_path for k in names_a						) and len(weight.shape						) > 2 and ".attentions." not in new_path:
																					SCREAMING_SNAKE_CASE__						:	Optional[int]									=       weight[:, :, 0]
														else:
																					SCREAMING_SNAKE_CASE__						:	str									=       weight

def lowercase_  (       _snake_case       ,_snake_case=0						):
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       []
							for old_item in old_list:
														SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       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 lowercase_  (       _snake_case       ,_snake_case=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 lowercase_  (       _snake_case       ,_snake_case=0						):
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       []
							for old_item in old_list:
														mapping.append({"""old""": old_item, """new""": old_item}						)

							return mapping

def lowercase_  (       _snake_case       ,_snake_case=0						):
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       []
							for old_item in old_list:
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       old_item.replace("""in_layers.0"""       ,"""norm1"""						)
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       new_item.replace("""in_layers.2"""       ,"""conv1"""						)

														SCREAMING_SNAKE_CASE__						:	int									=       new_item.replace("""out_layers.0"""       ,"""norm2"""						)
														SCREAMING_SNAKE_CASE__						:	int									=       new_item.replace("""out_layers.3"""       ,"""conv2"""						)

														SCREAMING_SNAKE_CASE__						:	int									=       new_item.replace("""emb_layers.1"""       ,"""time_emb_proj"""						)
														SCREAMING_SNAKE_CASE__						:	List[str]									=       new_item.replace("""skip_connection"""       ,"""conv_shortcut"""						)

														SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       shave_segments(__UpperCamelCase       ,n_shave_prefix_segments=__UpperCamelCase						)

														if "temopral_conv" not in old_item:
																					mapping.append({"""old""": old_item, """new""": new_item}						)

							return mapping

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case=None       ,_snake_case=False						):
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       {}
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       list(checkpoint.keys()						)

							SCREAMING_SNAKE_CASE__						:	List[str]									=       """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						) > 100 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"""						):
																												SCREAMING_SNAKE_CASE__						:	List[Any]									=       """model_ema.""" + """""".join(key.split("""."""						)[1:]						)
																												SCREAMING_SNAKE_CASE__						:	List[str]									=       checkpoint.pop(__UpperCamelCase						)
							else:
														if sum(k.startswith("""model_ema"""						) for k in keys						) > 100:
																					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:
																					SCREAMING_SNAKE_CASE__						:	List[str]									=       checkpoint.pop(__UpperCamelCase						)

							SCREAMING_SNAKE_CASE__						:	str									=       {}

							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       unet_state_dict["""time_embed.0.weight"""]
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       unet_state_dict["""time_embed.0.bias"""]
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       unet_state_dict["""time_embed.2.weight"""]
							SCREAMING_SNAKE_CASE__						:	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":
														SCREAMING_SNAKE_CASE__						:	str									=       unet_state_dict["""label_emb.0.0.weight"""]
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       unet_state_dict["""label_emb.0.0.bias"""]
														SCREAMING_SNAKE_CASE__						:	int									=       unet_state_dict["""label_emb.0.2.weight"""]
														SCREAMING_SNAKE_CASE__						:	str									=       unet_state_dict["""label_emb.0.2.bias"""]
							else:
														raise NotImplementedError(f'''Not implemented `class_embed_type`: {config['class_embed_type']}'''						)

							SCREAMING_SNAKE_CASE__						:	int									=       unet_state_dict["""input_blocks.0.0.weight"""]
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       unet_state_dict["""input_blocks.0.0.bias"""]

							SCREAMING_SNAKE_CASE__						:	Tuple									=       [v for v in unet_state_dict if v.startswith("""input_blocks.0.1"""						)]
							SCREAMING_SNAKE_CASE__						:	Dict									=       renew_attention_paths(__UpperCamelCase						)
							SCREAMING_SNAKE_CASE__						:	Dict									=       {"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
							assign_to_checkpoint(__UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ,additional_replacements=[meta_path]       ,config=__UpperCamelCase						)

							SCREAMING_SNAKE_CASE__						:	Dict									=       unet_state_dict["""out.0.weight"""]
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       unet_state_dict["""out.0.bias"""]
							SCREAMING_SNAKE_CASE__						:	Any									=       unet_state_dict["""out.2.weight"""]
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       unet_state_dict["""out.2.bias"""]

							# Retrieves the keys for the input blocks only
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       len({""".""".join(layer.split("""."""						)[:2]						) for layer in unet_state_dict if """input_blocks""" in layer}						)
							SCREAMING_SNAKE_CASE__						:	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(__UpperCamelCase						)
							}

							# Retrieves the keys for the middle blocks only
							SCREAMING_SNAKE_CASE__						:	int									=       len({""".""".join(layer.split("""."""						)[:2]						) for layer in unet_state_dict if """middle_block""" in layer}						)
							SCREAMING_SNAKE_CASE__						:	str									=       {
							    layer_id: [key for key in unet_state_dict if f'''middle_block.{layer_id}''' in key]
							    for layer_id in range(__UpperCamelCase						)
							}

							# Retrieves the keys for the output blocks only
							SCREAMING_SNAKE_CASE__						:	List[str]									=       len({""".""".join(layer.split("""."""						)[:2]						) for layer in unet_state_dict if """output_blocks""" in layer}						)
							SCREAMING_SNAKE_CASE__						:	int									=       {
							    layer_id: [key for key in unet_state_dict if f'''output_blocks.{layer_id}''' in key]
							    for layer_id in range(__UpperCamelCase						)
							}

							for i in range(1       ,__UpperCamelCase						):
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       (i - 1) // (config["""layers_per_block"""] + 1)
														SCREAMING_SNAKE_CASE__						:	str									=       (i - 1) % (config["""layers_per_block"""] + 1)

														SCREAMING_SNAKE_CASE__						:	Any									=       [
														    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
														]
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       [key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]
														SCREAMING_SNAKE_CASE__						:	int									=       [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:
																					SCREAMING_SNAKE_CASE__						:	str									=       unet_state_dict.pop(
																					    f'''input_blocks.{i}.op.weight'''						)
																					SCREAMING_SNAKE_CASE__						:	List[Any]									=       unet_state_dict.pop(
																					    f'''input_blocks.{i}.op.bias'''						)

														SCREAMING_SNAKE_CASE__						:	Dict									=       renew_resnet_paths(__UpperCamelCase						)
														SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       {"""old""": f'''input_blocks.{i}.0''', """new""": f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
														assign_to_checkpoint(
														    __UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ,additional_replacements=[meta_path]       ,config=__UpperCamelCase						)

														SCREAMING_SNAKE_CASE__						:	List[str]									=       [key for key in resnets if """temopral_conv""" in key]
														SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       renew_temp_conv_paths(__UpperCamelCase						)
														SCREAMING_SNAKE_CASE__						:	Tuple									=       {
														    """old""": f'''input_blocks.{i}.0.temopral_conv''',
														    """new""": f'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
														}
														assign_to_checkpoint(
														    __UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ,additional_replacements=[meta_path]       ,config=__UpperCamelCase						)

														if len(__UpperCamelCase						):
																					SCREAMING_SNAKE_CASE__						:	Tuple									=       renew_attention_paths(__UpperCamelCase						)
																					SCREAMING_SNAKE_CASE__						:	List[str]									=       {"""old""": f'''input_blocks.{i}.1''', """new""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
																					assign_to_checkpoint(
																					    __UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ,additional_replacements=[meta_path]       ,config=__UpperCamelCase						)

														if len(__UpperCamelCase						):
																					SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       renew_attention_paths(__UpperCamelCase						)
																					SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       {
																					    """old""": f'''input_blocks.{i}.2''',
																					    """new""": f'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
																					}
																					assign_to_checkpoint(
																					    __UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ,additional_replacements=[meta_path]       ,config=__UpperCamelCase						)

							SCREAMING_SNAKE_CASE__						:	List[Any]									=       middle_blocks[0]
							SCREAMING_SNAKE_CASE__						:	Tuple									=       [key for key in resnet_a if """temopral_conv""" in key]
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       middle_blocks[1]
							SCREAMING_SNAKE_CASE__						:	Tuple									=       middle_blocks[2]
							SCREAMING_SNAKE_CASE__						:	Dict									=       middle_blocks[3]
							SCREAMING_SNAKE_CASE__						:	Dict									=       [key for key in resnet_a if """temopral_conv""" in key]

							SCREAMING_SNAKE_CASE__						:	str									=       renew_resnet_paths(__UpperCamelCase						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       {"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
							assign_to_checkpoint(
							    __UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ,config=__UpperCamelCase       ,additional_replacements=[meta_path]						)

							SCREAMING_SNAKE_CASE__						:	Dict									=       renew_temp_conv_paths(__UpperCamelCase						)
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       {"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
							assign_to_checkpoint(
							    __UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ,config=__UpperCamelCase       ,additional_replacements=[meta_path]						)

							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       renew_resnet_paths(__UpperCamelCase						)
							SCREAMING_SNAKE_CASE__						:	str									=       {"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
							assign_to_checkpoint(
							    __UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ,config=__UpperCamelCase       ,additional_replacements=[meta_path]						)

							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       renew_temp_conv_paths(__UpperCamelCase						)
							SCREAMING_SNAKE_CASE__						:	str									=       {"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
							assign_to_checkpoint(
							    __UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ,config=__UpperCamelCase       ,additional_replacements=[meta_path]						)

							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       renew_attention_paths(__UpperCamelCase						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       {"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
							assign_to_checkpoint(
							    __UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ,additional_replacements=[meta_path]       ,config=__UpperCamelCase						)

							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       renew_attention_paths(__UpperCamelCase						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       {"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
							assign_to_checkpoint(
							    __UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ,additional_replacements=[meta_path]       ,config=__UpperCamelCase						)

							for i in range(__UpperCamelCase						):
														SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       i // (config["""layers_per_block"""] + 1)
														SCREAMING_SNAKE_CASE__						:	str									=       i % (config["""layers_per_block"""] + 1)
														SCREAMING_SNAKE_CASE__						:	int									=       [shave_segments(__UpperCamelCase       ,2						) for name in output_blocks[i]]
														SCREAMING_SNAKE_CASE__						:	Any									=       {}

														for layer in output_block_layers:
																					SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	List[Any]									=       layer.split("""."""						)[0], shave_segments(__UpperCamelCase       ,1						)
																					if layer_id in output_block_list:
																												output_block_list[layer_id].append(__UpperCamelCase						)
																					else:
																												SCREAMING_SNAKE_CASE__						:	Any									=       [layer_name]

														if len(__UpperCamelCase						) > 1:
																					SCREAMING_SNAKE_CASE__						:	List[Any]									=       [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
																					SCREAMING_SNAKE_CASE__						:	Any									=       [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]
																					SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       [key for key in output_blocks[i] if f'''output_blocks.{i}.2''' in key]

																					SCREAMING_SNAKE_CASE__						:	Optional[int]									=       renew_resnet_paths(__UpperCamelCase						)
																					SCREAMING_SNAKE_CASE__						:	int									=       renew_resnet_paths(__UpperCamelCase						)

																					SCREAMING_SNAKE_CASE__						:	str									=       {"""old""": f'''output_blocks.{i}.0''', """new""": f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
																					assign_to_checkpoint(
																					    __UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ,additional_replacements=[meta_path]       ,config=__UpperCamelCase						)

																					SCREAMING_SNAKE_CASE__						:	Any									=       [key for key in resnets if """temopral_conv""" in key]
																					SCREAMING_SNAKE_CASE__						:	Optional[int]									=       renew_temp_conv_paths(__UpperCamelCase						)
																					SCREAMING_SNAKE_CASE__						:	int									=       {
																					    """old""": f'''output_blocks.{i}.0.temopral_conv''',
																					    """new""": f'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
																					}
																					assign_to_checkpoint(
																					    __UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ,additional_replacements=[meta_path]       ,config=__UpperCamelCase						)

																					SCREAMING_SNAKE_CASE__						:	Dict									=       {k: sorted(__UpperCamelCase						) for k, v in output_block_list.items()}
																					if ["conv.bias", "conv.weight"] in output_block_list.values():
																												SCREAMING_SNAKE_CASE__						:	List[Any]									=       list(output_block_list.values()						).index(["""conv.bias""", """conv.weight"""]						)
																												SCREAMING_SNAKE_CASE__						:	Tuple									=       unet_state_dict[
																												    f'''output_blocks.{i}.{index}.conv.weight'''
																												]
																												SCREAMING_SNAKE_CASE__						:	List[Any]									=       unet_state_dict[
																												    f'''output_blocks.{i}.{index}.conv.bias'''
																												]

																												# Clear attentions as they have been attributed above.
																												if len(__UpperCamelCase						) == 2:
																																			SCREAMING_SNAKE_CASE__						:	str									=       []

																					if len(__UpperCamelCase						):
																												SCREAMING_SNAKE_CASE__						:	Optional[int]									=       renew_attention_paths(__UpperCamelCase						)
																												SCREAMING_SNAKE_CASE__						:	Dict									=       {
																												    """old""": f'''output_blocks.{i}.1''',
																												    """new""": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
																												}
																												assign_to_checkpoint(
																												    __UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ,additional_replacements=[meta_path]       ,config=__UpperCamelCase						)

																					if len(__UpperCamelCase						):
																												SCREAMING_SNAKE_CASE__						:	List[str]									=       renew_attention_paths(__UpperCamelCase						)
																												SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       {
																												    """old""": f'''output_blocks.{i}.2''',
																												    """new""": f'''up_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
																												}
																												assign_to_checkpoint(
																												    __UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ,additional_replacements=[meta_path]       ,config=__UpperCamelCase						)
														else:
																					SCREAMING_SNAKE_CASE__						:	List[str]									=       renew_resnet_paths(__UpperCamelCase       ,n_shave_prefix_segments=1						)
																					for path in resnet_0_paths:
																												SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       """.""".join(["""output_blocks""", str(__UpperCamelCase						), path["""old"""]]						)
																												SCREAMING_SNAKE_CASE__						:	Dict									=       """.""".join(["""up_blocks""", str(__UpperCamelCase						), """resnets""", str(__UpperCamelCase						), path["""new"""]]						)
																												SCREAMING_SNAKE_CASE__						:	List[Any]									=       unet_state_dict[old_path]

																					SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       [l for l in output_block_layers if """temopral_conv""" in l]
																					for path in temopral_conv_paths:
																												SCREAMING_SNAKE_CASE__						:	Tuple									=       path.split("""temopral_conv."""						)[-1]
																												SCREAMING_SNAKE_CASE__						:	str									=       """.""".join(["""output_blocks""", str(__UpperCamelCase						), str(__UpperCamelCase						), """temopral_conv""", pruned_path]						)
																												SCREAMING_SNAKE_CASE__						:	Dict									=       """.""".join(["""up_blocks""", str(__UpperCamelCase						), """temp_convs""", str(__UpperCamelCase						), pruned_path]						)
																												SCREAMING_SNAKE_CASE__						:	Any									=       unet_state_dict[old_path]

							return new_checkpoint


if __name__ == "__main__":
					UpperCAmelCase__				:   str   							=			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.')
					UpperCAmelCase__				:   Dict   							=			parser.parse_args()

					UpperCAmelCase__				:   Union[str, Any]   							=			torch.load(args.checkpoint_path, map_location='cpu')
					UpperCAmelCase__				:   str   							=			UNetaDConditionModel()

					UpperCAmelCase__				:   int   							=			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

					UpperCAmelCase__				:   Optional[Any]   							=			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
					UpperCAmelCase__				:   Dict   							=			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 --






"""simple docstring"""




def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	Any									=       index
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       2 * index + 1
							SCREAMING_SNAKE_CASE__						:	Any									=       2 * index + 2
							if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
														SCREAMING_SNAKE_CASE__						:	str									=       left_index

							if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
														SCREAMING_SNAKE_CASE__						:	Any									=       right_index

							if largest != index:
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Tuple									=       unsorted[index], unsorted[largest]
														heapify(_snake_case       ,_snake_case       ,_snake_case						)

def lowercase_  (       _snake_case						):
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       len(_snake_case						)
							for i in range(n // 2 - 1       ,-1       ,-1						):
														heapify(_snake_case       ,_snake_case       ,_snake_case						)
							for i in range(n - 1       ,0       ,-1						):
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Any									=       unsorted[i], unsorted[0]
														heapify(_snake_case       ,0       ,_snake_case						)
							return unsorted


if __name__ == "__main__":
					UpperCAmelCase__				:   Optional[Any]   							=			input('Enter numbers separated by a comma:\n').strip()
					UpperCAmelCase__				:   Optional[int]   							=			[int(item) for item in user_input.split(',')]
					print(heap_sort(unsorted))




